diff --git a/404.html b/404.html
index 17178b1..df2531e 100644
--- a/404.html
+++ b/404.html
@@ -14,7 +14,7 @@
       
       
       <link rel="icon" href="/sgp-tools/assets/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.49">
+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.31">
     
     
       
@@ -22,7 +22,7 @@
       
     
     
-      <link rel="stylesheet" href="/sgp-tools/assets/stylesheets/main.6f8fc17f.min.css">
+      <link rel="stylesheet" href="/sgp-tools/assets/stylesheets/main.3cba04c6.min.css">
       
         
         <link rel="stylesheet" href="/sgp-tools/assets/stylesheets/palette.06af60db.min.css">
@@ -50,7 +50,7 @@
     
       <link rel="stylesheet" href="/sgp-tools/stylesheets/extra.css">
     
-    <script>__md_scope=new URL("/sgp-tools/",location),__md_hash=e=>[...e].reduce(((e,_)=>(e<<5)-e+_.charCodeAt(0)),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("/sgp-tools/",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
   
@@ -59,7 +59,7 @@
   
   
 
-<script id="__analytics">function __md_analytics(){function e(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],e("js",new Date),e("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",(function(){document.forms.search&&document.forms.search.query.addEventListener("blur",(function(){this.value&&e("event","search",{search_term:this.value})}));document$.subscribe((function(){var t=document.forms.feedback;if(void 0!==t)for(var a of t.querySelectorAll("[type=submit]"))a.addEventListener("click",(function(a){a.preventDefault();var n=document.location.pathname,d=this.getAttribute("data-md-value");e("event","feedback",{page:n,data:d}),t.firstElementChild.disabled=!0;var r=t.querySelector(".md-feedback__note [data-md-value='"+d+"']");r&&(r.hidden=!1)})),t.hidden=!1})),location$.subscribe((function(t){e("config","G-Z2TVV3MF1S",{page_path:t.pathname})}))}));var t=document.createElement("script");t.async=!0,t.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",t)}</script>
+<script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",function(){document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document$.subscribe(function(){var a=document.forms.feedback;if(void 0!==a)for(var e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}),location$.subscribe(function(e){n("config","G-Z2TVV3MF1S",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
   
     <script>"undefined"!=typeof __md_analytics&&__md_analytics()</script>
   
@@ -142,7 +142,7 @@
     <!-- Button to open drawer -->
     <label class="md-header__button md-icon" for="__drawer">
       
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3zm0 5h18v2H3zm0 5h18v2H3z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
 
     <!-- Header title -->
@@ -173,7 +173,7 @@
     <input class="md-option" data-md-color-media="(prefers-color-scheme: light)" data-md-color-scheme="default" data-md-color-primary="custom" data-md-color-accent="custom"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_0">
     
       <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5s-1.65.15-2.39.42zM3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29zm.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14zM20.65 7l-1.77 3.79a7.02 7.02 0 0 0-2.38-4.15zm-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29zM12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5c-.84 0-1.65.15-2.39.42L12 2M3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29L3.34 7m.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14L3.36 17M20.65 7l-1.77 3.79a7.023 7.023 0 0 0-2.38-4.15l4.15.36m-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29L20.64 17M12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44L12 22Z"/></svg>
       </label>
     
   
@@ -183,7 +183,7 @@
     <input class="md-option" data-md-color-media="(prefers-color-scheme: dark)" data-md-color-scheme="slate" data-md-color-primary="custom" data-md-color-accent="custom"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_1">
     
       <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3zm3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95zm-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3 3.19.09m3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95 2.06.05m-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31Z"/></svg>
       </label>
     
   
@@ -193,7 +193,7 @@
 
     <!-- User preference: color palette -->
     
-      <script>var palette=__md_get("__palette");if(palette&&palette.color){if("(prefers-color-scheme)"===palette.color.media){var media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']");palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent")}for(var[key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
     
 
     <!-- Site language selector -->
@@ -203,7 +203,7 @@
     
       <label class="md-header__button md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
 
       <!-- Search interface -->
@@ -214,20 +214,20 @@
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
           <a href="javascript:void(0)" class="md-search__icon md-icon" title="Share" aria-label="Share" data-clipboard data-clipboard-text="" data-md-component="search-share" tabindex="-1">
             
-            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7s-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91s2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08"/></svg>
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7 0-.24-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91 1.61 0 2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08Z"/></svg>
           </a>
         
         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
           
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12z"/></svg>
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
       
@@ -254,7 +254,7 @@
         <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -364,6 +364,26 @@
   
     
     
+      <li class="md-tabs__item">
+        <a href="/sgp-tools/blueos-sgp-tools.html" class="md-tabs__link">
+          
+  
+    
+  
+  BlueOS
+
+        </a>
+      </li>
+    
+  
+
+      
+        
+  
+  
+  
+    
+    
       <li class="md-tabs__item">
         <a href="/sgp-tools/examples/AIPP_non_point_FoV.html" class="md-tabs__link">
           
@@ -460,7 +480,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
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+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -1302,6 +1322,78 @@
   
     
     
+      
+        
+      
+    
+    
+      
+      
+        
+      
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        
+          
+          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    BlueOS
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_5">
+            <span class="md-nav__icon md-icon"></span>
+            BlueOS
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="/sgp-tools/blueos-sgp-tools.html" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    blueos-sgp-tools
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    
+    
       
         
       
@@ -1327,10 +1419,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
         
           
-          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+          <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
             
   
   <span class="md-ellipsis">
@@ -1341,8 +1433,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_5">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1504,7 +1596,7 @@ <h1>404 - Not found</h1>
         
           <button type="button" class="md-top md-icon" data-md-component="top" hidden>
   
-  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8z"/></svg>
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg>
   Back to top
 </button>
         
@@ -1534,10 +1626,10 @@ <h1>404 - Not found</h1>
     </div>
     
     
-    <script id="__config" type="application/json">{"base": "/sgp-tools/", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "/sgp-tools/assets/javascripts/workers/search.6ce7567c.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "/sgp-tools/", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "/sgp-tools/assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="/sgp-tools/assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="/sgp-tools/assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
   </body>
diff --git a/api/kernels/Neural-Kernel.html b/api/kernels/Neural-Kernel.html
index a46879a..c723655 100644
--- a/api/kernels/Neural-Kernel.html
+++ b/api/kernels/Neural-Kernel.html
@@ -20,7 +20,7 @@
       
       
       <link rel="icon" href="../../assets/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.49">
+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.31">
     
     
       
@@ -28,7 +28,7 @@
       
     
     
-      <link rel="stylesheet" href="../../assets/stylesheets/main.6f8fc17f.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.3cba04c6.min.css">
       
         
         <link rel="stylesheet" href="../../assets/stylesheets/palette.06af60db.min.css">
@@ -56,7 +56,7 @@
     
       <link rel="stylesheet" href="../../stylesheets/extra.css">
     
-    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce(((e,_)=>(e<<5)-e+_.charCodeAt(0)),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
   
@@ -65,7 +65,7 @@
   
   
 
-<script id="__analytics">function __md_analytics(){function e(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],e("js",new Date),e("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",(function(){document.forms.search&&document.forms.search.query.addEventListener("blur",(function(){this.value&&e("event","search",{search_term:this.value})}));document$.subscribe((function(){var t=document.forms.feedback;if(void 0!==t)for(var a of t.querySelectorAll("[type=submit]"))a.addEventListener("click",(function(a){a.preventDefault();var n=document.location.pathname,d=this.getAttribute("data-md-value");e("event","feedback",{page:n,data:d}),t.firstElementChild.disabled=!0;var r=t.querySelector(".md-feedback__note [data-md-value='"+d+"']");r&&(r.hidden=!1)})),t.hidden=!1})),location$.subscribe((function(t){e("config","G-Z2TVV3MF1S",{page_path:t.pathname})}))}));var t=document.createElement("script");t.async=!0,t.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",t)}</script>
+<script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",function(){document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document$.subscribe(function(){var a=document.forms.feedback;if(void 0!==a)for(var e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}),location$.subscribe(function(e){n("config","G-Z2TVV3MF1S",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
   
     <script>"undefined"!=typeof __md_analytics&&__md_analytics()</script>
   
@@ -153,7 +153,7 @@
     <!-- Button to open drawer -->
     <label class="md-header__button md-icon" for="__drawer">
       
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3zm0 5h18v2H3zm0 5h18v2H3z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
 
     <!-- Header title -->
@@ -184,7 +184,7 @@
     <input class="md-option" data-md-color-media="(prefers-color-scheme: light)" data-md-color-scheme="default" data-md-color-primary="custom" data-md-color-accent="custom"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_0">
     
       <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5s-1.65.15-2.39.42zM3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29zm.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14zM20.65 7l-1.77 3.79a7.02 7.02 0 0 0-2.38-4.15zm-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29zM12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5c-.84 0-1.65.15-2.39.42L12 2M3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29L3.34 7m.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14L3.36 17M20.65 7l-1.77 3.79a7.023 7.023 0 0 0-2.38-4.15l4.15.36m-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29L20.64 17M12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44L12 22Z"/></svg>
       </label>
     
   
@@ -194,7 +194,7 @@
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       <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3zm3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95zm-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3 3.19.09m3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95 2.06.05m-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31Z"/></svg>
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@@ -204,7 +204,7 @@
 
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+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
     
 
     <!-- Site language selector -->
@@ -214,7 +214,7 @@
     
       <label class="md-header__button md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
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@@ -225,20 +225,20 @@
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+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
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+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7 0-.24-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91 1.61 0 2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08Z"/></svg>
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+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
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@@ -265,7 +265,7 @@
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-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
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   <div class="md-source__repository">
     GitHub
@@ -379,6 +379,26 @@
   
     
     
+      <li class="md-tabs__item">
+        <a href="../../blueos-sgp-tools.html" class="md-tabs__link">
+          
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+  
+  BlueOS
+
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+    
+    
       <li class="md-tabs__item">
         <a href="../../examples/AIPP_non_point_FoV.html" class="md-tabs__link">
           
@@ -475,7 +495,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -1420,6 +1440,78 @@
   
     
     
+      
+        
+      
+    
+    
+      
+      
+        
+      
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
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+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        
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+          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
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+  <span class="md-ellipsis">
+    BlueOS
+  </span>
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+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_5">
+            <span class="md-nav__icon md-icon"></span>
+            BlueOS
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
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+  
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+  
+    <li class="md-nav__item">
+      <a href="../../blueos-sgp-tools.html" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    blueos-sgp-tools
+  </span>
+  
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+    </li>
+  
+
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+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
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@@ -1445,10 +1537,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
         
           
-          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+          <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
             
   
   <span class="md-ellipsis">
@@ -1459,8 +1551,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_5">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1631,12 +1723,7 @@ <h2 id="sgptools.kernels.neural_kernel" class="doc doc-heading">
 
     <div class="doc doc-contents first">
 
-        <p>Provides a neural spectral kernel function along with an initialization function</p>
-
-
-
-
-
+      <p>Provides a neural spectral kernel function along with an initialization function</p>
 
 
 
@@ -1665,7 +1752,7 @@ <h3 id="sgptools.kernels.neural_kernel.NeuralSpectralKernel" class="doc doc-head
               Bases: <code><span title="gpflow.kernels.Kernel">Kernel</span></code></p>
 
 
-        <p>Neural Spectral Kernel function (non-stationary kernel function). 
+      <p>Neural Spectral Kernel function (non-stationary kernel function). 
 Based on the implementation from the following <a href="https://github.com/sremes/nssm-gp/tree/master?tab=readme-ov-file">repo</a></p>
 
 
@@ -1688,9 +1775,7 @@ <h3 id="sgptools.kernels.neural_kernel.NeuralSpectralKernel" class="doc doc-head
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>input_dim</code>
-            </td>
+            <td><code>input_dim</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -1704,9 +1789,7 @@ <h3 id="sgptools.kernels.neural_kernel.NeuralSpectralKernel" class="doc doc-head
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>active_dims</code>
-            </td>
+            <td><code>active_dims</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -1720,9 +1803,7 @@ <h3 id="sgptools.kernels.neural_kernel.NeuralSpectralKernel" class="doc doc-head
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>Q</code>
-            </td>
+            <td><code>Q</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -1736,9 +1817,7 @@ <h3 id="sgptools.kernels.neural_kernel.NeuralSpectralKernel" class="doc doc-head
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>hidden_sizes</code>
-            </td>
+            <td><code>hidden_sizes</code></td>
             <td>
                   <code>list</code>
             </td>
@@ -1754,11 +1833,6 @@ <h3 id="sgptools.kernels.neural_kernel.NeuralSpectralKernel" class="doc doc-head
       </tbody>
     </table>
 
-
-
-
-
-
               <details class="quote">
                 <summary>Source code in <code>sgptools/kernels/neural_kernel.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-34"> 34</a></span>
@@ -1968,7 +2042,7 @@ <h4 id="sgptools.kernels.neural_kernel.NeuralSpectralKernel.K" class="doc doc-he
 
     <div class="doc doc-contents ">
 
-        <p>Computes the covariances between/amongst the input variables</p>
+      <p>Computes the covariances between/amongst the input variables</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -1983,9 +2057,7 @@ <h4 id="sgptools.kernels.neural_kernel.NeuralSpectralKernel.K" class="doc doc-he
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>X</code>
-            </td>
+            <td><code>X</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1999,9 +2071,7 @@ <h4 id="sgptools.kernels.neural_kernel.NeuralSpectralKernel.K" class="doc doc-he
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>X2</code>
-            </td>
+            <td><code>X2</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2170,7 +2240,7 @@ <h3 id="sgptools.kernels.neural_kernel.init_neural_kernel" class="doc doc-headin
 
     <div class="doc doc-contents ">
 
-        <p>Helper function to initialize a Neural Spectral Kernel function (non-stationary kernel function). 
+      <p>Helper function to initialize a Neural Spectral Kernel function (non-stationary kernel function). 
 Based on the implementation from the following <a href="https://github.com/sremes/nssm-gp/tree/master?tab=readme-ov-file">repo</a></p>
 
 
@@ -2193,9 +2263,7 @@ <h3 id="sgptools.kernels.neural_kernel.init_neural_kernel" class="doc doc-headin
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>x</code>
-            </td>
+            <td><code>x</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2209,9 +2277,7 @@ <h3 id="sgptools.kernels.neural_kernel.init_neural_kernel" class="doc doc-headin
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>y</code>
-            </td>
+            <td><code>y</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2225,9 +2291,7 @@ <h3 id="sgptools.kernels.neural_kernel.init_neural_kernel" class="doc doc-headin
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>inducing_variable</code>
-            </td>
+            <td><code>inducing_variable</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2241,9 +2305,7 @@ <h3 id="sgptools.kernels.neural_kernel.init_neural_kernel" class="doc doc-headin
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>Q</code>
-            </td>
+            <td><code>Q</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -2257,9 +2319,7 @@ <h3 id="sgptools.kernels.neural_kernel.init_neural_kernel" class="doc doc-headin
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>n_inits</code>
-            </td>
+            <td><code>n_inits</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -2273,9 +2333,7 @@ <h3 id="sgptools.kernels.neural_kernel.init_neural_kernel" class="doc doc-headin
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>hidden_sizes</code>
-            </td>
+            <td><code>hidden_sizes</code></td>
             <td>
                   <code>list</code>
             </td>
@@ -2403,7 +2461,7 @@ <h3 id="sgptools.kernels.neural_kernel.init_neural_kernel" class="doc doc-headin
         
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   Back to top
 </button>
         
@@ -2433,10 +2491,10 @@ <h3 id="sgptools.kernels.neural_kernel.init_neural_kernel" class="doc doc-headin
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+    <script id="__config" type="application/json">{"base": "../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="../../assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="../../assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
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diff --git a/api/kernels/index.html b/api/kernels/index.html
index 563eb51..becb714 100644
--- a/api/kernels/index.html
+++ b/api/kernels/index.html
@@ -20,7 +20,7 @@
       
       
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+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.31">
     
     
       
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+      <link rel="stylesheet" href="../../assets/stylesheets/main.3cba04c6.min.css">
       
         
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       </nav>
       
@@ -265,7 +265,7 @@
         <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -379,6 +379,26 @@
   
     
     
+      <li class="md-tabs__item">
+        <a href="../../blueos-sgp-tools.html" class="md-tabs__link">
+          
+  
+    
+  
+  BlueOS
+
+        </a>
+      </li>
+    
+  
+
+      
+        
+  
+  
+  
+    
+    
       <li class="md-tabs__item">
         <a href="../../examples/AIPP_non_point_FoV.html" class="md-tabs__link">
           
@@ -475,7 +495,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -1336,6 +1356,78 @@
   
     
     
+      
+        
+      
+    
+    
+      
+      
+        
+      
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        
+          
+          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    BlueOS
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_5">
+            <span class="md-nav__icon md-icon"></span>
+            BlueOS
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../blueos-sgp-tools.html" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    blueos-sgp-tools
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    
+    
       
         
       
@@ -1361,10 +1453,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
         
           
-          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+          <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
             
   
   <span class="md-ellipsis">
@@ -1375,8 +1467,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_5">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1551,18 +1643,13 @@ <h2 id="sgptools.kernels" class="doc doc-heading">
 
     <div class="doc doc-contents first">
 
-        <p>Special kernel functions in this package:</p>
+      <p>Special kernel functions in this package:</p>
 <ul>
 <li><code>neural_kernel</code>: Provides a neural spectral kernel function that uses a mixture of multilayer perceptrons</li>
 </ul>
 
 
 
-
-
-
-
-
   <div class="doc doc-children">
 
 
@@ -1604,7 +1691,7 @@ <h2 id="sgptools.kernels" class="doc doc-heading">
         
           <button type="button" class="md-top md-icon" data-md-component="top" hidden>
   
-  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8z"/></svg>
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg>
   Back to top
 </button>
         
@@ -1634,10 +1721,10 @@ <h2 id="sgptools.kernels" class="doc doc-heading">
     </div>
     
     
-    <script id="__config" type="application/json">{"base": "../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../assets/javascripts/workers/search.6ce7567c.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="../../assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="../../assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
   </body>
diff --git a/api/models/Bayesian-Optimization.html b/api/models/Bayesian-Optimization.html
index 265ea8d..87a816e 100644
--- a/api/models/Bayesian-Optimization.html
+++ b/api/models/Bayesian-Optimization.html
@@ -20,7 +20,7 @@
       
       
       <link rel="icon" href="../../assets/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.49">
+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.31">
     
     
       
@@ -28,7 +28,7 @@
       
     
     
-      <link rel="stylesheet" href="../../assets/stylesheets/main.6f8fc17f.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.3cba04c6.min.css">
       
         
         <link rel="stylesheet" href="../../assets/stylesheets/palette.06af60db.min.css">
@@ -56,7 +56,7 @@
     
       <link rel="stylesheet" href="../../stylesheets/extra.css">
     
-    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce(((e,_)=>(e<<5)-e+_.charCodeAt(0)),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
   
@@ -65,7 +65,7 @@
   
   
 
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+<script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",function(){document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document$.subscribe(function(){var a=document.forms.feedback;if(void 0!==a)for(var e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}),location$.subscribe(function(e){n("config","G-Z2TVV3MF1S",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
   
     <script>"undefined"!=typeof __md_analytics&&__md_analytics()</script>
   
@@ -153,7 +153,7 @@
     <!-- Button to open drawer -->
     <label class="md-header__button md-icon" for="__drawer">
       
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3zm0 5h18v2H3zm0 5h18v2H3z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
 
     <!-- Header title -->
@@ -184,7 +184,7 @@
     <input class="md-option" data-md-color-media="(prefers-color-scheme: light)" data-md-color-scheme="default" data-md-color-primary="custom" data-md-color-accent="custom"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_0">
     
       <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5s-1.65.15-2.39.42zM3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29zm.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14zM20.65 7l-1.77 3.79a7.02 7.02 0 0 0-2.38-4.15zm-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29zM12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5c-.84 0-1.65.15-2.39.42L12 2M3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29L3.34 7m.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14L3.36 17M20.65 7l-1.77 3.79a7.023 7.023 0 0 0-2.38-4.15l4.15.36m-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29L20.64 17M12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44L12 22Z"/></svg>
       </label>
     
   
@@ -194,7 +194,7 @@
     <input class="md-option" data-md-color-media="(prefers-color-scheme: dark)" data-md-color-scheme="slate" data-md-color-primary="custom" data-md-color-accent="custom"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_1">
     
       <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3zm3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95zm-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3 3.19.09m3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95 2.06.05m-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31Z"/></svg>
       </label>
     
   
@@ -204,7 +204,7 @@
 
     <!-- User preference: color palette -->
     
-      <script>var palette=__md_get("__palette");if(palette&&palette.color){if("(prefers-color-scheme)"===palette.color.media){var media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']");palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent")}for(var[key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
     
 
     <!-- Site language selector -->
@@ -214,7 +214,7 @@
     
       <label class="md-header__button md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
 
       <!-- Search interface -->
@@ -225,20 +225,20 @@
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
           <a href="javascript:void(0)" class="md-search__icon md-icon" title="Share" aria-label="Share" data-clipboard data-clipboard-text="" data-md-component="search-share" tabindex="-1">
             
-            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7s-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91s2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08"/></svg>
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7 0-.24-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91 1.61 0 2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08Z"/></svg>
           </a>
         
         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
           
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12z"/></svg>
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
       
@@ -265,7 +265,7 @@
         <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -379,6 +379,26 @@
   
     
     
+      <li class="md-tabs__item">
+        <a href="../../blueos-sgp-tools.html" class="md-tabs__link">
+          
+  
+    
+  
+  BlueOS
+
+        </a>
+      </li>
+    
+  
+
+      
+        
+  
+  
+  
+    
+    
       <li class="md-tabs__item">
         <a href="../../examples/AIPP_non_point_FoV.html" class="md-tabs__link">
           
@@ -475,7 +495,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
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   <div class="md-source__repository">
     GitHub
@@ -1420,6 +1440,78 @@
   
     
     
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             Examples
           </label>
@@ -1633,11 +1725,6 @@ <h2 id="sgptools.models.bo" class="doc doc-heading">
 
 
 
-
-
-
-
-
   <div class="doc doc-children">
 
 
@@ -1661,7 +1748,7 @@ <h3 id="sgptools.models.bo.BayesianOpt" class="doc doc-heading">
     <div class="doc doc-contents ">
 
 
-        <p>Class for optimizing sensor placements using Bayesian Optimization</p>
+      <p>Class for optimizing sensor placements using Bayesian Optimization</p>
 
 
 <details class="refer-to-the-following-papers-for-more-details" open>
@@ -1684,9 +1771,7 @@ <h3 id="sgptools.models.bo.BayesianOpt" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>X_train</code>
-            </td>
+            <td><code>X_train</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1700,9 +1785,7 @@ <h3 id="sgptools.models.bo.BayesianOpt" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>noise_variance</code>
-            </td>
+            <td><code>noise_variance</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -1716,9 +1799,7 @@ <h3 id="sgptools.models.bo.BayesianOpt" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>kernel</code>
-            </td>
+            <td><code>kernel</code></td>
             <td>
                   <code><span title="gpflow.kernels.Kernel">Kernel</span></code>
             </td>
@@ -1732,9 +1813,7 @@ <h3 id="sgptools.models.bo.BayesianOpt" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>transform</code>
-            </td>
+            <td><code>transform</code></td>
             <td>
                   <code>Transform</code>
             </td>
@@ -1750,11 +1829,6 @@ <h3 id="sgptools.models.bo.BayesianOpt" class="doc doc-heading">
       </tbody>
     </table>
 
-
-
-
-
-
               <details class="quote">
                 <summary>Source code in <code>sgptools/models/bo.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-21"> 21</a></span>
@@ -1978,7 +2052,7 @@ <h4 id="sgptools.models.bo.BayesianOpt.objective" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Objective function (GP-based Mutual Information)</p>
+      <p>Objective function (GP-based Mutual Information)</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -1993,9 +2067,7 @@ <h4 id="sgptools.models.bo.BayesianOpt.objective" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>x&lt;i&gt;</code>
-            </td>
+            <td><code>x&lt;i&gt;</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2073,7 +2145,7 @@ <h4 id="sgptools.models.bo.BayesianOpt.optimize" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Optimizes the sensor placements using Bayesian Optimization without any constraints</p>
+      <p>Optimizes the sensor placements using Bayesian Optimization without any constraints</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -2088,9 +2160,7 @@ <h4 id="sgptools.models.bo.BayesianOpt.optimize" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>num_sensors</code>
-            </td>
+            <td><code>num_sensors</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -2104,9 +2174,7 @@ <h4 id="sgptools.models.bo.BayesianOpt.optimize" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>max_steps</code>
-            </td>
+            <td><code>max_steps</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -2120,9 +2188,7 @@ <h4 id="sgptools.models.bo.BayesianOpt.optimize" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>X_init</code>
-            </td>
+            <td><code>X_init</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2136,9 +2202,7 @@ <h4 id="sgptools.models.bo.BayesianOpt.optimize" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>init_points</code>
-            </td>
+            <td><code>init_points</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -2153,9 +2217,7 @@ <h4 id="sgptools.models.bo.BayesianOpt.optimize" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>verbose</code>
-            </td>
+            <td><code>verbose</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -2169,9 +2231,7 @@ <h4 id="sgptools.models.bo.BayesianOpt.optimize" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>seed</code>
-            </td>
+            <td><code>seed</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -2355,7 +2415,7 @@ <h4 id="sgptools.models.bo.BayesianOpt.optimize" class="doc doc-heading">
         
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+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg>
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@@ -2385,10 +2445,10 @@ <h4 id="sgptools.models.bo.BayesianOpt.optimize" class="doc doc-heading">
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-      <script src="../../assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="../../assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
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diff --git a/api/models/Continuous-SGP.html b/api/models/Continuous-SGP.html
index e67f0ac..bc5627d 100644
--- a/api/models/Continuous-SGP.html
+++ b/api/models/Continuous-SGP.html
@@ -20,7 +20,7 @@
       
       
       <link rel="icon" href="../../assets/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.49">
+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.31">
     
     
       
@@ -28,7 +28,7 @@
       
     
     
-      <link rel="stylesheet" href="../../assets/stylesheets/main.6f8fc17f.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.3cba04c6.min.css">
       
         
         <link rel="stylesheet" href="../../assets/stylesheets/palette.06af60db.min.css">
@@ -56,7 +56,7 @@
     
       <link rel="stylesheet" href="../../stylesheets/extra.css">
     
-    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce(((e,_)=>(e<<5)-e+_.charCodeAt(0)),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
   
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+<script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",function(){document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document$.subscribe(function(){var a=document.forms.feedback;if(void 0!==a)for(var e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}),location$.subscribe(function(e){n("config","G-Z2TVV3MF1S",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
   
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@@ -153,7 +153,7 @@
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+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
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       <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5s-1.65.15-2.39.42zM3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29zm.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14zM20.65 7l-1.77 3.79a7.02 7.02 0 0 0-2.38-4.15zm-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29zM12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5c-.84 0-1.65.15-2.39.42L12 2M3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29L3.34 7m.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14L3.36 17M20.65 7l-1.77 3.79a7.023 7.023 0 0 0-2.38-4.15l4.15.36m-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29L20.64 17M12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44L12 22Z"/></svg>
       </label>
     
   
@@ -194,7 +194,7 @@
     <input class="md-option" data-md-color-media="(prefers-color-scheme: dark)" data-md-color-scheme="slate" data-md-color-primary="custom" data-md-color-accent="custom"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_1">
     
       <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3zm3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95zm-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3 3.19.09m3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95 2.06.05m-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31Z"/></svg>
       </label>
     
   
@@ -204,7 +204,7 @@
 
     <!-- User preference: color palette -->
     
-      <script>var palette=__md_get("__palette");if(palette&&palette.color){if("(prefers-color-scheme)"===palette.color.media){var media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']");palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent")}for(var[key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
     
 
     <!-- Site language selector -->
@@ -214,7 +214,7 @@
     
       <label class="md-header__button md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
 
       <!-- Search interface -->
@@ -225,20 +225,20 @@
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
           <a href="javascript:void(0)" class="md-search__icon md-icon" title="Share" aria-label="Share" data-clipboard data-clipboard-text="" data-md-component="search-share" tabindex="-1">
             
-            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7s-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91s2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08"/></svg>
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7 0-.24-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91 1.61 0 2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08Z"/></svg>
           </a>
         
         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
           
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12z"/></svg>
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
       
@@ -265,7 +265,7 @@
         <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -379,6 +379,26 @@
   
     
     
+      <li class="md-tabs__item">
+        <a href="../../blueos-sgp-tools.html" class="md-tabs__link">
+          
+  
+    
+  
+  BlueOS
+
+        </a>
+      </li>
+    
+  
+
+      
+        
+  
+  
+  
+    
+    
       <li class="md-tabs__item">
         <a href="../../examples/AIPP_non_point_FoV.html" class="md-tabs__link">
           
@@ -475,7 +495,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
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   <div class="md-source__repository">
     GitHub
@@ -1396,6 +1416,78 @@
   
     
     
+      
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+  
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+    <li class="md-nav__item">
+      <a href="../../blueos-sgp-tools.html" class="md-nav__link">
+        
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+  <span class="md-ellipsis">
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+  
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+              
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+        </nav>
+      
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+  
+
+    
+      
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@@ -1435,8 +1527,8 @@
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-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
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+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1609,11 +1701,6 @@ <h2 id="sgptools.models.continuous_sgp" class="doc doc-heading">
 
 
 
-
-
-
-
-
   <div class="doc doc-children">
 
 
@@ -1635,7 +1722,7 @@ <h3 id="sgptools.models.continuous_sgp.continuous_sgp" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Get sensor placement solutions using the Continuous-SGP method</p>
+      <p>Get sensor placement solutions using the Continuous-SGP method</p>
 
 
 <details class="refer-to-the-following-papers-for-more-details" open>
@@ -1658,9 +1745,7 @@ <h3 id="sgptools.models.continuous_sgp.continuous_sgp" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>num_inducing</code>
-            </td>
+            <td><code>num_inducing</code></td>
             <td>
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@@ -1674,9 +1759,7 @@ <h3 id="sgptools.models.continuous_sgp.continuous_sgp" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>X_train</code>
-            </td>
+            <td><code>X_train</code></td>
             <td>
                   <code>ndarray</code>
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@@ -1690,9 +1773,7 @@ <h3 id="sgptools.models.continuous_sgp.continuous_sgp" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>noise_variance</code>
-            </td>
+            <td><code>noise_variance</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -1706,9 +1787,7 @@ <h3 id="sgptools.models.continuous_sgp.continuous_sgp" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>kernel</code>
-            </td>
+            <td><code>kernel</code></td>
             <td>
                   <code><span title="gpflow.kernels.Kernel">Kernel</span></code>
             </td>
@@ -1722,9 +1801,7 @@ <h3 id="sgptools.models.continuous_sgp.continuous_sgp" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>transform</code>
-            </td>
+            <td><code>transform</code></td>
             <td>
                   <code>Transform</code>
             </td>
@@ -1738,9 +1815,7 @@ <h3 id="sgptools.models.continuous_sgp.continuous_sgp" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>Xu_init</code>
-            </td>
+            <td><code>Xu_init</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1754,9 +1829,7 @@ <h3 id="sgptools.models.continuous_sgp.continuous_sgp" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>Xu_time</code>
-            </td>
+            <td><code>Xu_time</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1770,9 +1843,7 @@ <h3 id="sgptools.models.continuous_sgp.continuous_sgp" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>orientation</code>
-            </td>
+            <td><code>orientation</code></td>
             <td>
                   <code>bool</code>
             </td>
@@ -1952,7 +2023,7 @@ <h3 id="sgptools.models.continuous_sgp.continuous_sgp" class="doc doc-heading">
         
           <button type="button" class="md-top md-icon" data-md-component="top" hidden>
   
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+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg>
   Back to top
 </button>
         
@@ -1982,10 +2053,10 @@ <h3 id="sgptools.models.continuous_sgp.continuous_sgp" class="doc doc-heading">
     </div>
     
     
-    <script id="__config" type="application/json">{"base": "../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../assets/javascripts/workers/search.6ce7567c.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="../../assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="../../assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
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diff --git a/api/models/Genetic-Algorithm.html b/api/models/Genetic-Algorithm.html
index 8dac6d3..6822c61 100644
--- a/api/models/Genetic-Algorithm.html
+++ b/api/models/Genetic-Algorithm.html
@@ -20,7 +20,7 @@
       
       
       <link rel="icon" href="../../assets/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.49">
+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.31">
     
     
       
@@ -28,7 +28,7 @@
       
     
     
-      <link rel="stylesheet" href="../../assets/stylesheets/main.6f8fc17f.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.3cba04c6.min.css">
       
         
         <link rel="stylesheet" href="../../assets/stylesheets/palette.06af60db.min.css">
@@ -56,7 +56,7 @@
     
       <link rel="stylesheet" href="../../stylesheets/extra.css">
     
-    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce(((e,_)=>(e<<5)-e+_.charCodeAt(0)),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
   
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+<script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",function(){document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document$.subscribe(function(){var a=document.forms.feedback;if(void 0!==a)for(var e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}),location$.subscribe(function(e){n("config","G-Z2TVV3MF1S",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
   
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@@ -153,7 +153,7 @@
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+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
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       <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3zm3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95zm-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3 3.19.09m3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95 2.06.05m-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31Z"/></svg>
       </label>
     
   
@@ -204,7 +204,7 @@
 
     <!-- User preference: color palette -->
     
-      <script>var palette=__md_get("__palette");if(palette&&palette.color){if("(prefers-color-scheme)"===palette.color.media){var media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']");palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent")}for(var[key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
     
 
     <!-- Site language selector -->
@@ -214,7 +214,7 @@
     
       <label class="md-header__button md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
 
       <!-- Search interface -->
@@ -225,20 +225,20 @@
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
           <a href="javascript:void(0)" class="md-search__icon md-icon" title="Share" aria-label="Share" data-clipboard data-clipboard-text="" data-md-component="search-share" tabindex="-1">
             
-            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7s-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91s2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08"/></svg>
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7 0-.24-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91 1.61 0 2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08Z"/></svg>
           </a>
         
         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
           
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12z"/></svg>
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
       
@@ -265,7 +265,7 @@
         <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -379,6 +379,26 @@
   
     
     
+      <li class="md-tabs__item">
+        <a href="../../blueos-sgp-tools.html" class="md-tabs__link">
+          
+  
+    
+  
+  BlueOS
+
+        </a>
+      </li>
+    
+  
+
+      
+        
+  
+  
+  
+    
+    
       <li class="md-tabs__item">
         <a href="../../examples/AIPP_non_point_FoV.html" class="md-tabs__link">
           
@@ -475,7 +495,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -1429,6 +1449,78 @@
   
     
     
+      
+        
+      
+    
+    
+      
+      
+        
+      
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        
+          
+          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    BlueOS
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_5">
+            <span class="md-nav__icon md-icon"></span>
+            BlueOS
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../blueos-sgp-tools.html" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    blueos-sgp-tools
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
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+    
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@@ -1454,10 +1546,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
         
           
-          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+          <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
             
   
   <span class="md-ellipsis">
@@ -1468,8 +1560,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_5">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1642,11 +1734,6 @@ <h2 id="sgptools.models.cma_es" class="doc doc-heading">
 
 
 
-
-
-
-
-
   <div class="doc doc-children">
 
 
@@ -1670,7 +1757,7 @@ <h3 id="sgptools.models.cma_es.CMA_ES" class="doc doc-heading">
     <div class="doc doc-contents ">
 
 
-        <p>Class for optimizing sensor placements using CMA-ES (a genetic algorithm)</p>
+      <p>Class for optimizing sensor placements using CMA-ES (a genetic algorithm)</p>
 
 
 <details class="refer-to-the-following-paper-for-more-details" open>
@@ -1692,9 +1779,7 @@ <h3 id="sgptools.models.cma_es.CMA_ES" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>X_train</code>
-            </td>
+            <td><code>X_train</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1708,9 +1793,7 @@ <h3 id="sgptools.models.cma_es.CMA_ES" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>noise_variance</code>
-            </td>
+            <td><code>noise_variance</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -1724,9 +1807,7 @@ <h3 id="sgptools.models.cma_es.CMA_ES" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>kernel</code>
-            </td>
+            <td><code>kernel</code></td>
             <td>
                   <code><span title="gpflow.kernels.Kernel">Kernel</span></code>
             </td>
@@ -1740,9 +1821,7 @@ <h3 id="sgptools.models.cma_es.CMA_ES" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>distance_budget</code>
-            </td>
+            <td><code>distance_budget</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -1757,9 +1836,7 @@ <h3 id="sgptools.models.cma_es.CMA_ES" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>num_robots</code>
-            </td>
+            <td><code>num_robots</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -1774,9 +1851,7 @@ <h3 id="sgptools.models.cma_es.CMA_ES" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>transform</code>
-            </td>
+            <td><code>transform</code></td>
             <td>
                   <code>Transform</code>
             </td>
@@ -1792,11 +1867,6 @@ <h3 id="sgptools.models.cma_es.CMA_ES" class="doc doc-heading">
       </tbody>
     </table>
 
-
-
-
-
-
               <details class="quote">
                 <summary>Source code in <code>sgptools/models/cma_es.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-22"> 22</a></span>
@@ -2024,7 +2094,7 @@ <h4 id="sgptools.models.cma_es.CMA_ES.objective" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Objective function (GP-based Mutual Information)</p>
+      <p>Objective function (GP-based Mutual Information)</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -2039,9 +2109,7 @@ <h4 id="sgptools.models.cma_es.CMA_ES.objective" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>X</code>
-            </td>
+            <td><code>X</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2115,7 +2183,7 @@ <h4 id="sgptools.models.cma_es.CMA_ES.optimize" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Optimizes the sensor placements using CMA-ES without any constraints</p>
+      <p>Optimizes the sensor placements using CMA-ES without any constraints</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -2130,9 +2198,7 @@ <h4 id="sgptools.models.cma_es.CMA_ES.optimize" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>num_sensors</code>
-            </td>
+            <td><code>num_sensors</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -2146,9 +2212,7 @@ <h4 id="sgptools.models.cma_es.CMA_ES.optimize" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>max_steps</code>
-            </td>
+            <td><code>max_steps</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -2162,9 +2226,7 @@ <h4 id="sgptools.models.cma_es.CMA_ES.optimize" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>tol</code>
-            </td>
+            <td><code>tol</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -2178,9 +2240,7 @@ <h4 id="sgptools.models.cma_es.CMA_ES.optimize" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>X_init</code>
-            </td>
+            <td><code>X_init</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2194,9 +2254,7 @@ <h4 id="sgptools.models.cma_es.CMA_ES.optimize" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>verbose</code>
-            </td>
+            <td><code>verbose</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -2210,9 +2268,7 @@ <h4 id="sgptools.models.cma_es.CMA_ES.optimize" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>seed</code>
-            </td>
+            <td><code>seed</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -2349,7 +2405,7 @@ <h4 id="sgptools.models.cma_es.CMA_ES.update" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Update GP noise variance and kernel function parameters</p>
+      <p>Update GP noise variance and kernel function parameters</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -2364,9 +2420,7 @@ <h4 id="sgptools.models.cma_es.CMA_ES.update" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>noise_variance</code>
-            </td>
+            <td><code>noise_variance</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -2380,9 +2434,7 @@ <h4 id="sgptools.models.cma_es.CMA_ES.update" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>kernel</code>
-            </td>
+            <td><code>kernel</code></td>
             <td>
                   <code><span title="gpflow.kernels.Kernel">Kernel</span></code>
             </td>
@@ -2463,7 +2515,7 @@ <h4 id="sgptools.models.cma_es.CMA_ES.update" class="doc doc-heading">
         
           <button type="button" class="md-top md-icon" data-md-component="top" hidden>
   
-  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8z"/></svg>
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg>
   Back to top
 </button>
         
@@ -2493,10 +2545,10 @@ <h4 id="sgptools.models.cma_es.CMA_ES.update" class="doc doc-heading">
     </div>
     
     
-    <script id="__config" type="application/json">{"base": "../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../assets/javascripts/workers/search.6ce7567c.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="../../assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="../../assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
   </body>
diff --git a/api/models/Greedy-MI.html b/api/models/Greedy-MI.html
index 69ac31a..6ce7881 100644
--- a/api/models/Greedy-MI.html
+++ b/api/models/Greedy-MI.html
@@ -20,7 +20,7 @@
       
       
       <link rel="icon" href="../../assets/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.49">
+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.31">
     
     
       
@@ -28,7 +28,7 @@
       
     
     
-      <link rel="stylesheet" href="../../assets/stylesheets/main.6f8fc17f.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.3cba04c6.min.css">
       
         
         <link rel="stylesheet" href="../../assets/stylesheets/palette.06af60db.min.css">
@@ -56,7 +56,7 @@
     
       <link rel="stylesheet" href="../../stylesheets/extra.css">
     
-    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce(((e,_)=>(e<<5)-e+_.charCodeAt(0)),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
   
@@ -65,7 +65,7 @@
   
   
 
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+<script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",function(){document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document$.subscribe(function(){var a=document.forms.feedback;if(void 0!==a)for(var e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}),location$.subscribe(function(e){n("config","G-Z2TVV3MF1S",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
   
     <script>"undefined"!=typeof __md_analytics&&__md_analytics()</script>
   
@@ -153,7 +153,7 @@
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     <label class="md-header__button md-icon" for="__drawer">
       
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+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
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@@ -184,7 +184,7 @@
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+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
     
 
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@@ -214,7 +214,7 @@
     
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@@ -379,6 +379,26 @@
   
     
     
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@@ -475,7 +495,7 @@
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+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
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@@ -1420,6 +1440,78 @@
   
     
     
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@@ -1445,10 +1537,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
         
           
-          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+          <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
             
   
   <span class="md-ellipsis">
@@ -1459,8 +1551,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_5">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1633,11 +1725,6 @@ <h2 id="sgptools.models.greedy_mi" class="doc doc-heading">
 
 
 
-
-
-
-
-
   <div class="doc doc-children">
 
 
@@ -1661,7 +1748,7 @@ <h3 id="sgptools.models.greedy_mi.GreedyMI" class="doc doc-heading">
     <div class="doc doc-contents ">
 
 
-        <p>Helper class to compute mutual information using a Gaussian process for a given set of sensor locations.
+      <p>Helper class to compute mutual information using a Gaussian process for a given set of sensor locations.
 Used by <code>get_greedy_mi_sol</code> function to compute the solution sensor placements using the Greedy-MI method.</p>
 
 
@@ -1685,9 +1772,7 @@ <h3 id="sgptools.models.greedy_mi.GreedyMI" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>S</code>
-            </td>
+            <td><code>S</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1701,9 +1786,7 @@ <h3 id="sgptools.models.greedy_mi.GreedyMI" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>V</code>
-            </td>
+            <td><code>V</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1717,9 +1800,7 @@ <h3 id="sgptools.models.greedy_mi.GreedyMI" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>noise_variance</code>
-            </td>
+            <td><code>noise_variance</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -1733,9 +1814,7 @@ <h3 id="sgptools.models.greedy_mi.GreedyMI" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>kernel</code>
-            </td>
+            <td><code>kernel</code></td>
             <td>
                   <code><span title="gpflow.kernels.Kernel">Kernel</span></code>
             </td>
@@ -1749,9 +1828,7 @@ <h3 id="sgptools.models.greedy_mi.GreedyMI" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>transform</code>
-            </td>
+            <td><code>transform</code></td>
             <td>
                   <code>Transform</code>
             </td>
@@ -1767,11 +1844,6 @@ <h3 id="sgptools.models.greedy_mi.GreedyMI" class="doc doc-heading">
       </tbody>
     </table>
 
-
-
-
-
-
               <details class="quote">
                 <summary>Source code in <code>sgptools/models/greedy_mi.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-20">20</a></span>
@@ -1931,7 +2003,7 @@ <h4 id="sgptools.models.greedy_mi.GreedyMI.mutual_info" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Computes mutual information using the points <code>x</code> </p>
+      <p>Computes mutual information using the points <code>x</code> </p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -1946,9 +2018,7 @@ <h4 id="sgptools.models.greedy_mi.GreedyMI.mutual_info" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>x</code>
-            </td>
+            <td><code>x</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2098,7 +2168,7 @@ <h3 id="sgptools.models.greedy_mi.get_greedy_mi_sol" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Get sensor placement solutions using the GP-based mutual information approach (submodular objective function). 
+      <p>Get sensor placement solutions using the GP-based mutual information approach (submodular objective function). 
 Uses a greedy algorithm to select sensor placements from a given discrete set of candidates locations.</p>
 
 
@@ -2122,9 +2192,7 @@ <h3 id="sgptools.models.greedy_mi.get_greedy_mi_sol" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>num_sensors</code>
-            </td>
+            <td><code>num_sensors</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -2138,9 +2206,7 @@ <h3 id="sgptools.models.greedy_mi.get_greedy_mi_sol" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>candidates</code>
-            </td>
+            <td><code>candidates</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2154,9 +2220,7 @@ <h3 id="sgptools.models.greedy_mi.get_greedy_mi_sol" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>X_train</code>
-            </td>
+            <td><code>X_train</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2170,9 +2234,7 @@ <h3 id="sgptools.models.greedy_mi.get_greedy_mi_sol" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>noise_variance</code>
-            </td>
+            <td><code>noise_variance</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -2186,9 +2248,7 @@ <h3 id="sgptools.models.greedy_mi.get_greedy_mi_sol" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>kernel</code>
-            </td>
+            <td><code>kernel</code></td>
             <td>
                   <code><span title="gpflow.kernels.Kernel">Kernel</span></code>
             </td>
@@ -2202,9 +2262,7 @@ <h3 id="sgptools.models.greedy_mi.get_greedy_mi_sol" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>transform</code>
-            </td>
+            <td><code>transform</code></td>
             <td>
                   <code>Transform</code>
             </td>
@@ -2218,9 +2276,7 @@ <h3 id="sgptools.models.greedy_mi.get_greedy_mi_sol" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>optimizer</code>
-            </td>
+            <td><code>optimizer</code></td>
             <td>
                   <code>str</code>
             </td>
@@ -2353,7 +2409,7 @@ <h3 id="sgptools.models.greedy_mi.get_greedy_mi_sol" class="doc doc-heading">
         
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@@ -2383,10 +2439,10 @@ <h3 id="sgptools.models.greedy_mi.get_greedy_mi_sol" class="doc doc-heading">
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+      <script src="../../assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
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diff --git a/api/models/Greedy-SGP.html b/api/models/Greedy-SGP.html
index d9d08cb..b1d62d7 100644
--- a/api/models/Greedy-SGP.html
+++ b/api/models/Greedy-SGP.html
@@ -20,7 +20,7 @@
       
       
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-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.49">
+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.31">
     
     
       
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+      <link rel="stylesheet" href="../../assets/stylesheets/main.3cba04c6.min.css">
       
         
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@@ -56,7 +56,7 @@
     
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@@ -379,6 +379,26 @@
   
     
     
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         <a href="../../examples/AIPP_non_point_FoV.html" class="md-tabs__link">
           
@@ -475,7 +495,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
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@@ -1420,6 +1440,78 @@
   
     
     
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+            BlueOS
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+    <li class="md-nav__item">
+      <a href="../../blueos-sgp-tools.html" class="md-nav__link">
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@@ -1445,10 +1537,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
         
           
-          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+          <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
             
   
   <span class="md-ellipsis">
@@ -1459,8 +1551,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_5">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1633,11 +1725,6 @@ <h2 id="sgptools.models.greedy_sgp" class="doc doc-heading">
 
 
 
-
-
-
-
-
   <div class="doc doc-children">
 
 
@@ -1661,7 +1748,7 @@ <h3 id="sgptools.models.greedy_sgp.GreedySGP" class="doc doc-heading">
     <div class="doc doc-contents ">
 
 
-        <p>Helper class to compute SGP's ELBO/optimization bound for a given set of sensor locations.
+      <p>Helper class to compute SGP's ELBO/optimization bound for a given set of sensor locations.
 Used by <code>get_greedy_sgp_sol</code> function to compute the solution sensor placements using the Greedy-SGP method.</p>
 
 
@@ -1684,9 +1771,7 @@ <h3 id="sgptools.models.greedy_sgp.GreedySGP" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>num_inducing</code>
-            </td>
+            <td><code>num_inducing</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -1700,9 +1785,7 @@ <h3 id="sgptools.models.greedy_sgp.GreedySGP" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>S</code>
-            </td>
+            <td><code>S</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1716,9 +1799,7 @@ <h3 id="sgptools.models.greedy_sgp.GreedySGP" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>V</code>
-            </td>
+            <td><code>V</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1732,9 +1813,7 @@ <h3 id="sgptools.models.greedy_sgp.GreedySGP" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>noise_variance</code>
-            </td>
+            <td><code>noise_variance</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -1748,9 +1827,7 @@ <h3 id="sgptools.models.greedy_sgp.GreedySGP" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>kernel</code>
-            </td>
+            <td><code>kernel</code></td>
             <td>
                   <code><span title="gpflow.kernels.Kernel">Kernel</span></code>
             </td>
@@ -1764,9 +1841,7 @@ <h3 id="sgptools.models.greedy_sgp.GreedySGP" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>transform</code>
-            </td>
+            <td><code>transform</code></td>
             <td>
                   <code>Transform</code>
             </td>
@@ -1782,11 +1857,6 @@ <h3 id="sgptools.models.greedy_sgp.GreedySGP" class="doc doc-heading">
       </tbody>
     </table>
 
-
-
-
-
-
               <details class="quote">
                 <summary>Source code in <code>sgptools/models/greedy_sgp.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-20">20</a></span>
@@ -1912,7 +1982,7 @@ <h4 id="sgptools.models.greedy_sgp.GreedySGP.bound" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Computes the SGP's optimization bound using the inducing points <code>x</code> </p>
+      <p>Computes the SGP's optimization bound using the inducing points <code>x</code> </p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -1927,9 +1997,7 @@ <h4 id="sgptools.models.greedy_sgp.GreedySGP.bound" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>x</code>
-            </td>
+            <td><code>x</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2041,7 +2109,7 @@ <h3 id="sgptools.models.greedy_sgp.get_greedy_sgp_sol" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Get sensor placement solutions using the Greedy-SGP method. Uses a greedy algorithm to 
+      <p>Get sensor placement solutions using the Greedy-SGP method. Uses a greedy algorithm to 
 select sensor placements from a given discrete set of candidates locations.</p>
 
 
@@ -2064,9 +2132,7 @@ <h3 id="sgptools.models.greedy_sgp.get_greedy_sgp_sol" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>num_sensors</code>
-            </td>
+            <td><code>num_sensors</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -2080,9 +2146,7 @@ <h3 id="sgptools.models.greedy_sgp.get_greedy_sgp_sol" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>candidates</code>
-            </td>
+            <td><code>candidates</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2096,9 +2160,7 @@ <h3 id="sgptools.models.greedy_sgp.get_greedy_sgp_sol" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>X_train</code>
-            </td>
+            <td><code>X_train</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2112,9 +2174,7 @@ <h3 id="sgptools.models.greedy_sgp.get_greedy_sgp_sol" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>noise_variance</code>
-            </td>
+            <td><code>noise_variance</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -2128,9 +2188,7 @@ <h3 id="sgptools.models.greedy_sgp.get_greedy_sgp_sol" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>kernel</code>
-            </td>
+            <td><code>kernel</code></td>
             <td>
                   <code><span title="gpflow.kernels.Kernel">Kernel</span></code>
             </td>
@@ -2144,9 +2202,7 @@ <h3 id="sgptools.models.greedy_sgp.get_greedy_sgp_sol" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
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-  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8z"/></svg>
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg>
   Back to top
 </button>
         
@@ -2307,10 +2363,10 @@ <h3 id="sgptools.models.greedy_sgp.get_greedy_sgp_sol" class="doc doc-heading">
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+    <script id="__config" type="application/json">{"base": "../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="../../assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="../../assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
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diff --git a/api/models/core/AugmentedGPR.html b/api/models/core/AugmentedGPR.html
index 5610ea4..34a7c4f 100644
--- a/api/models/core/AugmentedGPR.html
+++ b/api/models/core/AugmentedGPR.html
@@ -20,7 +20,7 @@
       
       
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-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.49">
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-      <link rel="stylesheet" href="../../../assets/stylesheets/main.6f8fc17f.min.css">
+      <link rel="stylesheet" href="../../../assets/stylesheets/main.3cba04c6.min.css">
       
         
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+    <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
   
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+<script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",function(){document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document$.subscribe(function(){var a=document.forms.feedback;if(void 0!==a)for(var e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}),location$.subscribe(function(e){n("config","G-Z2TVV3MF1S",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
   
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+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
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           <a href="javascript:void(0)" class="md-search__icon md-icon" title="Share" aria-label="Share" data-clipboard data-clipboard-text="" data-md-component="search-share" tabindex="-1">
             
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     GitHub
@@ -379,6 +379,26 @@
   
     
     
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@@ -475,7 +495,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
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+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -1396,6 +1416,78 @@
   
     
     
+      
+        
+      
+    
+    
+      
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+        
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+    <li class="md-nav__item md-nav__item--nested">
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+          </label>
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+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_5">
+            <span class="md-nav__icon md-icon"></span>
+            BlueOS
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
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+  
+  
+    <li class="md-nav__item">
+      <a href="../../../blueos-sgp-tools.html" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    blueos-sgp-tools
+  </span>
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+              
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+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    
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@@ -1421,10 +1513,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
         
           
-          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+          <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
             
   
   <span class="md-ellipsis">
@@ -1435,8 +1527,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_5">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1607,12 +1699,7 @@ <h2 id="sgptools.models.core.augmented_gpr" class="doc doc-heading">
 
     <div class="doc doc-contents first">
 
-        <p>Provides a Gaussian process model with expand and aggregate functions</p>
-
-
-
-
-
+      <p>Provides a Gaussian process model with expand and aggregate functions</p>
 
 
 
@@ -1641,7 +1728,7 @@ <h3 id="sgptools.models.core.augmented_gpr.AugmentedGPR" class="doc doc-heading"
               Bases: <code><span title="gpflow.models.GPR">GPR</span></code></p>
 
 
-        <p>GPR model from the GPFlow library augmented to use a transform object's
+      <p>GPR model from the GPFlow library augmented to use a transform object's
 expand and aggregate functions on the data points where necessary.  </p>
 
 
@@ -1665,9 +1752,7 @@ <h3 id="sgptools.models.core.augmented_gpr.AugmentedGPR" class="doc doc-heading"
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>data</code>
-            </td>
+            <td><code>data</code></td>
             <td>
                   <code>tuple</code>
             </td>
@@ -1681,9 +1766,7 @@ <h3 id="sgptools.models.core.augmented_gpr.AugmentedGPR" class="doc doc-heading"
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>kernel</code>
-            </td>
+            <td><code>kernel</code></td>
             <td>
                   <code><span title="gpflow.kernels.Kernel">Kernel</span></code>
             </td>
@@ -1697,9 +1780,7 @@ <h3 id="sgptools.models.core.augmented_gpr.AugmentedGPR" class="doc doc-heading"
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>noise_variance</code>
-            </td>
+            <td><code>noise_variance</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -1713,9 +1794,7 @@ <h3 id="sgptools.models.core.augmented_gpr.AugmentedGPR" class="doc doc-heading"
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>transform</code>
-            </td>
+            <td><code>transform</code></td>
             <td>
                   <code><a class="autorefs autorefs-internal" title="sgptools.models.core.transformations.Transform" href="Transformations.html#sgptools.models.core.transformations.Transform">Transform</a></code>
             </td>
@@ -1731,11 +1810,6 @@ <h3 id="sgptools.models.core.augmented_gpr.AugmentedGPR" class="doc doc-heading"
       </tbody>
     </table>
 
-
-
-
-
-
               <details class="quote">
                 <summary>Source code in <code>sgptools/models/core/augmented_gpr.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-28">28</a></span>
@@ -1924,7 +1998,7 @@ <h3 id="sgptools.models.core.augmented_gpr.AugmentedGPR" class="doc doc-heading"
         
           <button type="button" class="md-top md-icon" data-md-component="top" hidden>
   
-  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8z"/></svg>
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg>
   Back to top
 </button>
         
@@ -1954,10 +2028,10 @@ <h3 id="sgptools.models.core.augmented_gpr.AugmentedGPR" class="doc doc-heading"
     </div>
     
     
-    <script id="__config" type="application/json">{"base": "../../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../../assets/javascripts/workers/search.6ce7567c.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "../../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="../../../assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="../../../assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
   </body>
diff --git a/api/models/core/AugmentedSGPR.html b/api/models/core/AugmentedSGPR.html
index 792a62f..e775be4 100644
--- a/api/models/core/AugmentedSGPR.html
+++ b/api/models/core/AugmentedSGPR.html
@@ -20,7 +20,7 @@
       
       
       <link rel="icon" href="../../../assets/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.49">
+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.31">
     
     
       
@@ -28,7 +28,7 @@
       
     
     
-      <link rel="stylesheet" href="../../../assets/stylesheets/main.6f8fc17f.min.css">
+      <link rel="stylesheet" href="../../../assets/stylesheets/main.3cba04c6.min.css">
       
         
         <link rel="stylesheet" href="../../../assets/stylesheets/palette.06af60db.min.css">
@@ -56,7 +56,7 @@
     
       <link rel="stylesheet" href="../../../stylesheets/extra.css">
     
-    <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce(((e,_)=>(e<<5)-e+_.charCodeAt(0)),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
   
@@ -65,7 +65,7 @@
   
   
 
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+<script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",function(){document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document$.subscribe(function(){var a=document.forms.feedback;if(void 0!==a)for(var e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}),location$.subscribe(function(e){n("config","G-Z2TVV3MF1S",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
   
     <script>"undefined"!=typeof __md_analytics&&__md_analytics()</script>
   
@@ -153,7 +153,7 @@
     <!-- Button to open drawer -->
     <label class="md-header__button md-icon" for="__drawer">
       
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3zm0 5h18v2H3zm0 5h18v2H3z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
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@@ -184,7 +184,7 @@
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       <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5s-1.65.15-2.39.42zM3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29zm.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14zM20.65 7l-1.77 3.79a7.02 7.02 0 0 0-2.38-4.15zm-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29zM12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5c-.84 0-1.65.15-2.39.42L12 2M3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29L3.34 7m.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14L3.36 17M20.65 7l-1.77 3.79a7.023 7.023 0 0 0-2.38-4.15l4.15.36m-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29L20.64 17M12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44L12 22Z"/></svg>
       </label>
     
   
@@ -194,7 +194,7 @@
     <input class="md-option" data-md-color-media="(prefers-color-scheme: dark)" data-md-color-scheme="slate" data-md-color-primary="custom" data-md-color-accent="custom"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_1">
     
       <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3zm3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95zm-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3 3.19.09m3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95 2.06.05m-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31Z"/></svg>
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@@ -204,7 +204,7 @@
 
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-      <script>var palette=__md_get("__palette");if(palette&&palette.color){if("(prefers-color-scheme)"===palette.color.media){var media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']");palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent")}for(var[key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
     
 
     <!-- Site language selector -->
@@ -214,7 +214,7 @@
     
       <label class="md-header__button md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
 
       <!-- Search interface -->
@@ -225,20 +225,20 @@
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
           <a href="javascript:void(0)" class="md-search__icon md-icon" title="Share" aria-label="Share" data-clipboard data-clipboard-text="" data-md-component="search-share" tabindex="-1">
             
-            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7s-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91s2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08"/></svg>
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7 0-.24-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91 1.61 0 2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08Z"/></svg>
           </a>
         
         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
           
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12z"/></svg>
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
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@@ -265,7 +265,7 @@
         <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -379,6 +379,26 @@
   
     
     
+      <li class="md-tabs__item">
+        <a href="../../../blueos-sgp-tools.html" class="md-tabs__link">
+          
+  
+    
+  
+  BlueOS
+
+        </a>
+      </li>
+    
+  
+
+      
+        
+  
+  
+  
+    
+    
       <li class="md-tabs__item">
         <a href="../../../examples/AIPP_non_point_FoV.html" class="md-tabs__link">
           
@@ -475,7 +495,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
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   <div class="md-source__repository">
     GitHub
@@ -1429,6 +1449,78 @@
   
     
     
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+      
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+  
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+    
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@@ -1454,10 +1546,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
         
           
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+          <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
             
   
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-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
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+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1640,12 +1732,7 @@ <h2 id="sgptools.models.core.augmented_sgpr" class="doc doc-heading">
 
     <div class="doc doc-contents first">
 
-        <p>Provides a sparse Gaussian process model with update, expand, and aggregate functions</p>
-
-
-
-
-
+      <p>Provides a sparse Gaussian process model with update, expand, and aggregate functions</p>
 
 
 
@@ -1674,7 +1761,7 @@ <h3 id="sgptools.models.core.augmented_sgpr.AugmentedSGPR" class="doc doc-headin
               Bases: <code><span title="gpflow.models.SGPR">SGPR</span></code></p>
 
 
-        <p>SGPR model from the GPFlow library augmented to use a transform object's
+      <p>SGPR model from the GPFlow library augmented to use a transform object's
 expand and aggregate functions on the inducing points where necessary. The object
 has an additional update function to update the kernel and noise variance parameters 
 (currently, the online updates part works only with RBF kernels).  </p>
@@ -1700,9 +1787,7 @@ <h3 id="sgptools.models.core.augmented_sgpr.AugmentedSGPR" class="doc doc-headin
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>data</code>
-            </td>
+            <td><code>data</code></td>
             <td>
                   <code>tuple</code>
             </td>
@@ -1716,9 +1801,7 @@ <h3 id="sgptools.models.core.augmented_sgpr.AugmentedSGPR" class="doc doc-headin
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>kernel</code>
-            </td>
+            <td><code>kernel</code></td>
             <td>
                   <code><span title="gpflow.kernels.Kernel">Kernel</span></code>
             </td>
@@ -1732,9 +1815,7 @@ <h3 id="sgptools.models.core.augmented_sgpr.AugmentedSGPR" class="doc doc-headin
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>noise_variance</code>
-            </td>
+            <td><code>noise_variance</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -1748,9 +1829,7 @@ <h3 id="sgptools.models.core.augmented_sgpr.AugmentedSGPR" class="doc doc-headin
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>inducing_variable</code>
-            </td>
+            <td><code>inducing_variable</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1764,9 +1843,7 @@ <h3 id="sgptools.models.core.augmented_sgpr.AugmentedSGPR" class="doc doc-headin
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>transform</code>
-            </td>
+            <td><code>transform</code></td>
             <td>
                   <code><a class="autorefs autorefs-internal" title="sgptools.models.core.transformations.Transform" href="Transformations.html#sgptools.models.core.transformations.Transform">Transform</a></code>
             </td>
@@ -1780,9 +1857,7 @@ <h3 id="sgptools.models.core.augmented_sgpr.AugmentedSGPR" class="doc doc-headin
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>inducing_variable_time</code>
-            </td>
+            <td><code>inducing_variable_time</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1799,11 +1874,6 @@ <h3 id="sgptools.models.core.augmented_sgpr.AugmentedSGPR" class="doc doc-headin
       </tbody>
     </table>
 
-
-
-
-
-
               <details class="quote">
                 <summary>Source code in <code>sgptools/models/core/augmented_sgpr.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-32"> 32</a></span>
@@ -2129,7 +2199,7 @@ <h4 id="sgptools.models.core.augmented_sgpr.AugmentedSGPR.elbo" class="doc doc-h
 
     <div class="doc doc-contents ">
 
-        <p>Construct a tensorflow function to compute the bound on the marginal
+      <p>Construct a tensorflow function to compute the bound on the marginal
 likelihood. For a derivation of the terms in here, see the associated
 SGPR notebook.</p>
 
@@ -2181,7 +2251,7 @@ <h4 id="sgptools.models.core.augmented_sgpr.AugmentedSGPR.predict_f" class="doc
 
     <div class="doc doc-contents ">
 
-        <p>Compute the mean and variance of the latent function at some new points
+      <p>Compute the mean and variance of the latent function at some new points
 Xnew. For a derivation of the terms in here, see the associated SGPR
 notebook.</p>
 
@@ -2301,7 +2371,7 @@ <h4 id="sgptools.models.core.augmented_sgpr.AugmentedSGPR.update" class="doc doc
 
     <div class="doc doc-contents ">
 
-        <p>Update SGP noise variance and kernel function parameters</p>
+      <p>Update SGP noise variance and kernel function parameters</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -2316,9 +2386,7 @@ <h4 id="sgptools.models.core.augmented_sgpr.AugmentedSGPR.update" class="doc doc
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>noise_variance</code>
-            </td>
+            <td><code>noise_variance</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -2332,9 +2400,7 @@ <h4 id="sgptools.models.core.augmented_sgpr.AugmentedSGPR.update" class="doc doc
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>kernel</code>
-            </td>
+            <td><code>kernel</code></td>
             <td>
                   <code><span title="gpflow.kernels.Kernel">Kernel</span></code>
             </td>
@@ -2417,7 +2483,7 @@ <h4 id="sgptools.models.core.augmented_sgpr.AugmentedSGPR.update" class="doc doc
         
           <button type="button" class="md-top md-icon" data-md-component="top" hidden>
   
-  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8z"/></svg>
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg>
   Back to top
 </button>
         
@@ -2447,10 +2513,10 @@ <h4 id="sgptools.models.core.augmented_sgpr.AugmentedSGPR.update" class="doc doc
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+    <script id="__config" type="application/json">{"base": "../../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="../../../assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="../../../assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
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diff --git a/api/models/core/OSGPR.html b/api/models/core/OSGPR.html
index d423dfc..6cd9efc 100644
--- a/api/models/core/OSGPR.html
+++ b/api/models/core/OSGPR.html
@@ -20,7 +20,7 @@
       
       
       <link rel="icon" href="../../../assets/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.49">
+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.31">
     
     
       
@@ -28,7 +28,7 @@
       
     
     
-      <link rel="stylesheet" href="../../../assets/stylesheets/main.6f8fc17f.min.css">
+      <link rel="stylesheet" href="../../../assets/stylesheets/main.3cba04c6.min.css">
       
         
         <link rel="stylesheet" href="../../../assets/stylesheets/palette.06af60db.min.css">
@@ -56,7 +56,7 @@
     
       <link rel="stylesheet" href="../../../stylesheets/extra.css">
     
-    <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce(((e,_)=>(e<<5)-e+_.charCodeAt(0)),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
   
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+<script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",function(){document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document$.subscribe(function(){var a=document.forms.feedback;if(void 0!==a)for(var e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}),location$.subscribe(function(e){n("config","G-Z2TVV3MF1S",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
   
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+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
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       <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5s-1.65.15-2.39.42zM3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29zm.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14zM20.65 7l-1.77 3.79a7.02 7.02 0 0 0-2.38-4.15zm-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29zM12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5c-.84 0-1.65.15-2.39.42L12 2M3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29L3.34 7m.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14L3.36 17M20.65 7l-1.77 3.79a7.023 7.023 0 0 0-2.38-4.15l4.15.36m-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29L20.64 17M12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44L12 22Z"/></svg>
       </label>
     
   
@@ -194,7 +194,7 @@
     <input class="md-option" data-md-color-media="(prefers-color-scheme: dark)" data-md-color-scheme="slate" data-md-color-primary="custom" data-md-color-accent="custom"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_1">
     
       <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3zm3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95zm-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3 3.19.09m3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95 2.06.05m-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31Z"/></svg>
       </label>
     
   
@@ -204,7 +204,7 @@
 
     <!-- User preference: color palette -->
     
-      <script>var palette=__md_get("__palette");if(palette&&palette.color){if("(prefers-color-scheme)"===palette.color.media){var media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']");palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent")}for(var[key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
     
 
     <!-- Site language selector -->
@@ -214,7 +214,7 @@
     
       <label class="md-header__button md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
 
       <!-- Search interface -->
@@ -225,20 +225,20 @@
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
           <a href="javascript:void(0)" class="md-search__icon md-icon" title="Share" aria-label="Share" data-clipboard data-clipboard-text="" data-md-component="search-share" tabindex="-1">
             
-            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7s-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91s2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08"/></svg>
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7 0-.24-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91 1.61 0 2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08Z"/></svg>
           </a>
         
         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
           
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12z"/></svg>
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
       
@@ -265,7 +265,7 @@
         <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -379,6 +379,26 @@
   
     
     
+      <li class="md-tabs__item">
+        <a href="../../../blueos-sgp-tools.html" class="md-tabs__link">
+          
+  
+    
+  
+  BlueOS
+
+        </a>
+      </li>
+    
+  
+
+      
+        
+  
+  
+  
+    
+    
       <li class="md-tabs__item">
         <a href="../../../examples/AIPP_non_point_FoV.html" class="md-tabs__link">
           
@@ -475,7 +495,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
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   </div>
   <div class="md-source__repository">
     GitHub
@@ -1438,6 +1458,78 @@
   
     
     
+      
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+      
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+        
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+    <li class="md-nav__item md-nav__item--nested">
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+            BlueOS
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+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
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+  
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+    <li class="md-nav__item">
+      <a href="../../../blueos-sgp-tools.html" class="md-nav__link">
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+    blueos-sgp-tools
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+        </nav>
+      
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+  
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+    
+      
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+  
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+  
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@@ -1463,10 +1555,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
         
           
-          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+          <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
             
   
   <span class="md-ellipsis">
@@ -1477,8 +1569,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_5">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1649,12 +1741,7 @@ <h2 id="sgptools.models.core.osgpr" class="doc doc-heading">
 
     <div class="doc doc-contents first">
 
-        <p>Provides a streaming sparse Gaussian process model along with initialization function</p>
-
-
-
-
-
+      <p>Provides a streaming sparse Gaussian process model along with initialization function</p>
 
 
 
@@ -1683,7 +1770,7 @@ <h3 id="sgptools.models.core.osgpr.OSGPR_VFE" class="doc doc-heading">
               Bases: <code><span title="gpflow.models.GPModel">GPModel</span></code>, <code><span title="gpflow.models.InternalDataTrainingLossMixin">InternalDataTrainingLossMixin</span></code></p>
 
 
-        <p>Online Sparse Variational GP regression model from <a href="https://github.com/thangbui/streaming_sparse_gp/tree/master">streaming_sparse_gp</a></p>
+      <p>Online Sparse Variational GP regression model from <a href="https://github.com/thangbui/streaming_sparse_gp/tree/master">streaming_sparse_gp</a></p>
 
 
 <details class="refer-to-the-following-paper-for-more-details" open>
@@ -1705,9 +1792,7 @@ <h3 id="sgptools.models.core.osgpr.OSGPR_VFE" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>data</code>
-            </td>
+            <td><code>data</code></td>
             <td>
                   <code>tuple</code>
             </td>
@@ -1721,9 +1806,7 @@ <h3 id="sgptools.models.core.osgpr.OSGPR_VFE" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>kernel</code>
-            </td>
+            <td><code>kernel</code></td>
             <td>
                   <code><span title="gpflow.kernels.Kernel">Kernel</span></code>
             </td>
@@ -1737,9 +1820,7 @@ <h3 id="sgptools.models.core.osgpr.OSGPR_VFE" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>mu_old</code>
-            </td>
+            <td><code>mu_old</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1753,9 +1834,7 @@ <h3 id="sgptools.models.core.osgpr.OSGPR_VFE" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>Su_old</code>
-            </td>
+            <td><code>Su_old</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1769,9 +1848,7 @@ <h3 id="sgptools.models.core.osgpr.OSGPR_VFE" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>Kaa_old</code>
-            </td>
+            <td><code>Kaa_old</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1785,9 +1862,7 @@ <h3 id="sgptools.models.core.osgpr.OSGPR_VFE" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>Z_old</code>
-            </td>
+            <td><code>Z_old</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1801,9 +1876,7 @@ <h3 id="sgptools.models.core.osgpr.OSGPR_VFE" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>Z</code>
-            </td>
+            <td><code>Z</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1817,9 +1890,7 @@ <h3 id="sgptools.models.core.osgpr.OSGPR_VFE" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>mean_function</code>
-            </td>
+            <td><code>mean_function</code></td>
             <td>
                   <code>function</code>
             </td>
@@ -1835,11 +1906,6 @@ <h3 id="sgptools.models.core.osgpr.OSGPR_VFE" class="doc doc-heading">
       </tbody>
     </table>
 
-
-
-
-
-
               <details class="quote">
                 <summary>Source code in <code>sgptools/models/core/osgpr.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-29"> 29</a></span>
@@ -2257,7 +2323,7 @@ <h4 id="sgptools.models.core.osgpr.OSGPR_VFE.maximum_log_likelihood_objective" c
 
     <div class="doc doc-contents ">
 
-        <p>Construct a tensorflow function to compute the bound on the marginal
+      <p>Construct a tensorflow function to compute the bound on the marginal
 likelihood.</p>
 
             <details class="quote">
@@ -2384,7 +2450,7 @@ <h4 id="sgptools.models.core.osgpr.OSGPR_VFE.predict_f" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Compute the mean and variance of the latent function at some new points
+      <p>Compute the mean and variance of the latent function at some new points
 Xnew.</p>
 
             <details class="quote">
@@ -2471,7 +2537,7 @@ <h4 id="sgptools.models.core.osgpr.OSGPR_VFE.update" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Configure the OSGPR to adapt to a new batch of data. 
+      <p>Configure the OSGPR to adapt to a new batch of data. 
 Note: The OSGPR needs to be trained using gradient-based approaches after update.</p>
 
 
@@ -2487,9 +2553,7 @@ <h4 id="sgptools.models.core.osgpr.OSGPR_VFE.update" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>data</code>
-            </td>
+            <td><code>data</code></td>
             <td>
                   <code>tuple</code>
             </td>
@@ -2576,7 +2640,7 @@ <h3 id="sgptools.models.core.osgpr.init_osgpr" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Initialize a VFE OSGPR model with an RBF kernel with 
+      <p>Initialize a VFE OSGPR model with an RBF kernel with 
 unit variance and lengthcales, and 0.001 noise variance.
 Used in the Online Continuous SGP approach. </p>
 
@@ -2593,9 +2657,7 @@ <h3 id="sgptools.models.core.osgpr.init_osgpr" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>X_train</code>
-            </td>
+            <td><code>X_train</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2611,9 +2673,7 @@ <h3 id="sgptools.models.core.osgpr.init_osgpr" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>num_inducing</code>
-            </td>
+            <td><code>num_inducing</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -2627,9 +2687,7 @@ <h3 id="sgptools.models.core.osgpr.init_osgpr" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>lengthscales</code>
-            </td>
+            <td><code>lengthscales</code></td>
             <td>
                   <code>float or list</code>
             </td>
@@ -2644,9 +2702,7 @@ <h3 id="sgptools.models.core.osgpr.init_osgpr" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>variance</code>
-            </td>
+            <td><code>variance</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -2660,9 +2716,7 @@ <h3 id="sgptools.models.core.osgpr.init_osgpr" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>noise_variance</code>
-            </td>
+            <td><code>noise_variance</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -2676,9 +2730,7 @@ <h3 id="sgptools.models.core.osgpr.init_osgpr" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>kernel</code>
-            </td>
+            <td><code>kernel</code></td>
             <td>
                   <code><span title="gpflow.kernels.Kernel">Kernel</span></code>
             </td>
@@ -2855,7 +2907,7 @@ <h3 id="sgptools.models.core.osgpr.init_osgpr" class="doc doc-heading">
         
           <button type="button" class="md-top md-icon" data-md-component="top" hidden>
   
-  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8z"/></svg>
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg>
   Back to top
 </button>
         
@@ -2885,10 +2937,10 @@ <h3 id="sgptools.models.core.osgpr.init_osgpr" class="doc doc-heading">
     </div>
     
     
-    <script id="__config" type="application/json">{"base": "../../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../../assets/javascripts/workers/search.6ce7567c.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "../../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="../../../assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="../../../assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
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diff --git a/api/models/core/Transformations.html b/api/models/core/Transformations.html
index d33e051..79db0d7 100644
--- a/api/models/core/Transformations.html
+++ b/api/models/core/Transformations.html
@@ -20,7 +20,7 @@
       
       
       <link rel="icon" href="../../../assets/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.49">
+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.31">
     
     
       
@@ -28,7 +28,7 @@
       
     
     
-      <link rel="stylesheet" href="../../../assets/stylesheets/main.6f8fc17f.min.css">
+      <link rel="stylesheet" href="../../../assets/stylesheets/main.3cba04c6.min.css">
       
         
         <link rel="stylesheet" href="../../../assets/stylesheets/palette.06af60db.min.css">
@@ -56,7 +56,7 @@
     
       <link rel="stylesheet" href="../../../stylesheets/extra.css">
     
-    <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce(((e,_)=>(e<<5)-e+_.charCodeAt(0)),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
   
@@ -65,7 +65,7 @@
   
   
 
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+<script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",function(){document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document$.subscribe(function(){var a=document.forms.feedback;if(void 0!==a)for(var e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}),location$.subscribe(function(e){n("config","G-Z2TVV3MF1S",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
   
     <script>"undefined"!=typeof __md_analytics&&__md_analytics()</script>
   
@@ -153,7 +153,7 @@
     <!-- Button to open drawer -->
     <label class="md-header__button md-icon" for="__drawer">
       
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3zm0 5h18v2H3zm0 5h18v2H3z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
 
     <!-- Header title -->
@@ -184,7 +184,7 @@
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       <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5s-1.65.15-2.39.42zM3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29zm.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14zM20.65 7l-1.77 3.79a7.02 7.02 0 0 0-2.38-4.15zm-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29zM12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5c-.84 0-1.65.15-2.39.42L12 2M3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29L3.34 7m.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14L3.36 17M20.65 7l-1.77 3.79a7.023 7.023 0 0 0-2.38-4.15l4.15.36m-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29L20.64 17M12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44L12 22Z"/></svg>
       </label>
     
   
@@ -194,7 +194,7 @@
     <input class="md-option" data-md-color-media="(prefers-color-scheme: dark)" data-md-color-scheme="slate" data-md-color-primary="custom" data-md-color-accent="custom"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_1">
     
       <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3zm3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95zm-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3 3.19.09m3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95 2.06.05m-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31Z"/></svg>
       </label>
     
   
@@ -204,7 +204,7 @@
 
     <!-- User preference: color palette -->
     
-      <script>var palette=__md_get("__palette");if(palette&&palette.color){if("(prefers-color-scheme)"===palette.color.media){var media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']");palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent")}for(var[key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
     
 
     <!-- Site language selector -->
@@ -214,7 +214,7 @@
     
       <label class="md-header__button md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
 
       <!-- Search interface -->
@@ -225,20 +225,20 @@
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
           <a href="javascript:void(0)" class="md-search__icon md-icon" title="Share" aria-label="Share" data-clipboard data-clipboard-text="" data-md-component="search-share" tabindex="-1">
             
-            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7s-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91s2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08"/></svg>
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7 0-.24-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91 1.61 0 2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08Z"/></svg>
           </a>
         
         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
           
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12z"/></svg>
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
       
@@ -265,7 +265,7 @@
         <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -379,6 +379,26 @@
   
     
     
+      <li class="md-tabs__item">
+        <a href="../../../blueos-sgp-tools.html" class="md-tabs__link">
+          
+  
+    
+  
+  BlueOS
+
+        </a>
+      </li>
+    
+  
+
+      
+        
+  
+  
+  
+    
+    
       <li class="md-tabs__item">
         <a href="../../../examples/AIPP_non_point_FoV.html" class="md-tabs__link">
           
@@ -475,7 +495,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
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+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -1573,6 +1593,78 @@
   
     
     
+      
+        
+      
+    
+    
+      
+      
+        
+      
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+    <li class="md-nav__item md-nav__item--nested">
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+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        
+          
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+            
+  
+  <span class="md-ellipsis">
+    BlueOS
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+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
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+            <span class="md-nav__icon md-icon"></span>
+            BlueOS
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+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../../blueos-sgp-tools.html" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    blueos-sgp-tools
+  </span>
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+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
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+  
+    
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@@ -1598,10 +1690,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
         
           
-          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+          <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
             
   
   <span class="md-ellipsis">
@@ -1612,8 +1704,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_5">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1784,12 +1876,7 @@ <h2 id="sgptools.models.core.transformations" class="doc doc-heading">
 
     <div class="doc doc-contents first">
 
-        <p>Provides transforms to model complex sensor field of views and handle informative path planning</p>
-
-
-
-
-
+      <p>Provides transforms to model complex sensor field of views and handle informative path planning</p>
 
 
 
@@ -1818,7 +1905,7 @@ <h3 id="sgptools.models.core.transformations.IPPTransform" class="doc doc-headin
               Bases: <code><a class="autorefs autorefs-internal" title="sgptools.models.core.transformations.Transform" href="#sgptools.models.core.transformations.Transform">Transform</a></code></p>
 
 
-        <p>Transform to model IPP problems</p>
+      <p>Transform to model IPP problems</p>
 
 
 <details class="usage-details" open>
@@ -1845,9 +1932,7 @@ <h3 id="sgptools.models.core.transformations.IPPTransform" class="doc doc-headin
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>sampling_rate</code>
-            </td>
+            <td><code>sampling_rate</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -1861,9 +1946,7 @@ <h3 id="sgptools.models.core.transformations.IPPTransform" class="doc doc-headin
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>distance_budget</code>
-            </td>
+            <td><code>distance_budget</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -1877,9 +1960,7 @@ <h3 id="sgptools.models.core.transformations.IPPTransform" class="doc doc-headin
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>num_robots</code>
-            </td>
+            <td><code>num_robots</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -1893,9 +1974,7 @@ <h3 id="sgptools.models.core.transformations.IPPTransform" class="doc doc-headin
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>Xu_fixed</code>
-            </td>
+            <td><code>Xu_fixed</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1909,9 +1988,7 @@ <h3 id="sgptools.models.core.transformations.IPPTransform" class="doc doc-headin
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>num_dim</code>
-            </td>
+            <td><code>num_dim</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -1925,9 +2002,7 @@ <h3 id="sgptools.models.core.transformations.IPPTransform" class="doc doc-headin
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>sensor_model</code>
-            </td>
+            <td><code>sensor_model</code></td>
             <td>
                   <code><a class="autorefs autorefs-internal" title="sgptools.models.core.transformations.Transform" href="#sgptools.models.core.transformations.Transform">Transform</a></code>
             </td>
@@ -1942,9 +2017,7 @@ <h3 id="sgptools.models.core.transformations.IPPTransform" class="doc doc-headin
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>aggregate_fov</code>
-            </td>
+            <td><code>aggregate_fov</code></td>
             <td>
                   <code>bool</code>
             </td>
@@ -1962,11 +2035,6 @@ <h3 id="sgptools.models.core.transformations.IPPTransform" class="doc doc-headin
       </tbody>
     </table>
 
-
-
-
-
-
               <details class="quote">
                 <summary>Source code in <code>sgptools/models/core/transformations.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-94"> 94</a></span>
@@ -2338,7 +2406,7 @@ <h4 id="sgptools.models.core.transformations.IPPTransform.aggregate" class="doc
 
     <div class="doc doc-contents ">
 
-        <p>Applies the aggregation transform to kernel matrices. Checks <code>sensor_model</code> 
+      <p>Applies the aggregation transform to kernel matrices. Checks <code>sensor_model</code> 
    and uses the appropriate aggregation transform. </p>
 
 
@@ -2354,9 +2422,7 @@ <h4 id="sgptools.models.core.transformations.IPPTransform.aggregate" class="doc
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>k</code>
-            </td>
+            <td><code>k</code></td>
             <td>
                   <code>tensor</code>
             </td>
@@ -2450,7 +2516,7 @@ <h4 id="sgptools.models.core.transformations.IPPTransform.constraints" class="do
 
     <div class="doc doc-contents ">
 
-        <p>Computes the distance constraint term that is added to the SGP's optimization function.
+      <p>Computes the distance constraint term that is added to the SGP's optimization function.
 Each robot can be assigned a different distance budget.</p>
 
 
@@ -2466,9 +2532,7 @@ <h4 id="sgptools.models.core.transformations.IPPTransform.constraints" class="do
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>Xu</code>
-            </td>
+            <td><code>Xu</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2563,7 +2627,7 @@ <h4 id="sgptools.models.core.transformations.IPPTransform.distance" class="doc d
 
     <div class="doc doc-contents ">
 
-        <p>Computes the distance incured by sequentially visiting the inducing points
+      <p>Computes the distance incured by sequentially visiting the inducing points
 Args:
     Xu (ndarray): (m, num_dim); Inducing points from which to compute the path lengths
                   <code>m</code> is the number of inducing points
@@ -2631,7 +2695,7 @@ <h4 id="sgptools.models.core.transformations.IPPTransform.expand" class="doc doc
 
     <div class="doc doc-contents ">
 
-        <p>Sample points between each pair of inducing points to form the path</p>
+      <p>Sample points between each pair of inducing points to form the path</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -2646,9 +2710,7 @@ <h4 id="sgptools.models.core.transformations.IPPTransform.expand" class="doc doc
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>Xu</code>
-            </td>
+            <td><code>Xu</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2662,9 +2724,7 @@ <h4 id="sgptools.models.core.transformations.IPPTransform.expand" class="doc doc
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>expand_sensor_model</code>
-            </td>
+            <td><code>expand_sensor_model</code></td>
             <td>
                   <code>bool</code>
             </td>
@@ -2802,7 +2862,7 @@ <h4 id="sgptools.models.core.transformations.IPPTransform.update_Xu_fixed" class
 
     <div class="doc doc-contents ">
 
-        <p>Function to update the visited waypoints</p>
+      <p>Function to update the visited waypoints</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -2817,9 +2877,7 @@ <h4 id="sgptools.models.core.transformations.IPPTransform.update_Xu_fixed" class
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>Xu_fixed</code>
-            </td>
+            <td><code>Xu_fixed</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2892,7 +2950,7 @@ <h3 id="sgptools.models.core.transformations.SquareHeightTransform" class="doc d
               Bases: <code><a class="autorefs autorefs-internal" title="sgptools.models.core.transformations.Transform" href="#sgptools.models.core.transformations.Transform">Transform</a></code></p>
 
 
-        <p>Non-point Transform to model a height-dependent square FoV</p>
+      <p>Non-point Transform to model a height-dependent square FoV</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -2907,9 +2965,7 @@ <h3 id="sgptools.models.core.transformations.SquareHeightTransform" class="doc d
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>num_side</code>
-            </td>
+            <td><code>num_side</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -2923,9 +2979,7 @@ <h3 id="sgptools.models.core.transformations.SquareHeightTransform" class="doc d
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>aggregate_fov</code>
-            </td>
+            <td><code>aggregate_fov</code></td>
             <td>
                   <code>bool</code>
             </td>
@@ -2942,11 +2996,6 @@ <h3 id="sgptools.models.core.transformations.SquareHeightTransform" class="doc d
       </tbody>
     </table>
 
-
-
-
-
-
               <details class="quote">
                 <summary>Source code in <code>sgptools/models/core/transformations.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-345">345</a></span>
@@ -3152,7 +3201,7 @@ <h4 id="sgptools.models.core.transformations.SquareHeightTransform.distance" cla
 
     <div class="doc doc-contents ">
 
-        <p>Computes the distance incured by sequentially visiting the inducing points
+      <p>Computes the distance incured by sequentially visiting the inducing points
 Args:
     Xu (ndarray): (m, 3); Inducing points from which to compute the path lengths.
                   <code>m</code> is the number of inducing points.</p>
@@ -3224,7 +3273,7 @@ <h4 id="sgptools.models.core.transformations.SquareHeightTransform.enable_aggreg
 
     <div class="doc doc-contents ">
 
-        <p>Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the 
+      <p>Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the 
 covariance matrix size.</p>
 
 
@@ -3240,9 +3289,7 @@ <h4 id="sgptools.models.core.transformations.SquareHeightTransform.enable_aggreg
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>size</code>
-            </td>
+            <td><code>size</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -3301,7 +3348,7 @@ <h4 id="sgptools.models.core.transformations.SquareHeightTransform.expand" class
 
     <div class="doc doc-contents ">
 
-        <p>Applies the expansion transform to the inducing points</p>
+      <p>Applies the expansion transform to the inducing points</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -3316,9 +3363,7 @@ <h4 id="sgptools.models.core.transformations.SquareHeightTransform.expand" class
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>Xu</code>
-            </td>
+            <td><code>Xu</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -3456,7 +3501,7 @@ <h3 id="sgptools.models.core.transformations.SquareTransform" class="doc doc-hea
               Bases: <code><a class="autorefs autorefs-internal" title="sgptools.models.core.transformations.Transform" href="#sgptools.models.core.transformations.Transform">Transform</a></code></p>
 
 
-        <p>Non-point Transform to model a square FoV. Only works for single robot cases. </p>
+      <p>Non-point Transform to model a square FoV. Only works for single robot cases. </p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -3471,9 +3516,7 @@ <h3 id="sgptools.models.core.transformations.SquareTransform" class="doc doc-hea
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>length</code>
-            </td>
+            <td><code>length</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -3487,9 +3530,7 @@ <h3 id="sgptools.models.core.transformations.SquareTransform" class="doc doc-hea
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>num_side</code>
-            </td>
+            <td><code>num_side</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -3503,9 +3544,7 @@ <h3 id="sgptools.models.core.transformations.SquareTransform" class="doc doc-hea
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>aggregate_fov</code>
-            </td>
+            <td><code>aggregate_fov</code></td>
             <td>
                   <code>bool</code>
             </td>
@@ -3522,11 +3561,6 @@ <h3 id="sgptools.models.core.transformations.SquareTransform" class="doc doc-hea
       </tbody>
     </table>
 
-
-
-
-
-
               <details class="quote">
                 <summary>Source code in <code>sgptools/models/core/transformations.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-268">268</a></span>
@@ -3704,7 +3738,7 @@ <h4 id="sgptools.models.core.transformations.SquareTransform.distance" class="do
 
     <div class="doc doc-contents ">
 
-        <p>Computes the distance incured by sequentially visiting the inducing points
+      <p>Computes the distance incured by sequentially visiting the inducing points
 Args:
     Xu (ndarray): (m, 3); Inducing points from which to compute the path lengths.
                   <code>m</code> is the number of inducing points.</p>
@@ -3776,7 +3810,7 @@ <h4 id="sgptools.models.core.transformations.SquareTransform.enable_aggregation"
 
     <div class="doc doc-contents ">
 
-        <p>Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the 
+      <p>Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the 
 covariance matrix size.</p>
 
 
@@ -3792,9 +3826,7 @@ <h4 id="sgptools.models.core.transformations.SquareTransform.enable_aggregation"
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>size</code>
-            </td>
+            <td><code>size</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -3853,7 +3885,7 @@ <h4 id="sgptools.models.core.transformations.SquareTransform.expand" class="doc
 
     <div class="doc doc-contents ">
 
-        <p>Applies the expansion transformation to the inducing points</p>
+      <p>Applies the expansion transformation to the inducing points</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -3868,9 +3900,7 @@ <h4 id="sgptools.models.core.transformations.SquareTransform.expand" class="doc
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>Xu</code>
-            </td>
+            <td><code>Xu</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -4000,7 +4030,7 @@ <h3 id="sgptools.models.core.transformations.Transform" class="doc doc-heading">
     <div class="doc doc-contents ">
 
 
-        <p>Base class for transformations of the inducing points, including expansion and aggregation transforms.</p>
+      <p>Base class for transformations of the inducing points, including expansion and aggregation transforms.</p>
 
 
 <details class="refer-to-the-following-papers-for-more-details" open>
@@ -4023,9 +4053,7 @@ <h3 id="sgptools.models.core.transformations.Transform" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>aggregation_size</code>
-            </td>
+            <td><code>aggregation_size</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -4039,9 +4067,7 @@ <h3 id="sgptools.models.core.transformations.Transform" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>constraint_weight</code>
-            </td>
+            <td><code>constraint_weight</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -4058,11 +4084,6 @@ <h3 id="sgptools.models.core.transformations.Transform" class="doc doc-heading">
       </tbody>
     </table>
 
-
-
-
-
-
               <details class="quote">
                 <summary>Source code in <code>sgptools/models/core/transformations.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-22">22</a></span>
@@ -4230,7 +4251,7 @@ <h4 id="sgptools.models.core.transformations.Transform.aggregate" class="doc doc
 
     <div class="doc doc-contents ">
 
-        <p>Applies the aggregation transform to kernel matrices</p>
+      <p>Applies the aggregation transform to kernel matrices</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -4245,9 +4266,7 @@ <h4 id="sgptools.models.core.transformations.Transform.aggregate" class="doc doc
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>k</code>
-            </td>
+            <td><code>k</code></td>
             <td>
                   <code>tensor</code>
             </td>
@@ -4371,7 +4390,7 @@ <h4 id="sgptools.models.core.transformations.Transform.constraints" class="doc d
 
     <div class="doc doc-contents ">
 
-        <p>Computes the constraint terms that are added to the SGP's optimization function</p>
+      <p>Computes the constraint terms that are added to the SGP's optimization function</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -4386,9 +4405,7 @@ <h4 id="sgptools.models.core.transformations.Transform.constraints" class="doc d
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>Xu</code>
-            </td>
+            <td><code>Xu</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -4465,7 +4482,7 @@ <h4 id="sgptools.models.core.transformations.Transform.expand" class="doc doc-he
 
     <div class="doc doc-contents ">
 
-        <p>Applies the expansion transform to the inducing points</p>
+      <p>Applies the expansion transform to the inducing points</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -4480,9 +4497,7 @@ <h4 id="sgptools.models.core.transformations.Transform.expand" class="doc doc-he
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>Xu</code>
-            </td>
+            <td><code>Xu</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -4559,7 +4574,7 @@ <h4 id="sgptools.models.core.transformations.Transform.expand" class="doc doc-he
         
           <button type="button" class="md-top md-icon" data-md-component="top" hidden>
   
-  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8z"/></svg>
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg>
   Back to top
 </button>
         
@@ -4589,10 +4604,10 @@ <h4 id="sgptools.models.core.transformations.Transform.expand" class="doc doc-he
     </div>
     
     
-    <script id="__config" type="application/json">{"base": "../../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../../assets/javascripts/workers/search.6ce7567c.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "../../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="../../../assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="../../../assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
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diff --git a/api/models/core/index.html b/api/models/core/index.html
index 6fa1714..92218d1 100644
--- a/api/models/core/index.html
+++ b/api/models/core/index.html
@@ -20,7 +20,7 @@
       
       
       <link rel="icon" href="../../../assets/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.49">
+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.31">
     
     
       
@@ -28,7 +28,7 @@
       
     
     
-      <link rel="stylesheet" href="../../../assets/stylesheets/main.6f8fc17f.min.css">
+      <link rel="stylesheet" href="../../../assets/stylesheets/main.3cba04c6.min.css">
       
         
         <link rel="stylesheet" href="../../../assets/stylesheets/palette.06af60db.min.css">
@@ -56,7 +56,7 @@
     
       <link rel="stylesheet" href="../../../stylesheets/extra.css">
     
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+    <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
   
@@ -65,7 +65,7 @@
   
   
 
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+<script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",function(){document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document$.subscribe(function(){var a=document.forms.feedback;if(void 0!==a)for(var e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}),location$.subscribe(function(e){n("config","G-Z2TVV3MF1S",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
   
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@@ -153,7 +153,7 @@
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+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
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       <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
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+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5c-.84 0-1.65.15-2.39.42L12 2M3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29L3.34 7m.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14L3.36 17M20.65 7l-1.77 3.79a7.023 7.023 0 0 0-2.38-4.15l4.15.36m-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29L20.64 17M12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44L12 22Z"/></svg>
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       <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
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@@ -214,7 +214,7 @@
     
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+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
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-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -379,6 +379,26 @@
   
     
     
+      <li class="md-tabs__item">
+        <a href="../../../blueos-sgp-tools.html" class="md-tabs__link">
+          
+  
+    
+  
+  BlueOS
+
+        </a>
+      </li>
+    
+  
+
+      
+        
+  
+  
+  
+    
+    
       <li class="md-tabs__item">
         <a href="../../../examples/AIPP_non_point_FoV.html" class="md-tabs__link">
           
@@ -475,7 +495,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -1336,6 +1356,78 @@
   
     
     
+      
+        
+      
+    
+    
+      
+      
+        
+      
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        
+          
+          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    BlueOS
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_5">
+            <span class="md-nav__icon md-icon"></span>
+            BlueOS
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../../blueos-sgp-tools.html" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    blueos-sgp-tools
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    
+    
       
         
       
@@ -1361,10 +1453,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
         
           
-          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+          <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
             
   
   <span class="md-ellipsis">
@@ -1375,8 +1467,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_5">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1551,7 +1643,7 @@ <h2 id="sgptools.models.core" class="doc doc-heading">
 
     <div class="doc doc-contents first">
 
-        <p>Core modules in this package:</p>
+      <p>Core modules in this package:</p>
 <ul>
 <li><code>augmented_gpr</code>: Provides a Gaussian process model with expand and aggregate functions</li>
 <li><code>augmented_sgpr</code>: Provides a sparse Gaussian process model with update, expand, and aggregate functions</li>
@@ -1561,11 +1653,6 @@ <h2 id="sgptools.models.core" class="doc doc-heading">
 
 
 
-
-
-
-
-
   <div class="doc doc-children">
 
 
@@ -1607,7 +1694,7 @@ <h2 id="sgptools.models.core" class="doc doc-heading">
         
           <button type="button" class="md-top md-icon" data-md-component="top" hidden>
   
-  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8z"/></svg>
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg>
   Back to top
 </button>
         
@@ -1637,10 +1724,10 @@ <h2 id="sgptools.models.core" class="doc doc-heading">
     </div>
     
     
-    <script id="__config" type="application/json">{"base": "../../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../../assets/javascripts/workers/search.6ce7567c.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "../../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="../../../assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="../../../assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
   </body>
diff --git a/api/models/index.html b/api/models/index.html
index 7600d37..9bee97e 100644
--- a/api/models/index.html
+++ b/api/models/index.html
@@ -20,7 +20,7 @@
       
       
       <link rel="icon" href="../../assets/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.49">
+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.31">
     
     
       
@@ -28,7 +28,7 @@
       
     
     
-      <link rel="stylesheet" href="../../assets/stylesheets/main.6f8fc17f.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.3cba04c6.min.css">
       
         
         <link rel="stylesheet" href="../../assets/stylesheets/palette.06af60db.min.css">
@@ -56,7 +56,7 @@
     
       <link rel="stylesheet" href="../../stylesheets/extra.css">
     
-    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce(((e,_)=>(e<<5)-e+_.charCodeAt(0)),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
   
@@ -65,7 +65,7 @@
   
   
 
-<script id="__analytics">function __md_analytics(){function e(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],e("js",new Date),e("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",(function(){document.forms.search&&document.forms.search.query.addEventListener("blur",(function(){this.value&&e("event","search",{search_term:this.value})}));document$.subscribe((function(){var t=document.forms.feedback;if(void 0!==t)for(var a of t.querySelectorAll("[type=submit]"))a.addEventListener("click",(function(a){a.preventDefault();var n=document.location.pathname,d=this.getAttribute("data-md-value");e("event","feedback",{page:n,data:d}),t.firstElementChild.disabled=!0;var r=t.querySelector(".md-feedback__note [data-md-value='"+d+"']");r&&(r.hidden=!1)})),t.hidden=!1})),location$.subscribe((function(t){e("config","G-Z2TVV3MF1S",{page_path:t.pathname})}))}));var t=document.createElement("script");t.async=!0,t.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",t)}</script>
+<script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",function(){document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document$.subscribe(function(){var a=document.forms.feedback;if(void 0!==a)for(var e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}),location$.subscribe(function(e){n("config","G-Z2TVV3MF1S",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
   
     <script>"undefined"!=typeof __md_analytics&&__md_analytics()</script>
   
@@ -153,7 +153,7 @@
     <!-- Button to open drawer -->
     <label class="md-header__button md-icon" for="__drawer">
       
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3zm0 5h18v2H3zm0 5h18v2H3z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
 
     <!-- Header title -->
@@ -184,7 +184,7 @@
     <input class="md-option" data-md-color-media="(prefers-color-scheme: light)" data-md-color-scheme="default" data-md-color-primary="custom" data-md-color-accent="custom"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_0">
     
       <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5s-1.65.15-2.39.42zM3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29zm.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14zM20.65 7l-1.77 3.79a7.02 7.02 0 0 0-2.38-4.15zm-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29zM12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5c-.84 0-1.65.15-2.39.42L12 2M3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29L3.34 7m.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14L3.36 17M20.65 7l-1.77 3.79a7.023 7.023 0 0 0-2.38-4.15l4.15.36m-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29L20.64 17M12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44L12 22Z"/></svg>
       </label>
     
   
@@ -194,7 +194,7 @@
     <input class="md-option" data-md-color-media="(prefers-color-scheme: dark)" data-md-color-scheme="slate" data-md-color-primary="custom" data-md-color-accent="custom"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_1">
     
       <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3zm3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95zm-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3 3.19.09m3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95 2.06.05m-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31Z"/></svg>
       </label>
     
   
@@ -204,7 +204,7 @@
 
     <!-- User preference: color palette -->
     
-      <script>var palette=__md_get("__palette");if(palette&&palette.color){if("(prefers-color-scheme)"===palette.color.media){var media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']");palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent")}for(var[key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
     
 
     <!-- Site language selector -->
@@ -214,7 +214,7 @@
     
       <label class="md-header__button md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
 
       <!-- Search interface -->
@@ -225,20 +225,20 @@
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
           <a href="javascript:void(0)" class="md-search__icon md-icon" title="Share" aria-label="Share" data-clipboard data-clipboard-text="" data-md-component="search-share" tabindex="-1">
             
-            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7s-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91s2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08"/></svg>
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7 0-.24-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91 1.61 0 2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08Z"/></svg>
           </a>
         
         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
           
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12z"/></svg>
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
       
@@ -265,7 +265,7 @@
         <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -379,6 +379,26 @@
   
     
     
+      <li class="md-tabs__item">
+        <a href="../../blueos-sgp-tools.html" class="md-tabs__link">
+          
+  
+    
+  
+  BlueOS
+
+        </a>
+      </li>
+    
+  
+
+      
+        
+  
+  
+  
+    
+    
       <li class="md-tabs__item">
         <a href="../../examples/AIPP_non_point_FoV.html" class="md-tabs__link">
           
@@ -475,7 +495,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -1336,6 +1356,78 @@
   
     
     
+      
+        
+      
+    
+    
+      
+      
+        
+      
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        
+          
+          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    BlueOS
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_5">
+            <span class="md-nav__icon md-icon"></span>
+            BlueOS
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../blueos-sgp-tools.html" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    blueos-sgp-tools
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    
+    
       
         
       
@@ -1361,10 +1453,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
         
           
-          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+          <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
             
   
   <span class="md-ellipsis">
@@ -1375,8 +1467,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_5">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1551,7 +1643,7 @@ <h2 id="sgptools.models" class="doc doc-heading">
 
     <div class="doc doc-contents first">
 
-        <p>Sensor placement and informative path planning methods in this package:</p>
+      <p>Sensor placement and informative path planning methods in this package:</p>
 <ul>
 <li><code>bo</code>: Provides a Bayesian optimization based approach that maximizes mutual-information to get sensor placements</li>
 <li><code>cma_es</code>: Provides a genetic algorithm (CMA-ES) based approach that maximizes mutual-information to get sensor placements</li>
@@ -1562,11 +1654,6 @@ <h2 id="sgptools.models" class="doc doc-heading">
 
 
 
-
-
-
-
-
   <div class="doc doc-children">
 
 
@@ -1608,7 +1695,7 @@ <h2 id="sgptools.models" class="doc doc-heading">
         
           <button type="button" class="md-top md-icon" data-md-component="top" hidden>
   
-  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8z"/></svg>
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg>
   Back to top
 </button>
         
@@ -1638,10 +1725,10 @@ <h2 id="sgptools.models" class="doc doc-heading">
     </div>
     
     
-    <script id="__config" type="application/json">{"base": "../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../assets/javascripts/workers/search.6ce7567c.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="../../assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="../../assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
   </body>
diff --git a/api/utils/Data.html b/api/utils/Data.html
index 0c778a3..92b7c31 100644
--- a/api/utils/Data.html
+++ b/api/utils/Data.html
@@ -20,7 +20,7 @@
       
       
       <link rel="icon" href="../../assets/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.49">
+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.31">
     
     
       
@@ -28,7 +28,7 @@
       
     
     
-      <link rel="stylesheet" href="../../assets/stylesheets/main.6f8fc17f.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.3cba04c6.min.css">
       
         
         <link rel="stylesheet" href="../../assets/stylesheets/palette.06af60db.min.css">
@@ -56,7 +56,7 @@
     
       <link rel="stylesheet" href="../../stylesheets/extra.css">
     
-    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce(((e,_)=>(e<<5)-e+_.charCodeAt(0)),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
   
@@ -65,7 +65,7 @@
   
   
 
-<script id="__analytics">function __md_analytics(){function e(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],e("js",new Date),e("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",(function(){document.forms.search&&document.forms.search.query.addEventListener("blur",(function(){this.value&&e("event","search",{search_term:this.value})}));document$.subscribe((function(){var t=document.forms.feedback;if(void 0!==t)for(var a of t.querySelectorAll("[type=submit]"))a.addEventListener("click",(function(a){a.preventDefault();var n=document.location.pathname,d=this.getAttribute("data-md-value");e("event","feedback",{page:n,data:d}),t.firstElementChild.disabled=!0;var r=t.querySelector(".md-feedback__note [data-md-value='"+d+"']");r&&(r.hidden=!1)})),t.hidden=!1})),location$.subscribe((function(t){e("config","G-Z2TVV3MF1S",{page_path:t.pathname})}))}));var t=document.createElement("script");t.async=!0,t.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",t)}</script>
+<script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",function(){document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document$.subscribe(function(){var a=document.forms.feedback;if(void 0!==a)for(var e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}),location$.subscribe(function(e){n("config","G-Z2TVV3MF1S",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
   
     <script>"undefined"!=typeof __md_analytics&&__md_analytics()</script>
   
@@ -153,7 +153,7 @@
     <!-- Button to open drawer -->
     <label class="md-header__button md-icon" for="__drawer">
       
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3zm0 5h18v2H3zm0 5h18v2H3z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
 
     <!-- Header title -->
@@ -184,7 +184,7 @@
     <input class="md-option" data-md-color-media="(prefers-color-scheme: light)" data-md-color-scheme="default" data-md-color-primary="custom" data-md-color-accent="custom"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_0">
     
       <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5s-1.65.15-2.39.42zM3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29zm.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14zM20.65 7l-1.77 3.79a7.02 7.02 0 0 0-2.38-4.15zm-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29zM12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5c-.84 0-1.65.15-2.39.42L12 2M3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29L3.34 7m.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14L3.36 17M20.65 7l-1.77 3.79a7.023 7.023 0 0 0-2.38-4.15l4.15.36m-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29L20.64 17M12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44L12 22Z"/></svg>
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@@ -194,7 +194,7 @@
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       <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3zm3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95zm-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3 3.19.09m3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95 2.06.05m-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31Z"/></svg>
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@@ -204,7 +204,7 @@
 
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+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
     
 
     <!-- Site language selector -->
@@ -214,7 +214,7 @@
     
       <label class="md-header__button md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
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@@ -225,20 +225,20 @@
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+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
         
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+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
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+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7 0-.24-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91 1.61 0 2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08Z"/></svg>
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+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
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@@ -265,7 +265,7 @@
         <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
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-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -379,6 +379,26 @@
   
     
     
+      <li class="md-tabs__item">
+        <a href="../../blueos-sgp-tools.html" class="md-tabs__link">
+          
+  
+    
+  
+  BlueOS
+
+        </a>
+      </li>
+    
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+
+      
+        
+  
+  
+  
+    
+    
       <li class="md-tabs__item">
         <a href="../../examples/AIPP_non_point_FoV.html" class="md-tabs__link">
           
@@ -475,7 +495,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -1441,6 +1461,78 @@
   
     
     
+      
+        
+      
+    
+    
+      
+      
+        
+      
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        
+          
+          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    BlueOS
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_5">
+            <span class="md-nav__icon md-icon"></span>
+            BlueOS
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../blueos-sgp-tools.html" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    blueos-sgp-tools
+  </span>
+  
+
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+    </li>
+  
+
+              
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+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
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+  
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@@ -1466,10 +1558,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
         
           
-          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+          <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
             
   
   <span class="md-ellipsis">
@@ -1480,8 +1572,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_5">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1654,11 +1746,6 @@ <h2 id="sgptools.utils.data" class="doc doc-heading">
 
 
 
-
-
-
-
-
   <div class="doc doc-children">
 
 
@@ -1680,7 +1767,7 @@ <h3 id="sgptools.utils.data.get_dataset" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Method to generate/load datasets and preprocess them for SP/IPP. The method uses kmeans to 
+      <p>Method to generate/load datasets and preprocess them for SP/IPP. The method uses kmeans to 
 generate train and test sets.</p>
 
 
@@ -1696,9 +1783,7 @@ <h3 id="sgptools.utils.data.get_dataset" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>dataset_path</code>
-            </td>
+            <td><code>dataset_path</code></td>
             <td>
                   <code>str</code>
             </td>
@@ -1712,9 +1797,7 @@ <h3 id="sgptools.utils.data.get_dataset" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>num_train</code>
-            </td>
+            <td><code>num_train</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -1728,9 +1811,7 @@ <h3 id="sgptools.utils.data.get_dataset" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>num_test</code>
-            </td>
+            <td><code>num_test</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -1744,9 +1825,7 @@ <h3 id="sgptools.utils.data.get_dataset" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>num_candidates</code>
-            </td>
+            <td><code>num_candidates</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -1969,7 +2048,7 @@ <h3 id="sgptools.utils.data.point_pos" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Generate a point at a distance d from a point at angle theta.</p>
+      <p>Generate a point at a distance d from a point at angle theta.</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -1984,9 +2063,7 @@ <h3 id="sgptools.utils.data.point_pos" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>point</code>
-            </td>
+            <td><code>point</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2000,9 +2077,7 @@ <h3 id="sgptools.utils.data.point_pos" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>d</code>
-            </td>
+            <td><code>d</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -2016,9 +2091,7 @@ <h3 id="sgptools.utils.data.point_pos" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>theta</code>
-            </td>
+            <td><code>theta</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -2113,7 +2186,7 @@ <h3 id="sgptools.utils.data.prep_synthetic_dataset" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Generates a 50x50 grid of synthetic elevation data using the diamond square algorithm.</p>
+      <p>Generates a 50x50 grid of synthetic elevation data using the diamond square algorithm.</p>
 
 
 <details class="refer-to-the-following-repo-for-more-details" open>
@@ -2135,9 +2208,7 @@ <h3 id="sgptools.utils.data.prep_synthetic_dataset" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>shape</code>
-            </td>
+            <td><code>shape</code></td>
             <td>
                   <code>tuple</code>
             </td>
@@ -2151,9 +2222,7 @@ <h3 id="sgptools.utils.data.prep_synthetic_dataset" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>min_height</code>
-            </td>
+            <td><code>min_height</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -2167,9 +2236,7 @@ <h3 id="sgptools.utils.data.prep_synthetic_dataset" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>max_height</code>
-            </td>
+            <td><code>max_height</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -2183,9 +2250,7 @@ <h3 id="sgptools.utils.data.prep_synthetic_dataset" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>roughness</code>
-            </td>
+            <td><code>roughness</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -2199,9 +2264,7 @@ <h3 id="sgptools.utils.data.prep_synthetic_dataset" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>random_seed</code>
-            </td>
+            <td><code>random_seed</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -2344,7 +2407,7 @@ <h3 id="sgptools.utils.data.prep_tif_dataset" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Load and preprocess a dataset from a GeoTIFF file (.tif file). The input features 
+      <p>Load and preprocess a dataset from a GeoTIFF file (.tif file). The input features 
 are set to the x and y pixel block coordinates and the labels are read from the file.
 The method also removes all invalid points.</p>
 <p>Large tif files 
@@ -2444,7 +2507,7 @@ <h3 id="sgptools.utils.data.remove_circle_patches" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Remove points inside polycircle patchesgons.</p>
+      <p>Remove points inside polycircle patchesgons.</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -2459,9 +2522,7 @@ <h3 id="sgptools.utils.data.remove_circle_patches" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>X</code>
-            </td>
+            <td><code>X</code></td>
             <td>
                   <code> (ndarray</code>
             </td>
@@ -2475,9 +2536,7 @@ <h3 id="sgptools.utils.data.remove_circle_patches" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>Y</code>
-            </td>
+            <td><code>Y</code></td>
             <td>
                   <code> (ndarray</code>
             </td>
@@ -2491,9 +2550,7 @@ <h3 id="sgptools.utils.data.remove_circle_patches" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>circle_patches</code>
-            </td>
+            <td><code>circle_patches</code></td>
             <td>
                   <code>list of matplotlib circle patches</code>
             </td>
@@ -2594,7 +2651,7 @@ <h3 id="sgptools.utils.data.remove_polygons" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Remove points inside polygons.</p>
+      <p>Remove points inside polygons.</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -2609,9 +2666,7 @@ <h3 id="sgptools.utils.data.remove_polygons" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>X</code>
-            </td>
+            <td><code>X</code></td>
             <td>
                   <code> (ndarray</code>
             </td>
@@ -2625,9 +2680,7 @@ <h3 id="sgptools.utils.data.remove_polygons" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>Y</code>
-            </td>
+            <td><code>Y</code></td>
             <td>
                   <code> (ndarray</code>
             </td>
@@ -2641,9 +2694,7 @@ <h3 id="sgptools.utils.data.remove_polygons" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>polygons</code>
-            </td>
+            <td><code>polygons</code></td>
             <td>
                   <code>list of matplotlib path polygon</code>
             </td>
@@ -2766,7 +2817,7 @@ <h3 id="sgptools.utils.data.remove_polygons" class="doc doc-heading">
         
           <button type="button" class="md-top md-icon" data-md-component="top" hidden>
   
-  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8z"/></svg>
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg>
   Back to top
 </button>
         
@@ -2796,10 +2847,10 @@ <h3 id="sgptools.utils.data.remove_polygons" class="doc doc-heading">
     </div>
     
     
-    <script id="__config" type="application/json">{"base": "../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../assets/javascripts/workers/search.6ce7567c.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="../../assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="../../assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
   </body>
diff --git a/api/utils/GPflow.html b/api/utils/GPflow.html
index 9bdfe2d..e9d2a3e 100644
--- a/api/utils/GPflow.html
+++ b/api/utils/GPflow.html
@@ -20,7 +20,7 @@
       
       
       <link rel="icon" href="../../assets/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.49">
+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.31">
     
     
       
@@ -28,7 +28,7 @@
       
     
     
-      <link rel="stylesheet" href="../../assets/stylesheets/main.6f8fc17f.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.3cba04c6.min.css">
       
         
         <link rel="stylesheet" href="../../assets/stylesheets/palette.06af60db.min.css">
@@ -56,7 +56,7 @@
     
       <link rel="stylesheet" href="../../stylesheets/extra.css">
     
-    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce(((e,_)=>(e<<5)-e+_.charCodeAt(0)),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
   
@@ -65,7 +65,7 @@
   
   
 
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+<script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",function(){document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document$.subscribe(function(){var a=document.forms.feedback;if(void 0!==a)for(var e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}),location$.subscribe(function(e){n("config","G-Z2TVV3MF1S",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
   
     <script>"undefined"!=typeof __md_analytics&&__md_analytics()</script>
   
@@ -153,7 +153,7 @@
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     <label class="md-header__button md-icon" for="__drawer">
       
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+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
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     <!-- Header title -->
@@ -184,7 +184,7 @@
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       <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5s-1.65.15-2.39.42zM3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29zm.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14zM20.65 7l-1.77 3.79a7.02 7.02 0 0 0-2.38-4.15zm-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29zM12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5c-.84 0-1.65.15-2.39.42L12 2M3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29L3.34 7m.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14L3.36 17M20.65 7l-1.77 3.79a7.023 7.023 0 0 0-2.38-4.15l4.15.36m-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29L20.64 17M12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44L12 22Z"/></svg>
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@@ -194,7 +194,7 @@
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       <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
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@@ -204,7 +204,7 @@
 
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+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
     
 
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+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
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+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
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@@ -379,6 +379,26 @@
   
     
     
+      <li class="md-tabs__item">
+        <a href="../../blueos-sgp-tools.html" class="md-tabs__link">
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         <a href="../../examples/AIPP_non_point_FoV.html" class="md-tabs__link">
           
@@ -475,7 +495,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
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@@ -1447,6 +1467,78 @@
   
     
     
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+      <a href="../../blueos-sgp-tools.html" class="md-nav__link">
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-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
         
           
-          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+          <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
             
   
   <span class="md-ellipsis">
@@ -1486,8 +1578,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_5">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1660,11 +1752,6 @@ <h2 id="sgptools.utils.gpflow" class="doc doc-heading">
 
 
 
-
-
-
-
-
   <div class="doc doc-children">
 
 
@@ -1690,7 +1777,7 @@ <h3 id="sgptools.utils.gpflow.TraceInducingPts" class="doc doc-heading">
               Bases: <code><span title="gpflow.monitor.MonitorTask">MonitorTask</span></code></p>
 
 
-        <p>GPflow monitoring task, used to trace the inducing points
+      <p>GPflow monitoring task, used to trace the inducing points
 states at every step during optimization. </p>
 
 
@@ -1706,9 +1793,7 @@ <h3 id="sgptools.utils.gpflow.TraceInducingPts" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>model</code>
-            </td>
+            <td><code>model</code></td>
             <td>
                   <code><span title="gpflow.models.sgpr">sgpr</span></code>
             </td>
@@ -1724,11 +1809,6 @@ <h3 id="sgptools.utils.gpflow.TraceInducingPts" class="doc doc-heading">
       </tbody>
     </table>
 
-
-
-
-
-
               <details class="quote">
                 <summary>Source code in <code>sgptools/utils/gpflow.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-123">123</a></span>
@@ -1824,7 +1904,7 @@ <h4 id="sgptools.utils.gpflow.TraceInducingPts.get_trace" class="doc doc-heading
 
     <div class="doc doc-contents ">
 
-        <p>Returns the inducing points collected at each optimization step</p>
+      <p>Returns the inducing points collected at each optimization step</p>
 
 
     <p><span class="doc-section-title">Returns:</span></p>
@@ -1892,7 +1972,7 @@ <h4 id="sgptools.utils.gpflow.TraceInducingPts.run" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Method used to extract the inducing points and 
+      <p>Method used to extract the inducing points and 
 apply IPP fixed points transform if available</p>
 
             <details class="quote">
@@ -1940,7 +2020,7 @@ <h3 id="sgptools.utils.gpflow.get_model_params" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Train a GP on the given training set. 
+      <p>Train a GP on the given training set. 
 Trains a sparse GP if the training set is larger than 1000 samples.</p>
 
 
@@ -1956,9 +2036,7 @@ <h3 id="sgptools.utils.gpflow.get_model_params" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>X_train</code>
-            </td>
+            <td><code>X_train</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1972,9 +2050,7 @@ <h3 id="sgptools.utils.gpflow.get_model_params" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>y_train</code>
-            </td>
+            <td><code>y_train</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1988,9 +2064,7 @@ <h3 id="sgptools.utils.gpflow.get_model_params" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>max_steps</code>
-            </td>
+            <td><code>max_steps</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -2004,9 +2078,7 @@ <h3 id="sgptools.utils.gpflow.get_model_params" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>lr</code>
-            </td>
+            <td><code>lr</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -2020,9 +2092,7 @@ <h3 id="sgptools.utils.gpflow.get_model_params" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>print_params</code>
-            </td>
+            <td><code>print_params</code></td>
             <td>
                   <code>bool</code>
             </td>
@@ -2036,9 +2106,7 @@ <h3 id="sgptools.utils.gpflow.get_model_params" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>lengthscales</code>
-            </td>
+            <td><code>lengthscales</code></td>
             <td>
                   <code>float or list</code>
             </td>
@@ -2053,9 +2121,7 @@ <h3 id="sgptools.utils.gpflow.get_model_params" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>variance</code>
-            </td>
+            <td><code>variance</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -2069,9 +2135,7 @@ <h3 id="sgptools.utils.gpflow.get_model_params" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>noise_variance</code>
-            </td>
+            <td><code>noise_variance</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -2085,9 +2149,7 @@ <h3 id="sgptools.utils.gpflow.get_model_params" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>kernel</code>
-            </td>
+            <td><code>kernel</code></td>
             <td>
                   <code><span title="gpflow.kernels.Kernel">Kernel</span></code>
             </td>
@@ -2101,9 +2163,7 @@ <h3 id="sgptools.utils.gpflow.get_model_params" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>return_gp</code>
-            </td>
+            <td><code>return_gp</code></td>
             <td>
                   <code>bool</code>
             </td>
@@ -2117,9 +2177,7 @@ <h3 id="sgptools.utils.gpflow.get_model_params" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>train_inducing_pts</code>
-            </td>
+            <td><code>train_inducing_pts</code></td>
             <td>
                   <code>bool</code>
             </td>
@@ -2134,9 +2192,7 @@ <h3 id="sgptools.utils.gpflow.get_model_params" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>num_inducing_pts</code>
-            </td>
+            <td><code>num_inducing_pts</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -2373,7 +2429,7 @@ <h3 id="sgptools.utils.gpflow.optimize_model" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Trains a GP/SGP model</p>
+      <p>Trains a GP/SGP model</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -2388,9 +2444,7 @@ <h3 id="sgptools.utils.gpflow.optimize_model" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>model</code>
-            </td>
+            <td><code>model</code></td>
             <td>
                   <code><span title="gpflow.models">models</span></code>
             </td>
@@ -2404,9 +2458,7 @@ <h3 id="sgptools.utils.gpflow.optimize_model" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>max_steps</code>
-            </td>
+            <td><code>max_steps</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -2420,9 +2472,7 @@ <h3 id="sgptools.utils.gpflow.optimize_model" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>kernel_grad</code>
-            </td>
+            <td><code>kernel_grad</code></td>
             <td>
                   <code>bool</code>
             </td>
@@ -2437,9 +2487,7 @@ <h3 id="sgptools.utils.gpflow.optimize_model" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>lr</code>
-            </td>
+            <td><code>lr</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -2453,9 +2501,7 @@ <h3 id="sgptools.utils.gpflow.optimize_model" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>optimizer</code>
-            </td>
+            <td><code>optimizer</code></td>
             <td>
                   <code>str</code>
             </td>
@@ -2469,9 +2515,7 @@ <h3 id="sgptools.utils.gpflow.optimize_model" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>method</code>
-            </td>
+            <td><code>method</code></td>
             <td>
                   <code>str</code>
             </td>
@@ -2486,9 +2530,7 @@ <h3 id="sgptools.utils.gpflow.optimize_model" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>verbose</code>
-            </td>
+            <td><code>verbose</code></td>
             <td>
                   <code>bool</code>
             </td>
@@ -2502,9 +2544,7 @@ <h3 id="sgptools.utils.gpflow.optimize_model" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>trace_fn</code>
-            </td>
+            <td><code>trace_fn</code></td>
             <td>
                   <code>str</code>
             </td>
@@ -2520,9 +2560,7 @@ <h3 id="sgptools.utils.gpflow.optimize_model" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>convergence_criterion</code>
-            </td>
+            <td><code>convergence_criterion</code></td>
             <td>
                   <code>bool</code>
             </td>
@@ -2537,9 +2575,7 @@ <h3 id="sgptools.utils.gpflow.optimize_model" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>trainable_variables</code>
-            </td>
+            <td><code>trainable_variables</code></td>
             <td>
                   <code>list</code>
             </td>
@@ -2553,9 +2589,7 @@ <h3 id="sgptools.utils.gpflow.optimize_model" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>tol</code>
-            </td>
+            <td><code>tol</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -2765,7 +2799,7 @@ <h3 id="sgptools.utils.gpflow.plot_loss" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Helper function to plot the training loss</p>
+      <p>Helper function to plot the training loss</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -2780,9 +2814,7 @@ <h3 id="sgptools.utils.gpflow.plot_loss" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>losses</code>
-            </td>
+            <td><code>losses</code></td>
             <td>
                   <code>list</code>
             </td>
@@ -2796,9 +2828,7 @@ <h3 id="sgptools.utils.gpflow.plot_loss" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>save_file</code>
-            </td>
+            <td><code>save_file</code></td>
             <td>
                   <code>str</code>
             </td>
@@ -2890,7 +2920,7 @@ <h3 id="sgptools.utils.gpflow.plot_loss" class="doc doc-heading">
         
           <button type="button" class="md-top md-icon" data-md-component="top" hidden>
   
-  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8z"/></svg>
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg>
   Back to top
 </button>
         
@@ -2920,10 +2950,10 @@ <h3 id="sgptools.utils.gpflow.plot_loss" class="doc doc-heading">
     </div>
     
     
-    <script id="__config" type="application/json">{"base": "../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../assets/javascripts/workers/search.6ce7567c.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="../../assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="../../assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
   </body>
diff --git a/api/utils/Metrics.html b/api/utils/Metrics.html
index fc1a85a..4a215b3 100644
--- a/api/utils/Metrics.html
+++ b/api/utils/Metrics.html
@@ -20,7 +20,7 @@
       
       
       <link rel="icon" href="../../assets/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.49">
+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.31">
     
     
       
@@ -28,7 +28,7 @@
       
     
     
-      <link rel="stylesheet" href="../../assets/stylesheets/main.6f8fc17f.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.3cba04c6.min.css">
       
         
         <link rel="stylesheet" href="../../assets/stylesheets/palette.06af60db.min.css">
@@ -56,7 +56,7 @@
     
       <link rel="stylesheet" href="../../stylesheets/extra.css">
     
-    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce(((e,_)=>(e<<5)-e+_.charCodeAt(0)),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
   
@@ -65,7 +65,7 @@
   
   
 
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+<script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",function(){document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document$.subscribe(function(){var a=document.forms.feedback;if(void 0!==a)for(var e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}),location$.subscribe(function(e){n("config","G-Z2TVV3MF1S",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
   
     <script>"undefined"!=typeof __md_analytics&&__md_analytics()</script>
   
@@ -153,7 +153,7 @@
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     <label class="md-header__button md-icon" for="__drawer">
       
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3zm0 5h18v2H3zm0 5h18v2H3z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
 
     <!-- Header title -->
@@ -184,7 +184,7 @@
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       <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5s-1.65.15-2.39.42zM3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29zm.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14zM20.65 7l-1.77 3.79a7.02 7.02 0 0 0-2.38-4.15zm-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29zM12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5c-.84 0-1.65.15-2.39.42L12 2M3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29L3.34 7m.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14L3.36 17M20.65 7l-1.77 3.79a7.023 7.023 0 0 0-2.38-4.15l4.15.36m-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29L20.64 17M12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44L12 22Z"/></svg>
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@@ -194,7 +194,7 @@
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       <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
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+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3 3.19.09m3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95 2.06.05m-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31Z"/></svg>
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@@ -204,7 +204,7 @@
 
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+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
     
 
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@@ -214,7 +214,7 @@
     
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+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
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       <label class="md-search__icon md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
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+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
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+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7 0-.24-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91 1.61 0 2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08Z"/></svg>
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+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
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@@ -265,7 +265,7 @@
         <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
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-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -379,6 +379,26 @@
   
     
     
+      <li class="md-tabs__item">
+        <a href="../../blueos-sgp-tools.html" class="md-tabs__link">
+          
+  
+    
+  
+  BlueOS
+
+        </a>
+      </li>
+    
+  
+
+      
+        
+  
+  
+  
+    
+    
       <li class="md-tabs__item">
         <a href="../../examples/AIPP_non_point_FoV.html" class="md-tabs__link">
           
@@ -475,7 +495,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -1450,6 +1470,78 @@
   
     
     
+      
+        
+      
+    
+    
+      
+      
+        
+      
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        
+          
+          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    BlueOS
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_5">
+            <span class="md-nav__icon md-icon"></span>
+            BlueOS
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../blueos-sgp-tools.html" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    blueos-sgp-tools
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    
+    
       
         
       
@@ -1475,10 +1567,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
         
           
-          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+          <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
             
   
   <span class="md-ellipsis">
@@ -1489,8 +1581,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_5">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1663,11 +1755,6 @@ <h2 id="sgptools.utils.metrics" class="doc doc-heading">
 
 
 
-
-
-
-
-
   <div class="doc doc-children">
 
 
@@ -1689,7 +1776,7 @@ <h3 id="sgptools.utils.metrics.gaussian_entropy" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Computes GP-based entropy from a kernel matrix</p>
+      <p>Computes GP-based entropy from a kernel matrix</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -1704,9 +1791,7 @@ <h3 id="sgptools.utils.metrics.gaussian_entropy" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>K</code>
-            </td>
+            <td><code>K</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1783,7 +1868,7 @@ <h3 id="sgptools.utils.metrics.get_distance" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Compute the length of a path (L2-norm)</p>
+      <p>Compute the length of a path (L2-norm)</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -1798,9 +1883,7 @@ <h3 id="sgptools.utils.metrics.get_distance" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>X</code>
-            </td>
+            <td><code>X</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1881,7 +1964,7 @@ <h3 id="sgptools.utils.metrics.get_elbo" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Computes the ELBO of the SGP, corrected to be positive</p>
+      <p>Computes the ELBO of the SGP, corrected to be positive</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -1896,9 +1979,7 @@ <h3 id="sgptools.utils.metrics.get_elbo" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>Xu</code>
-            </td>
+            <td><code>Xu</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1912,9 +1993,7 @@ <h3 id="sgptools.utils.metrics.get_elbo" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>X_env</code>
-            </td>
+            <td><code>X_env</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1928,9 +2007,7 @@ <h3 id="sgptools.utils.metrics.get_elbo" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>noise_variance</code>
-            </td>
+            <td><code>noise_variance</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -1944,9 +2021,7 @@ <h3 id="sgptools.utils.metrics.get_elbo" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>kernel</code>
-            </td>
+            <td><code>kernel</code></td>
             <td>
                   <code><span title="gpflow.kernels.Kernel">Kernel</span></code>
             </td>
@@ -1960,9 +2035,7 @@ <h3 id="sgptools.utils.metrics.get_elbo" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>baseline</code>
-            </td>
+            <td><code>baseline</code></td>
             <td>
                   <code>bool</code>
             </td>
@@ -2073,7 +2146,7 @@ <h3 id="sgptools.utils.metrics.get_kl" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Computes the KL divergence between the SGP and the GP</p>
+      <p>Computes the KL divergence between the SGP and the GP</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -2088,9 +2161,7 @@ <h3 id="sgptools.utils.metrics.get_kl" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>Xu</code>
-            </td>
+            <td><code>Xu</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2104,9 +2175,7 @@ <h3 id="sgptools.utils.metrics.get_kl" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>X_env</code>
-            </td>
+            <td><code>X_env</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2120,9 +2189,7 @@ <h3 id="sgptools.utils.metrics.get_kl" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>noise_variance</code>
-            </td>
+            <td><code>noise_variance</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -2136,9 +2203,7 @@ <h3 id="sgptools.utils.metrics.get_kl" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>kernel</code>
-            </td>
+            <td><code>kernel</code></td>
             <td>
                   <code><span title="gpflow.kernels.Kernel">Kernel</span></code>
             </td>
@@ -2261,7 +2326,7 @@ <h3 id="sgptools.utils.metrics.get_mi" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Computes mutual information between the sensing locations and the candidate locations</p>
+      <p>Computes mutual information between the sensing locations and the candidate locations</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -2276,9 +2341,7 @@ <h3 id="sgptools.utils.metrics.get_mi" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>Xu</code>
-            </td>
+            <td><code>Xu</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2292,9 +2355,7 @@ <h3 id="sgptools.utils.metrics.get_mi" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>candidate_locs</code>
-            </td>
+            <td><code>candidate_locs</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2308,9 +2369,7 @@ <h3 id="sgptools.utils.metrics.get_mi" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>noise_variance</code>
-            </td>
+            <td><code>noise_variance</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -2324,9 +2383,7 @@ <h3 id="sgptools.utils.metrics.get_mi" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>kernel</code>
-            </td>
+            <td><code>kernel</code></td>
             <td>
                   <code><span title="gpflow.kernels.Kernel">Kernel</span></code>
             </td>
@@ -2437,7 +2494,7 @@ <h3 id="sgptools.utils.metrics.get_reconstruction" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Computes the GP-based data field estimates with the solution placements as the training set</p>
+      <p>Computes the GP-based data field estimates with the solution placements as the training set</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -2452,9 +2509,7 @@ <h3 id="sgptools.utils.metrics.get_reconstruction" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>sensor_data</code>
-            </td>
+            <td><code>sensor_data</code></td>
             <td>
                   <code>ndarray tuple</code>
             </td>
@@ -2469,9 +2524,7 @@ <h3 id="sgptools.utils.metrics.get_reconstruction" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>X_test</code>
-            </td>
+            <td><code>X_test</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2485,9 +2538,7 @@ <h3 id="sgptools.utils.metrics.get_reconstruction" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>noise_variance</code>
-            </td>
+            <td><code>noise_variance</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -2501,9 +2552,7 @@ <h3 id="sgptools.utils.metrics.get_reconstruction" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>kernel</code>
-            </td>
+            <td><code>kernel</code></td>
             <td>
                   <code><span title="gpflow.kernels.Kernel">Kernel</span></code>
             </td>
@@ -2618,7 +2667,7 @@ <h3 id="sgptools.utils.metrics.get_rmse" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Computes the root-mean-square error between <code>y_pred</code> and <code>y_test</code></p>
+      <p>Computes the root-mean-square error between <code>y_pred</code> and <code>y_test</code></p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -2633,9 +2682,7 @@ <h3 id="sgptools.utils.metrics.get_rmse" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>y_pred</code>
-            </td>
+            <td><code>y_pred</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2649,9 +2696,7 @@ <h3 id="sgptools.utils.metrics.get_rmse" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>y_test</code>
-            </td>
+            <td><code>y_test</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2750,7 +2795,7 @@ <h3 id="sgptools.utils.metrics.get_rmse" class="doc doc-heading">
         
           <button type="button" class="md-top md-icon" data-md-component="top" hidden>
   
-  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8z"/></svg>
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg>
   Back to top
 </button>
         
@@ -2780,10 +2825,10 @@ <h3 id="sgptools.utils.metrics.get_rmse" class="doc doc-heading">
     </div>
     
     
-    <script id="__config" type="application/json">{"base": "../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../assets/javascripts/workers/search.6ce7567c.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="../../assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="../../assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
   </body>
diff --git a/api/utils/Misc.html b/api/utils/Misc.html
index 7f6160f..afcab25 100644
--- a/api/utils/Misc.html
+++ b/api/utils/Misc.html
@@ -20,7 +20,7 @@
       
       
       <link rel="icon" href="../../assets/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.49">
+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.31">
     
     
       
@@ -28,7 +28,7 @@
       
     
     
-      <link rel="stylesheet" href="../../assets/stylesheets/main.6f8fc17f.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.3cba04c6.min.css">
       
         
         <link rel="stylesheet" href="../../assets/stylesheets/palette.06af60db.min.css">
@@ -56,7 +56,7 @@
     
       <link rel="stylesheet" href="../../stylesheets/extra.css">
     
-    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce(((e,_)=>(e<<5)-e+_.charCodeAt(0)),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
   
@@ -65,7 +65,7 @@
   
   
 
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+<script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",function(){document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document$.subscribe(function(){var a=document.forms.feedback;if(void 0!==a)for(var e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}),location$.subscribe(function(e){n("config","G-Z2TVV3MF1S",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
   
     <script>"undefined"!=typeof __md_analytics&&__md_analytics()</script>
   
@@ -153,7 +153,7 @@
     <!-- Button to open drawer -->
     <label class="md-header__button md-icon" for="__drawer">
       
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3zm0 5h18v2H3zm0 5h18v2H3z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
 
     <!-- Header title -->
@@ -184,7 +184,7 @@
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       <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5s-1.65.15-2.39.42zM3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29zm.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14zM20.65 7l-1.77 3.79a7.02 7.02 0 0 0-2.38-4.15zm-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29zM12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5c-.84 0-1.65.15-2.39.42L12 2M3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29L3.34 7m.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14L3.36 17M20.65 7l-1.77 3.79a7.023 7.023 0 0 0-2.38-4.15l4.15.36m-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29L20.64 17M12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44L12 22Z"/></svg>
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@@ -194,7 +194,7 @@
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       <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3zm3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95zm-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3 3.19.09m3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95 2.06.05m-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31Z"/></svg>
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@@ -204,7 +204,7 @@
 
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+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
     
 
     <!-- Site language selector -->
@@ -214,7 +214,7 @@
     
       <label class="md-header__button md-icon" for="__search">
         
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+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
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       <!-- Search interface -->
@@ -225,20 +225,20 @@
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       <label class="md-search__icon md-icon" for="__search">
         
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+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
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-            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7s-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91s2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08"/></svg>
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7 0-.24-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91 1.61 0 2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08Z"/></svg>
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-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12z"/></svg>
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
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@@ -265,7 +265,7 @@
         <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -379,6 +379,26 @@
   
     
     
+      <li class="md-tabs__item">
+        <a href="../../blueos-sgp-tools.html" class="md-tabs__link">
+          
+  
+    
+  
+  BlueOS
+
+        </a>
+      </li>
+    
+  
+
+      
+        
+  
+  
+  
+    
+    
       <li class="md-tabs__item">
         <a href="../../examples/AIPP_non_point_FoV.html" class="md-tabs__link">
           
@@ -475,7 +495,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -1441,6 +1461,78 @@
   
     
     
+      
+        
+      
+    
+    
+      
+      
+        
+      
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        
+          
+          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    BlueOS
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_5">
+            <span class="md-nav__icon md-icon"></span>
+            BlueOS
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../blueos-sgp-tools.html" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    blueos-sgp-tools
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    
+    
       
         
       
@@ -1466,10 +1558,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
         
           
-          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+          <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
             
   
   <span class="md-ellipsis">
@@ -1480,8 +1572,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_5">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1654,11 +1746,6 @@ <h2 id="sgptools.utils.misc" class="doc doc-heading">
 
 
 
-
-
-
-
-
   <div class="doc doc-children">
 
 
@@ -1680,7 +1767,7 @@ <h3 id="sgptools.utils.misc.cont2disc" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Map continuous space locations to a discrete set of candidate location</p>
+      <p>Map continuous space locations to a discrete set of candidate location</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -1695,9 +1782,7 @@ <h3 id="sgptools.utils.misc.cont2disc" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>Xu</code>
-            </td>
+            <td><code>Xu</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1711,9 +1796,7 @@ <h3 id="sgptools.utils.misc.cont2disc" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>candidates</code>
-            </td>
+            <td><code>candidates</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1727,9 +1810,7 @@ <h3 id="sgptools.utils.misc.cont2disc" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>candidate_labels</code>
-            </td>
+            <td><code>candidate_labels</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1855,7 +1936,7 @@ <h3 id="sgptools.utils.misc.get_inducing_pts" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Selects a subset of the data points to be used as inducing points. 
+      <p>Selects a subset of the data points to be used as inducing points. 
 The default approach uses kmeans to select the subset. </p>
 
 
@@ -1871,9 +1952,7 @@ <h3 id="sgptools.utils.misc.get_inducing_pts" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>data</code>
-            </td>
+            <td><code>data</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1887,9 +1966,7 @@ <h3 id="sgptools.utils.misc.get_inducing_pts" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>num_inducing</code>
-            </td>
+            <td><code>num_inducing</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -1903,9 +1980,7 @@ <h3 id="sgptools.utils.misc.get_inducing_pts" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>orientation</code>
-            </td>
+            <td><code>orientation</code></td>
             <td>
                   <code>bool</code>
             </td>
@@ -1920,9 +1995,7 @@ <h3 id="sgptools.utils.misc.get_inducing_pts" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>random</code>
-            </td>
+            <td><code>random</code></td>
             <td>
                   <code>bool</code>
             </td>
@@ -2034,7 +2107,7 @@ <h3 id="sgptools.utils.misc.interpolate_path" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Interpolate additional points between the given waypoints to simulate continuous sensing robots</p>
+      <p>Interpolate additional points between the given waypoints to simulate continuous sensing robots</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -2049,9 +2122,7 @@ <h3 id="sgptools.utils.misc.interpolate_path" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>waypoints</code>
-            </td>
+            <td><code>waypoints</code></td>
             <td>
                   <code>(n, d)</code>
             </td>
@@ -2065,9 +2136,7 @@ <h3 id="sgptools.utils.misc.interpolate_path" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>sampling_rate</code>
-            </td>
+            <td><code>sampling_rate</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -2158,7 +2227,7 @@ <h3 id="sgptools.utils.misc.plot_paths" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Function to plot the IPP solution paths</p>
+      <p>Function to plot the IPP solution paths</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -2173,9 +2242,7 @@ <h3 id="sgptools.utils.misc.plot_paths" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>paths</code>
-            </td>
+            <td><code>paths</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2189,9 +2256,7 @@ <h3 id="sgptools.utils.misc.plot_paths" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>candidates</code>
-            </td>
+            <td><code>candidates</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2205,9 +2270,7 @@ <h3 id="sgptools.utils.misc.plot_paths" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>title</code>
-            </td>
+            <td><code>title</code></td>
             <td>
                   <code>str</code>
             </td>
@@ -2289,7 +2352,7 @@ <h3 id="sgptools.utils.misc.ploygon2candidats" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Sample unlabeled candidates within a polygon</p>
+      <p>Sample unlabeled candidates within a polygon</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -2304,9 +2367,7 @@ <h3 id="sgptools.utils.misc.ploygon2candidats" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>vertices</code>
-            </td>
+            <td><code>vertices</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2320,9 +2381,7 @@ <h3 id="sgptools.utils.misc.ploygon2candidats" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>num_samples</code>
-            </td>
+            <td><code>num_samples</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -2336,9 +2395,7 @@ <h3 id="sgptools.utils.misc.ploygon2candidats" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>random_seed</code>
-            </td>
+            <td><code>random_seed</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -2433,7 +2490,7 @@ <h3 id="sgptools.utils.misc.project_waypoints" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Project the waypoints back to the candidate set while retaining the 
+      <p>Project the waypoints back to the candidate set while retaining the 
 waypoint visitation order.</p>
 
 
@@ -2449,9 +2506,7 @@ <h3 id="sgptools.utils.misc.project_waypoints" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>waypoints</code>
-            </td>
+            <td><code>waypoints</code></td>
             <td>
                   <code>(n, d)</code>
             </td>
@@ -2465,9 +2520,7 @@ <h3 id="sgptools.utils.misc.project_waypoints" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>candidates</code>
-            </td>
+            <td><code>candidates</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -2572,7 +2625,7 @@ <h3 id="sgptools.utils.misc.project_waypoints" class="doc doc-heading">
         
           <button type="button" class="md-top md-icon" data-md-component="top" hidden>
   
-  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8z"/></svg>
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg>
   Back to top
 </button>
         
@@ -2602,10 +2655,10 @@ <h3 id="sgptools.utils.misc.project_waypoints" class="doc doc-heading">
     </div>
     
     
-    <script id="__config" type="application/json">{"base": "../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../assets/javascripts/workers/search.6ce7567c.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="../../assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="../../assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
   </body>
diff --git a/api/utils/TSP.html b/api/utils/TSP.html
index 7482246..ace3e1a 100644
--- a/api/utils/TSP.html
+++ b/api/utils/TSP.html
@@ -20,7 +20,7 @@
       
       
       <link rel="icon" href="../../assets/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.49">
+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.31">
     
     
       
@@ -28,7 +28,7 @@
       
     
     
-      <link rel="stylesheet" href="../../assets/stylesheets/main.6f8fc17f.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.3cba04c6.min.css">
       
         
         <link rel="stylesheet" href="../../assets/stylesheets/palette.06af60db.min.css">
@@ -56,7 +56,7 @@
     
       <link rel="stylesheet" href="../../stylesheets/extra.css">
     
-    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce(((e,_)=>(e<<5)-e+_.charCodeAt(0)),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
   
@@ -65,7 +65,7 @@
   
   
 
-<script id="__analytics">function __md_analytics(){function e(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],e("js",new Date),e("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",(function(){document.forms.search&&document.forms.search.query.addEventListener("blur",(function(){this.value&&e("event","search",{search_term:this.value})}));document$.subscribe((function(){var t=document.forms.feedback;if(void 0!==t)for(var a of t.querySelectorAll("[type=submit]"))a.addEventListener("click",(function(a){a.preventDefault();var n=document.location.pathname,d=this.getAttribute("data-md-value");e("event","feedback",{page:n,data:d}),t.firstElementChild.disabled=!0;var r=t.querySelector(".md-feedback__note [data-md-value='"+d+"']");r&&(r.hidden=!1)})),t.hidden=!1})),location$.subscribe((function(t){e("config","G-Z2TVV3MF1S",{page_path:t.pathname})}))}));var t=document.createElement("script");t.async=!0,t.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",t)}</script>
+<script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",function(){document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document$.subscribe(function(){var a=document.forms.feedback;if(void 0!==a)for(var e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}),location$.subscribe(function(e){n("config","G-Z2TVV3MF1S",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
   
     <script>"undefined"!=typeof __md_analytics&&__md_analytics()</script>
   
@@ -153,7 +153,7 @@
     <!-- Button to open drawer -->
     <label class="md-header__button md-icon" for="__drawer">
       
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3zm0 5h18v2H3zm0 5h18v2H3z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
 
     <!-- Header title -->
@@ -184,7 +184,7 @@
     <input class="md-option" data-md-color-media="(prefers-color-scheme: light)" data-md-color-scheme="default" data-md-color-primary="custom" data-md-color-accent="custom"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_0">
     
       <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5s-1.65.15-2.39.42zM3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29zm.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14zM20.65 7l-1.77 3.79a7.02 7.02 0 0 0-2.38-4.15zm-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29zM12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5c-.84 0-1.65.15-2.39.42L12 2M3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29L3.34 7m.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14L3.36 17M20.65 7l-1.77 3.79a7.023 7.023 0 0 0-2.38-4.15l4.15.36m-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29L20.64 17M12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44L12 22Z"/></svg>
       </label>
     
   
@@ -194,7 +194,7 @@
     <input class="md-option" data-md-color-media="(prefers-color-scheme: dark)" data-md-color-scheme="slate" data-md-color-primary="custom" data-md-color-accent="custom"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_1">
     
       <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3zm3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95zm-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3 3.19.09m3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95 2.06.05m-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31Z"/></svg>
       </label>
     
   
@@ -204,7 +204,7 @@
 
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-      <script>var palette=__md_get("__palette");if(palette&&palette.color){if("(prefers-color-scheme)"===palette.color.media){var media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']");palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent")}for(var[key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
     
 
     <!-- Site language selector -->
@@ -214,7 +214,7 @@
     
       <label class="md-header__button md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
 
       <!-- Search interface -->
@@ -225,20 +225,20 @@
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
           <a href="javascript:void(0)" class="md-search__icon md-icon" title="Share" aria-label="Share" data-clipboard data-clipboard-text="" data-md-component="search-share" tabindex="-1">
             
-            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7s-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91s2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08"/></svg>
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7 0-.24-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91 1.61 0 2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08Z"/></svg>
           </a>
         
         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
           
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12z"/></svg>
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
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@@ -265,7 +265,7 @@
         <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -379,6 +379,26 @@
   
     
     
+      <li class="md-tabs__item">
+        <a href="../../blueos-sgp-tools.html" class="md-tabs__link">
+          
+  
+    
+  
+  BlueOS
+
+        </a>
+      </li>
+    
+  
+
+      
+        
+  
+  
+  
+    
+    
       <li class="md-tabs__item">
         <a href="../../examples/AIPP_non_point_FoV.html" class="md-tabs__link">
           
@@ -475,7 +495,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -1405,6 +1425,78 @@
   
     
     
+      
+        
+      
+    
+    
+      
+      
+        
+      
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        
+          
+          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    BlueOS
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_5">
+            <span class="md-nav__icon md-icon"></span>
+            BlueOS
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../blueos-sgp-tools.html" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    blueos-sgp-tools
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    
+    
       
         
       
@@ -1430,10 +1522,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
         
           
-          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+          <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
             
   
   <span class="md-ellipsis">
@@ -1444,8 +1536,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_5">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1618,11 +1710,6 @@ <h2 id="sgptools.utils.tsp" class="doc doc-heading">
 
 
 
-
-
-
-
-
   <div class="doc doc-children">
 
 
@@ -1644,7 +1731,7 @@ <h3 id="sgptools.utils.tsp.resample_path" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Function to map path with arbitrary number of waypoints to 
+      <p>Function to map path with arbitrary number of waypoints to 
 inducing points path with fixed number of waypoints</p>
 
 
@@ -1660,9 +1747,7 @@ <h3 id="sgptools.utils.tsp.resample_path" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>waypoints</code>
-            </td>
+            <td><code>waypoints</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1676,9 +1761,7 @@ <h3 id="sgptools.utils.tsp.resample_path" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>num_inducing</code>
-            </td>
+            <td><code>num_inducing</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -1779,7 +1862,7 @@ <h3 id="sgptools.utils.tsp.run_tsp" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Method to run TSP/VRP with arbitrary start and end nodes, 
+      <p>Method to run TSP/VRP with arbitrary start and end nodes, 
 and without any distance constraint</p>
 
 
@@ -1795,9 +1878,7 @@ <h3 id="sgptools.utils.tsp.run_tsp" class="doc doc-heading">
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-                <code>nodes</code>
-            </td>
+            <td><code>nodes</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1811,9 +1892,7 @@ <h3 id="sgptools.utils.tsp.run_tsp" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>num_vehicles</code>
-            </td>
+            <td><code>num_vehicles</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -1827,9 +1906,7 @@ <h3 id="sgptools.utils.tsp.run_tsp" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>max_dist</code>
-            </td>
+            <td><code>max_dist</code></td>
             <td>
                   <code>float</code>
             </td>
@@ -1843,9 +1920,7 @@ <h3 id="sgptools.utils.tsp.run_tsp" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>depth</code>
-            </td>
+            <td><code>depth</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -1859,9 +1934,7 @@ <h3 id="sgptools.utils.tsp.run_tsp" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>resample</code>
-            </td>
+            <td><code>resample</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -1876,9 +1949,7 @@ <h3 id="sgptools.utils.tsp.run_tsp" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>start_nodes</code>
-            </td>
+            <td><code>start_nodes</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1893,9 +1964,7 @@ <h3 id="sgptools.utils.tsp.run_tsp" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>end_nodes</code>
-            </td>
+            <td><code>end_nodes</code></td>
             <td>
                   <code>ndarray</code>
             </td>
@@ -1910,9 +1979,7 @@ <h3 id="sgptools.utils.tsp.run_tsp" class="doc doc-heading">
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td>
-                <code>time_limit</code>
-            </td>
+            <td><code>time_limit</code></td>
             <td>
                   <code>int</code>
             </td>
@@ -2253,7 +2320,7 @@ <h3 id="sgptools.utils.tsp.run_tsp" class="doc doc-heading">
         
           <button type="button" class="md-top md-icon" data-md-component="top" hidden>
   
-  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8z"/></svg>
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg>
   Back to top
 </button>
         
@@ -2283,10 +2350,10 @@ <h3 id="sgptools.utils.tsp.run_tsp" class="doc doc-heading">
     </div>
     
     
-    <script id="__config" type="application/json">{"base": "../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../assets/javascripts/workers/search.6ce7567c.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="../../assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="../../assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
   </body>
diff --git a/api/utils/index.html b/api/utils/index.html
index 8e38caa..24e9ad1 100644
--- a/api/utils/index.html
+++ b/api/utils/index.html
@@ -20,7 +20,7 @@
       
       
       <link rel="icon" href="../../assets/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.49">
+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.31">
     
     
       
@@ -28,7 +28,7 @@
       
     
     
-      <link rel="stylesheet" href="../../assets/stylesheets/main.6f8fc17f.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.3cba04c6.min.css">
       
         
         <link rel="stylesheet" href="../../assets/stylesheets/palette.06af60db.min.css">
@@ -56,7 +56,7 @@
     
       <link rel="stylesheet" href="../../stylesheets/extra.css">
     
-    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce(((e,_)=>(e<<5)-e+_.charCodeAt(0)),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
   
@@ -65,7 +65,7 @@
   
   
 
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+<script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",function(){document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document$.subscribe(function(){var a=document.forms.feedback;if(void 0!==a)for(var e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}),location$.subscribe(function(e){n("config","G-Z2TVV3MF1S",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
   
     <script>"undefined"!=typeof __md_analytics&&__md_analytics()</script>
   
@@ -153,7 +153,7 @@
     <!-- Button to open drawer -->
     <label class="md-header__button md-icon" for="__drawer">
       
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3zm0 5h18v2H3zm0 5h18v2H3z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
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@@ -184,7 +184,7 @@
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       <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5s-1.65.15-2.39.42zM3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29zm.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14zM20.65 7l-1.77 3.79a7.02 7.02 0 0 0-2.38-4.15zm-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29zM12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5c-.84 0-1.65.15-2.39.42L12 2M3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29L3.34 7m.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14L3.36 17M20.65 7l-1.77 3.79a7.023 7.023 0 0 0-2.38-4.15l4.15.36m-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29L20.64 17M12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44L12 22Z"/></svg>
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@@ -194,7 +194,7 @@
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       <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3zm3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95zm-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3 3.19.09m3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95 2.06.05m-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31Z"/></svg>
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@@ -204,7 +204,7 @@
 
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-      <script>var palette=__md_get("__palette");if(palette&&palette.color){if("(prefers-color-scheme)"===palette.color.media){var media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']");palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent")}for(var[key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
     
 
     <!-- Site language selector -->
@@ -214,7 +214,7 @@
     
       <label class="md-header__button md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
 
       <!-- Search interface -->
@@ -225,20 +225,20 @@
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
           <a href="javascript:void(0)" class="md-search__icon md-icon" title="Share" aria-label="Share" data-clipboard data-clipboard-text="" data-md-component="search-share" tabindex="-1">
             
-            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7s-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91s2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08"/></svg>
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7 0-.24-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91 1.61 0 2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08Z"/></svg>
           </a>
         
         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
           
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12z"/></svg>
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
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@@ -265,7 +265,7 @@
         <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -379,6 +379,26 @@
   
     
     
+      <li class="md-tabs__item">
+        <a href="../../blueos-sgp-tools.html" class="md-tabs__link">
+          
+  
+    
+  
+  BlueOS
+
+        </a>
+      </li>
+    
+  
+
+      
+        
+  
+  
+  
+    
+    
       <li class="md-tabs__item">
         <a href="../../examples/AIPP_non_point_FoV.html" class="md-tabs__link">
           
@@ -475,7 +495,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -1336,6 +1356,78 @@
   
     
     
+      
+        
+      
+    
+    
+      
+      
+        
+      
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        
+          
+          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    BlueOS
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_5">
+            <span class="md-nav__icon md-icon"></span>
+            BlueOS
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../blueos-sgp-tools.html" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    blueos-sgp-tools
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    
+    
       
         
       
@@ -1361,10 +1453,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
         
           
-          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+          <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
             
   
   <span class="md-ellipsis">
@@ -1375,8 +1467,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_5">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1551,7 +1643,7 @@ <h2 id="sgptools.utils" class="doc doc-heading">
 
     <div class="doc doc-contents first">
 
-        <p>General utilities to support the functionalities of this package:</p>
+      <p>General utilities to support the functionalities of this package:</p>
 <ul>
 <li><code>data</code>: Provides utilities to preprocess datasets</li>
 <li><code>gpflow</code>: Provides utilities to interface with GPflow</li>
@@ -1562,11 +1654,6 @@ <h2 id="sgptools.utils" class="doc doc-heading">
 
 
 
-
-
-
-
-
   <div class="doc doc-children">
 
 
@@ -1608,7 +1695,7 @@ <h2 id="sgptools.utils" class="doc doc-heading">
         
           <button type="button" class="md-top md-icon" data-md-component="top" hidden>
   
-  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8z"/></svg>
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg>
   Back to top
 </button>
         
@@ -1638,10 +1725,10 @@ <h2 id="sgptools.utils" class="doc doc-heading">
     </div>
     
     
-    <script id="__config" type="application/json">{"base": "../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../assets/javascripts/workers/search.6ce7567c.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "../..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="../../assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="../../assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
   </body>
diff --git a/assets/_mkdocstrings.css b/assets/_mkdocstrings.css
index b500381..85449ec 100644
--- a/assets/_mkdocstrings.css
+++ b/assets/_mkdocstrings.css
@@ -26,33 +26,20 @@
   float: right;
 }
 
-/* Parameter headings must be inline, not blocks. */
-.doc-heading-parameter {
-  display: inline;
-}
-
-/* Prefer space on the right, not the left of parameter permalinks. */
-.doc-heading-parameter .headerlink {
-  margin-left: 0 !important;
-  margin-right: 0.2rem;
-}
-
 /* Backward-compatibility: docstring section titles in bold. */
 .doc-section-title {
   font-weight: bold;
 }
 
 /* Symbols in Navigation and ToC. */
-:root, :host,
+:root,
 [data-md-color-scheme="default"] {
-  --doc-symbol-parameter-fg-color: #df50af;
   --doc-symbol-attribute-fg-color: #953800;
   --doc-symbol-function-fg-color: #8250df;
   --doc-symbol-method-fg-color: #8250df;
   --doc-symbol-class-fg-color: #0550ae;
   --doc-symbol-module-fg-color: #5cad0f;
 
-  --doc-symbol-parameter-bg-color: #df50af1a;
   --doc-symbol-attribute-bg-color: #9538001a;
   --doc-symbol-function-bg-color: #8250df1a;
   --doc-symbol-method-bg-color: #8250df1a;
@@ -61,14 +48,12 @@
 }
 
 [data-md-color-scheme="slate"] {
-  --doc-symbol-parameter-fg-color: #ffa8cc;
   --doc-symbol-attribute-fg-color: #ffa657;
   --doc-symbol-function-fg-color: #d2a8ff;
   --doc-symbol-method-fg-color: #d2a8ff;
   --doc-symbol-class-fg-color: #79c0ff;
   --doc-symbol-module-fg-color: #baff79;
 
-  --doc-symbol-parameter-bg-color: #ffa8cc1a;
   --doc-symbol-attribute-bg-color: #ffa6571a;
   --doc-symbol-function-bg-color: #d2a8ff1a;
   --doc-symbol-method-bg-color: #d2a8ff1a;
@@ -83,15 +68,6 @@ code.doc-symbol {
   font-weight: bold;
 }
 
-code.doc-symbol-parameter {
-  color: var(--doc-symbol-parameter-fg-color);
-  background-color: var(--doc-symbol-parameter-bg-color);
-}
-
-code.doc-symbol-parameter::after {
-  content: "param";
-}
-
 code.doc-symbol-attribute {
   color: var(--doc-symbol-attribute-fg-color);
   background-color: var(--doc-symbol-attribute-bg-color);
diff --git a/assets/favicon.png b/assets/favicon.png
index 51669cf..e6396f2 100644
Binary files a/assets/favicon.png and b/assets/favicon.png differ
diff --git a/assets/javascripts/bundle.88dd0f4e.min.js b/assets/javascripts/bundle.88dd0f4e.min.js
deleted file mode 100644
index fb8f310..0000000
--- a/assets/javascripts/bundle.88dd0f4e.min.js
+++ /dev/null
@@ -1,16 +0,0 @@
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.state-title{fill:var(--md-mermaid-label-fg-color)!important;font-family:var(--md-mermaid-font-family)}g.stateGroup .composit{fill:var(--md-mermaid-label-bg-color)}.nodeLabel,.nodeLabel p{color:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}a .nodeLabel{text-decoration:underline}.node circle.state-end,.node circle.state-start,.start-state{fill:var(--md-mermaid-edge-color);stroke:none}.end-state-inner,.end-state-outer{fill:var(--md-mermaid-edge-color)}.end-state-inner,.node circle.state-end{stroke:var(--md-mermaid-label-bg-color)}.transition{stroke:var(--md-mermaid-edge-color)}[id^=state-fork] rect,[id^=state-join] rect{fill:var(--md-mermaid-edge-color)!important;stroke:none!important}.statediagram-cluster.statediagram-cluster .inner{fill:var(--md-default-bg-color)}.statediagram-cluster rect{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}.statediagram-state rect.divider{fill:var(--md-default-fg-color--lightest);stroke:var(--md-default-fg-color--lighter)}defs #statediagram-barbEnd{stroke:var(--md-mermaid-edge-color)}.entityTitleText{fill:var(--md-mermaid-label-fg-color)}.attributeBoxEven,.attributeBoxOdd{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}.entityBox{fill:var(--md-mermaid-label-bg-color);stroke:var(--md-mermaid-node-fg-color)}.entityLabel{fill:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.relationshipLabelBox{fill:var(--md-mermaid-label-bg-color);fill-opacity:1;background-color:var(--md-mermaid-label-bg-color);opacity:1}.relationshipLabel{fill:var(--md-mermaid-label-fg-color)}.relationshipLine{stroke:var(--md-mermaid-edge-color)}defs #ONE_OR_MORE_END *,defs #ONE_OR_MORE_START *,defs #ONLY_ONE_END *,defs #ONLY_ONE_START *,defs #ZERO_OR_MORE_END *,defs #ZERO_OR_MORE_START *,defs #ZERO_OR_ONE_END *,defs #ZERO_OR_ONE_START *{stroke:var(--md-mermaid-edge-color)!important}defs #ZERO_OR_MORE_END circle,defs #ZERO_OR_MORE_START circle{fill:var(--md-mermaid-label-bg-color)}text:not([class]):last-child{fill:var(--md-mermaid-label-fg-color)}.actor{fill:var(--md-mermaid-sequence-actor-bg-color);stroke:var(--md-mermaid-sequence-actor-border-color)}text.actor>tspan{fill:var(--md-mermaid-sequence-actor-fg-color);font-family:var(--md-mermaid-font-family)}line{stroke:var(--md-mermaid-sequence-actor-line-color)}.actor-man circle,.actor-man line{fill:var(--md-mermaid-sequence-actorman-bg-color);stroke:var(--md-mermaid-sequence-actorman-line-color)}.messageLine0,.messageLine1{stroke:var(--md-mermaid-sequence-message-line-color)}.note{fill:var(--md-mermaid-sequence-note-bg-color);stroke:var(--md-mermaid-sequence-note-border-color)}.loopText,.loopText>tspan,.messageText,.noteText>tspan{stroke:none;font-family:var(--md-mermaid-font-family)!important}.messageText{fill:var(--md-mermaid-sequence-message-fg-color)}.loopText,.loopText>tspan{fill:var(--md-mermaid-sequence-loop-fg-color)}.noteText>tspan{fill:var(--md-mermaid-sequence-note-fg-color)}#arrowhead path{fill:var(--md-mermaid-sequence-message-line-color);stroke:none}.loopLine{fill:var(--md-mermaid-sequence-loop-bg-color);stroke:var(--md-mermaid-sequence-loop-border-color)}.labelBox{fill:var(--md-mermaid-sequence-label-bg-color);stroke:none}.labelText,.labelText>span{fill:var(--md-mermaid-sequence-label-fg-color);font-family:var(--md-mermaid-font-family)}.sequenceNumber{fill:var(--md-mermaid-sequence-number-fg-color)}rect.rect{fill:var(--md-mermaid-sequence-box-bg-color);stroke:none}rect.rect+text.text{fill:var(--md-mermaid-sequence-box-fg-color)}defs #sequencenumber{fill:var(--md-mermaid-sequence-number-bg-color)!important}";var Gr,Qa=0;function Ka(){return typeof mermaid=="undefined"||mermaid instanceof Element?Tt("https://unpkg.com/mermaid@11/dist/mermaid.min.js"):I(void 0)}function Wn(e){return e.classList.remove("mermaid"),Gr||(Gr=Ka().pipe(w(()=>mermaid.initialize({startOnLoad:!1,themeCSS:Un,sequence:{actorFontSize:"16px",messageFontSize:"16px",noteFontSize:"16px"}})),m(()=>{}),G(1))),Gr.subscribe(()=>co(this,null,function*(){e.classList.add("mermaid");let t=`__mermaid_${Qa++}`,r=x("div",{class:"mermaid"}),o=e.textContent,{svg:n,fn:i}=yield mermaid.render(t,o),a=r.attachShadow({mode:"closed"});a.innerHTML=n,e.replaceWith(r),i==null||i(a)})),Gr.pipe(m(()=>({ref:e})))}var Dn=x("table");function Vn(e){return e.replaceWith(Dn),Dn.replaceWith(An(e)),I({ref:e})}function Ya(e){let t=e.find(r=>r.checked)||e[0];return O(...e.map(r=>h(r,"change").pipe(m(()=>R(`label[for="${r.id}"]`))))).pipe(Q(R(`label[for="${t.id}"]`)),m(r=>({active:r})))}function Nn(e,{viewport$:t,target$:r}){let o=R(".tabbed-labels",e),n=P(":scope > input",e),i=Kr("prev");e.append(i);let a=Kr("next");return e.append(a),C(()=>{let s=new g,p=s.pipe(Z(),ie(!0));z([s,ge(e),tt(e)]).pipe(W(p),Me(1,me)).subscribe({next([{active:c},l]){let f=Ve(c),{width:u}=ce(c);e.style.setProperty("--md-indicator-x",`${f.x}px`),e.style.setProperty("--md-indicator-width",`${u}px`);let d=pr(o);(f.x<d.x||f.x+u>d.x+l.width)&&o.scrollTo({left:Math.max(0,f.x-16),behavior:"smooth"})},complete(){e.style.removeProperty("--md-indicator-x"),e.style.removeProperty("--md-indicator-width")}}),z([Ne(o),ge(o)]).pipe(W(p)).subscribe(([c,l])=>{let f=St(o);i.hidden=c.x<16,a.hidden=c.x>f.width-l.width-16}),O(h(i,"click").pipe(m(()=>-1)),h(a,"click").pipe(m(()=>1))).pipe(W(p)).subscribe(c=>{let{width:l}=ce(o);o.scrollBy({left:l*c,behavior:"smooth"})}),r.pipe(W(p),b(c=>n.includes(c))).subscribe(c=>c.click()),o.classList.add("tabbed-labels--linked");for(let c of n){let l=R(`label[for="${c.id}"]`);l.replaceChildren(x("a",{href:`#${l.htmlFor}`,tabIndex:-1},...Array.from(l.childNodes))),h(l.firstElementChild,"click").pipe(W(p),b(f=>!(f.metaKey||f.ctrlKey)),w(f=>{f.preventDefault(),f.stopPropagation()})).subscribe(()=>{history.replaceState({},"",`#${l.htmlFor}`),l.click()})}return B("content.tabs.link")&&s.pipe(Ce(1),re(t)).subscribe(([{active:c},{offset:l}])=>{let f=c.innerText.trim();if(c.hasAttribute("data-md-switching"))c.removeAttribute("data-md-switching");else{let u=e.offsetTop-l.y;for(let y of P("[data-tabs]"))for(let L of P(":scope > input",y)){let X=R(`label[for="${L.id}"]`);if(X!==c&&X.innerText.trim()===f){X.setAttribute("data-md-switching",""),L.click();break}}window.scrollTo({top:e.offsetTop-u});let d=__md_get("__tabs")||[];__md_set("__tabs",[...new Set([f,...d])])}}),s.pipe(W(p)).subscribe(()=>{for(let c of P("audio, video",e))c.pause()}),Ya(n).pipe(w(c=>s.next(c)),_(()=>s.complete()),m(c=>$({ref:e},c)))}).pipe(Ke(se))}function zn(e,{viewport$:t,target$:r,print$:o}){return O(...P(".annotate:not(.highlight)",e).map(n=>Pn(n,{target$:r,print$:o})),...P("pre:not(.mermaid) > code",e).map(n=>jn(n,{target$:r,print$:o})),...P("pre.mermaid",e).map(n=>Wn(n)),...P("table:not([class])",e).map(n=>Vn(n)),...P("details",e).map(n=>Fn(n,{target$:r,print$:o})),...P("[data-tabs]",e).map(n=>Nn(n,{viewport$:t,target$:r})),...P("[title]",e).filter(()=>B("content.tooltips")).map(n=>mt(n,{viewport$:t})))}function Ba(e,{alert$:t}){return t.pipe(v(r=>O(I(!0),I(!1).pipe(Ge(2e3))).pipe(m(o=>({message:r,active:o})))))}function qn(e,t){let r=R(".md-typeset",e);return C(()=>{let o=new g;return o.subscribe(({message:n,active:i})=>{e.classList.toggle("md-dialog--active",i),r.textContent=n}),Ba(e,t).pipe(w(n=>o.next(n)),_(()=>o.complete()),m(n=>$({ref:e},n)))})}var Ga=0;function Ja(e,t){document.body.append(e);let{width:r}=ce(e);e.style.setProperty("--md-tooltip-width",`${r}px`),e.remove();let o=cr(t),n=typeof o!="undefined"?Ne(o):I({x:0,y:0}),i=O(et(t),$t(t)).pipe(K());return z([i,n]).pipe(m(([a,s])=>{let{x:p,y:c}=Ve(t),l=ce(t),f=t.closest("table");return f&&t.parentElement&&(p+=f.offsetLeft+t.parentElement.offsetLeft,c+=f.offsetTop+t.parentElement.offsetTop),{active:a,offset:{x:p-s.x+l.width/2-r/2,y:c-s.y+l.height+8}}}))}function Qn(e){let t=e.title;if(!t.length)return S;let r=`__tooltip_${Ga++}`,o=Rt(r,"inline"),n=R(".md-typeset",o);return n.innerHTML=t,C(()=>{let i=new g;return i.subscribe({next({offset:a}){o.style.setProperty("--md-tooltip-x",`${a.x}px`),o.style.setProperty("--md-tooltip-y",`${a.y}px`)},complete(){o.style.removeProperty("--md-tooltip-x"),o.style.removeProperty("--md-tooltip-y")}}),O(i.pipe(b(({active:a})=>a)),i.pipe(_e(250),b(({active:a})=>!a))).subscribe({next({active:a}){a?(e.insertAdjacentElement("afterend",o),e.setAttribute("aria-describedby",r),e.removeAttribute("title")):(o.remove(),e.removeAttribute("aria-describedby"),e.setAttribute("title",t))},complete(){o.remove(),e.removeAttribute("aria-describedby"),e.setAttribute("title",t)}}),i.pipe(Me(16,me)).subscribe(({active:a})=>{o.classList.toggle("md-tooltip--active",a)}),i.pipe(pt(125,me),b(()=>!!e.offsetParent),m(()=>e.offsetParent.getBoundingClientRect()),m(({x:a})=>a)).subscribe({next(a){a?o.style.setProperty("--md-tooltip-0",`${-a}px`):o.style.removeProperty("--md-tooltip-0")},complete(){o.style.removeProperty("--md-tooltip-0")}}),Ja(o,e).pipe(w(a=>i.next(a)),_(()=>i.complete()),m(a=>$({ref:e},a)))}).pipe(Ke(se))}function Xa({viewport$:e}){if(!B("header.autohide"))return I(!1);let t=e.pipe(m(({offset:{y:n}})=>n),Be(2,1),m(([n,i])=>[n<i,i]),ee(0)),r=z([e,t]).pipe(b(([{offset:n},[,i]])=>Math.abs(i-n.y)>100),m(([,[n]])=>n),K()),o=ze("search");return z([e,o]).pipe(m(([{offset:n},i])=>n.y>400&&!i),K(),v(n=>n?r:I(!1)),Q(!1))}function Kn(e,t){return C(()=>z([ge(e),Xa(t)])).pipe(m(([{height:r},o])=>({height:r,hidden:o})),K((r,o)=>r.height===o.height&&r.hidden===o.hidden),G(1))}function Yn(e,{header$:t,main$:r}){return C(()=>{let o=new g,n=o.pipe(Z(),ie(!0));o.pipe(ee("active"),He(t)).subscribe(([{active:a},{hidden:s}])=>{e.classList.toggle("md-header--shadow",a&&!s),e.hidden=s});let i=ue(P("[title]",e)).pipe(b(()=>B("content.tooltips")),ne(a=>Qn(a)));return r.subscribe(o),t.pipe(W(n),m(a=>$({ref:e},a)),Re(i.pipe(W(n))))})}function Za(e,{viewport$:t,header$:r}){return mr(e,{viewport$:t,header$:r}).pipe(m(({offset:{y:o}})=>{let{height:n}=ce(e);return{active:o>=n}}),ee("active"))}function Bn(e,t){return C(()=>{let r=new g;r.subscribe({next({active:n}){e.classList.toggle("md-header__title--active",n)},complete(){e.classList.remove("md-header__title--active")}});let o=fe(".md-content h1");return typeof o=="undefined"?S:Za(o,t).pipe(w(n=>r.next(n)),_(()=>r.complete()),m(n=>$({ref:e},n)))})}function Gn(e,{viewport$:t,header$:r}){let o=r.pipe(m(({height:i})=>i),K()),n=o.pipe(v(()=>ge(e).pipe(m(({height:i})=>({top:e.offsetTop,bottom:e.offsetTop+i})),ee("bottom"))));return z([o,n,t]).pipe(m(([i,{top:a,bottom:s},{offset:{y:p},size:{height:c}}])=>(c=Math.max(0,c-Math.max(0,a-p,i)-Math.max(0,c+p-s)),{offset:a-i,height:c,active:a-i<=p})),K((i,a)=>i.offset===a.offset&&i.height===a.height&&i.active===a.active))}function es(e){let t=__md_get("__palette")||{index:e.findIndex(o=>matchMedia(o.getAttribute("data-md-color-media")).matches)},r=Math.max(0,Math.min(t.index,e.length-1));return I(...e).pipe(ne(o=>h(o,"change").pipe(m(()=>o))),Q(e[r]),m(o=>({index:e.indexOf(o),color:{media:o.getAttribute("data-md-color-media"),scheme:o.getAttribute("data-md-color-scheme"),primary:o.getAttribute("data-md-color-primary"),accent:o.getAttribute("data-md-color-accent")}})),G(1))}function Jn(e){let t=P("input",e),r=x("meta",{name:"theme-color"});document.head.appendChild(r);let o=x("meta",{name:"color-scheme"});document.head.appendChild(o);let n=Pt("(prefers-color-scheme: light)");return C(()=>{let i=new g;return i.subscribe(a=>{if(document.body.setAttribute("data-md-color-switching",""),a.color.media==="(prefers-color-scheme)"){let s=matchMedia("(prefers-color-scheme: light)"),p=document.querySelector(s.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: 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t=ti(document);for(let[o,n]of ti(e))t.has(o)?t.delete(o):document.head.appendChild(n);for(let o of t.values()){let n=o.getAttribute("name");n!=="theme-color"&&n!=="color-scheme"&&o.remove()}let r=Se("container");return We(P("script",r)).pipe(v(o=>{let n=e.createElement("script");if(o.src){for(let i of o.getAttributeNames())n.setAttribute(i,o.getAttribute(i));return o.replaceWith(n),new j(i=>{n.onload=()=>i.complete()})}else return n.textContent=o.textContent,o.replaceWith(n),S}),Z(),ie(document))}function oi({location$:e,viewport$:t,progress$:r}){let o=xe();if(location.protocol==="file:")return S;let n=ur(o.base);I(document).subscribe(ri);let i=h(document.body,"click").pipe(He(n),v(([p,c])=>os(p,c)),pe()),a=h(window,"popstate").pipe(m(ye),pe());i.pipe(re(t)).subscribe(([p,{offset:c}])=>{history.replaceState(c,""),history.pushState(null,"",p)}),O(i,a).subscribe(e);let s=e.pipe(ee("pathname"),v(p=>fn(p,{progress$:r}).pipe(de(()=>(lt(p,!0),S)))),v(ri),v(ns),pe());return 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hs(e,{viewport$:t,header$:r}){return ge(document.body).pipe(v(()=>mr(e,{header$:r,viewport$:t})),m(({offset:{y:o}})=>({hidden:o>=10})),ee("hidden"))}function yi(e,t){return C(()=>{let r=new g;return r.subscribe({next({hidden:o}){e.hidden=o},complete(){e.hidden=!1}}),(B("navigation.tabs.sticky")?I({hidden:!1}):hs(e,t)).pipe(w(o=>r.next(o)),_(()=>r.complete()),m(o=>$({ref:e},o)))})}function bs(e,{viewport$:t,header$:r}){let o=new Map,n=P(".md-nav__link",e);for(let s of n){let p=decodeURIComponent(s.hash.substring(1)),c=fe(`[id="${p}"]`);typeof c!="undefined"&&o.set(s,c)}let i=r.pipe(ee("height"),m(({height:s})=>{let p=Se("main"),c=R(":scope > :first-child",p);return s+.8*(c.offsetTop-p.offsetTop)}),pe());return ge(document.body).pipe(ee("height"),v(s=>C(()=>{let p=[];return I([...o].reduce((c,[l,f])=>{for(;p.length&&o.get(p[p.length-1]).tagName>=f.tagName;)p.pop();let u=f.offsetTop;for(;!u&&f.parentElement;)f=f.parentElement,u=f.offsetTop;let 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vs(e,{viewport$:t,main$:r,target$:o}){let n=t.pipe(m(({offset:{y:a}})=>a),Be(2,1),m(([a,s])=>a>s&&s>0),K()),i=r.pipe(m(({active:a})=>a));return z([i,n]).pipe(m(([a,s])=>!(a&&s)),K(),W(o.pipe(Ce(1))),ie(!0),ct({delay:250}),m(a=>({hidden:a})))}function Ei(e,{viewport$:t,header$:r,main$:o,target$:n}){let i=new g,a=i.pipe(Z(),ie(!0));return i.subscribe({next({hidden:s}){e.hidden=s,s?(e.setAttribute("tabindex","-1"),e.blur()):e.removeAttribute("tabindex")},complete(){e.style.top="",e.hidden=!0,e.removeAttribute("tabindex")}}),r.pipe(W(a),ee("height")).subscribe(({height:s})=>{e.style.top=`${s+16}px`}),h(e,"click").subscribe(s=>{s.preventDefault(),window.scrollTo({top:0})}),vs(e,{viewport$:t,main$:o,target$:n}).pipe(w(s=>i.next(s)),_(()=>i.complete()),m(s=>$({ref:e},s)))}function wi({document$:e,viewport$:t}){e.pipe(v(()=>P(".md-ellipsis")),ne(r=>tt(r).pipe(W(e.pipe(Ce(1))),b(o=>o),m(()=>r),Te(1))),b(r=>r.offsetWidth<r.scrollWidth),ne(r=>{let o=r.innerText,n=r.closest("a")||r;return n.title=o,B("content.tooltips")?mt(n,{viewport$:t}).pipe(W(e.pipe(Ce(1))),_(()=>n.removeAttribute("title"))):S})).subscribe(),B("content.tooltips")&&e.pipe(v(()=>P(".md-status")),ne(r=>mt(r,{viewport$:t}))).subscribe()}function Ti({document$:e,tablet$:t}){e.pipe(v(()=>P(".md-toggle--indeterminate")),w(r=>{r.indeterminate=!0,r.checked=!1}),ne(r=>h(r,"change").pipe(Dr(()=>r.classList.contains("md-toggle--indeterminate")),m(()=>r))),re(t)).subscribe(([r,o])=>{r.classList.remove("md-toggle--indeterminate"),o&&(r.checked=!1)})}function gs(){return/(iPad|iPhone|iPod)/.test(navigator.userAgent)}function Si({document$:e}){e.pipe(v(()=>P("[data-md-scrollfix]")),w(t=>t.removeAttribute("data-md-scrollfix")),b(gs),ne(t=>h(t,"touchstart").pipe(m(()=>t)))).subscribe(t=>{let r=t.scrollTop;r===0?t.scrollTop=1:r+t.offsetHeight===t.scrollHeight&&(t.scrollTop=r-1)})}function 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o=="string"?o=document.createTextNode(o):o.parentNode&&o.parentNode.removeChild(o),r?t.insertBefore(this.previousSibling,o):t.replaceChild(o,this)}}}));function ys(){return location.protocol==="file:"?Tt(`${new URL("search/search_index.js",eo.base)}`).pipe(m(()=>__index),G(1)):je(new URL("search/search_index.json",eo.base))}document.documentElement.classList.remove("no-js");document.documentElement.classList.add("js");var ot=Go(),Ut=sn(),Lt=ln(Ut),to=an(),Oe=gn(),hr=Pt("(min-width: 960px)"),Mi=Pt("(min-width: 1220px)"),_i=mn(),eo=xe(),Ai=document.forms.namedItem("search")?ys():Ye,ro=new g;Zn({alert$:ro});var oo=new g;B("navigation.instant")&&oi({location$:Ut,viewport$:Oe,progress$:oo}).subscribe(ot);var Li;((Li=eo.version)==null?void 0:Li.provider)==="mike"&&ci({document$:ot});O(Ut,Lt).pipe(Ge(125)).subscribe(()=>{Je("drawer",!1),Je("search",!1)});to.pipe(b(({mode:e})=>e==="global")).subscribe(e=>{switch(e.type){case"p":case",":let t=fe("link[rel=prev]");typeof t!="undefined"&&lt(t);break;case"n":case".":let r=fe("link[rel=next]");typeof r!="undefined"&&lt(r);break;case"Enter":let o=Ie();o instanceof HTMLLabelElement&&o.click()}});wi({viewport$:Oe,document$:ot});Ti({document$:ot,tablet$:hr});Si({document$:ot});Oi({viewport$:Oe,tablet$:hr});var rt=Kn(Se("header"),{viewport$:Oe}),Ft=ot.pipe(m(()=>Se("main")),v(e=>Gn(e,{viewport$:Oe,header$:rt})),G(1)),xs=O(...ae("consent").map(e=>En(e,{target$:Lt})),...ae("dialog").map(e=>qn(e,{alert$:ro})),...ae("palette").map(e=>Jn(e)),...ae("progress").map(e=>Xn(e,{progress$:oo})),...ae("search").map(e=>ui(e,{index$:Ai,keyboard$:to})),...ae("source").map(e=>gi(e))),Es=C(()=>O(...ae("announce").map(e=>xn(e)),...ae("content").map(e=>zn(e,{viewport$:Oe,target$:Lt,print$:_i})),...ae("content").map(e=>B("search.highlight")?di(e,{index$:Ai,location$:Ut}):S),...ae("header").map(e=>Yn(e,{viewport$:Oe,header$:rt,main$:Ft})),...ae("header-title").map(e=>Bn(e,{viewport$:Oe,header$:rt})),...ae("sidebar").map(e=>e.getAttribute("data-md-type")==="navigation"?Nr(Mi,()=>Zr(e,{viewport$:Oe,header$:rt,main$:Ft})):Nr(hr,()=>Zr(e,{viewport$:Oe,header$:rt,main$:Ft}))),...ae("tabs").map(e=>yi(e,{viewport$:Oe,header$:rt})),...ae("toc").map(e=>xi(e,{viewport$:Oe,header$:rt,main$:Ft,target$:Lt})),...ae("top").map(e=>Ei(e,{viewport$:Oe,header$:rt,main$:Ft,target$:Lt})))),Ci=ot.pipe(v(()=>Es),Re(xs),G(1));Ci.subscribe();window.document$=ot;window.location$=Ut;window.target$=Lt;window.keyboard$=to;window.viewport$=Oe;window.tablet$=hr;window.screen$=Mi;window.print$=_i;window.alert$=ro;window.progress$=oo;window.component$=Ci;})();
-//# sourceMappingURL=bundle.88dd0f4e.min.js.map
-
diff --git a/assets/javascripts/bundle.88dd0f4e.min.js.map b/assets/javascripts/bundle.88dd0f4e.min.js.map
deleted file mode 100644
index dab2a87..0000000
--- a/assets/javascripts/bundle.88dd0f4e.min.js.map
+++ /dev/null
@@ -1,7 +0,0 @@
-{
-  "version": 3,
-  "sources": ["node_modules/focus-visible/dist/focus-visible.js", "node_modules/escape-html/index.js", "node_modules/clipboard/dist/clipboard.js", "src/templates/assets/javascripts/bundle.ts", "node_modules/tslib/tslib.es6.mjs", "node_modules/rxjs/src/internal/util/isFunction.ts", "node_modules/rxjs/src/internal/util/createErrorClass.ts", "node_modules/rxjs/src/internal/util/UnsubscriptionError.ts", "node_modules/rxjs/src/internal/util/arrRemove.ts", "node_modules/rxjs/src/internal/Subscription.ts", "node_modules/rxjs/src/internal/config.ts", "node_modules/rxjs/src/internal/scheduler/timeoutProvider.ts", "node_modules/rxjs/src/internal/util/reportUnhandledError.ts", "node_modules/rxjs/src/internal/util/noop.ts", "node_modules/rxjs/src/internal/NotificationFactories.ts", "node_modules/rxjs/src/internal/util/errorContext.ts", "node_modules/rxjs/src/internal/Subscriber.ts", "node_modules/rxjs/src/internal/symbol/observable.ts", "node_modules/rxjs/src/internal/util/identity.ts", "node_modules/rxjs/src/internal/util/pipe.ts", "node_modules/rxjs/src/internal/Observable.ts", "node_modules/rxjs/src/internal/util/lift.ts", "node_modules/rxjs/src/internal/operators/OperatorSubscriber.ts", "node_modules/rxjs/src/internal/scheduler/animationFrameProvider.ts", "node_modules/rxjs/src/internal/util/ObjectUnsubscribedError.ts", "node_modules/rxjs/src/internal/Subject.ts", "node_modules/rxjs/src/internal/BehaviorSubject.ts", "node_modules/rxjs/src/internal/scheduler/dateTimestampProvider.ts", "node_modules/rxjs/src/internal/ReplaySubject.ts", "node_modules/rxjs/src/internal/scheduler/Action.ts", "node_modules/rxjs/src/internal/scheduler/intervalProvider.ts", "node_modules/rxjs/src/internal/scheduler/AsyncAction.ts", "node_modules/rxjs/src/internal/Scheduler.ts", "node_modules/rxjs/src/internal/scheduler/AsyncScheduler.ts", "node_modules/rxjs/src/internal/scheduler/async.ts", "node_modules/rxjs/src/internal/scheduler/QueueAction.ts", "node_modules/rxjs/src/internal/scheduler/QueueScheduler.ts", "node_modules/rxjs/src/internal/scheduler/queue.ts", "node_modules/rxjs/src/internal/scheduler/AnimationFrameAction.ts", "node_modules/rxjs/src/internal/scheduler/AnimationFrameScheduler.ts", "node_modules/rxjs/src/internal/scheduler/animationFrame.ts", "node_modules/rxjs/src/internal/observable/empty.ts", "node_modules/rxjs/src/internal/util/isScheduler.ts", "node_modules/rxjs/src/internal/util/args.ts", "node_modules/rxjs/src/internal/util/isArrayLike.ts", "node_modules/rxjs/src/internal/util/isPromise.ts", "node_modules/rxjs/src/internal/util/isInteropObservable.ts", "node_modules/rxjs/src/internal/util/isAsyncIterable.ts", "node_modules/rxjs/src/internal/util/throwUnobservableError.ts", "node_modules/rxjs/src/internal/symbol/iterator.ts", "node_modules/rxjs/src/internal/util/isIterable.ts", "node_modules/rxjs/src/internal/util/isReadableStreamLike.ts", "node_modules/rxjs/src/internal/observable/innerFrom.ts", "node_modules/rxjs/src/internal/util/executeSchedule.ts", "node_modules/rxjs/src/internal/operators/observeOn.ts", "node_modules/rxjs/src/internal/operators/subscribeOn.ts", "node_modules/rxjs/src/internal/scheduled/scheduleObservable.ts", "node_modules/rxjs/src/internal/scheduled/schedulePromise.ts", "node_modules/rxjs/src/internal/scheduled/scheduleArray.ts", "node_modules/rxjs/src/internal/scheduled/scheduleIterable.ts", "node_modules/rxjs/src/internal/scheduled/scheduleAsyncIterable.ts", "node_modules/rxjs/src/internal/scheduled/scheduleReadableStreamLike.ts", "node_modules/rxjs/src/internal/scheduled/scheduled.ts", "node_modules/rxjs/src/internal/observable/from.ts", "node_modules/rxjs/src/internal/observable/of.ts", "node_modules/rxjs/src/internal/observable/throwError.ts", "node_modules/rxjs/src/internal/util/EmptyError.ts", "node_modules/rxjs/src/internal/util/isDate.ts", "node_modules/rxjs/src/internal/operators/map.ts", "node_modules/rxjs/src/internal/util/mapOneOrManyArgs.ts", "node_modules/rxjs/src/internal/util/argsArgArrayOrObject.ts", "node_modules/rxjs/src/internal/util/createObject.ts", "node_modules/rxjs/src/internal/observable/combineLatest.ts", "node_modules/rxjs/src/internal/operators/mergeInternals.ts", "node_modules/rxjs/src/internal/operators/mergeMap.ts", "node_modules/rxjs/src/internal/operators/mergeAll.ts", "node_modules/rxjs/src/internal/operators/concatAll.ts", "node_modules/rxjs/src/internal/observable/concat.ts", "node_modules/rxjs/src/internal/observable/defer.ts", "node_modules/rxjs/src/internal/observable/fromEvent.ts", "node_modules/rxjs/src/internal/observable/fromEventPattern.ts", "node_modules/rxjs/src/internal/observable/timer.ts", "node_modules/rxjs/src/internal/observable/merge.ts", "node_modules/rxjs/src/internal/observable/never.ts", "node_modules/rxjs/src/internal/util/argsOrArgArray.ts", "node_modules/rxjs/src/internal/operators/filter.ts", "node_modules/rxjs/src/internal/observable/zip.ts", "node_modules/rxjs/src/internal/operators/audit.ts", "node_modules/rxjs/src/internal/operators/auditTime.ts", "node_modules/rxjs/src/internal/operators/bufferCount.ts", "node_modules/rxjs/src/internal/operators/catchError.ts", "node_modules/rxjs/src/internal/operators/scanInternals.ts", "node_modules/rxjs/src/internal/operators/combineLatest.ts", "node_modules/rxjs/src/internal/operators/combineLatestWith.ts", "node_modules/rxjs/src/internal/operators/debounce.ts", "node_modules/rxjs/src/internal/operators/debounceTime.ts", "node_modules/rxjs/src/internal/operators/defaultIfEmpty.ts", "node_modules/rxjs/src/internal/operators/take.ts", "node_modules/rxjs/src/internal/operators/ignoreElements.ts", "node_modules/rxjs/src/internal/operators/mapTo.ts", "node_modules/rxjs/src/internal/operators/delayWhen.ts", "node_modules/rxjs/src/internal/operators/delay.ts", "node_modules/rxjs/src/internal/operators/distinctUntilChanged.ts", "node_modules/rxjs/src/internal/operators/distinctUntilKeyChanged.ts", "node_modules/rxjs/src/internal/operators/throwIfEmpty.ts", "node_modules/rxjs/src/internal/operators/endWith.ts", "node_modules/rxjs/src/internal/operators/finalize.ts", "node_modules/rxjs/src/internal/operators/first.ts", "node_modules/rxjs/src/internal/operators/takeLast.ts", "node_modules/rxjs/src/internal/operators/merge.ts", "node_modules/rxjs/src/internal/operators/mergeWith.ts", "node_modules/rxjs/src/internal/operators/repeat.ts", "node_modules/rxjs/src/internal/operators/scan.ts", "node_modules/rxjs/src/internal/operators/share.ts", "node_modules/rxjs/src/internal/operators/shareReplay.ts", "node_modules/rxjs/src/internal/operators/skip.ts", "node_modules/rxjs/src/internal/operators/skipUntil.ts", "node_modules/rxjs/src/internal/operators/startWith.ts", "node_modules/rxjs/src/internal/operators/switchMap.ts", "node_modules/rxjs/src/internal/operators/takeUntil.ts", "node_modules/rxjs/src/internal/operators/takeWhile.ts", "node_modules/rxjs/src/internal/operators/tap.ts", "node_modules/rxjs/src/internal/operators/throttle.ts", "node_modules/rxjs/src/internal/operators/throttleTime.ts", "node_modules/rxjs/src/internal/operators/withLatestFrom.ts", "node_modules/rxjs/src/internal/operators/zip.ts", "node_modules/rxjs/src/internal/operators/zipWith.ts", "src/templates/assets/javascripts/browser/document/index.ts", "src/templates/assets/javascripts/browser/element/_/index.ts", "src/templates/assets/javascripts/browser/element/focus/index.ts", "src/templates/assets/javascripts/browser/element/hover/index.ts", "src/templates/assets/javascripts/utilities/h/index.ts", "src/templates/assets/javascripts/utilities/round/index.ts", "src/templates/assets/javascripts/browser/script/index.ts", "src/templates/assets/javascripts/browser/element/size/_/index.ts", "src/templates/assets/javascripts/browser/element/size/content/index.ts", "src/templates/assets/javascripts/browser/element/offset/_/index.ts", "src/templates/assets/javascripts/browser/element/offset/content/index.ts", "src/templates/assets/javascripts/browser/element/visibility/index.ts", "src/templates/assets/javascripts/browser/toggle/index.ts", "src/templates/assets/javascripts/browser/keyboard/index.ts", "src/templates/assets/javascripts/browser/location/_/index.ts", "src/templates/assets/javascripts/browser/location/hash/index.ts", "src/templates/assets/javascripts/browser/media/index.ts", "src/templates/assets/javascripts/browser/request/index.ts", "src/templates/assets/javascripts/browser/viewport/offset/index.ts", "src/templates/assets/javascripts/browser/viewport/size/index.ts", "src/templates/assets/javascripts/browser/viewport/_/index.ts", "src/templates/assets/javascripts/browser/viewport/at/index.ts", "src/templates/assets/javascripts/browser/worker/index.ts", "src/templates/assets/javascripts/_/index.ts", "src/templates/assets/javascripts/components/_/index.ts", "src/templates/assets/javascripts/components/announce/index.ts", "src/templates/assets/javascripts/components/consent/index.ts", "src/templates/assets/javascripts/templates/tooltip/index.tsx", "src/templates/assets/javascripts/templates/annotation/index.tsx", "src/templates/assets/javascripts/templates/clipboard/index.tsx", "src/templates/assets/javascripts/templates/search/index.tsx", "src/templates/assets/javascripts/templates/source/index.tsx", "src/templates/assets/javascripts/templates/tabbed/index.tsx", "src/templates/assets/javascripts/templates/table/index.tsx", "src/templates/assets/javascripts/templates/version/index.tsx", "src/templates/assets/javascripts/components/tooltip2/index.ts", "src/templates/assets/javascripts/components/content/annotation/_/index.ts", "src/templates/assets/javascripts/components/content/annotation/list/index.ts", "src/templates/assets/javascripts/components/content/annotation/block/index.ts", "src/templates/assets/javascripts/components/content/code/_/index.ts", "src/templates/assets/javascripts/components/content/details/index.ts", "src/templates/assets/javascripts/components/content/mermaid/index.css", "src/templates/assets/javascripts/components/content/mermaid/index.ts", "src/templates/assets/javascripts/components/content/table/index.ts", "src/templates/assets/javascripts/components/content/tabs/index.ts", "src/templates/assets/javascripts/components/content/_/index.ts", "src/templates/assets/javascripts/components/dialog/index.ts", "src/templates/assets/javascripts/components/tooltip/index.ts", "src/templates/assets/javascripts/components/header/_/index.ts", "src/templates/assets/javascripts/components/header/title/index.ts", "src/templates/assets/javascripts/components/main/index.ts", "src/templates/assets/javascripts/components/palette/index.ts", "src/templates/assets/javascripts/components/progress/index.ts", "src/templates/assets/javascripts/integrations/clipboard/index.ts", "src/templates/assets/javascripts/integrations/sitemap/index.ts", "src/templates/assets/javascripts/integrations/instant/index.ts", "src/templates/assets/javascripts/integrations/search/highlighter/index.ts", "src/templates/assets/javascripts/integrations/search/worker/message/index.ts", "src/templates/assets/javascripts/integrations/search/worker/_/index.ts", "src/templates/assets/javascripts/integrations/version/findurl/index.ts", "src/templates/assets/javascripts/integrations/version/index.ts", "src/templates/assets/javascripts/components/search/query/index.ts", "src/templates/assets/javascripts/components/search/result/index.ts", "src/templates/assets/javascripts/components/search/share/index.ts", "src/templates/assets/javascripts/components/search/suggest/index.ts", "src/templates/assets/javascripts/components/search/_/index.ts", "src/templates/assets/javascripts/components/search/highlight/index.ts", "src/templates/assets/javascripts/components/sidebar/index.ts", "src/templates/assets/javascripts/components/source/facts/github/index.ts", "src/templates/assets/javascripts/components/source/facts/gitlab/index.ts", "src/templates/assets/javascripts/components/source/facts/_/index.ts", "src/templates/assets/javascripts/components/source/_/index.ts", "src/templates/assets/javascripts/components/tabs/index.ts", "src/templates/assets/javascripts/components/toc/index.ts", "src/templates/assets/javascripts/components/top/index.ts", "src/templates/assets/javascripts/patches/ellipsis/index.ts", "src/templates/assets/javascripts/patches/indeterminate/index.ts", "src/templates/assets/javascripts/patches/scrollfix/index.ts", "src/templates/assets/javascripts/patches/scrolllock/index.ts", "src/templates/assets/javascripts/polyfills/index.ts"],
-  "sourcesContent": ["(function (global, factory) {\n  typeof exports === 'object' && typeof module !== 'undefined' ? factory() :\n  typeof define === 'function' && define.amd ? define(factory) :\n  (factory());\n}(this, (function () { 'use strict';\n\n  /**\n   * Applies the :focus-visible polyfill at the given scope.\n   * A scope in this case is either the top-level Document or a Shadow Root.\n   *\n   * @param {(Document|ShadowRoot)} scope\n   * @see https://github.com/WICG/focus-visible\n   */\n  function applyFocusVisiblePolyfill(scope) {\n    var hadKeyboardEvent = true;\n    var hadFocusVisibleRecently = false;\n    var hadFocusVisibleRecentlyTimeout = null;\n\n    var inputTypesAllowlist = {\n      text: true,\n      search: true,\n      url: true,\n      tel: true,\n      email: true,\n      password: true,\n      number: true,\n      date: true,\n      month: true,\n      week: true,\n      time: true,\n      datetime: true,\n      'datetime-local': true\n    };\n\n    /**\n     * Helper function for legacy browsers and iframes which sometimes focus\n     * elements like document, body, and non-interactive SVG.\n     * @param {Element} el\n     */\n    function isValidFocusTarget(el) {\n      if (\n        el &&\n        el !== document &&\n        el.nodeName !== 'HTML' &&\n        el.nodeName !== 'BODY' &&\n        'classList' in el &&\n        'contains' in el.classList\n      ) {\n        return true;\n      }\n      return false;\n    }\n\n    /**\n     * Computes whether the given element should automatically trigger the\n     * `focus-visible` class being added, i.e. whether it should always match\n     * `:focus-visible` when focused.\n     * @param {Element} el\n     * @return {boolean}\n     */\n    function focusTriggersKeyboardModality(el) {\n      var type = el.type;\n      var tagName = el.tagName;\n\n      if (tagName === 'INPUT' && inputTypesAllowlist[type] && !el.readOnly) {\n        return true;\n      }\n\n      if (tagName === 'TEXTAREA' && !el.readOnly) {\n        return true;\n      }\n\n      if (el.isContentEditable) {\n        return true;\n      }\n\n      return false;\n    }\n\n    /**\n     * Add the `focus-visible` class to the given element if it was not added by\n     * the author.\n     * @param {Element} el\n     */\n    function addFocusVisibleClass(el) {\n      if (el.classList.contains('focus-visible')) {\n        return;\n      }\n      el.classList.add('focus-visible');\n      el.setAttribute('data-focus-visible-added', '');\n    }\n\n    /**\n     * Remove the `focus-visible` class from the given element if it was not\n     * originally added by the author.\n     * @param {Element} el\n     */\n    function removeFocusVisibleClass(el) {\n      if (!el.hasAttribute('data-focus-visible-added')) {\n        return;\n      }\n      el.classList.remove('focus-visible');\n      el.removeAttribute('data-focus-visible-added');\n    }\n\n    /**\n     * If the most recent user interaction was via the keyboard;\n     * and the key press did not include a meta, alt/option, or control key;\n     * then the modality is keyboard. Otherwise, the modality is not keyboard.\n     * Apply `focus-visible` to any current active element and keep track\n     * of our keyboard modality state with `hadKeyboardEvent`.\n     * @param {KeyboardEvent} e\n     */\n    function onKeyDown(e) {\n      if (e.metaKey || e.altKey || e.ctrlKey) {\n        return;\n      }\n\n      if (isValidFocusTarget(scope.activeElement)) {\n        addFocusVisibleClass(scope.activeElement);\n      }\n\n      hadKeyboardEvent = true;\n    }\n\n    /**\n     * If at any point a user clicks with a pointing device, ensure that we change\n     * the modality away from keyboard.\n     * This avoids the situation where a user presses a key on an already focused\n     * element, and then clicks on a different element, focusing it with a\n     * pointing device, while we still think we're in keyboard modality.\n     * @param {Event} e\n     */\n    function onPointerDown(e) {\n      hadKeyboardEvent = false;\n    }\n\n    /**\n     * On `focus`, add the `focus-visible` class to the target if:\n     * - the target received focus as a result of keyboard navigation, or\n     * - the event target is an element that will likely require interaction\n     *   via the keyboard (e.g. a text box)\n     * @param {Event} e\n     */\n    function onFocus(e) {\n      // Prevent IE from focusing the document or HTML element.\n      if (!isValidFocusTarget(e.target)) {\n        return;\n      }\n\n      if (hadKeyboardEvent || focusTriggersKeyboardModality(e.target)) {\n        addFocusVisibleClass(e.target);\n      }\n    }\n\n    /**\n     * On `blur`, remove the `focus-visible` class from the target.\n     * @param {Event} e\n     */\n    function onBlur(e) {\n      if (!isValidFocusTarget(e.target)) {\n        return;\n      }\n\n      if (\n        e.target.classList.contains('focus-visible') ||\n        e.target.hasAttribute('data-focus-visible-added')\n      ) {\n        // To detect a tab/window switch, we look for a blur event followed\n        // rapidly by a visibility change.\n        // If we don't see a visibility change within 100ms, it's probably a\n        // regular focus change.\n        hadFocusVisibleRecently = true;\n        window.clearTimeout(hadFocusVisibleRecentlyTimeout);\n        hadFocusVisibleRecentlyTimeout = window.setTimeout(function() {\n          hadFocusVisibleRecently = false;\n        }, 100);\n        removeFocusVisibleClass(e.target);\n      }\n    }\n\n    /**\n     * If the user changes tabs, keep track of whether or not the previously\n     * focused element had .focus-visible.\n     * @param {Event} e\n     */\n    function onVisibilityChange(e) {\n      if (document.visibilityState === 'hidden') {\n        // If the tab becomes active again, the browser will handle calling focus\n        // on the element (Safari actually calls it twice).\n        // If this tab change caused a blur on an element with focus-visible,\n        // re-apply the class when the user switches back to the tab.\n        if (hadFocusVisibleRecently) {\n          hadKeyboardEvent = true;\n        }\n        addInitialPointerMoveListeners();\n      }\n    }\n\n    /**\n     * Add a group of listeners to detect usage of any pointing devices.\n     * These listeners will be added when the polyfill first loads, and anytime\n     * the window is blurred, so that they are active when the window regains\n     * focus.\n     */\n    function addInitialPointerMoveListeners() {\n      document.addEventListener('mousemove', onInitialPointerMove);\n      document.addEventListener('mousedown', onInitialPointerMove);\n      document.addEventListener('mouseup', onInitialPointerMove);\n      document.addEventListener('pointermove', onInitialPointerMove);\n      document.addEventListener('pointerdown', onInitialPointerMove);\n      document.addEventListener('pointerup', onInitialPointerMove);\n      document.addEventListener('touchmove', onInitialPointerMove);\n      document.addEventListener('touchstart', onInitialPointerMove);\n      document.addEventListener('touchend', onInitialPointerMove);\n    }\n\n    function removeInitialPointerMoveListeners() {\n      document.removeEventListener('mousemove', onInitialPointerMove);\n      document.removeEventListener('mousedown', onInitialPointerMove);\n      document.removeEventListener('mouseup', onInitialPointerMove);\n      document.removeEventListener('pointermove', onInitialPointerMove);\n      document.removeEventListener('pointerdown', onInitialPointerMove);\n      document.removeEventListener('pointerup', onInitialPointerMove);\n      document.removeEventListener('touchmove', onInitialPointerMove);\n      document.removeEventListener('touchstart', onInitialPointerMove);\n      document.removeEventListener('touchend', onInitialPointerMove);\n    }\n\n    /**\n     * When the polfyill first loads, assume the user is in keyboard modality.\n     * If any event is received from a pointing device (e.g. mouse, pointer,\n     * touch), turn off keyboard modality.\n     * This accounts for situations where focus enters the page from the URL bar.\n     * @param {Event} e\n     */\n    function onInitialPointerMove(e) {\n      // Work around a Safari quirk that fires a mousemove on <html> whenever the\n      // window blurs, even if you're tabbing out of the page. \u00AF\\_(\u30C4)_/\u00AF\n      if (e.target.nodeName && e.target.nodeName.toLowerCase() === 'html') {\n        return;\n      }\n\n      hadKeyboardEvent = false;\n      removeInitialPointerMoveListeners();\n    }\n\n    // For some kinds of state, we are interested in changes at the global scope\n    // only. For example, global pointer input, global key presses and global\n    // visibility change should affect the state at every scope:\n    document.addEventListener('keydown', onKeyDown, true);\n    document.addEventListener('mousedown', onPointerDown, true);\n    document.addEventListener('pointerdown', onPointerDown, true);\n    document.addEventListener('touchstart', onPointerDown, true);\n    document.addEventListener('visibilitychange', onVisibilityChange, true);\n\n    addInitialPointerMoveListeners();\n\n    // For focus and blur, we specifically care about state changes in the local\n    // scope. This is because focus / blur events that originate from within a\n    // shadow root are not re-dispatched from the host element if it was already\n    // the active element in its own scope:\n    scope.addEventListener('focus', onFocus, true);\n    scope.addEventListener('blur', onBlur, true);\n\n    // We detect that a node is a ShadowRoot by ensuring that it is a\n    // DocumentFragment and also has a host property. This check covers native\n    // implementation and polyfill implementation transparently. If we only cared\n    // about the native implementation, we could just check if the scope was\n    // an instance of a ShadowRoot.\n    if (scope.nodeType === Node.DOCUMENT_FRAGMENT_NODE && scope.host) {\n      // Since a ShadowRoot is a special kind of DocumentFragment, it does not\n      // have a root element to add a class to. So, we add this attribute to the\n      // host element instead:\n      scope.host.setAttribute('data-js-focus-visible', '');\n    } else if (scope.nodeType === Node.DOCUMENT_NODE) {\n      document.documentElement.classList.add('js-focus-visible');\n      document.documentElement.setAttribute('data-js-focus-visible', '');\n    }\n  }\n\n  // It is important to wrap all references to global window and document in\n  // these checks to support server-side rendering use cases\n  // @see https://github.com/WICG/focus-visible/issues/199\n  if (typeof window !== 'undefined' && typeof document !== 'undefined') {\n    // Make the polyfill helper globally available. This can be used as a signal\n    // to interested libraries that wish to coordinate with the polyfill for e.g.,\n    // applying the polyfill to a shadow root:\n    window.applyFocusVisiblePolyfill = applyFocusVisiblePolyfill;\n\n    // Notify interested libraries of the polyfill's presence, in case the\n    // polyfill was loaded lazily:\n    var event;\n\n    try {\n      event = new CustomEvent('focus-visible-polyfill-ready');\n    } catch (error) {\n      // IE11 does not support using CustomEvent as a constructor directly:\n      event = document.createEvent('CustomEvent');\n      event.initCustomEvent('focus-visible-polyfill-ready', false, false, {});\n    }\n\n    window.dispatchEvent(event);\n  }\n\n  if (typeof document !== 'undefined') {\n    // Apply the polyfill to the global document, so that no JavaScript\n    // coordination is required to use the polyfill in the top-level document:\n    applyFocusVisiblePolyfill(document);\n  }\n\n})));\n", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param  {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n  var str = '' + string;\n  var match = matchHtmlRegExp.exec(str);\n\n  if (!match) {\n    return str;\n  }\n\n  var escape;\n  var html = '';\n  var index = 0;\n  var lastIndex = 0;\n\n  for (index = match.index; index < str.length; index++) {\n    switch (str.charCodeAt(index)) {\n      case 34: // \"\n        escape = '&quot;';\n        break;\n      case 38: // &\n        escape = '&amp;';\n        break;\n      case 39: // '\n        escape = '&#39;';\n        break;\n      case 60: // <\n        escape = '&lt;';\n        break;\n      case 62: // >\n        escape = '&gt;';\n        break;\n      default:\n        continue;\n    }\n\n    if (lastIndex !== index) {\n      html += str.substring(lastIndex, index);\n    }\n\n    lastIndex = index + 1;\n    html += escape;\n  }\n\n  return lastIndex !== index\n    ? html + str.substring(lastIndex, index)\n    : html;\n}\n", "/*!\n * clipboard.js v2.0.11\n * https://clipboardjs.com/\n *\n * Licensed MIT \u00A9 Zeno Rocha\n */\n(function webpackUniversalModuleDefinition(root, factory) {\n\tif(typeof exports === 'object' && typeof module === 'object')\n\t\tmodule.exports = factory();\n\telse if(typeof define === 'function' && define.amd)\n\t\tdefine([], factory);\n\telse if(typeof exports === 'object')\n\t\texports[\"ClipboardJS\"] = factory();\n\telse\n\t\troot[\"ClipboardJS\"] = factory();\n})(this, function() {\nreturn /******/ (function() { // webpackBootstrap\n/******/ \tvar __webpack_modules__ = ({\n\n/***/ 686:\n/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {\n\n\"use strict\";\n\n// EXPORTS\n__webpack_require__.d(__webpack_exports__, {\n  \"default\": function() { return /* binding */ clipboard; }\n});\n\n// EXTERNAL MODULE: ./node_modules/tiny-emitter/index.js\nvar tiny_emitter = __webpack_require__(279);\nvar tiny_emitter_default = /*#__PURE__*/__webpack_require__.n(tiny_emitter);\n// EXTERNAL MODULE: ./node_modules/good-listener/src/listen.js\nvar listen = __webpack_require__(370);\nvar listen_default = /*#__PURE__*/__webpack_require__.n(listen);\n// EXTERNAL MODULE: ./node_modules/select/src/select.js\nvar src_select = __webpack_require__(817);\nvar select_default = /*#__PURE__*/__webpack_require__.n(src_select);\n;// CONCATENATED MODULE: ./src/common/command.js\n/**\n * Executes a given operation type.\n * @param {String} type\n * @return {Boolean}\n */\nfunction command(type) {\n  try {\n    return document.execCommand(type);\n  } catch (err) {\n    return false;\n  }\n}\n;// CONCATENATED MODULE: ./src/actions/cut.js\n\n\n/**\n * Cut action wrapper.\n * @param {String|HTMLElement} target\n * @return {String}\n */\n\nvar ClipboardActionCut = function ClipboardActionCut(target) {\n  var selectedText = select_default()(target);\n  command('cut');\n  return selectedText;\n};\n\n/* harmony default export */ var actions_cut = (ClipboardActionCut);\n;// CONCATENATED MODULE: ./src/common/create-fake-element.js\n/**\n * Creates a fake textarea element with a value.\n * @param {String} value\n * @return {HTMLElement}\n */\nfunction createFakeElement(value) {\n  var isRTL = document.documentElement.getAttribute('dir') === 'rtl';\n  var fakeElement = document.createElement('textarea'); // Prevent zooming on iOS\n\n  fakeElement.style.fontSize = '12pt'; // Reset box model\n\n  fakeElement.style.border = '0';\n  fakeElement.style.padding = '0';\n  fakeElement.style.margin = '0'; // Move element out of screen horizontally\n\n  fakeElement.style.position = 'absolute';\n  fakeElement.style[isRTL ? 'right' : 'left'] = '-9999px'; // Move element to the same position vertically\n\n  var yPosition = window.pageYOffset || document.documentElement.scrollTop;\n  fakeElement.style.top = \"\".concat(yPosition, \"px\");\n  fakeElement.setAttribute('readonly', '');\n  fakeElement.value = value;\n  return fakeElement;\n}\n;// CONCATENATED MODULE: ./src/actions/copy.js\n\n\n\n/**\n * Create fake copy action wrapper using a fake element.\n * @param {String} target\n * @param {Object} options\n * @return {String}\n */\n\nvar fakeCopyAction = function fakeCopyAction(value, options) {\n  var fakeElement = createFakeElement(value);\n  options.container.appendChild(fakeElement);\n  var selectedText = select_default()(fakeElement);\n  command('copy');\n  fakeElement.remove();\n  return selectedText;\n};\n/**\n * Copy action wrapper.\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @return {String}\n */\n\n\nvar ClipboardActionCopy = function ClipboardActionCopy(target) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n    container: document.body\n  };\n  var selectedText = '';\n\n  if (typeof target === 'string') {\n    selectedText = fakeCopyAction(target, options);\n  } else if (target instanceof HTMLInputElement && !['text', 'search', 'url', 'tel', 'password'].includes(target === null || target === void 0 ? void 0 : target.type)) {\n    // If input type doesn't support `setSelectionRange`. Simulate it. https://developer.mozilla.org/en-US/docs/Web/API/HTMLInputElement/setSelectionRange\n    selectedText = fakeCopyAction(target.value, options);\n  } else {\n    selectedText = select_default()(target);\n    command('copy');\n  }\n\n  return selectedText;\n};\n\n/* harmony default export */ var actions_copy = (ClipboardActionCopy);\n;// CONCATENATED MODULE: ./src/actions/default.js\nfunction _typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return _typeof(obj); }\n\n\n\n/**\n * Inner function which performs selection from either `text` or `target`\n * properties and then executes copy or cut operations.\n * @param {Object} options\n */\n\nvar ClipboardActionDefault = function ClipboardActionDefault() {\n  var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n  // Defines base properties passed from constructor.\n  var _options$action = options.action,\n      action = _options$action === void 0 ? 'copy' : _options$action,\n      container = options.container,\n      target = options.target,\n      text = options.text; // Sets the `action` to be performed which can be either 'copy' or 'cut'.\n\n  if (action !== 'copy' && action !== 'cut') {\n    throw new Error('Invalid \"action\" value, use either \"copy\" or \"cut\"');\n  } // Sets the `target` property using an element that will be have its content copied.\n\n\n  if (target !== undefined) {\n    if (target && _typeof(target) === 'object' && target.nodeType === 1) {\n      if (action === 'copy' && target.hasAttribute('disabled')) {\n        throw new Error('Invalid \"target\" attribute. Please use \"readonly\" instead of \"disabled\" attribute');\n      }\n\n      if (action === 'cut' && (target.hasAttribute('readonly') || target.hasAttribute('disabled'))) {\n        throw new Error('Invalid \"target\" attribute. You can\\'t cut text from elements with \"readonly\" or \"disabled\" attributes');\n      }\n    } else {\n      throw new Error('Invalid \"target\" value, use a valid Element');\n    }\n  } // Define selection strategy based on `text` property.\n\n\n  if (text) {\n    return actions_copy(text, {\n      container: container\n    });\n  } // Defines which selection strategy based on `target` property.\n\n\n  if (target) {\n    return action === 'cut' ? actions_cut(target) : actions_copy(target, {\n      container: container\n    });\n  }\n};\n\n/* harmony default export */ var actions_default = (ClipboardActionDefault);\n;// CONCATENATED MODULE: ./src/clipboard.js\nfunction clipboard_typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { clipboard_typeof = function _typeof(obj) { return typeof obj; }; } else { clipboard_typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return clipboard_typeof(obj); }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\nfunction _inherits(subClass, superClass) { if (typeof superClass !== \"function\" && superClass !== null) { throw new TypeError(\"Super expression must either be null or a function\"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }\n\nfunction _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }\n\nfunction _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }\n\nfunction _possibleConstructorReturn(self, call) { if (call && (clipboard_typeof(call) === \"object\" || typeof call === \"function\")) { return call; } return _assertThisInitialized(self); }\n\nfunction _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError(\"this hasn't been initialised - super() hasn't been called\"); } return self; }\n\nfunction _isNativeReflectConstruct() { if (typeof Reflect === \"undefined\" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === \"function\") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }\n\nfunction _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }\n\n\n\n\n\n\n/**\n * Helper function to retrieve attribute value.\n * @param {String} suffix\n * @param {Element} element\n */\n\nfunction getAttributeValue(suffix, element) {\n  var attribute = \"data-clipboard-\".concat(suffix);\n\n  if (!element.hasAttribute(attribute)) {\n    return;\n  }\n\n  return element.getAttribute(attribute);\n}\n/**\n * Base class which takes one or more elements, adds event listeners to them,\n * and instantiates a new `ClipboardAction` on each click.\n */\n\n\nvar Clipboard = /*#__PURE__*/function (_Emitter) {\n  _inherits(Clipboard, _Emitter);\n\n  var _super = _createSuper(Clipboard);\n\n  /**\n   * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n   * @param {Object} options\n   */\n  function Clipboard(trigger, options) {\n    var _this;\n\n    _classCallCheck(this, Clipboard);\n\n    _this = _super.call(this);\n\n    _this.resolveOptions(options);\n\n    _this.listenClick(trigger);\n\n    return _this;\n  }\n  /**\n   * Defines if attributes would be resolved using internal setter functions\n   * or custom functions that were passed in the constructor.\n   * @param {Object} options\n   */\n\n\n  _createClass(Clipboard, [{\n    key: \"resolveOptions\",\n    value: function resolveOptions() {\n      var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n      this.action = typeof options.action === 'function' ? options.action : this.defaultAction;\n      this.target = typeof options.target === 'function' ? options.target : this.defaultTarget;\n      this.text = typeof options.text === 'function' ? options.text : this.defaultText;\n      this.container = clipboard_typeof(options.container) === 'object' ? options.container : document.body;\n    }\n    /**\n     * Adds a click event listener to the passed trigger.\n     * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n     */\n\n  }, {\n    key: \"listenClick\",\n    value: function listenClick(trigger) {\n      var _this2 = this;\n\n      this.listener = listen_default()(trigger, 'click', function (e) {\n        return _this2.onClick(e);\n      });\n    }\n    /**\n     * Defines a new `ClipboardAction` on each click event.\n     * @param {Event} e\n     */\n\n  }, {\n    key: \"onClick\",\n    value: function onClick(e) {\n      var trigger = e.delegateTarget || e.currentTarget;\n      var action = this.action(trigger) || 'copy';\n      var text = actions_default({\n        action: action,\n        container: this.container,\n        target: this.target(trigger),\n        text: this.text(trigger)\n      }); // Fires an event based on the copy operation result.\n\n      this.emit(text ? 'success' : 'error', {\n        action: action,\n        text: text,\n        trigger: trigger,\n        clearSelection: function clearSelection() {\n          if (trigger) {\n            trigger.focus();\n          }\n\n          window.getSelection().removeAllRanges();\n        }\n      });\n    }\n    /**\n     * Default `action` lookup function.\n     * @param {Element} trigger\n     */\n\n  }, {\n    key: \"defaultAction\",\n    value: function defaultAction(trigger) {\n      return getAttributeValue('action', trigger);\n    }\n    /**\n     * Default `target` lookup function.\n     * @param {Element} trigger\n     */\n\n  }, {\n    key: \"defaultTarget\",\n    value: function defaultTarget(trigger) {\n      var selector = getAttributeValue('target', trigger);\n\n      if (selector) {\n        return document.querySelector(selector);\n      }\n    }\n    /**\n     * Allow fire programmatically a copy action\n     * @param {String|HTMLElement} target\n     * @param {Object} options\n     * @returns Text copied.\n     */\n\n  }, {\n    key: \"defaultText\",\n\n    /**\n     * Default `text` lookup function.\n     * @param {Element} trigger\n     */\n    value: function defaultText(trigger) {\n      return getAttributeValue('text', trigger);\n    }\n    /**\n     * Destroy lifecycle.\n     */\n\n  }, {\n    key: \"destroy\",\n    value: function destroy() {\n      this.listener.destroy();\n    }\n  }], [{\n    key: \"copy\",\n    value: function copy(target) {\n      var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n        container: document.body\n      };\n      return actions_copy(target, options);\n    }\n    /**\n     * Allow fire programmatically a cut action\n     * @param {String|HTMLElement} target\n     * @returns Text cutted.\n     */\n\n  }, {\n    key: \"cut\",\n    value: function cut(target) {\n      return actions_cut(target);\n    }\n    /**\n     * Returns the support of the given action, or all actions if no action is\n     * given.\n     * @param {String} [action]\n     */\n\n  }, {\n    key: \"isSupported\",\n    value: function isSupported() {\n      var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ['copy', 'cut'];\n      var actions = typeof action === 'string' ? [action] : action;\n      var support = !!document.queryCommandSupported;\n      actions.forEach(function (action) {\n        support = support && !!document.queryCommandSupported(action);\n      });\n      return support;\n    }\n  }]);\n\n  return Clipboard;\n}((tiny_emitter_default()));\n\n/* harmony default export */ var clipboard = (Clipboard);\n\n/***/ }),\n\n/***/ 828:\n/***/ (function(module) {\n\nvar DOCUMENT_NODE_TYPE = 9;\n\n/**\n * A polyfill for Element.matches()\n */\nif (typeof Element !== 'undefined' && !Element.prototype.matches) {\n    var proto = Element.prototype;\n\n    proto.matches = proto.matchesSelector ||\n                    proto.mozMatchesSelector ||\n                    proto.msMatchesSelector ||\n                    proto.oMatchesSelector ||\n                    proto.webkitMatchesSelector;\n}\n\n/**\n * Finds the closest parent that matches a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @return {Function}\n */\nfunction closest (element, selector) {\n    while (element && element.nodeType !== DOCUMENT_NODE_TYPE) {\n        if (typeof element.matches === 'function' &&\n            element.matches(selector)) {\n          return element;\n        }\n        element = element.parentNode;\n    }\n}\n\nmodule.exports = closest;\n\n\n/***/ }),\n\n/***/ 438:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar closest = __webpack_require__(828);\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction _delegate(element, selector, type, callback, useCapture) {\n    var listenerFn = listener.apply(this, arguments);\n\n    element.addEventListener(type, listenerFn, useCapture);\n\n    return {\n        destroy: function() {\n            element.removeEventListener(type, listenerFn, useCapture);\n        }\n    }\n}\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element|String|Array} [elements]\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction delegate(elements, selector, type, callback, useCapture) {\n    // Handle the regular Element usage\n    if (typeof elements.addEventListener === 'function') {\n        return _delegate.apply(null, arguments);\n    }\n\n    // Handle Element-less usage, it defaults to global delegation\n    if (typeof type === 'function') {\n        // Use `document` as the first parameter, then apply arguments\n        // This is a short way to .unshift `arguments` without running into deoptimizations\n        return _delegate.bind(null, document).apply(null, arguments);\n    }\n\n    // Handle Selector-based usage\n    if (typeof elements === 'string') {\n        elements = document.querySelectorAll(elements);\n    }\n\n    // Handle Array-like based usage\n    return Array.prototype.map.call(elements, function (element) {\n        return _delegate(element, selector, type, callback, useCapture);\n    });\n}\n\n/**\n * Finds closest match and invokes callback.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Function}\n */\nfunction listener(element, selector, type, callback) {\n    return function(e) {\n        e.delegateTarget = closest(e.target, selector);\n\n        if (e.delegateTarget) {\n            callback.call(element, e);\n        }\n    }\n}\n\nmodule.exports = delegate;\n\n\n/***/ }),\n\n/***/ 879:\n/***/ (function(__unused_webpack_module, exports) {\n\n/**\n * Check if argument is a HTML element.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.node = function(value) {\n    return value !== undefined\n        && value instanceof HTMLElement\n        && value.nodeType === 1;\n};\n\n/**\n * Check if argument is a list of HTML elements.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.nodeList = function(value) {\n    var type = Object.prototype.toString.call(value);\n\n    return value !== undefined\n        && (type === '[object NodeList]' || type === '[object HTMLCollection]')\n        && ('length' in value)\n        && (value.length === 0 || exports.node(value[0]));\n};\n\n/**\n * Check if argument is a string.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.string = function(value) {\n    return typeof value === 'string'\n        || value instanceof String;\n};\n\n/**\n * Check if argument is a function.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.fn = function(value) {\n    var type = Object.prototype.toString.call(value);\n\n    return type === '[object Function]';\n};\n\n\n/***/ }),\n\n/***/ 370:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar is = __webpack_require__(879);\nvar delegate = __webpack_require__(438);\n\n/**\n * Validates all params and calls the right\n * listener function based on its target type.\n *\n * @param {String|HTMLElement|HTMLCollection|NodeList} target\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listen(target, type, callback) {\n    if (!target && !type && !callback) {\n        throw new Error('Missing required arguments');\n    }\n\n    if (!is.string(type)) {\n        throw new TypeError('Second argument must be a String');\n    }\n\n    if (!is.fn(callback)) {\n        throw new TypeError('Third argument must be a Function');\n    }\n\n    if (is.node(target)) {\n        return listenNode(target, type, callback);\n    }\n    else if (is.nodeList(target)) {\n        return listenNodeList(target, type, callback);\n    }\n    else if (is.string(target)) {\n        return listenSelector(target, type, callback);\n    }\n    else {\n        throw new TypeError('First argument must be a String, HTMLElement, HTMLCollection, or NodeList');\n    }\n}\n\n/**\n * Adds an event listener to a HTML element\n * and returns a remove listener function.\n *\n * @param {HTMLElement} node\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNode(node, type, callback) {\n    node.addEventListener(type, callback);\n\n    return {\n        destroy: function() {\n            node.removeEventListener(type, callback);\n        }\n    }\n}\n\n/**\n * Add an event listener to a list of HTML elements\n * and returns a remove listener function.\n *\n * @param {NodeList|HTMLCollection} nodeList\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNodeList(nodeList, type, callback) {\n    Array.prototype.forEach.call(nodeList, function(node) {\n        node.addEventListener(type, callback);\n    });\n\n    return {\n        destroy: function() {\n            Array.prototype.forEach.call(nodeList, function(node) {\n                node.removeEventListener(type, callback);\n            });\n        }\n    }\n}\n\n/**\n * Add an event listener to a selector\n * and returns a remove listener function.\n *\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenSelector(selector, type, callback) {\n    return delegate(document.body, selector, type, callback);\n}\n\nmodule.exports = listen;\n\n\n/***/ }),\n\n/***/ 817:\n/***/ (function(module) {\n\nfunction select(element) {\n    var selectedText;\n\n    if (element.nodeName === 'SELECT') {\n        element.focus();\n\n        selectedText = element.value;\n    }\n    else if (element.nodeName === 'INPUT' || element.nodeName === 'TEXTAREA') {\n        var isReadOnly = element.hasAttribute('readonly');\n\n        if (!isReadOnly) {\n            element.setAttribute('readonly', '');\n        }\n\n        element.select();\n        element.setSelectionRange(0, element.value.length);\n\n        if (!isReadOnly) {\n            element.removeAttribute('readonly');\n        }\n\n        selectedText = element.value;\n    }\n    else {\n        if (element.hasAttribute('contenteditable')) {\n            element.focus();\n        }\n\n        var selection = window.getSelection();\n        var range = document.createRange();\n\n        range.selectNodeContents(element);\n        selection.removeAllRanges();\n        selection.addRange(range);\n\n        selectedText = selection.toString();\n    }\n\n    return selectedText;\n}\n\nmodule.exports = select;\n\n\n/***/ }),\n\n/***/ 279:\n/***/ (function(module) {\n\nfunction E () {\n  // Keep this empty so it's easier to inherit from\n  // (via https://github.com/lipsmack from https://github.com/scottcorgan/tiny-emitter/issues/3)\n}\n\nE.prototype = {\n  on: function (name, callback, ctx) {\n    var e = this.e || (this.e = {});\n\n    (e[name] || (e[name] = [])).push({\n      fn: callback,\n      ctx: ctx\n    });\n\n    return this;\n  },\n\n  once: function (name, callback, ctx) {\n    var self = this;\n    function listener () {\n      self.off(name, listener);\n      callback.apply(ctx, arguments);\n    };\n\n    listener._ = callback\n    return this.on(name, listener, ctx);\n  },\n\n  emit: function (name) {\n    var data = [].slice.call(arguments, 1);\n    var evtArr = ((this.e || (this.e = {}))[name] || []).slice();\n    var i = 0;\n    var len = evtArr.length;\n\n    for (i; i < len; i++) {\n      evtArr[i].fn.apply(evtArr[i].ctx, data);\n    }\n\n    return this;\n  },\n\n  off: function (name, callback) {\n    var e = this.e || (this.e = {});\n    var evts = e[name];\n    var liveEvents = [];\n\n    if (evts && callback) {\n      for (var i = 0, len = evts.length; i < len; i++) {\n        if (evts[i].fn !== callback && evts[i].fn._ !== callback)\n          liveEvents.push(evts[i]);\n      }\n    }\n\n    // Remove event from queue to prevent memory leak\n    // Suggested by https://github.com/lazd\n    // Ref: https://github.com/scottcorgan/tiny-emitter/commit/c6ebfaa9bc973b33d110a84a307742b7cf94c953#commitcomment-5024910\n\n    (liveEvents.length)\n      ? e[name] = liveEvents\n      : delete e[name];\n\n    return this;\n  }\n};\n\nmodule.exports = E;\nmodule.exports.TinyEmitter = E;\n\n\n/***/ })\n\n/******/ \t});\n/************************************************************************/\n/******/ \t// The module cache\n/******/ \tvar __webpack_module_cache__ = {};\n/******/ \t\n/******/ \t// The require function\n/******/ \tfunction __webpack_require__(moduleId) {\n/******/ \t\t// Check if module is in cache\n/******/ \t\tif(__webpack_module_cache__[moduleId]) {\n/******/ \t\t\treturn __webpack_module_cache__[moduleId].exports;\n/******/ \t\t}\n/******/ \t\t// Create a new module (and put it into the cache)\n/******/ \t\tvar module = __webpack_module_cache__[moduleId] = {\n/******/ \t\t\t// no module.id needed\n/******/ \t\t\t// no module.loaded needed\n/******/ \t\t\texports: {}\n/******/ \t\t};\n/******/ \t\n/******/ \t\t// Execute the module function\n/******/ \t\t__webpack_modules__[moduleId](module, module.exports, __webpack_require__);\n/******/ \t\n/******/ \t\t// Return the exports of the module\n/******/ \t\treturn module.exports;\n/******/ \t}\n/******/ \t\n/************************************************************************/\n/******/ \t/* webpack/runtime/compat get default export */\n/******/ \t!function() {\n/******/ \t\t// getDefaultExport function for compatibility with non-harmony modules\n/******/ \t\t__webpack_require__.n = function(module) {\n/******/ \t\t\tvar getter = module && module.__esModule ?\n/******/ \t\t\t\tfunction() { return module['default']; } :\n/******/ \t\t\t\tfunction() { return module; };\n/******/ \t\t\t__webpack_require__.d(getter, { a: getter });\n/******/ \t\t\treturn getter;\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/define property getters */\n/******/ \t!function() {\n/******/ \t\t// define getter functions for harmony exports\n/******/ \t\t__webpack_require__.d = function(exports, definition) {\n/******/ \t\t\tfor(var key in definition) {\n/******/ \t\t\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n/******/ \t\t\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n/******/ \t\t\t\t}\n/******/ \t\t\t}\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/hasOwnProperty shorthand */\n/******/ \t!function() {\n/******/ \t\t__webpack_require__.o = function(obj, prop) { return Object.prototype.hasOwnProperty.call(obj, prop); }\n/******/ \t}();\n/******/ \t\n/************************************************************************/\n/******/ \t// module exports must be returned from runtime so entry inlining is disabled\n/******/ \t// startup\n/******/ \t// Load entry module and return exports\n/******/ \treturn __webpack_require__(686);\n/******/ })()\n.default;\n});", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport \"focus-visible\"\n\nimport {\n  EMPTY,\n  NEVER,\n  Observable,\n  Subject,\n  defer,\n  delay,\n  filter,\n  map,\n  merge,\n  mergeWith,\n  shareReplay,\n  switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"./_\"\nimport {\n  at,\n  getActiveElement,\n  getOptionalElement,\n  requestJSON,\n  setLocation,\n  setToggle,\n  watchDocument,\n  watchKeyboard,\n  watchLocation,\n  watchLocationTarget,\n  watchMedia,\n  watchPrint,\n  watchScript,\n  watchViewport\n} from \"./browser\"\nimport {\n  getComponentElement,\n  getComponentElements,\n  mountAnnounce,\n  mountBackToTop,\n  mountConsent,\n  mountContent,\n  mountDialog,\n  mountHeader,\n  mountHeaderTitle,\n  mountPalette,\n  mountProgress,\n  mountSearch,\n  mountSearchHiglight,\n  mountSidebar,\n  mountSource,\n  mountTableOfContents,\n  mountTabs,\n  watchHeader,\n  watchMain\n} from \"./components\"\nimport {\n  SearchIndex,\n  setupClipboardJS,\n  setupInstantNavigation,\n  setupVersionSelector\n} from \"./integrations\"\nimport {\n  patchEllipsis,\n  patchIndeterminate,\n  patchScrollfix,\n  patchScrolllock\n} from \"./patches\"\nimport \"./polyfills\"\n\n/* ----------------------------------------------------------------------------\n * Functions - @todo refactor\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch search index\n *\n * @returns Search index observable\n */\nfunction fetchSearchIndex(): Observable<SearchIndex> {\n  if (location.protocol === \"file:\") {\n    return watchScript(\n      `${new URL(\"search/search_index.js\", config.base)}`\n    )\n      .pipe(\n        // @ts-ignore - @todo fix typings\n        map(() => __index),\n        shareReplay(1)\n      )\n  } else {\n    return requestJSON<SearchIndex>(\n      new URL(\"search/search_index.json\", config.base)\n    )\n  }\n}\n\n/* ----------------------------------------------------------------------------\n * Application\n * ------------------------------------------------------------------------- */\n\n/* Yay, JavaScript is available */\ndocument.documentElement.classList.remove(\"no-js\")\ndocument.documentElement.classList.add(\"js\")\n\n/* Set up navigation observables and subjects */\nconst document$ = watchDocument()\nconst location$ = watchLocation()\nconst target$   = watchLocationTarget(location$)\nconst keyboard$ = watchKeyboard()\n\n/* Set up media observables */\nconst viewport$ = watchViewport()\nconst tablet$   = watchMedia(\"(min-width: 960px)\")\nconst screen$   = watchMedia(\"(min-width: 1220px)\")\nconst print$    = watchPrint()\n\n/* Retrieve search index, if search is enabled */\nconst config = configuration()\nconst index$ = document.forms.namedItem(\"search\")\n  ? fetchSearchIndex()\n  : NEVER\n\n/* Set up Clipboard.js integration */\nconst alert$ = new Subject<string>()\nsetupClipboardJS({ alert$ })\n\n/* Set up progress indicator */\nconst progress$ = new Subject<number>()\n\n/* Set up instant navigation, if enabled */\nif (feature(\"navigation.instant\"))\n  setupInstantNavigation({ location$, viewport$, progress$ })\n    .subscribe(document$)\n\n/* Set up version selector */\nif (config.version?.provider === \"mike\")\n  setupVersionSelector({ document$ })\n\n/* Always close drawer and search on navigation */\nmerge(location$, target$)\n  .pipe(\n    delay(125)\n  )\n    .subscribe(() => {\n      setToggle(\"drawer\", false)\n      setToggle(\"search\", false)\n    })\n\n/* Set up global keyboard handlers */\nkeyboard$\n  .pipe(\n    filter(({ mode }) => mode === \"global\")\n  )\n    .subscribe(key => {\n      switch (key.type) {\n\n        /* Go to previous page */\n        case \"p\":\n        case \",\":\n          const prev = getOptionalElement<HTMLLinkElement>(\"link[rel=prev]\")\n          if (typeof prev !== \"undefined\")\n            setLocation(prev)\n          break\n\n        /* Go to next page */\n        case \"n\":\n        case \".\":\n          const next = getOptionalElement<HTMLLinkElement>(\"link[rel=next]\")\n          if (typeof next !== \"undefined\")\n            setLocation(next)\n          break\n\n        /* Expand navigation, see https://bit.ly/3ZjG5io */\n        case \"Enter\":\n          const active = getActiveElement()\n          if (active instanceof HTMLLabelElement)\n            active.click()\n      }\n    })\n\n/* Set up patches */\npatchEllipsis({ viewport$, document$ })\npatchIndeterminate({ document$, tablet$ })\npatchScrollfix({ document$ })\npatchScrolllock({ viewport$, tablet$ })\n\n/* Set up header and main area observable */\nconst header$ = watchHeader(getComponentElement(\"header\"), { viewport$ })\nconst main$ = document$\n  .pipe(\n    map(() => getComponentElement(\"main\")),\n    switchMap(el => watchMain(el, { viewport$, header$ })),\n    shareReplay(1)\n  )\n\n/* Set up control component observables */\nconst control$ = merge(\n\n  /* Consent */\n  ...getComponentElements(\"consent\")\n    .map(el => mountConsent(el, { target$ })),\n\n  /* Dialog */\n  ...getComponentElements(\"dialog\")\n    .map(el => mountDialog(el, { alert$ })),\n\n  /* Color palette */\n  ...getComponentElements(\"palette\")\n    .map(el => mountPalette(el)),\n\n  /* Progress bar */\n  ...getComponentElements(\"progress\")\n    .map(el => mountProgress(el, { progress$ })),\n\n  /* Search */\n  ...getComponentElements(\"search\")\n    .map(el => mountSearch(el, { index$, keyboard$ })),\n\n  /* Repository information */\n  ...getComponentElements(\"source\")\n    .map(el => mountSource(el))\n)\n\n/* Set up content component observables */\nconst content$ = defer(() => merge(\n\n  /* Announcement bar */\n  ...getComponentElements(\"announce\")\n    .map(el => mountAnnounce(el)),\n\n  /* Content */\n  ...getComponentElements(\"content\")\n    .map(el => mountContent(el, { viewport$, target$, print$ })),\n\n  /* Search highlighting */\n  ...getComponentElements(\"content\")\n    .map(el => feature(\"search.highlight\")\n      ? mountSearchHiglight(el, { index$, location$ })\n      : EMPTY\n    ),\n\n  /* Header */\n  ...getComponentElements(\"header\")\n    .map(el => mountHeader(el, { viewport$, header$, main$ })),\n\n  /* Header title */\n  ...getComponentElements(\"header-title\")\n    .map(el => mountHeaderTitle(el, { viewport$, header$ })),\n\n  /* Sidebar */\n  ...getComponentElements(\"sidebar\")\n    .map(el => el.getAttribute(\"data-md-type\") === \"navigation\"\n      ? at(screen$, () => mountSidebar(el, { viewport$, header$, main$ }))\n      : at(tablet$, () => mountSidebar(el, { viewport$, header$, main$ }))\n    ),\n\n  /* Navigation tabs */\n  ...getComponentElements(\"tabs\")\n    .map(el => mountTabs(el, { viewport$, header$ })),\n\n  /* Table of contents */\n  ...getComponentElements(\"toc\")\n    .map(el => mountTableOfContents(el, {\n      viewport$, header$, main$, target$\n    })),\n\n  /* Back-to-top button */\n  ...getComponentElements(\"top\")\n    .map(el => mountBackToTop(el, { viewport$, header$, main$, target$ }))\n))\n\n/* Set up component observables */\nconst component$ = document$\n  .pipe(\n    switchMap(() => content$),\n    mergeWith(control$),\n    shareReplay(1)\n  )\n\n/* Subscribe to all components */\ncomponent$.subscribe()\n\n/* ----------------------------------------------------------------------------\n * Exports\n * ------------------------------------------------------------------------- */\n\nwindow.document$  = document$          /* Document observable */\nwindow.location$  = location$          /* Location subject */\nwindow.target$    = target$            /* Location target observable */\nwindow.keyboard$  = keyboard$          /* Keyboard observable */\nwindow.viewport$  = viewport$          /* Viewport observable */\nwindow.tablet$    = tablet$            /* Media tablet observable */\nwindow.screen$    = screen$            /* Media screen observable */\nwindow.print$     = print$             /* Media print observable */\nwindow.alert$     = alert$             /* Alert subject */\nwindow.progress$  = progress$          /* Progress indicator subject */\nwindow.component$ = component$         /* Component observable */\n", "/******************************************************************************\nCopyright (c) Microsoft Corporation.\n\nPermission to use, copy, modify, and/or distribute this software for any\npurpose with or without fee is hereby granted.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH\nREGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY\nAND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,\nINDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM\nLOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR\nOTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR\nPERFORMANCE OF THIS SOFTWARE.\n***************************************************************************** */\n/* global Reflect, Promise, SuppressedError, Symbol, Iterator */\n\nvar extendStatics = function(d, b) {\n  extendStatics = Object.setPrototypeOf ||\n      ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||\n      function (d, b) { for (var p in b) if (Object.prototype.hasOwnProperty.call(b, p)) d[p] = b[p]; };\n  return extendStatics(d, b);\n};\n\nexport function __extends(d, b) {\n  if (typeof b !== \"function\" && b !== null)\n      throw new TypeError(\"Class extends value \" + String(b) + \" is not a constructor or null\");\n  extendStatics(d, b);\n  function __() { this.constructor = d; }\n  d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());\n}\n\nexport var __assign = function() {\n  __assign = Object.assign || function __assign(t) {\n      for (var s, i = 1, n = arguments.length; i < n; i++) {\n          s = arguments[i];\n          for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];\n      }\n      return t;\n  }\n  return __assign.apply(this, arguments);\n}\n\nexport function __rest(s, e) {\n  var t = {};\n  for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)\n      t[p] = s[p];\n  if (s != null && typeof Object.getOwnPropertySymbols === \"function\")\n      for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {\n          if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i]))\n              t[p[i]] = s[p[i]];\n      }\n  return t;\n}\n\nexport function __decorate(decorators, target, key, desc) {\n  var c = arguments.length, r = c < 3 ? target : desc === null ? desc = Object.getOwnPropertyDescriptor(target, key) : desc, d;\n  if (typeof Reflect === \"object\" && typeof Reflect.decorate === \"function\") r = Reflect.decorate(decorators, target, key, desc);\n  else for (var i = decorators.length - 1; i >= 0; i--) if (d = decorators[i]) r = (c < 3 ? d(r) : c > 3 ? d(target, key, r) : d(target, key)) || r;\n  return c > 3 && r && Object.defineProperty(target, key, r), r;\n}\n\nexport function __param(paramIndex, decorator) {\n  return function (target, key) { decorator(target, key, paramIndex); }\n}\n\nexport function __esDecorate(ctor, descriptorIn, decorators, contextIn, initializers, extraInitializers) {\n  function accept(f) { if (f !== void 0 && typeof f !== \"function\") throw new TypeError(\"Function expected\"); return f; }\n  var kind = contextIn.kind, key = kind === \"getter\" ? \"get\" : kind === \"setter\" ? \"set\" : \"value\";\n  var target = !descriptorIn && ctor ? contextIn[\"static\"] ? ctor : ctor.prototype : null;\n  var descriptor = descriptorIn || (target ? Object.getOwnPropertyDescriptor(target, contextIn.name) : {});\n  var _, done = false;\n  for (var i = decorators.length - 1; i >= 0; i--) {\n      var context = {};\n      for (var p in contextIn) context[p] = p === \"access\" ? {} : contextIn[p];\n      for (var p in contextIn.access) context.access[p] = contextIn.access[p];\n      context.addInitializer = function (f) { if (done) throw new TypeError(\"Cannot add initializers after decoration has completed\"); extraInitializers.push(accept(f || null)); };\n      var result = (0, decorators[i])(kind === \"accessor\" ? { get: descriptor.get, set: descriptor.set } : descriptor[key], context);\n      if (kind === \"accessor\") {\n          if (result === void 0) continue;\n          if (result === null || typeof result !== \"object\") throw new TypeError(\"Object expected\");\n          if (_ = accept(result.get)) descriptor.get = _;\n          if (_ = accept(result.set)) descriptor.set = _;\n          if (_ = accept(result.init)) initializers.unshift(_);\n      }\n      else if (_ = accept(result)) {\n          if (kind === \"field\") initializers.unshift(_);\n          else descriptor[key] = _;\n      }\n  }\n  if (target) Object.defineProperty(target, contextIn.name, descriptor);\n  done = true;\n};\n\nexport function __runInitializers(thisArg, initializers, value) {\n  var useValue = arguments.length > 2;\n  for (var i = 0; i < initializers.length; i++) {\n      value = useValue ? initializers[i].call(thisArg, value) : initializers[i].call(thisArg);\n  }\n  return useValue ? value : void 0;\n};\n\nexport function __propKey(x) {\n  return typeof x === \"symbol\" ? x : \"\".concat(x);\n};\n\nexport function __setFunctionName(f, name, prefix) {\n  if (typeof name === \"symbol\") name = name.description ? \"[\".concat(name.description, \"]\") : \"\";\n  return Object.defineProperty(f, \"name\", { configurable: true, value: prefix ? \"\".concat(prefix, \" \", name) : name });\n};\n\nexport function __metadata(metadataKey, metadataValue) {\n  if (typeof Reflect === \"object\" && typeof Reflect.metadata === \"function\") return Reflect.metadata(metadataKey, metadataValue);\n}\n\nexport function __awaiter(thisArg, _arguments, P, generator) {\n  function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }\n  return new (P || (P = Promise))(function (resolve, reject) {\n      function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }\n      function rejected(value) { try { step(generator[\"throw\"](value)); } catch (e) { reject(e); } }\n      function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }\n      step((generator = generator.apply(thisArg, _arguments || [])).next());\n  });\n}\n\nexport function __generator(thisArg, body) {\n  var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g = Object.create((typeof Iterator === \"function\" ? Iterator : Object).prototype);\n  return g.next = verb(0), g[\"throw\"] = verb(1), g[\"return\"] = verb(2), typeof Symbol === \"function\" && (g[Symbol.iterator] = function() { return this; }), g;\n  function verb(n) { return function (v) { return step([n, v]); }; }\n  function step(op) {\n      if (f) throw new TypeError(\"Generator is already executing.\");\n      while (g && (g = 0, op[0] && (_ = 0)), _) try {\n          if (f = 1, y && (t = op[0] & 2 ? y[\"return\"] : op[0] ? y[\"throw\"] || ((t = y[\"return\"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;\n          if (y = 0, t) op = [op[0] & 2, t.value];\n          switch (op[0]) {\n              case 0: case 1: t = op; break;\n              case 4: _.label++; return { value: op[1], done: false };\n              case 5: _.label++; y = op[1]; op = [0]; continue;\n              case 7: op = _.ops.pop(); _.trys.pop(); continue;\n              default:\n                  if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }\n                  if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }\n                  if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }\n                  if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }\n                  if (t[2]) _.ops.pop();\n                  _.trys.pop(); continue;\n          }\n          op = body.call(thisArg, _);\n      } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }\n      if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };\n  }\n}\n\nexport var __createBinding = Object.create ? (function(o, m, k, k2) {\n  if (k2 === undefined) k2 = k;\n  var desc = Object.getOwnPropertyDescriptor(m, k);\n  if (!desc || (\"get\" in desc ? !m.__esModule : desc.writable || desc.configurable)) {\n      desc = { enumerable: true, get: function() { return m[k]; } };\n  }\n  Object.defineProperty(o, k2, desc);\n}) : (function(o, m, k, k2) {\n  if (k2 === undefined) k2 = k;\n  o[k2] = m[k];\n});\n\nexport function __exportStar(m, o) {\n  for (var p in m) if (p !== \"default\" && !Object.prototype.hasOwnProperty.call(o, p)) __createBinding(o, m, p);\n}\n\nexport function __values(o) {\n  var s = typeof Symbol === \"function\" && Symbol.iterator, m = s && o[s], i = 0;\n  if (m) return m.call(o);\n  if (o && typeof o.length === \"number\") return {\n      next: function () {\n          if (o && i >= o.length) o = void 0;\n          return { value: o && o[i++], done: !o };\n      }\n  };\n  throw new TypeError(s ? \"Object is not iterable.\" : \"Symbol.iterator is not defined.\");\n}\n\nexport function __read(o, n) {\n  var m = typeof Symbol === \"function\" && o[Symbol.iterator];\n  if (!m) return o;\n  var i = m.call(o), r, ar = [], e;\n  try {\n      while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value);\n  }\n  catch (error) { e = { error: error }; }\n  finally {\n      try {\n          if (r && !r.done && (m = i[\"return\"])) m.call(i);\n      }\n      finally { if (e) throw e.error; }\n  }\n  return ar;\n}\n\n/** @deprecated */\nexport function __spread() {\n  for (var ar = [], i = 0; i < arguments.length; i++)\n      ar = ar.concat(__read(arguments[i]));\n  return ar;\n}\n\n/** @deprecated */\nexport function __spreadArrays() {\n  for (var s = 0, i = 0, il = arguments.length; i < il; i++) s += arguments[i].length;\n  for (var r = Array(s), k = 0, i = 0; i < il; i++)\n      for (var a = arguments[i], j = 0, jl = a.length; j < jl; j++, k++)\n          r[k] = a[j];\n  return r;\n}\n\nexport function __spreadArray(to, from, pack) {\n  if (pack || arguments.length === 2) for (var i = 0, l = from.length, ar; i < l; i++) {\n      if (ar || !(i in from)) {\n          if (!ar) ar = Array.prototype.slice.call(from, 0, i);\n          ar[i] = from[i];\n      }\n  }\n  return to.concat(ar || Array.prototype.slice.call(from));\n}\n\nexport function __await(v) {\n  return this instanceof __await ? (this.v = v, this) : new __await(v);\n}\n\nexport function __asyncGenerator(thisArg, _arguments, generator) {\n  if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\n  var g = generator.apply(thisArg, _arguments || []), i, q = [];\n  return i = Object.create((typeof AsyncIterator === \"function\" ? AsyncIterator : Object).prototype), verb(\"next\"), verb(\"throw\"), verb(\"return\", awaitReturn), i[Symbol.asyncIterator] = function () { return this; }, i;\n  function awaitReturn(f) { return function (v) { return Promise.resolve(v).then(f, reject); }; }\n  function verb(n, f) { if (g[n]) { i[n] = function (v) { return new Promise(function (a, b) { q.push([n, v, a, b]) > 1 || resume(n, v); }); }; if (f) i[n] = f(i[n]); } }\n  function resume(n, v) { try { step(g[n](v)); } catch (e) { settle(q[0][3], e); } }\n  function step(r) { r.value instanceof __await ? Promise.resolve(r.value.v).then(fulfill, reject) : settle(q[0][2], r); }\n  function fulfill(value) { resume(\"next\", value); }\n  function reject(value) { resume(\"throw\", value); }\n  function settle(f, v) { if (f(v), q.shift(), q.length) resume(q[0][0], q[0][1]); }\n}\n\nexport function __asyncDelegator(o) {\n  var i, p;\n  return i = {}, verb(\"next\"), verb(\"throw\", function (e) { throw e; }), verb(\"return\"), i[Symbol.iterator] = function () { return this; }, i;\n  function verb(n, f) { i[n] = o[n] ? function (v) { return (p = !p) ? { value: __await(o[n](v)), done: false } : f ? f(v) : v; } : f; }\n}\n\nexport function __asyncValues(o) {\n  if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\n  var m = o[Symbol.asyncIterator], i;\n  return m ? m.call(o) : (o = typeof __values === \"function\" ? __values(o) : o[Symbol.iterator](), i = {}, verb(\"next\"), verb(\"throw\"), verb(\"return\"), i[Symbol.asyncIterator] = function () { return this; }, i);\n  function verb(n) { i[n] = o[n] && function (v) { return new Promise(function (resolve, reject) { v = o[n](v), settle(resolve, reject, v.done, v.value); }); }; }\n  function settle(resolve, reject, d, v) { Promise.resolve(v).then(function(v) { resolve({ value: v, done: d }); }, reject); }\n}\n\nexport function __makeTemplateObject(cooked, raw) {\n  if (Object.defineProperty) { Object.defineProperty(cooked, \"raw\", { value: raw }); } else { cooked.raw = raw; }\n  return cooked;\n};\n\nvar __setModuleDefault = Object.create ? (function(o, v) {\n  Object.defineProperty(o, \"default\", { enumerable: true, value: v });\n}) : function(o, v) {\n  o[\"default\"] = v;\n};\n\nexport function __importStar(mod) {\n  if (mod && mod.__esModule) return mod;\n  var result = {};\n  if (mod != null) for (var k in mod) if (k !== \"default\" && Object.prototype.hasOwnProperty.call(mod, k)) __createBinding(result, mod, k);\n  __setModuleDefault(result, mod);\n  return result;\n}\n\nexport function __importDefault(mod) {\n  return (mod && mod.__esModule) ? mod : { default: mod };\n}\n\nexport function __classPrivateFieldGet(receiver, state, kind, f) {\n  if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a getter\");\n  if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot read private member from an object whose class did not declare it\");\n  return kind === \"m\" ? f : kind === \"a\" ? f.call(receiver) : f ? f.value : state.get(receiver);\n}\n\nexport function __classPrivateFieldSet(receiver, state, value, kind, f) {\n  if (kind === \"m\") throw new TypeError(\"Private method is not writable\");\n  if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a setter\");\n  if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot write private member to an object whose class did not declare it\");\n  return (kind === \"a\" ? f.call(receiver, value) : f ? f.value = value : state.set(receiver, value)), value;\n}\n\nexport function __classPrivateFieldIn(state, receiver) {\n  if (receiver === null || (typeof receiver !== \"object\" && typeof receiver !== \"function\")) throw new TypeError(\"Cannot use 'in' operator on non-object\");\n  return typeof state === \"function\" ? receiver === state : state.has(receiver);\n}\n\nexport function __addDisposableResource(env, value, async) {\n  if (value !== null && value !== void 0) {\n    if (typeof value !== \"object\" && typeof value !== \"function\") throw new TypeError(\"Object expected.\");\n    var dispose, inner;\n    if (async) {\n      if (!Symbol.asyncDispose) throw new TypeError(\"Symbol.asyncDispose is not defined.\");\n      dispose = value[Symbol.asyncDispose];\n    }\n    if (dispose === void 0) {\n      if (!Symbol.dispose) throw new TypeError(\"Symbol.dispose is not defined.\");\n      dispose = value[Symbol.dispose];\n      if (async) inner = dispose;\n    }\n    if (typeof dispose !== \"function\") throw new TypeError(\"Object not disposable.\");\n    if (inner) dispose = function() { try { inner.call(this); } catch (e) { return Promise.reject(e); } };\n    env.stack.push({ value: value, dispose: dispose, async: async });\n  }\n  else if (async) {\n    env.stack.push({ async: true });\n  }\n  return value;\n}\n\nvar _SuppressedError = typeof SuppressedError === \"function\" ? SuppressedError : function (error, suppressed, message) {\n  var e = new Error(message);\n  return e.name = \"SuppressedError\", e.error = error, e.suppressed = suppressed, e;\n};\n\nexport function __disposeResources(env) {\n  function fail(e) {\n    env.error = env.hasError ? new _SuppressedError(e, env.error, \"An error was suppressed during disposal.\") : e;\n    env.hasError = true;\n  }\n  var r, s = 0;\n  function next() {\n    while (r = env.stack.pop()) {\n      try {\n        if (!r.async && s === 1) return s = 0, env.stack.push(r), Promise.resolve().then(next);\n        if (r.dispose) {\n          var result = r.dispose.call(r.value);\n          if (r.async) return s |= 2, Promise.resolve(result).then(next, function(e) { fail(e); return next(); });\n        }\n        else s |= 1;\n      }\n      catch (e) {\n        fail(e);\n      }\n    }\n    if (s === 1) return env.hasError ? Promise.reject(env.error) : Promise.resolve();\n    if (env.hasError) throw env.error;\n  }\n  return next();\n}\n\nexport default {\n  __extends,\n  __assign,\n  __rest,\n  __decorate,\n  __param,\n  __metadata,\n  __awaiter,\n  __generator,\n  __createBinding,\n  __exportStar,\n  __values,\n  __read,\n  __spread,\n  __spreadArrays,\n  __spreadArray,\n  __await,\n  __asyncGenerator,\n  __asyncDelegator,\n  __asyncValues,\n  __makeTemplateObject,\n  __importStar,\n  __importDefault,\n  __classPrivateFieldGet,\n  __classPrivateFieldSet,\n  __classPrivateFieldIn,\n  __addDisposableResource,\n  __disposeResources,\n};\n", "/**\n * Returns true if the object is a function.\n * @param value The value to check\n */\nexport function isFunction(value: any): value is (...args: any[]) => any {\n  return typeof value === 'function';\n}\n", "/**\n * Used to create Error subclasses until the community moves away from ES5.\n *\n * This is because compiling from TypeScript down to ES5 has issues with subclassing Errors\n * as well as other built-in types: https://github.com/Microsoft/TypeScript/issues/12123\n *\n * @param createImpl A factory function to create the actual constructor implementation. The returned\n * function should be a named function that calls `_super` internally.\n */\nexport function createErrorClass<T>(createImpl: (_super: any) => any): T {\n  const _super = (instance: any) => {\n    Error.call(instance);\n    instance.stack = new Error().stack;\n  };\n\n  const ctorFunc = createImpl(_super);\n  ctorFunc.prototype = Object.create(Error.prototype);\n  ctorFunc.prototype.constructor = ctorFunc;\n  return ctorFunc;\n}\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface UnsubscriptionError extends Error {\n  readonly errors: any[];\n}\n\nexport interface UnsubscriptionErrorCtor {\n  /**\n   * @deprecated Internal implementation detail. Do not construct error instances.\n   * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n   */\n  new (errors: any[]): UnsubscriptionError;\n}\n\n/**\n * An error thrown when one or more errors have occurred during the\n * `unsubscribe` of a {@link Subscription}.\n */\nexport const UnsubscriptionError: UnsubscriptionErrorCtor = createErrorClass(\n  (_super) =>\n    function UnsubscriptionErrorImpl(this: any, errors: (Error | string)[]) {\n      _super(this);\n      this.message = errors\n        ? `${errors.length} errors occurred during unsubscription:\n${errors.map((err, i) => `${i + 1}) ${err.toString()}`).join('\\n  ')}`\n        : '';\n      this.name = 'UnsubscriptionError';\n      this.errors = errors;\n    }\n);\n", "/**\n * Removes an item from an array, mutating it.\n * @param arr The array to remove the item from\n * @param item The item to remove\n */\nexport function arrRemove<T>(arr: T[] | undefined | null, item: T) {\n  if (arr) {\n    const index = arr.indexOf(item);\n    0 <= index && arr.splice(index, 1);\n  }\n}\n", "import { isFunction } from './util/isFunction';\nimport { UnsubscriptionError } from './util/UnsubscriptionError';\nimport { SubscriptionLike, TeardownLogic, Unsubscribable } from './types';\nimport { arrRemove } from './util/arrRemove';\n\n/**\n * Represents a disposable resource, such as the execution of an Observable. A\n * Subscription has one important method, `unsubscribe`, that takes no argument\n * and just disposes the resource held by the subscription.\n *\n * Additionally, subscriptions may be grouped together through the `add()`\n * method, which will attach a child Subscription to the current Subscription.\n * When a Subscription is unsubscribed, all its children (and its grandchildren)\n * will be unsubscribed as well.\n *\n * @class Subscription\n */\nexport class Subscription implements SubscriptionLike {\n  /** @nocollapse */\n  public static EMPTY = (() => {\n    const empty = new Subscription();\n    empty.closed = true;\n    return empty;\n  })();\n\n  /**\n   * A flag to indicate whether this Subscription has already been unsubscribed.\n   */\n  public closed = false;\n\n  private _parentage: Subscription[] | Subscription | null = null;\n\n  /**\n   * The list of registered finalizers to execute upon unsubscription. Adding and removing from this\n   * list occurs in the {@link #add} and {@link #remove} methods.\n   */\n  private _finalizers: Exclude<TeardownLogic, void>[] | null = null;\n\n  /**\n   * @param initialTeardown A function executed first as part of the finalization\n   * process that is kicked off when {@link #unsubscribe} is called.\n   */\n  constructor(private initialTeardown?: () => void) {}\n\n  /**\n   * Disposes the resources held by the subscription. May, for instance, cancel\n   * an ongoing Observable execution or cancel any other type of work that\n   * started when the Subscription was created.\n   * @return {void}\n   */\n  unsubscribe(): void {\n    let errors: any[] | undefined;\n\n    if (!this.closed) {\n      this.closed = true;\n\n      // Remove this from it's parents.\n      const { _parentage } = this;\n      if (_parentage) {\n        this._parentage = null;\n        if (Array.isArray(_parentage)) {\n          for (const parent of _parentage) {\n            parent.remove(this);\n          }\n        } else {\n          _parentage.remove(this);\n        }\n      }\n\n      const { initialTeardown: initialFinalizer } = this;\n      if (isFunction(initialFinalizer)) {\n        try {\n          initialFinalizer();\n        } catch (e) {\n          errors = e instanceof UnsubscriptionError ? e.errors : [e];\n        }\n      }\n\n      const { _finalizers } = this;\n      if (_finalizers) {\n        this._finalizers = null;\n        for (const finalizer of _finalizers) {\n          try {\n            execFinalizer(finalizer);\n          } catch (err) {\n            errors = errors ?? [];\n            if (err instanceof UnsubscriptionError) {\n              errors = [...errors, ...err.errors];\n            } else {\n              errors.push(err);\n            }\n          }\n        }\n      }\n\n      if (errors) {\n        throw new UnsubscriptionError(errors);\n      }\n    }\n  }\n\n  /**\n   * Adds a finalizer to this subscription, so that finalization will be unsubscribed/called\n   * when this subscription is unsubscribed. If this subscription is already {@link #closed},\n   * because it has already been unsubscribed, then whatever finalizer is passed to it\n   * will automatically be executed (unless the finalizer itself is also a closed subscription).\n   *\n   * Closed Subscriptions cannot be added as finalizers to any subscription. Adding a closed\n   * subscription to a any subscription will result in no operation. (A noop).\n   *\n   * Adding a subscription to itself, or adding `null` or `undefined` will not perform any\n   * operation at all. (A noop).\n   *\n   * `Subscription` instances that are added to this instance will automatically remove themselves\n   * if they are unsubscribed. Functions and {@link Unsubscribable} objects that you wish to remove\n   * will need to be removed manually with {@link #remove}\n   *\n   * @param teardown The finalization logic to add to this subscription.\n   */\n  add(teardown: TeardownLogic): void {\n    // Only add the finalizer if it's not undefined\n    // and don't add a subscription to itself.\n    if (teardown && teardown !== this) {\n      if (this.closed) {\n        // If this subscription is already closed,\n        // execute whatever finalizer is handed to it automatically.\n        execFinalizer(teardown);\n      } else {\n        if (teardown instanceof Subscription) {\n          // We don't add closed subscriptions, and we don't add the same subscription\n          // twice. Subscription unsubscribe is idempotent.\n          if (teardown.closed || teardown._hasParent(this)) {\n            return;\n          }\n          teardown._addParent(this);\n        }\n        (this._finalizers = this._finalizers ?? []).push(teardown);\n      }\n    }\n  }\n\n  /**\n   * Checks to see if a this subscription already has a particular parent.\n   * This will signal that this subscription has already been added to the parent in question.\n   * @param parent the parent to check for\n   */\n  private _hasParent(parent: Subscription) {\n    const { _parentage } = this;\n    return _parentage === parent || (Array.isArray(_parentage) && _parentage.includes(parent));\n  }\n\n  /**\n   * Adds a parent to this subscription so it can be removed from the parent if it\n   * unsubscribes on it's own.\n   *\n   * NOTE: THIS ASSUMES THAT {@link _hasParent} HAS ALREADY BEEN CHECKED.\n   * @param parent The parent subscription to add\n   */\n  private _addParent(parent: Subscription) {\n    const { _parentage } = this;\n    this._parentage = Array.isArray(_parentage) ? (_parentage.push(parent), _parentage) : _parentage ? [_parentage, parent] : parent;\n  }\n\n  /**\n   * Called on a child when it is removed via {@link #remove}.\n   * @param parent The parent to remove\n   */\n  private _removeParent(parent: Subscription) {\n    const { _parentage } = this;\n    if (_parentage === parent) {\n      this._parentage = null;\n    } else if (Array.isArray(_parentage)) {\n      arrRemove(_parentage, parent);\n    }\n  }\n\n  /**\n   * Removes a finalizer from this subscription that was previously added with the {@link #add} method.\n   *\n   * Note that `Subscription` instances, when unsubscribed, will automatically remove themselves\n   * from every other `Subscription` they have been added to. This means that using the `remove` method\n   * is not a common thing and should be used thoughtfully.\n   *\n   * If you add the same finalizer instance of a function or an unsubscribable object to a `Subscription` instance\n   * more than once, you will need to call `remove` the same number of times to remove all instances.\n   *\n   * All finalizer instances are removed to free up memory upon unsubscription.\n   *\n   * @param teardown The finalizer to remove from this subscription\n   */\n  remove(teardown: Exclude<TeardownLogic, void>): void {\n    const { _finalizers } = this;\n    _finalizers && arrRemove(_finalizers, teardown);\n\n    if (teardown instanceof Subscription) {\n      teardown._removeParent(this);\n    }\n  }\n}\n\nexport const EMPTY_SUBSCRIPTION = Subscription.EMPTY;\n\nexport function isSubscription(value: any): value is Subscription {\n  return (\n    value instanceof Subscription ||\n    (value && 'closed' in value && isFunction(value.remove) && isFunction(value.add) && isFunction(value.unsubscribe))\n  );\n}\n\nfunction execFinalizer(finalizer: Unsubscribable | (() => void)) {\n  if (isFunction(finalizer)) {\n    finalizer();\n  } else {\n    finalizer.unsubscribe();\n  }\n}\n", "import { Subscriber } from './Subscriber';\nimport { ObservableNotification } from './types';\n\n/**\n * The {@link GlobalConfig} object for RxJS. It is used to configure things\n * like how to react on unhandled errors.\n */\nexport const config: GlobalConfig = {\n  onUnhandledError: null,\n  onStoppedNotification: null,\n  Promise: undefined,\n  useDeprecatedSynchronousErrorHandling: false,\n  useDeprecatedNextContext: false,\n};\n\n/**\n * The global configuration object for RxJS, used to configure things\n * like how to react on unhandled errors. Accessible via {@link config}\n * object.\n */\nexport interface GlobalConfig {\n  /**\n   * A registration point for unhandled errors from RxJS. These are errors that\n   * cannot were not handled by consuming code in the usual subscription path. For\n   * example, if you have this configured, and you subscribe to an observable without\n   * providing an error handler, errors from that subscription will end up here. This\n   * will _always_ be called asynchronously on another job in the runtime. This is because\n   * we do not want errors thrown in this user-configured handler to interfere with the\n   * behavior of the library.\n   */\n  onUnhandledError: ((err: any) => void) | null;\n\n  /**\n   * A registration point for notifications that cannot be sent to subscribers because they\n   * have completed, errored or have been explicitly unsubscribed. By default, next, complete\n   * and error notifications sent to stopped subscribers are noops. However, sometimes callers\n   * might want a different behavior. For example, with sources that attempt to report errors\n   * to stopped subscribers, a caller can configure RxJS to throw an unhandled error instead.\n   * This will _always_ be called asynchronously on another job in the runtime. This is because\n   * we do not want errors thrown in this user-configured handler to interfere with the\n   * behavior of the library.\n   */\n  onStoppedNotification: ((notification: ObservableNotification<any>, subscriber: Subscriber<any>) => void) | null;\n\n  /**\n   * The promise constructor used by default for {@link Observable#toPromise toPromise} and {@link Observable#forEach forEach}\n   * methods.\n   *\n   * @deprecated As of version 8, RxJS will no longer support this sort of injection of a\n   * Promise constructor. If you need a Promise implementation other than native promises,\n   * please polyfill/patch Promise as you see appropriate. Will be removed in v8.\n   */\n  Promise?: PromiseConstructorLike;\n\n  /**\n   * If true, turns on synchronous error rethrowing, which is a deprecated behavior\n   * in v6 and higher. This behavior enables bad patterns like wrapping a subscribe\n   * call in a try/catch block. It also enables producer interference, a nasty bug\n   * where a multicast can be broken for all observers by a downstream consumer with\n   * an unhandled error. DO NOT USE THIS FLAG UNLESS IT'S NEEDED TO BUY TIME\n   * FOR MIGRATION REASONS.\n   *\n   * @deprecated As of version 8, RxJS will no longer support synchronous throwing\n   * of unhandled errors. All errors will be thrown on a separate call stack to prevent bad\n   * behaviors described above. Will be removed in v8.\n   */\n  useDeprecatedSynchronousErrorHandling: boolean;\n\n  /**\n   * If true, enables an as-of-yet undocumented feature from v5: The ability to access\n   * `unsubscribe()` via `this` context in `next` functions created in observers passed\n   * to `subscribe`.\n   *\n   * This is being removed because the performance was severely problematic, and it could also cause\n   * issues when types other than POJOs are passed to subscribe as subscribers, as they will likely have\n   * their `this` context overwritten.\n   *\n   * @deprecated As of version 8, RxJS will no longer support altering the\n   * context of next functions provided as part of an observer to Subscribe. Instead,\n   * you will have access to a subscription or a signal or token that will allow you to do things like\n   * unsubscribe and test closed status. Will be removed in v8.\n   */\n  useDeprecatedNextContext: boolean;\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetTimeoutFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearTimeoutFunction = (handle: TimerHandle) => void;\n\ninterface TimeoutProvider {\n  setTimeout: SetTimeoutFunction;\n  clearTimeout: ClearTimeoutFunction;\n  delegate:\n    | {\n        setTimeout: SetTimeoutFunction;\n        clearTimeout: ClearTimeoutFunction;\n      }\n    | undefined;\n}\n\nexport const timeoutProvider: TimeoutProvider = {\n  // When accessing the delegate, use the variable rather than `this` so that\n  // the functions can be called without being bound to the provider.\n  setTimeout(handler: () => void, timeout?: number, ...args) {\n    const { delegate } = timeoutProvider;\n    if (delegate?.setTimeout) {\n      return delegate.setTimeout(handler, timeout, ...args);\n    }\n    return setTimeout(handler, timeout, ...args);\n  },\n  clearTimeout(handle) {\n    const { delegate } = timeoutProvider;\n    return (delegate?.clearTimeout || clearTimeout)(handle as any);\n  },\n  delegate: undefined,\n};\n", "import { config } from '../config';\nimport { timeoutProvider } from '../scheduler/timeoutProvider';\n\n/**\n * Handles an error on another job either with the user-configured {@link onUnhandledError},\n * or by throwing it on that new job so it can be picked up by `window.onerror`, `process.on('error')`, etc.\n *\n * This should be called whenever there is an error that is out-of-band with the subscription\n * or when an error hits a terminal boundary of the subscription and no error handler was provided.\n *\n * @param err the error to report\n */\nexport function reportUnhandledError(err: any) {\n  timeoutProvider.setTimeout(() => {\n    const { onUnhandledError } = config;\n    if (onUnhandledError) {\n      // Execute the user-configured error handler.\n      onUnhandledError(err);\n    } else {\n      // Throw so it is picked up by the runtime's uncaught error mechanism.\n      throw err;\n    }\n  });\n}\n", "/* tslint:disable:no-empty */\nexport function noop() { }\n", "import { CompleteNotification, NextNotification, ErrorNotification } from './types';\n\n/**\n * A completion object optimized for memory use and created to be the\n * same \"shape\" as other notifications in v8.\n * @internal\n */\nexport const COMPLETE_NOTIFICATION = (() => createNotification('C', undefined, undefined) as CompleteNotification)();\n\n/**\n * Internal use only. Creates an optimized error notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function errorNotification(error: any): ErrorNotification {\n  return createNotification('E', undefined, error) as any;\n}\n\n/**\n * Internal use only. Creates an optimized next notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function nextNotification<T>(value: T) {\n  return createNotification('N', value, undefined) as NextNotification<T>;\n}\n\n/**\n * Ensures that all notifications created internally have the same \"shape\" in v8.\n *\n * TODO: This is only exported to support a crazy legacy test in `groupBy`.\n * @internal\n */\nexport function createNotification(kind: 'N' | 'E' | 'C', value: any, error: any) {\n  return {\n    kind,\n    value,\n    error,\n  };\n}\n", "import { config } from '../config';\n\nlet context: { errorThrown: boolean; error: any } | null = null;\n\n/**\n * Handles dealing with errors for super-gross mode. Creates a context, in which\n * any synchronously thrown errors will be passed to {@link captureError}. Which\n * will record the error such that it will be rethrown after the call back is complete.\n * TODO: Remove in v8\n * @param cb An immediately executed function.\n */\nexport function errorContext(cb: () => void) {\n  if (config.useDeprecatedSynchronousErrorHandling) {\n    const isRoot = !context;\n    if (isRoot) {\n      context = { errorThrown: false, error: null };\n    }\n    cb();\n    if (isRoot) {\n      const { errorThrown, error } = context!;\n      context = null;\n      if (errorThrown) {\n        throw error;\n      }\n    }\n  } else {\n    // This is the general non-deprecated path for everyone that\n    // isn't crazy enough to use super-gross mode (useDeprecatedSynchronousErrorHandling)\n    cb();\n  }\n}\n\n/**\n * Captures errors only in super-gross mode.\n * @param err the error to capture\n */\nexport function captureError(err: any) {\n  if (config.useDeprecatedSynchronousErrorHandling && context) {\n    context.errorThrown = true;\n    context.error = err;\n  }\n}\n", "import { isFunction } from './util/isFunction';\nimport { Observer, ObservableNotification } from './types';\nimport { isSubscription, Subscription } from './Subscription';\nimport { config } from './config';\nimport { reportUnhandledError } from './util/reportUnhandledError';\nimport { noop } from './util/noop';\nimport { nextNotification, errorNotification, COMPLETE_NOTIFICATION } from './NotificationFactories';\nimport { timeoutProvider } from './scheduler/timeoutProvider';\nimport { captureError } from './util/errorContext';\n\n/**\n * Implements the {@link Observer} interface and extends the\n * {@link Subscription} class. While the {@link Observer} is the public API for\n * consuming the values of an {@link Observable}, all Observers get converted to\n * a Subscriber, in order to provide Subscription-like capabilities such as\n * `unsubscribe`. Subscriber is a common type in RxJS, and crucial for\n * implementing operators, but it is rarely used as a public API.\n *\n * @class Subscriber<T>\n */\nexport class Subscriber<T> extends Subscription implements Observer<T> {\n  /**\n   * A static factory for a Subscriber, given a (potentially partial) definition\n   * of an Observer.\n   * @param next The `next` callback of an Observer.\n   * @param error The `error` callback of an\n   * Observer.\n   * @param complete The `complete` callback of an\n   * Observer.\n   * @return A Subscriber wrapping the (partially defined)\n   * Observer represented by the given arguments.\n   * @nocollapse\n   * @deprecated Do not use. Will be removed in v8. There is no replacement for this\n   * method, and there is no reason to be creating instances of `Subscriber` directly.\n   * If you have a specific use case, please file an issue.\n   */\n  static create<T>(next?: (x?: T) => void, error?: (e?: any) => void, complete?: () => void): Subscriber<T> {\n    return new SafeSubscriber(next, error, complete);\n  }\n\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  protected isStopped: boolean = false;\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  protected destination: Subscriber<any> | Observer<any>; // this `any` is the escape hatch to erase extra type param (e.g. R)\n\n  /**\n   * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n   * There is no reason to directly create an instance of Subscriber. This type is exported for typings reasons.\n   */\n  constructor(destination?: Subscriber<any> | Observer<any>) {\n    super();\n    if (destination) {\n      this.destination = destination;\n      // Automatically chain subscriptions together here.\n      // if destination is a Subscription, then it is a Subscriber.\n      if (isSubscription(destination)) {\n        destination.add(this);\n      }\n    } else {\n      this.destination = EMPTY_OBSERVER;\n    }\n  }\n\n  /**\n   * The {@link Observer} callback to receive notifications of type `next` from\n   * the Observable, with a value. The Observable may call this method 0 or more\n   * times.\n   * @param {T} [value] The `next` value.\n   * @return {void}\n   */\n  next(value?: T): void {\n    if (this.isStopped) {\n      handleStoppedNotification(nextNotification(value), this);\n    } else {\n      this._next(value!);\n    }\n  }\n\n  /**\n   * The {@link Observer} callback to receive notifications of type `error` from\n   * the Observable, with an attached `Error`. Notifies the Observer that\n   * the Observable has experienced an error condition.\n   * @param {any} [err] The `error` exception.\n   * @return {void}\n   */\n  error(err?: any): void {\n    if (this.isStopped) {\n      handleStoppedNotification(errorNotification(err), this);\n    } else {\n      this.isStopped = true;\n      this._error(err);\n    }\n  }\n\n  /**\n   * The {@link Observer} callback to receive a valueless notification of type\n   * `complete` from the Observable. Notifies the Observer that the Observable\n   * has finished sending push-based notifications.\n   * @return {void}\n   */\n  complete(): void {\n    if (this.isStopped) {\n      handleStoppedNotification(COMPLETE_NOTIFICATION, this);\n    } else {\n      this.isStopped = true;\n      this._complete();\n    }\n  }\n\n  unsubscribe(): void {\n    if (!this.closed) {\n      this.isStopped = true;\n      super.unsubscribe();\n      this.destination = null!;\n    }\n  }\n\n  protected _next(value: T): void {\n    this.destination.next(value);\n  }\n\n  protected _error(err: any): void {\n    try {\n      this.destination.error(err);\n    } finally {\n      this.unsubscribe();\n    }\n  }\n\n  protected _complete(): void {\n    try {\n      this.destination.complete();\n    } finally {\n      this.unsubscribe();\n    }\n  }\n}\n\n/**\n * This bind is captured here because we want to be able to have\n * compatibility with monoid libraries that tend to use a method named\n * `bind`. In particular, a library called Monio requires this.\n */\nconst _bind = Function.prototype.bind;\n\nfunction bind<Fn extends (...args: any[]) => any>(fn: Fn, thisArg: any): Fn {\n  return _bind.call(fn, thisArg);\n}\n\n/**\n * Internal optimization only, DO NOT EXPOSE.\n * @internal\n */\nclass ConsumerObserver<T> implements Observer<T> {\n  constructor(private partialObserver: Partial<Observer<T>>) {}\n\n  next(value: T): void {\n    const { partialObserver } = this;\n    if (partialObserver.next) {\n      try {\n        partialObserver.next(value);\n      } catch (error) {\n        handleUnhandledError(error);\n      }\n    }\n  }\n\n  error(err: any): void {\n    const { partialObserver } = this;\n    if (partialObserver.error) {\n      try {\n        partialObserver.error(err);\n      } catch (error) {\n        handleUnhandledError(error);\n      }\n    } else {\n      handleUnhandledError(err);\n    }\n  }\n\n  complete(): void {\n    const { partialObserver } = this;\n    if (partialObserver.complete) {\n      try {\n        partialObserver.complete();\n      } catch (error) {\n        handleUnhandledError(error);\n      }\n    }\n  }\n}\n\nexport class SafeSubscriber<T> extends Subscriber<T> {\n  constructor(\n    observerOrNext?: Partial<Observer<T>> | ((value: T) => void) | null,\n    error?: ((e?: any) => void) | null,\n    complete?: (() => void) | null\n  ) {\n    super();\n\n    let partialObserver: Partial<Observer<T>>;\n    if (isFunction(observerOrNext) || !observerOrNext) {\n      // The first argument is a function, not an observer. The next\n      // two arguments *could* be observers, or they could be empty.\n      partialObserver = {\n        next: (observerOrNext ?? undefined) as (((value: T) => void) | undefined),\n        error: error ?? undefined,\n        complete: complete ?? undefined,\n      };\n    } else {\n      // The first argument is a partial observer.\n      let context: any;\n      if (this && config.useDeprecatedNextContext) {\n        // This is a deprecated path that made `this.unsubscribe()` available in\n        // next handler functions passed to subscribe. This only exists behind a flag\n        // now, as it is *very* slow.\n        context = Object.create(observerOrNext);\n        context.unsubscribe = () => this.unsubscribe();\n        partialObserver = {\n          next: observerOrNext.next && bind(observerOrNext.next, context),\n          error: observerOrNext.error && bind(observerOrNext.error, context),\n          complete: observerOrNext.complete && bind(observerOrNext.complete, context),\n        };\n      } else {\n        // The \"normal\" path. Just use the partial observer directly.\n        partialObserver = observerOrNext;\n      }\n    }\n\n    // Wrap the partial observer to ensure it's a full observer, and\n    // make sure proper error handling is accounted for.\n    this.destination = new ConsumerObserver(partialObserver);\n  }\n}\n\nfunction handleUnhandledError(error: any) {\n  if (config.useDeprecatedSynchronousErrorHandling) {\n    captureError(error);\n  } else {\n    // Ideal path, we report this as an unhandled error,\n    // which is thrown on a new call stack.\n    reportUnhandledError(error);\n  }\n}\n\n/**\n * An error handler used when no error handler was supplied\n * to the SafeSubscriber -- meaning no error handler was supplied\n * do the `subscribe` call on our observable.\n * @param err The error to handle\n */\nfunction defaultErrorHandler(err: any) {\n  throw err;\n}\n\n/**\n * A handler for notifications that cannot be sent to a stopped subscriber.\n * @param notification The notification being sent\n * @param subscriber The stopped subscriber\n */\nfunction handleStoppedNotification(notification: ObservableNotification<any>, subscriber: Subscriber<any>) {\n  const { onStoppedNotification } = config;\n  onStoppedNotification && timeoutProvider.setTimeout(() => onStoppedNotification(notification, subscriber));\n}\n\n/**\n * The observer used as a stub for subscriptions where the user did not\n * pass any arguments to `subscribe`. Comes with the default error handling\n * behavior.\n */\nexport const EMPTY_OBSERVER: Readonly<Observer<any>> & { closed: true } = {\n  closed: true,\n  next: noop,\n  error: defaultErrorHandler,\n  complete: noop,\n};\n", "/**\n * Symbol.observable or a string \"@@observable\". Used for interop\n *\n * @deprecated We will no longer be exporting this symbol in upcoming versions of RxJS.\n * Instead polyfill and use Symbol.observable directly *or* use https://www.npmjs.com/package/symbol-observable\n */\nexport const observable: string | symbol = (() => (typeof Symbol === 'function' && Symbol.observable) || '@@observable')();\n", "/**\n * This function takes one parameter and just returns it. Simply put,\n * this is like `<T>(x: T): T => x`.\n *\n * ## Examples\n *\n * This is useful in some cases when using things like `mergeMap`\n *\n * ```ts\n * import { interval, take, map, range, mergeMap, identity } from 'rxjs';\n *\n * const source$ = interval(1000).pipe(take(5));\n *\n * const result$ = source$.pipe(\n *   map(i => range(i)),\n *   mergeMap(identity) // same as mergeMap(x => x)\n * );\n *\n * result$.subscribe({\n *   next: console.log\n * });\n * ```\n *\n * Or when you want to selectively apply an operator\n *\n * ```ts\n * import { interval, take, identity } from 'rxjs';\n *\n * const shouldLimit = () => Math.random() < 0.5;\n *\n * const source$ = interval(1000);\n *\n * const result$ = source$.pipe(shouldLimit() ? take(5) : identity);\n *\n * result$.subscribe({\n *   next: console.log\n * });\n * ```\n *\n * @param x Any value that is returned by this function\n * @returns The value passed as the first parameter to this function\n */\nexport function identity<T>(x: T): T {\n  return x;\n}\n", "import { identity } from './identity';\nimport { UnaryFunction } from '../types';\n\nexport function pipe(): typeof identity;\nexport function pipe<T, A>(fn1: UnaryFunction<T, A>): UnaryFunction<T, A>;\nexport function pipe<T, A, B>(fn1: UnaryFunction<T, A>, fn2: UnaryFunction<A, B>): UnaryFunction<T, B>;\nexport function pipe<T, A, B, C>(fn1: UnaryFunction<T, A>, fn2: UnaryFunction<A, B>, fn3: UnaryFunction<B, C>): UnaryFunction<T, C>;\nexport function pipe<T, A, B, C, D>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>\n): UnaryFunction<T, D>;\nexport function pipe<T, A, B, C, D, E>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>,\n  fn5: UnaryFunction<D, E>\n): UnaryFunction<T, E>;\nexport function pipe<T, A, B, C, D, E, F>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>,\n  fn5: UnaryFunction<D, E>,\n  fn6: UnaryFunction<E, F>\n): UnaryFunction<T, F>;\nexport function pipe<T, A, B, C, D, E, F, G>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>,\n  fn5: UnaryFunction<D, E>,\n  fn6: UnaryFunction<E, F>,\n  fn7: UnaryFunction<F, G>\n): UnaryFunction<T, G>;\nexport function pipe<T, A, B, C, D, E, F, G, H>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>,\n  fn5: UnaryFunction<D, E>,\n  fn6: UnaryFunction<E, F>,\n  fn7: UnaryFunction<F, G>,\n  fn8: UnaryFunction<G, H>\n): UnaryFunction<T, H>;\nexport function pipe<T, A, B, C, D, E, F, G, H, I>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>,\n  fn5: UnaryFunction<D, E>,\n  fn6: UnaryFunction<E, F>,\n  fn7: UnaryFunction<F, G>,\n  fn8: UnaryFunction<G, H>,\n  fn9: UnaryFunction<H, I>\n): UnaryFunction<T, I>;\nexport function pipe<T, A, B, C, D, E, F, G, H, I>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>,\n  fn5: UnaryFunction<D, E>,\n  fn6: UnaryFunction<E, F>,\n  fn7: UnaryFunction<F, G>,\n  fn8: UnaryFunction<G, H>,\n  fn9: UnaryFunction<H, I>,\n  ...fns: UnaryFunction<any, any>[]\n): UnaryFunction<T, unknown>;\n\n/**\n * pipe() can be called on one or more functions, each of which can take one argument (\"UnaryFunction\")\n * and uses it to return a value.\n * It returns a function that takes one argument, passes it to the first UnaryFunction, and then\n * passes the result to the next one, passes that result to the next one, and so on.  \n */\nexport function pipe(...fns: Array<UnaryFunction<any, any>>): UnaryFunction<any, any> {\n  return pipeFromArray(fns);\n}\n\n/** @internal */\nexport function pipeFromArray<T, R>(fns: Array<UnaryFunction<T, R>>): UnaryFunction<T, R> {\n  if (fns.length === 0) {\n    return identity as UnaryFunction<any, any>;\n  }\n\n  if (fns.length === 1) {\n    return fns[0];\n  }\n\n  return function piped(input: T): R {\n    return fns.reduce((prev: any, fn: UnaryFunction<T, R>) => fn(prev), input as any);\n  };\n}\n", "import { Operator } from './Operator';\nimport { SafeSubscriber, Subscriber } from './Subscriber';\nimport { isSubscription, Subscription } from './Subscription';\nimport { TeardownLogic, OperatorFunction, Subscribable, Observer } from './types';\nimport { observable as Symbol_observable } from './symbol/observable';\nimport { pipeFromArray } from './util/pipe';\nimport { config } from './config';\nimport { isFunction } from './util/isFunction';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A representation of any set of values over any amount of time. This is the most basic building block\n * of RxJS.\n *\n * @class Observable<T>\n */\nexport class Observable<T> implements Subscribable<T> {\n  /**\n   * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n   */\n  source: Observable<any> | undefined;\n\n  /**\n   * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n   */\n  operator: Operator<any, T> | undefined;\n\n  /**\n   * @constructor\n   * @param {Function} subscribe the function that is called when the Observable is\n   * initially subscribed to. This function is given a Subscriber, to which new values\n   * can be `next`ed, or an `error` method can be called to raise an error, or\n   * `complete` can be called to notify of a successful completion.\n   */\n  constructor(subscribe?: (this: Observable<T>, subscriber: Subscriber<T>) => TeardownLogic) {\n    if (subscribe) {\n      this._subscribe = subscribe;\n    }\n  }\n\n  // HACK: Since TypeScript inherits static properties too, we have to\n  // fight against TypeScript here so Subject can have a different static create signature\n  /**\n   * Creates a new Observable by calling the Observable constructor\n   * @owner Observable\n   * @method create\n   * @param {Function} subscribe? the subscriber function to be passed to the Observable constructor\n   * @return {Observable} a new observable\n   * @nocollapse\n   * @deprecated Use `new Observable()` instead. Will be removed in v8.\n   */\n  static create: (...args: any[]) => any = <T>(subscribe?: (subscriber: Subscriber<T>) => TeardownLogic) => {\n    return new Observable<T>(subscribe);\n  };\n\n  /**\n   * Creates a new Observable, with this Observable instance as the source, and the passed\n   * operator defined as the new observable's operator.\n   * @method lift\n   * @param operator the operator defining the operation to take on the observable\n   * @return a new observable with the Operator applied\n   * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n   * If you have implemented an operator using `lift`, it is recommended that you create an\n   * operator by simply returning `new Observable()` directly. See \"Creating new operators from\n   * scratch\" section here: https://rxjs.dev/guide/operators\n   */\n  lift<R>(operator?: Operator<T, R>): Observable<R> {\n    const observable = new Observable<R>();\n    observable.source = this;\n    observable.operator = operator;\n    return observable;\n  }\n\n  subscribe(observerOrNext?: Partial<Observer<T>> | ((value: T) => void)): Subscription;\n  /** @deprecated Instead of passing separate callback arguments, use an observer argument. Signatures taking separate callback arguments will be removed in v8. Details: https://rxjs.dev/deprecations/subscribe-arguments */\n  subscribe(next?: ((value: T) => void) | null, error?: ((error: any) => void) | null, complete?: (() => void) | null): Subscription;\n  /**\n   * Invokes an execution of an Observable and registers Observer handlers for notifications it will emit.\n   *\n   * <span class=\"informal\">Use it when you have all these Observables, but still nothing is happening.</span>\n   *\n   * `subscribe` is not a regular operator, but a method that calls Observable's internal `subscribe` function. It\n   * might be for example a function that you passed to Observable's constructor, but most of the time it is\n   * a library implementation, which defines what will be emitted by an Observable, and when it be will emitted. This means\n   * that calling `subscribe` is actually the moment when Observable starts its work, not when it is created, as it is often\n   * the thought.\n   *\n   * Apart from starting the execution of an Observable, this method allows you to listen for values\n   * that an Observable emits, as well as for when it completes or errors. You can achieve this in two\n   * of the following ways.\n   *\n   * The first way is creating an object that implements {@link Observer} interface. It should have methods\n   * defined by that interface, but note that it should be just a regular JavaScript object, which you can create\n   * yourself in any way you want (ES6 class, classic function constructor, object literal etc.). In particular, do\n   * not attempt to use any RxJS implementation details to create Observers - you don't need them. Remember also\n   * that your object does not have to implement all methods. If you find yourself creating a method that doesn't\n   * do anything, you can simply omit it. Note however, if the `error` method is not provided and an error happens,\n   * it will be thrown asynchronously. Errors thrown asynchronously cannot be caught using `try`/`catch`. Instead,\n   * use the {@link onUnhandledError} configuration option or use a runtime handler (like `window.onerror` or\n   * `process.on('error)`) to be notified of unhandled errors. Because of this, it's recommended that you provide\n   * an `error` method to avoid missing thrown errors.\n   *\n   * The second way is to give up on Observer object altogether and simply provide callback functions in place of its methods.\n   * This means you can provide three functions as arguments to `subscribe`, where the first function is equivalent\n   * of a `next` method, the second of an `error` method and the third of a `complete` method. Just as in case of an Observer,\n   * if you do not need to listen for something, you can omit a function by passing `undefined` or `null`,\n   * since `subscribe` recognizes these functions by where they were placed in function call. When it comes\n   * to the `error` function, as with an Observer, if not provided, errors emitted by an Observable will be thrown asynchronously.\n   *\n   * You can, however, subscribe with no parameters at all. This may be the case where you're not interested in terminal events\n   * and you also handled emissions internally by using operators (e.g. using `tap`).\n   *\n   * Whichever style of calling `subscribe` you use, in both cases it returns a Subscription object.\n   * This object allows you to call `unsubscribe` on it, which in turn will stop the work that an Observable does and will clean\n   * up all resources that an Observable used. Note that cancelling a subscription will not call `complete` callback\n   * provided to `subscribe` function, which is reserved for a regular completion signal that comes from an Observable.\n   *\n   * Remember that callbacks provided to `subscribe` are not guaranteed to be called asynchronously.\n   * It is an Observable itself that decides when these functions will be called. For example {@link of}\n   * by default emits all its values synchronously. Always check documentation for how given Observable\n   * will behave when subscribed and if its default behavior can be modified with a `scheduler`.\n   *\n   * #### Examples\n   *\n   * Subscribe with an {@link guide/observer Observer}\n   *\n   * ```ts\n   * import { of } from 'rxjs';\n   *\n   * const sumObserver = {\n   *   sum: 0,\n   *   next(value) {\n   *     console.log('Adding: ' + value);\n   *     this.sum = this.sum + value;\n   *   },\n   *   error() {\n   *     // We actually could just remove this method,\n   *     // since we do not really care about errors right now.\n   *   },\n   *   complete() {\n   *     console.log('Sum equals: ' + this.sum);\n   *   }\n   * };\n   *\n   * of(1, 2, 3) // Synchronously emits 1, 2, 3 and then completes.\n   *   .subscribe(sumObserver);\n   *\n   * // Logs:\n   * // 'Adding: 1'\n   * // 'Adding: 2'\n   * // 'Adding: 3'\n   * // 'Sum equals: 6'\n   * ```\n   *\n   * Subscribe with functions ({@link deprecations/subscribe-arguments deprecated})\n   *\n   * ```ts\n   * import { of } from 'rxjs'\n   *\n   * let sum = 0;\n   *\n   * of(1, 2, 3).subscribe(\n   *   value => {\n   *     console.log('Adding: ' + value);\n   *     sum = sum + value;\n   *   },\n   *   undefined,\n   *   () => console.log('Sum equals: ' + sum)\n   * );\n   *\n   * // Logs:\n   * // 'Adding: 1'\n   * // 'Adding: 2'\n   * // 'Adding: 3'\n   * // 'Sum equals: 6'\n   * ```\n   *\n   * Cancel a subscription\n   *\n   * ```ts\n   * import { interval } from 'rxjs';\n   *\n   * const subscription = interval(1000).subscribe({\n   *   next(num) {\n   *     console.log(num)\n   *   },\n   *   complete() {\n   *     // Will not be called, even when cancelling subscription.\n   *     console.log('completed!');\n   *   }\n   * });\n   *\n   * setTimeout(() => {\n   *   subscription.unsubscribe();\n   *   console.log('unsubscribed!');\n   * }, 2500);\n   *\n   * // Logs:\n   * // 0 after 1s\n   * // 1 after 2s\n   * // 'unsubscribed!' after 2.5s\n   * ```\n   *\n   * @param {Observer|Function} observerOrNext (optional) Either an observer with methods to be called,\n   * or the first of three possible handlers, which is the handler for each value emitted from the subscribed\n   * Observable.\n   * @param {Function} error (optional) A handler for a terminal event resulting from an error. If no error handler is provided,\n   * the error will be thrown asynchronously as unhandled.\n   * @param {Function} complete (optional) A handler for a terminal event resulting from successful completion.\n   * @return {Subscription} a subscription reference to the registered handlers\n   * @method subscribe\n   */\n  subscribe(\n    observerOrNext?: Partial<Observer<T>> | ((value: T) => void) | null,\n    error?: ((error: any) => void) | null,\n    complete?: (() => void) | null\n  ): Subscription {\n    const subscriber = isSubscriber(observerOrNext) ? observerOrNext : new SafeSubscriber(observerOrNext, error, complete);\n\n    errorContext(() => {\n      const { operator, source } = this;\n      subscriber.add(\n        operator\n          ? // We're dealing with a subscription in the\n            // operator chain to one of our lifted operators.\n            operator.call(subscriber, source)\n          : source\n          ? // If `source` has a value, but `operator` does not, something that\n            // had intimate knowledge of our API, like our `Subject`, must have\n            // set it. We're going to just call `_subscribe` directly.\n            this._subscribe(subscriber)\n          : // In all other cases, we're likely wrapping a user-provided initializer\n            // function, so we need to catch errors and handle them appropriately.\n            this._trySubscribe(subscriber)\n      );\n    });\n\n    return subscriber;\n  }\n\n  /** @internal */\n  protected _trySubscribe(sink: Subscriber<T>): TeardownLogic {\n    try {\n      return this._subscribe(sink);\n    } catch (err) {\n      // We don't need to return anything in this case,\n      // because it's just going to try to `add()` to a subscription\n      // above.\n      sink.error(err);\n    }\n  }\n\n  /**\n   * Used as a NON-CANCELLABLE means of subscribing to an observable, for use with\n   * APIs that expect promises, like `async/await`. You cannot unsubscribe from this.\n   *\n   * **WARNING**: Only use this with observables you *know* will complete. If the source\n   * observable does not complete, you will end up with a promise that is hung up, and\n   * potentially all of the state of an async function hanging out in memory. To avoid\n   * this situation, look into adding something like {@link timeout}, {@link take},\n   * {@link takeWhile}, or {@link takeUntil} amongst others.\n   *\n   * #### Example\n   *\n   * ```ts\n   * import { interval, take } from 'rxjs';\n   *\n   * const source$ = interval(1000).pipe(take(4));\n   *\n   * async function getTotal() {\n   *   let total = 0;\n   *\n   *   await source$.forEach(value => {\n   *     total += value;\n   *     console.log('observable -> ' + value);\n   *   });\n   *\n   *   return total;\n   * }\n   *\n   * getTotal().then(\n   *   total => console.log('Total: ' + total)\n   * );\n   *\n   * // Expected:\n   * // 'observable -> 0'\n   * // 'observable -> 1'\n   * // 'observable -> 2'\n   * // 'observable -> 3'\n   * // 'Total: 6'\n   * ```\n   *\n   * @param next a handler for each value emitted by the observable\n   * @return a promise that either resolves on observable completion or\n   *  rejects with the handled error\n   */\n  forEach(next: (value: T) => void): Promise<void>;\n\n  /**\n   * @param next a handler for each value emitted by the observable\n   * @param promiseCtor a constructor function used to instantiate the Promise\n   * @return a promise that either resolves on observable completion or\n   *  rejects with the handled error\n   * @deprecated Passing a Promise constructor will no longer be available\n   * in upcoming versions of RxJS. This is because it adds weight to the library, for very\n   * little benefit. If you need this functionality, it is recommended that you either\n   * polyfill Promise, or you create an adapter to convert the returned native promise\n   * to whatever promise implementation you wanted. Will be removed in v8.\n   */\n  forEach(next: (value: T) => void, promiseCtor: PromiseConstructorLike): Promise<void>;\n\n  forEach(next: (value: T) => void, promiseCtor?: PromiseConstructorLike): Promise<void> {\n    promiseCtor = getPromiseCtor(promiseCtor);\n\n    return new promiseCtor<void>((resolve, reject) => {\n      const subscriber = new SafeSubscriber<T>({\n        next: (value) => {\n          try {\n            next(value);\n          } catch (err) {\n            reject(err);\n            subscriber.unsubscribe();\n          }\n        },\n        error: reject,\n        complete: resolve,\n      });\n      this.subscribe(subscriber);\n    }) as Promise<void>;\n  }\n\n  /** @internal */\n  protected _subscribe(subscriber: Subscriber<any>): TeardownLogic {\n    return this.source?.subscribe(subscriber);\n  }\n\n  /**\n   * An interop point defined by the es7-observable spec https://github.com/zenparsing/es-observable\n   * @method Symbol.observable\n   * @return {Observable} this instance of the observable\n   */\n  [Symbol_observable]() {\n    return this;\n  }\n\n  /* tslint:disable:max-line-length */\n  pipe(): Observable<T>;\n  pipe<A>(op1: OperatorFunction<T, A>): Observable<A>;\n  pipe<A, B>(op1: OperatorFunction<T, A>, op2: OperatorFunction<A, B>): Observable<B>;\n  pipe<A, B, C>(op1: OperatorFunction<T, A>, op2: OperatorFunction<A, B>, op3: OperatorFunction<B, C>): Observable<C>;\n  pipe<A, B, C, D>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>\n  ): Observable<D>;\n  pipe<A, B, C, D, E>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>,\n    op5: OperatorFunction<D, E>\n  ): Observable<E>;\n  pipe<A, B, C, D, E, F>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>,\n    op5: OperatorFunction<D, E>,\n    op6: OperatorFunction<E, F>\n  ): Observable<F>;\n  pipe<A, B, C, D, E, F, G>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>,\n    op5: OperatorFunction<D, E>,\n    op6: OperatorFunction<E, F>,\n    op7: OperatorFunction<F, G>\n  ): Observable<G>;\n  pipe<A, B, C, D, E, F, G, H>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>,\n    op5: OperatorFunction<D, E>,\n    op6: OperatorFunction<E, F>,\n    op7: OperatorFunction<F, G>,\n    op8: OperatorFunction<G, H>\n  ): Observable<H>;\n  pipe<A, B, C, D, E, F, G, H, I>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>,\n    op5: OperatorFunction<D, E>,\n    op6: OperatorFunction<E, F>,\n    op7: OperatorFunction<F, G>,\n    op8: OperatorFunction<G, H>,\n    op9: OperatorFunction<H, I>\n  ): Observable<I>;\n  pipe<A, B, C, D, E, F, G, H, I>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>,\n    op5: OperatorFunction<D, E>,\n    op6: OperatorFunction<E, F>,\n    op7: OperatorFunction<F, G>,\n    op8: OperatorFunction<G, H>,\n    op9: OperatorFunction<H, I>,\n    ...operations: OperatorFunction<any, any>[]\n  ): Observable<unknown>;\n  /* tslint:enable:max-line-length */\n\n  /**\n   * Used to stitch together functional operators into a chain.\n   * @method pipe\n   * @return {Observable} the Observable result of all of the operators having\n   * been called in the order they were passed in.\n   *\n   * ## Example\n   *\n   * ```ts\n   * import { interval, filter, map, scan } from 'rxjs';\n   *\n   * interval(1000)\n   *   .pipe(\n   *     filter(x => x % 2 === 0),\n   *     map(x => x + x),\n   *     scan((acc, x) => acc + x)\n   *   )\n   *   .subscribe(x => console.log(x));\n   * ```\n   */\n  pipe(...operations: OperatorFunction<any, any>[]): Observable<any> {\n    return pipeFromArray(operations)(this);\n  }\n\n  /* tslint:disable:max-line-length */\n  /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n  toPromise(): Promise<T | undefined>;\n  /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n  toPromise(PromiseCtor: typeof Promise): Promise<T | undefined>;\n  /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n  toPromise(PromiseCtor: PromiseConstructorLike): Promise<T | undefined>;\n  /* tslint:enable:max-line-length */\n\n  /**\n   * Subscribe to this Observable and get a Promise resolving on\n   * `complete` with the last emission (if any).\n   *\n   * **WARNING**: Only use this with observables you *know* will complete. If the source\n   * observable does not complete, you will end up with a promise that is hung up, and\n   * potentially all of the state of an async function hanging out in memory. To avoid\n   * this situation, look into adding something like {@link timeout}, {@link take},\n   * {@link takeWhile}, or {@link takeUntil} amongst others.\n   *\n   * @method toPromise\n   * @param [promiseCtor] a constructor function used to instantiate\n   * the Promise\n   * @return A Promise that resolves with the last value emit, or\n   * rejects on an error. If there were no emissions, Promise\n   * resolves with undefined.\n   * @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise\n   */\n  toPromise(promiseCtor?: PromiseConstructorLike): Promise<T | undefined> {\n    promiseCtor = getPromiseCtor(promiseCtor);\n\n    return new promiseCtor((resolve, reject) => {\n      let value: T | undefined;\n      this.subscribe(\n        (x: T) => (value = x),\n        (err: any) => reject(err),\n        () => resolve(value)\n      );\n    }) as Promise<T | undefined>;\n  }\n}\n\n/**\n * Decides between a passed promise constructor from consuming code,\n * A default configured promise constructor, and the native promise\n * constructor and returns it. If nothing can be found, it will throw\n * an error.\n * @param promiseCtor The optional promise constructor to passed by consuming code\n */\nfunction getPromiseCtor(promiseCtor: PromiseConstructorLike | undefined) {\n  return promiseCtor ?? config.Promise ?? Promise;\n}\n\nfunction isObserver<T>(value: any): value is Observer<T> {\n  return value && isFunction(value.next) && isFunction(value.error) && isFunction(value.complete);\n}\n\nfunction isSubscriber<T>(value: any): value is Subscriber<T> {\n  return (value && value instanceof Subscriber) || (isObserver(value) && isSubscription(value));\n}\n", "import { Observable } from '../Observable';\nimport { Subscriber } from '../Subscriber';\nimport { OperatorFunction } from '../types';\nimport { isFunction } from './isFunction';\n\n/**\n * Used to determine if an object is an Observable with a lift function.\n */\nexport function hasLift(source: any): source is { lift: InstanceType<typeof Observable>['lift'] } {\n  return isFunction(source?.lift);\n}\n\n/**\n * Creates an `OperatorFunction`. Used to define operators throughout the library in a concise way.\n * @param init The logic to connect the liftedSource to the subscriber at the moment of subscription.\n */\nexport function operate<T, R>(\n  init: (liftedSource: Observable<T>, subscriber: Subscriber<R>) => (() => void) | void\n): OperatorFunction<T, R> {\n  return (source: Observable<T>) => {\n    if (hasLift(source)) {\n      return source.lift(function (this: Subscriber<R>, liftedSource: Observable<T>) {\n        try {\n          return init(liftedSource, this);\n        } catch (err) {\n          this.error(err);\n        }\n      });\n    }\n    throw new TypeError('Unable to lift unknown Observable type');\n  };\n}\n", "import { Subscriber } from '../Subscriber';\n\n/**\n * Creates an instance of an `OperatorSubscriber`.\n * @param destination The downstream subscriber.\n * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n * and send to the `destination` error handler.\n * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n * this handler are sent to the `destination` error handler.\n * @param onFinalize Additional teardown logic here. This will only be called on teardown if the\n * subscriber itself is not already closed. This is called after all other teardown logic is executed.\n */\nexport function createOperatorSubscriber<T>(\n  destination: Subscriber<any>,\n  onNext?: (value: T) => void,\n  onComplete?: () => void,\n  onError?: (err: any) => void,\n  onFinalize?: () => void\n): Subscriber<T> {\n  return new OperatorSubscriber(destination, onNext, onComplete, onError, onFinalize);\n}\n\n/**\n * A generic helper for allowing operators to be created with a Subscriber and\n * use closures to capture necessary state from the operator function itself.\n */\nexport class OperatorSubscriber<T> extends Subscriber<T> {\n  /**\n   * Creates an instance of an `OperatorSubscriber`.\n   * @param destination The downstream subscriber.\n   * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n   * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n   * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n   * and send to the `destination` error handler.\n   * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n   * this handler are sent to the `destination` error handler.\n   * @param onFinalize Additional finalization logic here. This will only be called on finalization if the\n   * subscriber itself is not already closed. This is called after all other finalization logic is executed.\n   * @param shouldUnsubscribe An optional check to see if an unsubscribe call should truly unsubscribe.\n   * NOTE: This currently **ONLY** exists to support the strange behavior of {@link groupBy}, where unsubscription\n   * to the resulting observable does not actually disconnect from the source if there are active subscriptions\n   * to any grouped observable. (DO NOT EXPOSE OR USE EXTERNALLY!!!)\n   */\n  constructor(\n    destination: Subscriber<any>,\n    onNext?: (value: T) => void,\n    onComplete?: () => void,\n    onError?: (err: any) => void,\n    private onFinalize?: () => void,\n    private shouldUnsubscribe?: () => boolean\n  ) {\n    // It's important - for performance reasons - that all of this class's\n    // members are initialized and that they are always initialized in the same\n    // order. This will ensure that all OperatorSubscriber instances have the\n    // same hidden class in V8. This, in turn, will help keep the number of\n    // hidden classes involved in property accesses within the base class as\n    // low as possible. If the number of hidden classes involved exceeds four,\n    // the property accesses will become megamorphic and performance penalties\n    // will be incurred - i.e. inline caches won't be used.\n    //\n    // The reasons for ensuring all instances have the same hidden class are\n    // further discussed in this blog post from Benedikt Meurer:\n    // https://benediktmeurer.de/2018/03/23/impact-of-polymorphism-on-component-based-frameworks-like-react/\n    super(destination);\n    this._next = onNext\n      ? function (this: OperatorSubscriber<T>, value: T) {\n          try {\n            onNext(value);\n          } catch (err) {\n            destination.error(err);\n          }\n        }\n      : super._next;\n    this._error = onError\n      ? function (this: OperatorSubscriber<T>, err: any) {\n          try {\n            onError(err);\n          } catch (err) {\n            // Send any errors that occur down stream.\n            destination.error(err);\n          } finally {\n            // Ensure finalization.\n            this.unsubscribe();\n          }\n        }\n      : super._error;\n    this._complete = onComplete\n      ? function (this: OperatorSubscriber<T>) {\n          try {\n            onComplete();\n          } catch (err) {\n            // Send any errors that occur down stream.\n            destination.error(err);\n          } finally {\n            // Ensure finalization.\n            this.unsubscribe();\n          }\n        }\n      : super._complete;\n  }\n\n  unsubscribe() {\n    if (!this.shouldUnsubscribe || this.shouldUnsubscribe()) {\n      const { closed } = this;\n      super.unsubscribe();\n      // Execute additional teardown if we have any and we didn't already do so.\n      !closed && this.onFinalize?.();\n    }\n  }\n}\n", "import { Subscription } from '../Subscription';\n\ninterface AnimationFrameProvider {\n  schedule(callback: FrameRequestCallback): Subscription;\n  requestAnimationFrame: typeof requestAnimationFrame;\n  cancelAnimationFrame: typeof cancelAnimationFrame;\n  delegate:\n    | {\n        requestAnimationFrame: typeof requestAnimationFrame;\n        cancelAnimationFrame: typeof cancelAnimationFrame;\n      }\n    | undefined;\n}\n\nexport const animationFrameProvider: AnimationFrameProvider = {\n  // When accessing the delegate, use the variable rather than `this` so that\n  // the functions can be called without being bound to the provider.\n  schedule(callback) {\n    let request = requestAnimationFrame;\n    let cancel: typeof cancelAnimationFrame | undefined = cancelAnimationFrame;\n    const { delegate } = animationFrameProvider;\n    if (delegate) {\n      request = delegate.requestAnimationFrame;\n      cancel = delegate.cancelAnimationFrame;\n    }\n    const handle = request((timestamp) => {\n      // Clear the cancel function. The request has been fulfilled, so\n      // attempting to cancel the request upon unsubscription would be\n      // pointless.\n      cancel = undefined;\n      callback(timestamp);\n    });\n    return new Subscription(() => cancel?.(handle));\n  },\n  requestAnimationFrame(...args) {\n    const { delegate } = animationFrameProvider;\n    return (delegate?.requestAnimationFrame || requestAnimationFrame)(...args);\n  },\n  cancelAnimationFrame(...args) {\n    const { delegate } = animationFrameProvider;\n    return (delegate?.cancelAnimationFrame || cancelAnimationFrame)(...args);\n  },\n  delegate: undefined,\n};\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface ObjectUnsubscribedError extends Error {}\n\nexport interface ObjectUnsubscribedErrorCtor {\n  /**\n   * @deprecated Internal implementation detail. Do not construct error instances.\n   * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n   */\n  new (): ObjectUnsubscribedError;\n}\n\n/**\n * An error thrown when an action is invalid because the object has been\n * unsubscribed.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n *\n * @class ObjectUnsubscribedError\n */\nexport const ObjectUnsubscribedError: ObjectUnsubscribedErrorCtor = createErrorClass(\n  (_super) =>\n    function ObjectUnsubscribedErrorImpl(this: any) {\n      _super(this);\n      this.name = 'ObjectUnsubscribedError';\n      this.message = 'object unsubscribed';\n    }\n);\n", "import { Operator } from './Operator';\nimport { Observable } from './Observable';\nimport { Subscriber } from './Subscriber';\nimport { Subscription, EMPTY_SUBSCRIPTION } from './Subscription';\nimport { Observer, SubscriptionLike, TeardownLogic } from './types';\nimport { ObjectUnsubscribedError } from './util/ObjectUnsubscribedError';\nimport { arrRemove } from './util/arrRemove';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A Subject is a special type of Observable that allows values to be\n * multicasted to many Observers. Subjects are like EventEmitters.\n *\n * Every Subject is an Observable and an Observer. You can subscribe to a\n * Subject, and you can call next to feed values as well as error and complete.\n */\nexport class Subject<T> extends Observable<T> implements SubscriptionLike {\n  closed = false;\n\n  private currentObservers: Observer<T>[] | null = null;\n\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  observers: Observer<T>[] = [];\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  isStopped = false;\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  hasError = false;\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  thrownError: any = null;\n\n  /**\n   * Creates a \"subject\" by basically gluing an observer to an observable.\n   *\n   * @nocollapse\n   * @deprecated Recommended you do not use. Will be removed at some point in the future. Plans for replacement still under discussion.\n   */\n  static create: (...args: any[]) => any = <T>(destination: Observer<T>, source: Observable<T>): AnonymousSubject<T> => {\n    return new AnonymousSubject<T>(destination, source);\n  };\n\n  constructor() {\n    // NOTE: This must be here to obscure Observable's constructor.\n    super();\n  }\n\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  lift<R>(operator: Operator<T, R>): Observable<R> {\n    const subject = new AnonymousSubject(this, this);\n    subject.operator = operator as any;\n    return subject as any;\n  }\n\n  /** @internal */\n  protected _throwIfClosed() {\n    if (this.closed) {\n      throw new ObjectUnsubscribedError();\n    }\n  }\n\n  next(value: T) {\n    errorContext(() => {\n      this._throwIfClosed();\n      if (!this.isStopped) {\n        if (!this.currentObservers) {\n          this.currentObservers = Array.from(this.observers);\n        }\n        for (const observer of this.currentObservers) {\n          observer.next(value);\n        }\n      }\n    });\n  }\n\n  error(err: any) {\n    errorContext(() => {\n      this._throwIfClosed();\n      if (!this.isStopped) {\n        this.hasError = this.isStopped = true;\n        this.thrownError = err;\n        const { observers } = this;\n        while (observers.length) {\n          observers.shift()!.error(err);\n        }\n      }\n    });\n  }\n\n  complete() {\n    errorContext(() => {\n      this._throwIfClosed();\n      if (!this.isStopped) {\n        this.isStopped = true;\n        const { observers } = this;\n        while (observers.length) {\n          observers.shift()!.complete();\n        }\n      }\n    });\n  }\n\n  unsubscribe() {\n    this.isStopped = this.closed = true;\n    this.observers = this.currentObservers = null!;\n  }\n\n  get observed() {\n    return this.observers?.length > 0;\n  }\n\n  /** @internal */\n  protected _trySubscribe(subscriber: Subscriber<T>): TeardownLogic {\n    this._throwIfClosed();\n    return super._trySubscribe(subscriber);\n  }\n\n  /** @internal */\n  protected _subscribe(subscriber: Subscriber<T>): Subscription {\n    this._throwIfClosed();\n    this._checkFinalizedStatuses(subscriber);\n    return this._innerSubscribe(subscriber);\n  }\n\n  /** @internal */\n  protected _innerSubscribe(subscriber: Subscriber<any>) {\n    const { hasError, isStopped, observers } = this;\n    if (hasError || isStopped) {\n      return EMPTY_SUBSCRIPTION;\n    }\n    this.currentObservers = null;\n    observers.push(subscriber);\n    return new Subscription(() => {\n      this.currentObservers = null;\n      arrRemove(observers, subscriber);\n    });\n  }\n\n  /** @internal */\n  protected _checkFinalizedStatuses(subscriber: Subscriber<any>) {\n    const { hasError, thrownError, isStopped } = this;\n    if (hasError) {\n      subscriber.error(thrownError);\n    } else if (isStopped) {\n      subscriber.complete();\n    }\n  }\n\n  /**\n   * Creates a new Observable with this Subject as the source. You can do this\n   * to create custom Observer-side logic of the Subject and conceal it from\n   * code that uses the Observable.\n   * @return {Observable} Observable that the Subject casts to\n   */\n  asObservable(): Observable<T> {\n    const observable: any = new Observable<T>();\n    observable.source = this;\n    return observable;\n  }\n}\n\n/**\n * @class AnonymousSubject<T>\n */\nexport class AnonymousSubject<T> extends Subject<T> {\n  constructor(\n    /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n    public destination?: Observer<T>,\n    source?: Observable<T>\n  ) {\n    super();\n    this.source = source;\n  }\n\n  next(value: T) {\n    this.destination?.next?.(value);\n  }\n\n  error(err: any) {\n    this.destination?.error?.(err);\n  }\n\n  complete() {\n    this.destination?.complete?.();\n  }\n\n  /** @internal */\n  protected _subscribe(subscriber: Subscriber<T>): Subscription {\n    return this.source?.subscribe(subscriber) ?? EMPTY_SUBSCRIPTION;\n  }\n}\n", "import { Subject } from './Subject';\nimport { Subscriber } from './Subscriber';\nimport { Subscription } from './Subscription';\n\n/**\n * A variant of Subject that requires an initial value and emits its current\n * value whenever it is subscribed to.\n *\n * @class BehaviorSubject<T>\n */\nexport class BehaviorSubject<T> extends Subject<T> {\n  constructor(private _value: T) {\n    super();\n  }\n\n  get value(): T {\n    return this.getValue();\n  }\n\n  /** @internal */\n  protected _subscribe(subscriber: Subscriber<T>): Subscription {\n    const subscription = super._subscribe(subscriber);\n    !subscription.closed && subscriber.next(this._value);\n    return subscription;\n  }\n\n  getValue(): T {\n    const { hasError, thrownError, _value } = this;\n    if (hasError) {\n      throw thrownError;\n    }\n    this._throwIfClosed();\n    return _value;\n  }\n\n  next(value: T): void {\n    super.next((this._value = value));\n  }\n}\n", "import { TimestampProvider } from '../types';\n\ninterface DateTimestampProvider extends TimestampProvider {\n  delegate: TimestampProvider | undefined;\n}\n\nexport const dateTimestampProvider: DateTimestampProvider = {\n  now() {\n    // Use the variable rather than `this` so that the function can be called\n    // without being bound to the provider.\n    return (dateTimestampProvider.delegate || Date).now();\n  },\n  delegate: undefined,\n};\n", "import { Subject } from './Subject';\nimport { TimestampProvider } from './types';\nimport { Subscriber } from './Subscriber';\nimport { Subscription } from './Subscription';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * A variant of {@link Subject} that \"replays\" old values to new subscribers by emitting them when they first subscribe.\n *\n * `ReplaySubject` has an internal buffer that will store a specified number of values that it has observed. Like `Subject`,\n * `ReplaySubject` \"observes\" values by having them passed to its `next` method. When it observes a value, it will store that\n * value for a time determined by the configuration of the `ReplaySubject`, as passed to its constructor.\n *\n * When a new subscriber subscribes to the `ReplaySubject` instance, it will synchronously emit all values in its buffer in\n * a First-In-First-Out (FIFO) manner. The `ReplaySubject` will also complete, if it has observed completion; and it will\n * error if it has observed an error.\n *\n * There are two main configuration items to be concerned with:\n *\n * 1. `bufferSize` - This will determine how many items are stored in the buffer, defaults to infinite.\n * 2. `windowTime` - The amount of time to hold a value in the buffer before removing it from the buffer.\n *\n * Both configurations may exist simultaneously. So if you would like to buffer a maximum of 3 values, as long as the values\n * are less than 2 seconds old, you could do so with a `new ReplaySubject(3, 2000)`.\n *\n * ### Differences with BehaviorSubject\n *\n * `BehaviorSubject` is similar to `new ReplaySubject(1)`, with a couple of exceptions:\n *\n * 1. `BehaviorSubject` comes \"primed\" with a single value upon construction.\n * 2. `ReplaySubject` will replay values, even after observing an error, where `BehaviorSubject` will not.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n * @see {@link shareReplay}\n */\nexport class ReplaySubject<T> extends Subject<T> {\n  private _buffer: (T | number)[] = [];\n  private _infiniteTimeWindow = true;\n\n  /**\n   * @param bufferSize The size of the buffer to replay on subscription\n   * @param windowTime The amount of time the buffered items will stay buffered\n   * @param timestampProvider An object with a `now()` method that provides the current timestamp. This is used to\n   * calculate the amount of time something has been buffered.\n   */\n  constructor(\n    private _bufferSize = Infinity,\n    private _windowTime = Infinity,\n    private _timestampProvider: TimestampProvider = dateTimestampProvider\n  ) {\n    super();\n    this._infiniteTimeWindow = _windowTime === Infinity;\n    this._bufferSize = Math.max(1, _bufferSize);\n    this._windowTime = Math.max(1, _windowTime);\n  }\n\n  next(value: T): void {\n    const { isStopped, _buffer, _infiniteTimeWindow, _timestampProvider, _windowTime } = this;\n    if (!isStopped) {\n      _buffer.push(value);\n      !_infiniteTimeWindow && _buffer.push(_timestampProvider.now() + _windowTime);\n    }\n    this._trimBuffer();\n    super.next(value);\n  }\n\n  /** @internal */\n  protected _subscribe(subscriber: Subscriber<T>): Subscription {\n    this._throwIfClosed();\n    this._trimBuffer();\n\n    const subscription = this._innerSubscribe(subscriber);\n\n    const { _infiniteTimeWindow, _buffer } = this;\n    // We use a copy here, so reentrant code does not mutate our array while we're\n    // emitting it to a new subscriber.\n    const copy = _buffer.slice();\n    for (let i = 0; i < copy.length && !subscriber.closed; i += _infiniteTimeWindow ? 1 : 2) {\n      subscriber.next(copy[i] as T);\n    }\n\n    this._checkFinalizedStatuses(subscriber);\n\n    return subscription;\n  }\n\n  private _trimBuffer() {\n    const { _bufferSize, _timestampProvider, _buffer, _infiniteTimeWindow } = this;\n    // If we don't have an infinite buffer size, and we're over the length,\n    // use splice to truncate the old buffer values off. Note that we have to\n    // double the size for instances where we're not using an infinite time window\n    // because we're storing the values and the timestamps in the same array.\n    const adjustedBufferSize = (_infiniteTimeWindow ? 1 : 2) * _bufferSize;\n    _bufferSize < Infinity && adjustedBufferSize < _buffer.length && _buffer.splice(0, _buffer.length - adjustedBufferSize);\n\n    // Now, if we're not in an infinite time window, remove all values where the time is\n    // older than what is allowed.\n    if (!_infiniteTimeWindow) {\n      const now = _timestampProvider.now();\n      let last = 0;\n      // Search the array for the first timestamp that isn't expired and\n      // truncate the buffer up to that point.\n      for (let i = 1; i < _buffer.length && (_buffer[i] as number) <= now; i += 2) {\n        last = i;\n      }\n      last && _buffer.splice(0, last + 1);\n    }\n  }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Subscription } from '../Subscription';\nimport { SchedulerAction } from '../types';\n\n/**\n * A unit of work to be executed in a `scheduler`. An action is typically\n * created from within a {@link SchedulerLike} and an RxJS user does not need to concern\n * themselves about creating and manipulating an Action.\n *\n * ```ts\n * class Action<T> extends Subscription {\n *   new (scheduler: Scheduler, work: (state?: T) => void);\n *   schedule(state?: T, delay: number = 0): Subscription;\n * }\n * ```\n *\n * @class Action<T>\n */\nexport class Action<T> extends Subscription {\n  constructor(scheduler: Scheduler, work: (this: SchedulerAction<T>, state?: T) => void) {\n    super();\n  }\n  /**\n   * Schedules this action on its parent {@link SchedulerLike} for execution. May be passed\n   * some context object, `state`. May happen at some point in the future,\n   * according to the `delay` parameter, if specified.\n   * @param {T} [state] Some contextual data that the `work` function uses when\n   * called by the Scheduler.\n   * @param {number} [delay] Time to wait before executing the work, where the\n   * time unit is implicit and defined by the Scheduler.\n   * @return {void}\n   */\n  public schedule(state?: T, delay: number = 0): Subscription {\n    return this;\n  }\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetIntervalFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearIntervalFunction = (handle: TimerHandle) => void;\n\ninterface IntervalProvider {\n  setInterval: SetIntervalFunction;\n  clearInterval: ClearIntervalFunction;\n  delegate:\n    | {\n        setInterval: SetIntervalFunction;\n        clearInterval: ClearIntervalFunction;\n      }\n    | undefined;\n}\n\nexport const intervalProvider: IntervalProvider = {\n  // When accessing the delegate, use the variable rather than `this` so that\n  // the functions can be called without being bound to the provider.\n  setInterval(handler: () => void, timeout?: number, ...args) {\n    const { delegate } = intervalProvider;\n    if (delegate?.setInterval) {\n      return delegate.setInterval(handler, timeout, ...args);\n    }\n    return setInterval(handler, timeout, ...args);\n  },\n  clearInterval(handle) {\n    const { delegate } = intervalProvider;\n    return (delegate?.clearInterval || clearInterval)(handle as any);\n  },\n  delegate: undefined,\n};\n", "import { Action } from './Action';\nimport { SchedulerAction } from '../types';\nimport { Subscription } from '../Subscription';\nimport { AsyncScheduler } from './AsyncScheduler';\nimport { intervalProvider } from './intervalProvider';\nimport { arrRemove } from '../util/arrRemove';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncAction<T> extends Action<T> {\n  public id: TimerHandle | undefined;\n  public state?: T;\n  // @ts-ignore: Property has no initializer and is not definitely assigned\n  public delay: number;\n  protected pending: boolean = false;\n\n  constructor(protected scheduler: AsyncScheduler, protected work: (this: SchedulerAction<T>, state?: T) => void) {\n    super(scheduler, work);\n  }\n\n  public schedule(state?: T, delay: number = 0): Subscription {\n    if (this.closed) {\n      return this;\n    }\n\n    // Always replace the current state with the new state.\n    this.state = state;\n\n    const id = this.id;\n    const scheduler = this.scheduler;\n\n    //\n    // Important implementation note:\n    //\n    // Actions only execute once by default, unless rescheduled from within the\n    // scheduled callback. This allows us to implement single and repeat\n    // actions via the same code path, without adding API surface area, as well\n    // as mimic traditional recursion but across asynchronous boundaries.\n    //\n    // However, JS runtimes and timers distinguish between intervals achieved by\n    // serial `setTimeout` calls vs. a single `setInterval` call. An interval of\n    // serial `setTimeout` calls can be individually delayed, which delays\n    // scheduling the next `setTimeout`, and so on. `setInterval` attempts to\n    // guarantee the interval callback will be invoked more precisely to the\n    // interval period, regardless of load.\n    //\n    // Therefore, we use `setInterval` to schedule single and repeat actions.\n    // If the action reschedules itself with the same delay, the interval is not\n    // canceled. If the action doesn't reschedule, or reschedules with a\n    // different delay, the interval will be canceled after scheduled callback\n    // execution.\n    //\n    if (id != null) {\n      this.id = this.recycleAsyncId(scheduler, id, delay);\n    }\n\n    // Set the pending flag indicating that this action has been scheduled, or\n    // has recursively rescheduled itself.\n    this.pending = true;\n\n    this.delay = delay;\n    // If this action has already an async Id, don't request a new one.\n    this.id = this.id ?? this.requestAsyncId(scheduler, this.id, delay);\n\n    return this;\n  }\n\n  protected requestAsyncId(scheduler: AsyncScheduler, _id?: TimerHandle, delay: number = 0): TimerHandle {\n    return intervalProvider.setInterval(scheduler.flush.bind(scheduler, this), delay);\n  }\n\n  protected recycleAsyncId(_scheduler: AsyncScheduler, id?: TimerHandle, delay: number | null = 0): TimerHandle | undefined {\n    // If this action is rescheduled with the same delay time, don't clear the interval id.\n    if (delay != null && this.delay === delay && this.pending === false) {\n      return id;\n    }\n    // Otherwise, if the action's delay time is different from the current delay,\n    // or the action has been rescheduled before it's executed, clear the interval id\n    if (id != null) {\n      intervalProvider.clearInterval(id);\n    }\n\n    return undefined;\n  }\n\n  /**\n   * Immediately executes this action and the `work` it contains.\n   * @return {any}\n   */\n  public execute(state: T, delay: number): any {\n    if (this.closed) {\n      return new Error('executing a cancelled action');\n    }\n\n    this.pending = false;\n    const error = this._execute(state, delay);\n    if (error) {\n      return error;\n    } else if (this.pending === false && this.id != null) {\n      // Dequeue if the action didn't reschedule itself. Don't call\n      // unsubscribe(), because the action could reschedule later.\n      // For example:\n      // ```\n      // scheduler.schedule(function doWork(counter) {\n      //   /* ... I'm a busy worker bee ... */\n      //   var originalAction = this;\n      //   /* wait 100ms before rescheduling the action */\n      //   setTimeout(function () {\n      //     originalAction.schedule(counter + 1);\n      //   }, 100);\n      // }, 1000);\n      // ```\n      this.id = this.recycleAsyncId(this.scheduler, this.id, null);\n    }\n  }\n\n  protected _execute(state: T, _delay: number): any {\n    let errored: boolean = false;\n    let errorValue: any;\n    try {\n      this.work(state);\n    } catch (e) {\n      errored = true;\n      // HACK: Since code elsewhere is relying on the \"truthiness\" of the\n      // return here, we can't have it return \"\" or 0 or false.\n      // TODO: Clean this up when we refactor schedulers mid-version-8 or so.\n      errorValue = e ? e : new Error('Scheduled action threw falsy error');\n    }\n    if (errored) {\n      this.unsubscribe();\n      return errorValue;\n    }\n  }\n\n  unsubscribe() {\n    if (!this.closed) {\n      const { id, scheduler } = this;\n      const { actions } = scheduler;\n\n      this.work = this.state = this.scheduler = null!;\n      this.pending = false;\n\n      arrRemove(actions, this);\n      if (id != null) {\n        this.id = this.recycleAsyncId(scheduler, id, null);\n      }\n\n      this.delay = null!;\n      super.unsubscribe();\n    }\n  }\n}\n", "import { Action } from './scheduler/Action';\nimport { Subscription } from './Subscription';\nimport { SchedulerLike, SchedulerAction } from './types';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * An execution context and a data structure to order tasks and schedule their\n * execution. Provides a notion of (potentially virtual) time, through the\n * `now()` getter method.\n *\n * Each unit of work in a Scheduler is called an `Action`.\n *\n * ```ts\n * class Scheduler {\n *   now(): number;\n *   schedule(work, delay?, state?): Subscription;\n * }\n * ```\n *\n * @class Scheduler\n * @deprecated Scheduler is an internal implementation detail of RxJS, and\n * should not be used directly. Rather, create your own class and implement\n * {@link SchedulerLike}. Will be made internal in v8.\n */\nexport class Scheduler implements SchedulerLike {\n  public static now: () => number = dateTimestampProvider.now;\n\n  constructor(private schedulerActionCtor: typeof Action, now: () => number = Scheduler.now) {\n    this.now = now;\n  }\n\n  /**\n   * A getter method that returns a number representing the current time\n   * (at the time this function was called) according to the scheduler's own\n   * internal clock.\n   * @return {number} A number that represents the current time. May or may not\n   * have a relation to wall-clock time. May or may not refer to a time unit\n   * (e.g. milliseconds).\n   */\n  public now: () => number;\n\n  /**\n   * Schedules a function, `work`, for execution. May happen at some point in\n   * the future, according to the `delay` parameter, if specified. May be passed\n   * some context object, `state`, which will be passed to the `work` function.\n   *\n   * The given arguments will be processed an stored as an Action object in a\n   * queue of actions.\n   *\n   * @param {function(state: ?T): ?Subscription} work A function representing a\n   * task, or some unit of work to be executed by the Scheduler.\n   * @param {number} [delay] Time to wait before executing the work, where the\n   * time unit is implicit and defined by the Scheduler itself.\n   * @param {T} [state] Some contextual data that the `work` function uses when\n   * called by the Scheduler.\n   * @return {Subscription} A subscription in order to be able to unsubscribe\n   * the scheduled work.\n   */\n  public schedule<T>(work: (this: SchedulerAction<T>, state?: T) => void, delay: number = 0, state?: T): Subscription {\n    return new this.schedulerActionCtor<T>(this, work).schedule(state, delay);\n  }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Action } from './Action';\nimport { AsyncAction } from './AsyncAction';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncScheduler extends Scheduler {\n  public actions: Array<AsyncAction<any>> = [];\n  /**\n   * A flag to indicate whether the Scheduler is currently executing a batch of\n   * queued actions.\n   * @type {boolean}\n   * @internal\n   */\n  public _active: boolean = false;\n  /**\n   * An internal ID used to track the latest asynchronous task such as those\n   * coming from `setTimeout`, `setInterval`, `requestAnimationFrame`, and\n   * others.\n   * @type {any}\n   * @internal\n   */\n  public _scheduled: TimerHandle | undefined;\n\n  constructor(SchedulerAction: typeof Action, now: () => number = Scheduler.now) {\n    super(SchedulerAction, now);\n  }\n\n  public flush(action: AsyncAction<any>): void {\n    const { actions } = this;\n\n    if (this._active) {\n      actions.push(action);\n      return;\n    }\n\n    let error: any;\n    this._active = true;\n\n    do {\n      if ((error = action.execute(action.state, action.delay))) {\n        break;\n      }\n    } while ((action = actions.shift()!)); // exhaust the scheduler queue\n\n    this._active = false;\n\n    if (error) {\n      while ((action = actions.shift()!)) {\n        action.unsubscribe();\n      }\n      throw error;\n    }\n  }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\n/**\n *\n * Async Scheduler\n *\n * <span class=\"informal\">Schedule task as if you used setTimeout(task, duration)</span>\n *\n * `async` scheduler schedules tasks asynchronously, by putting them on the JavaScript\n * event loop queue. It is best used to delay tasks in time or to schedule tasks repeating\n * in intervals.\n *\n * If you just want to \"defer\" task, that is to perform it right after currently\n * executing synchronous code ends (commonly achieved by `setTimeout(deferredTask, 0)`),\n * better choice will be the {@link asapScheduler} scheduler.\n *\n * ## Examples\n * Use async scheduler to delay task\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * const task = () => console.log('it works!');\n *\n * asyncScheduler.schedule(task, 2000);\n *\n * // After 2 seconds logs:\n * // \"it works!\"\n * ```\n *\n * Use async scheduler to repeat task in intervals\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * function task(state) {\n *   console.log(state);\n *   this.schedule(state + 1, 1000); // `this` references currently executing Action,\n *                                   // which we reschedule with new state and delay\n * }\n *\n * asyncScheduler.schedule(task, 3000, 0);\n *\n * // Logs:\n * // 0 after 3s\n * // 1 after 4s\n * // 2 after 5s\n * // 3 after 6s\n * ```\n */\n\nexport const asyncScheduler = new AsyncScheduler(AsyncAction);\n\n/**\n * @deprecated Renamed to {@link asyncScheduler}. Will be removed in v8.\n */\nexport const async = asyncScheduler;\n", "import { AsyncAction } from './AsyncAction';\nimport { Subscription } from '../Subscription';\nimport { QueueScheduler } from './QueueScheduler';\nimport { SchedulerAction } from '../types';\nimport { TimerHandle } from './timerHandle';\n\nexport class QueueAction<T> extends AsyncAction<T> {\n  constructor(protected scheduler: QueueScheduler, protected work: (this: SchedulerAction<T>, state?: T) => void) {\n    super(scheduler, work);\n  }\n\n  public schedule(state?: T, delay: number = 0): Subscription {\n    if (delay > 0) {\n      return super.schedule(state, delay);\n    }\n    this.delay = delay;\n    this.state = state;\n    this.scheduler.flush(this);\n    return this;\n  }\n\n  public execute(state: T, delay: number): any {\n    return delay > 0 || this.closed ? super.execute(state, delay) : this._execute(state, delay);\n  }\n\n  protected requestAsyncId(scheduler: QueueScheduler, id?: TimerHandle, delay: number = 0): TimerHandle {\n    // If delay exists and is greater than 0, or if the delay is null (the\n    // action wasn't rescheduled) but was originally scheduled as an async\n    // action, then recycle as an async action.\n\n    if ((delay != null && delay > 0) || (delay == null && this.delay > 0)) {\n      return super.requestAsyncId(scheduler, id, delay);\n    }\n\n    // Otherwise flush the scheduler starting with this action.\n    scheduler.flush(this);\n\n    // HACK: In the past, this was returning `void`. However, `void` isn't a valid\n    // `TimerHandle`, and generally the return value here isn't really used. So the\n    // compromise is to return `0` which is both \"falsy\" and a valid `TimerHandle`,\n    // as opposed to refactoring every other instanceo of `requestAsyncId`.\n    return 0;\n  }\n}\n", "import { AsyncScheduler } from './AsyncScheduler';\n\nexport class QueueScheduler extends AsyncScheduler {\n}\n", "import { QueueAction } from './QueueAction';\nimport { QueueScheduler } from './QueueScheduler';\n\n/**\n *\n * Queue Scheduler\n *\n * <span class=\"informal\">Put every next task on a queue, instead of executing it immediately</span>\n *\n * `queue` scheduler, when used with delay, behaves the same as {@link asyncScheduler} scheduler.\n *\n * When used without delay, it schedules given task synchronously - executes it right when\n * it is scheduled. However when called recursively, that is when inside the scheduled task,\n * another task is scheduled with queue scheduler, instead of executing immediately as well,\n * that task will be put on a queue and wait for current one to finish.\n *\n * This means that when you execute task with `queue` scheduler, you are sure it will end\n * before any other task scheduled with that scheduler will start.\n *\n * ## Examples\n * Schedule recursively first, then do something\n * ```ts\n * import { queueScheduler } from 'rxjs';\n *\n * queueScheduler.schedule(() => {\n *   queueScheduler.schedule(() => console.log('second')); // will not happen now, but will be put on a queue\n *\n *   console.log('first');\n * });\n *\n * // Logs:\n * // \"first\"\n * // \"second\"\n * ```\n *\n * Reschedule itself recursively\n * ```ts\n * import { queueScheduler } from 'rxjs';\n *\n * queueScheduler.schedule(function(state) {\n *   if (state !== 0) {\n *     console.log('before', state);\n *     this.schedule(state - 1); // `this` references currently executing Action,\n *                               // which we reschedule with new state\n *     console.log('after', state);\n *   }\n * }, 0, 3);\n *\n * // In scheduler that runs recursively, you would expect:\n * // \"before\", 3\n * // \"before\", 2\n * // \"before\", 1\n * // \"after\", 1\n * // \"after\", 2\n * // \"after\", 3\n *\n * // But with queue it logs:\n * // \"before\", 3\n * // \"after\", 3\n * // \"before\", 2\n * // \"after\", 2\n * // \"before\", 1\n * // \"after\", 1\n * ```\n */\n\nexport const queueScheduler = new QueueScheduler(QueueAction);\n\n/**\n * @deprecated Renamed to {@link queueScheduler}. Will be removed in v8.\n */\nexport const queue = queueScheduler;\n", "import { AsyncAction } from './AsyncAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\nimport { SchedulerAction } from '../types';\nimport { animationFrameProvider } from './animationFrameProvider';\nimport { TimerHandle } from './timerHandle';\n\nexport class AnimationFrameAction<T> extends AsyncAction<T> {\n  constructor(protected scheduler: AnimationFrameScheduler, protected work: (this: SchedulerAction<T>, state?: T) => void) {\n    super(scheduler, work);\n  }\n\n  protected requestAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle {\n    // If delay is greater than 0, request as an async action.\n    if (delay !== null && delay > 0) {\n      return super.requestAsyncId(scheduler, id, delay);\n    }\n    // Push the action to the end of the scheduler queue.\n    scheduler.actions.push(this);\n    // If an animation frame has already been requested, don't request another\n    // one. If an animation frame hasn't been requested yet, request one. Return\n    // the current animation frame request id.\n    return scheduler._scheduled || (scheduler._scheduled = animationFrameProvider.requestAnimationFrame(() => scheduler.flush(undefined)));\n  }\n\n  protected recycleAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle | undefined {\n    // If delay exists and is greater than 0, or if the delay is null (the\n    // action wasn't rescheduled) but was originally scheduled as an async\n    // action, then recycle as an async action.\n    if (delay != null ? delay > 0 : this.delay > 0) {\n      return super.recycleAsyncId(scheduler, id, delay);\n    }\n    // If the scheduler queue has no remaining actions with the same async id,\n    // cancel the requested animation frame and set the scheduled flag to\n    // undefined so the next AnimationFrameAction will request its own.\n    const { actions } = scheduler;\n    if (id != null && actions[actions.length - 1]?.id !== id) {\n      animationFrameProvider.cancelAnimationFrame(id as number);\n      scheduler._scheduled = undefined;\n    }\n    // Return undefined so the action knows to request a new async id if it's rescheduled.\n    return undefined;\n  }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\nexport class AnimationFrameScheduler extends AsyncScheduler {\n  public flush(action?: AsyncAction<any>): void {\n    this._active = true;\n    // The async id that effects a call to flush is stored in _scheduled.\n    // Before executing an action, it's necessary to check the action's async\n    // id to determine whether it's supposed to be executed in the current\n    // flush.\n    // Previous implementations of this method used a count to determine this,\n    // but that was unsound, as actions that are unsubscribed - i.e. cancelled -\n    // are removed from the actions array and that can shift actions that are\n    // scheduled to be executed in a subsequent flush into positions at which\n    // they are executed within the current flush.\n    const flushId = this._scheduled;\n    this._scheduled = undefined;\n\n    const { actions } = this;\n    let error: any;\n    action = action || actions.shift()!;\n\n    do {\n      if ((error = action.execute(action.state, action.delay))) {\n        break;\n      }\n    } while ((action = actions[0]) && action.id === flushId && actions.shift());\n\n    this._active = false;\n\n    if (error) {\n      while ((action = actions[0]) && action.id === flushId && actions.shift()) {\n        action.unsubscribe();\n      }\n      throw error;\n    }\n  }\n}\n", "import { AnimationFrameAction } from './AnimationFrameAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\n\n/**\n *\n * Animation Frame Scheduler\n *\n * <span class=\"informal\">Perform task when `window.requestAnimationFrame` would fire</span>\n *\n * When `animationFrame` scheduler is used with delay, it will fall back to {@link asyncScheduler} scheduler\n * behaviour.\n *\n * Without delay, `animationFrame` scheduler can be used to create smooth browser animations.\n * It makes sure scheduled task will happen just before next browser content repaint,\n * thus performing animations as efficiently as possible.\n *\n * ## Example\n * Schedule div height animation\n * ```ts\n * // html: <div style=\"background: #0ff;\"></div>\n * import { animationFrameScheduler } from 'rxjs';\n *\n * const div = document.querySelector('div');\n *\n * animationFrameScheduler.schedule(function(height) {\n *   div.style.height = height + \"px\";\n *\n *   this.schedule(height + 1);  // `this` references currently executing Action,\n *                               // which we reschedule with new state\n * }, 0, 0);\n *\n * // You will see a div element growing in height\n * ```\n */\n\nexport const animationFrameScheduler = new AnimationFrameScheduler(AnimationFrameAction);\n\n/**\n * @deprecated Renamed to {@link animationFrameScheduler}. Will be removed in v8.\n */\nexport const animationFrame = animationFrameScheduler;\n", "import { Observable } from '../Observable';\nimport { SchedulerLike } from '../types';\n\n/**\n * A simple Observable that emits no items to the Observer and immediately\n * emits a complete notification.\n *\n * <span class=\"informal\">Just emits 'complete', and nothing else.</span>\n *\n * ![](empty.png)\n *\n * A simple Observable that only emits the complete notification. It can be used\n * for composing with other Observables, such as in a {@link mergeMap}.\n *\n * ## Examples\n *\n * Log complete notification\n *\n * ```ts\n * import { EMPTY } from 'rxjs';\n *\n * EMPTY.subscribe({\n *   next: () => console.log('Next'),\n *   complete: () => console.log('Complete!')\n * });\n *\n * // Outputs\n * // Complete!\n * ```\n *\n * Emit the number 7, then complete\n *\n * ```ts\n * import { EMPTY, startWith } from 'rxjs';\n *\n * const result = EMPTY.pipe(startWith(7));\n * result.subscribe(x => console.log(x));\n *\n * // Outputs\n * // 7\n * ```\n *\n * Map and flatten only odd numbers to the sequence `'a'`, `'b'`, `'c'`\n *\n * ```ts\n * import { interval, mergeMap, of, EMPTY } from 'rxjs';\n *\n * const interval$ = interval(1000);\n * const result = interval$.pipe(\n *   mergeMap(x => x % 2 === 1 ? of('a', 'b', 'c') : EMPTY),\n * );\n * result.subscribe(x => console.log(x));\n *\n * // Results in the following to the console:\n * // x is equal to the count on the interval, e.g. (0, 1, 2, 3, ...)\n * // x will occur every 1000ms\n * // if x % 2 is equal to 1, print a, b, c (each on its own)\n * // if x % 2 is not equal to 1, nothing will be output\n * ```\n *\n * @see {@link Observable}\n * @see {@link NEVER}\n * @see {@link of}\n * @see {@link throwError}\n */\nexport const EMPTY = new Observable<never>((subscriber) => subscriber.complete());\n\n/**\n * @param scheduler A {@link SchedulerLike} to use for scheduling\n * the emission of the complete notification.\n * @deprecated Replaced with the {@link EMPTY} constant or {@link scheduled} (e.g. `scheduled([], scheduler)`). Will be removed in v8.\n */\nexport function empty(scheduler?: SchedulerLike) {\n  return scheduler ? emptyScheduled(scheduler) : EMPTY;\n}\n\nfunction emptyScheduled(scheduler: SchedulerLike) {\n  return new Observable<never>((subscriber) => scheduler.schedule(() => subscriber.complete()));\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport function isScheduler(value: any): value is SchedulerLike {\n  return value && isFunction(value.schedule);\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\nimport { isScheduler } from './isScheduler';\n\nfunction last<T>(arr: T[]): T | undefined {\n  return arr[arr.length - 1];\n}\n\nexport function popResultSelector(args: any[]): ((...args: unknown[]) => unknown) | undefined {\n  return isFunction(last(args)) ? args.pop() : undefined;\n}\n\nexport function popScheduler(args: any[]): SchedulerLike | undefined {\n  return isScheduler(last(args)) ? args.pop() : undefined;\n}\n\nexport function popNumber(args: any[], defaultValue: number): number {\n  return typeof last(args) === 'number' ? args.pop()! : defaultValue;\n}\n", "export const isArrayLike = (<T>(x: any): x is ArrayLike<T> => x && typeof x.length === 'number' && typeof x !== 'function');", "import { isFunction } from \"./isFunction\";\n\n/**\n * Tests to see if the object is \"thennable\".\n * @param value the object to test\n */\nexport function isPromise(value: any): value is PromiseLike<any> {\n  return isFunction(value?.then);\n}\n", "import { InteropObservable } from '../types';\nimport { observable as Symbol_observable } from '../symbol/observable';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being Observable (but not necessary an Rx Observable) */\nexport function isInteropObservable(input: any): input is InteropObservable<any> {\n  return isFunction(input[Symbol_observable]);\n}\n", "import { isFunction } from './isFunction';\n\nexport function isAsyncIterable<T>(obj: any): obj is AsyncIterable<T> {\n  return Symbol.asyncIterator && isFunction(obj?.[Symbol.asyncIterator]);\n}\n", "/**\n * Creates the TypeError to throw if an invalid object is passed to `from` or `scheduled`.\n * @param input The object that was passed.\n */\nexport function createInvalidObservableTypeError(input: any) {\n  // TODO: We should create error codes that can be looked up, so this can be less verbose.\n  return new TypeError(\n    `You provided ${\n      input !== null && typeof input === 'object' ? 'an invalid object' : `'${input}'`\n    } where a stream was expected. You can provide an Observable, Promise, ReadableStream, Array, AsyncIterable, or Iterable.`\n  );\n}\n", "export function getSymbolIterator(): symbol {\n  if (typeof Symbol !== 'function' || !Symbol.iterator) {\n    return '@@iterator' as any;\n  }\n\n  return Symbol.iterator;\n}\n\nexport const iterator = getSymbolIterator();\n", "import { iterator as Symbol_iterator } from '../symbol/iterator';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being an Iterable */\nexport function isIterable(input: any): input is Iterable<any> {\n  return isFunction(input?.[Symbol_iterator]);\n}\n", "import { ReadableStreamLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport async function* readableStreamLikeToAsyncGenerator<T>(readableStream: ReadableStreamLike<T>): AsyncGenerator<T> {\n  const reader = readableStream.getReader();\n  try {\n    while (true) {\n      const { value, done } = await reader.read();\n      if (done) {\n        return;\n      }\n      yield value!;\n    }\n  } finally {\n    reader.releaseLock();\n  }\n}\n\nexport function isReadableStreamLike<T>(obj: any): obj is ReadableStreamLike<T> {\n  // We don't want to use instanceof checks because they would return\n  // false for instances from another Realm, like an <iframe>.\n  return isFunction(obj?.getReader);\n}\n", "import { isArrayLike } from '../util/isArrayLike';\nimport { isPromise } from '../util/isPromise';\nimport { Observable } from '../Observable';\nimport { ObservableInput, ObservedValueOf, ReadableStreamLike } from '../types';\nimport { isInteropObservable } from '../util/isInteropObservable';\nimport { isAsyncIterable } from '../util/isAsyncIterable';\nimport { createInvalidObservableTypeError } from '../util/throwUnobservableError';\nimport { isIterable } from '../util/isIterable';\nimport { isReadableStreamLike, readableStreamLikeToAsyncGenerator } from '../util/isReadableStreamLike';\nimport { Subscriber } from '../Subscriber';\nimport { isFunction } from '../util/isFunction';\nimport { reportUnhandledError } from '../util/reportUnhandledError';\nimport { observable as Symbol_observable } from '../symbol/observable';\n\nexport function innerFrom<O extends ObservableInput<any>>(input: O): Observable<ObservedValueOf<O>>;\nexport function innerFrom<T>(input: ObservableInput<T>): Observable<T> {\n  if (input instanceof Observable) {\n    return input;\n  }\n  if (input != null) {\n    if (isInteropObservable(input)) {\n      return fromInteropObservable(input);\n    }\n    if (isArrayLike(input)) {\n      return fromArrayLike(input);\n    }\n    if (isPromise(input)) {\n      return fromPromise(input);\n    }\n    if (isAsyncIterable(input)) {\n      return fromAsyncIterable(input);\n    }\n    if (isIterable(input)) {\n      return fromIterable(input);\n    }\n    if (isReadableStreamLike(input)) {\n      return fromReadableStreamLike(input);\n    }\n  }\n\n  throw createInvalidObservableTypeError(input);\n}\n\n/**\n * Creates an RxJS Observable from an object that implements `Symbol.observable`.\n * @param obj An object that properly implements `Symbol.observable`.\n */\nexport function fromInteropObservable<T>(obj: any) {\n  return new Observable((subscriber: Subscriber<T>) => {\n    const obs = obj[Symbol_observable]();\n    if (isFunction(obs.subscribe)) {\n      return obs.subscribe(subscriber);\n    }\n    // Should be caught by observable subscribe function error handling.\n    throw new TypeError('Provided object does not correctly implement Symbol.observable');\n  });\n}\n\n/**\n * Synchronously emits the values of an array like and completes.\n * This is exported because there are creation functions and operators that need to\n * make direct use of the same logic, and there's no reason to make them run through\n * `from` conditionals because we *know* they're dealing with an array.\n * @param array The array to emit values from\n */\nexport function fromArrayLike<T>(array: ArrayLike<T>) {\n  return new Observable((subscriber: Subscriber<T>) => {\n    // Loop over the array and emit each value. Note two things here:\n    // 1. We're making sure that the subscriber is not closed on each loop.\n    //    This is so we don't continue looping over a very large array after\n    //    something like a `take`, `takeWhile`, or other synchronous unsubscription\n    //    has already unsubscribed.\n    // 2. In this form, reentrant code can alter that array we're looping over.\n    //    This is a known issue, but considered an edge case. The alternative would\n    //    be to copy the array before executing the loop, but this has\n    //    performance implications.\n    for (let i = 0; i < array.length && !subscriber.closed; i++) {\n      subscriber.next(array[i]);\n    }\n    subscriber.complete();\n  });\n}\n\nexport function fromPromise<T>(promise: PromiseLike<T>) {\n  return new Observable((subscriber: Subscriber<T>) => {\n    promise\n      .then(\n        (value) => {\n          if (!subscriber.closed) {\n            subscriber.next(value);\n            subscriber.complete();\n          }\n        },\n        (err: any) => subscriber.error(err)\n      )\n      .then(null, reportUnhandledError);\n  });\n}\n\nexport function fromIterable<T>(iterable: Iterable<T>) {\n  return new Observable((subscriber: Subscriber<T>) => {\n    for (const value of iterable) {\n      subscriber.next(value);\n      if (subscriber.closed) {\n        return;\n      }\n    }\n    subscriber.complete();\n  });\n}\n\nexport function fromAsyncIterable<T>(asyncIterable: AsyncIterable<T>) {\n  return new Observable((subscriber: Subscriber<T>) => {\n    process(asyncIterable, subscriber).catch((err) => subscriber.error(err));\n  });\n}\n\nexport function fromReadableStreamLike<T>(readableStream: ReadableStreamLike<T>) {\n  return fromAsyncIterable(readableStreamLikeToAsyncGenerator(readableStream));\n}\n\nasync function process<T>(asyncIterable: AsyncIterable<T>, subscriber: Subscriber<T>) {\n  for await (const value of asyncIterable) {\n    subscriber.next(value);\n    // A side-effect may have closed our subscriber,\n    // check before the next iteration.\n    if (subscriber.closed) {\n      return;\n    }\n  }\n  subscriber.complete();\n}\n", "import { Subscription } from '../Subscription';\nimport { SchedulerAction, SchedulerLike } from '../types';\n\nexport function executeSchedule(\n  parentSubscription: Subscription,\n  scheduler: SchedulerLike,\n  work: () => void,\n  delay: number,\n  repeat: true\n): void;\nexport function executeSchedule(\n  parentSubscription: Subscription,\n  scheduler: SchedulerLike,\n  work: () => void,\n  delay?: number,\n  repeat?: false\n): Subscription;\n\nexport function executeSchedule(\n  parentSubscription: Subscription,\n  scheduler: SchedulerLike,\n  work: () => void,\n  delay = 0,\n  repeat = false\n): Subscription | void {\n  const scheduleSubscription = scheduler.schedule(function (this: SchedulerAction<any>) {\n    work();\n    if (repeat) {\n      parentSubscription.add(this.schedule(null, delay));\n    } else {\n      this.unsubscribe();\n    }\n  }, delay);\n\n  parentSubscription.add(scheduleSubscription);\n\n  if (!repeat) {\n    // Because user-land scheduler implementations are unlikely to properly reuse\n    // Actions for repeat scheduling, we can't trust that the returned subscription\n    // will control repeat subscription scenarios. So we're trying to avoid using them\n    // incorrectly within this library.\n    return scheduleSubscription;\n  }\n}\n", "/** @prettier */\nimport { MonoTypeOperatorFunction, SchedulerLike } from '../types';\nimport { executeSchedule } from '../util/executeSchedule';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * Re-emits all notifications from source Observable with specified scheduler.\n *\n * <span class=\"informal\">Ensure a specific scheduler is used, from outside of an Observable.</span>\n *\n * `observeOn` is an operator that accepts a scheduler as a first parameter, which will be used to reschedule\n * notifications emitted by the source Observable. It might be useful, if you do not have control over\n * internal scheduler of a given Observable, but want to control when its values are emitted nevertheless.\n *\n * Returned Observable emits the same notifications (nexted values, complete and error events) as the source Observable,\n * but rescheduled with provided scheduler. Note that this doesn't mean that source Observables internal\n * scheduler will be replaced in any way. Original scheduler still will be used, but when the source Observable emits\n * notification, it will be immediately scheduled again - this time with scheduler passed to `observeOn`.\n * An anti-pattern would be calling `observeOn` on Observable that emits lots of values synchronously, to split\n * that emissions into asynchronous chunks. For this to happen, scheduler would have to be passed into the source\n * Observable directly (usually into the operator that creates it). `observeOn` simply delays notifications a\n * little bit more, to ensure that they are emitted at expected moments.\n *\n * As a matter of fact, `observeOn` accepts second parameter, which specifies in milliseconds with what delay notifications\n * will be emitted. The main difference between {@link delay} operator and `observeOn` is that `observeOn`\n * will delay all notifications - including error notifications - while `delay` will pass through error\n * from source Observable immediately when it is emitted. In general it is highly recommended to use `delay` operator\n * for any kind of delaying of values in the stream, while using `observeOn` to specify which scheduler should be used\n * for notification emissions in general.\n *\n * ## Example\n *\n * Ensure values in subscribe are called just before browser repaint\n *\n * ```ts\n * import { interval, observeOn, animationFrameScheduler } from 'rxjs';\n *\n * const someDiv = document.createElement('div');\n * someDiv.style.cssText = 'width: 200px;background: #09c';\n * document.body.appendChild(someDiv);\n * const intervals = interval(10);      // Intervals are scheduled\n *                                      // with async scheduler by default...\n * intervals.pipe(\n *   observeOn(animationFrameScheduler) // ...but we will observe on animationFrame\n * )                                    // scheduler to ensure smooth animation.\n * .subscribe(val => {\n *   someDiv.style.height = val + 'px';\n * });\n * ```\n *\n * @see {@link delay}\n *\n * @param scheduler Scheduler that will be used to reschedule notifications from source Observable.\n * @param delay Number of milliseconds that states with what delay every notification should be rescheduled.\n * @return A function that returns an Observable that emits the same\n * notifications as the source Observable, but with provided scheduler.\n */\nexport function observeOn<T>(scheduler: SchedulerLike, delay = 0): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => executeSchedule(subscriber, scheduler, () => subscriber.next(value), delay),\n        () => executeSchedule(subscriber, scheduler, () => subscriber.complete(), delay),\n        (err) => executeSchedule(subscriber, scheduler, () => subscriber.error(err), delay)\n      )\n    );\n  });\n}\n", "import { MonoTypeOperatorFunction, SchedulerLike } from '../types';\nimport { operate } from '../util/lift';\n\n/**\n * Asynchronously subscribes Observers to this Observable on the specified {@link SchedulerLike}.\n *\n * With `subscribeOn` you can decide what type of scheduler a specific Observable will be using when it is subscribed to.\n *\n * Schedulers control the speed and order of emissions to observers from an Observable stream.\n *\n * ![](subscribeOn.png)\n *\n * ## Example\n *\n * Given the following code:\n *\n * ```ts\n * import { of, merge } from 'rxjs';\n *\n * const a = of(1, 2, 3);\n * const b = of(4, 5, 6);\n *\n * merge(a, b).subscribe(console.log);\n *\n * // Outputs\n * // 1\n * // 2\n * // 3\n * // 4\n * // 5\n * // 6\n * ```\n *\n * Both Observable `a` and `b` will emit their values directly and synchronously once they are subscribed to.\n *\n * If we instead use the `subscribeOn` operator declaring that we want to use the {@link asyncScheduler} for values emitted by Observable `a`:\n *\n * ```ts\n * import { of, subscribeOn, asyncScheduler, merge } from 'rxjs';\n *\n * const a = of(1, 2, 3).pipe(subscribeOn(asyncScheduler));\n * const b = of(4, 5, 6);\n *\n * merge(a, b).subscribe(console.log);\n *\n * // Outputs\n * // 4\n * // 5\n * // 6\n * // 1\n * // 2\n * // 3\n * ```\n *\n * The reason for this is that Observable `b` emits its values directly and synchronously like before\n * but the emissions from `a` are scheduled on the event loop because we are now using the {@link asyncScheduler} for that specific Observable.\n *\n * @param scheduler The {@link SchedulerLike} to perform subscription actions on.\n * @param delay A delay to pass to the scheduler to delay subscriptions\n * @return A function that returns an Observable modified so that its\n * subscriptions happen on the specified {@link SchedulerLike}.\n */\nexport function subscribeOn<T>(scheduler: SchedulerLike, delay: number = 0): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    subscriber.add(scheduler.schedule(() => source.subscribe(subscriber), delay));\n  });\n}\n", "import { innerFrom } from '../observable/innerFrom';\nimport { observeOn } from '../operators/observeOn';\nimport { subscribeOn } from '../operators/subscribeOn';\nimport { InteropObservable, SchedulerLike } from '../types';\n\nexport function scheduleObservable<T>(input: InteropObservable<T>, scheduler: SchedulerLike) {\n  return innerFrom(input).pipe(subscribeOn(scheduler), observeOn(scheduler));\n}\n", "import { innerFrom } from '../observable/innerFrom';\nimport { observeOn } from '../operators/observeOn';\nimport { subscribeOn } from '../operators/subscribeOn';\nimport { SchedulerLike } from '../types';\n\nexport function schedulePromise<T>(input: PromiseLike<T>, scheduler: SchedulerLike) {\n  return innerFrom(input).pipe(subscribeOn(scheduler), observeOn(scheduler));\n}\n", "import { Observable } from '../Observable';\nimport { SchedulerLike } from '../types';\n\nexport function scheduleArray<T>(input: ArrayLike<T>, scheduler: SchedulerLike) {\n  return new Observable<T>((subscriber) => {\n    // The current array index.\n    let i = 0;\n    // Start iterating over the array like on a schedule.\n    return scheduler.schedule(function () {\n      if (i === input.length) {\n        // If we have hit the end of the array like in the\n        // previous job, we can complete.\n        subscriber.complete();\n      } else {\n        // Otherwise let's next the value at the current index,\n        // then increment our index.\n        subscriber.next(input[i++]);\n        // If the last emission didn't cause us to close the subscriber\n        // (via take or some side effect), reschedule the job and we'll\n        // make another pass.\n        if (!subscriber.closed) {\n          this.schedule();\n        }\n      }\n    });\n  });\n}\n", "import { Observable } from '../Observable';\nimport { SchedulerLike } from '../types';\nimport { iterator as Symbol_iterator } from '../symbol/iterator';\nimport { isFunction } from '../util/isFunction';\nimport { executeSchedule } from '../util/executeSchedule';\n\n/**\n * Used in {@link scheduled} to create an observable from an Iterable.\n * @param input The iterable to create an observable from\n * @param scheduler The scheduler to use\n */\nexport function scheduleIterable<T>(input: Iterable<T>, scheduler: SchedulerLike) {\n  return new Observable<T>((subscriber) => {\n    let iterator: Iterator<T, T>;\n\n    // Schedule the initial creation of the iterator from\n    // the iterable. This is so the code in the iterable is\n    // not called until the scheduled job fires.\n    executeSchedule(subscriber, scheduler, () => {\n      // Create the iterator.\n      iterator = (input as any)[Symbol_iterator]();\n\n      executeSchedule(\n        subscriber,\n        scheduler,\n        () => {\n          let value: T;\n          let done: boolean | undefined;\n          try {\n            // Pull the value out of the iterator\n            ({ value, done } = iterator.next());\n          } catch (err) {\n            // We got an error while pulling from the iterator\n            subscriber.error(err);\n            return;\n          }\n\n          if (done) {\n            // If it is \"done\" we just complete. This mimics the\n            // behavior of JavaScript's `for..of` consumption of\n            // iterables, which will not emit the value from an iterator\n            // result of `{ done: true: value: 'here' }`.\n            subscriber.complete();\n          } else {\n            // The iterable is not done, emit the value.\n            subscriber.next(value);\n          }\n        },\n        0,\n        true\n      );\n    });\n\n    // During finalization, if we see this iterator has a `return` method,\n    // then we know it is a Generator, and not just an Iterator. So we call\n    // the `return()` function. This will ensure that any `finally { }` blocks\n    // inside of the generator we can hit will be hit properly.\n    return () => isFunction(iterator?.return) && iterator.return();\n  });\n}\n", "import { SchedulerLike } from '../types';\nimport { Observable } from '../Observable';\nimport { executeSchedule } from '../util/executeSchedule';\n\nexport function scheduleAsyncIterable<T>(input: AsyncIterable<T>, scheduler: SchedulerLike) {\n  if (!input) {\n    throw new Error('Iterable cannot be null');\n  }\n  return new Observable<T>((subscriber) => {\n    executeSchedule(subscriber, scheduler, () => {\n      const iterator = input[Symbol.asyncIterator]();\n      executeSchedule(\n        subscriber,\n        scheduler,\n        () => {\n          iterator.next().then((result) => {\n            if (result.done) {\n              // This will remove the subscriptions from\n              // the parent subscription.\n              subscriber.complete();\n            } else {\n              subscriber.next(result.value);\n            }\n          });\n        },\n        0,\n        true\n      );\n    });\n  });\n}\n", "import { SchedulerLike, ReadableStreamLike } from '../types';\nimport { Observable } from '../Observable';\nimport { scheduleAsyncIterable } from './scheduleAsyncIterable';\nimport { readableStreamLikeToAsyncGenerator } from '../util/isReadableStreamLike';\n\nexport function scheduleReadableStreamLike<T>(input: ReadableStreamLike<T>, scheduler: SchedulerLike): Observable<T> {\n  return scheduleAsyncIterable(readableStreamLikeToAsyncGenerator(input), scheduler);\n}\n", "import { scheduleObservable } from './scheduleObservable';\nimport { schedulePromise } from './schedulePromise';\nimport { scheduleArray } from './scheduleArray';\nimport { scheduleIterable } from './scheduleIterable';\nimport { scheduleAsyncIterable } from './scheduleAsyncIterable';\nimport { isInteropObservable } from '../util/isInteropObservable';\nimport { isPromise } from '../util/isPromise';\nimport { isArrayLike } from '../util/isArrayLike';\nimport { isIterable } from '../util/isIterable';\nimport { ObservableInput, SchedulerLike } from '../types';\nimport { Observable } from '../Observable';\nimport { isAsyncIterable } from '../util/isAsyncIterable';\nimport { createInvalidObservableTypeError } from '../util/throwUnobservableError';\nimport { isReadableStreamLike } from '../util/isReadableStreamLike';\nimport { scheduleReadableStreamLike } from './scheduleReadableStreamLike';\n\n/**\n * Converts from a common {@link ObservableInput} type to an observable where subscription and emissions\n * are scheduled on the provided scheduler.\n *\n * @see {@link from}\n * @see {@link of}\n *\n * @param input The observable, array, promise, iterable, etc you would like to schedule\n * @param scheduler The scheduler to use to schedule the subscription and emissions from\n * the returned observable.\n */\nexport function scheduled<T>(input: ObservableInput<T>, scheduler: SchedulerLike): Observable<T> {\n  if (input != null) {\n    if (isInteropObservable(input)) {\n      return scheduleObservable(input, scheduler);\n    }\n    if (isArrayLike(input)) {\n      return scheduleArray(input, scheduler);\n    }\n    if (isPromise(input)) {\n      return schedulePromise(input, scheduler);\n    }\n    if (isAsyncIterable(input)) {\n      return scheduleAsyncIterable(input, scheduler);\n    }\n    if (isIterable(input)) {\n      return scheduleIterable(input, scheduler);\n    }\n    if (isReadableStreamLike(input)) {\n      return scheduleReadableStreamLike(input, scheduler);\n    }\n  }\n  throw createInvalidObservableTypeError(input);\n}\n", "import { Observable } from '../Observable';\nimport { ObservableInput, SchedulerLike, ObservedValueOf } from '../types';\nimport { scheduled } from '../scheduled/scheduled';\nimport { innerFrom } from './innerFrom';\n\nexport function from<O extends ObservableInput<any>>(input: O): Observable<ObservedValueOf<O>>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function from<O extends ObservableInput<any>>(input: O, scheduler: SchedulerLike | undefined): Observable<ObservedValueOf<O>>;\n\n/**\n * Creates an Observable from an Array, an array-like object, a Promise, an iterable object, or an Observable-like object.\n *\n * <span class=\"informal\">Converts almost anything to an Observable.</span>\n *\n * ![](from.png)\n *\n * `from` converts various other objects and data types into Observables. It also converts a Promise, an array-like, or an\n * <a href=\"https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Iteration_protocols#iterable\" target=\"_blank\">iterable</a>\n * object into an Observable that emits the items in that promise, array, or iterable. A String, in this context, is treated\n * as an array of characters. Observable-like objects (contains a function named with the ES2015 Symbol for Observable) can also be\n * converted through this operator.\n *\n * ## Examples\n *\n * Converts an array to an Observable\n *\n * ```ts\n * import { from } from 'rxjs';\n *\n * const array = [10, 20, 30];\n * const result = from(array);\n *\n * result.subscribe(x => console.log(x));\n *\n * // Logs:\n * // 10\n * // 20\n * // 30\n * ```\n *\n * Convert an infinite iterable (from a generator) to an Observable\n *\n * ```ts\n * import { from, take } from 'rxjs';\n *\n * function* generateDoubles(seed) {\n *    let i = seed;\n *    while (true) {\n *      yield i;\n *      i = 2 * i; // double it\n *    }\n * }\n *\n * const iterator = generateDoubles(3);\n * const result = from(iterator).pipe(take(10));\n *\n * result.subscribe(x => console.log(x));\n *\n * // Logs:\n * // 3\n * // 6\n * // 12\n * // 24\n * // 48\n * // 96\n * // 192\n * // 384\n * // 768\n * // 1536\n * ```\n *\n * With `asyncScheduler`\n *\n * ```ts\n * import { from, asyncScheduler } from 'rxjs';\n *\n * console.log('start');\n *\n * const array = [10, 20, 30];\n * const result = from(array, asyncScheduler);\n *\n * result.subscribe(x => console.log(x));\n *\n * console.log('end');\n *\n * // Logs:\n * // 'start'\n * // 'end'\n * // 10\n * // 20\n * // 30\n * ```\n *\n * @see {@link fromEvent}\n * @see {@link fromEventPattern}\n *\n * @param {ObservableInput<T>} A subscription object, a Promise, an Observable-like,\n * an Array, an iterable, or an array-like object to be converted.\n * @param {SchedulerLike} An optional {@link SchedulerLike} on which to schedule the emission of values.\n * @return {Observable<T>}\n */\nexport function from<T>(input: ObservableInput<T>, scheduler?: SchedulerLike): Observable<T> {\n  return scheduler ? scheduled(input, scheduler) : innerFrom(input);\n}\n", "import { SchedulerLike, ValueFromArray } from '../types';\nimport { Observable } from '../Observable';\nimport { popScheduler } from '../util/args';\nimport { from } from './from';\n\n// Devs are more likely to pass null or undefined than they are a scheduler\n// without accompanying values. To make things easier for (naughty) devs who\n// use the `strictNullChecks: false` TypeScript compiler option, these\n// overloads with explicit null and undefined values are included.\n\nexport function of(value: null): Observable<null>;\nexport function of(value: undefined): Observable<undefined>;\n\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function of(scheduler: SchedulerLike): Observable<never>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function of<A extends readonly unknown[]>(...valuesAndScheduler: [...A, SchedulerLike]): Observable<ValueFromArray<A>>;\n\nexport function of(): Observable<never>;\n/** @deprecated Do not specify explicit type parameters. Signatures with type parameters that cannot be inferred will be removed in v8. */\nexport function of<T>(): Observable<T>;\nexport function of<T>(value: T): Observable<T>;\nexport function of<A extends readonly unknown[]>(...values: A): Observable<ValueFromArray<A>>;\n\n/**\n * Converts the arguments to an observable sequence.\n *\n * <span class=\"informal\">Each argument becomes a `next` notification.</span>\n *\n * ![](of.png)\n *\n * Unlike {@link from}, it does not do any flattening and emits each argument in whole\n * as a separate `next` notification.\n *\n * ## Examples\n *\n * Emit the values `10, 20, 30`\n *\n * ```ts\n * import { of } from 'rxjs';\n *\n * of(10, 20, 30)\n *   .subscribe({\n *     next: value => console.log('next:', value),\n *     error: err => console.log('error:', err),\n *     complete: () => console.log('the end'),\n *   });\n *\n * // Outputs\n * // next: 10\n * // next: 20\n * // next: 30\n * // the end\n * ```\n *\n * Emit the array `[1, 2, 3]`\n *\n * ```ts\n * import { of } from 'rxjs';\n *\n * of([1, 2, 3])\n *   .subscribe({\n *     next: value => console.log('next:', value),\n *     error: err => console.log('error:', err),\n *     complete: () => console.log('the end'),\n *   });\n *\n * // Outputs\n * // next: [1, 2, 3]\n * // the end\n * ```\n *\n * @see {@link from}\n * @see {@link range}\n *\n * @param {...T} values A comma separated list of arguments you want to be emitted\n * @return {Observable} An Observable that emits the arguments\n * described above and then completes.\n */\nexport function of<T>(...args: Array<T | SchedulerLike>): Observable<T> {\n  const scheduler = popScheduler(args);\n  return from(args as T[], scheduler);\n}\n", "import { Observable } from '../Observable';\nimport { Subscriber } from '../Subscriber';\nimport { SchedulerLike } from '../types';\nimport { isFunction } from '../util/isFunction';\n\n/**\n * Creates an observable that will create an error instance and push it to the consumer as an error\n * immediately upon subscription.\n *\n * <span class=\"informal\">Just errors and does nothing else</span>\n *\n * ![](throw.png)\n *\n * This creation function is useful for creating an observable that will create an error and error every\n * time it is subscribed to. Generally, inside of most operators when you might want to return an errored\n * observable, this is unnecessary. In most cases, such as in the inner return of {@link concatMap},\n * {@link mergeMap}, {@link defer}, and many others, you can simply throw the error, and RxJS will pick\n * that up and notify the consumer of the error.\n *\n * ## Example\n *\n * Create a simple observable that will create a new error with a timestamp and log it\n * and the message every time you subscribe to it\n *\n * ```ts\n * import { throwError } from 'rxjs';\n *\n * let errorCount = 0;\n *\n * const errorWithTimestamp$ = throwError(() => {\n *   const error: any = new Error(`This is error number ${ ++errorCount }`);\n *   error.timestamp = Date.now();\n *   return error;\n * });\n *\n * errorWithTimestamp$.subscribe({\n *   error: err => console.log(err.timestamp, err.message)\n * });\n *\n * errorWithTimestamp$.subscribe({\n *   error: err => console.log(err.timestamp, err.message)\n * });\n *\n * // Logs the timestamp and a new error message for each subscription\n * ```\n *\n * ### Unnecessary usage\n *\n * Using `throwError` inside of an operator or creation function\n * with a callback, is usually not necessary\n *\n * ```ts\n * import { of, concatMap, timer, throwError } from 'rxjs';\n *\n * const delays$ = of(1000, 2000, Infinity, 3000);\n *\n * delays$.pipe(\n *   concatMap(ms => {\n *     if (ms < 10000) {\n *       return timer(ms);\n *     } else {\n *       // This is probably overkill.\n *       return throwError(() => new Error(`Invalid time ${ ms }`));\n *     }\n *   })\n * )\n * .subscribe({\n *   next: console.log,\n *   error: console.error\n * });\n * ```\n *\n * You can just throw the error instead\n *\n * ```ts\n * import { of, concatMap, timer } from 'rxjs';\n *\n * const delays$ = of(1000, 2000, Infinity, 3000);\n *\n * delays$.pipe(\n *   concatMap(ms => {\n *     if (ms < 10000) {\n *       return timer(ms);\n *     } else {\n *       // Cleaner and easier to read for most folks.\n *       throw new Error(`Invalid time ${ ms }`);\n *     }\n *   })\n * )\n * .subscribe({\n *   next: console.log,\n *   error: console.error\n * });\n * ```\n *\n * @param errorFactory A factory function that will create the error instance that is pushed.\n */\nexport function throwError(errorFactory: () => any): Observable<never>;\n\n/**\n * Returns an observable that will error with the specified error immediately upon subscription.\n *\n * @param error The error instance to emit\n * @deprecated Support for passing an error value will be removed in v8. Instead, pass a factory function to `throwError(() => new Error('test'))`. This is\n * because it will create the error at the moment it should be created and capture a more appropriate stack trace. If\n * for some reason you need to create the error ahead of time, you can still do that: `const err = new Error('test'); throwError(() => err);`.\n */\nexport function throwError(error: any): Observable<never>;\n\n/**\n * Notifies the consumer of an error using a given scheduler by scheduling it at delay `0` upon subscription.\n *\n * @param errorOrErrorFactory An error instance or error factory\n * @param scheduler A scheduler to use to schedule the error notification\n * @deprecated The `scheduler` parameter will be removed in v8.\n * Use `throwError` in combination with {@link observeOn}: `throwError(() => new Error('test')).pipe(observeOn(scheduler));`.\n * Details: https://rxjs.dev/deprecations/scheduler-argument\n */\nexport function throwError(errorOrErrorFactory: any, scheduler: SchedulerLike): Observable<never>;\n\nexport function throwError(errorOrErrorFactory: any, scheduler?: SchedulerLike): Observable<never> {\n  const errorFactory = isFunction(errorOrErrorFactory) ? errorOrErrorFactory : () => errorOrErrorFactory;\n  const init = (subscriber: Subscriber<never>) => subscriber.error(errorFactory());\n  return new Observable(scheduler ? (subscriber) => scheduler.schedule(init as any, 0, subscriber) : init);\n}\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface EmptyError extends Error {}\n\nexport interface EmptyErrorCtor {\n  /**\n   * @deprecated Internal implementation detail. Do not construct error instances.\n   * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n   */\n  new (): EmptyError;\n}\n\n/**\n * An error thrown when an Observable or a sequence was queried but has no\n * elements.\n *\n * @see {@link first}\n * @see {@link last}\n * @see {@link single}\n * @see {@link firstValueFrom}\n * @see {@link lastValueFrom}\n *\n * @class EmptyError\n */\nexport const EmptyError: EmptyErrorCtor = createErrorClass((_super) => function EmptyErrorImpl(this: any) {\n  _super(this);\n  this.name = 'EmptyError';\n  this.message = 'no elements in sequence';\n});\n", "/**\n * Checks to see if a value is not only a `Date` object,\n * but a *valid* `Date` object that can be converted to a\n * number. For example, `new Date('blah')` is indeed an\n * `instanceof Date`, however it cannot be converted to a\n * number.\n */\nexport function isValidDate(value: any): value is Date {\n  return value instanceof Date && !isNaN(value as any);\n}\n", "import { OperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\nexport function map<T, R>(project: (value: T, index: number) => R): OperatorFunction<T, R>;\n/** @deprecated Use a closure instead of a `thisArg`. Signatures accepting a `thisArg` will be removed in v8. */\nexport function map<T, R, A>(project: (this: A, value: T, index: number) => R, thisArg: A): OperatorFunction<T, R>;\n\n/**\n * Applies a given `project` function to each value emitted by the source\n * Observable, and emits the resulting values as an Observable.\n *\n * <span class=\"informal\">Like [Array.prototype.map()](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/map),\n * it passes each source value through a transformation function to get\n * corresponding output values.</span>\n *\n * ![](map.png)\n *\n * Similar to the well known `Array.prototype.map` function, this operator\n * applies a projection to each value and emits that projection in the output\n * Observable.\n *\n * ## Example\n *\n * Map every click to the `clientX` position of that click\n *\n * ```ts\n * import { fromEvent, map } from 'rxjs';\n *\n * const clicks = fromEvent<PointerEvent>(document, 'click');\n * const positions = clicks.pipe(map(ev => ev.clientX));\n *\n * positions.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link mapTo}\n * @see {@link pluck}\n *\n * @param {function(value: T, index: number): R} project The function to apply\n * to each `value` emitted by the source Observable. The `index` parameter is\n * the number `i` for the i-th emission that has happened since the\n * subscription, starting from the number `0`.\n * @param {any} [thisArg] An optional argument to define what `this` is in the\n * `project` function.\n * @return A function that returns an Observable that emits the values from the\n * source Observable transformed by the given `project` function.\n */\nexport function map<T, R>(project: (value: T, index: number) => R, thisArg?: any): OperatorFunction<T, R> {\n  return operate((source, subscriber) => {\n    // The index of the value from the source. Used with projection.\n    let index = 0;\n    // Subscribe to the source, all errors and completions are sent along\n    // to the consumer.\n    source.subscribe(\n      createOperatorSubscriber(subscriber, (value: T) => {\n        // Call the projection function with the appropriate this context,\n        // and send the resulting value to the consumer.\n        subscriber.next(project.call(thisArg, value, index++));\n      })\n    );\n  });\n}\n", "import { OperatorFunction } from \"../types\";\nimport { map } from \"../operators/map\";\n\nconst { isArray } = Array;\n\nfunction callOrApply<T, R>(fn: ((...values: T[]) => R), args: T|T[]): R {\n    return isArray(args) ? fn(...args) : fn(args);\n}\n\n/**\n * Used in several -- mostly deprecated -- situations where we need to \n * apply a list of arguments or a single argument to a result selector.\n */\nexport function mapOneOrManyArgs<T, R>(fn: ((...values: T[]) => R)): OperatorFunction<T|T[], R> {\n    return map(args => callOrApply(fn, args))\n}", "const { isArray } = Array;\nconst { getPrototypeOf, prototype: objectProto, keys: getKeys } = Object;\n\n/**\n * Used in functions where either a list of arguments, a single array of arguments, or a\n * dictionary of arguments can be returned. Returns an object with an `args` property with\n * the arguments in an array, if it is a dictionary, it will also return the `keys` in another\n * property.\n */\nexport function argsArgArrayOrObject<T, O extends Record<string, T>>(args: T[] | [O] | [T[]]): { args: T[]; keys: string[] | null } {\n  if (args.length === 1) {\n    const first = args[0];\n    if (isArray(first)) {\n      return { args: first, keys: null };\n    }\n    if (isPOJO(first)) {\n      const keys = getKeys(first);\n      return {\n        args: keys.map((key) => first[key]),\n        keys,\n      };\n    }\n  }\n\n  return { args: args as T[], keys: null };\n}\n\nfunction isPOJO(obj: any): obj is object {\n  return obj && typeof obj === 'object' && getPrototypeOf(obj) === objectProto;\n}\n", "export function createObject(keys: string[], values: any[]) {\n  return keys.reduce((result, key, i) => ((result[key] = values[i]), result), {} as any);\n}\n", "import { Observable } from '../Observable';\nimport { ObservableInput, SchedulerLike, ObservedValueOf, ObservableInputTuple } from '../types';\nimport { argsArgArrayOrObject } from '../util/argsArgArrayOrObject';\nimport { Subscriber } from '../Subscriber';\nimport { from } from './from';\nimport { identity } from '../util/identity';\nimport { Subscription } from '../Subscription';\nimport { mapOneOrManyArgs } from '../util/mapOneOrManyArgs';\nimport { popResultSelector, popScheduler } from '../util/args';\nimport { createObject } from '../util/createObject';\nimport { createOperatorSubscriber } from '../operators/OperatorSubscriber';\nimport { AnyCatcher } from '../AnyCatcher';\nimport { executeSchedule } from '../util/executeSchedule';\n\n// combineLatest(any)\n// We put this first because we need to catch cases where the user has supplied\n// _exactly `any`_ as the argument. Since `any` literally matches _anything_,\n// we don't want it to randomly hit one of the other type signatures below,\n// as we have no idea at build-time what type we should be returning when given an any.\n\n/**\n * You have passed `any` here, we can't figure out if it is\n * an array or an object, so you're getting `unknown`. Use better types.\n * @param arg Something typed as `any`\n */\nexport function combineLatest<T extends AnyCatcher>(arg: T): Observable<unknown>;\n\n// combineLatest([a, b, c])\nexport function combineLatest(sources: []): Observable<never>;\nexport function combineLatest<A extends readonly unknown[]>(sources: readonly [...ObservableInputTuple<A>]): Observable<A>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `combineLatestAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function combineLatest<A extends readonly unknown[], R>(\n  sources: readonly [...ObservableInputTuple<A>],\n  resultSelector: (...values: A) => R,\n  scheduler: SchedulerLike\n): Observable<R>;\nexport function combineLatest<A extends readonly unknown[], R>(\n  sources: readonly [...ObservableInputTuple<A>],\n  resultSelector: (...values: A) => R\n): Observable<R>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `combineLatestAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function combineLatest<A extends readonly unknown[]>(\n  sources: readonly [...ObservableInputTuple<A>],\n  scheduler: SchedulerLike\n): Observable<A>;\n\n// combineLatest(a, b, c)\n/** @deprecated Pass an array of sources instead. The rest-parameters signature will be removed in v8. Details: https://rxjs.dev/deprecations/array-argument */\nexport function combineLatest<A extends readonly unknown[]>(...sources: [...ObservableInputTuple<A>]): Observable<A>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `combineLatestAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function combineLatest<A extends readonly unknown[], R>(\n  ...sourcesAndResultSelectorAndScheduler: [...ObservableInputTuple<A>, (...values: A) => R, SchedulerLike]\n): Observable<R>;\n/** @deprecated Pass an array of sources instead. The rest-parameters signature will be removed in v8. Details: https://rxjs.dev/deprecations/array-argument */\nexport function combineLatest<A extends readonly unknown[], R>(\n  ...sourcesAndResultSelector: [...ObservableInputTuple<A>, (...values: A) => R]\n): Observable<R>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `combineLatestAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function combineLatest<A extends readonly unknown[]>(\n  ...sourcesAndScheduler: [...ObservableInputTuple<A>, SchedulerLike]\n): Observable<A>;\n\n// combineLatest({a, b, c})\nexport function combineLatest(sourcesObject: { [K in any]: never }): Observable<never>;\nexport function combineLatest<T extends Record<string, ObservableInput<any>>>(\n  sourcesObject: T\n): Observable<{ [K in keyof T]: ObservedValueOf<T[K]> }>;\n\n/**\n * Combines multiple Observables to create an Observable whose values are\n * calculated from the latest values of each of its input Observables.\n *\n * <span class=\"informal\">Whenever any input Observable emits a value, it\n * computes a formula using the latest values from all the inputs, then emits\n * the output of that formula.</span>\n *\n * ![](combineLatest.png)\n *\n * `combineLatest` combines the values from all the Observables passed in the\n * observables array. This is done by subscribing to each Observable in order and,\n * whenever any Observable emits, collecting an array of the most recent\n * values from each Observable. So if you pass `n` Observables to this operator,\n * the returned Observable will always emit an array of `n` values, in an order\n * corresponding to the order of the passed Observables (the value from the first Observable\n * will be at index 0 of the array and so on).\n *\n * Static version of `combineLatest` accepts an array of Observables. Note that an array of\n * Observables is a good choice, if you don't know beforehand how many Observables\n * you will combine. Passing an empty array will result in an Observable that\n * completes immediately.\n *\n * To ensure the output array always has the same length, `combineLatest` will\n * actually wait for all input Observables to emit at least once,\n * before it starts emitting results. This means if some Observable emits\n * values before other Observables started emitting, all these values but the last\n * will be lost. On the other hand, if some Observable does not emit a value but\n * completes, resulting Observable will complete at the same moment without\n * emitting anything, since it will now be impossible to include a value from the\n * completed Observable in the resulting array. Also, if some input Observable does\n * not emit any value and never completes, `combineLatest` will also never emit\n * and never complete, since, again, it will wait for all streams to emit some\n * value.\n *\n * If at least one Observable was passed to `combineLatest` and all passed Observables\n * emitted something, the resulting Observable will complete when all combined\n * streams complete. So even if some Observable completes, the result of\n * `combineLatest` will still emit values when other Observables do. In case\n * of a completed Observable, its value from now on will always be the last\n * emitted value. On the other hand, if any Observable errors, `combineLatest`\n * will error immediately as well, and all other Observables will be unsubscribed.\n *\n * ## Examples\n *\n * Combine two timer Observables\n *\n * ```ts\n * import { timer, combineLatest } from 'rxjs';\n *\n * const firstTimer = timer(0, 1000); // emit 0, 1, 2... after every second, starting from now\n * const secondTimer = timer(500, 1000); // emit 0, 1, 2... after every second, starting 0,5s from now\n * const combinedTimers = combineLatest([firstTimer, secondTimer]);\n * combinedTimers.subscribe(value => console.log(value));\n * // Logs\n * // [0, 0] after 0.5s\n * // [1, 0] after 1s\n * // [1, 1] after 1.5s\n * // [2, 1] after 2s\n * ```\n *\n * Combine a dictionary of Observables\n *\n * ```ts\n * import { of, delay, startWith, combineLatest } from 'rxjs';\n *\n * const observables = {\n *   a: of(1).pipe(delay(1000), startWith(0)),\n *   b: of(5).pipe(delay(5000), startWith(0)),\n *   c: of(10).pipe(delay(10000), startWith(0))\n * };\n * const combined = combineLatest(observables);\n * combined.subscribe(value => console.log(value));\n * // Logs\n * // { a: 0, b: 0, c: 0 } immediately\n * // { a: 1, b: 0, c: 0 } after 1s\n * // { a: 1, b: 5, c: 0 } after 5s\n * // { a: 1, b: 5, c: 10 } after 10s\n * ```\n *\n * Combine an array of Observables\n *\n * ```ts\n * import { of, delay, startWith, combineLatest } from 'rxjs';\n *\n * const observables = [1, 5, 10].map(\n *   n => of(n).pipe(\n *     delay(n * 1000), // emit 0 and then emit n after n seconds\n *     startWith(0)\n *   )\n * );\n * const combined = combineLatest(observables);\n * combined.subscribe(value => console.log(value));\n * // Logs\n * // [0, 0, 0] immediately\n * // [1, 0, 0] after 1s\n * // [1, 5, 0] after 5s\n * // [1, 5, 10] after 10s\n * ```\n *\n * Use map operator to dynamically calculate the Body-Mass Index\n *\n * ```ts\n * import { of, combineLatest, map } from 'rxjs';\n *\n * const weight = of(70, 72, 76, 79, 75);\n * const height = of(1.76, 1.77, 1.78);\n * const bmi = combineLatest([weight, height]).pipe(\n *   map(([w, h]) => w / (h * h)),\n * );\n * bmi.subscribe(x => console.log('BMI is ' + x));\n *\n * // With output to console:\n * // BMI is 24.212293388429753\n * // BMI is 23.93948099205209\n * // BMI is 23.671253629592222\n * ```\n *\n * @see {@link combineLatestAll}\n * @see {@link merge}\n * @see {@link withLatestFrom}\n *\n * @param {ObservableInput} [observables] An array of input Observables to combine with each other.\n * An array of Observables must be given as the first argument.\n * @param {function} [project] An optional function to project the values from\n * the combined latest values into a new value on the output Observable.\n * @param {SchedulerLike} [scheduler=null] The {@link SchedulerLike} to use for subscribing to\n * each input Observable.\n * @return {Observable} An Observable of projected values from the most recent\n * values from each input Observable, or an array of the most recent values from\n * each input Observable.\n */\nexport function combineLatest<O extends ObservableInput<any>, R>(...args: any[]): Observable<R> | Observable<ObservedValueOf<O>[]> {\n  const scheduler = popScheduler(args);\n  const resultSelector = popResultSelector(args);\n\n  const { args: observables, keys } = argsArgArrayOrObject(args);\n\n  if (observables.length === 0) {\n    // If no observables are passed, or someone has passed an empty array\n    // of observables, or even an empty object POJO, we need to just\n    // complete (EMPTY), but we have to honor the scheduler provided if any.\n    return from([], scheduler as any);\n  }\n\n  const result = new Observable<ObservedValueOf<O>[]>(\n    combineLatestInit(\n      observables as ObservableInput<ObservedValueOf<O>>[],\n      scheduler,\n      keys\n        ? // A handler for scrubbing the array of args into a dictionary.\n          (values) => createObject(keys, values)\n        : // A passthrough to just return the array\n          identity\n    )\n  );\n\n  return resultSelector ? (result.pipe(mapOneOrManyArgs(resultSelector)) as Observable<R>) : result;\n}\n\nexport function combineLatestInit(\n  observables: ObservableInput<any>[],\n  scheduler?: SchedulerLike,\n  valueTransform: (values: any[]) => any = identity\n) {\n  return (subscriber: Subscriber<any>) => {\n    // The outer subscription. We're capturing this in a function\n    // because we may have to schedule it.\n    maybeSchedule(\n      scheduler,\n      () => {\n        const { length } = observables;\n        // A store for the values each observable has emitted so far. We match observable to value on index.\n        const values = new Array(length);\n        // The number of currently active subscriptions, as they complete, we decrement this number to see if\n        // we are all done combining values, so we can complete the result.\n        let active = length;\n        // The number of inner sources that still haven't emitted the first value\n        // We need to track this because all sources need to emit one value in order\n        // to start emitting values.\n        let remainingFirstValues = length;\n        // The loop to kick off subscription. We're keying everything on index `i` to relate the observables passed\n        // in to the slot in the output array or the key in the array of keys in the output dictionary.\n        for (let i = 0; i < length; i++) {\n          maybeSchedule(\n            scheduler,\n            () => {\n              const source = from(observables[i], scheduler as any);\n              let hasFirstValue = false;\n              source.subscribe(\n                createOperatorSubscriber(\n                  subscriber,\n                  (value) => {\n                    // When we get a value, record it in our set of values.\n                    values[i] = value;\n                    if (!hasFirstValue) {\n                      // If this is our first value, record that.\n                      hasFirstValue = true;\n                      remainingFirstValues--;\n                    }\n                    if (!remainingFirstValues) {\n                      // We're not waiting for any more\n                      // first values, so we can emit!\n                      subscriber.next(valueTransform(values.slice()));\n                    }\n                  },\n                  () => {\n                    if (!--active) {\n                      // We only complete the result if we have no more active\n                      // inner observables.\n                      subscriber.complete();\n                    }\n                  }\n                )\n              );\n            },\n            subscriber\n          );\n        }\n      },\n      subscriber\n    );\n  };\n}\n\n/**\n * A small utility to handle the couple of locations where we want to schedule if a scheduler was provided,\n * but we don't if there was no scheduler.\n */\nfunction maybeSchedule(scheduler: SchedulerLike | undefined, execute: () => void, subscription: Subscription) {\n  if (scheduler) {\n    executeSchedule(subscription, scheduler, execute);\n  } else {\n    execute();\n  }\n}\n", "import { Observable } from '../Observable';\nimport { innerFrom } from '../observable/innerFrom';\nimport { Subscriber } from '../Subscriber';\nimport { ObservableInput, SchedulerLike } from '../types';\nimport { executeSchedule } from '../util/executeSchedule';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * A process embodying the general \"merge\" strategy. This is used in\n * `mergeMap` and `mergeScan` because the logic is otherwise nearly identical.\n * @param source The original source observable\n * @param subscriber The consumer subscriber\n * @param project The projection function to get our inner sources\n * @param concurrent The number of concurrent inner subscriptions\n * @param onBeforeNext Additional logic to apply before nexting to our consumer\n * @param expand If `true` this will perform an \"expand\" strategy, which differs only\n * in that it recurses, and the inner subscription must be schedule-able.\n * @param innerSubScheduler A scheduler to use to schedule inner subscriptions,\n * this is to support the expand strategy, mostly, and should be deprecated\n */\nexport function mergeInternals<T, R>(\n  source: Observable<T>,\n  subscriber: Subscriber<R>,\n  project: (value: T, index: number) => ObservableInput<R>,\n  concurrent: number,\n  onBeforeNext?: (innerValue: R) => void,\n  expand?: boolean,\n  innerSubScheduler?: SchedulerLike,\n  additionalFinalizer?: () => void\n) {\n  // Buffered values, in the event of going over our concurrency limit\n  const buffer: T[] = [];\n  // The number of active inner subscriptions.\n  let active = 0;\n  // An index to pass to our accumulator function\n  let index = 0;\n  // Whether or not the outer source has completed.\n  let isComplete = false;\n\n  /**\n   * Checks to see if we can complete our result or not.\n   */\n  const checkComplete = () => {\n    // If the outer has completed, and nothing is left in the buffer,\n    // and we don't have any active inner subscriptions, then we can\n    // Emit the state and complete.\n    if (isComplete && !buffer.length && !active) {\n      subscriber.complete();\n    }\n  };\n\n  // If we're under our concurrency limit, just start the inner subscription, otherwise buffer and wait.\n  const outerNext = (value: T) => (active < concurrent ? doInnerSub(value) : buffer.push(value));\n\n  const doInnerSub = (value: T) => {\n    // If we're expanding, we need to emit the outer values and the inner values\n    // as the inners will \"become outers\" in a way as they are recursively fed\n    // back to the projection mechanism.\n    expand && subscriber.next(value as any);\n\n    // Increment the number of active subscriptions so we can track it\n    // against our concurrency limit later.\n    active++;\n\n    // A flag used to show that the inner observable completed.\n    // This is checked during finalization to see if we should\n    // move to the next item in the buffer, if there is on.\n    let innerComplete = false;\n\n    // Start our inner subscription.\n    innerFrom(project(value, index++)).subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (innerValue) => {\n          // `mergeScan` has additional handling here. For example\n          // taking the inner value and updating state.\n          onBeforeNext?.(innerValue);\n\n          if (expand) {\n            // If we're expanding, then just recurse back to our outer\n            // handler. It will emit the value first thing.\n            outerNext(innerValue as any);\n          } else {\n            // Otherwise, emit the inner value.\n            subscriber.next(innerValue);\n          }\n        },\n        () => {\n          // Flag that we have completed, so we know to check the buffer\n          // during finalization.\n          innerComplete = true;\n        },\n        // Errors are passed to the destination.\n        undefined,\n        () => {\n          // During finalization, if the inner completed (it wasn't errored or\n          // cancelled), then we want to try the next item in the buffer if\n          // there is one.\n          if (innerComplete) {\n            // We have to wrap this in a try/catch because it happens during\n            // finalization, possibly asynchronously, and we want to pass\n            // any errors that happen (like in a projection function) to\n            // the outer Subscriber.\n            try {\n              // INNER SOURCE COMPLETE\n              // Decrement the active count to ensure that the next time\n              // we try to call `doInnerSub`, the number is accurate.\n              active--;\n              // If we have more values in the buffer, try to process those\n              // Note that this call will increment `active` ahead of the\n              // next conditional, if there were any more inner subscriptions\n              // to start.\n              while (buffer.length && active < concurrent) {\n                const bufferedValue = buffer.shift()!;\n                // Particularly for `expand`, we need to check to see if a scheduler was provided\n                // for when we want to start our inner subscription. Otherwise, we just start\n                // are next inner subscription.\n                if (innerSubScheduler) {\n                  executeSchedule(subscriber, innerSubScheduler, () => doInnerSub(bufferedValue));\n                } else {\n                  doInnerSub(bufferedValue);\n                }\n              }\n              // Check to see if we can complete, and complete if so.\n              checkComplete();\n            } catch (err) {\n              subscriber.error(err);\n            }\n          }\n        }\n      )\n    );\n  };\n\n  // Subscribe to our source observable.\n  source.subscribe(\n    createOperatorSubscriber(subscriber, outerNext, () => {\n      // Outer completed, make a note of it, and check to see if we can complete everything.\n      isComplete = true;\n      checkComplete();\n    })\n  );\n\n  // Additional finalization (for when the destination is torn down).\n  // Other finalization is added implicitly via subscription above.\n  return () => {\n    additionalFinalizer?.();\n  };\n}\n", "import { ObservableInput, OperatorFunction, ObservedValueOf } from '../types';\nimport { map } from './map';\nimport { innerFrom } from '../observable/innerFrom';\nimport { operate } from '../util/lift';\nimport { mergeInternals } from './mergeInternals';\nimport { isFunction } from '../util/isFunction';\n\n/* tslint:disable:max-line-length */\nexport function mergeMap<T, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  concurrent?: number\n): OperatorFunction<T, ObservedValueOf<O>>;\n/** @deprecated The `resultSelector` parameter will be removed in v8. Use an inner `map` instead. Details: https://rxjs.dev/deprecations/resultSelector */\nexport function mergeMap<T, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  resultSelector: undefined,\n  concurrent?: number\n): OperatorFunction<T, ObservedValueOf<O>>;\n/** @deprecated The `resultSelector` parameter will be removed in v8. Use an inner `map` instead. Details: https://rxjs.dev/deprecations/resultSelector */\nexport function mergeMap<T, R, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  resultSelector: (outerValue: T, innerValue: ObservedValueOf<O>, outerIndex: number, innerIndex: number) => R,\n  concurrent?: number\n): OperatorFunction<T, R>;\n/* tslint:enable:max-line-length */\n\n/**\n * Projects each source value to an Observable which is merged in the output\n * Observable.\n *\n * <span class=\"informal\">Maps each value to an Observable, then flattens all of\n * these inner Observables using {@link mergeAll}.</span>\n *\n * ![](mergeMap.png)\n *\n * Returns an Observable that emits items based on applying a function that you\n * supply to each item emitted by the source Observable, where that function\n * returns an Observable, and then merging those resulting Observables and\n * emitting the results of this merger.\n *\n * ## Example\n *\n * Map and flatten each letter to an Observable ticking every 1 second\n *\n * ```ts\n * import { of, mergeMap, interval, map } from 'rxjs';\n *\n * const letters = of('a', 'b', 'c');\n * const result = letters.pipe(\n *   mergeMap(x => interval(1000).pipe(map(i => x + i)))\n * );\n *\n * result.subscribe(x => console.log(x));\n *\n * // Results in the following:\n * // a0\n * // b0\n * // c0\n * // a1\n * // b1\n * // c1\n * // continues to list a, b, c every second with respective ascending integers\n * ```\n *\n * @see {@link concatMap}\n * @see {@link exhaustMap}\n * @see {@link merge}\n * @see {@link mergeAll}\n * @see {@link mergeMapTo}\n * @see {@link mergeScan}\n * @see {@link switchMap}\n *\n * @param {function(value: T, ?index: number): ObservableInput} project A function\n * that, when applied to an item emitted by the source Observable, returns an\n * Observable.\n * @param {number} [concurrent=Infinity] Maximum number of input\n * Observables being subscribed to concurrently.\n * @return A function that returns an Observable that emits the result of\n * applying the projection function (and the optional deprecated\n * `resultSelector`) to each item emitted by the source Observable and merging\n * the results of the Observables obtained from this transformation.\n */\nexport function mergeMap<T, R, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  resultSelector?: ((outerValue: T, innerValue: ObservedValueOf<O>, outerIndex: number, innerIndex: number) => R) | number,\n  concurrent: number = Infinity\n): OperatorFunction<T, ObservedValueOf<O> | R> {\n  if (isFunction(resultSelector)) {\n    // DEPRECATED PATH\n    return mergeMap((a, i) => map((b: any, ii: number) => resultSelector(a, b, i, ii))(innerFrom(project(a, i))), concurrent);\n  } else if (typeof resultSelector === 'number') {\n    concurrent = resultSelector;\n  }\n\n  return operate((source, subscriber) => mergeInternals(source, subscriber, project, concurrent));\n}\n", "import { mergeMap } from './mergeMap';\nimport { identity } from '../util/identity';\nimport { OperatorFunction, ObservableInput, ObservedValueOf } from '../types';\n\n/**\n * Converts a higher-order Observable into a first-order Observable which\n * concurrently delivers all values that are emitted on the inner Observables.\n *\n * <span class=\"informal\">Flattens an Observable-of-Observables.</span>\n *\n * ![](mergeAll.png)\n *\n * `mergeAll` subscribes to an Observable that emits Observables, also known as\n * a higher-order Observable. Each time it observes one of these emitted inner\n * Observables, it subscribes to that and delivers all the values from the\n * inner Observable on the output Observable. The output Observable only\n * completes once all inner Observables have completed. Any error delivered by\n * a inner Observable will be immediately emitted on the output Observable.\n *\n * ## Examples\n *\n * Spawn a new interval Observable for each click event, and blend their outputs as one Observable\n *\n * ```ts\n * import { fromEvent, map, interval, mergeAll } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const higherOrder = clicks.pipe(map(() => interval(1000)));\n * const firstOrder = higherOrder.pipe(mergeAll());\n *\n * firstOrder.subscribe(x => console.log(x));\n * ```\n *\n * Count from 0 to 9 every second for each click, but only allow 2 concurrent timers\n *\n * ```ts\n * import { fromEvent, map, interval, take, mergeAll } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const higherOrder = clicks.pipe(\n *   map(() => interval(1000).pipe(take(10)))\n * );\n * const firstOrder = higherOrder.pipe(mergeAll(2));\n *\n * firstOrder.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link combineLatestAll}\n * @see {@link concatAll}\n * @see {@link exhaustAll}\n * @see {@link merge}\n * @see {@link mergeMap}\n * @see {@link mergeMapTo}\n * @see {@link mergeScan}\n * @see {@link switchAll}\n * @see {@link switchMap}\n * @see {@link zipAll}\n *\n * @param {number} [concurrent=Infinity] Maximum number of inner\n * Observables being subscribed to concurrently.\n * @return A function that returns an Observable that emits values coming from\n * all the inner Observables emitted by the source Observable.\n */\nexport function mergeAll<O extends ObservableInput<any>>(concurrent: number = Infinity): OperatorFunction<O, ObservedValueOf<O>> {\n  return mergeMap(identity, concurrent);\n}\n", "import { mergeAll } from './mergeAll';\nimport { OperatorFunction, ObservableInput, ObservedValueOf } from '../types';\n\n/**\n * Converts a higher-order Observable into a first-order Observable by\n * concatenating the inner Observables in order.\n *\n * <span class=\"informal\">Flattens an Observable-of-Observables by putting one\n * inner Observable after the other.</span>\n *\n * ![](concatAll.svg)\n *\n * Joins every Observable emitted by the source (a higher-order Observable), in\n * a serial fashion. It subscribes to each inner Observable only after the\n * previous inner Observable has completed, and merges all of their values into\n * the returned observable.\n *\n * __Warning:__ If the source Observable emits Observables quickly and\n * endlessly, and the inner Observables it emits generally complete slower than\n * the source emits, you can run into memory issues as the incoming Observables\n * collect in an unbounded buffer.\n *\n * Note: `concatAll` is equivalent to `mergeAll` with concurrency parameter set\n * to `1`.\n *\n * ## Example\n *\n * For each click event, tick every second from 0 to 3, with no concurrency\n *\n * ```ts\n * import { fromEvent, map, interval, take, concatAll } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const higherOrder = clicks.pipe(\n *   map(() => interval(1000).pipe(take(4)))\n * );\n * const firstOrder = higherOrder.pipe(concatAll());\n * firstOrder.subscribe(x => console.log(x));\n *\n * // Results in the following:\n * // (results are not concurrent)\n * // For every click on the \"document\" it will emit values 0 to 3 spaced\n * // on a 1000ms interval\n * // one click = 1000ms-> 0 -1000ms-> 1 -1000ms-> 2 -1000ms-> 3\n * ```\n *\n * @see {@link combineLatestAll}\n * @see {@link concat}\n * @see {@link concatMap}\n * @see {@link concatMapTo}\n * @see {@link exhaustAll}\n * @see {@link mergeAll}\n * @see {@link switchAll}\n * @see {@link switchMap}\n * @see {@link zipAll}\n *\n * @return A function that returns an Observable emitting values from all the\n * inner Observables concatenated.\n */\nexport function concatAll<O extends ObservableInput<any>>(): OperatorFunction<O, ObservedValueOf<O>> {\n  return mergeAll(1);\n}\n", "import { Observable } from '../Observable';\nimport { ObservableInputTuple, SchedulerLike } from '../types';\nimport { concatAll } from '../operators/concatAll';\nimport { popScheduler } from '../util/args';\nimport { from } from './from';\n\nexport function concat<T extends readonly unknown[]>(...inputs: [...ObservableInputTuple<T>]): Observable<T[number]>;\nexport function concat<T extends readonly unknown[]>(\n  ...inputsAndScheduler: [...ObservableInputTuple<T>, SchedulerLike]\n): Observable<T[number]>;\n\n/**\n * Creates an output Observable which sequentially emits all values from the first given\n * Observable and then moves on to the next.\n *\n * <span class=\"informal\">Concatenates multiple Observables together by\n * sequentially emitting their values, one Observable after the other.</span>\n *\n * ![](concat.png)\n *\n * `concat` joins multiple Observables together, by subscribing to them one at a time and\n * merging their results into the output Observable. You can pass either an array of\n * Observables, or put them directly as arguments. Passing an empty array will result\n * in Observable that completes immediately.\n *\n * `concat` will subscribe to first input Observable and emit all its values, without\n * changing or affecting them in any way. When that Observable completes, it will\n * subscribe to then next Observable passed and, again, emit its values. This will be\n * repeated, until the operator runs out of Observables. When last input Observable completes,\n * `concat` will complete as well. At any given moment only one Observable passed to operator\n * emits values. If you would like to emit values from passed Observables concurrently, check out\n * {@link merge} instead, especially with optional `concurrent` parameter. As a matter of fact,\n * `concat` is an equivalent of `merge` operator with `concurrent` parameter set to `1`.\n *\n * Note that if some input Observable never completes, `concat` will also never complete\n * and Observables following the one that did not complete will never be subscribed. On the other\n * hand, if some Observable simply completes immediately after it is subscribed, it will be\n * invisible for `concat`, which will just move on to the next Observable.\n *\n * If any Observable in chain errors, instead of passing control to the next Observable,\n * `concat` will error immediately as well. Observables that would be subscribed after\n * the one that emitted error, never will.\n *\n * If you pass to `concat` the same Observable many times, its stream of values\n * will be \"replayed\" on every subscription, which means you can repeat given Observable\n * as many times as you like. If passing the same Observable to `concat` 1000 times becomes tedious,\n * you can always use {@link repeat}.\n *\n * ## Examples\n *\n * Concatenate a timer counting from 0 to 3 with a synchronous sequence from 1 to 10\n *\n * ```ts\n * import { interval, take, range, concat } from 'rxjs';\n *\n * const timer = interval(1000).pipe(take(4));\n * const sequence = range(1, 10);\n * const result = concat(timer, sequence);\n * result.subscribe(x => console.log(x));\n *\n * // results in:\n * // 0 -1000ms-> 1 -1000ms-> 2 -1000ms-> 3 -immediate-> 1 ... 10\n * ```\n *\n * Concatenate 3 Observables\n *\n * ```ts\n * import { interval, take, concat } from 'rxjs';\n *\n * const timer1 = interval(1000).pipe(take(10));\n * const timer2 = interval(2000).pipe(take(6));\n * const timer3 = interval(500).pipe(take(10));\n *\n * const result = concat(timer1, timer2, timer3);\n * result.subscribe(x => console.log(x));\n *\n * // results in the following:\n * // (Prints to console sequentially)\n * // -1000ms-> 0 -1000ms-> 1 -1000ms-> ... 9\n * // -2000ms-> 0 -2000ms-> 1 -2000ms-> ... 5\n * // -500ms-> 0 -500ms-> 1 -500ms-> ... 9\n * ```\n *\n * Concatenate the same Observable to repeat it\n *\n * ```ts\n * import { interval, take, concat } from 'rxjs';\n *\n * const timer = interval(1000).pipe(take(2));\n *\n * concat(timer, timer) // concatenating the same Observable!\n *   .subscribe({\n *     next: value => console.log(value),\n *     complete: () => console.log('...and it is done!')\n *   });\n *\n * // Logs:\n * // 0 after 1s\n * // 1 after 2s\n * // 0 after 3s\n * // 1 after 4s\n * // '...and it is done!' also after 4s\n * ```\n *\n * @see {@link concatAll}\n * @see {@link concatMap}\n * @see {@link concatMapTo}\n * @see {@link startWith}\n * @see {@link endWith}\n *\n * @param args Input Observables to concatenate.\n */\nexport function concat(...args: any[]): Observable<unknown> {\n  return concatAll()(from(args, popScheduler(args)));\n}\n", "import { Observable } from '../Observable';\nimport { ObservedValueOf, ObservableInput } from '../types';\nimport { innerFrom } from './innerFrom';\n\n/**\n * Creates an Observable that, on subscribe, calls an Observable factory to\n * make an Observable for each new Observer.\n *\n * <span class=\"informal\">Creates the Observable lazily, that is, only when it\n * is subscribed.\n * </span>\n *\n * ![](defer.png)\n *\n * `defer` allows you to create an Observable only when the Observer\n * subscribes. It waits until an Observer subscribes to it, calls the given\n * factory function to get an Observable -- where a factory function typically\n * generates a new Observable -- and subscribes the Observer to this Observable.\n * In case the factory function returns a falsy value, then EMPTY is used as\n * Observable instead. Last but not least, an exception during the factory\n * function call is transferred to the Observer by calling `error`.\n *\n * ## Example\n *\n * Subscribe to either an Observable of clicks or an Observable of interval, at random\n *\n * ```ts\n * import { defer, fromEvent, interval } from 'rxjs';\n *\n * const clicksOrInterval = defer(() => {\n *   return Math.random() > 0.5\n *     ? fromEvent(document, 'click')\n *     : interval(1000);\n * });\n * clicksOrInterval.subscribe(x => console.log(x));\n *\n * // Results in the following behavior:\n * // If the result of Math.random() is greater than 0.5 it will listen\n * // for clicks anywhere on the \"document\"; when document is clicked it\n * // will log a MouseEvent object to the console. If the result is less\n * // than 0.5 it will emit ascending numbers, one every second(1000ms).\n * ```\n *\n * @see {@link Observable}\n *\n * @param {function(): ObservableInput} observableFactory The Observable\n * factory function to invoke for each Observer that subscribes to the output\n * Observable. May also return a Promise, which will be converted on the fly\n * to an Observable.\n * @return {Observable} An Observable whose Observers' subscriptions trigger\n * an invocation of the given Observable factory function.\n */\nexport function defer<R extends ObservableInput<any>>(observableFactory: () => R): Observable<ObservedValueOf<R>> {\n  return new Observable<ObservedValueOf<R>>((subscriber) => {\n    innerFrom(observableFactory()).subscribe(subscriber);\n  });\n}\n", "import { innerFrom } from '../observable/innerFrom';\nimport { Observable } from '../Observable';\nimport { mergeMap } from '../operators/mergeMap';\nimport { isArrayLike } from '../util/isArrayLike';\nimport { isFunction } from '../util/isFunction';\nimport { mapOneOrManyArgs } from '../util/mapOneOrManyArgs';\n\n// These constants are used to create handler registry functions using array mapping below.\nconst nodeEventEmitterMethods = ['addListener', 'removeListener'] as const;\nconst eventTargetMethods = ['addEventListener', 'removeEventListener'] as const;\nconst jqueryMethods = ['on', 'off'] as const;\n\nexport interface NodeStyleEventEmitter {\n  addListener(eventName: string | symbol, handler: NodeEventHandler): this;\n  removeListener(eventName: string | symbol, handler: NodeEventHandler): this;\n}\n\nexport type NodeEventHandler = (...args: any[]) => void;\n\n// For APIs that implement `addListener` and `removeListener` methods that may\n// not use the same arguments or return EventEmitter values\n// such as React Native\nexport interface NodeCompatibleEventEmitter {\n  addListener(eventName: string, handler: NodeEventHandler): void | {};\n  removeListener(eventName: string, handler: NodeEventHandler): void | {};\n}\n\n// Use handler types like those in @types/jquery. See:\n// https://github.com/DefinitelyTyped/DefinitelyTyped/blob/847731ba1d7fa6db6b911c0e43aa0afe596e7723/types/jquery/misc.d.ts#L6395\nexport interface JQueryStyleEventEmitter<TContext, T> {\n  on(eventName: string, handler: (this: TContext, t: T, ...args: any[]) => any): void;\n  off(eventName: string, handler: (this: TContext, t: T, ...args: any[]) => any): void;\n}\n\nexport interface EventListenerObject<E> {\n  handleEvent(evt: E): void;\n}\n\nexport interface HasEventTargetAddRemove<E> {\n  addEventListener(\n    type: string,\n    listener: ((evt: E) => void) | EventListenerObject<E> | null,\n    options?: boolean | AddEventListenerOptions\n  ): void;\n  removeEventListener(\n    type: string,\n    listener: ((evt: E) => void) | EventListenerObject<E> | null,\n    options?: EventListenerOptions | boolean\n  ): void;\n}\n\nexport interface EventListenerOptions {\n  capture?: boolean;\n  passive?: boolean;\n  once?: boolean;\n}\n\nexport interface AddEventListenerOptions extends EventListenerOptions {\n  once?: boolean;\n  passive?: boolean;\n}\n\nexport function fromEvent<T>(target: HasEventTargetAddRemove<T> | ArrayLike<HasEventTargetAddRemove<T>>, eventName: string): Observable<T>;\nexport function fromEvent<T, R>(\n  target: HasEventTargetAddRemove<T> | ArrayLike<HasEventTargetAddRemove<T>>,\n  eventName: string,\n  resultSelector: (event: T) => R\n): Observable<R>;\nexport function fromEvent<T>(\n  target: HasEventTargetAddRemove<T> | ArrayLike<HasEventTargetAddRemove<T>>,\n  eventName: string,\n  options: EventListenerOptions\n): Observable<T>;\nexport function fromEvent<T, R>(\n  target: HasEventTargetAddRemove<T> | ArrayLike<HasEventTargetAddRemove<T>>,\n  eventName: string,\n  options: EventListenerOptions,\n  resultSelector: (event: T) => R\n): Observable<R>;\n\nexport function fromEvent(target: NodeStyleEventEmitter | ArrayLike<NodeStyleEventEmitter>, eventName: string): Observable<unknown>;\n/** @deprecated Do not specify explicit type parameters. Signatures with type parameters that cannot be inferred will be removed in v8. */\nexport function fromEvent<T>(target: NodeStyleEventEmitter | ArrayLike<NodeStyleEventEmitter>, eventName: string): Observable<T>;\nexport function fromEvent<R>(\n  target: NodeStyleEventEmitter | ArrayLike<NodeStyleEventEmitter>,\n  eventName: string,\n  resultSelector: (...args: any[]) => R\n): Observable<R>;\n\nexport function fromEvent(\n  target: NodeCompatibleEventEmitter | ArrayLike<NodeCompatibleEventEmitter>,\n  eventName: string\n): Observable<unknown>;\n/** @deprecated Do not specify explicit type parameters. Signatures with type parameters that cannot be inferred will be removed in v8. */\nexport function fromEvent<T>(target: NodeCompatibleEventEmitter | ArrayLike<NodeCompatibleEventEmitter>, eventName: string): Observable<T>;\nexport function fromEvent<R>(\n  target: NodeCompatibleEventEmitter | ArrayLike<NodeCompatibleEventEmitter>,\n  eventName: string,\n  resultSelector: (...args: any[]) => R\n): Observable<R>;\n\nexport function fromEvent<T>(\n  target: JQueryStyleEventEmitter<any, T> | ArrayLike<JQueryStyleEventEmitter<any, T>>,\n  eventName: string\n): Observable<T>;\nexport function fromEvent<T, R>(\n  target: JQueryStyleEventEmitter<any, T> | ArrayLike<JQueryStyleEventEmitter<any, T>>,\n  eventName: string,\n  resultSelector: (value: T, ...args: any[]) => R\n): Observable<R>;\n\n/**\n * Creates an Observable that emits events of a specific type coming from the\n * given event target.\n *\n * <span class=\"informal\">Creates an Observable from DOM events, or Node.js\n * EventEmitter events or others.</span>\n *\n * ![](fromEvent.png)\n *\n * `fromEvent` accepts as a first argument event target, which is an object with methods\n * for registering event handler functions. As a second argument it takes string that indicates\n * type of event we want to listen for. `fromEvent` supports selected types of event targets,\n * which are described in detail below. If your event target does not match any of the ones listed,\n * you should use {@link fromEventPattern}, which can be used on arbitrary APIs.\n * When it comes to APIs supported by `fromEvent`, their methods for adding and removing event\n * handler functions have different names, but they all accept a string describing event type\n * and function itself, which will be called whenever said event happens.\n *\n * Every time resulting Observable is subscribed, event handler function will be registered\n * to event target on given event type. When that event fires, value\n * passed as a first argument to registered function will be emitted by output Observable.\n * When Observable is unsubscribed, function will be unregistered from event target.\n *\n * Note that if event target calls registered function with more than one argument, second\n * and following arguments will not appear in resulting stream. In order to get access to them,\n * you can pass to `fromEvent` optional project function, which will be called with all arguments\n * passed to event handler. Output Observable will then emit value returned by project function,\n * instead of the usual value.\n *\n * Remember that event targets listed below are checked via duck typing. It means that\n * no matter what kind of object you have and no matter what environment you work in,\n * you can safely use `fromEvent` on that object if it exposes described methods (provided\n * of course they behave as was described above). So for example if Node.js library exposes\n * event target which has the same method names as DOM EventTarget, `fromEvent` is still\n * a good choice.\n *\n * If the API you use is more callback then event handler oriented (subscribed\n * callback function fires only once and thus there is no need to manually\n * unregister it), you should use {@link bindCallback} or {@link bindNodeCallback}\n * instead.\n *\n * `fromEvent` supports following types of event targets:\n *\n * **DOM EventTarget**\n *\n * This is an object with `addEventListener` and `removeEventListener` methods.\n *\n * In the browser, `addEventListener` accepts - apart from event type string and event\n * handler function arguments - optional third parameter, which is either an object or boolean,\n * both used for additional configuration how and when passed function will be called. When\n * `fromEvent` is used with event target of that type, you can provide this values\n * as third parameter as well.\n *\n * **Node.js EventEmitter**\n *\n * An object with `addListener` and `removeListener` methods.\n *\n * **JQuery-style event target**\n *\n * An object with `on` and `off` methods\n *\n * **DOM NodeList**\n *\n * List of DOM Nodes, returned for example by `document.querySelectorAll` or `Node.childNodes`.\n *\n * Although this collection is not event target in itself, `fromEvent` will iterate over all Nodes\n * it contains and install event handler function in every of them. When returned Observable\n * is unsubscribed, function will be removed from all Nodes.\n *\n * **DOM HtmlCollection**\n *\n * Just as in case of NodeList it is a collection of DOM nodes. Here as well event handler function is\n * installed and removed in each of elements.\n *\n *\n * ## Examples\n *\n * Emit clicks happening on the DOM document\n *\n * ```ts\n * import { fromEvent } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * clicks.subscribe(x => console.log(x));\n *\n * // Results in:\n * // MouseEvent object logged to console every time a click\n * // occurs on the document.\n * ```\n *\n * Use `addEventListener` with capture option\n *\n * ```ts\n * import { fromEvent } from 'rxjs';\n *\n * const div = document.createElement('div');\n * div.style.cssText = 'width: 200px; height: 200px; background: #09c;';\n * document.body.appendChild(div);\n *\n * // note optional configuration parameter which will be passed to addEventListener\n * const clicksInDocument = fromEvent(document, 'click', { capture: true });\n * const clicksInDiv = fromEvent(div, 'click');\n *\n * clicksInDocument.subscribe(() => console.log('document'));\n * clicksInDiv.subscribe(() => console.log('div'));\n *\n * // By default events bubble UP in DOM tree, so normally\n * // when we would click on div in document\n * // \"div\" would be logged first and then \"document\".\n * // Since we specified optional `capture` option, document\n * // will catch event when it goes DOWN DOM tree, so console\n * // will log \"document\" and then \"div\".\n * ```\n *\n * @see {@link bindCallback}\n * @see {@link bindNodeCallback}\n * @see {@link fromEventPattern}\n *\n * @param {FromEventTarget<T>} target The DOM EventTarget, Node.js\n * EventEmitter, JQuery-like event target, NodeList or HTMLCollection to attach the event handler to.\n * @param {string} eventName The event name of interest, being emitted by the\n * `target`.\n * @param {EventListenerOptions} [options] Options to pass through to addEventListener\n * @return {Observable<T>}\n */\nexport function fromEvent<T>(\n  target: any,\n  eventName: string,\n  options?: EventListenerOptions | ((...args: any[]) => T),\n  resultSelector?: (...args: any[]) => T\n): Observable<T> {\n  if (isFunction(options)) {\n    resultSelector = options;\n    options = undefined;\n  }\n  if (resultSelector) {\n    return fromEvent<T>(target, eventName, options as EventListenerOptions).pipe(mapOneOrManyArgs(resultSelector));\n  }\n\n  // Figure out our add and remove methods. In order to do this,\n  // we are going to analyze the target in a preferred order, if\n  // the target matches a given signature, we take the two \"add\" and \"remove\"\n  // method names and apply them to a map to create opposite versions of the\n  // same function. This is because they all operate in duplicate pairs,\n  // `addListener(name, handler)`, `removeListener(name, handler)`, for example.\n  // The call only differs by method name, as to whether or not you're adding or removing.\n  const [add, remove] =\n    // If it is an EventTarget, we need to use a slightly different method than the other two patterns.\n    isEventTarget(target)\n      ? eventTargetMethods.map((methodName) => (handler: any) => target[methodName](eventName, handler, options as EventListenerOptions))\n      : // In all other cases, the call pattern is identical with the exception of the method names.\n      isNodeStyleEventEmitter(target)\n      ? nodeEventEmitterMethods.map(toCommonHandlerRegistry(target, eventName))\n      : isJQueryStyleEventEmitter(target)\n      ? jqueryMethods.map(toCommonHandlerRegistry(target, eventName))\n      : [];\n\n  // If add is falsy, it's because we didn't match a pattern above.\n  // Check to see if it is an ArrayLike, because if it is, we want to\n  // try to apply fromEvent to all of it's items. We do this check last,\n  // because there are may be some types that are both ArrayLike *and* implement\n  // event registry points, and we'd rather delegate to that when possible.\n  if (!add) {\n    if (isArrayLike(target)) {\n      return mergeMap((subTarget: any) => fromEvent(subTarget, eventName, options as EventListenerOptions))(\n        innerFrom(target)\n      ) as Observable<T>;\n    }\n  }\n\n  // If add is falsy and we made it here, it's because we didn't\n  // match any valid target objects above.\n  if (!add) {\n    throw new TypeError('Invalid event target');\n  }\n\n  return new Observable<T>((subscriber) => {\n    // The handler we are going to register. Forwards the event object, by itself, or\n    // an array of arguments to the event handler, if there is more than one argument,\n    // to the consumer.\n    const handler = (...args: any[]) => subscriber.next(1 < args.length ? args : args[0]);\n    // Do the work of adding the handler to the target.\n    add(handler);\n    // When we finalize, we want to remove the handler and free up memory.\n    return () => remove!(handler);\n  });\n}\n\n/**\n * Used to create `add` and `remove` functions to register and unregister event handlers\n * from a target in the most common handler pattern, where there are only two arguments.\n * (e.g.  `on(name, fn)`, `off(name, fn)`, `addListener(name, fn)`, or `removeListener(name, fn)`)\n * @param target The target we're calling methods on\n * @param eventName The event name for the event we're creating register or unregister functions for\n */\nfunction toCommonHandlerRegistry(target: any, eventName: string) {\n  return (methodName: string) => (handler: any) => target[methodName](eventName, handler);\n}\n\n/**\n * Checks to see if the target implements the required node-style EventEmitter methods\n * for adding and removing event handlers.\n * @param target the object to check\n */\nfunction isNodeStyleEventEmitter(target: any): target is NodeStyleEventEmitter {\n  return isFunction(target.addListener) && isFunction(target.removeListener);\n}\n\n/**\n * Checks to see if the target implements the required jQuery-style EventEmitter methods\n * for adding and removing event handlers.\n * @param target the object to check\n */\nfunction isJQueryStyleEventEmitter(target: any): target is JQueryStyleEventEmitter<any, any> {\n  return isFunction(target.on) && isFunction(target.off);\n}\n\n/**\n * Checks to see if the target implements the required EventTarget methods\n * for adding and removing event handlers.\n * @param target the object to check\n */\nfunction isEventTarget(target: any): target is HasEventTargetAddRemove<any> {\n  return isFunction(target.addEventListener) && isFunction(target.removeEventListener);\n}\n", "import { Observable } from '../Observable';\nimport { isFunction } from '../util/isFunction';\nimport { NodeEventHandler } from './fromEvent';\nimport { mapOneOrManyArgs } from '../util/mapOneOrManyArgs';\n\n/* tslint:disable:max-line-length */\nexport function fromEventPattern<T>(\n  addHandler: (handler: NodeEventHandler) => any,\n  removeHandler?: (handler: NodeEventHandler, signal?: any) => void\n): Observable<T>;\nexport function fromEventPattern<T>(\n  addHandler: (handler: NodeEventHandler) => any,\n  removeHandler?: (handler: NodeEventHandler, signal?: any) => void,\n  resultSelector?: (...args: any[]) => T\n): Observable<T>;\n/* tslint:enable:max-line-length */\n\n/**\n * Creates an Observable from an arbitrary API for registering event handlers.\n *\n * <span class=\"informal\">When that method for adding event handler was something {@link fromEvent}\n * was not prepared for.</span>\n *\n * ![](fromEventPattern.png)\n *\n * `fromEventPattern` allows you to convert into an Observable any API that supports registering handler functions\n * for events. It is similar to {@link fromEvent}, but far\n * more flexible. In fact, all use cases of {@link fromEvent} could be easily handled by\n * `fromEventPattern` (although in slightly more verbose way).\n *\n * This operator accepts as a first argument an `addHandler` function, which will be injected with\n * handler parameter. That handler is actually an event handler function that you now can pass\n * to API expecting it. `addHandler` will be called whenever Observable\n * returned by the operator is subscribed, so registering handler in API will not\n * necessarily happen when `fromEventPattern` is called.\n *\n * After registration, every time an event that we listen to happens,\n * Observable returned by `fromEventPattern` will emit value that event handler\n * function was called with. Note that if event handler was called with more\n * than one argument, second and following arguments will not appear in the Observable.\n *\n * If API you are using allows to unregister event handlers as well, you can pass to `fromEventPattern`\n * another function - `removeHandler` - as a second parameter. It will be injected\n * with the same handler function as before, which now you can use to unregister\n * it from the API. `removeHandler` will be called when consumer of resulting Observable\n * unsubscribes from it.\n *\n * In some APIs unregistering is actually handled differently. Method registering an event handler\n * returns some kind of token, which is later used to identify which function should\n * be unregistered or it itself has method that unregisters event handler.\n * If that is the case with your API, make sure token returned\n * by registering method is returned by `addHandler`. Then it will be passed\n * as a second argument to `removeHandler`, where you will be able to use it.\n *\n * If you need access to all event handler parameters (not only the first one),\n * or you need to transform them in any way, you can call `fromEventPattern` with optional\n * third parameter - project function which will accept all arguments passed to\n * event handler when it is called. Whatever is returned from project function will appear on\n * resulting stream instead of usual event handlers first argument. This means\n * that default project can be thought of as function that takes its first parameter\n * and ignores the rest.\n *\n * ## Examples\n *\n * Emits clicks happening on the DOM document\n *\n * ```ts\n * import { fromEventPattern } from 'rxjs';\n *\n * function addClickHandler(handler) {\n *   document.addEventListener('click', handler);\n * }\n *\n * function removeClickHandler(handler) {\n *   document.removeEventListener('click', handler);\n * }\n *\n * const clicks = fromEventPattern(\n *   addClickHandler,\n *   removeClickHandler\n * );\n * clicks.subscribe(x => console.log(x));\n *\n * // Whenever you click anywhere in the browser, DOM MouseEvent\n * // object will be logged.\n * ```\n *\n * Use with API that returns cancellation token\n *\n * ```ts\n * import { fromEventPattern } from 'rxjs';\n *\n * const token = someAPI.registerEventHandler(function() {});\n * someAPI.unregisterEventHandler(token); // this APIs cancellation method accepts\n *                                        // not handler itself, but special token.\n *\n * const someAPIObservable = fromEventPattern(\n *   function(handler) { return someAPI.registerEventHandler(handler); }, // Note that we return the token here...\n *   function(handler, token) { someAPI.unregisterEventHandler(token); }  // ...to then use it here.\n * );\n * ```\n *\n * Use with project function\n *\n * ```ts\n * import { fromEventPattern } from 'rxjs';\n *\n * someAPI.registerEventHandler((eventType, eventMessage) => {\n *   console.log(eventType, eventMessage); // Logs 'EVENT_TYPE' 'EVENT_MESSAGE' to console.\n * });\n *\n * const someAPIObservable = fromEventPattern(\n *   handler => someAPI.registerEventHandler(handler),\n *   handler => someAPI.unregisterEventHandler(handler)\n *   (eventType, eventMessage) => eventType + ' --- ' + eventMessage // without that function only 'EVENT_TYPE'\n * );                                                                // would be emitted by the Observable\n *\n * someAPIObservable.subscribe(value => console.log(value));\n *\n * // Logs:\n * // 'EVENT_TYPE --- EVENT_MESSAGE'\n * ```\n *\n * @see {@link fromEvent}\n * @see {@link bindCallback}\n * @see {@link bindNodeCallback}\n *\n * @param {function(handler: Function): any} addHandler A function that takes\n * a `handler` function as argument and attaches it somehow to the actual\n * source of events.\n * @param {function(handler: Function, token?: any): void} [removeHandler] A function that\n * takes a `handler` function as an argument and removes it from the event source. If `addHandler`\n * returns some kind of token, `removeHandler` function will have it as a second parameter.\n * @param {function(...args: any): T} [project] A function to\n * transform results. It takes the arguments from the event handler and\n * should return a single value.\n * @return {Observable<T>} Observable which, when an event happens, emits first parameter\n * passed to registered event handler. Alternatively it emits whatever project function returns\n * at that moment.\n */\nexport function fromEventPattern<T>(\n  addHandler: (handler: NodeEventHandler) => any,\n  removeHandler?: (handler: NodeEventHandler, signal?: any) => void,\n  resultSelector?: (...args: any[]) => T\n): Observable<T | T[]> {\n  if (resultSelector) {\n    return fromEventPattern<T>(addHandler, removeHandler).pipe(mapOneOrManyArgs(resultSelector));\n  }\n\n  return new Observable<T | T[]>((subscriber) => {\n    const handler = (...e: T[]) => subscriber.next(e.length === 1 ? e[0] : e);\n    const retValue = addHandler(handler);\n    return isFunction(removeHandler) ? () => removeHandler(handler, retValue) : undefined;\n  });\n}\n", "import { Observable } from '../Observable';\nimport { SchedulerLike } from '../types';\nimport { async as asyncScheduler } from '../scheduler/async';\nimport { isScheduler } from '../util/isScheduler';\nimport { isValidDate } from '../util/isDate';\n\n/**\n * Creates an observable that will wait for a specified time period, or exact date, before\n * emitting the number 0.\n *\n * <span class=\"informal\">Used to emit a notification after a delay.</span>\n *\n * This observable is useful for creating delays in code, or racing against other values\n * for ad-hoc timeouts.\n *\n * The `delay` is specified by default in milliseconds, however providing a custom scheduler could\n * create a different behavior.\n *\n * ## Examples\n *\n * Wait 3 seconds and start another observable\n *\n * You might want to use `timer` to delay subscription to an\n * observable by a set amount of time. Here we use a timer with\n * {@link concatMapTo} or {@link concatMap} in order to wait\n * a few seconds and start a subscription to a source.\n *\n * ```ts\n * import { of, timer, concatMap } from 'rxjs';\n *\n * // This could be any observable\n * const source = of(1, 2, 3);\n *\n * timer(3000)\n *   .pipe(concatMap(() => source))\n *   .subscribe(console.log);\n * ```\n *\n * Take all values until the start of the next minute\n *\n * Using a `Date` as the trigger for the first emission, you can\n * do things like wait until midnight to fire an event, or in this case,\n * wait until a new minute starts (chosen so the example wouldn't take\n * too long to run) in order to stop watching a stream. Leveraging\n * {@link takeUntil}.\n *\n * ```ts\n * import { interval, takeUntil, timer } from 'rxjs';\n *\n * // Build a Date object that marks the\n * // next minute.\n * const currentDate = new Date();\n * const startOfNextMinute = new Date(\n *   currentDate.getFullYear(),\n *   currentDate.getMonth(),\n *   currentDate.getDate(),\n *   currentDate.getHours(),\n *   currentDate.getMinutes() + 1\n * );\n *\n * // This could be any observable stream\n * const source = interval(1000);\n *\n * const result = source.pipe(\n *   takeUntil(timer(startOfNextMinute))\n * );\n *\n * result.subscribe(console.log);\n * ```\n *\n * ### Known Limitations\n *\n * - The {@link asyncScheduler} uses `setTimeout` which has limitations for how far in the future it can be scheduled.\n *\n * - If a `scheduler` is provided that returns a timestamp other than an epoch from `now()`, and\n * a `Date` object is passed to the `dueTime` argument, the calculation for when the first emission\n * should occur will be incorrect. In this case, it would be best to do your own calculations\n * ahead of time, and pass a `number` in as the `dueTime`.\n *\n * @param due If a `number`, the amount of time in milliseconds to wait before emitting.\n * If a `Date`, the exact time at which to emit.\n * @param scheduler The scheduler to use to schedule the delay. Defaults to {@link asyncScheduler}.\n */\nexport function timer(due: number | Date, scheduler?: SchedulerLike): Observable<0>;\n\n/**\n * Creates an observable that starts an interval after a specified delay, emitting incrementing numbers -- starting at `0` --\n * on each interval after words.\n *\n * The `delay` and `intervalDuration` are specified by default in milliseconds, however providing a custom scheduler could\n * create a different behavior.\n *\n * ## Example\n *\n * ### Start an interval that starts right away\n *\n * Since {@link interval} waits for the passed delay before starting,\n * sometimes that's not ideal. You may want to start an interval immediately.\n * `timer` works well for this. Here we have both side-by-side so you can\n * see them in comparison.\n *\n * Note that this observable will never complete.\n *\n * ```ts\n * import { timer, interval } from 'rxjs';\n *\n * timer(0, 1000).subscribe(n => console.log('timer', n));\n * interval(1000).subscribe(n => console.log('interval', n));\n * ```\n *\n * ### Known Limitations\n *\n * - The {@link asyncScheduler} uses `setTimeout` which has limitations for how far in the future it can be scheduled.\n *\n * - If a `scheduler` is provided that returns a timestamp other than an epoch from `now()`, and\n * a `Date` object is passed to the `dueTime` argument, the calculation for when the first emission\n * should occur will be incorrect. In this case, it would be best to do your own calculations\n * ahead of time, and pass a `number` in as the `startDue`.\n * @param startDue If a `number`, is the time to wait before starting the interval.\n * If a `Date`, is the exact time at which to start the interval.\n * @param intervalDuration The delay between each value emitted in the interval. Passing a\n * negative number here will result in immediate completion after the first value is emitted, as though\n * no `intervalDuration` was passed at all.\n * @param scheduler The scheduler to use to schedule the delay. Defaults to {@link asyncScheduler}.\n */\nexport function timer(startDue: number | Date, intervalDuration: number, scheduler?: SchedulerLike): Observable<number>;\n\n/**\n * @deprecated The signature allowing `undefined` to be passed for `intervalDuration` will be removed in v8. Use the `timer(dueTime, scheduler?)` signature instead.\n */\nexport function timer(dueTime: number | Date, unused: undefined, scheduler?: SchedulerLike): Observable<0>;\n\nexport function timer(\n  dueTime: number | Date = 0,\n  intervalOrScheduler?: number | SchedulerLike,\n  scheduler: SchedulerLike = asyncScheduler\n): Observable<number> {\n  // Since negative intervalDuration is treated as though no\n  // interval was specified at all, we start with a negative number.\n  let intervalDuration = -1;\n\n  if (intervalOrScheduler != null) {\n    // If we have a second argument, and it's a scheduler,\n    // override the scheduler we had defaulted. Otherwise,\n    // it must be an interval.\n    if (isScheduler(intervalOrScheduler)) {\n      scheduler = intervalOrScheduler;\n    } else {\n      // Note that this *could* be negative, in which case\n      // it's like not passing an intervalDuration at all.\n      intervalDuration = intervalOrScheduler;\n    }\n  }\n\n  return new Observable((subscriber) => {\n    // If a valid date is passed, calculate how long to wait before\n    // executing the first value... otherwise, if it's a number just schedule\n    // that many milliseconds (or scheduler-specified unit size) in the future.\n    let due = isValidDate(dueTime) ? +dueTime - scheduler!.now() : dueTime;\n\n    if (due < 0) {\n      // Ensure we don't schedule in the future.\n      due = 0;\n    }\n\n    // The incrementing value we emit.\n    let n = 0;\n\n    // Start the timer.\n    return scheduler.schedule(function () {\n      if (!subscriber.closed) {\n        // Emit the next value and increment.\n        subscriber.next(n++);\n\n        if (0 <= intervalDuration) {\n          // If we have a interval after the initial timer,\n          // reschedule with the period.\n          this.schedule(undefined, intervalDuration);\n        } else {\n          // We didn't have an interval. So just complete.\n          subscriber.complete();\n        }\n      }\n    }, due);\n  });\n}\n", "import { Observable } from '../Observable';\nimport { ObservableInput, ObservableInputTuple, SchedulerLike } from '../types';\nimport { mergeAll } from '../operators/mergeAll';\nimport { innerFrom } from './innerFrom';\nimport { EMPTY } from './empty';\nimport { popNumber, popScheduler } from '../util/args';\nimport { from } from './from';\n\nexport function merge<A extends readonly unknown[]>(...sources: [...ObservableInputTuple<A>]): Observable<A[number]>;\nexport function merge<A extends readonly unknown[]>(...sourcesAndConcurrency: [...ObservableInputTuple<A>, number?]): Observable<A[number]>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `mergeAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function merge<A extends readonly unknown[]>(\n  ...sourcesAndScheduler: [...ObservableInputTuple<A>, SchedulerLike?]\n): Observable<A[number]>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `mergeAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function merge<A extends readonly unknown[]>(\n  ...sourcesAndConcurrencyAndScheduler: [...ObservableInputTuple<A>, number?, SchedulerLike?]\n): Observable<A[number]>;\n\n/**\n * Creates an output Observable which concurrently emits all values from every\n * given input Observable.\n *\n * <span class=\"informal\">Flattens multiple Observables together by blending\n * their values into one Observable.</span>\n *\n * ![](merge.png)\n *\n * `merge` subscribes to each given input Observable (as arguments), and simply\n * forwards (without doing any transformation) all the values from all the input\n * Observables to the output Observable. The output Observable only completes\n * once all input Observables have completed. Any error delivered by an input\n * Observable will be immediately emitted on the output Observable.\n *\n * ## Examples\n *\n * Merge together two Observables: 1s interval and clicks\n *\n * ```ts\n * import { merge, fromEvent, interval } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const timer = interval(1000);\n * const clicksOrTimer = merge(clicks, timer);\n * clicksOrTimer.subscribe(x => console.log(x));\n *\n * // Results in the following:\n * // timer will emit ascending values, one every second(1000ms) to console\n * // clicks logs MouseEvents to console every time the \"document\" is clicked\n * // Since the two streams are merged you see these happening\n * // as they occur.\n * ```\n *\n * Merge together 3 Observables, but run only 2 concurrently\n *\n * ```ts\n * import { interval, take, merge } from 'rxjs';\n *\n * const timer1 = interval(1000).pipe(take(10));\n * const timer2 = interval(2000).pipe(take(6));\n * const timer3 = interval(500).pipe(take(10));\n *\n * const concurrent = 2; // the argument\n * const merged = merge(timer1, timer2, timer3, concurrent);\n * merged.subscribe(x => console.log(x));\n *\n * // Results in the following:\n * // - First timer1 and timer2 will run concurrently\n * // - timer1 will emit a value every 1000ms for 10 iterations\n * // - timer2 will emit a value every 2000ms for 6 iterations\n * // - after timer1 hits its max iteration, timer2 will\n * //   continue, and timer3 will start to run concurrently with timer2\n * // - when timer2 hits its max iteration it terminates, and\n * //   timer3 will continue to emit a value every 500ms until it is complete\n * ```\n *\n * @see {@link mergeAll}\n * @see {@link mergeMap}\n * @see {@link mergeMapTo}\n * @see {@link mergeScan}\n *\n * @param {...ObservableInput} observables Input Observables to merge together.\n * @param {number} [concurrent=Infinity] Maximum number of input\n * Observables being subscribed to concurrently.\n * @param {SchedulerLike} [scheduler=null] The {@link SchedulerLike} to use for managing\n * concurrency of input Observables.\n * @return {Observable} an Observable that emits items that are the result of\n * every input Observable.\n */\nexport function merge(...args: (ObservableInput<unknown> | number | SchedulerLike)[]): Observable<unknown> {\n  const scheduler = popScheduler(args);\n  const concurrent = popNumber(args, Infinity);\n  const sources = args as ObservableInput<unknown>[];\n  return !sources.length\n    ? // No source provided\n      EMPTY\n    : sources.length === 1\n    ? // One source? Just return it.\n      innerFrom(sources[0])\n    : // Merge all sources\n      mergeAll(concurrent)(from(sources, scheduler));\n}\n", "import { Observable } from '../Observable';\nimport { noop } from '../util/noop';\n\n/**\n * An Observable that emits no items to the Observer and never completes.\n *\n * ![](never.png)\n *\n * A simple Observable that emits neither values nor errors nor the completion\n * notification. It can be used for testing purposes or for composing with other\n * Observables. Please note that by never emitting a complete notification, this\n * Observable keeps the subscription from being disposed automatically.\n * Subscriptions need to be manually disposed.\n *\n * ##  Example\n *\n * Emit the number 7, then never emit anything else (not even complete)\n *\n * ```ts\n * import { NEVER, startWith } from 'rxjs';\n *\n * const info = () => console.log('Will not be called');\n *\n * const result = NEVER.pipe(startWith(7));\n * result.subscribe({\n *   next: x => console.log(x),\n *   error: info,\n *   complete: info\n * });\n * ```\n *\n * @see {@link Observable}\n * @see {@link EMPTY}\n * @see {@link of}\n * @see {@link throwError}\n */\nexport const NEVER = new Observable<never>(noop);\n\n/**\n * @deprecated Replaced with the {@link NEVER} constant. Will be removed in v8.\n */\nexport function never() {\n  return NEVER;\n}\n", "const { isArray } = Array;\n\n/**\n * Used in operators and functions that accept either a list of arguments, or an array of arguments\n * as a single argument.\n */\nexport function argsOrArgArray<T>(args: (T | T[])[]): T[] {\n  return args.length === 1 && isArray(args[0]) ? args[0] : (args as T[]);\n}\n", "import { OperatorFunction, MonoTypeOperatorFunction, TruthyTypesOf } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/** @deprecated Use a closure instead of a `thisArg`. Signatures accepting a `thisArg` will be removed in v8. */\nexport function filter<T, S extends T, A>(predicate: (this: A, value: T, index: number) => value is S, thisArg: A): OperatorFunction<T, S>;\nexport function filter<T, S extends T>(predicate: (value: T, index: number) => value is S): OperatorFunction<T, S>;\nexport function filter<T>(predicate: BooleanConstructor): OperatorFunction<T, TruthyTypesOf<T>>;\n/** @deprecated Use a closure instead of a `thisArg`. Signatures accepting a `thisArg` will be removed in v8. */\nexport function filter<T, A>(predicate: (this: A, value: T, index: number) => boolean, thisArg: A): MonoTypeOperatorFunction<T>;\nexport function filter<T>(predicate: (value: T, index: number) => boolean): MonoTypeOperatorFunction<T>;\n\n/**\n * Filter items emitted by the source Observable by only emitting those that\n * satisfy a specified predicate.\n *\n * <span class=\"informal\">Like\n * [Array.prototype.filter()](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/filter),\n * it only emits a value from the source if it passes a criterion function.</span>\n *\n * ![](filter.png)\n *\n * Similar to the well-known `Array.prototype.filter` method, this operator\n * takes values from the source Observable, passes them through a `predicate`\n * function and only emits those values that yielded `true`.\n *\n * ## Example\n *\n * Emit only click events whose target was a DIV element\n *\n * ```ts\n * import { fromEvent, filter } from 'rxjs';\n *\n * const div = document.createElement('div');\n * div.style.cssText = 'width: 200px; height: 200px; background: #09c;';\n * document.body.appendChild(div);\n *\n * const clicks = fromEvent(document, 'click');\n * const clicksOnDivs = clicks.pipe(filter(ev => (<HTMLElement>ev.target).tagName === 'DIV'));\n * clicksOnDivs.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link distinct}\n * @see {@link distinctUntilChanged}\n * @see {@link distinctUntilKeyChanged}\n * @see {@link ignoreElements}\n * @see {@link partition}\n * @see {@link skip}\n *\n * @param predicate A function that\n * evaluates each value emitted by the source Observable. If it returns `true`,\n * the value is emitted, if `false` the value is not passed to the output\n * Observable. The `index` parameter is the number `i` for the i-th source\n * emission that has happened since the subscription, starting from the number\n * `0`.\n * @param thisArg An optional argument to determine the value of `this`\n * in the `predicate` function.\n * @return A function that returns an Observable that emits items from the\n * source Observable that satisfy the specified `predicate`.\n */\nexport function filter<T>(predicate: (value: T, index: number) => boolean, thisArg?: any): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    // An index passed to our predicate function on each call.\n    let index = 0;\n\n    // Subscribe to the source, all errors and completions are\n    // forwarded to the consumer.\n    source.subscribe(\n      // Call the predicate with the appropriate `this` context,\n      // if the predicate returns `true`, then send the value\n      // to the consumer.\n      createOperatorSubscriber(subscriber, (value) => predicate.call(thisArg, value, index++) && subscriber.next(value))\n    );\n  });\n}\n", "import { Observable } from '../Observable';\nimport { ObservableInputTuple } from '../types';\nimport { innerFrom } from './innerFrom';\nimport { argsOrArgArray } from '../util/argsOrArgArray';\nimport { EMPTY } from './empty';\nimport { createOperatorSubscriber } from '../operators/OperatorSubscriber';\nimport { popResultSelector } from '../util/args';\n\nexport function zip<A extends readonly unknown[]>(sources: [...ObservableInputTuple<A>]): Observable<A>;\nexport function zip<A extends readonly unknown[], R>(\n  sources: [...ObservableInputTuple<A>],\n  resultSelector: (...values: A) => R\n): Observable<R>;\nexport function zip<A extends readonly unknown[]>(...sources: [...ObservableInputTuple<A>]): Observable<A>;\nexport function zip<A extends readonly unknown[], R>(\n  ...sourcesAndResultSelector: [...ObservableInputTuple<A>, (...values: A) => R]\n): Observable<R>;\n\n/**\n * Combines multiple Observables to create an Observable whose values are calculated from the values, in order, of each\n * of its input Observables.\n *\n * If the last parameter is a function, this function is used to compute the created value from the input values.\n * Otherwise, an array of the input values is returned.\n *\n * ## Example\n *\n * Combine age and name from different sources\n *\n * ```ts\n * import { of, zip, map } from 'rxjs';\n *\n * const age$ = of(27, 25, 29);\n * const name$ = of('Foo', 'Bar', 'Beer');\n * const isDev$ = of(true, true, false);\n *\n * zip(age$, name$, isDev$).pipe(\n *   map(([age, name, isDev]) => ({ age, name, isDev }))\n * )\n * .subscribe(x => console.log(x));\n *\n * // Outputs\n * // { age: 27, name: 'Foo', isDev: true }\n * // { age: 25, name: 'Bar', isDev: true }\n * // { age: 29, name: 'Beer', isDev: false }\n * ```\n *\n * @param sources\n * @return {Observable<R>}\n */\nexport function zip(...args: unknown[]): Observable<unknown> {\n  const resultSelector = popResultSelector(args);\n\n  const sources = argsOrArgArray(args) as Observable<unknown>[];\n\n  return sources.length\n    ? new Observable<unknown[]>((subscriber) => {\n        // A collection of buffers of values from each source.\n        // Keyed by the same index with which the sources were passed in.\n        let buffers: unknown[][] = sources.map(() => []);\n\n        // An array of flags of whether or not the sources have completed.\n        // This is used to check to see if we should complete the result.\n        // Keyed by the same index with which the sources were passed in.\n        let completed = sources.map(() => false);\n\n        // When everything is done, release the arrays above.\n        subscriber.add(() => {\n          buffers = completed = null!;\n        });\n\n        // Loop over our sources and subscribe to each one. The index `i` is\n        // especially important here, because we use it in closures below to\n        // access the related buffers and completion properties\n        for (let sourceIndex = 0; !subscriber.closed && sourceIndex < sources.length; sourceIndex++) {\n          innerFrom(sources[sourceIndex]).subscribe(\n            createOperatorSubscriber(\n              subscriber,\n              (value) => {\n                buffers[sourceIndex].push(value);\n                // if every buffer has at least one value in it, then we\n                // can shift out the oldest value from each buffer and emit\n                // them as an array.\n                if (buffers.every((buffer) => buffer.length)) {\n                  const result: any = buffers.map((buffer) => buffer.shift()!);\n                  // Emit the array. If theres' a result selector, use that.\n                  subscriber.next(resultSelector ? resultSelector(...result) : result);\n                  // If any one of the sources is both complete and has an empty buffer\n                  // then we complete the result. This is because we cannot possibly have\n                  // any more values to zip together.\n                  if (buffers.some((buffer, i) => !buffer.length && completed[i])) {\n                    subscriber.complete();\n                  }\n                }\n              },\n              () => {\n                // This source completed. Mark it as complete so we can check it later\n                // if we have to.\n                completed[sourceIndex] = true;\n                // But, if this complete source has nothing in its buffer, then we\n                // can complete the result, because we can't possibly have any more\n                // values from this to zip together with the other values.\n                !buffers[sourceIndex].length && subscriber.complete();\n              }\n            )\n          );\n        }\n\n        // When everything is done, release the arrays above.\n        return () => {\n          buffers = completed = null!;\n        };\n      })\n    : EMPTY;\n}\n", "import { Subscriber } from '../Subscriber';\nimport { MonoTypeOperatorFunction, ObservableInput } from '../types';\n\nimport { operate } from '../util/lift';\nimport { innerFrom } from '../observable/innerFrom';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * Ignores source values for a duration determined by another Observable, then\n * emits the most recent value from the source Observable, then repeats this\n * process.\n *\n * <span class=\"informal\">It's like {@link auditTime}, but the silencing\n * duration is determined by a second Observable.</span>\n *\n * ![](audit.svg)\n *\n * `audit` is similar to `throttle`, but emits the last value from the silenced\n * time window, instead of the first value. `audit` emits the most recent value\n * from the source Observable on the output Observable as soon as its internal\n * timer becomes disabled, and ignores source values while the timer is enabled.\n * Initially, the timer is disabled. As soon as the first source value arrives,\n * the timer is enabled by calling the `durationSelector` function with the\n * source value, which returns the \"duration\" Observable. When the duration\n * Observable emits a value, the timer is disabled, then the most\n * recent source value is emitted on the output Observable, and this process\n * repeats for the next source value.\n *\n * ## Example\n *\n * Emit clicks at a rate of at most one click per second\n *\n * ```ts\n * import { fromEvent, audit, interval } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(audit(ev => interval(1000)));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link auditTime}\n * @see {@link debounce}\n * @see {@link delayWhen}\n * @see {@link sample}\n * @see {@link throttle}\n *\n * @param durationSelector A function\n * that receives a value from the source Observable, for computing the silencing\n * duration, returned as an Observable or a Promise.\n * @return A function that returns an Observable that performs rate-limiting of\n * emissions from the source Observable.\n */\nexport function audit<T>(durationSelector: (value: T) => ObservableInput<any>): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    let hasValue = false;\n    let lastValue: T | null = null;\n    let durationSubscriber: Subscriber<any> | null = null;\n    let isComplete = false;\n\n    const endDuration = () => {\n      durationSubscriber?.unsubscribe();\n      durationSubscriber = null;\n      if (hasValue) {\n        hasValue = false;\n        const value = lastValue!;\n        lastValue = null;\n        subscriber.next(value);\n      }\n      isComplete && subscriber.complete();\n    };\n\n    const cleanupDuration = () => {\n      durationSubscriber = null;\n      isComplete && subscriber.complete();\n    };\n\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => {\n          hasValue = true;\n          lastValue = value;\n          if (!durationSubscriber) {\n            innerFrom(durationSelector(value)).subscribe(\n              (durationSubscriber = createOperatorSubscriber(subscriber, endDuration, cleanupDuration))\n            );\n          }\n        },\n        () => {\n          isComplete = true;\n          (!hasValue || !durationSubscriber || durationSubscriber.closed) && subscriber.complete();\n        }\n      )\n    );\n  });\n}\n", "import { asyncScheduler } from '../scheduler/async';\nimport { audit } from './audit';\nimport { timer } from '../observable/timer';\nimport { MonoTypeOperatorFunction, SchedulerLike } from '../types';\n\n/**\n * Ignores source values for `duration` milliseconds, then emits the most recent\n * value from the source Observable, then repeats this process.\n *\n * <span class=\"informal\">When it sees a source value, it ignores that plus\n * the next ones for `duration` milliseconds, and then it emits the most recent\n * value from the source.</span>\n *\n * ![](auditTime.png)\n *\n * `auditTime` is similar to `throttleTime`, but emits the last value from the\n * silenced time window, instead of the first value. `auditTime` emits the most\n * recent value from the source Observable on the output Observable as soon as\n * its internal timer becomes disabled, and ignores source values while the\n * timer is enabled. Initially, the timer is disabled. As soon as the first\n * source value arrives, the timer is enabled. After `duration` milliseconds (or\n * the time unit determined internally by the optional `scheduler`) has passed,\n * the timer is disabled, then the most recent source value is emitted on the\n * output Observable, and this process repeats for the next source value.\n * Optionally takes a {@link SchedulerLike} for managing timers.\n *\n * ## Example\n *\n * Emit clicks at a rate of at most one click per second\n *\n * ```ts\n * import { fromEvent, auditTime } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(auditTime(1000));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link audit}\n * @see {@link debounceTime}\n * @see {@link delay}\n * @see {@link sampleTime}\n * @see {@link throttleTime}\n *\n * @param {number} duration Time to wait before emitting the most recent source\n * value, measured in milliseconds or the time unit determined internally\n * by the optional `scheduler`.\n * @param {SchedulerLike} [scheduler=async] The {@link SchedulerLike} to use for\n * managing the timers that handle the rate-limiting behavior.\n * @return A function that returns an Observable that performs rate-limiting of\n * emissions from the source Observable.\n */\nexport function auditTime<T>(duration: number, scheduler: SchedulerLike = asyncScheduler): MonoTypeOperatorFunction<T> {\n  return audit(() => timer(duration, scheduler));\n}\n", "import { OperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { arrRemove } from '../util/arrRemove';\n\n/**\n * Buffers the source Observable values until the size hits the maximum\n * `bufferSize` given.\n *\n * <span class=\"informal\">Collects values from the past as an array, and emits\n * that array only when its size reaches `bufferSize`.</span>\n *\n * ![](bufferCount.png)\n *\n * Buffers a number of values from the source Observable by `bufferSize` then\n * emits the buffer and clears it, and starts a new buffer each\n * `startBufferEvery` values. If `startBufferEvery` is not provided or is\n * `null`, then new buffers are started immediately at the start of the source\n * and when each buffer closes and is emitted.\n *\n * ## Examples\n *\n * Emit the last two click events as an array\n *\n * ```ts\n * import { fromEvent, bufferCount } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const buffered = clicks.pipe(bufferCount(2));\n * buffered.subscribe(x => console.log(x));\n * ```\n *\n * On every click, emit the last two click events as an array\n *\n * ```ts\n * import { fromEvent, bufferCount } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const buffered = clicks.pipe(bufferCount(2, 1));\n * buffered.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link buffer}\n * @see {@link bufferTime}\n * @see {@link bufferToggle}\n * @see {@link bufferWhen}\n * @see {@link pairwise}\n * @see {@link windowCount}\n *\n * @param {number} bufferSize The maximum size of the buffer emitted.\n * @param {number} [startBufferEvery] Interval at which to start a new buffer.\n * For example if `startBufferEvery` is `2`, then a new buffer will be started\n * on every other value from the source. A new buffer is started at the\n * beginning of the source by default.\n * @return A function that returns an Observable of arrays of buffered values.\n */\nexport function bufferCount<T>(bufferSize: number, startBufferEvery: number | null = null): OperatorFunction<T, T[]> {\n  // If no `startBufferEvery` value was supplied, then we're\n  // opening and closing on the bufferSize itself.\n  startBufferEvery = startBufferEvery ?? bufferSize;\n\n  return operate((source, subscriber) => {\n    let buffers: T[][] = [];\n    let count = 0;\n\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => {\n          let toEmit: T[][] | null = null;\n\n          // Check to see if we need to start a buffer.\n          // This will start one at the first value, and then\n          // a new one every N after that.\n          if (count++ % startBufferEvery! === 0) {\n            buffers.push([]);\n          }\n\n          // Push our value into our active buffers.\n          for (const buffer of buffers) {\n            buffer.push(value);\n            // Check to see if we're over the bufferSize\n            // if we are, record it so we can emit it later.\n            // If we emitted it now and removed it, it would\n            // mutate the `buffers` array while we're looping\n            // over it.\n            if (bufferSize <= buffer.length) {\n              toEmit = toEmit ?? [];\n              toEmit.push(buffer);\n            }\n          }\n\n          if (toEmit) {\n            // We have found some buffers that are over the\n            // `bufferSize`. Emit them, and remove them from our\n            // buffers list.\n            for (const buffer of toEmit) {\n              arrRemove(buffers, buffer);\n              subscriber.next(buffer);\n            }\n          }\n        },\n        () => {\n          // When the source completes, emit all of our\n          // active buffers.\n          for (const buffer of buffers) {\n            subscriber.next(buffer);\n          }\n          subscriber.complete();\n        },\n        // Pass all errors through to consumer.\n        undefined,\n        () => {\n          // Clean up our memory when we finalize\n          buffers = null!;\n        }\n      )\n    );\n  });\n}\n", "import { Observable } from '../Observable';\n\nimport { ObservableInput, OperatorFunction, ObservedValueOf } from '../types';\nimport { Subscription } from '../Subscription';\nimport { innerFrom } from '../observable/innerFrom';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { operate } from '../util/lift';\n\n/* tslint:disable:max-line-length */\nexport function catchError<T, O extends ObservableInput<any>>(\n  selector: (err: any, caught: Observable<T>) => O\n): OperatorFunction<T, T | ObservedValueOf<O>>;\n/* tslint:enable:max-line-length */\n\n/**\n * Catches errors on the observable to be handled by returning a new observable or throwing an error.\n *\n * <span class=\"informal\">\n * It only listens to the error channel and ignores notifications.\n * Handles errors from the source observable, and maps them to a new observable.\n * The error may also be rethrown, or a new error can be thrown to emit an error from the result.\n * </span>\n *\n * ![](catch.png)\n *\n * This operator handles errors, but forwards along all other events to the resulting observable.\n * If the source observable terminates with an error, it will map that error to a new observable,\n * subscribe to it, and forward all of its events to the resulting observable.\n *\n * ## Examples\n *\n * Continue with a different Observable when there's an error\n *\n * ```ts\n * import { of, map, catchError } from 'rxjs';\n *\n * of(1, 2, 3, 4, 5)\n *   .pipe(\n *     map(n => {\n *       if (n === 4) {\n *         throw 'four!';\n *       }\n *       return n;\n *     }),\n *     catchError(err => of('I', 'II', 'III', 'IV', 'V'))\n *   )\n *   .subscribe(x => console.log(x));\n *   // 1, 2, 3, I, II, III, IV, V\n * ```\n *\n * Retry the caught source Observable again in case of error, similar to `retry()` operator\n *\n * ```ts\n * import { of, map, catchError, take } from 'rxjs';\n *\n * of(1, 2, 3, 4, 5)\n *   .pipe(\n *     map(n => {\n *       if (n === 4) {\n *         throw 'four!';\n *       }\n *       return n;\n *     }),\n *     catchError((err, caught) => caught),\n *     take(30)\n *   )\n *   .subscribe(x => console.log(x));\n *   // 1, 2, 3, 1, 2, 3, ...\n * ```\n *\n * Throw a new error when the source Observable throws an error\n *\n * ```ts\n * import { of, map, catchError } from 'rxjs';\n *\n * of(1, 2, 3, 4, 5)\n *   .pipe(\n *     map(n => {\n *       if (n === 4) {\n *         throw 'four!';\n *       }\n *       return n;\n *     }),\n *     catchError(err => {\n *       throw 'error in source. Details: ' + err;\n *     })\n *   )\n *   .subscribe({\n *     next: x => console.log(x),\n *     error: err => console.log(err)\n *   });\n *   // 1, 2, 3, error in source. Details: four!\n * ```\n *\n * @see {@link onErrorResumeNext}\n * @see {@link repeat}\n * @see {@link repeatWhen}\n * @see {@link retry }\n * @see {@link retryWhen}\n *\n * @param {function} selector a function that takes as arguments `err`, which is the error, and `caught`, which\n * is the source observable, in case you'd like to \"retry\" that observable by returning it again. Whatever observable\n * is returned by the `selector` will be used to continue the observable chain.\n * @return A function that returns an Observable that originates from either\n * the source or the Observable returned by the `selector` function.\n */\nexport function catchError<T, O extends ObservableInput<any>>(\n  selector: (err: any, caught: Observable<T>) => O\n): OperatorFunction<T, T | ObservedValueOf<O>> {\n  return operate((source, subscriber) => {\n    let innerSub: Subscription | null = null;\n    let syncUnsub = false;\n    let handledResult: Observable<ObservedValueOf<O>>;\n\n    innerSub = source.subscribe(\n      createOperatorSubscriber(subscriber, undefined, undefined, (err) => {\n        handledResult = innerFrom(selector(err, catchError(selector)(source)));\n        if (innerSub) {\n          innerSub.unsubscribe();\n          innerSub = null;\n          handledResult.subscribe(subscriber);\n        } else {\n          // We don't have an innerSub yet, that means the error was synchronous\n          // because the subscribe call hasn't returned yet.\n          syncUnsub = true;\n        }\n      })\n    );\n\n    if (syncUnsub) {\n      // We have a synchronous error, we need to make sure to\n      // finalize right away. This ensures that callbacks in the `finalize` operator are called\n      // at the right time, and that finalization occurs at the expected\n      // time between the source error and the subscription to the\n      // next observable.\n      innerSub.unsubscribe();\n      innerSub = null;\n      handledResult!.subscribe(subscriber);\n    }\n  });\n}\n", "import { Observable } from '../Observable';\nimport { Subscriber } from '../Subscriber';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * A basic scan operation. This is used for `scan` and `reduce`.\n * @param accumulator The accumulator to use\n * @param seed The seed value for the state to accumulate\n * @param hasSeed Whether or not a seed was provided\n * @param emitOnNext Whether or not to emit the state on next\n * @param emitBeforeComplete Whether or not to emit the before completion\n */\n\nexport function scanInternals<V, A, S>(\n  accumulator: (acc: V | A | S, value: V, index: number) => A,\n  seed: S,\n  hasSeed: boolean,\n  emitOnNext: boolean,\n  emitBeforeComplete?: undefined | true\n) {\n  return (source: Observable<V>, subscriber: Subscriber<any>) => {\n    // Whether or not we have state yet. This will only be\n    // false before the first value arrives if we didn't get\n    // a seed value.\n    let hasState = hasSeed;\n    // The state that we're tracking, starting with the seed,\n    // if there is one, and then updated by the return value\n    // from the accumulator on each emission.\n    let state: any = seed;\n    // An index to pass to the accumulator function.\n    let index = 0;\n\n    // Subscribe to our source. All errors and completions are passed through.\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => {\n          // Always increment the index.\n          const i = index++;\n          // Set the state\n          state = hasState\n            ? // We already have state, so we can get the new state from the accumulator\n              accumulator(state, value, i)\n            : // We didn't have state yet, a seed value was not provided, so\n\n              // we set the state to the first value, and mark that we have state now\n              ((hasState = true), value);\n\n          // Maybe send it to the consumer.\n          emitOnNext && subscriber.next(state);\n        },\n        // If an onComplete was given, call it, otherwise\n        // just pass through the complete notification to the consumer.\n        emitBeforeComplete &&\n          (() => {\n            hasState && subscriber.next(state);\n            subscriber.complete();\n          })\n      )\n    );\n  };\n}\n", "import { combineLatestInit } from '../observable/combineLatest';\nimport { ObservableInput, ObservableInputTuple, OperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { argsOrArgArray } from '../util/argsOrArgArray';\nimport { mapOneOrManyArgs } from '../util/mapOneOrManyArgs';\nimport { pipe } from '../util/pipe';\nimport { popResultSelector } from '../util/args';\n\n/** @deprecated Replaced with {@link combineLatestWith}. Will be removed in v8. */\nexport function combineLatest<T, A extends readonly unknown[], R>(\n  sources: [...ObservableInputTuple<A>],\n  project: (...values: [T, ...A]) => R\n): OperatorFunction<T, R>;\n/** @deprecated Replaced with {@link combineLatestWith}. Will be removed in v8. */\nexport function combineLatest<T, A extends readonly unknown[], R>(sources: [...ObservableInputTuple<A>]): OperatorFunction<T, [T, ...A]>;\n\n/** @deprecated Replaced with {@link combineLatestWith}. Will be removed in v8. */\nexport function combineLatest<T, A extends readonly unknown[], R>(\n  ...sourcesAndProject: [...ObservableInputTuple<A>, (...values: [T, ...A]) => R]\n): OperatorFunction<T, R>;\n/** @deprecated Replaced with {@link combineLatestWith}. Will be removed in v8. */\nexport function combineLatest<T, A extends readonly unknown[], R>(...sources: [...ObservableInputTuple<A>]): OperatorFunction<T, [T, ...A]>;\n\n/**\n * @deprecated Replaced with {@link combineLatestWith}. Will be removed in v8.\n */\nexport function combineLatest<T, R>(...args: (ObservableInput<any> | ((...values: any[]) => R))[]): OperatorFunction<T, unknown> {\n  const resultSelector = popResultSelector(args);\n  return resultSelector\n    ? pipe(combineLatest(...(args as Array<ObservableInput<any>>)), mapOneOrManyArgs(resultSelector))\n    : operate((source, subscriber) => {\n        combineLatestInit([source, ...argsOrArgArray(args)])(subscriber);\n      });\n}\n", "import { ObservableInputTuple, OperatorFunction, Cons } from '../types';\nimport { combineLatest } from './combineLatest';\n\n/**\n * Create an observable that combines the latest values from all passed observables and the source\n * into arrays and emits them.\n *\n * Returns an observable, that when subscribed to, will subscribe to the source observable and all\n * sources provided as arguments. Once all sources emit at least one value, all of the latest values\n * will be emitted as an array. After that, every time any source emits a value, all of the latest values\n * will be emitted as an array.\n *\n * This is a useful operator for eagerly calculating values based off of changed inputs.\n *\n * ## Example\n *\n * Simple concatenation of values from two inputs\n *\n * ```ts\n * import { fromEvent, combineLatestWith, map } from 'rxjs';\n *\n * // Setup: Add two inputs to the page\n * const input1 = document.createElement('input');\n * document.body.appendChild(input1);\n * const input2 = document.createElement('input');\n * document.body.appendChild(input2);\n *\n * // Get streams of changes\n * const input1Changes$ = fromEvent(input1, 'change');\n * const input2Changes$ = fromEvent(input2, 'change');\n *\n * // Combine the changes by adding them together\n * input1Changes$.pipe(\n *   combineLatestWith(input2Changes$),\n *   map(([e1, e2]) => (<HTMLInputElement>e1.target).value + ' - ' + (<HTMLInputElement>e2.target).value)\n * )\n * .subscribe(x => console.log(x));\n * ```\n *\n * @param otherSources the other sources to subscribe to.\n * @return A function that returns an Observable that emits the latest\n * emissions from both source and provided Observables.\n */\nexport function combineLatestWith<T, A extends readonly unknown[]>(\n  ...otherSources: [...ObservableInputTuple<A>]\n): OperatorFunction<T, Cons<T, A>> {\n  return combineLatest(...otherSources);\n}\n", "import { Subscriber } from '../Subscriber';\nimport { MonoTypeOperatorFunction, ObservableInput } from '../types';\nimport { operate } from '../util/lift';\nimport { noop } from '../util/noop';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { innerFrom } from '../observable/innerFrom';\n\n/**\n * Emits a notification from the source Observable only after a particular time span\n * determined by another Observable has passed without another source emission.\n *\n * <span class=\"informal\">It's like {@link debounceTime}, but the time span of\n * emission silence is determined by a second Observable.</span>\n *\n * ![](debounce.svg)\n *\n * `debounce` delays notifications emitted by the source Observable, but drops previous\n * pending delayed emissions if a new notification arrives on the source Observable.\n * This operator keeps track of the most recent notification from the source\n * Observable, and spawns a duration Observable by calling the\n * `durationSelector` function. The notification is emitted only when the duration\n * Observable emits a next notification, and if no other notification was emitted on\n * the source Observable since the duration Observable was spawned. If a new\n * notification appears before the duration Observable emits, the previous notification will\n * not be emitted and a new duration is scheduled from `durationSelector` is scheduled.\n * If the completing event happens during the scheduled duration the last cached notification\n * is emitted before the completion event is forwarded to the output observable.\n * If the error event happens during the scheduled duration or after it only the error event is\n * forwarded to the output observable. The cache notification is not emitted in this case.\n *\n * Like {@link debounceTime}, this is a rate-limiting operator, and also a\n * delay-like operator since output emissions do not necessarily occur at the\n * same time as they did on the source Observable.\n *\n * ## Example\n *\n * Emit the most recent click after a burst of clicks\n *\n * ```ts\n * import { fromEvent, scan, debounce, interval } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(\n *   scan(i => ++i, 1),\n *   debounce(i => interval(200 * i))\n * );\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link audit}\n * @see {@link auditTime}\n * @see {@link debounceTime}\n * @see {@link delay}\n * @see {@link sample}\n * @see {@link sampleTime}\n * @see {@link throttle}\n * @see {@link throttleTime}\n *\n * @param durationSelector A function\n * that receives a value from the source Observable, for computing the timeout\n * duration for each source value, returned as an Observable or a Promise.\n * @return A function that returns an Observable that delays the emissions of\n * the source Observable by the specified duration Observable returned by\n * `durationSelector`, and may drop some values if they occur too frequently.\n */\nexport function debounce<T>(durationSelector: (value: T) => ObservableInput<any>): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    let hasValue = false;\n    let lastValue: T | null = null;\n    // The subscriber/subscription for the current debounce, if there is one.\n    let durationSubscriber: Subscriber<any> | null = null;\n\n    const emit = () => {\n      // Unsubscribe any current debounce subscription we have,\n      // we only cared about the first notification from it, and we\n      // want to clean that subscription up as soon as possible.\n      durationSubscriber?.unsubscribe();\n      durationSubscriber = null;\n      if (hasValue) {\n        // We have a value! Free up memory first, then emit the value.\n        hasValue = false;\n        const value = lastValue!;\n        lastValue = null;\n        subscriber.next(value);\n      }\n    };\n\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value: T) => {\n          // Cancel any pending debounce duration. We don't\n          // need to null it out here yet tho, because we're just going\n          // to create another one in a few lines.\n          durationSubscriber?.unsubscribe();\n          hasValue = true;\n          lastValue = value;\n          // Capture our duration subscriber, so we can unsubscribe it when we're notified\n          // and we're going to emit the value.\n          durationSubscriber = createOperatorSubscriber(subscriber, emit, noop);\n          // Subscribe to the duration.\n          innerFrom(durationSelector(value)).subscribe(durationSubscriber);\n        },\n        () => {\n          // Source completed.\n          // Emit any pending debounced values then complete\n          emit();\n          subscriber.complete();\n        },\n        // Pass all errors through to consumer\n        undefined,\n        () => {\n          // Finalization.\n          lastValue = durationSubscriber = null;\n        }\n      )\n    );\n  });\n}\n", "import { asyncScheduler } from '../scheduler/async';\nimport { Subscription } from '../Subscription';\nimport { MonoTypeOperatorFunction, SchedulerAction, SchedulerLike } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * Emits a notification from the source Observable only after a particular time span\n * has passed without another source emission.\n *\n * <span class=\"informal\">It's like {@link delay}, but passes only the most\n * recent notification from each burst of emissions.</span>\n *\n * ![](debounceTime.png)\n *\n * `debounceTime` delays notifications emitted by the source Observable, but drops\n * previous pending delayed emissions if a new notification arrives on the source\n * Observable. This operator keeps track of the most recent notification from the\n * source Observable, and emits that only when `dueTime` has passed\n * without any other notification appearing on the source Observable. If a new value\n * appears before `dueTime` silence occurs, the previous notification will be dropped\n * and will not be emitted and a new `dueTime` is scheduled.\n * If the completing event happens during `dueTime` the last cached notification\n * is emitted before the completion event is forwarded to the output observable.\n * If the error event happens during `dueTime` or after it only the error event is\n * forwarded to the output observable. The cache notification is not emitted in this case.\n *\n * This is a rate-limiting operator, because it is impossible for more than one\n * notification to be emitted in any time window of duration `dueTime`, but it is also\n * a delay-like operator since output emissions do not occur at the same time as\n * they did on the source Observable. Optionally takes a {@link SchedulerLike} for\n * managing timers.\n *\n * ## Example\n *\n * Emit the most recent click after a burst of clicks\n *\n * ```ts\n * import { fromEvent, debounceTime } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(debounceTime(1000));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link audit}\n * @see {@link auditTime}\n * @see {@link debounce}\n * @see {@link sample}\n * @see {@link sampleTime}\n * @see {@link throttle}\n * @see {@link throttleTime}\n *\n * @param {number} dueTime The timeout duration in milliseconds (or the time\n * unit determined internally by the optional `scheduler`) for the window of\n * time required to wait for emission silence before emitting the most recent\n * source value.\n * @param {SchedulerLike} [scheduler=async] The {@link SchedulerLike} to use for\n * managing the timers that handle the timeout for each value.\n * @return A function that returns an Observable that delays the emissions of\n * the source Observable by the specified `dueTime`, and may drop some values\n * if they occur too frequently.\n */\nexport function debounceTime<T>(dueTime: number, scheduler: SchedulerLike = asyncScheduler): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    let activeTask: Subscription | null = null;\n    let lastValue: T | null = null;\n    let lastTime: number | null = null;\n\n    const emit = () => {\n      if (activeTask) {\n        // We have a value! Free up memory first, then emit the value.\n        activeTask.unsubscribe();\n        activeTask = null;\n        const value = lastValue!;\n        lastValue = null;\n        subscriber.next(value);\n      }\n    };\n    function emitWhenIdle(this: SchedulerAction<unknown>) {\n      // This is called `dueTime` after the first value\n      // but we might have received new values during this window!\n\n      const targetTime = lastTime! + dueTime;\n      const now = scheduler.now();\n      if (now < targetTime) {\n        // On that case, re-schedule to the new target\n        activeTask = this.schedule(undefined, targetTime - now);\n        subscriber.add(activeTask);\n        return;\n      }\n\n      emit();\n    }\n\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value: T) => {\n          lastValue = value;\n          lastTime = scheduler.now();\n\n          // Only set up a task if it's not already up\n          if (!activeTask) {\n            activeTask = scheduler.schedule(emitWhenIdle, dueTime);\n            subscriber.add(activeTask);\n          }\n        },\n        () => {\n          // Source completed.\n          // Emit any pending debounced values then complete\n          emit();\n          subscriber.complete();\n        },\n        // Pass all errors through to consumer.\n        undefined,\n        () => {\n          // Finalization.\n          lastValue = activeTask = null;\n        }\n      )\n    );\n  });\n}\n", "import { OperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * Emits a given value if the source Observable completes without emitting any\n * `next` value, otherwise mirrors the source Observable.\n *\n * <span class=\"informal\">If the source Observable turns out to be empty, then\n * this operator will emit a default value.</span>\n *\n * ![](defaultIfEmpty.png)\n *\n * `defaultIfEmpty` emits the values emitted by the source Observable or a\n * specified default value if the source Observable is empty (completes without\n * having emitted any `next` value).\n *\n * ## Example\n *\n * If no clicks happen in 5 seconds, then emit 'no clicks'\n *\n * ```ts\n * import { fromEvent, takeUntil, interval, defaultIfEmpty } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const clicksBeforeFive = clicks.pipe(takeUntil(interval(5000)));\n * const result = clicksBeforeFive.pipe(defaultIfEmpty('no clicks'));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link empty}\n * @see {@link last}\n *\n * @param defaultValue The default value used if the source\n * Observable is empty.\n * @return A function that returns an Observable that emits either the\n * specified `defaultValue` if the source Observable emits no items, or the\n * values emitted by the source Observable.\n */\nexport function defaultIfEmpty<T, R>(defaultValue: R): OperatorFunction<T, T | R> {\n  return operate((source, subscriber) => {\n    let hasValue = false;\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => {\n          hasValue = true;\n          subscriber.next(value);\n        },\n        () => {\n          if (!hasValue) {\n            subscriber.next(defaultValue!);\n          }\n          subscriber.complete();\n        }\n      )\n    );\n  });\n}\n", "import { MonoTypeOperatorFunction } from '../types';\nimport { EMPTY } from '../observable/empty';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * Emits only the first `count` values emitted by the source Observable.\n *\n * <span class=\"informal\">Takes the first `count` values from the source, then\n * completes.</span>\n *\n * ![](take.png)\n *\n * `take` returns an Observable that emits only the first `count` values emitted\n * by the source Observable. If the source emits fewer than `count` values then\n * all of its values are emitted. After that, it completes, regardless if the\n * source completes.\n *\n * ## Example\n *\n * Take the first 5 seconds of an infinite 1-second interval Observable\n *\n * ```ts\n * import { interval, take } from 'rxjs';\n *\n * const intervalCount = interval(1000);\n * const takeFive = intervalCount.pipe(take(5));\n * takeFive.subscribe(x => console.log(x));\n *\n * // Logs:\n * // 0\n * // 1\n * // 2\n * // 3\n * // 4\n * ```\n *\n * @see {@link takeLast}\n * @see {@link takeUntil}\n * @see {@link takeWhile}\n * @see {@link skip}\n *\n * @param count The maximum number of `next` values to emit.\n * @return A function that returns an Observable that emits only the first\n * `count` values emitted by the source Observable, or all of the values from\n * the source if the source emits fewer than `count` values.\n */\nexport function take<T>(count: number): MonoTypeOperatorFunction<T> {\n  return count <= 0\n    ? // If we are taking no values, that's empty.\n      () => EMPTY\n    : operate((source, subscriber) => {\n        let seen = 0;\n        source.subscribe(\n          createOperatorSubscriber(subscriber, (value) => {\n            // Increment the number of values we have seen,\n            // then check it against the allowed count to see\n            // if we are still letting values through.\n            if (++seen <= count) {\n              subscriber.next(value);\n              // If we have met or passed our allowed count,\n              // we need to complete. We have to do <= here,\n              // because re-entrant code will increment `seen` twice.\n              if (count <= seen) {\n                subscriber.complete();\n              }\n            }\n          })\n        );\n      });\n}\n", "import { OperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { noop } from '../util/noop';\n\n/**\n * Ignores all items emitted by the source Observable and only passes calls of `complete` or `error`.\n *\n * ![](ignoreElements.png)\n *\n * The `ignoreElements` operator suppresses all items emitted by the source Observable,\n * but allows its termination notification (either `error` or `complete`) to pass through unchanged.\n *\n * If you do not care about the items being emitted by an Observable, but you do want to be notified\n * when it completes or when it terminates with an error, you can apply the `ignoreElements` operator\n * to the Observable, which will ensure that it will never call its observers\u2019 `next` handlers.\n *\n * ## Example\n *\n * Ignore all `next` emissions from the source\n *\n * ```ts\n * import { of, ignoreElements } from 'rxjs';\n *\n * of('you', 'talking', 'to', 'me')\n *   .pipe(ignoreElements())\n *   .subscribe({\n *     next: word => console.log(word),\n *     error: err => console.log('error:', err),\n *     complete: () => console.log('the end'),\n *   });\n *\n * // result:\n * // 'the end'\n * ```\n *\n * @return A function that returns an empty Observable that only calls\n * `complete` or `error`, based on which one is called by the source\n * Observable.\n */\nexport function ignoreElements(): OperatorFunction<unknown, never> {\n  return operate((source, subscriber) => {\n    source.subscribe(createOperatorSubscriber(subscriber, noop));\n  });\n}\n", "import { OperatorFunction } from '../types';\nimport { map } from './map';\n\n/** @deprecated To be removed in v9. Use {@link map} instead: `map(() => value)`. */\nexport function mapTo<R>(value: R): OperatorFunction<unknown, R>;\n/**\n * @deprecated Do not specify explicit type parameters. Signatures with type parameters\n * that cannot be inferred will be removed in v8. `mapTo` itself will be removed in v9,\n * use {@link map} instead: `map(() => value)`.\n * */\nexport function mapTo<T, R>(value: R): OperatorFunction<T, R>;\n\n/**\n * Emits the given constant value on the output Observable every time the source\n * Observable emits a value.\n *\n * <span class=\"informal\">Like {@link map}, but it maps every source value to\n * the same output value every time.</span>\n *\n * ![](mapTo.png)\n *\n * Takes a constant `value` as argument, and emits that whenever the source\n * Observable emits a value. In other words, ignores the actual source value,\n * and simply uses the emission moment to know when to emit the given `value`.\n *\n * ## Example\n *\n * Map every click to the string `'Hi'`\n *\n * ```ts\n * import { fromEvent, mapTo } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const greetings = clicks.pipe(mapTo('Hi'));\n *\n * greetings.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link map}\n *\n * @param value The value to map each source value to.\n * @return A function that returns an Observable that emits the given `value`\n * every time the source Observable emits.\n * @deprecated To be removed in v9. Use {@link map} instead: `map(() => value)`.\n */\nexport function mapTo<R>(value: R): OperatorFunction<unknown, R> {\n  return map(() => value);\n}\n", "import { Observable } from '../Observable';\nimport { MonoTypeOperatorFunction, ObservableInput } from '../types';\nimport { concat } from '../observable/concat';\nimport { take } from './take';\nimport { ignoreElements } from './ignoreElements';\nimport { mapTo } from './mapTo';\nimport { mergeMap } from './mergeMap';\nimport { innerFrom } from '../observable/innerFrom';\n\n/** @deprecated The `subscriptionDelay` parameter will be removed in v8. */\nexport function delayWhen<T>(\n  delayDurationSelector: (value: T, index: number) => ObservableInput<any>,\n  subscriptionDelay: Observable<any>\n): MonoTypeOperatorFunction<T>;\nexport function delayWhen<T>(delayDurationSelector: (value: T, index: number) => ObservableInput<any>): MonoTypeOperatorFunction<T>;\n\n/**\n * Delays the emission of items from the source Observable by a given time span\n * determined by the emissions of another Observable.\n *\n * <span class=\"informal\">It's like {@link delay}, but the time span of the\n * delay duration is determined by a second Observable.</span>\n *\n * ![](delayWhen.png)\n *\n * `delayWhen` operator shifts each emitted value from the source Observable by\n * a time span determined by another Observable. When the source emits a value,\n * the `delayDurationSelector` function is called with the value emitted from\n * the source Observable as the first argument to the `delayDurationSelector`.\n * The `delayDurationSelector` function should return an {@link ObservableInput},\n * that is internally converted to an Observable that is called the \"duration\"\n * Observable.\n *\n * The source value is emitted on the output Observable only when the \"duration\"\n * Observable emits ({@link guide/glossary-and-semantics#next next}s) any value.\n * Upon that, the \"duration\" Observable gets unsubscribed.\n *\n * Before RxJS V7, the {@link guide/glossary-and-semantics#complete completion}\n * of the \"duration\" Observable would have been triggering the emission of the\n * source value to the output Observable, but with RxJS V7, this is not the case\n * anymore.\n *\n * Only next notifications (from the \"duration\" Observable) trigger values from\n * the source Observable to be passed to the output Observable. If the \"duration\"\n * Observable only emits the complete notification (without next), the value\n * emitted by the source Observable will never get to the output Observable - it\n * will be swallowed. If the \"duration\" Observable errors, the error will be\n * propagated to the output Observable.\n *\n * Optionally, `delayWhen` takes a second argument, `subscriptionDelay`, which\n * is an Observable. When `subscriptionDelay` emits its first value or\n * completes, the source Observable is subscribed to and starts behaving like\n * described in the previous paragraph. If `subscriptionDelay` is not provided,\n * `delayWhen` will subscribe to the source Observable as soon as the output\n * Observable is subscribed.\n *\n * ## Example\n *\n * Delay each click by a random amount of time, between 0 and 5 seconds\n *\n * ```ts\n * import { fromEvent, delayWhen, interval } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const delayedClicks = clicks.pipe(\n *   delayWhen(() => interval(Math.random() * 5000))\n * );\n * delayedClicks.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link delay}\n * @see {@link throttle}\n * @see {@link throttleTime}\n * @see {@link debounce}\n * @see {@link debounceTime}\n * @see {@link sample}\n * @see {@link sampleTime}\n * @see {@link audit}\n * @see {@link auditTime}\n *\n * @param delayDurationSelector A function that returns an `ObservableInput` for\n * each `value` emitted by the source Observable, which is then used to delay the\n * emission of that `value` on the output Observable until the `ObservableInput`\n * returned from this function emits a next value. When called, beside `value`,\n * this function receives a zero-based `index` of the emission order.\n * @param subscriptionDelay An Observable that triggers the subscription to the\n * source Observable once it emits any value.\n * @return A function that returns an Observable that delays the emissions of\n * the source Observable by an amount of time specified by the Observable\n * returned by `delayDurationSelector`.\n */\nexport function delayWhen<T>(\n  delayDurationSelector: (value: T, index: number) => ObservableInput<any>,\n  subscriptionDelay?: Observable<any>\n): MonoTypeOperatorFunction<T> {\n  if (subscriptionDelay) {\n    // DEPRECATED PATH\n    return (source: Observable<T>) =>\n      concat(subscriptionDelay.pipe(take(1), ignoreElements()), source.pipe(delayWhen(delayDurationSelector)));\n  }\n\n  return mergeMap((value, index) => innerFrom(delayDurationSelector(value, index)).pipe(take(1), mapTo(value)));\n}\n", "import { asyncScheduler } from '../scheduler/async';\nimport { MonoTypeOperatorFunction, SchedulerLike } from '../types';\nimport { delayWhen } from './delayWhen';\nimport { timer } from '../observable/timer';\n\n/**\n * Delays the emission of items from the source Observable by a given timeout or\n * until a given Date.\n *\n * <span class=\"informal\">Time shifts each item by some specified amount of\n * milliseconds.</span>\n *\n * ![](delay.svg)\n *\n * If the delay argument is a Number, this operator time shifts the source\n * Observable by that amount of time expressed in milliseconds. The relative\n * time intervals between the values are preserved.\n *\n * If the delay argument is a Date, this operator time shifts the start of the\n * Observable execution until the given date occurs.\n *\n * ## Examples\n *\n * Delay each click by one second\n *\n * ```ts\n * import { fromEvent, delay } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const delayedClicks = clicks.pipe(delay(1000)); // each click emitted after 1 second\n * delayedClicks.subscribe(x => console.log(x));\n * ```\n *\n * Delay all clicks until a future date happens\n *\n * ```ts\n * import { fromEvent, delay } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const date = new Date('March 15, 2050 12:00:00'); // in the future\n * const delayedClicks = clicks.pipe(delay(date)); // click emitted only after that date\n * delayedClicks.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link delayWhen}\n * @see {@link throttle}\n * @see {@link throttleTime}\n * @see {@link debounce}\n * @see {@link debounceTime}\n * @see {@link sample}\n * @see {@link sampleTime}\n * @see {@link audit}\n * @see {@link auditTime}\n *\n * @param {number|Date} due The delay duration in milliseconds (a `number`) or\n * a `Date` until which the emission of the source items is delayed.\n * @param {SchedulerLike} [scheduler=async] The {@link SchedulerLike} to use for\n * managing the timers that handle the time-shift for each item.\n * @return A function that returns an Observable that delays the emissions of\n * the source Observable by the specified timeout or Date.\n */\nexport function delay<T>(due: number | Date, scheduler: SchedulerLike = asyncScheduler): MonoTypeOperatorFunction<T> {\n  const duration = timer(due, scheduler);\n  return delayWhen(() => duration);\n}\n", "import { MonoTypeOperatorFunction } from '../types';\nimport { identity } from '../util/identity';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\nexport function distinctUntilChanged<T>(comparator?: (previous: T, current: T) => boolean): MonoTypeOperatorFunction<T>;\nexport function distinctUntilChanged<T, K>(\n  comparator: (previous: K, current: K) => boolean,\n  keySelector: (value: T) => K\n): MonoTypeOperatorFunction<T>;\n\n/**\n * Returns a result {@link Observable} that emits all values pushed by the source observable if they\n * are distinct in comparison to the last value the result observable emitted.\n *\n * When provided without parameters or with the first parameter (`{@link distinctUntilChanged#comparator comparator}`),\n * it behaves like this:\n *\n * 1. It will always emit the first value from the source.\n * 2. For all subsequent values pushed by the source, they will be compared to the previously emitted values\n *    using the provided `comparator` or an `===` equality check.\n * 3. If the value pushed by the source is determined to be unequal by this check, that value is emitted and\n *    becomes the new \"previously emitted value\" internally.\n *\n * When the second parameter (`{@link distinctUntilChanged#keySelector keySelector}`) is provided, the behavior\n * changes:\n *\n * 1. It will always emit the first value from the source.\n * 2. The `keySelector` will be run against all values, including the first value.\n * 3. For all values after the first, the selected key will be compared against the key selected from\n *    the previously emitted value using the `comparator`.\n * 4. If the keys are determined to be unequal by this check, the value (not the key), is emitted\n *    and the selected key from that value is saved for future comparisons against other keys.\n *\n * ## Examples\n *\n * A very basic example with no `{@link distinctUntilChanged#comparator comparator}`. Note that `1` is emitted more than once,\n * because it's distinct in comparison to the _previously emitted_ value,\n * not in comparison to _all other emitted values_.\n *\n * ```ts\n * import { of, distinctUntilChanged } from 'rxjs';\n *\n * of(1, 1, 1, 2, 2, 2, 1, 1, 3, 3)\n *   .pipe(distinctUntilChanged())\n *   .subscribe(console.log);\n * // Logs: 1, 2, 1, 3\n * ```\n *\n * With a `{@link distinctUntilChanged#comparator comparator}`, you can do custom comparisons. Let's say\n * you only want to emit a value when all of its components have\n * changed:\n *\n * ```ts\n * import { of, distinctUntilChanged } from 'rxjs';\n *\n * const totallyDifferentBuilds$ = of(\n *   { engineVersion: '1.1.0', transmissionVersion: '1.2.0' },\n *   { engineVersion: '1.1.0', transmissionVersion: '1.4.0' },\n *   { engineVersion: '1.3.0', transmissionVersion: '1.4.0' },\n *   { engineVersion: '1.3.0', transmissionVersion: '1.5.0' },\n *   { engineVersion: '2.0.0', transmissionVersion: '1.5.0' }\n * ).pipe(\n *   distinctUntilChanged((prev, curr) => {\n *     return (\n *       prev.engineVersion === curr.engineVersion ||\n *       prev.transmissionVersion === curr.transmissionVersion\n *     );\n *   })\n * );\n *\n * totallyDifferentBuilds$.subscribe(console.log);\n *\n * // Logs:\n * // { engineVersion: '1.1.0', transmissionVersion: '1.2.0' }\n * // { engineVersion: '1.3.0', transmissionVersion: '1.4.0' }\n * // { engineVersion: '2.0.0', transmissionVersion: '1.5.0' }\n * ```\n *\n * You can also provide a custom `{@link distinctUntilChanged#comparator comparator}` to check that emitted\n * changes are only in one direction. Let's say you only want to get\n * the next record temperature:\n *\n * ```ts\n * import { of, distinctUntilChanged } from 'rxjs';\n *\n * const temps$ = of(30, 31, 20, 34, 33, 29, 35, 20);\n *\n * const recordHighs$ = temps$.pipe(\n *   distinctUntilChanged((prevHigh, temp) => {\n *     // If the current temp is less than\n *     // or the same as the previous record,\n *     // the record hasn't changed.\n *     return temp <= prevHigh;\n *   })\n * );\n *\n * recordHighs$.subscribe(console.log);\n * // Logs: 30, 31, 34, 35\n * ```\n *\n * Selecting update events only when the `updatedBy` field shows\n * the account changed hands.\n *\n * ```ts\n * import { of, distinctUntilChanged } from 'rxjs';\n *\n * // A stream of updates to a given account\n * const accountUpdates$ = of(\n *   { updatedBy: 'blesh', data: [] },\n *   { updatedBy: 'blesh', data: [] },\n *   { updatedBy: 'ncjamieson', data: [] },\n *   { updatedBy: 'ncjamieson', data: [] },\n *   { updatedBy: 'blesh', data: [] }\n * );\n *\n * // We only want the events where it changed hands\n * const changedHands$ = accountUpdates$.pipe(\n *   distinctUntilChanged(undefined, update => update.updatedBy)\n * );\n *\n * changedHands$.subscribe(console.log);\n * // Logs:\n * // { updatedBy: 'blesh', data: Array[0] }\n * // { updatedBy: 'ncjamieson', data: Array[0] }\n * // { updatedBy: 'blesh', data: Array[0] }\n * ```\n *\n * @see {@link distinct}\n * @see {@link distinctUntilKeyChanged}\n *\n * @param comparator A function used to compare the previous and current keys for\n * equality. Defaults to a `===` check.\n * @param keySelector Used to select a key value to be passed to the `comparator`.\n *\n * @return A function that returns an Observable that emits items from the\n * source Observable with distinct values.\n */\nexport function distinctUntilChanged<T, K>(\n  comparator?: (previous: K, current: K) => boolean,\n  keySelector: (value: T) => K = identity as (value: T) => K\n): MonoTypeOperatorFunction<T> {\n  // We've been allowing `null` do be passed as the `compare`, so we can't do\n  // a default value for the parameter, because that will only work\n  // for `undefined`.\n  comparator = comparator ?? defaultCompare;\n\n  return operate((source, subscriber) => {\n    // The previous key, used to compare against keys selected\n    // from new arrivals to determine \"distinctiveness\".\n    let previousKey: K;\n    // Whether or not this is the first value we've gotten.\n    let first = true;\n\n    source.subscribe(\n      createOperatorSubscriber(subscriber, (value) => {\n        // We always call the key selector.\n        const currentKey = keySelector(value);\n\n        // If it's the first value, we always emit it.\n        // Otherwise, we compare this key to the previous key, and\n        // if the comparer returns false, we emit.\n        if (first || !comparator!(previousKey, currentKey)) {\n          // Update our state *before* we emit the value\n          // as emission can be the source of re-entrant code\n          // in functional libraries like this. We only really\n          // need to do this if it's the first value, or if the\n          // key we're tracking in previous needs to change.\n          first = false;\n          previousKey = currentKey;\n\n          // Emit the value!\n          subscriber.next(value);\n        }\n      })\n    );\n  });\n}\n\nfunction defaultCompare(a: any, b: any) {\n  return a === b;\n}\n", "import { distinctUntilChanged } from './distinctUntilChanged';\nimport { MonoTypeOperatorFunction } from '../types';\n\n/* tslint:disable:max-line-length */\nexport function distinctUntilKeyChanged<T>(key: keyof T): MonoTypeOperatorFunction<T>;\nexport function distinctUntilKeyChanged<T, K extends keyof T>(key: K, compare: (x: T[K], y: T[K]) => boolean): MonoTypeOperatorFunction<T>;\n/* tslint:enable:max-line-length */\n\n/**\n * Returns an Observable that emits all items emitted by the source Observable that are distinct by comparison from the previous item,\n * using a property accessed by using the key provided to check if the two items are distinct.\n *\n * If a comparator function is provided, then it will be called for each item to test for whether or not that value should be emitted.\n *\n * If a comparator function is not provided, an equality check is used by default.\n *\n * ## Examples\n *\n * An example comparing the name of persons\n *\n * ```ts\n * import { of, distinctUntilKeyChanged } from 'rxjs';\n *\n * of(\n *   { age: 4, name: 'Foo' },\n *   { age: 7, name: 'Bar' },\n *   { age: 5, name: 'Foo' },\n *   { age: 6, name: 'Foo' }\n * ).pipe(\n *   distinctUntilKeyChanged('name')\n * )\n * .subscribe(x => console.log(x));\n *\n * // displays:\n * // { age: 4, name: 'Foo' }\n * // { age: 7, name: 'Bar' }\n * // { age: 5, name: 'Foo' }\n * ```\n *\n * An example comparing the first letters of the name\n *\n * ```ts\n * import { of, distinctUntilKeyChanged } from 'rxjs';\n *\n * of(\n *   { age: 4, name: 'Foo1' },\n *   { age: 7, name: 'Bar' },\n *   { age: 5, name: 'Foo2' },\n *   { age: 6, name: 'Foo3' }\n * ).pipe(\n *   distinctUntilKeyChanged('name', (x, y) => x.substring(0, 3) === y.substring(0, 3))\n * )\n * .subscribe(x => console.log(x));\n *\n * // displays:\n * // { age: 4, name: 'Foo1' }\n * // { age: 7, name: 'Bar' }\n * // { age: 5, name: 'Foo2' }\n * ```\n *\n * @see {@link distinct}\n * @see {@link distinctUntilChanged}\n *\n * @param {string} key String key for object property lookup on each item.\n * @param {function} [compare] Optional comparison function called to test if an item is distinct from the previous item in the source.\n * @return A function that returns an Observable that emits items from the\n * source Observable with distinct values based on the key specified.\n */\nexport function distinctUntilKeyChanged<T, K extends keyof T>(key: K, compare?: (x: T[K], y: T[K]) => boolean): MonoTypeOperatorFunction<T> {\n  return distinctUntilChanged((x: T, y: T) => compare ? compare(x[key], y[key]) : x[key] === y[key]);\n}\n", "import { EmptyError } from '../util/EmptyError';\nimport { MonoTypeOperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * If the source observable completes without emitting a value, it will emit\n * an error. The error will be created at that time by the optional\n * `errorFactory` argument, otherwise, the error will be {@link EmptyError}.\n *\n * ![](throwIfEmpty.png)\n *\n * ## Example\n *\n * Throw an error if the document wasn't clicked within 1 second\n *\n * ```ts\n * import { fromEvent, takeUntil, timer, throwIfEmpty } from 'rxjs';\n *\n * const click$ = fromEvent(document, 'click');\n *\n * click$.pipe(\n *   takeUntil(timer(1000)),\n *   throwIfEmpty(() => new Error('The document was not clicked within 1 second'))\n * )\n * .subscribe({\n *   next() {\n *    console.log('The document was clicked');\n *   },\n *   error(err) {\n *     console.error(err.message);\n *   }\n * });\n * ```\n *\n * @param errorFactory A factory function called to produce the\n * error to be thrown when the source observable completes without emitting a\n * value.\n * @return A function that returns an Observable that throws an error if the\n * source Observable completed without emitting.\n */\nexport function throwIfEmpty<T>(errorFactory: () => any = defaultErrorFactory): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    let hasValue = false;\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => {\n          hasValue = true;\n          subscriber.next(value);\n        },\n        () => (hasValue ? subscriber.complete() : subscriber.error(errorFactory()))\n      )\n    );\n  });\n}\n\nfunction defaultErrorFactory() {\n  return new EmptyError();\n}\n", "/** prettier */\nimport { Observable } from '../Observable';\nimport { concat } from '../observable/concat';\nimport { of } from '../observable/of';\nimport { MonoTypeOperatorFunction, SchedulerLike, OperatorFunction, ValueFromArray } from '../types';\n\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `concatAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function endWith<T>(scheduler: SchedulerLike): MonoTypeOperatorFunction<T>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `concatAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function endWith<T, A extends unknown[] = T[]>(\n  ...valuesAndScheduler: [...A, SchedulerLike]\n): OperatorFunction<T, T | ValueFromArray<A>>;\n\nexport function endWith<T, A extends unknown[] = T[]>(...values: A): OperatorFunction<T, T | ValueFromArray<A>>;\n\n/**\n * Returns an observable that will emit all values from the source, then synchronously emit\n * the provided value(s) immediately after the source completes.\n *\n * NOTE: Passing a last argument of a Scheduler is _deprecated_, and may result in incorrect\n * types in TypeScript.\n *\n * This is useful for knowing when an observable ends. Particularly when paired with an\n * operator like {@link takeUntil}\n *\n * ![](endWith.png)\n *\n * ## Example\n *\n * Emit values to know when an interval starts and stops. The interval will\n * stop when a user clicks anywhere on the document.\n *\n * ```ts\n * import { interval, map, fromEvent, startWith, takeUntil, endWith } from 'rxjs';\n *\n * const ticker$ = interval(5000).pipe(\n *   map(() => 'tick')\n * );\n *\n * const documentClicks$ = fromEvent(document, 'click');\n *\n * ticker$.pipe(\n *   startWith('interval started'),\n *   takeUntil(documentClicks$),\n *   endWith('interval ended by click')\n * )\n * .subscribe(x => console.log(x));\n *\n * // Result (assuming a user clicks after 15 seconds)\n * // 'interval started'\n * // 'tick'\n * // 'tick'\n * // 'tick'\n * // 'interval ended by click'\n * ```\n *\n * @see {@link startWith}\n * @see {@link concat}\n * @see {@link takeUntil}\n *\n * @param values Items you want the modified Observable to emit last.\n * @return A function that returns an Observable that emits all values from the\n * source, then synchronously emits the provided value(s) immediately after the\n * source completes.\n */\nexport function endWith<T>(...values: Array<T | SchedulerLike>): MonoTypeOperatorFunction<T> {\n  return (source: Observable<T>) => concat(source, of(...values)) as Observable<T>;\n}\n", "import { MonoTypeOperatorFunction } from '../types';\nimport { operate } from '../util/lift';\n\n/**\n * Returns an Observable that mirrors the source Observable, but will call a specified function when\n * the source terminates on complete or error.\n * The specified function will also be called when the subscriber explicitly unsubscribes.\n *\n * ## Examples\n *\n * Execute callback function when the observable completes\n *\n * ```ts\n * import { interval, take, finalize } from 'rxjs';\n *\n * // emit value in sequence every 1 second\n * const source = interval(1000);\n * const example = source.pipe(\n *   take(5), //take only the first 5 values\n *   finalize(() => console.log('Sequence complete')) // Execute when the observable completes\n * );\n * const subscribe = example.subscribe(val => console.log(val));\n *\n * // results:\n * // 0\n * // 1\n * // 2\n * // 3\n * // 4\n * // 'Sequence complete'\n * ```\n *\n * Execute callback function when the subscriber explicitly unsubscribes\n *\n * ```ts\n * import { interval, finalize, tap, noop, timer } from 'rxjs';\n *\n * const source = interval(100).pipe(\n *   finalize(() => console.log('[finalize] Called')),\n *   tap({\n *     next: () => console.log('[next] Called'),\n *     error: () => console.log('[error] Not called'),\n *     complete: () => console.log('[tap complete] Not called')\n *   })\n * );\n *\n * const sub = source.subscribe({\n *   next: x => console.log(x),\n *   error: noop,\n *   complete: () => console.log('[complete] Not called')\n * });\n *\n * timer(150).subscribe(() => sub.unsubscribe());\n *\n * // results:\n * // '[next] Called'\n * // 0\n * // '[finalize] Called'\n * ```\n *\n * @param {function} callback Function to be called when source terminates.\n * @return A function that returns an Observable that mirrors the source, but\n * will call the specified function on termination.\n */\nexport function finalize<T>(callback: () => void): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    // TODO: This try/finally was only added for `useDeprecatedSynchronousErrorHandling`.\n    // REMOVE THIS WHEN THAT HOT GARBAGE IS REMOVED IN V8.\n    try {\n      source.subscribe(subscriber);\n    } finally {\n      subscriber.add(callback);\n    }\n  });\n}\n", "import { Observable } from '../Observable';\nimport { EmptyError } from '../util/EmptyError';\nimport { OperatorFunction, TruthyTypesOf } from '../types';\nimport { filter } from './filter';\nimport { take } from './take';\nimport { defaultIfEmpty } from './defaultIfEmpty';\nimport { throwIfEmpty } from './throwIfEmpty';\nimport { identity } from '../util/identity';\n\nexport function first<T, D = T>(predicate?: null, defaultValue?: D): OperatorFunction<T, T | D>;\nexport function first<T>(predicate: BooleanConstructor): OperatorFunction<T, TruthyTypesOf<T>>;\nexport function first<T, D>(predicate: BooleanConstructor, defaultValue: D): OperatorFunction<T, TruthyTypesOf<T> | D>;\nexport function first<T, S extends T>(\n  predicate: (value: T, index: number, source: Observable<T>) => value is S,\n  defaultValue?: S\n): OperatorFunction<T, S>;\nexport function first<T, S extends T, D>(\n  predicate: (value: T, index: number, source: Observable<T>) => value is S,\n  defaultValue: D\n): OperatorFunction<T, S | D>;\nexport function first<T, D = T>(\n  predicate: (value: T, index: number, source: Observable<T>) => boolean,\n  defaultValue?: D\n): OperatorFunction<T, T | D>;\n\n/**\n * Emits only the first value (or the first value that meets some condition)\n * emitted by the source Observable.\n *\n * <span class=\"informal\">Emits only the first value. Or emits only the first\n * value that passes some test.</span>\n *\n * ![](first.png)\n *\n * If called with no arguments, `first` emits the first value of the source\n * Observable, then completes. If called with a `predicate` function, `first`\n * emits the first value of the source that matches the specified condition. Throws an error if\n * `defaultValue` was not provided and a matching element is not found.\n *\n * ## Examples\n *\n * Emit only the first click that happens on the DOM\n *\n * ```ts\n * import { fromEvent, first } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(first());\n * result.subscribe(x => console.log(x));\n * ```\n *\n * Emits the first click that happens on a DIV\n *\n * ```ts\n * import { fromEvent, first } from 'rxjs';\n *\n * const div = document.createElement('div');\n * div.style.cssText = 'width: 200px; height: 200px; background: #09c;';\n * document.body.appendChild(div);\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(first(ev => (<HTMLElement>ev.target).tagName === 'DIV'));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link filter}\n * @see {@link find}\n * @see {@link take}\n *\n * @throws {EmptyError} Delivers an EmptyError to the Observer's `error`\n * callback if the Observable completes before any `next` notification was sent.\n * This is how `first()` is different from {@link take}(1) which completes instead.\n *\n * @param {function(value: T, index: number, source: Observable<T>): boolean} [predicate]\n * An optional function called with each item to test for condition matching.\n * @param {D} [defaultValue] The default value emitted in case no valid value\n * was found on the source.\n * @return A function that returns an Observable that emits the first item that\n * matches the condition.\n */\nexport function first<T, D>(\n  predicate?: ((value: T, index: number, source: Observable<T>) => boolean) | null,\n  defaultValue?: D\n): OperatorFunction<T, T | D> {\n  const hasDefaultValue = arguments.length >= 2;\n  return (source: Observable<T>) =>\n    source.pipe(\n      predicate ? filter((v, i) => predicate(v, i, source)) : identity,\n      take(1),\n      hasDefaultValue ? defaultIfEmpty(defaultValue!) : throwIfEmpty(() => new EmptyError())\n    );\n}\n", "import { EMPTY } from '../observable/empty';\nimport { MonoTypeOperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * Waits for the source to complete, then emits the last N values from the source,\n * as specified by the `count` argument.\n *\n * ![](takeLast.png)\n *\n * `takeLast` results in an observable that will hold values up to `count` values in memory,\n * until the source completes. It then pushes all values in memory to the consumer, in the\n * order they were received from the source, then notifies the consumer that it is\n * complete.\n *\n * If for some reason the source completes before the `count` supplied to `takeLast` is reached,\n * all values received until that point are emitted, and then completion is notified.\n *\n * **Warning**: Using `takeLast` with an observable that never completes will result\n * in an observable that never emits a value.\n *\n * ## Example\n *\n * Take the last 3 values of an Observable with many values\n *\n * ```ts\n * import { range, takeLast } from 'rxjs';\n *\n * const many = range(1, 100);\n * const lastThree = many.pipe(takeLast(3));\n * lastThree.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link take}\n * @see {@link takeUntil}\n * @see {@link takeWhile}\n * @see {@link skip}\n *\n * @param count The maximum number of values to emit from the end of\n * the sequence of values emitted by the source Observable.\n * @return A function that returns an Observable that emits at most the last\n * `count` values emitted by the source Observable.\n */\nexport function takeLast<T>(count: number): MonoTypeOperatorFunction<T> {\n  return count <= 0\n    ? () => EMPTY\n    : operate((source, subscriber) => {\n        // This buffer will hold the values we are going to emit\n        // when the source completes. Since we only want to take the\n        // last N values, we can't emit until we're sure we're not getting\n        // any more values.\n        let buffer: T[] = [];\n        source.subscribe(\n          createOperatorSubscriber(\n            subscriber,\n            (value) => {\n              // Add the most recent value onto the end of our buffer.\n              buffer.push(value);\n              // If our buffer is now larger than the number of values we\n              // want to take, we remove the oldest value from the buffer.\n              count < buffer.length && buffer.shift();\n            },\n            () => {\n              // The source completed, we now know what are last values\n              // are, emit them in the order they were received.\n              for (const value of buffer) {\n                subscriber.next(value);\n              }\n              subscriber.complete();\n            },\n            // Errors are passed through to the consumer\n            undefined,\n            () => {\n              // During finalization release the values in our buffer.\n              buffer = null!;\n            }\n          )\n        );\n      });\n}\n", "import { ObservableInput, ObservableInputTuple, OperatorFunction, SchedulerLike } from '../types';\nimport { operate } from '../util/lift';\nimport { argsOrArgArray } from '../util/argsOrArgArray';\nimport { mergeAll } from './mergeAll';\nimport { popNumber, popScheduler } from '../util/args';\nimport { from } from '../observable/from';\n\n/** @deprecated Replaced with {@link mergeWith}. Will be removed in v8. */\nexport function merge<T, A extends readonly unknown[]>(...sources: [...ObservableInputTuple<A>]): OperatorFunction<T, T | A[number]>;\n/** @deprecated Replaced with {@link mergeWith}. Will be removed in v8. */\nexport function merge<T, A extends readonly unknown[]>(\n  ...sourcesAndConcurrency: [...ObservableInputTuple<A>, number]\n): OperatorFunction<T, T | A[number]>;\n/** @deprecated Replaced with {@link mergeWith}. Will be removed in v8. */\nexport function merge<T, A extends readonly unknown[]>(\n  ...sourcesAndScheduler: [...ObservableInputTuple<A>, SchedulerLike]\n): OperatorFunction<T, T | A[number]>;\n/** @deprecated Replaced with {@link mergeWith}. Will be removed in v8. */\nexport function merge<T, A extends readonly unknown[]>(\n  ...sourcesAndConcurrencyAndScheduler: [...ObservableInputTuple<A>, number, SchedulerLike]\n): OperatorFunction<T, T | A[number]>;\n\nexport function merge<T>(...args: unknown[]): OperatorFunction<T, unknown> {\n  const scheduler = popScheduler(args);\n  const concurrent = popNumber(args, Infinity);\n  args = argsOrArgArray(args);\n\n  return operate((source, subscriber) => {\n    mergeAll(concurrent)(from([source, ...(args as ObservableInput<T>[])], scheduler)).subscribe(subscriber);\n  });\n}\n", "import { ObservableInputTuple, OperatorFunction } from '../types';\nimport { merge } from './merge';\n\n/**\n * Merge the values from all observables to a single observable result.\n *\n * Creates an observable, that when subscribed to, subscribes to the source\n * observable, and all other sources provided as arguments. All values from\n * every source are emitted from the resulting subscription.\n *\n * When all sources complete, the resulting observable will complete.\n *\n * When any source errors, the resulting observable will error.\n *\n * ## Example\n *\n * Joining all outputs from multiple user input event streams\n *\n * ```ts\n * import { fromEvent, map, mergeWith } from 'rxjs';\n *\n * const clicks$ = fromEvent(document, 'click').pipe(map(() => 'click'));\n * const mousemoves$ = fromEvent(document, 'mousemove').pipe(map(() => 'mousemove'));\n * const dblclicks$ = fromEvent(document, 'dblclick').pipe(map(() => 'dblclick'));\n *\n * mousemoves$\n *   .pipe(mergeWith(clicks$, dblclicks$))\n *   .subscribe(x => console.log(x));\n *\n * // result (assuming user interactions)\n * // 'mousemove'\n * // 'mousemove'\n * // 'mousemove'\n * // 'click'\n * // 'click'\n * // 'dblclick'\n * ```\n *\n * @see {@link merge}\n *\n * @param otherSources the sources to combine the current source with.\n * @return A function that returns an Observable that merges the values from\n * all given Observables.\n */\nexport function mergeWith<T, A extends readonly unknown[]>(\n  ...otherSources: [...ObservableInputTuple<A>]\n): OperatorFunction<T, T | A[number]> {\n  return merge(...otherSources);\n}\n", "import { Subscription } from '../Subscription';\nimport { EMPTY } from '../observable/empty';\nimport { operate } from '../util/lift';\nimport { MonoTypeOperatorFunction, ObservableInput } from '../types';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { innerFrom } from '../observable/innerFrom';\nimport { timer } from '../observable/timer';\n\nexport interface RepeatConfig {\n  /**\n   * The number of times to repeat the source. Defaults to `Infinity`.\n   */\n  count?: number;\n\n  /**\n   * If a `number`, will delay the repeat of the source by that number of milliseconds.\n   * If a function, it will provide the number of times the source has been subscribed to,\n   * and the return value should be a valid observable input that will notify when the source\n   * should be repeated. If the notifier observable is empty, the result will complete.\n   */\n  delay?: number | ((count: number) => ObservableInput<any>);\n}\n\n/**\n * Returns an Observable that will resubscribe to the source stream when the source stream completes.\n *\n * <span class=\"informal\">Repeats all values emitted on the source. It's like {@link retry}, but for non error cases.</span>\n *\n * ![](repeat.png)\n *\n * Repeat will output values from a source until the source completes, then it will resubscribe to the\n * source a specified number of times, with a specified delay. Repeat can be particularly useful in\n * combination with closing operators like {@link take}, {@link takeUntil}, {@link first}, or {@link takeWhile},\n * as it can be used to restart a source again from scratch.\n *\n * Repeat is very similar to {@link retry}, where {@link retry} will resubscribe to the source in the error case, but\n * `repeat` will resubscribe if the source completes.\n *\n * Note that `repeat` will _not_ catch errors. Use {@link retry} for that.\n *\n * - `repeat(0)` returns an empty observable\n * - `repeat()` will repeat forever\n * - `repeat({ delay: 200 })` will repeat forever, with a delay of 200ms between repetitions.\n * - `repeat({ count: 2, delay: 400 })` will repeat twice, with a delay of 400ms between repetitions.\n * - `repeat({ delay: (count) => timer(count * 1000) })` will repeat forever, but will have a delay that grows by one second for each repetition.\n *\n * ## Example\n *\n * Repeat a message stream\n *\n * ```ts\n * import { of, repeat } from 'rxjs';\n *\n * const source = of('Repeat message');\n * const result = source.pipe(repeat(3));\n *\n * result.subscribe(x => console.log(x));\n *\n * // Results\n * // 'Repeat message'\n * // 'Repeat message'\n * // 'Repeat message'\n * ```\n *\n * Repeat 3 values, 2 times\n *\n * ```ts\n * import { interval, take, repeat } from 'rxjs';\n *\n * const source = interval(1000);\n * const result = source.pipe(take(3), repeat(2));\n *\n * result.subscribe(x => console.log(x));\n *\n * // Results every second\n * // 0\n * // 1\n * // 2\n * // 0\n * // 1\n * // 2\n * ```\n *\n * Defining two complex repeats with delays on the same source.\n * Note that the second repeat cannot be called until the first\n * repeat as exhausted it's count.\n *\n * ```ts\n * import { defer, of, repeat } from 'rxjs';\n *\n * const source = defer(() => {\n *    return of(`Hello, it is ${new Date()}`)\n * });\n *\n * source.pipe(\n *    // Repeat 3 times with a delay of 1 second between repetitions\n *    repeat({\n *      count: 3,\n *      delay: 1000,\n *    }),\n *\n *    // *Then* repeat forever, but with an exponential step-back\n *    // maxing out at 1 minute.\n *    repeat({\n *      delay: (count) => timer(Math.min(60000, 2 ^ count * 1000))\n *    })\n * )\n * ```\n *\n * @see {@link repeatWhen}\n * @see {@link retry}\n *\n * @param count The number of times the source Observable items are repeated, a count of 0 will yield\n * an empty Observable.\n */\nexport function repeat<T>(countOrConfig?: number | RepeatConfig): MonoTypeOperatorFunction<T> {\n  let count = Infinity;\n  let delay: RepeatConfig['delay'];\n\n  if (countOrConfig != null) {\n    if (typeof countOrConfig === 'object') {\n      ({ count = Infinity, delay } = countOrConfig);\n    } else {\n      count = countOrConfig;\n    }\n  }\n\n  return count <= 0\n    ? () => EMPTY\n    : operate((source, subscriber) => {\n        let soFar = 0;\n        let sourceSub: Subscription | null;\n\n        const resubscribe = () => {\n          sourceSub?.unsubscribe();\n          sourceSub = null;\n          if (delay != null) {\n            const notifier = typeof delay === 'number' ? timer(delay) : innerFrom(delay(soFar));\n            const notifierSubscriber = createOperatorSubscriber(subscriber, () => {\n              notifierSubscriber.unsubscribe();\n              subscribeToSource();\n            });\n            notifier.subscribe(notifierSubscriber);\n          } else {\n            subscribeToSource();\n          }\n        };\n\n        const subscribeToSource = () => {\n          let syncUnsub = false;\n          sourceSub = source.subscribe(\n            createOperatorSubscriber(subscriber, undefined, () => {\n              if (++soFar < count) {\n                if (sourceSub) {\n                  resubscribe();\n                } else {\n                  syncUnsub = true;\n                }\n              } else {\n                subscriber.complete();\n              }\n            })\n          );\n\n          if (syncUnsub) {\n            resubscribe();\n          }\n        };\n\n        subscribeToSource();\n      });\n}\n", "import { OperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { scanInternals } from './scanInternals';\n\nexport function scan<V, A = V>(accumulator: (acc: A | V, value: V, index: number) => A): OperatorFunction<V, V | A>;\nexport function scan<V, A>(accumulator: (acc: A, value: V, index: number) => A, seed: A): OperatorFunction<V, A>;\nexport function scan<V, A, S>(accumulator: (acc: A | S, value: V, index: number) => A, seed: S): OperatorFunction<V, A>;\n\n// TODO: link to a \"redux pattern\" section in the guide (location TBD)\n\n/**\n * Useful for encapsulating and managing state. Applies an accumulator (or \"reducer function\")\n * to each value from the source after an initial state is established -- either via\n * a `seed` value (second argument), or from the first value from the source.\n *\n * <span class=\"informal\">It's like {@link reduce}, but emits the current\n * accumulation state after each update</span>\n *\n * ![](scan.png)\n *\n * This operator maintains an internal state and emits it after processing each value as follows:\n *\n * 1. First value arrives\n *   - If a `seed` value was supplied (as the second argument to `scan`), let `state = seed` and `value = firstValue`.\n *   - If NO `seed` value was supplied (no second argument), let `state = firstValue` and go to 3.\n * 2. Let `state = accumulator(state, value)`.\n *   - If an error is thrown by `accumulator`, notify the consumer of an error. The process ends.\n * 3. Emit `state`.\n * 4. Next value arrives, let `value = nextValue`, go to 2.\n *\n * ## Examples\n *\n * An average of previous numbers. This example shows how\n * not providing a `seed` can prime the stream with the\n * first value from the source.\n *\n * ```ts\n * import { of, scan, map } from 'rxjs';\n *\n * const numbers$ = of(1, 2, 3);\n *\n * numbers$\n *   .pipe(\n *     // Get the sum of the numbers coming in.\n *     scan((total, n) => total + n),\n *     // Get the average by dividing the sum by the total number\n *     // received so far (which is 1 more than the zero-based index).\n *     map((sum, index) => sum / (index + 1))\n *   )\n *   .subscribe(console.log);\n * ```\n *\n * The Fibonacci sequence. This example shows how you can use\n * a seed to prime accumulation process. Also... you know... Fibonacci.\n * So important to like, computers and stuff that its whiteboarded\n * in job interviews. Now you can show them the Rx version! (Please don't, haha)\n *\n * ```ts\n * import { interval, scan, map, startWith } from 'rxjs';\n *\n * const firstTwoFibs = [0, 1];\n * // An endless stream of Fibonacci numbers.\n * const fibonacci$ = interval(1000).pipe(\n *   // Scan to get the fibonacci numbers (after 0, 1)\n *   scan(([a, b]) => [b, a + b], firstTwoFibs),\n *   // Get the second number in the tuple, it's the one you calculated\n *   map(([, n]) => n),\n *   // Start with our first two digits :)\n *   startWith(...firstTwoFibs)\n * );\n *\n * fibonacci$.subscribe(console.log);\n * ```\n *\n * @see {@link expand}\n * @see {@link mergeScan}\n * @see {@link reduce}\n * @see {@link switchScan}\n *\n * @param accumulator A \"reducer function\". This will be called for each value after an initial state is\n * acquired.\n * @param seed The initial state. If this is not provided, the first value from the source will\n * be used as the initial state, and emitted without going through the accumulator. All subsequent values\n * will be processed by the accumulator function. If this is provided, all values will go through\n * the accumulator function.\n * @return A function that returns an Observable of the accumulated values.\n */\nexport function scan<V, A, S>(accumulator: (acc: V | A | S, value: V, index: number) => A, seed?: S): OperatorFunction<V, V | A> {\n  // providing a seed of `undefined` *should* be valid and trigger\n  // hasSeed! so don't use `seed !== undefined` checks!\n  // For this reason, we have to check it here at the original call site\n  // otherwise inside Operator/Subscriber we won't know if `undefined`\n  // means they didn't provide anything or if they literally provided `undefined`\n  return operate(scanInternals(accumulator, seed as S, arguments.length >= 2, true));\n}\n", "import { innerFrom } from '../observable/innerFrom';\nimport { Subject } from '../Subject';\nimport { SafeSubscriber } from '../Subscriber';\nimport { Subscription } from '../Subscription';\nimport { MonoTypeOperatorFunction, SubjectLike, ObservableInput } from '../types';\nimport { operate } from '../util/lift';\n\nexport interface ShareConfig<T> {\n  /**\n   * The factory used to create the subject that will connect the source observable to\n   * multicast consumers.\n   */\n  connector?: () => SubjectLike<T>;\n  /**\n   * If `true`, the resulting observable will reset internal state on error from source and return to a \"cold\" state. This\n   * allows the resulting observable to be \"retried\" in the event of an error.\n   * If `false`, when an error comes from the source it will push the error into the connecting subject, and the subject\n   * will remain the connecting subject, meaning the resulting observable will not go \"cold\" again, and subsequent retries\n   * or resubscriptions will resubscribe to that same subject. In all cases, RxJS subjects will emit the same error again, however\n   * {@link ReplaySubject} will also push its buffered values before pushing the error.\n   * It is also possible to pass a notifier factory returning an `ObservableInput` instead which grants more fine-grained\n   * control over how and when the reset should happen. This allows behaviors like conditional or delayed resets.\n   */\n  resetOnError?: boolean | ((error: any) => ObservableInput<any>);\n  /**\n   * If `true`, the resulting observable will reset internal state on completion from source and return to a \"cold\" state. This\n   * allows the resulting observable to be \"repeated\" after it is done.\n   * If `false`, when the source completes, it will push the completion through the connecting subject, and the subject\n   * will remain the connecting subject, meaning the resulting observable will not go \"cold\" again, and subsequent repeats\n   * or resubscriptions will resubscribe to that same subject.\n   * It is also possible to pass a notifier factory returning an `ObservableInput` instead which grants more fine-grained\n   * control over how and when the reset should happen. This allows behaviors like conditional or delayed resets.\n   */\n  resetOnComplete?: boolean | (() => ObservableInput<any>);\n  /**\n   * If `true`, when the number of subscribers to the resulting observable reaches zero due to those subscribers unsubscribing, the\n   * internal state will be reset and the resulting observable will return to a \"cold\" state. This means that the next\n   * time the resulting observable is subscribed to, a new subject will be created and the source will be subscribed to\n   * again.\n   * If `false`, when the number of subscribers to the resulting observable reaches zero due to unsubscription, the subject\n   * will remain connected to the source, and new subscriptions to the result will be connected through that same subject.\n   * It is also possible to pass a notifier factory returning an `ObservableInput` instead which grants more fine-grained\n   * control over how and when the reset should happen. This allows behaviors like conditional or delayed resets.\n   */\n  resetOnRefCountZero?: boolean | (() => ObservableInput<any>);\n}\n\nexport function share<T>(): MonoTypeOperatorFunction<T>;\n\nexport function share<T>(options: ShareConfig<T>): MonoTypeOperatorFunction<T>;\n\n/**\n * Returns a new Observable that multicasts (shares) the original Observable. As long as there is at least one\n * Subscriber this Observable will be subscribed and emitting data. When all subscribers have unsubscribed it will\n * unsubscribe from the source Observable. Because the Observable is multicasting it makes the stream `hot`.\n * This is an alias for `multicast(() => new Subject()), refCount()`.\n *\n * The subscription to the underlying source Observable can be reset (unsubscribe and resubscribe for new subscribers),\n * if the subscriber count to the shared observable drops to 0, or if the source Observable errors or completes. It is\n * possible to use notifier factories for the resets to allow for behaviors like conditional or delayed resets. Please\n * note that resetting on error or complete of the source Observable does not behave like a transparent retry or restart\n * of the source because the error or complete will be forwarded to all subscribers and their subscription will be\n * closed. Only new subscribers after a reset on error or complete happened will cause a fresh subscription to the\n * source. To achieve transparent retries or restarts pipe the source through appropriate operators before sharing.\n *\n * ![](share.png)\n *\n * ## Example\n *\n * Generate new multicast Observable from the `source` Observable value\n *\n * ```ts\n * import { interval, tap, map, take, share } from 'rxjs';\n *\n * const source = interval(1000).pipe(\n *   tap(x => console.log('Processing: ', x)),\n *   map(x => x * x),\n *   take(6),\n *   share()\n * );\n *\n * source.subscribe(x => console.log('subscription 1: ', x));\n * source.subscribe(x => console.log('subscription 2: ', x));\n *\n * // Logs:\n * // Processing: 0\n * // subscription 1: 0\n * // subscription 2: 0\n * // Processing: 1\n * // subscription 1: 1\n * // subscription 2: 1\n * // Processing: 2\n * // subscription 1: 4\n * // subscription 2: 4\n * // Processing: 3\n * // subscription 1: 9\n * // subscription 2: 9\n * // Processing: 4\n * // subscription 1: 16\n * // subscription 2: 16\n * // Processing: 5\n * // subscription 1: 25\n * // subscription 2: 25\n * ```\n *\n * ## Example with notifier factory: Delayed reset\n *\n * ```ts\n * import { interval, take, share, timer } from 'rxjs';\n *\n * const source = interval(1000).pipe(\n *   take(3),\n *   share({\n *     resetOnRefCountZero: () => timer(1000)\n *   })\n * );\n *\n * const subscriptionOne = source.subscribe(x => console.log('subscription 1: ', x));\n * setTimeout(() => subscriptionOne.unsubscribe(), 1300);\n *\n * setTimeout(() => source.subscribe(x => console.log('subscription 2: ', x)), 1700);\n *\n * setTimeout(() => source.subscribe(x => console.log('subscription 3: ', x)), 5000);\n *\n * // Logs:\n * // subscription 1:  0\n * // (subscription 1 unsubscribes here)\n * // (subscription 2 subscribes here ~400ms later, source was not reset)\n * // subscription 2:  1\n * // subscription 2:  2\n * // (subscription 2 unsubscribes here)\n * // (subscription 3 subscribes here ~2000ms later, source did reset before)\n * // subscription 3:  0\n * // subscription 3:  1\n * // subscription 3:  2\n * ```\n *\n * @see {@link shareReplay}\n *\n * @return A function that returns an Observable that mirrors the source.\n */\nexport function share<T>(options: ShareConfig<T> = {}): MonoTypeOperatorFunction<T> {\n  const { connector = () => new Subject<T>(), resetOnError = true, resetOnComplete = true, resetOnRefCountZero = true } = options;\n  // It's necessary to use a wrapper here, as the _operator_ must be\n  // referentially transparent. Otherwise, it cannot be used in calls to the\n  // static `pipe` function - to create a partial pipeline.\n  //\n  // The _operator function_ - the function returned by the _operator_ - will\n  // not be referentially transparent - as it shares its source - but the\n  // _operator function_ is called when the complete pipeline is composed via a\n  // call to a source observable's `pipe` method - not when the static `pipe`\n  // function is called.\n  return (wrapperSource) => {\n    let connection: SafeSubscriber<T> | undefined;\n    let resetConnection: Subscription | undefined;\n    let subject: SubjectLike<T> | undefined;\n    let refCount = 0;\n    let hasCompleted = false;\n    let hasErrored = false;\n\n    const cancelReset = () => {\n      resetConnection?.unsubscribe();\n      resetConnection = undefined;\n    };\n    // Used to reset the internal state to a \"cold\"\n    // state, as though it had never been subscribed to.\n    const reset = () => {\n      cancelReset();\n      connection = subject = undefined;\n      hasCompleted = hasErrored = false;\n    };\n    const resetAndUnsubscribe = () => {\n      // We need to capture the connection before\n      // we reset (if we need to reset).\n      const conn = connection;\n      reset();\n      conn?.unsubscribe();\n    };\n\n    return operate<T, T>((source, subscriber) => {\n      refCount++;\n      if (!hasErrored && !hasCompleted) {\n        cancelReset();\n      }\n\n      // Create the subject if we don't have one yet. Grab a local reference to\n      // it as well, which avoids non-null assertions when using it and, if we\n      // connect to it now, then error/complete need a reference after it was\n      // reset.\n      const dest = (subject = subject ?? connector());\n\n      // Add the finalization directly to the subscriber - instead of returning it -\n      // so that the handling of the subscriber's unsubscription will be wired\n      // up _before_ the subscription to the source occurs. This is done so that\n      // the assignment to the source connection's `closed` property will be seen\n      // by synchronous firehose sources.\n      subscriber.add(() => {\n        refCount--;\n\n        // If we're resetting on refCount === 0, and it's 0, we only want to do\n        // that on \"unsubscribe\", really. Resetting on error or completion is a different\n        // configuration.\n        if (refCount === 0 && !hasErrored && !hasCompleted) {\n          resetConnection = handleReset(resetAndUnsubscribe, resetOnRefCountZero);\n        }\n      });\n\n      // The following line adds the subscription to the subscriber passed.\n      // Basically, `subscriber === dest.subscribe(subscriber)` is `true`.\n      dest.subscribe(subscriber);\n\n      if (\n        !connection &&\n        // Check this shareReplay is still activate - it can be reset to 0\n        // and be \"unsubscribed\" _before_ it actually subscribes.\n        // If we were to subscribe then, it'd leak and get stuck.\n        refCount > 0\n      ) {\n        // We need to create a subscriber here - rather than pass an observer and\n        // assign the returned subscription to connection - because it's possible\n        // for reentrant subscriptions to the shared observable to occur and in\n        // those situations we want connection to be already-assigned so that we\n        // don't create another connection to the source.\n        connection = new SafeSubscriber({\n          next: (value) => dest.next(value),\n          error: (err) => {\n            hasErrored = true;\n            cancelReset();\n            resetConnection = handleReset(reset, resetOnError, err);\n            dest.error(err);\n          },\n          complete: () => {\n            hasCompleted = true;\n            cancelReset();\n            resetConnection = handleReset(reset, resetOnComplete);\n            dest.complete();\n          },\n        });\n        innerFrom(source).subscribe(connection);\n      }\n    })(wrapperSource);\n  };\n}\n\nfunction handleReset<T extends unknown[] = never[]>(\n  reset: () => void,\n  on: boolean | ((...args: T) => ObservableInput<any>),\n  ...args: T\n): Subscription | undefined {\n  if (on === true) {\n    reset();\n    return;\n  }\n\n  if (on === false) {\n    return;\n  }\n\n  const onSubscriber = new SafeSubscriber({\n    next: () => {\n      onSubscriber.unsubscribe();\n      reset();\n    },\n  });\n\n  return innerFrom(on(...args)).subscribe(onSubscriber);\n}\n", "import { ReplaySubject } from '../ReplaySubject';\nimport { MonoTypeOperatorFunction, SchedulerLike } from '../types';\nimport { share } from './share';\n\nexport interface ShareReplayConfig {\n  bufferSize?: number;\n  windowTime?: number;\n  refCount: boolean;\n  scheduler?: SchedulerLike;\n}\n\nexport function shareReplay<T>(config: ShareReplayConfig): MonoTypeOperatorFunction<T>;\nexport function shareReplay<T>(bufferSize?: number, windowTime?: number, scheduler?: SchedulerLike): MonoTypeOperatorFunction<T>;\n\n/**\n * Share source and replay specified number of emissions on subscription.\n *\n * This operator is a specialization of `replay` that connects to a source observable\n * and multicasts through a `ReplaySubject` constructed with the specified arguments.\n * A successfully completed source will stay cached in the `shareReplay`ed observable forever,\n * but an errored source can be retried.\n *\n * ## Why use `shareReplay`?\n *\n * You generally want to use `shareReplay` when you have side-effects or taxing computations\n * that you do not wish to be executed amongst multiple subscribers.\n * It may also be valuable in situations where you know you will have late subscribers to\n * a stream that need access to previously emitted values.\n * This ability to replay values on subscription is what differentiates {@link share} and `shareReplay`.\n *\n * ## Reference counting\n *\n * By default `shareReplay` will use `refCount` of false, meaning that it will _not_ unsubscribe the\n * source when the reference counter drops to zero, i.e. the inner `ReplaySubject` will _not_ be unsubscribed\n * (and potentially run for ever).\n * This is the default as it is expected that `shareReplay` is often used to keep around expensive to setup\n * observables which we want to keep running instead of having to do the expensive setup again.\n *\n * As of RXJS version 6.4.0 a new overload signature was added to allow for manual control over what\n * happens when the operators internal reference counter drops to zero.\n * If `refCount` is true, the source will be unsubscribed from once the reference count drops to zero, i.e.\n * the inner `ReplaySubject` will be unsubscribed. All new subscribers will receive value emissions from a\n * new `ReplaySubject` which in turn will cause a new subscription to the source observable.\n *\n * ## Examples\n *\n * Example with a third subscriber coming late to the party\n *\n * ```ts\n * import { interval, take, shareReplay } from 'rxjs';\n *\n * const shared$ = interval(2000).pipe(\n *   take(6),\n *   shareReplay(3)\n * );\n *\n * shared$.subscribe(x => console.log('sub A: ', x));\n * shared$.subscribe(y => console.log('sub B: ', y));\n *\n * setTimeout(() => {\n *   shared$.subscribe(y => console.log('sub C: ', y));\n * }, 11000);\n *\n * // Logs:\n * // (after ~2000 ms)\n * // sub A: 0\n * // sub B: 0\n * // (after ~4000 ms)\n * // sub A: 1\n * // sub B: 1\n * // (after ~6000 ms)\n * // sub A: 2\n * // sub B: 2\n * // (after ~8000 ms)\n * // sub A: 3\n * // sub B: 3\n * // (after ~10000 ms)\n * // sub A: 4\n * // sub B: 4\n * // (after ~11000 ms, sub C gets the last 3 values)\n * // sub C: 2\n * // sub C: 3\n * // sub C: 4\n * // (after ~12000 ms)\n * // sub A: 5\n * // sub B: 5\n * // sub C: 5\n * ```\n *\n * Example for `refCount` usage\n *\n * ```ts\n * import { Observable, tap, interval, shareReplay, take } from 'rxjs';\n *\n * const log = <T>(name: string, source: Observable<T>) => source.pipe(\n *   tap({\n *     subscribe: () => console.log(`${ name }: subscribed`),\n *     next: value => console.log(`${ name }: ${ value }`),\n *     complete: () => console.log(`${ name }: completed`),\n *     finalize: () => console.log(`${ name }: unsubscribed`)\n *   })\n * );\n *\n * const obs$ = log('source', interval(1000));\n *\n * const shared$ = log('shared', obs$.pipe(\n *   shareReplay({ bufferSize: 1, refCount: true }),\n *   take(2)\n * ));\n *\n * shared$.subscribe(x => console.log('sub A: ', x));\n * shared$.subscribe(y => console.log('sub B: ', y));\n *\n * // PRINTS:\n * // shared: subscribed <-- reference count = 1\n * // source: subscribed\n * // shared: subscribed <-- reference count = 2\n * // source: 0\n * // shared: 0\n * // sub A: 0\n * // shared: 0\n * // sub B: 0\n * // source: 1\n * // shared: 1\n * // sub A: 1\n * // shared: completed <-- take(2) completes the subscription for sub A\n * // shared: unsubscribed <-- reference count = 1\n * // shared: 1\n * // sub B: 1\n * // shared: completed <-- take(2) completes the subscription for sub B\n * // shared: unsubscribed <-- reference count = 0\n * // source: unsubscribed <-- replaySubject unsubscribes from source observable because the reference count dropped to 0 and refCount is true\n *\n * // In case of refCount being false, the unsubscribe is never called on the source and the source would keep on emitting, even if no subscribers\n * // are listening.\n * // source: 2\n * // source: 3\n * // source: 4\n * // ...\n * ```\n *\n * @see {@link publish}\n * @see {@link share}\n * @see {@link publishReplay}\n *\n * @param configOrBufferSize Maximum element count of the replay buffer or {@link ShareReplayConfig configuration}\n * object.\n * @param windowTime Maximum time length of the replay buffer in milliseconds.\n * @param scheduler Scheduler where connected observers within the selector function\n * will be invoked on.\n * @return A function that returns an Observable sequence that contains the\n * elements of a sequence produced by multicasting the source sequence within a\n * selector function.\n */\nexport function shareReplay<T>(\n  configOrBufferSize?: ShareReplayConfig | number,\n  windowTime?: number,\n  scheduler?: SchedulerLike\n): MonoTypeOperatorFunction<T> {\n  let bufferSize: number;\n  let refCount = false;\n  if (configOrBufferSize && typeof configOrBufferSize === 'object') {\n    ({ bufferSize = Infinity, windowTime = Infinity, refCount = false, scheduler } = configOrBufferSize);\n  } else {\n    bufferSize = (configOrBufferSize ?? Infinity) as number;\n  }\n  return share<T>({\n    connector: () => new ReplaySubject(bufferSize, windowTime, scheduler),\n    resetOnError: true,\n    resetOnComplete: false,\n    resetOnRefCountZero: refCount,\n  });\n}\n", "import { MonoTypeOperatorFunction } from '../types';\nimport { filter } from './filter';\n\n/**\n * Returns an Observable that skips the first `count` items emitted by the source Observable.\n *\n * ![](skip.png)\n *\n * Skips the values until the sent notifications are equal or less than provided skip count. It raises\n * an error if skip count is equal or more than the actual number of emits and source raises an error.\n *\n * ## Example\n *\n * Skip the values before the emission\n *\n * ```ts\n * import { interval, skip } from 'rxjs';\n *\n * // emit every half second\n * const source = interval(500);\n * // skip the first 10 emitted values\n * const result = source.pipe(skip(10));\n *\n * result.subscribe(value => console.log(value));\n * // output: 10...11...12...13...\n * ```\n *\n * @see {@link last}\n * @see {@link skipWhile}\n * @see {@link skipUntil}\n * @see {@link skipLast}\n *\n * @param {Number} count - The number of times, items emitted by source Observable should be skipped.\n * @return A function that returns an Observable that skips the first `count`\n * values emitted by the source Observable.\n */\nexport function skip<T>(count: number): MonoTypeOperatorFunction<T> {\n  return filter((_, index) => count <= index);\n}\n", "import { MonoTypeOperatorFunction, ObservableInput } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { innerFrom } from '../observable/innerFrom';\nimport { noop } from '../util/noop';\n\n/**\n * Returns an Observable that skips items emitted by the source Observable until a second Observable emits an item.\n *\n * The `skipUntil` operator causes the observable stream to skip the emission of values until the passed in observable\n * emits the first value. This can be particularly useful in combination with user interactions, responses of HTTP\n * requests or waiting for specific times to pass by.\n *\n * ![](skipUntil.png)\n *\n * Internally, the `skipUntil` operator subscribes to the passed in `notifier` `ObservableInput` (which gets converted\n * to an Observable) in order to recognize the emission of its first value. When `notifier` emits next, the operator\n * unsubscribes from it and starts emitting the values of the *source* observable until it completes or errors. It\n * will never let the *source* observable emit any values if the `notifier` completes or throws an error without\n * emitting a value before.\n *\n * ## Example\n *\n * In the following example, all emitted values of the interval observable are skipped until the user clicks anywhere\n * within the page\n *\n * ```ts\n * import { interval, fromEvent, skipUntil } from 'rxjs';\n *\n * const intervalObservable = interval(1000);\n * const click = fromEvent(document, 'click');\n *\n * const emitAfterClick = intervalObservable.pipe(\n *   skipUntil(click)\n * );\n * // clicked at 4.6s. output: 5...6...7...8........ or\n * // clicked at 7.3s. output: 8...9...10..11.......\n * emitAfterClick.subscribe(value => console.log(value));\n * ```\n *\n * @see {@link last}\n * @see {@link skip}\n * @see {@link skipWhile}\n * @see {@link skipLast}\n *\n * @param notifier An `ObservableInput` that has to emit an item before the source Observable elements begin to\n * be mirrored by the resulting Observable.\n * @return A function that returns an Observable that skips items from the\n * source Observable until the `notifier` Observable emits an item, then emits the\n * remaining items.\n */\nexport function skipUntil<T>(notifier: ObservableInput<any>): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    let taking = false;\n\n    const skipSubscriber = createOperatorSubscriber(\n      subscriber,\n      () => {\n        skipSubscriber?.unsubscribe();\n        taking = true;\n      },\n      noop\n    );\n\n    innerFrom(notifier).subscribe(skipSubscriber);\n\n    source.subscribe(createOperatorSubscriber(subscriber, (value) => taking && subscriber.next(value)));\n  });\n}\n", "import { concat } from '../observable/concat';\nimport { OperatorFunction, SchedulerLike, ValueFromArray } from '../types';\nimport { popScheduler } from '../util/args';\nimport { operate } from '../util/lift';\n\n// Devs are more likely to pass null or undefined than they are a scheduler\n// without accompanying values. To make things easier for (naughty) devs who\n// use the `strictNullChecks: false` TypeScript compiler option, these\n// overloads with explicit null and undefined values are included.\n\nexport function startWith<T>(value: null): OperatorFunction<T, T | null>;\nexport function startWith<T>(value: undefined): OperatorFunction<T, T | undefined>;\n\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `concatAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function startWith<T, A extends readonly unknown[] = T[]>(\n  ...valuesAndScheduler: [...A, SchedulerLike]\n): OperatorFunction<T, T | ValueFromArray<A>>;\nexport function startWith<T, A extends readonly unknown[] = T[]>(...values: A): OperatorFunction<T, T | ValueFromArray<A>>;\n\n/**\n * Returns an observable that, at the moment of subscription, will synchronously emit all\n * values provided to this operator, then subscribe to the source and mirror all of its emissions\n * to subscribers.\n *\n * This is a useful way to know when subscription has occurred on an existing observable.\n *\n * <span class=\"informal\">First emits its arguments in order, and then any\n * emissions from the source.</span>\n *\n * ![](startWith.png)\n *\n * ## Examples\n *\n * Emit a value when a timer starts.\n *\n * ```ts\n * import { timer, map, startWith } from 'rxjs';\n *\n * timer(1000)\n *   .pipe(\n *     map(() => 'timer emit'),\n *     startWith('timer start')\n *   )\n *   .subscribe(x => console.log(x));\n *\n * // results:\n * // 'timer start'\n * // 'timer emit'\n * ```\n *\n * @param values Items you want the modified Observable to emit first.\n * @return A function that returns an Observable that synchronously emits\n * provided values before subscribing to the source Observable.\n *\n * @see {@link endWith}\n * @see {@link finalize}\n * @see {@link concat}\n */\nexport function startWith<T, D>(...values: D[]): OperatorFunction<T, T | D> {\n  const scheduler = popScheduler(values);\n  return operate((source, subscriber) => {\n    // Here we can't pass `undefined` as a scheduler, because if we did, the\n    // code inside of `concat` would be confused by the `undefined`, and treat it\n    // like an invalid observable. So we have to split it two different ways.\n    (scheduler ? concat(values, source, scheduler) : concat(values, source)).subscribe(subscriber);\n  });\n}\n", "import { Subscriber } from '../Subscriber';\nimport { ObservableInput, OperatorFunction, ObservedValueOf } from '../types';\nimport { innerFrom } from '../observable/innerFrom';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/* tslint:disable:max-line-length */\nexport function switchMap<T, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O\n): OperatorFunction<T, ObservedValueOf<O>>;\n/** @deprecated The `resultSelector` parameter will be removed in v8. Use an inner `map` instead. Details: https://rxjs.dev/deprecations/resultSelector */\nexport function switchMap<T, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  resultSelector: undefined\n): OperatorFunction<T, ObservedValueOf<O>>;\n/** @deprecated The `resultSelector` parameter will be removed in v8. Use an inner `map` instead. Details: https://rxjs.dev/deprecations/resultSelector */\nexport function switchMap<T, R, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  resultSelector: (outerValue: T, innerValue: ObservedValueOf<O>, outerIndex: number, innerIndex: number) => R\n): OperatorFunction<T, R>;\n/* tslint:enable:max-line-length */\n\n/**\n * Projects each source value to an Observable which is merged in the output\n * Observable, emitting values only from the most recently projected Observable.\n *\n * <span class=\"informal\">Maps each value to an Observable, then flattens all of\n * these inner Observables using {@link switchAll}.</span>\n *\n * ![](switchMap.png)\n *\n * Returns an Observable that emits items based on applying a function that you\n * supply to each item emitted by the source Observable, where that function\n * returns an (so-called \"inner\") Observable. Each time it observes one of these\n * inner Observables, the output Observable begins emitting the items emitted by\n * that inner Observable. When a new inner Observable is emitted, `switchMap`\n * stops emitting items from the earlier-emitted inner Observable and begins\n * emitting items from the new one. It continues to behave like this for\n * subsequent inner Observables.\n *\n * ## Example\n *\n * Generate new Observable according to source Observable values\n *\n * ```ts\n * import { of, switchMap } from 'rxjs';\n *\n * const switched = of(1, 2, 3).pipe(switchMap(x => of(x, x ** 2, x ** 3)));\n * switched.subscribe(x => console.log(x));\n * // outputs\n * // 1\n * // 1\n * // 1\n * // 2\n * // 4\n * // 8\n * // 3\n * // 9\n * // 27\n * ```\n *\n * Restart an interval Observable on every click event\n *\n * ```ts\n * import { fromEvent, switchMap, interval } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(switchMap(() => interval(1000)));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link concatMap}\n * @see {@link exhaustMap}\n * @see {@link mergeMap}\n * @see {@link switchAll}\n * @see {@link switchMapTo}\n *\n * @param {function(value: T, index: number): ObservableInput} project A function\n * that, when applied to an item emitted by the source Observable, returns an\n * Observable.\n * @return A function that returns an Observable that emits the result of\n * applying the projection function (and the optional deprecated\n * `resultSelector`) to each item emitted by the source Observable and taking\n * only the values from the most recently projected inner Observable.\n */\nexport function switchMap<T, R, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  resultSelector?: (outerValue: T, innerValue: ObservedValueOf<O>, outerIndex: number, innerIndex: number) => R\n): OperatorFunction<T, ObservedValueOf<O> | R> {\n  return operate((source, subscriber) => {\n    let innerSubscriber: Subscriber<ObservedValueOf<O>> | null = null;\n    let index = 0;\n    // Whether or not the source subscription has completed\n    let isComplete = false;\n\n    // We only complete the result if the source is complete AND we don't have an active inner subscription.\n    // This is called both when the source completes and when the inners complete.\n    const checkComplete = () => isComplete && !innerSubscriber && subscriber.complete();\n\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => {\n          // Cancel the previous inner subscription if there was one\n          innerSubscriber?.unsubscribe();\n          let innerIndex = 0;\n          const outerIndex = index++;\n          // Start the next inner subscription\n          innerFrom(project(value, outerIndex)).subscribe(\n            (innerSubscriber = createOperatorSubscriber(\n              subscriber,\n              // When we get a new inner value, next it through. Note that this is\n              // handling the deprecate result selector here. This is because with this architecture\n              // it ends up being smaller than using the map operator.\n              (innerValue) => subscriber.next(resultSelector ? resultSelector(value, innerValue, outerIndex, innerIndex++) : innerValue),\n              () => {\n                // The inner has completed. Null out the inner subscriber to\n                // free up memory and to signal that we have no inner subscription\n                // currently.\n                innerSubscriber = null!;\n                checkComplete();\n              }\n            ))\n          );\n        },\n        () => {\n          isComplete = true;\n          checkComplete();\n        }\n      )\n    );\n  });\n}\n", "import { MonoTypeOperatorFunction, ObservableInput } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { innerFrom } from '../observable/innerFrom';\nimport { noop } from '../util/noop';\n\n/**\n * Emits the values emitted by the source Observable until a `notifier`\n * Observable emits a value.\n *\n * <span class=\"informal\">Lets values pass until a second Observable,\n * `notifier`, emits a value. Then, it completes.</span>\n *\n * ![](takeUntil.png)\n *\n * `takeUntil` subscribes and begins mirroring the source Observable. It also\n * monitors a second Observable, `notifier` that you provide. If the `notifier`\n * emits a value, the output Observable stops mirroring the source Observable\n * and completes. If the `notifier` doesn't emit any value and completes\n * then `takeUntil` will pass all values.\n *\n * ## Example\n *\n * Tick every second until the first click happens\n *\n * ```ts\n * import { interval, fromEvent, takeUntil } from 'rxjs';\n *\n * const source = interval(1000);\n * const clicks = fromEvent(document, 'click');\n * const result = source.pipe(takeUntil(clicks));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link take}\n * @see {@link takeLast}\n * @see {@link takeWhile}\n * @see {@link skip}\n *\n * @param {Observable} notifier The Observable whose first emitted value will\n * cause the output Observable of `takeUntil` to stop emitting values from the\n * source Observable.\n * @return A function that returns an Observable that emits the values from the\n * source Observable until `notifier` emits its first value.\n */\nexport function takeUntil<T>(notifier: ObservableInput<any>): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    innerFrom(notifier).subscribe(createOperatorSubscriber(subscriber, () => subscriber.complete(), noop));\n    !subscriber.closed && source.subscribe(subscriber);\n  });\n}\n", "import { OperatorFunction, MonoTypeOperatorFunction, TruthyTypesOf } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\nexport function takeWhile<T>(predicate: BooleanConstructor, inclusive: true): MonoTypeOperatorFunction<T>;\nexport function takeWhile<T>(predicate: BooleanConstructor, inclusive: false): OperatorFunction<T, TruthyTypesOf<T>>;\nexport function takeWhile<T>(predicate: BooleanConstructor): OperatorFunction<T, TruthyTypesOf<T>>;\nexport function takeWhile<T, S extends T>(predicate: (value: T, index: number) => value is S): OperatorFunction<T, S>;\nexport function takeWhile<T, S extends T>(predicate: (value: T, index: number) => value is S, inclusive: false): OperatorFunction<T, S>;\nexport function takeWhile<T>(predicate: (value: T, index: number) => boolean, inclusive?: boolean): MonoTypeOperatorFunction<T>;\n\n/**\n * Emits values emitted by the source Observable so long as each value satisfies\n * the given `predicate`, and then completes as soon as this `predicate` is not\n * satisfied.\n *\n * <span class=\"informal\">Takes values from the source only while they pass the\n * condition given. When the first value does not satisfy, it completes.</span>\n *\n * ![](takeWhile.png)\n *\n * `takeWhile` subscribes and begins mirroring the source Observable. Each value\n * emitted on the source is given to the `predicate` function which returns a\n * boolean, representing a condition to be satisfied by the source values. The\n * output Observable emits the source values until such time as the `predicate`\n * returns false, at which point `takeWhile` stops mirroring the source\n * Observable and completes the output Observable.\n *\n * ## Example\n *\n * Emit click events only while the clientX property is greater than 200\n *\n * ```ts\n * import { fromEvent, takeWhile } from 'rxjs';\n *\n * const clicks = fromEvent<PointerEvent>(document, 'click');\n * const result = clicks.pipe(takeWhile(ev => ev.clientX > 200));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link take}\n * @see {@link takeLast}\n * @see {@link takeUntil}\n * @see {@link skip}\n *\n * @param {function(value: T, index: number): boolean} predicate A function that\n * evaluates a value emitted by the source Observable and returns a boolean.\n * Also takes the (zero-based) index as the second argument.\n * @param {boolean} inclusive When set to `true` the value that caused\n * `predicate` to return `false` will also be emitted.\n * @return A function that returns an Observable that emits values from the\n * source Observable so long as each value satisfies the condition defined by\n * the `predicate`, then completes.\n */\nexport function takeWhile<T>(predicate: (value: T, index: number) => boolean, inclusive = false): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    let index = 0;\n    source.subscribe(\n      createOperatorSubscriber(subscriber, (value) => {\n        const result = predicate(value, index++);\n        (result || inclusive) && subscriber.next(value);\n        !result && subscriber.complete();\n      })\n    );\n  });\n}\n", "import { MonoTypeOperatorFunction, Observer } from '../types';\nimport { isFunction } from '../util/isFunction';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { identity } from '../util/identity';\n\n/**\n * An extension to the {@link Observer} interface used only by the {@link tap} operator.\n *\n * It provides a useful set of callbacks a user can register to do side-effects in\n * cases other than what the usual {@link Observer} callbacks are\n * ({@link guide/glossary-and-semantics#next next},\n * {@link guide/glossary-and-semantics#error error} and/or\n * {@link guide/glossary-and-semantics#complete complete}).\n *\n * ## Example\n *\n * ```ts\n * import { fromEvent, switchMap, tap, interval, take } from 'rxjs';\n *\n * const source$ = fromEvent(document, 'click');\n * const result$ = source$.pipe(\n *   switchMap((_, i) => i % 2 === 0\n *     ? fromEvent(document, 'mousemove').pipe(\n *         tap({\n *           subscribe: () => console.log('Subscribed to the mouse move events after click #' + i),\n *           unsubscribe: () => console.log('Mouse move events #' + i + ' unsubscribed'),\n *           finalize: () => console.log('Mouse move events #' + i + ' finalized')\n *         })\n *       )\n *     : interval(1_000).pipe(\n *         take(5),\n *         tap({\n *           subscribe: () => console.log('Subscribed to the 1-second interval events after click #' + i),\n *           unsubscribe: () => console.log('1-second interval events #' + i + ' unsubscribed'),\n *           finalize: () => console.log('1-second interval events #' + i + ' finalized')\n *         })\n *       )\n *   )\n * );\n *\n * const subscription = result$.subscribe({\n *   next: console.log\n * });\n *\n * setTimeout(() => {\n *   console.log('Unsubscribe after 60 seconds');\n *   subscription.unsubscribe();\n * }, 60_000);\n * ```\n */\nexport interface TapObserver<T> extends Observer<T> {\n  /**\n   * The callback that `tap` operator invokes at the moment when the source Observable\n   * gets subscribed to.\n   */\n  subscribe: () => void;\n  /**\n   * The callback that `tap` operator invokes when an explicit\n   * {@link guide/glossary-and-semantics#unsubscription unsubscribe} happens. It won't get invoked on\n   * `error` or `complete` events.\n   */\n  unsubscribe: () => void;\n  /**\n   * The callback that `tap` operator invokes when any kind of\n   * {@link guide/glossary-and-semantics#finalization finalization} happens - either when\n   * the source Observable `error`s or `complete`s or when it gets explicitly unsubscribed\n   * by the user. There is no difference in using this callback or the {@link finalize}\n   * operator, but if you're already using `tap` operator, you can use this callback\n   * instead. You'd get the same result in either case.\n   */\n  finalize: () => void;\n}\nexport function tap<T>(observerOrNext?: Partial<TapObserver<T>> | ((value: T) => void)): MonoTypeOperatorFunction<T>;\n/** @deprecated Instead of passing separate callback arguments, use an observer argument. Signatures taking separate callback arguments will be removed in v8. Details: https://rxjs.dev/deprecations/subscribe-arguments */\nexport function tap<T>(\n  next?: ((value: T) => void) | null,\n  error?: ((error: any) => void) | null,\n  complete?: (() => void) | null\n): MonoTypeOperatorFunction<T>;\n\n/**\n * Used to perform side-effects for notifications from the source observable\n *\n * <span class=\"informal\">Used when you want to affect outside state with a notification without altering the notification</span>\n *\n * ![](tap.png)\n *\n * Tap is designed to allow the developer a designated place to perform side effects. While you _could_ perform side-effects\n * inside of a `map` or a `mergeMap`, that would make their mapping functions impure, which isn't always a big deal, but will\n * make it so you can't do things like memoize those functions. The `tap` operator is designed solely for such side-effects to\n * help you remove side-effects from other operations.\n *\n * For any notification, next, error, or complete, `tap` will call the appropriate callback you have provided to it, via a function\n * reference, or a partial observer, then pass that notification down the stream.\n *\n * The observable returned by `tap` is an exact mirror of the source, with one exception: Any error that occurs -- synchronously -- in a handler\n * provided to `tap` will be emitted as an error from the returned observable.\n *\n * > Be careful! You can mutate objects as they pass through the `tap` operator's handlers.\n *\n * The most common use of `tap` is actually for debugging. You can place a `tap(console.log)` anywhere\n * in your observable `pipe`, log out the notifications as they are emitted by the source returned by the previous\n * operation.\n *\n * ## Examples\n *\n * Check a random number before it is handled. Below is an observable that will use a random number between 0 and 1,\n * and emit `'big'` or `'small'` depending on the size of that number. But we wanted to log what the original number\n * was, so we have added a `tap(console.log)`.\n *\n * ```ts\n * import { of, tap, map } from 'rxjs';\n *\n * of(Math.random()).pipe(\n *   tap(console.log),\n *   map(n => n > 0.5 ? 'big' : 'small')\n * ).subscribe(console.log);\n * ```\n *\n * Using `tap` to analyze a value and force an error. Below is an observable where in our system we only\n * want to emit numbers 3 or less we get from another source. We can force our observable to error\n * using `tap`.\n *\n * ```ts\n * import { of, tap } from 'rxjs';\n *\n * const source = of(1, 2, 3, 4, 5);\n *\n * source.pipe(\n *   tap(n => {\n *     if (n > 3) {\n *       throw new TypeError(`Value ${ n } is greater than 3`);\n *     }\n *   })\n * )\n * .subscribe({ next: console.log, error: err => console.log(err.message) });\n * ```\n *\n * We want to know when an observable completes before moving on to the next observable. The system\n * below will emit a random series of `'X'` characters from 3 different observables in sequence. The\n * only way we know when one observable completes and moves to the next one, in this case, is because\n * we have added a `tap` with the side effect of logging to console.\n *\n * ```ts\n * import { of, concatMap, interval, take, map, tap } from 'rxjs';\n *\n * of(1, 2, 3).pipe(\n *   concatMap(n => interval(1000).pipe(\n *     take(Math.round(Math.random() * 10)),\n *     map(() => 'X'),\n *     tap({ complete: () => console.log(`Done with ${ n }`) })\n *   ))\n * )\n * .subscribe(console.log);\n * ```\n *\n * @see {@link finalize}\n * @see {@link TapObserver}\n *\n * @param observerOrNext A next handler or partial observer\n * @param error An error handler\n * @param complete A completion handler\n * @return A function that returns an Observable identical to the source, but\n * runs the specified Observer or callback(s) for each item.\n */\nexport function tap<T>(\n  observerOrNext?: Partial<TapObserver<T>> | ((value: T) => void) | null,\n  error?: ((e: any) => void) | null,\n  complete?: (() => void) | null\n): MonoTypeOperatorFunction<T> {\n  // We have to check to see not only if next is a function,\n  // but if error or complete were passed. This is because someone\n  // could technically call tap like `tap(null, fn)` or `tap(null, null, fn)`.\n  const tapObserver =\n    isFunction(observerOrNext) || error || complete\n      ? // tslint:disable-next-line: no-object-literal-type-assertion\n        ({ next: observerOrNext as Exclude<typeof observerOrNext, Partial<TapObserver<T>>>, error, complete } as Partial<TapObserver<T>>)\n      : observerOrNext;\n\n  return tapObserver\n    ? operate((source, subscriber) => {\n        tapObserver.subscribe?.();\n        let isUnsub = true;\n        source.subscribe(\n          createOperatorSubscriber(\n            subscriber,\n            (value) => {\n              tapObserver.next?.(value);\n              subscriber.next(value);\n            },\n            () => {\n              isUnsub = false;\n              tapObserver.complete?.();\n              subscriber.complete();\n            },\n            (err) => {\n              isUnsub = false;\n              tapObserver.error?.(err);\n              subscriber.error(err);\n            },\n            () => {\n              if (isUnsub) {\n                tapObserver.unsubscribe?.();\n              }\n              tapObserver.finalize?.();\n            }\n          )\n        );\n      })\n    : // Tap was called with no valid tap observer or handler\n      // (e.g. `tap(null, null, null)` or `tap(null)` or `tap()`)\n      // so we're going to just mirror the source.\n      identity;\n}\n", "import { Subscription } from '../Subscription';\n\nimport { MonoTypeOperatorFunction, ObservableInput } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { innerFrom } from '../observable/innerFrom';\n\n/**\n * An object interface used by {@link throttle} or {@link throttleTime} that ensure\n * configuration options of these operators.\n *\n * @see {@link throttle}\n * @see {@link throttleTime}\n */\nexport interface ThrottleConfig {\n  /**\n   * If `true`, the resulting Observable will emit the first value from the source\n   * Observable at the **start** of the \"throttling\" process (when starting an\n   * internal timer that prevents other emissions from the source to pass through).\n   * If `false`, it will not emit the first value from the source Observable at the\n   * start of the \"throttling\" process.\n   *\n   * If not provided, defaults to: `true`.\n   */\n  leading?: boolean;\n  /**\n   * If `true`, the resulting Observable will emit the last value from the source\n   * Observable at the **end** of the \"throttling\" process (when ending an internal\n   * timer that prevents other emissions from the source to pass through).\n   * If `false`, it will not emit the last value from the source Observable at the\n   * end of the \"throttling\" process.\n   *\n   * If not provided, defaults to: `false`.\n   */\n  trailing?: boolean;\n}\n\n/**\n * Emits a value from the source Observable, then ignores subsequent source\n * values for a duration determined by another Observable, then repeats this\n * process.\n *\n * <span class=\"informal\">It's like {@link throttleTime}, but the silencing\n * duration is determined by a second Observable.</span>\n *\n * ![](throttle.svg)\n *\n * `throttle` emits the source Observable values on the output Observable\n * when its internal timer is disabled, and ignores source values when the timer\n * is enabled. Initially, the timer is disabled. As soon as the first source\n * value arrives, it is forwarded to the output Observable, and then the timer\n * is enabled by calling the `durationSelector` function with the source value,\n * which returns the \"duration\" Observable. When the duration Observable emits a\n * value, the timer is disabled, and this process repeats for the\n * next source value.\n *\n * ## Example\n *\n * Emit clicks at a rate of at most one click per second\n *\n * ```ts\n * import { fromEvent, throttle, interval } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(throttle(() => interval(1000)));\n *\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link audit}\n * @see {@link debounce}\n * @see {@link delayWhen}\n * @see {@link sample}\n * @see {@link throttleTime}\n *\n * @param durationSelector A function that receives a value from the source\n * Observable, for computing the silencing duration for each source value,\n * returned as an `ObservableInput`.\n * @param config A configuration object to define `leading` and `trailing`\n * behavior. Defaults to `{ leading: true, trailing: false }`.\n * @return A function that returns an Observable that performs the throttle\n * operation to limit the rate of emissions from the source.\n */\nexport function throttle<T>(durationSelector: (value: T) => ObservableInput<any>, config?: ThrottleConfig): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    const { leading = true, trailing = false } = config ?? {};\n    let hasValue = false;\n    let sendValue: T | null = null;\n    let throttled: Subscription | null = null;\n    let isComplete = false;\n\n    const endThrottling = () => {\n      throttled?.unsubscribe();\n      throttled = null;\n      if (trailing) {\n        send();\n        isComplete && subscriber.complete();\n      }\n    };\n\n    const cleanupThrottling = () => {\n      throttled = null;\n      isComplete && subscriber.complete();\n    };\n\n    const startThrottle = (value: T) =>\n      (throttled = innerFrom(durationSelector(value)).subscribe(createOperatorSubscriber(subscriber, endThrottling, cleanupThrottling)));\n\n    const send = () => {\n      if (hasValue) {\n        // Ensure we clear out our value and hasValue flag\n        // before we emit, otherwise reentrant code can cause\n        // issues here.\n        hasValue = false;\n        const value = sendValue!;\n        sendValue = null;\n        // Emit the value.\n        subscriber.next(value);\n        !isComplete && startThrottle(value);\n      }\n    };\n\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        // Regarding the presence of throttled.closed in the following\n        // conditions, if a synchronous duration selector is specified - weird,\n        // but legal - an already-closed subscription will be assigned to\n        // throttled, so the subscription's closed property needs to be checked,\n        // too.\n        (value) => {\n          hasValue = true;\n          sendValue = value;\n          !(throttled && !throttled.closed) && (leading ? send() : startThrottle(value));\n        },\n        () => {\n          isComplete = true;\n          !(trailing && hasValue && throttled && !throttled.closed) && subscriber.complete();\n        }\n      )\n    );\n  });\n}\n", "import { asyncScheduler } from '../scheduler/async';\nimport { throttle, ThrottleConfig } from './throttle';\nimport { MonoTypeOperatorFunction, SchedulerLike } from '../types';\nimport { timer } from '../observable/timer';\n\n/**\n * Emits a value from the source Observable, then ignores subsequent source\n * values for `duration` milliseconds, then repeats this process.\n *\n * <span class=\"informal\">Lets a value pass, then ignores source values for the\n * next `duration` milliseconds.</span>\n *\n * ![](throttleTime.png)\n *\n * `throttleTime` emits the source Observable values on the output Observable\n * when its internal timer is disabled, and ignores source values when the timer\n * is enabled. Initially, the timer is disabled. As soon as the first source\n * value arrives, it is forwarded to the output Observable, and then the timer\n * is enabled. After `duration` milliseconds (or the time unit determined\n * internally by the optional `scheduler`) has passed, the timer is disabled,\n * and this process repeats for the next source value. Optionally takes a\n * {@link SchedulerLike} for managing timers.\n *\n * ## Examples\n *\n * ### Limit click rate\n *\n * Emit clicks at a rate of at most one click per second\n *\n * ```ts\n * import { fromEvent, throttleTime } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(throttleTime(1000));\n *\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link auditTime}\n * @see {@link debounceTime}\n * @see {@link delay}\n * @see {@link sampleTime}\n * @see {@link throttle}\n *\n * @param duration Time to wait before emitting another value after\n * emitting the last value, measured in milliseconds or the time unit determined\n * internally by the optional `scheduler`.\n * @param scheduler The {@link SchedulerLike} to use for\n * managing the timers that handle the throttling. Defaults to {@link asyncScheduler}.\n * @param config A configuration object to define `leading` and\n * `trailing` behavior. Defaults to `{ leading: true, trailing: false }`.\n * @return A function that returns an Observable that performs the throttle\n * operation to limit the rate of emissions from the source.\n */\nexport function throttleTime<T>(\n  duration: number,\n  scheduler: SchedulerLike = asyncScheduler,\n  config?: ThrottleConfig\n): MonoTypeOperatorFunction<T> {\n  const duration$ = timer(duration, scheduler);\n  return throttle(() => duration$, config);\n}\n", "import { OperatorFunction, ObservableInputTuple } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { innerFrom } from '../observable/innerFrom';\nimport { identity } from '../util/identity';\nimport { noop } from '../util/noop';\nimport { popResultSelector } from '../util/args';\n\nexport function withLatestFrom<T, O extends unknown[]>(...inputs: [...ObservableInputTuple<O>]): OperatorFunction<T, [T, ...O]>;\n\nexport function withLatestFrom<T, O extends unknown[], R>(\n  ...inputs: [...ObservableInputTuple<O>, (...value: [T, ...O]) => R]\n): OperatorFunction<T, R>;\n\n/**\n * Combines the source Observable with other Observables to create an Observable\n * whose values are calculated from the latest values of each, only when the\n * source emits.\n *\n * <span class=\"informal\">Whenever the source Observable emits a value, it\n * computes a formula using that value plus the latest values from other input\n * Observables, then emits the output of that formula.</span>\n *\n * ![](withLatestFrom.png)\n *\n * `withLatestFrom` combines each value from the source Observable (the\n * instance) with the latest values from the other input Observables only when\n * the source emits a value, optionally using a `project` function to determine\n * the value to be emitted on the output Observable. All input Observables must\n * emit at least one value before the output Observable will emit a value.\n *\n * ## Example\n *\n * On every click event, emit an array with the latest timer event plus the click event\n *\n * ```ts\n * import { fromEvent, interval, withLatestFrom } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const timer = interval(1000);\n * const result = clicks.pipe(withLatestFrom(timer));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link combineLatest}\n *\n * @param {ObservableInput} other An input Observable to combine with the source\n * Observable. More than one input Observables may be given as argument.\n * @param {Function} [project] Projection function for combining values\n * together. Receives all values in order of the Observables passed, where the\n * first parameter is a value from the source Observable. (e.g.\n * `a.pipe(withLatestFrom(b, c), map(([a1, b1, c1]) => a1 + b1 + c1))`). If this is not\n * passed, arrays will be emitted on the output Observable.\n * @return A function that returns an Observable of projected values from the\n * most recent values from each input Observable, or an array of the most\n * recent values from each input Observable.\n */\nexport function withLatestFrom<T, R>(...inputs: any[]): OperatorFunction<T, R | any[]> {\n  const project = popResultSelector(inputs) as ((...args: any[]) => R) | undefined;\n\n  return operate((source, subscriber) => {\n    const len = inputs.length;\n    const otherValues = new Array(len);\n    // An array of whether or not the other sources have emitted. Matched with them by index.\n    // TODO: At somepoint, we should investigate the performance implications here, and look\n    // into using a `Set()` and checking the `size` to see if we're ready.\n    let hasValue = inputs.map(() => false);\n    // Flipped true when we have at least one value from all other sources and\n    // we are ready to start emitting values.\n    let ready = false;\n\n    // Other sources. Note that here we are not checking `subscriber.closed`,\n    // this causes all inputs to be subscribed to, even if nothing can be emitted\n    // from them. This is an important distinction because subscription constitutes\n    // a side-effect.\n    for (let i = 0; i < len; i++) {\n      innerFrom(inputs[i]).subscribe(\n        createOperatorSubscriber(\n          subscriber,\n          (value) => {\n            otherValues[i] = value;\n            if (!ready && !hasValue[i]) {\n              // If we're not ready yet, flag to show this observable has emitted.\n              hasValue[i] = true;\n              // Intentionally terse code.\n              // If all of our other observables have emitted, set `ready` to `true`,\n              // so we know we can start emitting values, then clean up the `hasValue` array,\n              // because we don't need it anymore.\n              (ready = hasValue.every(identity)) && (hasValue = null!);\n            }\n          },\n          // Completing one of the other sources has\n          // no bearing on the completion of our result.\n          noop\n        )\n      );\n    }\n\n    // Source subscription\n    source.subscribe(\n      createOperatorSubscriber(subscriber, (value) => {\n        if (ready) {\n          // We have at least one value from the other sources. Go ahead and emit.\n          const values = [value, ...otherValues];\n          subscriber.next(project ? project(...values) : values);\n        }\n      })\n    );\n  });\n}\n", "import { zip as zipStatic } from '../observable/zip';\nimport { ObservableInput, ObservableInputTuple, OperatorFunction, Cons } from '../types';\nimport { operate } from '../util/lift';\n\n/** @deprecated Replaced with {@link zipWith}. Will be removed in v8. */\nexport function zip<T, A extends readonly unknown[]>(otherInputs: [...ObservableInputTuple<A>]): OperatorFunction<T, Cons<T, A>>;\n/** @deprecated Replaced with {@link zipWith}. Will be removed in v8. */\nexport function zip<T, A extends readonly unknown[], R>(\n  otherInputsAndProject: [...ObservableInputTuple<A>],\n  project: (...values: Cons<T, A>) => R\n): OperatorFunction<T, R>;\n/** @deprecated Replaced with {@link zipWith}. Will be removed in v8. */\nexport function zip<T, A extends readonly unknown[]>(...otherInputs: [...ObservableInputTuple<A>]): OperatorFunction<T, Cons<T, A>>;\n/** @deprecated Replaced with {@link zipWith}. Will be removed in v8. */\nexport function zip<T, A extends readonly unknown[], R>(\n  ...otherInputsAndProject: [...ObservableInputTuple<A>, (...values: Cons<T, A>) => R]\n): OperatorFunction<T, R>;\n\n/**\n * @deprecated Replaced with {@link zipWith}. Will be removed in v8.\n */\nexport function zip<T, R>(...sources: Array<ObservableInput<any> | ((...values: Array<any>) => R)>): OperatorFunction<T, any> {\n  return operate((source, subscriber) => {\n    zipStatic(source as ObservableInput<any>, ...(sources as Array<ObservableInput<any>>)).subscribe(subscriber);\n  });\n}\n", "import { ObservableInputTuple, OperatorFunction, Cons } from '../types';\nimport { zip } from './zip';\n\n/**\n * Subscribes to the source, and the observable inputs provided as arguments, and combines their values, by index, into arrays.\n *\n * What is meant by \"combine by index\": The first value from each will be made into a single array, then emitted,\n * then the second value from each will be combined into a single array and emitted, then the third value\n * from each will be combined into a single array and emitted, and so on.\n *\n * This will continue until it is no longer able to combine values of the same index into an array.\n *\n * After the last value from any one completed source is emitted in an array, the resulting observable will complete,\n * as there is no way to continue \"zipping\" values together by index.\n *\n * Use-cases for this operator are limited. There are memory concerns if one of the streams is emitting\n * values at a much faster rate than the others. Usage should likely be limited to streams that emit\n * at a similar pace, or finite streams of known length.\n *\n * In many cases, authors want `combineLatestWith` and not `zipWith`.\n *\n * @param otherInputs other observable inputs to collate values from.\n * @return A function that returns an Observable that emits items by index\n * combined from the source Observable and provided Observables, in form of an\n * array.\n */\nexport function zipWith<T, A extends readonly unknown[]>(...otherInputs: [...ObservableInputTuple<A>]): OperatorFunction<T, Cons<T, A>> {\n  return zip(...otherInputs);\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  ReplaySubject,\n  Subject,\n  fromEvent\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch document\n *\n * Documents are implemented as subjects, so all downstream observables are\n * automatically updated when a new document is emitted.\n *\n * @returns Document subject\n */\nexport function watchDocument(): Subject<Document> {\n  const document$ = new ReplaySubject<Document>(1)\n  fromEvent(document, \"DOMContentLoaded\", { once: true })\n    .subscribe(() => document$.next(document))\n\n  /* Return document */\n  return document$\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve all elements matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getElements<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T][]\n\nexport function getElements<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T[]\n\nexport function getElements<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T[] {\n  return Array.from(node.querySelectorAll<T>(selector))\n}\n\n/**\n * Retrieve an element matching a query selector or throw a reference error\n *\n * Note that this function assumes that the element is present. If unsure if an\n * element is existent, use the `getOptionalElement` function instead.\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getElement<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T]\n\nexport function getElement<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T\n\nexport function getElement<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T {\n  const el = getOptionalElement<T>(selector, node)\n  if (typeof el === \"undefined\")\n    throw new ReferenceError(\n      `Missing element: expected \"${selector}\" to be present`\n    )\n\n  /* Return element */\n  return el\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Retrieve an optional element matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element or nothing\n */\nexport function getOptionalElement<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T] | undefined\n\nexport function getOptionalElement<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T | undefined\n\nexport function getOptionalElement<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T | undefined {\n  return node.querySelector<T>(selector) || undefined\n}\n\n/**\n * Retrieve the currently active element\n *\n * @returns Element or nothing\n */\nexport function getActiveElement(): HTMLElement | undefined {\n  return (\n    document.activeElement?.shadowRoot?.activeElement as HTMLElement ??\n    document.activeElement as HTMLElement ??\n    undefined\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  debounceTime,\n  distinctUntilChanged,\n  fromEvent,\n  map,\n  merge,\n  shareReplay,\n  startWith\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Focus observable\n *\n * Previously, this observer used `focus` and `blur` events to determine whether\n * an element is focused, but this doesn't work if there are focusable elements\n * within the elements itself. A better solutions are `focusin` and `focusout`\n * events, which bubble up the tree and allow for more fine-grained control.\n *\n * `debounceTime` is necessary, because when a focus change happens inside an\n * element, the observable would first emit `false` and then `true` again.\n */\nconst observer$ = merge(\n  fromEvent(document.body, \"focusin\"),\n  fromEvent(document.body, \"focusout\")\n)\n  .pipe(\n    debounceTime(1),\n    startWith(undefined),\n    map(() => getActiveElement() || document.body),\n    shareReplay(1)\n  )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element focus\n *\n * @param el - Element\n *\n * @returns Element focus observable\n */\nexport function watchElementFocus(\n  el: HTMLElement\n): Observable<boolean> {\n  return observer$\n    .pipe(\n      map(active => el.contains(active)),\n      distinctUntilChanged()\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  debounce,\n  defer,\n  fromEvent,\n  identity,\n  map,\n  merge,\n  startWith,\n  timer\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element hover\n *\n * The second parameter allows to specify a timeout in milliseconds after which\n * the hover state will be reset to `false`. This is useful for tooltips which\n * should disappear after a certain amount of time, in order to allow the user\n * to move the cursor from the host to the tooltip.\n *\n * @param el - Element\n * @param timeout - Timeout\n *\n * @returns Element hover observable\n */\nexport function watchElementHover(\n  el: HTMLElement, timeout?: number\n): Observable<boolean> {\n  return defer(() => merge(\n    fromEvent(el, \"mouseenter\").pipe(map(() => true)),\n    fromEvent(el, \"mouseleave\").pipe(map(() => false))\n  )\n    .pipe(\n      timeout ? debounce(active => timer(+!active * timeout)) : identity,\n      startWith(el.matches(\":hover\"))\n    )\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { JSX as JSXInternal } from \"preact\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * HTML attributes\n */\ntype Attributes =\n  & JSXInternal.HTMLAttributes\n  & JSXInternal.SVGAttributes\n  & Record<string, any>\n\n/**\n * Child element\n */\ntype Child =\n  | ChildNode\n  | HTMLElement\n  | Text\n  | string\n  | number\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Append a child node to an element\n *\n * @param el - Element\n * @param child - Child node(s)\n */\nfunction appendChild(el: HTMLElement, child: Child | Child[]): void {\n\n  /* Handle primitive types (including raw HTML) */\n  if (typeof child === \"string\" || typeof child === \"number\") {\n    el.innerHTML += child.toString()\n\n  /* Handle nodes */\n  } else if (child instanceof Node) {\n    el.appendChild(child)\n\n  /* Handle nested children */\n  } else if (Array.isArray(child)) {\n    for (const node of child)\n      appendChild(el, node)\n  }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * JSX factory\n *\n * @template T - Element type\n *\n * @param tag - HTML tag\n * @param attributes - HTML attributes\n * @param children - Child elements\n *\n * @returns Element\n */\nexport function h<T extends keyof HTMLElementTagNameMap>(\n  tag: T, attributes?: Attributes | null, ...children: Child[]\n): HTMLElementTagNameMap[T]\n\nexport function h<T extends h.JSX.Element>(\n  tag: string, attributes?: Attributes | null, ...children: Child[]\n): T\n\nexport function h<T extends h.JSX.Element>(\n  tag: string, attributes?: Attributes | null, ...children: Child[]\n): T {\n  const el = document.createElement(tag)\n\n  /* Set attributes, if any */\n  if (attributes)\n    for (const attr of Object.keys(attributes)) {\n      if (typeof attributes[attr] === \"undefined\")\n        continue\n\n      /* Set default attribute or boolean */\n      if (typeof attributes[attr] !== \"boolean\")\n        el.setAttribute(attr, attributes[attr])\n      else\n        el.setAttribute(attr, \"\")\n    }\n\n  /* Append child nodes */\n  for (const child of children)\n    appendChild(el, child)\n\n  /* Return element */\n  return el as T\n}\n\n/* ----------------------------------------------------------------------------\n * Namespace\n * ------------------------------------------------------------------------- */\n\nexport declare namespace h {\n  namespace JSX {\n    type Element = HTMLElement\n    type IntrinsicElements = JSXInternal.IntrinsicElements\n  }\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Round a number for display with repository facts\n *\n * This is a reverse-engineered version of GitHub's weird rounding algorithm\n * for stars, forks and all other numbers. While all numbers below `1,000` are\n * returned as-is, bigger numbers are converted to fixed numbers:\n *\n * - `1,049` => `1k`\n * - `1,050` => `1.1k`\n * - `1,949` => `1.9k`\n * - `1,950` => `2k`\n *\n * @param value - Original value\n *\n * @returns Rounded value\n */\nexport function round(value: number): string {\n  if (value > 999) {\n    const digits = +((value - 950) % 1000 > 99)\n    return `${((value + 0.000001) / 1000).toFixed(digits)}k`\n  } else {\n    return value.toString()\n  }\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  defer,\n  finalize,\n  fromEvent,\n  map,\n  merge,\n  switchMap,\n  take,\n  throwError\n} from \"rxjs\"\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create and load a `script` element\n *\n * This function returns an observable that will emit when the script was\n * successfully loaded, or throw an error if it wasn't.\n *\n * @param src - Script URL\n *\n * @returns Script observable\n */\nexport function watchScript(src: string): Observable<void> {\n  const script = h(\"script\", { src })\n  return defer(() => {\n    document.head.appendChild(script)\n    return merge(\n      fromEvent(script, \"load\"),\n      fromEvent(script, \"error\")\n        .pipe(\n          switchMap(() => (\n            throwError(() => new ReferenceError(`Invalid script: ${src}`))\n          ))\n        )\n    )\n      .pipe(\n        map(() => undefined),\n        finalize(() => document.head.removeChild(script)),\n        take(1)\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  NEVER,\n  Observable,\n  Subject,\n  defer,\n  filter,\n  finalize,\n  map,\n  merge,\n  of,\n  shareReplay,\n  startWith,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport { watchScript } from \"../../../script\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Element offset\n */\nexport interface ElementSize {\n  width: number                        /* Element width */\n  height: number                       /* Element height */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Resize observer entry subject\n */\nconst entry$ = new Subject<ResizeObserverEntry>()\n\n/**\n * Resize observer observable\n *\n * This observable will create a `ResizeObserver` on the first subscription\n * and will automatically terminate it when there are no more subscribers.\n * It's quite important to centralize observation in a single `ResizeObserver`,\n * as the performance difference can be quite dramatic, as the link shows.\n *\n * If the browser doesn't have a `ResizeObserver` implementation available, a\n * polyfill is automatically downloaded from unpkg.com. This is also compatible\n * with the built-in privacy plugin, which will download the polyfill and put\n * it alongside the built site for self-hosting.\n *\n * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => (\n  typeof ResizeObserver === \"undefined\"\n    ? watchScript(\"https://unpkg.com/resize-observer-polyfill\")\n    : of(undefined)\n))\n  .pipe(\n    map(() => new ResizeObserver(entries => (\n      entries.forEach(entry => entry$.next(entry))\n    ))),\n    switchMap(observer => merge(NEVER, of(observer)).pipe(\n      finalize(() => observer.disconnect())\n    )),\n    shareReplay(1)\n  )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element size\n *\n * @param el - Element\n *\n * @returns Element size\n */\nexport function getElementSize(\n  el: HTMLElement\n): ElementSize {\n  return {\n    width:  el.offsetWidth,\n    height: el.offsetHeight\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element size\n *\n * This function returns an observable that subscribes to a single internal\n * instance of `ResizeObserver` upon subscription, and emit resize events until\n * termination. Note that this function should not be called with the same\n * element twice, as the first unsubscription will terminate observation.\n *\n * Sadly, we can't use the `DOMRect` objects returned by the observer, because\n * we need the emitted values to be consistent with `getElementSize`, which will\n * return the used values (rounded) and not actual values (unrounded). Thus, we\n * use the `offset*` properties. See the linked GitHub issue.\n *\n * @see https://bit.ly/3m0k3he - GitHub issue\n *\n * @param el - Element\n *\n * @returns Element size observable\n */\nexport function watchElementSize(\n  el: HTMLElement\n): Observable<ElementSize> {\n\n  // Compute target element - since inline elements cannot be observed by the\n  // current `ResizeObserver` implementation as provided by browsers, we need\n  // to determine the first containing parent element and use that one as a\n  // target, while we always compute the actual size from the element.\n  let target = el\n  while (target.clientWidth === 0)\n    if (target.parentElement)\n      target = target.parentElement\n    else\n      break\n\n  // Observe target element and recompute element size on resize - as described\n  // above, the target element is not necessarily the element of interest\n  return observer$.pipe(\n    tap(observer => observer.observe(target)),\n    switchMap(observer => entry$.pipe(\n      filter(entry => entry.target === target),\n      finalize(() => observer.unobserve(target))\n    )),\n    map(() => getElementSize(el)),\n    startWith(getElementSize(el))\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ElementSize } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element content size (= scroll width and height)\n *\n * @param el - Element\n *\n * @returns Element content size\n */\nexport function getElementContentSize(\n  el: HTMLElement\n): ElementSize {\n  return {\n    width:  el.scrollWidth,\n    height: el.scrollHeight\n  }\n}\n\n/**\n * Retrieve the overflowing container of an element, if any\n *\n * @param el - Element\n *\n * @returns Overflowing container or nothing\n */\nexport function getElementContainer(\n  el: HTMLElement\n): HTMLElement | undefined {\n  let parent = el.parentElement\n  while (parent)\n    if (\n      el.scrollWidth  <= parent.scrollWidth &&\n      el.scrollHeight <= parent.scrollHeight\n    )\n      parent = (el = parent).parentElement\n    else\n      break\n\n  /* Return overflowing container */\n  return parent ? el : undefined\n}\n\n/**\n * Retrieve all overflowing containers of an element, if any\n *\n * Note that this function has a slightly different behavior, so we should at\n * some point consider refactoring how overflowing containers are handled.\n *\n * @param el - Element\n *\n * @returns Overflowing containers\n */\nexport function getElementContainers(\n  el: HTMLElement\n): HTMLElement[] {\n  const containers: HTMLElement[] = []\n\n  // Walk up the DOM tree until we find an overflowing container\n  let parent = el.parentElement\n  while (parent) {\n    if (\n      el.clientWidth  > parent.clientWidth ||\n      el.clientHeight > parent.clientHeight\n    )\n      containers.push(parent)\n\n    // Continue with parent element\n    parent = (el = parent).parentElement\n  }\n\n  // If the page is short, the body might not be overflowing and there might be\n  // no other containers, which is why we need to make sure the body is present\n  if (containers.length === 0)\n    containers.push(document.documentElement)\n\n  // Return overflowing containers\n  return containers\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  animationFrameScheduler,\n  auditTime,\n  fromEvent,\n  map,\n  merge,\n  startWith\n} from \"rxjs\"\n\nimport { watchElementSize } from \"../../size\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Element offset\n */\nexport interface ElementOffset {\n  x: number                            /* Horizontal offset */\n  y: number                            /* Vertical offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element offset\n *\n * @param el - Element\n *\n * @returns Element offset\n */\nexport function getElementOffset(\n  el: HTMLElement\n): ElementOffset {\n  return {\n    x: el.offsetLeft,\n    y: el.offsetTop\n  }\n}\n\n/**\n * Retrieve absolute element offset\n *\n * @param el - Element\n *\n * @returns Element offset\n */\nexport function getElementOffsetAbsolute(\n  el: HTMLElement\n): ElementOffset {\n  const rect = el.getBoundingClientRect()\n  return {\n    x: rect.x + window.scrollX,\n    y: rect.y + window.scrollY\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element offset\n *\n * @param el - Element\n *\n * @returns Element offset observable\n */\nexport function watchElementOffset(\n  el: HTMLElement\n): Observable<ElementOffset> {\n  return merge(\n    fromEvent(window, \"load\"),\n    fromEvent(window, \"resize\")\n  )\n    .pipe(\n      auditTime(0, animationFrameScheduler),\n      map(() => getElementOffset(el)),\n      startWith(getElementOffset(el))\n    )\n}\n\n/**\n * Watch absolute element offset\n *\n * @param el - Element\n *\n * @returns Element offset observable\n */\nexport function watchElementOffsetAbsolute(\n  el: HTMLElement\n): Observable<ElementOffset> {\n  return merge(\n    watchElementOffset(el),\n    watchElementSize(document.body) // @todo find a better way for this\n  )\n    .pipe(\n      map(() => getElementOffsetAbsolute(el)),\n      startWith(getElementOffsetAbsolute(el))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  animationFrameScheduler,\n  auditTime,\n  fromEvent,\n  map,\n  merge,\n  startWith\n} from \"rxjs\"\n\nimport { ElementOffset } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element content offset (= scroll offset)\n *\n * @param el - Element\n *\n * @returns Element content offset\n */\nexport function getElementContentOffset(\n  el: HTMLElement\n): ElementOffset {\n  return {\n    x: el.scrollLeft,\n    y: el.scrollTop\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element content offset\n *\n * @param el - Element\n *\n * @returns Element content offset observable\n */\nexport function watchElementContentOffset(\n  el: HTMLElement\n): Observable<ElementOffset> {\n  return merge(\n    fromEvent(el, \"scroll\"),\n    fromEvent(window, \"scroll\"),\n    fromEvent(window, \"resize\")\n  )\n    .pipe(\n      auditTime(0, animationFrameScheduler),\n      map(() => getElementContentOffset(el)),\n      startWith(getElementContentOffset(el))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  NEVER,\n  Observable,\n  Subject,\n  defer,\n  distinctUntilChanged,\n  filter,\n  finalize,\n  map,\n  merge,\n  of,\n  shareReplay,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport {\n  getElementContentSize,\n  getElementSize,\n  watchElementContentOffset\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Intersection observer entry subject\n */\nconst entry$ = new Subject<IntersectionObserverEntry>()\n\n/**\n * Intersection observer observable\n *\n * This observable will create an `IntersectionObserver` on first subscription\n * and will automatically terminate it when there are no more subscribers.\n *\n * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => of(\n  new IntersectionObserver(entries => {\n    for (const entry of entries)\n      entry$.next(entry)\n  }, {\n    threshold: 0\n  })\n))\n  .pipe(\n    switchMap(observer => merge(NEVER, of(observer))\n      .pipe(\n        finalize(() => observer.disconnect())\n      )\n    ),\n    shareReplay(1)\n  )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element visibility\n *\n * @param el - Element\n *\n * @returns Element visibility observable\n */\nexport function watchElementVisibility(\n  el: HTMLElement\n): Observable<boolean> {\n  return observer$\n    .pipe(\n      tap(observer => observer.observe(el)),\n      switchMap(observer => entry$\n        .pipe(\n          filter(({ target }) => target === el),\n          finalize(() => observer.unobserve(el)),\n          map(({ isIntersecting }) => isIntersecting)\n        )\n      )\n    )\n}\n\n/**\n * Watch element boundary\n *\n * This function returns an observable which emits whether the bottom content\n * boundary (= scroll offset) of an element is within a certain threshold.\n *\n * @param el - Element\n * @param threshold - Threshold\n *\n * @returns Element boundary observable\n */\nexport function watchElementBoundary(\n  el: HTMLElement, threshold = 16\n): Observable<boolean> {\n  return watchElementContentOffset(el)\n    .pipe(\n      map(({ y }) => {\n        const visible = getElementSize(el)\n        const content = getElementContentSize(el)\n        return y >= (\n          content.height - visible.height - threshold\n        )\n      }),\n      distinctUntilChanged()\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  startWith\n} from \"rxjs\"\n\nimport { getElement } from \"../element\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Toggle\n */\nexport type Toggle =\n  | \"drawer\"                           /* Toggle for drawer */\n  | \"search\"                           /* Toggle for search */\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Toggle map\n */\nconst toggles: Record<Toggle, HTMLInputElement> = {\n  drawer: getElement(\"[data-md-toggle=drawer]\"),\n  search: getElement(\"[data-md-toggle=search]\")\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve the value of a toggle\n *\n * @param name - Toggle\n *\n * @returns Toggle value\n */\nexport function getToggle(name: Toggle): boolean {\n  return toggles[name].checked\n}\n\n/**\n * Set toggle\n *\n * Simulating a click event seems to be the most cross-browser compatible way\n * of changing the value while also emitting a `change` event. Before, Material\n * used `CustomEvent` to programmatically change the value of a toggle, but this\n * is a much simpler and cleaner solution which doesn't require a polyfill.\n *\n * @param name - Toggle\n * @param value - Toggle value\n */\nexport function setToggle(name: Toggle, value: boolean): void {\n  if (toggles[name].checked !== value)\n    toggles[name].click()\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch toggle\n *\n * @param name - Toggle\n *\n * @returns Toggle value observable\n */\nexport function watchToggle(name: Toggle): Observable<boolean> {\n  const el = toggles[name]\n  return fromEvent(el, \"change\")\n    .pipe(\n      map(() => el.checked),\n      startWith(el.checked)\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  filter,\n  fromEvent,\n  map,\n  merge,\n  share,\n  startWith,\n  switchMap\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../element\"\nimport { getToggle } from \"../toggle\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Keyboard mode\n */\nexport type KeyboardMode =\n  | \"global\"                           /* Global */\n  | \"search\"                           /* Search is open */\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Keyboard\n */\nexport interface Keyboard {\n  mode: KeyboardMode                   /* Keyboard mode */\n  type: string                         /* Key type */\n  claim(): void                        /* Key claim */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Check whether an element may receive keyboard input\n *\n * @param el - Element\n * @param type - Key type\n *\n * @returns Test result\n */\nfunction isSusceptibleToKeyboard(\n  el: HTMLElement, type: string\n): boolean {\n  switch (el.constructor) {\n\n    /* Input elements */\n    case HTMLInputElement:\n      /* @ts-expect-error - omit unnecessary type cast */\n      if (el.type === \"radio\")\n        return /^Arrow/.test(type)\n      else\n        return true\n\n    /* Select element and textarea */\n    case HTMLSelectElement:\n    case HTMLTextAreaElement:\n      return true\n\n    /* Everything else */\n    default:\n      return el.isContentEditable\n  }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch composition events\n *\n * @returns Composition observable\n */\nexport function watchComposition(): Observable<boolean> {\n  return merge(\n    fromEvent(window, \"compositionstart\").pipe(map(() => true)),\n    fromEvent(window, \"compositionend\").pipe(map(() => false))\n  )\n    .pipe(\n      startWith(false)\n    )\n}\n\n/**\n * Watch keyboard\n *\n * @returns Keyboard observable\n */\nexport function watchKeyboard(): Observable<Keyboard> {\n  const keyboard$ = fromEvent<KeyboardEvent>(window, \"keydown\")\n    .pipe(\n      filter(ev => !(ev.metaKey || ev.ctrlKey)),\n      map(ev => ({\n        mode: getToggle(\"search\") ? \"search\" : \"global\",\n        type: ev.key,\n        claim() {\n          ev.preventDefault()\n          ev.stopPropagation()\n        }\n      } as Keyboard)),\n      filter(({ mode, type }) => {\n        if (mode === \"global\") {\n          const active = getActiveElement()\n          if (typeof active !== \"undefined\")\n            return !isSusceptibleToKeyboard(active, type)\n        }\n        return true\n      }),\n      share()\n    )\n\n  /* Don't emit during composition events - see https://bit.ly/3te3Wl8 */\n  return watchComposition()\n    .pipe(\n      switchMap(active => !active ? keyboard$ : EMPTY)\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Subject } from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve location\n *\n * This function returns a `URL` object (and not `Location`) to normalize the\n * typings across the application. Furthermore, locations need to be tracked\n * without setting them and `Location` is a singleton which represents the\n * current location.\n *\n * @returns URL\n */\nexport function getLocation(): URL {\n  return new URL(location.href)\n}\n\n/**\n * Set location\n *\n * If instant navigation is enabled, this function creates a temporary anchor\n * element, sets the `href` attribute, appends it to the body, clicks it, and\n * then removes it again. The event will bubble up the DOM and trigger be\n * intercepted by the instant loading business logic.\n *\n * Note that we must append and remove the anchor element, or the event will\n * not bubble up the DOM, making it impossible to intercept it.\n *\n * @param url - URL to navigate to\n * @param navigate - Force navigation\n */\nexport function setLocation(\n  url: URL | HTMLLinkElement, navigate = false\n): void {\n  if (feature(\"navigation.instant\") && !navigate) {\n    const el = h(\"a\", { href: url.href })\n    document.body.appendChild(el)\n    el.click()\n    el.remove()\n\n  // If we're not using instant navigation, and the page should not be reloaded\n  // just instruct the browser to navigate to the given URL\n  } else {\n    location.href = url.href\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch location\n *\n * @returns Location subject\n */\nexport function watchLocation(): Subject<URL> {\n  return new Subject<URL>()\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  filter,\n  fromEvent,\n  map,\n  merge,\n  shareReplay,\n  startWith\n} from \"rxjs\"\n\nimport { getOptionalElement } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve location hash\n *\n * @returns Location hash\n */\nexport function getLocationHash(): string {\n  return location.hash.slice(1)\n}\n\n/**\n * Set location hash\n *\n * Setting a new fragment identifier via `location.hash` will have no effect\n * if the value doesn't change. When a new fragment identifier is set, we want\n * the browser to target the respective element at all times, which is why we\n * use this dirty little trick.\n *\n * @param hash - Location hash\n */\nexport function setLocationHash(hash: string): void {\n  const el = h(\"a\", { href: hash })\n  el.addEventListener(\"click\", ev => ev.stopPropagation())\n  el.click()\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch location hash\n *\n * @param location$ - Location observable\n *\n * @returns Location hash observable\n */\nexport function watchLocationHash(\n  location$: Observable<URL>\n): Observable<string> {\n  return merge(\n    fromEvent<HashChangeEvent>(window, \"hashchange\"),\n    location$\n  )\n    .pipe(\n      map(getLocationHash),\n      startWith(getLocationHash()),\n      filter(hash => hash.length > 0),\n      shareReplay(1)\n    )\n}\n\n/**\n * Watch location target\n *\n * @param location$ - Location observable\n *\n * @returns Location target observable\n */\nexport function watchLocationTarget(\n  location$: Observable<URL>\n): Observable<HTMLElement> {\n  return watchLocationHash(location$)\n    .pipe(\n      map(id => getOptionalElement(`[id=\"${id}\"]`)!),\n      filter(el => typeof el !== \"undefined\")\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  fromEvent,\n  fromEventPattern,\n  map,\n  merge,\n  startWith,\n  switchMap\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch media query\n *\n * Note that although `MediaQueryList.addListener` is deprecated we have to\n * use it, because it's the only way to ensure proper downward compatibility.\n *\n * @see https://bit.ly/3dUBH2m - GitHub issue\n *\n * @param query - Media query\n *\n * @returns Media observable\n */\nexport function watchMedia(query: string): Observable<boolean> {\n  const media = matchMedia(query)\n  return fromEventPattern<boolean>(next => (\n    media.addListener(() => next(media.matches))\n  ))\n    .pipe(\n      startWith(media.matches)\n    )\n}\n\n/**\n * Watch print mode\n *\n * @returns Print observable\n */\nexport function watchPrint(): Observable<boolean> {\n  const media = matchMedia(\"print\")\n  return merge(\n    fromEvent(window, \"beforeprint\").pipe(map(() => true)),\n    fromEvent(window, \"afterprint\").pipe(map(() => false))\n  )\n    .pipe(\n      startWith(media.matches)\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Toggle an observable with a media observable\n *\n * @template T - Data type\n *\n * @param query$ - Media observable\n * @param factory - Observable factory\n *\n * @returns Toggled observable\n */\nexport function at<T>(\n  query$: Observable<boolean>, factory: () => Observable<T>\n): Observable<T> {\n  return query$\n    .pipe(\n      switchMap(active => active ? factory() : EMPTY)\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  map,\n  shareReplay,\n  switchMap\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Options\n */\ninterface Options {\n  progress$?: Subject<number>          // Progress subject\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the given URL\n *\n * This function returns an observable that emits the response as a blob and\n * completes, or emits an error if the request failed. The caller can cancel\n * the request by unsubscribing at any time, which will automatically abort\n * the inflight request and complete the observable.\n *\n * Note that we use `XMLHTTPRequest` not because we're nostalgic, but because\n * it's the only way to get progress events for downloads and also allow for\n * cancellation of requests, as the official Fetch API does not support this\n * yet, even though we're already in 2024.\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function request(\n  url: URL | string, options?: Options\n): Observable<Blob> {\n  return new Observable<Blob>(observer => {\n    const req = new XMLHttpRequest()\n    req.open(\"GET\", `${url}`)\n    req.responseType = \"blob\"\n\n    // Handle response\n    req.addEventListener(\"load\", () => {\n      if (req.status >= 200 && req.status < 300) {\n        observer.next(req.response)\n        observer.complete()\n\n      // Every response that is not in the 2xx range is considered an error\n      } else {\n        observer.error(new Error(req.statusText))\n      }\n    })\n\n    // Handle network errors\n    req.addEventListener(\"error\", () => {\n      observer.error(new Error(\"Network error\"))\n    })\n\n    // Handle aborted requests\n    req.addEventListener(\"abort\", () => {\n      observer.complete()\n    })\n\n    // Handle download progress\n    if (typeof options?.progress$ !== \"undefined\") {\n      req.addEventListener(\"progress\", event => {\n        if (event.lengthComputable) {\n          options.progress$!.next((event.loaded / event.total) * 100)\n\n        // Hack: Chromium doesn't report the total number of bytes if content\n        // is compressed, so we need this fallback - see https://t.ly/ZXofI\n        } else {\n          const length = req.getResponseHeader(\"Content-Length\") ?? 0\n          options.progress$!.next((event.loaded / +length) * 100)\n        }\n      })\n\n      // Immediately set progress to 5% to indicate that we're loading\n      options.progress$.next(5)\n    }\n\n    // Send request and automatically abort request upon unsubscription\n    req.send()\n    return () => req.abort()\n  })\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Fetch JSON from the given URL\n *\n * @template T - Data type\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestJSON<T>(\n  url: URL | string, options?: Options\n): Observable<T> {\n  return request(url, options)\n    .pipe(\n      switchMap(res => res.text()),\n      map(body => JSON.parse(body) as T),\n      shareReplay(1)\n    )\n}\n\n/**\n * Fetch HTML from the given URL\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestHTML(\n  url: URL | string, options?: Options\n): Observable<Document> {\n  const dom = new DOMParser()\n  return request(url, options)\n    .pipe(\n      switchMap(res => res.text()),\n      map(res => dom.parseFromString(res, \"text/html\")),\n      shareReplay(1)\n    )\n}\n\n/**\n * Fetch XML from the given URL\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestXML(\n  url: URL | string, options?: Options\n): Observable<Document> {\n  const dom = new DOMParser()\n  return request(url, options)\n    .pipe(\n      switchMap(res => res.text()),\n      map(res => dom.parseFromString(res, \"text/xml\")),\n      shareReplay(1)\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  merge,\n  startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport offset\n */\nexport interface ViewportOffset {\n  x: number                            /* Horizontal offset */\n  y: number                            /* Vertical offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve viewport offset\n *\n * On iOS Safari, viewport offset can be negative due to overflow scrolling.\n * As this may induce strange behaviors downstream, we'll just limit it to 0.\n *\n * @returns Viewport offset\n */\nexport function getViewportOffset(): ViewportOffset {\n  return {\n    x: Math.max(0, scrollX),\n    y: Math.max(0, scrollY)\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport offset\n *\n * @returns Viewport offset observable\n */\nexport function watchViewportOffset(): Observable<ViewportOffset> {\n  return merge(\n    fromEvent(window, \"scroll\", { passive: true }),\n    fromEvent(window, \"resize\", { passive: true })\n  )\n    .pipe(\n      map(getViewportOffset),\n      startWith(getViewportOffset())\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport size\n */\nexport interface ViewportSize {\n  width: number                        /* Viewport width */\n  height: number                       /* Viewport height */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve viewport size\n *\n * @returns Viewport size\n */\nexport function getViewportSize(): ViewportSize {\n  return {\n    width:  innerWidth,\n    height: innerHeight\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport size\n *\n * @returns Viewport size observable\n */\nexport function watchViewportSize(): Observable<ViewportSize> {\n  return fromEvent(window, \"resize\", { passive: true })\n    .pipe(\n      map(getViewportSize),\n      startWith(getViewportSize())\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  map,\n  shareReplay\n} from \"rxjs\"\n\nimport {\n  ViewportOffset,\n  watchViewportOffset\n} from \"../offset\"\nimport {\n  ViewportSize,\n  watchViewportSize\n} from \"../size\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport\n */\nexport interface Viewport {\n  offset: ViewportOffset               /* Viewport offset */\n  size: ViewportSize                   /* Viewport size */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport\n *\n * @returns Viewport observable\n */\nexport function watchViewport(): Observable<Viewport> {\n  return combineLatest([\n    watchViewportOffset(),\n    watchViewportSize()\n  ])\n    .pipe(\n      map(([offset, size]) => ({ offset, size })),\n      shareReplay(1)\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  distinctUntilKeyChanged,\n  map\n} from \"rxjs\"\n\nimport { Header } from \"~/components\"\n\nimport { getElementOffset } from \"../../element\"\nimport { Viewport } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport relative to element\n *\n * @param el - Element\n * @param options - Options\n *\n * @returns Viewport observable\n */\nexport function watchViewportAt(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<Viewport> {\n  const size$ = viewport$\n    .pipe(\n      distinctUntilKeyChanged(\"size\")\n    )\n\n  /* Compute element offset */\n  const offset$ = combineLatest([size$, header$])\n    .pipe(\n      map(() => getElementOffset(el))\n    )\n\n  /* Compute relative viewport, return hot observable */\n  return combineLatest([header$, viewport$, offset$])\n    .pipe(\n      map(([{ height }, { offset, size }, { x, y }]) => ({\n        offset: {\n          x: offset.x - x,\n          y: offset.y - y + height\n        },\n        size\n      }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  endWith,\n  fromEvent,\n  ignoreElements,\n  mergeWith,\n  share,\n  takeUntil\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Worker message\n */\nexport interface WorkerMessage {\n  type: unknown                        /* Message type */\n  data?: unknown                       /* Message data */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create an observable for receiving from a web worker\n *\n * @template T - Data type\n *\n * @param worker - Web worker\n *\n * @returns Message observable\n */\nfunction recv<T>(worker: Worker): Observable<T> {\n  return fromEvent<MessageEvent<T>, T>(worker, \"message\", ev => ev.data)\n}\n\n/**\n * Create a subject for sending to a web worker\n *\n * @template T - Data type\n *\n * @param worker - Web worker\n *\n * @returns Message subject\n */\nfunction send<T>(worker: Worker): Subject<T> {\n  const send$ = new Subject<T>()\n  send$.subscribe(data => worker.postMessage(data))\n\n  /* Return message subject */\n  return send$\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a bidirectional communication channel to a web worker\n *\n * @template T - Data type\n *\n * @param url - Worker URL\n * @param worker - Worker\n *\n * @returns Worker subject\n */\nexport function watchWorker<T extends WorkerMessage>(\n  url: string, worker = new Worker(url)\n): Subject<T> {\n  const recv$ = recv<T>(worker)\n  const send$ = send<T>(worker)\n\n  /* Create worker subject and forward messages */\n  const worker$ = new Subject<T>()\n  worker$.subscribe(send$)\n\n  /* Return worker subject */\n  const done$ = send$.pipe(ignoreElements(), endWith(true))\n  return worker$\n    .pipe(\n      ignoreElements(),\n      mergeWith(recv$.pipe(takeUntil(done$))),\n      share()\n    ) as Subject<T>\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { getElement, getLocation } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Feature flag\n */\nexport type Flag =\n  | \"announce.dismiss\"                 /* Dismissable announcement bar */\n  | \"content.code.annotate\"            /* Code annotations */\n  | \"content.code.copy\"                /* Code copy button */\n  | \"content.lazy\"                     /* Lazy content elements */\n  | \"content.tabs.link\"                /* Link content tabs */\n  | \"content.tooltips\"                 /* Tooltips */\n  | \"header.autohide\"                  /* Hide header */\n  | \"navigation.expand\"                /* Automatic expansion */\n  | \"navigation.indexes\"               /* Section pages */\n  | \"navigation.instant\"               /* Instant navigation */\n  | \"navigation.instant.progress\"      /* Instant navigation progress */\n  | \"navigation.sections\"              /* Section navigation */\n  | \"navigation.tabs\"                  /* Tabs navigation */\n  | \"navigation.tabs.sticky\"           /* Tabs navigation (sticky) */\n  | \"navigation.top\"                   /* Back-to-top button */\n  | \"navigation.tracking\"              /* Anchor tracking */\n  | \"search.highlight\"                 /* Search highlighting */\n  | \"search.share\"                     /* Search sharing */\n  | \"search.suggest\"                   /* Search suggestions */\n  | \"toc.follow\"                       /* Following table of contents */\n  | \"toc.integrate\"                    /* Integrated table of contents */\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Translation\n */\nexport type Translation =\n  | \"clipboard.copy\"                   /* Copy to clipboard */\n  | \"clipboard.copied\"                 /* Copied to clipboard */\n  | \"search.result.placeholder\"        /* Type to start searching */\n  | \"search.result.none\"               /* No matching documents */\n  | \"search.result.one\"                /* 1 matching document */\n  | \"search.result.other\"              /* # matching documents */\n  | \"search.result.more.one\"           /* 1 more on this page */\n  | \"search.result.more.other\"         /* # more on this page */\n  | \"search.result.term.missing\"       /* Missing */\n  | \"select.version\"                   /* Version selector */\n\n/**\n * Translations\n */\nexport type Translations =\n  Record<Translation, string>\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Versioning\n */\nexport interface Versioning {\n  provider: \"mike\"                     /* Version provider */\n  default?: string | string[]          /* Default version */\n  alias?: boolean                      /* Show alias */\n}\n\n/**\n * Configuration\n */\nexport interface Config {\n  base: string                         /* Base URL */\n  features: Flag[]                     /* Feature flags */\n  translations: Translations           /* Translations */\n  search: string                       /* Search worker URL */\n  tags?: Record<string, string>        /* Tags mapping */\n  version?: Versioning                 /* Versioning */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve global configuration and make base URL absolute\n */\nconst script = getElement(\"#__config\")\nconst config: Config = JSON.parse(script.textContent!)\nconfig.base = `${new URL(config.base, getLocation())}`\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve global configuration\n *\n * @returns Global configuration\n */\nexport function configuration(): Config {\n  return config\n}\n\n/**\n * Check whether a feature flag is enabled\n *\n * @param flag - Feature flag\n *\n * @returns Test result\n */\nexport function feature(flag: Flag): boolean {\n  return config.features.includes(flag)\n}\n\n/**\n * Retrieve the translation for the given key\n *\n * @param key - Key to be translated\n * @param value - Positional value, if any\n *\n * @returns Translation\n */\nexport function translation(\n  key: Translation, value?: string | number\n): string {\n  return typeof value !== \"undefined\"\n    ? config.translations[key].replace(\"#\", value.toString())\n    : config.translations[key]\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { getElement, getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Component type\n */\nexport type ComponentType =\n  | \"announce\"                         /* Announcement bar */\n  | \"container\"                        /* Container */\n  | \"consent\"                          /* Consent */\n  | \"content\"                          /* Content */\n  | \"dialog\"                           /* Dialog */\n  | \"header\"                           /* Header */\n  | \"header-title\"                     /* Header title */\n  | \"header-topic\"                     /* Header topic */\n  | \"main\"                             /* Main area */\n  | \"outdated\"                         /* Version warning */\n  | \"palette\"                          /* Color palette */\n  | \"progress\"                         /* Progress indicator */\n  | \"search\"                           /* Search */\n  | \"search-query\"                     /* Search input */\n  | \"search-result\"                    /* Search results */\n  | \"search-share\"                     /* Search sharing */\n  | \"search-suggest\"                   /* Search suggestions */\n  | \"sidebar\"                          /* Sidebar */\n  | \"skip\"                             /* Skip link */\n  | \"source\"                           /* Repository information */\n  | \"tabs\"                             /* Navigation tabs */\n  | \"toc\"                              /* Table of contents */\n  | \"top\"                              /* Back-to-top button */\n\n/**\n * Component\n *\n * @template T - Component type\n * @template U - Reference type\n */\nexport type Component<\n  T extends {} = {},\n  U extends HTMLElement = HTMLElement\n> =\n  T & {\n    ref: U                             /* Component reference */\n  }\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Component type map\n */\ninterface ComponentTypeMap {\n  \"announce\": HTMLElement              /* Announcement bar */\n  \"container\": HTMLElement             /* Container */\n  \"consent\": HTMLElement               /* Consent */\n  \"content\": HTMLElement               /* Content */\n  \"dialog\": HTMLElement                /* Dialog */\n  \"header\": HTMLElement                /* Header */\n  \"header-title\": HTMLElement          /* Header title */\n  \"header-topic\": HTMLElement          /* Header topic */\n  \"main\": HTMLElement                  /* Main area */\n  \"outdated\": HTMLElement              /* Version warning */\n  \"palette\": HTMLElement               /* Color palette */\n  \"progress\": HTMLElement              /* Progress indicator */\n  \"search\": HTMLElement                /* Search */\n  \"search-query\": HTMLInputElement     /* Search input */\n  \"search-result\": HTMLElement         /* Search results */\n  \"search-share\": HTMLAnchorElement    /* Search sharing */\n  \"search-suggest\": HTMLElement        /* Search suggestions */\n  \"sidebar\": HTMLElement               /* Sidebar */\n  \"skip\": HTMLAnchorElement            /* Skip link */\n  \"source\": HTMLAnchorElement          /* Repository information */\n  \"tabs\": HTMLElement                  /* Navigation tabs */\n  \"toc\": HTMLElement                   /* Table of contents */\n  \"top\": HTMLAnchorElement             /* Back-to-top button */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve the element for a given component or throw a reference error\n *\n * @template T - Component type\n *\n * @param type - Component type\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getComponentElement<T extends ComponentType>(\n  type: T, node: ParentNode = document\n): ComponentTypeMap[T] {\n  return getElement(`[data-md-component=${type}]`, node)\n}\n\n/**\n * Retrieve all elements for a given component\n *\n * @template T - Component type\n *\n * @param type - Component type\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getComponentElements<T extends ComponentType>(\n  type: T, node: ParentNode = document\n): ComponentTypeMap[T][] {\n  return getElements(`[data-md-component=${type}]`, node)\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  finalize,\n  fromEvent,\n  map,\n  tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport { getElement } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Announcement bar\n */\nexport interface Announce {\n  hash: number                        /* Content hash */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch announcement bar\n *\n * @param el - Announcement bar element\n *\n * @returns Announcement bar observable\n */\nexport function watchAnnounce(\n  el: HTMLElement\n): Observable<Announce> {\n  const button = getElement(\".md-typeset > :first-child\", el)\n  return fromEvent(button, \"click\", { once: true })\n    .pipe(\n      map(() => getElement(\".md-typeset\", el)),\n      map(content => ({ hash: __md_hash(content.innerHTML) }))\n    )\n}\n\n/**\n * Mount announcement bar\n *\n * @param el - Announcement bar element\n *\n * @returns Announcement bar component observable\n */\nexport function mountAnnounce(\n  el: HTMLElement\n): Observable<Component<Announce>> {\n  if (!feature(\"announce.dismiss\") || !el.childElementCount)\n    return EMPTY\n\n  /* Support instant navigation - see https://t.ly/3FTme */\n  if (!el.hidden) {\n    const content = getElement(\".md-typeset\", el)\n    if (__md_hash(content.innerHTML) === __md_get(\"__announce\"))\n      el.hidden = true\n  }\n\n  /* Mount component on subscription */\n  return defer(() => {\n    const push$ = new Subject<Announce>()\n    push$.subscribe(({ hash }) => {\n      el.hidden = true\n\n      /* Persist preference in local storage */\n      __md_set<number>(\"__announce\", hash)\n    })\n\n    /* Create and return component */\n    return watchAnnounce(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  finalize,\n  map,\n  tap\n} from \"rxjs\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Consent\n */\nexport interface Consent {\n  hidden: boolean                      /* Consent is hidden */\n}\n\n/**\n * Consent defaults\n */\nexport interface ConsentDefaults {\n  analytics?: boolean                  /* Consent for Analytics */\n  github?: boolean                     /* Consent for GitHub */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  target$: Observable<HTMLElement>     /* Target observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch consent\n *\n * @param el - Consent element\n * @param options - Options\n *\n * @returns Consent observable\n */\nexport function watchConsent(\n  el: HTMLElement, { target$ }: WatchOptions\n): Observable<Consent> {\n  return target$\n    .pipe(\n      map(target => ({ hidden: target !== el }))\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount consent\n *\n * @param el - Consent element\n * @param options - Options\n *\n * @returns Consent component observable\n */\nexport function mountConsent(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<Consent>> {\n  const internal$ = new Subject<Consent>()\n  internal$.subscribe(({ hidden }) => {\n    el.hidden = hidden\n  })\n\n  /* Create and return component */\n  return watchConsent(el, options)\n    .pipe(\n      tap(state => internal$.next(state)),\n      finalize(() => internal$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ComponentChild } from \"preact\"\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Tooltip style\n */\nexport type TooltipStyle =\n  | \"inline\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a tooltip\n *\n * @param id - Tooltip identifier\n * @param style - Tooltip style\n *\n * @returns Element\n */\nexport function renderTooltip(\n  id?: string, style?: TooltipStyle\n): HTMLElement {\n  if (style === \"inline\") { // @todo refactor control flow\n    return (\n      <div class=\"md-tooltip md-tooltip--inline\" id={id} role=\"tooltip\">\n        <div class=\"md-tooltip__inner md-typeset\"></div>\n      </div>\n    )\n  } else {\n    return (\n      <div class=\"md-tooltip\" id={id} role=\"tooltip\">\n        <div class=\"md-tooltip__inner md-typeset\"></div>\n      </div>\n    )\n  }\n}\n\n// @todo: rename\nexport function renderInlineTooltip2(\n  ...children: ComponentChild[]\n): HTMLElement {\n  return (\n    <div class=\"md-tooltip2\" role=\"tooltip\">\n      <div class=\"md-tooltip2__inner md-typeset\">\n        {children}\n      </div>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\nimport { renderTooltip } from \"../tooltip\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render an annotation\n *\n * @param id - Annotation identifier\n * @param prefix - Tooltip identifier prefix\n *\n * @returns Element\n */\nexport function renderAnnotation(\n  id: string | number, prefix?: string\n): HTMLElement {\n  prefix = prefix ? `${prefix}_annotation_${id}` : undefined\n\n  /* Render tooltip with anchor, if given */\n  if (prefix) {\n    const anchor = prefix ? `#${prefix}` : undefined\n    return (\n      <aside class=\"md-annotation\" tabIndex={0}>\n        {renderTooltip(prefix)}\n        <a href={anchor} class=\"md-annotation__index\" tabIndex={-1}>\n          <span data-md-annotation-id={id}></span>\n        </a>\n      </aside>\n    )\n  } else {\n    return (\n      <aside class=\"md-annotation\" tabIndex={0}>\n        {renderTooltip(prefix)}\n        <span class=\"md-annotation__index\" tabIndex={-1}>\n          <span data-md-annotation-id={id}></span>\n        </span>\n      </aside>\n    )\n  }\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { translation } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a 'copy-to-clipboard' button\n *\n * @param id - Unique identifier\n *\n * @returns Element\n */\nexport function renderClipboardButton(id: string): HTMLElement {\n  return (\n    <button\n      class=\"md-clipboard md-icon\"\n      title={translation(\"clipboard.copy\")}\n      data-clipboard-target={`#${id} > code`}\n    ></button>\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\nimport { ComponentChild } from \"preact\"\n\nimport { configuration, feature, translation } from \"~/_\"\nimport { SearchItem } from \"~/integrations/search\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Render flag\n */\nconst enum Flag {\n  TEASER = 1,                          /* Render teaser */\n  PARENT = 2                           /* Render as parent */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper function\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a search document\n *\n * @param document - Search document\n * @param flag - Render flags\n *\n * @returns Element\n */\nfunction renderSearchDocument(\n  document: SearchItem, flag: Flag\n): HTMLElement {\n  const parent = flag & Flag.PARENT\n  const teaser = flag & Flag.TEASER\n\n  /* Render missing query terms */\n  const missing = Object.keys(document.terms)\n    .filter(key => !document.terms[key])\n    .reduce<ComponentChild[]>((list, key) => [\n      ...list, <del>{escapeHTML(key)}</del>, \" \"\n    ], [])\n    .slice(0, -1)\n\n  /* Assemble query string for highlighting */\n  const config = configuration()\n  const url = new URL(document.location, config.base)\n  if (feature(\"search.highlight\"))\n    url.searchParams.set(\"h\", Object.entries(document.terms)\n      .filter(([, match]) => match)\n      .reduce((highlight, [value]) => `${highlight} ${value}`.trim(), \"\")\n    )\n\n  /* Render article or section, depending on flags */\n  const { tags } = configuration()\n  return (\n    <a href={`${url}`} class=\"md-search-result__link\" tabIndex={-1}>\n      <article\n        class=\"md-search-result__article md-typeset\"\n        data-md-score={document.score.toFixed(2)}\n      >\n        {parent > 0 && <div class=\"md-search-result__icon md-icon\"></div>}\n        {parent > 0 && <h1>{document.title}</h1>}\n        {parent <= 0 && <h2>{document.title}</h2>}\n        {teaser > 0 && document.text.length > 0 &&\n          document.text\n        }\n        {document.tags && (\n          <nav class=\"md-tags\">\n            {document.tags.map(tag => {\n              const type = tags\n                ? tag in tags\n                  ? `md-tag-icon md-tag--${tags[tag]}`\n                  : \"md-tag-icon\"\n                : \"\"\n              return (\n                <span class={`md-tag ${type}`}>{tag}</span>\n              )\n            })}\n          </nav>\n        )}\n        {teaser > 0 && missing.length > 0 &&\n          <p class=\"md-search-result__terms\">\n            {translation(\"search.result.term.missing\")}: {...missing}\n          </p>\n        }\n      </article>\n    </a>\n  )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a search result\n *\n * @param result - Search result\n *\n * @returns Element\n */\nexport function renderSearchResultItem(\n  result: SearchItem[]\n): HTMLElement {\n  const threshold = result[0].score\n  const docs = [...result]\n\n  const config = configuration()\n\n  /* Find and extract parent article */\n  const parent = docs.findIndex(doc => {\n    const l = `${new URL(doc.location, config.base)}` // @todo hacky\n    return !l.includes(\"#\")\n  })\n  const [article] = docs.splice(parent, 1)\n\n  /* Determine last index above threshold */\n  let index = docs.findIndex(doc => doc.score < threshold)\n  if (index === -1)\n    index = docs.length\n\n  /* Partition sections */\n  const best = docs.slice(0, index)\n  const more = docs.slice(index)\n\n  /* Render children */\n  const children = [\n    renderSearchDocument(article, Flag.PARENT | +(!parent && index === 0)),\n    ...best.map(section => renderSearchDocument(section, Flag.TEASER)),\n    ...more.length ? [\n      <details class=\"md-search-result__more\">\n        <summary tabIndex={-1}>\n          <div>\n            {more.length > 0 && more.length === 1\n              ? translation(\"search.result.more.one\")\n              : translation(\"search.result.more.other\", more.length)\n            }\n          </div>\n        </summary>\n        {...more.map(section => renderSearchDocument(section, Flag.TEASER))}\n      </details>\n    ] : []\n  ]\n\n  /* Render search result */\n  return (\n    <li class=\"md-search-result__item\">\n      {children}\n    </li>\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { SourceFacts } from \"~/components\"\nimport { h, round } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render repository facts\n *\n * @param facts - Repository facts\n *\n * @returns Element\n */\nexport function renderSourceFacts(facts: SourceFacts): HTMLElement {\n  return (\n    <ul class=\"md-source__facts\">\n      {Object.entries(facts).map(([key, value]) => (\n        <li class={`md-source__fact md-source__fact--${key}`}>\n          {typeof value === \"number\" ? round(value) : value}\n        </li>\n      ))}\n    </ul>\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Tabbed control type\n */\ntype TabbedControlType =\n  | \"prev\"\n  | \"next\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render control for content tabs\n *\n * @param type - Control type\n *\n * @returns Element\n */\nexport function renderTabbedControl(\n  type: TabbedControlType\n): HTMLElement {\n  const classes = `tabbed-control tabbed-control--${type}`\n  return (\n    <div class={classes} hidden>\n      <button class=\"tabbed-button\" tabIndex={-1} aria-hidden=\"true\"></button>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a table inside a wrapper to improve scrolling on mobile\n *\n * @param table - Table element\n *\n * @returns Element\n */\nexport function renderTable(table: HTMLElement): HTMLElement {\n  return (\n    <div class=\"md-typeset__scrollwrap\">\n      <div class=\"md-typeset__table\">\n        {table}\n      </div>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { configuration, translation } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Version properties\n */\nexport interface VersionProperties {\n  hidden?: boolean                     /* Version is hidden */\n}\n\n/**\n * Version\n */\nexport interface Version {\n  version: string                      /* Version identifier */\n  title: string                        /* Version title */\n  aliases: string[]                    /* Version aliases */\n  properties?: VersionProperties       /* Version properties */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a version\n *\n * @param version - Version\n *\n * @returns Element\n */\nfunction renderVersion(version: Version): HTMLElement {\n  const config = configuration()\n\n  /* Ensure trailing slash - see https://bit.ly/3rL5u3f */\n  const url = new URL(`../${version.version}/`, config.base)\n  return (\n    <li class=\"md-version__item\">\n      <a href={`${url}`} class=\"md-version__link\">\n        {version.title}\n        {config.version?.alias && version.aliases.length > 0 && (\n          <span class=\"md-version__alias\">\n            {version.aliases[0]}\n          </span>\n        )}\n      </a>\n    </li>\n  )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a version selector\n *\n * @param versions - Versions\n * @param active - Active version\n *\n * @returns Element\n */\nexport function renderVersionSelector(\n  versions: Version[], active: Version\n): HTMLElement {\n  const config = configuration()\n  versions = versions.filter(version => !version.properties?.hidden)\n  return (\n    <div class=\"md-version\">\n      <button\n        class=\"md-version__current\"\n        aria-label={translation(\"select.version\")}\n      >\n        {active.title}\n        {config.version?.alias && active.aliases.length > 0 && (\n          <span class=\"md-version__alias\">\n            {active.aliases[0]}\n          </span>\n        )}\n      </button>\n      <ul class=\"md-version__list\">\n        {versions.map(renderVersion)}\n      </ul>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  BehaviorSubject,\n  EMPTY,\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  combineLatest,\n  combineLatestWith,\n  debounce,\n  defer,\n  distinctUntilChanged,\n  endWith,\n  filter,\n  finalize,\n  first,\n  ignoreElements,\n  map,\n  mergeMap,\n  observeOn,\n  queueScheduler,\n  share,\n  startWith,\n  switchMap,\n  tap,\n  throttleTime,\n  timer,\n  withLatestFrom\n} from \"rxjs\"\n\nimport {\n  ElementOffset,\n  Viewport,\n  getElement,\n  getElementContainers,\n  getElementOffsetAbsolute,\n  getElementSize,\n  watchElementContentOffset,\n  watchElementFocus,\n  watchElementHover\n} from \"~/browser\"\nimport { renderInlineTooltip2 } from \"~/templates\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Tooltip\n */\nexport interface Tooltip {\n  active: boolean                      // Tooltip is active\n  offset: ElementOffset                // Tooltip offset\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Dependencies\n */\ninterface Dependencies {\n  content$: Observable<HTMLElement>    // Tooltip content observable\n  viewport$: Observable<Viewport>      // Viewport observable\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Global sequence number for tooltips\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch tooltip\n *\n * This function tracks the tooltip host element, and deduces the active state\n * and offset of the tooltip from it. The active state is determined by whether\n * the host element is focused or hovered, and the offset is determined by the\n * host element's absolute position in the document.\n *\n * @param el - Tooltip host element\n *\n * @returns Tooltip observable\n */\nexport function watchTooltip2(\n  el: HTMLElement\n): Observable<Tooltip> {\n\n  // Compute whether tooltip should be shown - we need to watch both focus and\n  // hover events on the host element and emit if one of them is active. In case\n  // of a hover event, we keep the element visible for a short amount of time\n  // after the pointer left the host element for a better user experience.\n  const active$ =\n    combineLatest([\n      watchElementFocus(el),\n      watchElementHover(el)\n    ])\n      .pipe(\n        map(([focus, hover]) => focus || hover),\n        distinctUntilChanged()\n      )\n\n  // We need to determine all parent elements of the host element that are\n  // currently scrollable, as they might affect the position of the tooltip\n  // depending on their horizontal of vertical offset. We must track all of\n  // them and recompute the position of the tooltip if they change.\n  const offset$ =\n    defer(() => getElementContainers(el)).pipe(\n      mergeMap(watchElementContentOffset),\n      throttleTime(1),\n      // Note that we need to poll the value again if the active state changes,\n      // as otherwise the tooltip might be misplaced. This particularly happens\n      // when using third-party integrations like tablesort that change the\n      // position of elements \u2013 see https://t.ly/Y-V7X\n      combineLatestWith(active$),\n      map(() => getElementOffsetAbsolute(el)),\n    )\n\n  // Only track parent elements and compute offset of the tooltip host if the\n  // tooltip should be shown - we defer the computation of the offset until the\n  // tooltip becomes active for the first time. This is necessary, because we\n  // must also keep the tooltip active as long as it is focused or hovered.\n  return active$.pipe(\n    first(active => active),\n    switchMap(() => combineLatest([active$, offset$])),\n    map(([active, offset]) => ({ active, offset })),\n    share()\n  )\n}\n\n/**\n * Mount tooltip\n *\n * This function renders a tooltip with the content from the provided `content$`\n * observable as passed via the dependencies. If the returned element has a role\n * of type `dialog`, the tooltip is considered to be interactive, and rendered\n * either above or below the host element, depending on the available space.\n *\n * If the returned element has a role of type `tooltip`, the tooltip is always\n * rendered below the host element and considered to be non-interactive. This\n * allows us to reuse the same positioning logic for both interactive and\n * non-interactive tooltips, as it is largely the same.\n *\n * @param el - Tooltip host element\n * @param dependencies - Dependencies\n *\n * @returns Tooltip component observable\n */\nexport function mountTooltip2(\n  el: HTMLElement, dependencies: Dependencies\n): Observable<Component<Tooltip>> {\n  const { content$, viewport$ } = dependencies\n\n  // Compute unique tooltip id - this is necessary to associate the tooltip host\n  // element with the tooltip element for ARIA purposes\n  const id = `__tooltip2_${sequence++}`\n\n  // Create component on subscription\n  return defer(() => {\n    const push$ = new Subject<Tooltip>()\n\n    // Create subject to track tooltip presence and visibility - we use another\n    // purely internal subject to track the tooltip's presence and visibility,\n    // as the tooltip should be visible if the host element or tooltip itself\n    // is focused or hovered to allow for smooth pointer migration\n    const show$ = new BehaviorSubject(false)\n    push$.pipe(ignoreElements(), endWith(false))\n      .subscribe(show$)\n\n    // Create observable controlling tooltip element - we create and attach the\n    // tooltip only if it is actually present, in order to keep the number of\n    // elements low. We need to keep the tooltip visible for a short time after\n    // the pointer left the host element or tooltip itself. For this, we use an\n    // inner subscription to the tooltip observable, which we terminate when the\n    // tooltip should not be shown, automatically removing the element. Moreover\n    // we use the queue scheduler, which will schedule synchronously in case the\n    // tooltip should be shown, and asynchronously if it should be hidden.\n    const node$ = show$.pipe(\n      debounce(active => timer(+!active * 250, queueScheduler)),\n      distinctUntilChanged(),\n      switchMap(active => active ? content$ : EMPTY),\n      tap(node => node.id = id),\n      share()\n    )\n\n    // Compute tooltip presence and visibility - the tooltip should be shown if\n    // the host element or the tooltip itself is focused or hovered\n    combineLatest([\n      push$.pipe(map(({ active }) => active)),\n      node$.pipe(\n        switchMap(node => watchElementHover(node, 250)),\n        startWith(false)\n      )\n    ])\n      .pipe(map(states => states.some(active => active)))\n      .subscribe(show$)\n\n    // Compute tooltip origin - we need to compute the tooltip origin depending\n    // on the position of the host element, the viewport size, as well as the\n    // actual size of the tooltip, if positioned above. The tooltip must about\n    // to be rendered for this to be correct, which is why we do it here.\n    const origin$ = show$.pipe(\n      filter(active => active),\n      withLatestFrom(node$, viewport$),\n      map(([_, node, { size }]) => {\n        const host = el.getBoundingClientRect()\n        const x = host.width / 2\n\n        // If the tooltip is non-interactive, we always render it below the\n        // actual element because all operating systems do it that way\n        if (node.role === \"tooltip\") {\n          return { x, y: 8 + host.height }\n\n        // Otherwise, we determine where there is more space, and render the\n        // tooltip either above or below the host element\n        } else if (host.y >= size.height / 2) {\n          const { height } = getElementSize(node)\n          return { x, y: -16 - height }\n        } else {\n          return { x, y: +16 + host.height }\n        }\n      })\n    )\n\n    // Update tooltip position - we always need to update the position of the\n    // tooltip, as it might change depending on the viewport offset of the host\n    combineLatest([node$, push$, origin$])\n      .subscribe(([node, { offset }, origin]) => {\n        node.style.setProperty(\"--md-tooltip-host-x\", `${offset.x}px`)\n        node.style.setProperty(\"--md-tooltip-host-y\", `${offset.y}px`)\n\n        // Update tooltip origin - this is mainly set to determine the position\n        // of the tooltip tail, to show the direction it is originating from\n        node.style.setProperty(\"--md-tooltip-x\", `${origin.x}px`)\n        node.style.setProperty(\"--md-tooltip-y\", `${origin.y}px`)\n\n        // Update tooltip render location, i.e., whether the tooltip is shown\n        // above or below the host element, depending on the available space\n        node.classList.toggle(\"md-tooltip2--top\",    origin.y <  0)\n        node.classList.toggle(\"md-tooltip2--bottom\", origin.y >= 0)\n      })\n\n    // Update tooltip width - we only explicitly set the width of the tooltip\n    // if it is non-interactive, in case it should always be rendered centered\n    show$.pipe(\n      filter(active => active),\n      withLatestFrom(node$, (_, node) => node),\n      filter(node => node.role === \"tooltip\")\n    )\n      .subscribe(node => {\n        const size = getElementSize(getElement(\":scope > *\", node))\n\n        // Set tooltip width and remove tail by setting it to a width of zero -\n        // if authors want to keep the tail, we can move this to CSS later\n        node.style.setProperty(\"--md-tooltip-width\", `${size.width}px`)\n        node.style.setProperty(\"--md-tooltip-tail\",  `${0}px`)\n      })\n\n    // Update tooltip visibility - we defer to the next animation frame, because\n    // the tooltip must first be added to the document before we make it appear,\n    // or it will appear instantly without delay. Additionally, we need to keep\n    // the tooltip visible for a short time after the pointer left the host.\n    show$.pipe(\n      distinctUntilChanged(),\n      observeOn(animationFrameScheduler),\n      withLatestFrom(node$)\n    )\n      .subscribe(([active, node]) => {\n        node.classList.toggle(\"md-tooltip2--active\", active)\n      })\n\n    // Set up ARIA attributes when tooltip is visible\n    combineLatest([\n      show$.pipe(filter(active => active)),\n      node$\n    ])\n      .subscribe(([_, node]) => {\n        if (node.role === \"dialog\") {\n          el.setAttribute(\"aria-controls\", id)\n          el.setAttribute(\"aria-haspopup\", \"dialog\")\n        } else {\n          el.setAttribute(\"aria-describedby\", id)\n        }\n      })\n\n    // Remove ARIA attributes when tooltip is hidden\n    show$.pipe(filter(active => !active))\n      .subscribe(() => {\n        el.removeAttribute(\"aria-controls\")\n        el.removeAttribute(\"aria-describedby\")\n        el.removeAttribute(\"aria-haspopup\")\n      })\n\n    // Create and return component\n    return watchTooltip2(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n\n// ----------------------------------------------------------------------------\n\n/**\n * Mount inline tooltip\n *\n * @todo refactor this function\n *\n * @param el - Tooltip host element\n * @param dependencies - Dependencies\n * @param container - Container\n *\n * @returns Tooltip component observable\n */\nexport function mountInlineTooltip2(\n  el: HTMLElement, { viewport$ }: { viewport$: Observable<Viewport> },\n  container = document.body\n): Observable<Component<Tooltip>> {\n  return mountTooltip2(el, {\n    content$: new Observable<HTMLElement>(observer => {\n      const title = el.title\n      const node = renderInlineTooltip2(title)\n      observer.next(node)\n      el.removeAttribute(\"title\")\n      // Append tooltip and remove on unsubscription\n      container.append(node)\n      return () => {\n        node.remove()\n        el.setAttribute(\"title\", title)\n      }\n    }),\n    viewport$\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  auditTime,\n  combineLatest,\n  debounceTime,\n  defer,\n  delay,\n  endWith,\n  filter,\n  finalize,\n  fromEvent,\n  ignoreElements,\n  map,\n  merge,\n  switchMap,\n  take,\n  takeUntil,\n  tap,\n  throttleTime,\n  withLatestFrom\n} from \"rxjs\"\n\nimport {\n  ElementOffset,\n  getActiveElement,\n  getElementSize,\n  watchElementContentOffset,\n  watchElementFocus,\n  watchElementOffset,\n  watchElementVisibility\n} from \"~/browser\"\n\nimport { Component } from \"../../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Annotation\n */\nexport interface Annotation {\n  active: boolean                      /* Annotation is active */\n  offset: ElementOffset                /* Annotation offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch annotation\n *\n * @param el - Annotation element\n * @param container - Containing element\n *\n * @returns Annotation observable\n */\nexport function watchAnnotation(\n  el: HTMLElement, container: HTMLElement\n): Observable<Annotation> {\n  const offset$ = defer(() => combineLatest([\n    watchElementOffset(el),\n    watchElementContentOffset(container)\n  ]))\n    .pipe(\n      map(([{ x, y }, scroll]): ElementOffset => {\n        const { width, height } = getElementSize(el)\n        return ({\n          x: x - scroll.x + width  / 2,\n          y: y - scroll.y + height / 2\n        })\n      })\n    )\n\n  /* Actively watch annotation on focus */\n  return watchElementFocus(el)\n    .pipe(\n      switchMap(active => offset$\n        .pipe(\n          map(offset => ({ active, offset })),\n          take(+!active || Infinity)\n        )\n      )\n    )\n}\n\n/**\n * Mount annotation\n *\n * @param el - Annotation element\n * @param container - Containing element\n * @param options - Options\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotation(\n  el: HTMLElement, container: HTMLElement, { target$ }: MountOptions\n): Observable<Component<Annotation>> {\n  const [tooltip, index] = Array.from(el.children)\n\n  /* Mount component on subscription */\n  return defer(() => {\n    const push$ = new Subject<Annotation>()\n    const done$ = push$.pipe(ignoreElements(), endWith(true))\n    push$.subscribe({\n\n      /* Handle emission */\n      next({ offset }) {\n        el.style.setProperty(\"--md-tooltip-x\", `${offset.x}px`)\n        el.style.setProperty(\"--md-tooltip-y\", `${offset.y}px`)\n      },\n\n      /* Handle complete */\n      complete() {\n        el.style.removeProperty(\"--md-tooltip-x\")\n        el.style.removeProperty(\"--md-tooltip-y\")\n      }\n    })\n\n    /* Start animation only when annotation is visible */\n    watchElementVisibility(el)\n      .pipe(\n        takeUntil(done$)\n      )\n        .subscribe(visible => {\n          el.toggleAttribute(\"data-md-visible\", visible)\n        })\n\n    /* Toggle tooltip presence to mitigate empty lines when copying */\n    merge(\n      push$.pipe(filter(({ active }) => active)),\n      push$.pipe(debounceTime(250), filter(({ active }) => !active))\n    )\n      .subscribe({\n\n        /* Handle emission */\n        next({ active }) {\n          if (active)\n            el.prepend(tooltip)\n          else\n            tooltip.remove()\n        },\n\n        /* Handle complete */\n        complete() {\n          el.prepend(tooltip)\n        }\n      })\n\n    /* Toggle tooltip visibility */\n    push$\n      .pipe(\n        auditTime(16, animationFrameScheduler)\n      )\n        .subscribe(({ active }) => {\n          tooltip.classList.toggle(\"md-tooltip--active\", active)\n        })\n\n    /* Track relative origin of tooltip */\n    push$\n      .pipe(\n        throttleTime(125, animationFrameScheduler),\n        filter(() => !!el.offsetParent),\n        map(() => el.offsetParent!.getBoundingClientRect()),\n        map(({ x }) => x)\n      )\n        .subscribe({\n\n          /* Handle emission */\n          next(origin) {\n            if (origin)\n              el.style.setProperty(\"--md-tooltip-0\", `${-origin}px`)\n            else\n              el.style.removeProperty(\"--md-tooltip-0\")\n          },\n\n          /* Handle complete */\n          complete() {\n            el.style.removeProperty(\"--md-tooltip-0\")\n          }\n        })\n\n    /* Allow to copy link without scrolling to anchor */\n    fromEvent<MouseEvent>(index, \"click\")\n      .pipe(\n        takeUntil(done$),\n        filter(ev => !(ev.metaKey || ev.ctrlKey))\n      )\n        .subscribe(ev => {\n          ev.stopPropagation()\n          ev.preventDefault()\n        })\n\n    /* Allow to open link in new tab or blur on close */\n    fromEvent<MouseEvent>(index, \"mousedown\")\n      .pipe(\n        takeUntil(done$),\n        withLatestFrom(push$)\n      )\n        .subscribe(([ev, { active }]) => {\n\n          /* Open in new tab */\n          if (ev.button !== 0 || ev.metaKey || ev.ctrlKey) {\n            ev.preventDefault()\n\n          /* Close annotation */\n          } else if (active) {\n            ev.preventDefault()\n\n            /* Focus parent annotation, if any */\n            const parent = el.parentElement!.closest(\".md-annotation\")\n            if (parent instanceof HTMLElement)\n              parent.focus()\n            else\n              getActiveElement()?.blur()\n          }\n        })\n\n    /* Open and focus annotation on location target */\n    target$\n      .pipe(\n        takeUntil(done$),\n        filter(target => target === tooltip),\n        delay(125)\n      )\n        .subscribe(() => el.focus())\n\n    /* Create and return component */\n    return watchAnnotation(el, container)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  endWith,\n  finalize,\n  ignoreElements,\n  merge,\n  share,\n  takeUntil\n} from \"rxjs\"\n\nimport {\n  getElement,\n  getElements,\n  getOptionalElement\n} from \"~/browser\"\nimport { renderAnnotation } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n  Annotation,\n  mountAnnotation\n} from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find all annotation hosts in the containing element\n *\n * @param container - Containing element\n *\n * @returns Annotation hosts\n */\nfunction findHosts(container: HTMLElement): HTMLElement[] {\n  return container.tagName === \"CODE\"\n    ? getElements(\".c, .c1, .cm\", container)\n    : [container]\n}\n\n/**\n * Find all annotation markers in the containing element\n *\n * @param container - Containing element\n *\n * @returns Annotation markers\n */\nfunction findMarkers(container: HTMLElement): Text[] {\n  const markers: Text[] = []\n  for (const el of findHosts(container)) {\n    const nodes: Text[] = []\n\n    /* Find all text nodes in current element */\n    const it = document.createNodeIterator(el, NodeFilter.SHOW_TEXT)\n    for (let node = it.nextNode(); node; node = it.nextNode())\n      nodes.push(node as Text)\n\n    /* Find all markers in each text node */\n    for (let text of nodes) {\n      let match: RegExpExecArray | null\n\n      /* Split text at marker and add to list */\n      while ((match = /(\\(\\d+\\))(!)?/.exec(text.textContent!))) {\n        const [, id, force] = match\n        if (typeof force === \"undefined\") {\n          const marker = text.splitText(match.index)\n          text = marker.splitText(id.length)\n          markers.push(marker)\n\n        /* Replace entire text with marker */\n        } else {\n          text.textContent = id\n          markers.push(text)\n          break\n        }\n      }\n    }\n  }\n  return markers\n}\n\n/**\n * Swap the child nodes of two elements\n *\n * @param source - Source element\n * @param target - Target element\n */\nfunction swap(source: HTMLElement, target: HTMLElement): void {\n  target.append(...Array.from(source.childNodes))\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount annotation list\n *\n * This function analyzes the containing code block and checks for markers\n * referring to elements in the given annotation list. If no markers are found,\n * the list is left untouched. Otherwise, list elements are rendered as\n * annotations inside the code block.\n *\n * @param el - Annotation list element\n * @param container - Containing element\n * @param options - Options\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotationList(\n  el: HTMLElement, container: HTMLElement, { target$, print$ }: MountOptions\n): Observable<Component<Annotation>> {\n\n  /* Compute prefix for tooltip anchors */\n  const parent = container.closest(\"[id]\")\n  const prefix = parent?.id\n\n  /* Find and replace all markers with empty annotations */\n  const annotations = new Map<string, HTMLElement>()\n  for (const marker of findMarkers(container)) {\n    const [, id] = marker.textContent!.match(/\\((\\d+)\\)/)!\n    if (getOptionalElement(`:scope > li:nth-child(${id})`, el)) {\n      annotations.set(id, renderAnnotation(id, prefix))\n      marker.replaceWith(annotations.get(id)!)\n    }\n  }\n\n  /* Keep list if there are no annotations to render */\n  if (annotations.size === 0)\n    return EMPTY\n\n  /* Mount component on subscription */\n  return defer(() => {\n    const push$ = new Subject()\n    const done$ = push$.pipe(ignoreElements(), endWith(true))\n\n    /* Retrieve container pairs for swapping */\n    const pairs: [HTMLElement, HTMLElement][] = []\n    for (const [id, annotation] of annotations)\n      pairs.push([\n        getElement(\".md-typeset\", annotation),\n        getElement(`:scope > li:nth-child(${id})`, el)\n      ])\n\n    /* Handle print mode - see https://bit.ly/3rgPdpt */\n    print$.pipe(takeUntil(done$))\n      .subscribe(active => {\n        el.hidden = !active\n\n        /* Add class to discern list element */\n        el.classList.toggle(\"md-annotation-list\", active)\n\n        /* Show annotations in code block or list (print) */\n        for (const [inner, child] of pairs)\n          if (!active)\n            swap(child, inner)\n          else\n            swap(inner, child)\n      })\n\n    /* Create and return component */\n    return merge(...[...annotations]\n      .map(([, annotation]) => (\n        mountAnnotation(annotation, container, { target$ })\n      ))\n    )\n      .pipe(\n        finalize(() => push$.complete()),\n        share()\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { EMPTY, Observable, defer } from \"rxjs\"\n\nimport { Component } from \"../../../_\"\nimport { Annotation } from \"../_\"\nimport { mountAnnotationList } from \"../list\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find list element directly following a block\n *\n * @param el - Annotation block element\n *\n * @returns List element or nothing\n */\nfunction findList(el: HTMLElement): HTMLElement | undefined {\n  if (el.nextElementSibling) {\n    const sibling = el.nextElementSibling as HTMLElement\n    if (sibling.tagName === \"OL\")\n      return sibling\n\n    /* Skip empty paragraphs - see https://bit.ly/3r4ZJ2O */\n    else if (sibling.tagName === \"P\" && !sibling.children.length)\n      return findList(sibling)\n  }\n\n  /* Everything else */\n  return undefined\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount annotation block\n *\n * @param el - Annotation block element\n * @param options - Options\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotationBlock(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<Annotation>> {\n  return defer(() => {\n    const list = findList(el)\n    return typeof list !== \"undefined\"\n      ? mountAnnotationList(list, el, options)\n      : EMPTY\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  filter,\n  finalize,\n  map,\n  mergeWith,\n  switchMap,\n  take,\n  takeLast,\n  takeUntil,\n  tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  getElementContentSize,\n  getElements,\n  watchElementSize,\n  watchElementVisibility\n} from \"~/browser\"\nimport {\n  Tooltip,\n  mountInlineTooltip2\n} from \"~/components/tooltip2\"\nimport { renderClipboardButton } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n  Annotation,\n  mountAnnotationList\n} from \"../../annotation\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Code block overflow\n */\nexport interface Overflow {\n  scrollable: boolean                  /* Code block overflows */\n}\n\n/**\n * Code block\n */\nexport type CodeBlock =\n  | Overflow\n  | Annotation\n  | Tooltip\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Global sequence number for code blocks\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find candidate list element directly following a code block\n *\n * @param el - Code block element\n *\n * @returns List element or nothing\n */\nfunction findCandidateList(el: HTMLElement): HTMLElement | undefined {\n  if (el.nextElementSibling) {\n    const sibling = el.nextElementSibling as HTMLElement\n    if (sibling.tagName === \"OL\")\n      return sibling\n\n    /* Skip empty paragraphs - see https://bit.ly/3r4ZJ2O */\n    else if (sibling.tagName === \"P\" && !sibling.children.length)\n      return findCandidateList(sibling)\n  }\n\n  /* Everything else */\n  return undefined\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch code block\n *\n * This function monitors size changes of the viewport, as well as switches of\n * content tabs with embedded code blocks, as both may trigger overflow.\n *\n * @param el - Code block element\n *\n * @returns Code block observable\n */\nexport function watchCodeBlock(\n  el: HTMLElement\n): Observable<Overflow> {\n  return watchElementSize(el)\n    .pipe(\n      map(({ width }) => {\n        const content = getElementContentSize(el)\n        return {\n          scrollable: content.width > width\n        }\n      }),\n      distinctUntilKeyChanged(\"scrollable\")\n    )\n}\n\n/**\n * Mount code block\n *\n * This function ensures that an overflowing code block is focusable through\n * keyboard, so it can be scrolled without a mouse to improve on accessibility.\n * Furthermore, if code annotations are enabled, they are mounted if and only\n * if the code block is currently visible, e.g., not in a hidden content tab.\n *\n * Note that code blocks may be mounted eagerly or lazily. If they're mounted\n * lazily (on first visibility), code annotation anchor links will not work,\n * as they are evaluated on initial page load, and code annotations in general\n * might feel a little bumpier.\n *\n * @param el - Code block element\n * @param options - Options\n *\n * @returns Code block and annotation component observable\n */\nexport function mountCodeBlock(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<CodeBlock>> {\n  const { matches: hover } = matchMedia(\"(hover)\")\n\n  /* Defer mounting of code block - see https://bit.ly/3vHVoVD */\n  const factory$ = defer(() => {\n    const push$ = new Subject<Overflow>()\n    const done$ = push$.pipe(takeLast(1))\n    push$.subscribe(({ scrollable }) => {\n      if (scrollable && hover)\n        el.setAttribute(\"tabindex\", \"0\")\n      else\n        el.removeAttribute(\"tabindex\")\n    })\n\n    /* Render button for Clipboard.js integration */\n    const content$: Array<Observable<Component<CodeBlock>>> = []\n    if (ClipboardJS.isSupported()) {\n      if (el.closest(\".copy\") || (\n        feature(\"content.code.copy\") && !el.closest(\".no-copy\")\n      )) {\n        const parent = el.closest(\"pre\")!\n        parent.id = `__code_${sequence++}`\n\n        /* Mount tooltip, if enabled */\n        const button = renderClipboardButton(parent.id)\n        parent.insertBefore(button, el)\n        if (feature(\"content.tooltips\"))\n          content$.push(mountInlineTooltip2(button, { viewport$ }))\n      }\n    }\n\n    /* Handle code annotations */\n    const container = el.closest(\".highlight\")\n    if (container instanceof HTMLElement) {\n      const list = findCandidateList(container)\n\n      /* Mount code annotations, if enabled */\n      if (typeof list !== \"undefined\" && (\n        container.classList.contains(\"annotate\") ||\n        feature(\"content.code.annotate\")\n      )) {\n        const annotations$ = mountAnnotationList(list, el, options)\n        content$.push(\n          watchElementSize(container)\n            .pipe(\n              takeUntil(done$),\n              map(({ width, height }) => width && height),\n              distinctUntilChanged(),\n              switchMap(active => active ? annotations$ : EMPTY)\n            )\n        )\n      }\n    }\n\n    // If the code block has line spans, we can add this additional class to\n    // the code block element, which fixes the problem for highlighted code\n    // lines not stretching to the entirety of the screen when the code block\n    // overflows, e.g., on mobile - see\n    const spans = getElements(\":scope > span[id]\", el)\n    if (spans.length)\n      el.classList.add(\"md-code__content\")\n\n    /* Create and return component */\n    return watchCodeBlock(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state })),\n        mergeWith(...content$)\n      )\n  })\n\n  /* Mount code block lazily */\n  if (feature(\"content.lazy\"))\n    return watchElementVisibility(el)\n      .pipe(\n        filter(visible => visible),\n        take(1),\n        switchMap(() => factory$)\n      )\n\n  /* Mount code block */\n  return factory$\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  defer,\n  filter,\n  finalize,\n  map,\n  merge,\n  tap\n} from \"rxjs\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Details\n */\nexport interface Details {\n  action: \"open\" | \"close\"             /* Details state */\n  reveal?: boolean                     /* Details is revealed */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch details\n *\n * @param el - Details element\n * @param options - Options\n *\n * @returns Details observable\n */\nexport function watchDetails(\n  el: HTMLDetailsElement, { target$, print$ }: WatchOptions\n): Observable<Details> {\n  let open = true\n  return merge(\n\n    /* Open and focus details on location target */\n    target$\n      .pipe(\n        map(target => target.closest(\"details:not([open])\")!),\n        filter(details => el === details),\n        map(() => ({\n          action: \"open\", reveal: true\n        }) as Details)\n      ),\n\n    /* Open details on print and close afterwards */\n    print$\n      .pipe(\n        filter(active => active || !open),\n        tap(() => open = el.open),\n        map(active => ({\n          action: active ? \"open\" : \"close\"\n        }) as Details)\n      )\n  )\n}\n\n/**\n * Mount details\n *\n * This function ensures that `details` tags are opened on anchor jumps and\n * prior to printing, so the whole content of the page is visible.\n *\n * @param el - Details element\n * @param options - Options\n *\n * @returns Details component observable\n */\nexport function mountDetails(\n  el: HTMLDetailsElement, options: MountOptions\n): Observable<Component<Details>> {\n  return defer(() => {\n    const push$ = new Subject<Details>()\n    push$.subscribe(({ action, reveal }) => {\n      el.toggleAttribute(\"open\", action === \"open\")\n      if (reveal)\n        el.scrollIntoView()\n    })\n\n    /* Create and return component */\n    return watchDetails(el, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", ".node circle,.node ellipse,.node path,.node polygon,.node rect{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}marker{fill:var(--md-mermaid-edge-color)!important}.edgeLabel .label rect{fill:#0000}.flowchartTitleText{fill:var(--md-mermaid-label-fg-color)}.label{color:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.label foreignObject{line-height:normal;overflow:visible}.label div .edgeLabel{color:var(--md-mermaid-label-fg-color)}.edgeLabel,.edgeLabel p,.label div .edgeLabel{background-color:var(--md-mermaid-label-bg-color)}.edgeLabel,.edgeLabel p{fill:var(--md-mermaid-label-bg-color);color:var(--md-mermaid-edge-color)}.edgePath .path,.flowchart-link{stroke:var(--md-mermaid-edge-color);stroke-width:.05rem}.edgePath .arrowheadPath{fill:var(--md-mermaid-edge-color);stroke:none}.cluster rect{fill:var(--md-default-fg-color--lightest);stroke:var(--md-default-fg-color--lighter)}.cluster span{color:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}g #flowchart-circleEnd,g #flowchart-circleStart,g #flowchart-crossEnd,g #flowchart-crossStart,g #flowchart-pointEnd,g #flowchart-pointStart{stroke:none}.classDiagramTitleText{fill:var(--md-mermaid-label-fg-color)}g.classGroup line,g.classGroup rect{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}g.classGroup text{fill:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.classLabel .box{fill:var(--md-mermaid-label-bg-color);background-color:var(--md-mermaid-label-bg-color);opacity:1}.classLabel .label{fill:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.node .divider{stroke:var(--md-mermaid-node-fg-color)}.relation{stroke:var(--md-mermaid-edge-color)}.cardinality{fill:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.cardinality text{fill:inherit!important}defs #classDiagram-compositionEnd,defs #classDiagram-compositionStart,defs #classDiagram-dependencyEnd,defs #classDiagram-dependencyStart,defs #classDiagram-extensionEnd,defs #classDiagram-extensionStart{fill:var(--md-mermaid-edge-color)!important;stroke:var(--md-mermaid-edge-color)!important}defs #classDiagram-aggregationEnd,defs #classDiagram-aggregationStart{fill:var(--md-mermaid-label-bg-color)!important;stroke:var(--md-mermaid-edge-color)!important}.statediagramTitleText{fill:var(--md-mermaid-label-fg-color)}g.stateGroup rect{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}g.stateGroup .state-title{fill:var(--md-mermaid-label-fg-color)!important;font-family:var(--md-mermaid-font-family)}g.stateGroup .composit{fill:var(--md-mermaid-label-bg-color)}.nodeLabel,.nodeLabel p{color:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}a .nodeLabel{text-decoration:underline}.node circle.state-end,.node circle.state-start,.start-state{fill:var(--md-mermaid-edge-color);stroke:none}.end-state-inner,.end-state-outer{fill:var(--md-mermaid-edge-color)}.end-state-inner,.node circle.state-end{stroke:var(--md-mermaid-label-bg-color)}.transition{stroke:var(--md-mermaid-edge-color)}[id^=state-fork] rect,[id^=state-join] rect{fill:var(--md-mermaid-edge-color)!important;stroke:none!important}.statediagram-cluster.statediagram-cluster .inner{fill:var(--md-default-bg-color)}.statediagram-cluster rect{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}.statediagram-state rect.divider{fill:var(--md-default-fg-color--lightest);stroke:var(--md-default-fg-color--lighter)}defs #statediagram-barbEnd{stroke:var(--md-mermaid-edge-color)}.entityTitleText{fill:var(--md-mermaid-label-fg-color)}.attributeBoxEven,.attributeBoxOdd{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}.entityBox{fill:var(--md-mermaid-label-bg-color);stroke:var(--md-mermaid-node-fg-color)}.entityLabel{fill:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.relationshipLabelBox{fill:var(--md-mermaid-label-bg-color);fill-opacity:1;background-color:var(--md-mermaid-label-bg-color);opacity:1}.relationshipLabel{fill:var(--md-mermaid-label-fg-color)}.relationshipLine{stroke:var(--md-mermaid-edge-color)}defs #ONE_OR_MORE_END *,defs #ONE_OR_MORE_START *,defs #ONLY_ONE_END *,defs #ONLY_ONE_START *,defs #ZERO_OR_MORE_END *,defs #ZERO_OR_MORE_START *,defs #ZERO_OR_ONE_END *,defs #ZERO_OR_ONE_START *{stroke:var(--md-mermaid-edge-color)!important}defs #ZERO_OR_MORE_END circle,defs #ZERO_OR_MORE_START circle{fill:var(--md-mermaid-label-bg-color)}text:not([class]):last-child{fill:var(--md-mermaid-label-fg-color)}.actor{fill:var(--md-mermaid-sequence-actor-bg-color);stroke:var(--md-mermaid-sequence-actor-border-color)}text.actor>tspan{fill:var(--md-mermaid-sequence-actor-fg-color);font-family:var(--md-mermaid-font-family)}line{stroke:var(--md-mermaid-sequence-actor-line-color)}.actor-man circle,.actor-man line{fill:var(--md-mermaid-sequence-actorman-bg-color);stroke:var(--md-mermaid-sequence-actorman-line-color)}.messageLine0,.messageLine1{stroke:var(--md-mermaid-sequence-message-line-color)}.note{fill:var(--md-mermaid-sequence-note-bg-color);stroke:var(--md-mermaid-sequence-note-border-color)}.loopText,.loopText>tspan,.messageText,.noteText>tspan{stroke:none;font-family:var(--md-mermaid-font-family)!important}.messageText{fill:var(--md-mermaid-sequence-message-fg-color)}.loopText,.loopText>tspan{fill:var(--md-mermaid-sequence-loop-fg-color)}.noteText>tspan{fill:var(--md-mermaid-sequence-note-fg-color)}#arrowhead path{fill:var(--md-mermaid-sequence-message-line-color);stroke:none}.loopLine{fill:var(--md-mermaid-sequence-loop-bg-color);stroke:var(--md-mermaid-sequence-loop-border-color)}.labelBox{fill:var(--md-mermaid-sequence-label-bg-color);stroke:none}.labelText,.labelText>span{fill:var(--md-mermaid-sequence-label-fg-color);font-family:var(--md-mermaid-font-family)}.sequenceNumber{fill:var(--md-mermaid-sequence-number-fg-color)}rect.rect{fill:var(--md-mermaid-sequence-box-bg-color);stroke:none}rect.rect+text.text{fill:var(--md-mermaid-sequence-box-fg-color)}defs #sequencenumber{fill:var(--md-mermaid-sequence-number-bg-color)!important}", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  map,\n  of,\n  shareReplay,\n  tap\n} from \"rxjs\"\n\nimport { watchScript } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\nimport themeCSS from \"./index.css\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid diagram\n */\nexport interface Mermaid {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid instance observable\n */\nlet mermaid$: Observable<void>\n\n/**\n * Global sequence number for diagrams\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch Mermaid script\n *\n * @returns Mermaid scripts observable\n */\nfunction fetchScripts(): Observable<void> {\n  return typeof mermaid === \"undefined\" || mermaid instanceof Element\n    ? watchScript(\"https://unpkg.com/mermaid@11/dist/mermaid.min.js\")\n    : of(undefined)\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount Mermaid diagram\n *\n * @param el - Code block element\n *\n * @returns Mermaid diagram component observable\n */\nexport function mountMermaid(\n  el: HTMLElement\n): Observable<Component<Mermaid>> {\n  el.classList.remove(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n  mermaid$ ||= fetchScripts()\n    .pipe(\n      tap(() => mermaid.initialize({\n        startOnLoad: false,\n        themeCSS,\n        sequence: {\n          actorFontSize: \"16px\", // Hack: mitigate https://bit.ly/3y0NEi3\n          messageFontSize: \"16px\",\n          noteFontSize: \"16px\"\n        }\n      })),\n      map(() => undefined),\n      shareReplay(1)\n    )\n\n  /* Render diagram */\n  mermaid$.subscribe(async () => {\n    el.classList.add(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n    const id = `__mermaid_${sequence++}`\n\n    /* Create host element to replace code block */\n    const host = h(\"div\", { class: \"mermaid\" })\n    const text = el.textContent\n\n    /* Render and inject diagram */\n    const { svg, fn } = await mermaid.render(id, text)\n\n    /* Create a shadow root and inject diagram */\n    const shadow = host.attachShadow({ mode: \"closed\" })\n    shadow.innerHTML = svg\n\n    /* Replace code block with diagram and bind functions */\n    el.replaceWith(host)\n    fn?.(shadow)\n  })\n\n  /* Create and return component */\n  return mermaid$\n    .pipe(\n      map(() => ({ ref: el }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Observable, of } from \"rxjs\"\n\nimport { renderTable } from \"~/templates\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Data table\n */\nexport interface DataTable {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Sentinel for replacement\n */\nconst sentinel = h(\"table\")\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount data table\n *\n * This function wraps a data table in another scrollable container, so it can\n * be smoothly scrolled on smaller screen sizes and won't break the layout.\n *\n * @param el - Data table element\n *\n * @returns Data table component observable\n */\nexport function mountDataTable(\n  el: HTMLElement\n): Observable<Component<DataTable>> {\n  el.replaceWith(sentinel)\n  sentinel.replaceWith(renderTable(el))\n\n  /* Create and return component */\n  return of({ ref: el })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  asyncScheduler,\n  auditTime,\n  combineLatest,\n  defer,\n  endWith,\n  filter,\n  finalize,\n  fromEvent,\n  ignoreElements,\n  map,\n  merge,\n  skip,\n  startWith,\n  subscribeOn,\n  takeUntil,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  getElement,\n  getElementContentOffset,\n  getElementContentSize,\n  getElementOffset,\n  getElementSize,\n  getElements,\n  watchElementContentOffset,\n  watchElementSize,\n  watchElementVisibility\n} from \"~/browser\"\nimport { renderTabbedControl } from \"~/templates\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Content tabs\n */\nexport interface ContentTabs {\n  active: HTMLLabelElement             /* Active tab label */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch content tabs\n *\n * @param inputs - Content tabs input elements\n *\n * @returns Content tabs observable\n */\nexport function watchContentTabs(\n  inputs: HTMLInputElement[]\n): Observable<ContentTabs> {\n  const initial = inputs.find(input => input.checked) || inputs[0]\n  return merge(...inputs.map(input => fromEvent(input, \"change\")\n    .pipe(\n      map(() => getElement<HTMLLabelElement>(`label[for=\"${input.id}\"]`))\n    )\n  ))\n    .pipe(\n      startWith(getElement<HTMLLabelElement>(`label[for=\"${initial.id}\"]`)),\n      map(active => ({ active }))\n    )\n}\n\n/**\n * Mount content tabs\n *\n * @param el - Content tabs element\n * @param options - Options\n *\n * @returns Content tabs component observable\n */\nexport function mountContentTabs(\n  el: HTMLElement, { viewport$, target$ }: MountOptions\n): Observable<Component<ContentTabs>> {\n  const container = getElement(\".tabbed-labels\", el)\n  const inputs = getElements<HTMLInputElement>(\":scope > input\", el)\n\n  /* Render content tab previous button for pagination */\n  const prev = renderTabbedControl(\"prev\")\n  el.append(prev)\n\n  /* Render content tab next button for pagination */\n  const next = renderTabbedControl(\"next\")\n  el.append(next)\n\n  /* Mount component on subscription */\n  return defer(() => {\n    const push$ = new Subject<ContentTabs>()\n    const done$ = push$.pipe(ignoreElements(), endWith(true))\n    combineLatest([push$, watchElementSize(el), watchElementVisibility(el)])\n      .pipe(\n        takeUntil(done$),\n        auditTime(1, animationFrameScheduler)\n      )\n        .subscribe({\n\n          /* Handle emission */\n          next([{ active }, size]) {\n            const offset = getElementOffset(active)\n            const { width } = getElementSize(active)\n\n            /* Set tab indicator offset and width */\n            el.style.setProperty(\"--md-indicator-x\", `${offset.x}px`)\n            el.style.setProperty(\"--md-indicator-width\", `${width}px`)\n\n            /* Scroll container to active content tab */\n            const content = getElementContentOffset(container)\n            if (\n              offset.x         < content.x              ||\n              offset.x + width > content.x + size.width\n            )\n              container.scrollTo({\n                left: Math.max(0, offset.x - 16),\n                behavior: \"smooth\"\n              })\n          },\n\n          /* Handle complete */\n          complete() {\n            el.style.removeProperty(\"--md-indicator-x\")\n            el.style.removeProperty(\"--md-indicator-width\")\n          }\n        })\n\n    /* Hide content tab buttons on borders */\n    combineLatest([\n      watchElementContentOffset(container),\n      watchElementSize(container)\n    ])\n      .pipe(\n        takeUntil(done$)\n      )\n        .subscribe(([offset, size]) => {\n          const content = getElementContentSize(container)\n          prev.hidden = offset.x < 16\n          next.hidden = offset.x > content.width - size.width - 16\n        })\n\n    /* Paginate content tab container on click */\n    merge(\n      fromEvent(prev, \"click\").pipe(map(() => -1)),\n      fromEvent(next, \"click\").pipe(map(() => +1))\n    )\n      .pipe(\n        takeUntil(done$)\n      )\n        .subscribe(direction => {\n          const { width } = getElementSize(container)\n          container.scrollBy({\n            left: width * direction,\n            behavior: \"smooth\"\n          })\n        })\n\n    /* Switch to content tab target */\n    target$\n      .pipe(\n        takeUntil(done$),\n        filter(input => inputs.includes(input as HTMLInputElement))\n      )\n        .subscribe(input => input.click())\n\n    /* Add link to each content tab label */\n    container.classList.add(\"tabbed-labels--linked\")\n    for (const input of inputs) {\n      const label = getElement<HTMLLabelElement>(`label[for=\"${input.id}\"]`)\n      label.replaceChildren(h(\"a\", {\n        href: `#${label.htmlFor}`,\n        tabIndex: -1\n      }, ...Array.from(label.childNodes)))\n\n      /* Allow to copy link without scrolling to anchor */\n      fromEvent<MouseEvent>(label.firstElementChild!, \"click\")\n        .pipe(\n          takeUntil(done$),\n          filter(ev => !(ev.metaKey || ev.ctrlKey)),\n          tap(ev => {\n            ev.preventDefault()\n            ev.stopPropagation()\n          })\n        )\n          // @todo we might need to remove the anchor link on complete\n          .subscribe(() => {\n            history.replaceState({}, \"\", `#${label.htmlFor}`)\n            label.click()\n          })\n    }\n\n    /* Set up linking of content tabs, if enabled */\n    if (feature(\"content.tabs.link\"))\n      push$.pipe(\n        skip(1),\n        withLatestFrom(viewport$)\n      )\n        .subscribe(([{ active }, { offset }]) => {\n          const tab = active.innerText.trim()\n          if (active.hasAttribute(\"data-md-switching\")) {\n            active.removeAttribute(\"data-md-switching\")\n\n          /* Determine viewport offset of active tab */\n          } else {\n            const y = el.offsetTop - offset.y\n\n            /* Passively activate other tabs */\n            for (const set of getElements(\"[data-tabs]\"))\n              for (const input of getElements<HTMLInputElement>(\n                \":scope > input\", set\n              )) {\n                const label = getElement(`label[for=\"${input.id}\"]`)\n                if (\n                  label !== active &&\n                  label.innerText.trim() === tab\n                ) {\n                  label.setAttribute(\"data-md-switching\", \"\")\n                  input.click()\n                  break\n                }\n              }\n\n            /* Bring active tab into view */\n            window.scrollTo({\n              top: el.offsetTop - y\n            })\n\n            /* Persist active tabs in local storage */\n            const tabs = __md_get<string[]>(\"__tabs\") || []\n            __md_set(\"__tabs\", [...new Set([tab, ...tabs])])\n          }\n        })\n\n    /* Pause media (audio, video) on switch - see https://bit.ly/3Bk6cel */\n    push$.pipe(takeUntil(done$))\n      .subscribe(() => {\n        for (const media of getElements<HTMLAudioElement>(\"audio, video\", el))\n          media.pause()\n      })\n\n    /* Create and return component */\n    return watchContentTabs(inputs)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n    .pipe(\n      subscribeOn(asyncScheduler)\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Observable, merge } from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport { Viewport, getElements } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport {\n  Tooltip,\n  mountInlineTooltip2\n} from \"../../tooltip2\"\nimport {\n  Annotation,\n  mountAnnotationBlock\n} from \"../annotation\"\nimport {\n  CodeBlock,\n  mountCodeBlock\n} from \"../code\"\nimport {\n  Details,\n  mountDetails\n} from \"../details\"\nimport {\n  Mermaid,\n  mountMermaid\n} from \"../mermaid\"\nimport {\n  DataTable,\n  mountDataTable\n} from \"../table\"\nimport {\n  ContentTabs,\n  mountContentTabs\n} from \"../tabs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Content\n */\nexport type Content =\n  | Annotation\n  | CodeBlock\n  | ContentTabs\n  | DataTable\n  | Details\n  | Mermaid\n  | Tooltip\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount content\n *\n * This function mounts all components that are found in the content of the\n * actual article, including code blocks, data tables and details.\n *\n * @param el - Content element\n * @param options - Options\n *\n * @returns Content component observable\n */\nexport function mountContent(\n  el: HTMLElement, { viewport$, target$, print$ }: MountOptions\n): Observable<Component<Content>> {\n  return merge(\n\n    /* Annotations */\n    ...getElements(\".annotate:not(.highlight)\", el)\n      .map(child => mountAnnotationBlock(child, { target$, print$ })),\n\n    /* Code blocks */\n    ...getElements(\"pre:not(.mermaid) > code\", el)\n      .map(child => mountCodeBlock(child, { target$, print$ })),\n\n    /* Mermaid diagrams */\n    ...getElements(\"pre.mermaid\", el)\n      .map(child => mountMermaid(child)),\n\n    /* Data tables */\n    ...getElements(\"table:not([class])\", el)\n      .map(child => mountDataTable(child)),\n\n    /* Details */\n    ...getElements(\"details\", el)\n      .map(child => mountDetails(child, { target$, print$ })),\n\n    /* Content tabs */\n    ...getElements(\"[data-tabs]\", el)\n      .map(child => mountContentTabs(child, { viewport$, target$ })),\n\n    /* Tooltips */\n    ...getElements(\"[title]\", el)\n      .filter(() => feature(\"content.tooltips\"))\n      .map(child => mountInlineTooltip2(child, { viewport$ }))\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  defer,\n  delay,\n  finalize,\n  map,\n  merge,\n  of,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport { getElement } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Dialog\n */\nexport interface Dialog {\n  message: string                      /* Dialog message */\n  active: boolean                      /* Dialog is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  alert$: Subject<string>              /* Alert subject */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  alert$: Subject<string>              /* Alert subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch dialog\n *\n * @param _el - Dialog element\n * @param options - Options\n *\n * @returns Dialog observable\n */\nexport function watchDialog(\n  _el: HTMLElement, { alert$ }: WatchOptions\n): Observable<Dialog> {\n  return alert$\n    .pipe(\n      switchMap(message => merge(\n        of(true),\n        of(false).pipe(delay(2000))\n      )\n        .pipe(\n          map(active => ({ message, active }))\n        )\n      )\n    )\n}\n\n/**\n * Mount dialog\n *\n * This function reveals the dialog in the right corner when a new alert is\n * emitted through the subject that is passed as part of the options.\n *\n * @param el - Dialog element\n * @param options - Options\n *\n * @returns Dialog component observable\n */\nexport function mountDialog(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<Dialog>> {\n  const inner = getElement(\".md-typeset\", el)\n  return defer(() => {\n    const push$ = new Subject<Dialog>()\n    push$.subscribe(({ message, active }) => {\n      el.classList.toggle(\"md-dialog--active\", active)\n      inner.textContent = message\n    })\n\n    /* Create and return component */\n    return watchDialog(el, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  asyncScheduler,\n  auditTime,\n  combineLatest,\n  debounceTime,\n  defer,\n  distinctUntilChanged,\n  filter,\n  finalize,\n  map,\n  merge,\n  of,\n  subscribeOn,\n  tap,\n  throttleTime\n} from \"rxjs\"\n\nimport {\n  ElementOffset,\n  getElement,\n  getElementContainer,\n  getElementOffset,\n  getElementSize,\n  watchElementContentOffset,\n  watchElementFocus,\n  watchElementHover\n} from \"~/browser\"\nimport { renderTooltip } from \"~/templates\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Tooltip\n */\nexport interface Tooltip {\n  active: boolean                      /* Tooltip is active */\n  offset: ElementOffset                /* Tooltip offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Global sequence number for tooltips\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch tooltip\n *\n * This function will append the tooltip temporarily to compute its width,\n * which is necessary for correct centering, and then removing it again.\n *\n * @param el - Tooltip element\n * @param host - Host element\n *\n * @returns Tooltip observable\n */\nexport function watchTooltip(\n  el: HTMLElement, host: HTMLElement\n): Observable<Tooltip> {\n  document.body.append(el)\n\n  /* Compute width and remove tooltip immediately */\n  const { width } = getElementSize(el)\n  el.style.setProperty(\"--md-tooltip-width\", `${width}px`)\n  el.remove()\n\n  /* Retrieve and watch containing element */\n  const container = getElementContainer(host)\n  const scroll$ =\n    typeof container !== \"undefined\"\n      ? watchElementContentOffset(container)\n      : of({ x: 0, y: 0 })\n\n  /* Compute tooltip visibility */\n  const active$ = merge(\n    watchElementFocus(host),\n    watchElementHover(host)\n  )\n    .pipe(\n      distinctUntilChanged()\n    )\n\n  /* Compute tooltip offset */\n  return combineLatest([active$, scroll$])\n    .pipe(\n      map(([active, scroll]) => {\n        let { x, y } = getElementOffset(host)\n        const size = getElementSize(host)\n\n        /**\n         * Experimental: fix handling of tables - see https://bit.ly/3TQEj5O\n         *\n         * If this proves to be a viable fix, we should refactor tooltip\n         * positioning and somehow streamline the current process. This might\n         * also fix positioning for annotations inside tables, which is another\n         * limitation.\n         */\n        const table = host.closest(\"table\")\n        if (table && host.parentElement) {\n          x += table.offsetLeft + host.parentElement.offsetLeft\n          y += table.offsetTop  + host.parentElement.offsetTop\n        }\n        return {\n          active,\n          offset: {\n            x: x - scroll.x + size.width  / 2 - width / 2,\n            y: y - scroll.y + size.height + 8\n          }\n        }\n      })\n    )\n}\n\n/**\n * Mount tooltip\n *\n * @param el - Host element\n *\n * @returns Tooltip component observable\n */\nexport function mountTooltip(\n  el: HTMLElement\n): Observable<Component<Tooltip>> {\n  const title = el.title\n  if (!title.length)\n    return EMPTY\n\n  /* Render tooltip and set title from host element */\n  const id = `__tooltip_${sequence++}`\n  const tooltip = renderTooltip(id, \"inline\")\n  const typeset = getElement(\".md-typeset\", tooltip)\n  typeset.innerHTML = title\n\n  /* Mount component on subscription */\n  return defer(() => {\n    const push$ = new Subject<Tooltip>()\n    push$.subscribe({\n\n      /* Handle emission */\n      next({ offset }) {\n        tooltip.style.setProperty(\"--md-tooltip-x\", `${offset.x}px`)\n        tooltip.style.setProperty(\"--md-tooltip-y\", `${offset.y}px`)\n      },\n\n      /* Handle complete */\n      complete() {\n        tooltip.style.removeProperty(\"--md-tooltip-x\")\n        tooltip.style.removeProperty(\"--md-tooltip-y\")\n      }\n    })\n\n    /* Toggle tooltip presence to mitigate empty lines when copying */\n    merge(\n      push$.pipe(filter(({ active }) => active)),\n      push$.pipe(debounceTime(250), filter(({ active }) => !active))\n    )\n      .subscribe({\n\n        /* Handle emission */\n        next({ active }) {\n          if (active) {\n            el.insertAdjacentElement(\"afterend\", tooltip)\n            el.setAttribute(\"aria-describedby\", id)\n            el.removeAttribute(\"title\")\n          } else {\n            tooltip.remove()\n            el.removeAttribute(\"aria-describedby\")\n            el.setAttribute(\"title\", title)\n          }\n        },\n\n        /* Handle complete */\n        complete() {\n          tooltip.remove()\n          el.removeAttribute(\"aria-describedby\")\n          el.setAttribute(\"title\", title)\n        }\n      })\n\n    /* Toggle tooltip visibility */\n    push$\n      .pipe(\n        auditTime(16, animationFrameScheduler)\n      )\n        .subscribe(({ active }) => {\n          tooltip.classList.toggle(\"md-tooltip--active\", active)\n        })\n\n    // @todo - refactor positioning together with annotations \u2013 there are\n    // several things that overlap and are identical in handling\n\n    /* Track relative origin of tooltip */\n    push$\n      .pipe(\n        throttleTime(125, animationFrameScheduler),\n        filter(() => !!el.offsetParent),\n        map(() => el.offsetParent!.getBoundingClientRect()),\n        map(({ x }) => x)\n      )\n      .subscribe({\n\n        /* Handle emission */\n        next(origin) {\n          if (origin)\n            tooltip.style.setProperty(\"--md-tooltip-0\", `${-origin}px`)\n          else\n            tooltip.style.removeProperty(\"--md-tooltip-0\")\n        },\n\n        /* Handle complete */\n        complete() {\n          tooltip.style.removeProperty(\"--md-tooltip-0\")\n        }\n      })\n\n    /* Create and return component */\n    return watchTooltip(tooltip, el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n    .pipe(\n      subscribeOn(asyncScheduler)\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  bufferCount,\n  combineLatest,\n  combineLatestWith,\n  defer,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  endWith,\n  filter,\n  from,\n  ignoreElements,\n  map,\n  mergeMap,\n  mergeWith,\n  of,\n  shareReplay,\n  startWith,\n  switchMap,\n  takeUntil\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  getElements,\n  watchElementSize,\n  watchToggle\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Main } from \"../../main\"\nimport {\n  Tooltip,\n  mountTooltip\n} from \"../../tooltip\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Header\n */\nexport interface Header {\n  height: number                       /* Header visible height */\n  hidden: boolean                      /* Header is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  main$: Observable<Main>              /* Main area observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Compute whether the header is hidden\n *\n * If the user scrolls past a certain threshold, the header can be hidden when\n * scrolling down, and shown when scrolling up.\n *\n * @param options - Options\n *\n * @returns Toggle observable\n */\nfunction isHidden({ viewport$ }: WatchOptions): Observable<boolean> {\n  if (!feature(\"header.autohide\"))\n    return of(false)\n\n  /* Compute direction and turning point */\n  const direction$ = viewport$\n    .pipe(\n      map(({ offset: { y } }) => y),\n      bufferCount(2, 1),\n      map(([a, b]) => [a < b, b] as const),\n      distinctUntilKeyChanged(0)\n    )\n\n  /* Compute whether header should be hidden */\n  const hidden$ = combineLatest([viewport$, direction$])\n    .pipe(\n      filter(([{ offset }, [, y]]) => Math.abs(y - offset.y) > 100),\n      map(([, [direction]]) => direction),\n      distinctUntilChanged()\n    )\n\n  /* Compute threshold for hiding */\n  const search$ = watchToggle(\"search\")\n  return combineLatest([viewport$, search$])\n    .pipe(\n      map(([{ offset }, search]) => offset.y > 400 && !search),\n      distinctUntilChanged(),\n      switchMap(active => active ? hidden$ : of(false)),\n      startWith(false)\n    )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch header\n *\n * @param el - Header element\n * @param options - Options\n *\n * @returns Header observable\n */\nexport function watchHeader(\n  el: HTMLElement, options: WatchOptions\n): Observable<Header> {\n  return defer(() => combineLatest([\n    watchElementSize(el),\n    isHidden(options)\n  ]))\n    .pipe(\n      map(([{ height }, hidden]) => ({\n        height,\n        hidden\n      })),\n      distinctUntilChanged((a, b) => (\n        a.height === b.height &&\n        a.hidden === b.hidden\n      )),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount header\n *\n * This function manages the different states of the header, i.e. whether it's\n * hidden or rendered with a shadow. This depends heavily on the main area.\n *\n * @param el - Header element\n * @param options - Options\n *\n * @returns Header component observable\n */\nexport function mountHeader(\n  el: HTMLElement, { header$, main$ }: MountOptions\n): Observable<Component<Header | Tooltip>> {\n  return defer(() => {\n    const push$ = new Subject<Main>()\n    const done$ = push$.pipe(ignoreElements(), endWith(true))\n    push$\n      .pipe(\n        distinctUntilKeyChanged(\"active\"),\n        combineLatestWith(header$)\n      )\n        .subscribe(([{ active }, { hidden }]) => {\n          el.classList.toggle(\"md-header--shadow\", active && !hidden)\n          el.hidden = hidden\n        })\n\n    /* Mount tooltips, if enabled */\n    const tooltips = from(getElements(\"[title]\", el))\n      .pipe(\n        filter(() => feature(\"content.tooltips\")),\n        mergeMap(child => mountTooltip(child))\n      )\n\n    /* Link to main area */\n    main$.subscribe(push$)\n\n    /* Create and return component */\n    return header$\n      .pipe(\n        takeUntil(done$),\n        map(state => ({ ref: el, ...state })),\n        mergeWith(tooltips.pipe(takeUntil(done$)))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  distinctUntilKeyChanged,\n  finalize,\n  map,\n  tap\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  getElementSize,\n  getOptionalElement,\n  watchViewportAt\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Header } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Header\n */\nexport interface HeaderTitle {\n  active: boolean                      /* Header title is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch header title\n *\n * @param el - Heading element\n * @param options - Options\n *\n * @returns Header title observable\n */\nexport function watchHeaderTitle(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<HeaderTitle> {\n  return watchViewportAt(el, { viewport$, header$ })\n    .pipe(\n      map(({ offset: { y } }) => {\n        const { height } = getElementSize(el)\n        return {\n          active: y >= height\n        }\n      }),\n      distinctUntilKeyChanged(\"active\")\n    )\n}\n\n/**\n * Mount header title\n *\n * This function swaps the header title from the site title to the title of the\n * current page when the user scrolls past the first headline.\n *\n * @param el - Header title element\n * @param options - Options\n *\n * @returns Header title component observable\n */\nexport function mountHeaderTitle(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<HeaderTitle>> {\n  return defer(() => {\n    const push$ = new Subject<HeaderTitle>()\n    push$.subscribe({\n\n      /* Handle emission */\n      next({ active }) {\n        el.classList.toggle(\"md-header__title--active\", active)\n      },\n\n      /* Handle complete */\n      complete() {\n        el.classList.remove(\"md-header__title--active\")\n      }\n    })\n\n    /* Obtain headline, if any */\n    const heading = getOptionalElement(\".md-content h1\")\n    if (typeof heading === \"undefined\")\n      return EMPTY\n\n    /* Create and return component */\n    return watchHeaderTitle(heading, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  map,\n  switchMap\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  watchElementSize\n} from \"~/browser\"\n\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Main area\n */\nexport interface Main {\n  offset: number                       /* Main area top offset */\n  height: number                       /* Main area visible height */\n  active: boolean                      /* Main area is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch main area\n *\n * This function returns an observable that computes the visual parameters of\n * the main area which depends on the viewport vertical offset and height, as\n * well as the height of the header element, if the header is fixed.\n *\n * @param el - Main area element\n * @param options - Options\n *\n * @returns Main area observable\n */\nexport function watchMain(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<Main> {\n\n  /* Compute necessary adjustment for header */\n  const adjust$ = header$\n    .pipe(\n      map(({ height }) => height),\n      distinctUntilChanged()\n    )\n\n  /* Compute the main area's top and bottom borders */\n  const border$ = adjust$\n    .pipe(\n      switchMap(() => watchElementSize(el)\n        .pipe(\n          map(({ height }) => ({\n            top:    el.offsetTop,\n            bottom: el.offsetTop + height\n          })),\n          distinctUntilKeyChanged(\"bottom\")\n        )\n      )\n    )\n\n  /* Compute the main area's offset, visible height and if we scrolled past */\n  return combineLatest([adjust$, border$, viewport$])\n    .pipe(\n      map(([header, { top, bottom }, { offset: { y }, size: { height } }]) => {\n        height = Math.max(0, height\n          - Math.max(0, top    - y,  header)\n          - Math.max(0, height + y - bottom)\n        )\n        return {\n          offset: top - header,\n          height,\n          active: top - header <= y\n        }\n      }),\n      distinctUntilChanged((a, b) => (\n        a.offset === b.offset &&\n        a.height === b.height &&\n        a.active === b.active\n      ))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  asyncScheduler,\n  defer,\n  filter,\n  finalize,\n  fromEvent,\n  map,\n  mergeMap,\n  observeOn,\n  of,\n  repeat,\n  shareReplay,\n  skip,\n  startWith,\n  takeUntil,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { getElements, watchMedia } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\nimport {\n  Component,\n  getComponentElement\n} from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Palette colors\n */\nexport interface PaletteColor {\n  media?: string                       /* Media query */\n  scheme?: string                      /* Color scheme */\n  primary?: string                     /* Primary color */\n  accent?: string                      /* Accent color */\n}\n\n/**\n * Palette\n */\nexport interface Palette {\n  index: number                        /* Palette index */\n  color: PaletteColor                  /* Palette colors */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch color palette\n *\n * @param inputs - Color palette element\n *\n * @returns Color palette observable\n */\nexport function watchPalette(\n  inputs: HTMLInputElement[]\n): Observable<Palette> {\n  const current = __md_get<Palette>(\"__palette\") || {\n    index: inputs.findIndex(input => matchMedia(\n      input.getAttribute(\"data-md-color-media\")!\n    ).matches)\n  }\n\n  /* Emit changes in color palette */\n  const index = Math.max(0, Math.min(current.index, inputs.length - 1))\n  return of(...inputs)\n    .pipe(\n      mergeMap(input => fromEvent(input, \"change\").pipe(map(() => input))),\n      startWith(inputs[index]),\n      map(input => ({\n        index: inputs.indexOf(input),\n        color: {\n          media:   input.getAttribute(\"data-md-color-media\"),\n          scheme:  input.getAttribute(\"data-md-color-scheme\"),\n          primary: input.getAttribute(\"data-md-color-primary\"),\n          accent:  input.getAttribute(\"data-md-color-accent\")\n        }\n      } as Palette)),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount color palette\n *\n * @param el - Color palette element\n *\n * @returns Color palette component observable\n */\nexport function mountPalette(\n  el: HTMLElement\n): Observable<Component<Palette>> {\n  const inputs = getElements<HTMLInputElement>(\"input\", el)\n  const meta = h(\"meta\", { name: \"theme-color\" })\n  document.head.appendChild(meta)\n\n  // Add color scheme meta tag\n  const scheme = h(\"meta\", { name: \"color-scheme\" })\n  document.head.appendChild(scheme)\n\n  /* Mount component on subscription */\n  const media$ = watchMedia(\"(prefers-color-scheme: light)\")\n  return defer(() => {\n    const push$ = new Subject<Palette>()\n    push$.subscribe(palette => {\n      document.body.setAttribute(\"data-md-color-switching\", \"\")\n\n      /* Retrieve color palette for system preference */\n      if (palette.color.media === \"(prefers-color-scheme)\") {\n        const media = matchMedia(\"(prefers-color-scheme: light)\")\n        const input = document.querySelector(media.matches\n          ? \"[data-md-color-media='(prefers-color-scheme: light)']\"\n          : \"[data-md-color-media='(prefers-color-scheme: dark)']\"\n        )!\n\n        /* Retrieve colors for system preference */\n        palette.color.scheme  = input.getAttribute(\"data-md-color-scheme\")!\n        palette.color.primary = input.getAttribute(\"data-md-color-primary\")!\n        palette.color.accent  = input.getAttribute(\"data-md-color-accent\")!\n      }\n\n      /* Set color palette */\n      for (const [key, value] of Object.entries(palette.color))\n        document.body.setAttribute(`data-md-color-${key}`, value)\n\n      /* Set toggle visibility */\n      for (let index = 0; index < inputs.length; index++) {\n        const label = inputs[index].nextElementSibling\n        if (label instanceof HTMLElement)\n          label.hidden = palette.index !== index\n      }\n\n      /* Persist preference in local storage */\n      __md_set(\"__palette\", palette)\n    })\n\n    // Handle color switch on Enter or Space - see https://t.ly/YIhVj\n    fromEvent<KeyboardEvent>(el, \"keydown\").pipe(\n      filter(ev => ev.key === \"Enter\"),\n      withLatestFrom(push$, (_, palette) => palette)\n    )\n      .subscribe(({ index }) => {\n        index = (index + 1) % inputs.length\n        inputs[index].click()\n        inputs[index].focus()\n      })\n\n    /* Update theme-color meta tag */\n    push$\n      .pipe(\n        map(() => {\n          const header = getComponentElement(\"header\")\n          const style  = window.getComputedStyle(header)\n\n          // Set color scheme\n          scheme.content = style.colorScheme\n\n          /* Return color in hexadecimal format */\n          return style.backgroundColor.match(/\\d+/g)!\n            .map(value => (+value).toString(16).padStart(2, \"0\"))\n            .join(\"\")\n        })\n      )\n        .subscribe(color => meta.content = `#${color}`)\n\n    /* Revert transition durations after color switch */\n    push$.pipe(observeOn(asyncScheduler))\n      .subscribe(() => {\n        document.body.removeAttribute(\"data-md-color-switching\")\n      })\n\n    /* Create and return component */\n    return watchPalette(inputs)\n      .pipe(\n        takeUntil(media$.pipe(skip(1))),\n        repeat(),\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  defer,\n  finalize,\n  map,\n  tap\n} from \"rxjs\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Progress indicator\n */\nexport interface Progress {\n  value: number                        // Progress value\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  progress$: Subject<number>           // Progress subject\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount progress indicator\n *\n * @param el - Progress indicator element\n * @param options - Options\n *\n * @returns Progress indicator component observable\n */\nexport function mountProgress(\n  el: HTMLElement, { progress$ }: MountOptions\n): Observable<Component<Progress>> {\n\n  // Mount component on subscription\n  return defer(() => {\n    const push$ = new Subject<Progress>()\n    push$.subscribe(({ value }) => {\n      el.style.setProperty(\"--md-progress-value\", `${value}`)\n    })\n\n    // Create and return component\n    return progress$\n      .pipe(\n        tap(value => push$.next({ value })),\n        finalize(() => push$.complete()),\n        map(value => ({ ref: el, value }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n  Observable,\n  Subject,\n  map,\n  tap\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport { getElement } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n  alert$: Subject<string>              /* Alert subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Extract text to copy\n *\n * @param el - HTML element\n *\n * @returns Extracted text\n */\nfunction extract(el: HTMLElement): string {\n  el.setAttribute(\"data-md-copying\", \"\")\n  const copy = el.closest(\"[data-copy]\")\n  const text = copy\n    ? copy.getAttribute(\"data-copy\")!\n    : el.innerText\n  el.removeAttribute(\"data-md-copying\")\n  return text.trimEnd()\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up Clipboard.js integration\n *\n * @param options - Options\n */\nexport function setupClipboardJS(\n  { alert$ }: SetupOptions\n): void {\n  if (ClipboardJS.isSupported()) {\n    new Observable<ClipboardJS.Event>(subscriber => {\n      new ClipboardJS(\"[data-clipboard-target], [data-clipboard-text]\", {\n        text: el => (\n          el.getAttribute(\"data-clipboard-text\")! ||\n          extract(getElement(\n            el.getAttribute(\"data-clipboard-target\")!\n          ))\n        )\n      })\n        .on(\"success\", ev => subscriber.next(ev))\n    })\n      .pipe(\n        tap(ev => {\n          const trigger = ev.trigger as HTMLElement\n          trigger.focus()\n        }),\n        map(() => translation(\"clipboard.copied\"))\n      )\n        .subscribe(alert$)\n  }\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  catchError,\n  map,\n  of\n} from \"rxjs\"\n\nimport {\n  getElement,\n  getElements,\n  requestXML\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sitemap, i.e. a list of URLs\n */\nexport type Sitemap = Map<string, URL[]>\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Resolve URL to the given base URL\n *\n * When serving the site with instant navigation, MkDocs will set the hostname\n * to the value as specified in `dev_addr`, but the browser allows for several\n * hostnames to be used: `localhost`, `127.0.0.1` or even `0.0.0.0`, depending\n * on configuration. This function resolves the URL to the given hostname.\n *\n * @param url - URL\n * @param base - Base URL\n *\n * @returns Resolved URL\n */\nfunction resolve(url: URL, base: URL) {\n  url.protocol = base.protocol\n  url.hostname = base.hostname\n  return url\n}\n\n/**\n * Extract sitemap from document\n *\n * This function extracts the URLs and alternate links from the document, and\n * associates alternate links to the original URL as found in `loc`, allowing\n * the browser to navigate to the correct page when switching languages. The\n * format of the sitemap is expected to adhere to:\n *\n * ``` xml\n * <urlset>\n *   <url>\n *     <loc>...</loc>\n *     <xhtml:link rel=\"alternate\" hreflang=\"en\" href=\"...\"/>\n *     <xhtml:link rel=\"alternate\" hreflang=\"de\" href=\"...\"/>\n *     ...\n *   </url>\n *   ...\n * </urlset>\n * ```\n *\n * @param document - Document\n * @param base - Base URL\n *\n * @returns Sitemap\n */\nfunction extract(document: Document, base: URL): Sitemap {\n  const sitemap: Sitemap = new Map()\n  for (const el of getElements(\"url\", document)) {\n    const url = getElement(\"loc\", el)\n\n    // Create entry for location and add it to the list of links\n    const links = [resolve(new URL(url.textContent!), base)]\n    sitemap.set(`${links[0]}`, links)\n\n    // Attach alternate links to current entry\n    for (const link of getElements(\"[rel=alternate]\", el)) {\n      const href = link.getAttribute(\"href\")\n      if (href != null)\n        links.push(resolve(new URL(href), base))\n    }\n  }\n\n  // Return sitemap\n  return sitemap\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the sitemap for the given base URL\n *\n * If a network or parsing error occurs, we just default to an empty sitemap,\n * which means the caller should fall back to regular navigation.\n *\n * @param base - Base URL\n *\n * @returns Sitemap observable\n */\nexport function fetchSitemap(base: URL | string): Observable<Sitemap> {\n  return requestXML(new URL(\"sitemap.xml\", base))\n    .pipe(\n      map(document => extract(document, new URL(base))),\n      catchError(() => of(new Map())),\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  catchError,\n  combineLatestWith,\n  concat,\n  debounceTime,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  endWith,\n  fromEvent,\n  ignoreElements,\n  map,\n  merge,\n  of,\n  share,\n  switchMap,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { configuration, feature } from \"~/_\"\nimport {\n  Viewport,\n  getElements,\n  getLocation,\n  getOptionalElement,\n  requestHTML,\n  setLocation,\n  setLocationHash\n} from \"~/browser\"\nimport { getComponentElement } from \"~/components\"\n\nimport { Sitemap, fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n  location$: Subject<URL>              // Location subject\n  viewport$: Observable<Viewport>      // Viewport observable\n  progress$: Subject<number>           // Progress subject\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Handle clicks on internal URLs while skipping external URLs\n *\n * @param ev - Mouse event\n * @param sitemap - Sitemap\n *\n * @returns URL observable\n */\nfunction handle(\n  ev: MouseEvent, sitemap: Sitemap\n): Observable<URL> {\n  if (!(ev.target instanceof Element))\n    return EMPTY\n\n  // Skip, as target is not within a link - clicks on non-link elements are\n  // also captured, which we need to exclude from processing\n  const el = ev.target.closest(\"a\")\n  if (el === null)\n    return EMPTY\n\n  // Skip, as link opens in new window - we now know we have captured a click\n  // on a link, but the link either has a `target` property defined, or the\n  // user pressed the `meta` or `ctrl` key to open it in a new window. Thus,\n  // we need to filter this event as well.\n  if (el.target || ev.metaKey || ev.ctrlKey)\n    return EMPTY\n\n  // Next, we must check if the URL is relevant for us, i.e., if it's an\n  // internal link to a page that is managed by MkDocs. Only then we can be\n  // sure that the structure of the page to be loaded adheres to the current\n  // document structure and can subsequently be injected into it without doing\n  // a full reload. For this reason, we must canonicalize the URL by removing\n  // all search parameters and hash fragments.\n  const url = new URL(el.href)\n  url.search = url.hash = \"\"\n\n  // Skip, if URL is not included in the sitemap - this could be the case when\n  // linking between versions or languages, or to another page that the author\n  // included as part of the build, but that is not managed by MkDocs. In that\n  // case we must not continue with instant navigation.\n  if (!sitemap.has(`${url}`))\n    return EMPTY\n\n  // We now know that we have a link to an internal page, so we prevent the\n  // browser from navigation and emit the URL for instant navigation. Note that\n  // this also includes anchor links, which means we need to implement anchor\n  // positioning ourselves. The reason for this is that if we wouldn't manage\n  // anchor links as well, scroll restoration will not work correctly (e.g.\n  // following an anchor link and scrolling).\n  ev.preventDefault()\n  return of(new URL(el.href))\n}\n\n/**\n * Create a map of head elements for lookup and replacement\n *\n * @param document - Document\n *\n * @returns Tag map\n */\nfunction head(document: Document): Map<string, HTMLElement> {\n  const tags = new Map<string, HTMLElement>()\n  for (const el of getElements(\":scope > *\", document.head))\n    tags.set(el.outerHTML, el)\n\n  // Return tag map\n  return tags\n}\n\n/**\n * Resolve relative URLs in the given document\n *\n * This function resolves relative `href` and `src` attributes, which can belong\n * to all sorts of tags, like meta tags, links, images, scripts and more.\n *\n * @param document - Document\n *\n * @returns Document observable\n */\nfunction resolve(document: Document): Observable<Document> {\n  for (const el of getElements(\"[href], [src]\", document))\n    for (const key of [\"href\", \"src\"]) {\n      const value = el.getAttribute(key)\n      if (value && !/^(?:[a-z]+:)?\\/\\//i.test(value)) {\n        // @ts-expect-error - trick: self-assign to resolve URL\n        el[key] = el[key]\n        break\n      }\n    }\n\n  // Return document observable\n  return of(document)\n}\n\n/**\n * Inject the contents of a document into the current one\n *\n * @param next - Next document\n *\n * @returns Document observable\n */\nfunction inject(next: Document): Observable<Document> {\n  for (const selector of [\n    \"[data-md-component=announce]\",\n    \"[data-md-component=container]\",\n    \"[data-md-component=header-topic]\",\n    \"[data-md-component=outdated]\",\n    \"[data-md-component=logo]\",\n    \"[data-md-component=skip]\",\n    ...feature(\"navigation.tabs.sticky\")\n      ? [\"[data-md-component=tabs]\"]\n      : []\n  ]) {\n    const source = getOptionalElement(selector)\n    const target = getOptionalElement(selector, next)\n    if (\n      typeof source !== \"undefined\" &&\n      typeof target !== \"undefined\"\n    ) {\n      source.replaceWith(target)\n    }\n  }\n\n  // Update meta tags\n  const tags = head(document)\n  for (const [html, el] of head(next))\n    if (tags.has(html))\n      tags.delete(html)\n    else\n      document.head.appendChild(el)\n\n  // Remove meta tags that are not present in the new document\n  for (const el of tags.values()) {\n    const name = el.getAttribute(\"name\")\n    // @todo - find a better way to handle attributes we add dynamically in\n    // other components without mounting components on every navigation, as\n    // this might impact overall performance - see https://t.ly/ehp_O\n    if (name !== \"theme-color\" && name !== \"color-scheme\")\n      el.remove()\n  }\n\n  // After components and meta tags were replaced, re-evaluate scripts\n  // that were provided by the author as part of Markdown files\n  const container = getComponentElement(\"container\")\n  return concat(getElements(\"script\", container))\n    .pipe(\n      switchMap(el => {\n        const script = next.createElement(\"script\")\n        if (el.src) {\n          for (const name of el.getAttributeNames())\n            script.setAttribute(name, el.getAttribute(name)!)\n          el.replaceWith(script)\n\n          // Complete when script is loaded\n          return new Observable(observer => {\n            script.onload = () => observer.complete()\n          })\n\n        // Complete immediately\n        } else {\n          script.textContent = el.textContent\n          el.replaceWith(script)\n          return EMPTY\n        }\n      }),\n      ignoreElements(),\n      endWith(document)\n    )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up instant navigation\n *\n * This is a heavily orchestrated operation - see inline comments to learn how\n * this works with Material for MkDocs, and how you can hook into it.\n *\n * @param options - Options\n *\n * @returns Document observable\n */\nexport function setupInstantNavigation(\n  { location$, viewport$, progress$ }: SetupOptions\n): Observable<Document> {\n  const config = configuration()\n  if (location.protocol === \"file:\")\n    return EMPTY\n\n  // Load sitemap immediately, so we have it available when the user initiates\n  // the first navigation request without any perceivable delay\n  const sitemap$ = fetchSitemap(config.base)\n\n  // Since we might be on a slow connection, the user might trigger multiple\n  // instant navigation events that overlap. MkDocs produces relative URLs for\n  // all internal links, which becomes a problem in this case, because we need\n  // to change the base URL the moment the user clicks a link that should be\n  // intercepted in order to be consistent with popstate, which means that the\n  // base URL would now be incorrect when resolving another relative link from\n  // the same site. For this reason we always resolve all relative links to\n  // absolute links, so we can be sure this never happens.\n  of(document)\n    .subscribe(resolve)\n\n  // --------------------------------------------------------------------------\n  // Navigation interception\n  // --------------------------------------------------------------------------\n\n  // Intercept navigation - to keep the number of event listeners down we use\n  // the fact that uncaptured events bubble up to the body. This has the nice\n  // property that we don't need to detach and then re-attach event listeners\n  // when the document is replaced after a navigation event.\n  const instant$ =\n    fromEvent<MouseEvent>(document.body, \"click\")\n      .pipe(\n        combineLatestWith(sitemap$),\n        switchMap(([ev, sitemap]) => handle(ev, sitemap)),\n        share()\n      )\n\n  // Intercept history change events, e.g. when the user uses the browser's\n  // back or forward buttons, and emit new location for fetching and parsing\n  const history$ =\n    fromEvent<PopStateEvent>(window, \"popstate\")\n      .pipe(\n        map(getLocation),\n        share()\n      )\n\n  // While it would be better UX to defer navigation events until the document\n  // is fully fetched and parsed, we must schedule it here to synchronize with\n  // popstate events, as they are emitted immediately. Moreover we need to\n  // store the current viewport offset for scroll restoration later on.\n  instant$.pipe(withLatestFrom(viewport$))\n    .subscribe(([url, { offset }]) => {\n      history.replaceState(offset, \"\")\n      history.pushState(null, \"\", url)\n    })\n\n  // Emit URLs that should be fetched via instant navigation on location subject\n  // which was passed into this function. The state of instant navigation can be\n  // intercepted by other parts of the application, which can synchronously back\n  // up or restore state before or after instant navigation happens.\n  merge(instant$, history$)\n    .subscribe(location$)\n\n  // --------------------------------------------------------------------------\n  // Fetching and parsing\n  // --------------------------------------------------------------------------\n\n  // Fetch document - we deduplicate requests to the same location, so we don't\n  // end up with multiple requests for the same page. We use `switchMap`, since\n  // we want to cancel the previous request when a new one is triggered, which\n  // is automatically handled by the observable returned by `request`. This is\n  // essential to ensure a good user experience, as we don't want to load pages\n  // that are not needed anymore, e.g., when the user clicks multiple links in\n  // quick succession or on slow connections. If the request fails for some\n  // reason, we fall back and use regular navigation, forcing a reload.\n  const document$ =\n    location$.pipe(\n      distinctUntilKeyChanged(\"pathname\"),\n      switchMap(url => requestHTML(url, { progress$ })\n        .pipe(\n          catchError(() => {\n            setLocation(url, true)\n            return EMPTY\n          })\n        )\n      ),\n\n      // The document was successfully fetched and parsed, so we can inject its\n      // contents into the currently active document\n      switchMap(resolve),\n      switchMap(inject),\n      share()\n    )\n\n  // --------------------------------------------------------------------------\n  // Scroll restoration\n  // --------------------------------------------------------------------------\n\n  // Handle scroll restoration - we must restore the viewport offset after the\n  // document has been fetched and injected, and every time the user clicks an\n  // anchor that leads to an element on the same page, which might also happen\n  // when the user uses the back or forward button.\n  merge(\n    document$.pipe(withLatestFrom(location$, (_, url) => url)),\n\n    // Handle instant navigation events that are triggered by the user clicking\n    // on an anchor link with a hash fragment different from the current one, as\n    // well as from popstate events, which are emitted when the user navigates\n    // back and forth between pages. We use a two-layered subscription to scope\n    // the scroll restoration to the current page, as we don't need to restore\n    // the viewport offset when the user navigates to a different page, as this\n    // is already handled by the previous observable.\n    document$.pipe(\n      switchMap(() => location$),\n      distinctUntilKeyChanged(\"pathname\"),\n      switchMap(() => location$),\n      distinctUntilKeyChanged(\"hash\")\n    ),\n\n    // Handle instant navigation events that are triggered by the user clicking\n    // on an anchor link with the same hash fragment as the current one in the\n    // URL. It is essential that we only intercept those from instant navigation\n    // events and not from history change events, or we'll end up in and endless\n    // loop. The top-level history entry must be removed, as it will be replaced\n    // with a new one, which would otherwise lead to a duplicate entry.\n    location$.pipe(\n      distinctUntilChanged((a, b) => (\n        a.pathname === b.pathname &&\n        a.hash     === b.hash\n      )),\n      switchMap(() => instant$),\n      tap(() => history.back())\n    )\n  )\n    .subscribe(url => {\n\n      // Check if the current history entry has a state, which happens when the\n      // user presses the back or forward button to visit a page we've already\n      // seen. If there's no state, it means a new page was visited and we must\n      // scroll to the top, unless an anchor is given.\n      if (history.state !== null || !url.hash) {\n        window.scrollTo(0, history.state?.y ?? 0)\n      } else {\n        history.scrollRestoration = \"auto\"\n        setLocationHash(url.hash)\n        history.scrollRestoration = \"manual\"\n      }\n    })\n\n  // Disable scroll restoration when an instant navigation event occurs, so the\n  // browser does not immediately set the viewport offset to the prior history\n  // entry, scrolling to the position on the same page, which would look odd.\n  // Instead, we manually restore the position once the page has loaded.\n  location$.subscribe(() => {\n    history.scrollRestoration = \"manual\"\n  })\n\n  // Enable scroll restoration before window unloads - this is essential to\n  // ensure that full reloads (F5) restore the viewport offset correctly. If\n  // only popstate events wouldn't reset the viewport offset prior to their\n  // emission, we could just reset this in popstate. Meh.\n  fromEvent(window, \"beforeunload\")\n    .subscribe(() => {\n      history.scrollRestoration = \"auto\"\n    })\n\n  // Track viewport offset, so we can restore it when the user navigates back\n  // and forth between pages. Note that this must be debounced and cannot be\n  // done in popstate, as popstate has already removed the entry from the\n  // history, which means it is too late.\n  viewport$.pipe(\n    distinctUntilKeyChanged(\"offset\"),\n    debounceTime(100)\n  )\n    .subscribe(({ offset }) => {\n      history.replaceState(offset, \"\")\n    })\n\n  // Return document observable\n  return document$\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchConfig } from \"../config\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlight function\n *\n * @param value - Value\n *\n * @returns Highlighted value\n */\nexport type SearchHighlightFn = (value: string) => string\n\n/**\n * Search highlight factory function\n *\n * @param query - Query value\n *\n * @returns Search highlight function\n */\nexport type SearchHighlightFactoryFn = (query: string) => SearchHighlightFn\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search highlighter\n *\n * @param config - Search configuration\n *\n * @returns Search highlight factory function\n */\nexport function setupSearchHighlighter(\n  config: SearchConfig\n): SearchHighlightFactoryFn {\n  // Hack: temporarily remove pure lookaheads and lookbehinds\n  const regex = config.separator.split(\"|\").map(term => {\n    const temp = term.replace(/(\\(\\?[!=<][^)]+\\))/g, \"\")\n    return temp.length === 0 ? \"\uFFFD\" : term\n  })\n    .join(\"|\")\n\n  const separator = new RegExp(regex, \"img\")\n  const highlight = (_: unknown, data: string, term: string) => {\n    return `${data}<mark data-md-highlight>${term}</mark>`\n  }\n\n  /* Return factory function */\n  return (query: string) => {\n    query = query\n      .replace(/[\\s*+\\-:~^]+/g, \" \")\n      .trim()\n\n    /* Create search term match expression */\n    const match = new RegExp(`(^|${config.separator}|)(${\n      query\n        .replace(/[|\\\\{}()[\\]^$+*?.-]/g, \"\\\\$&\")\n        .replace(separator, \"|\")\n    })`, \"img\")\n\n    /* Highlight string value */\n    return value => escapeHTML(value)\n      .replace(match, highlight)\n      .replace(/<\\/mark>(\\s+)<mark[^>]*>/img, \"$1\")\n  }\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { SearchResult } from \"../../_\"\nimport { SearchIndex } from \"../../config\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search message type\n */\nexport const enum SearchMessageType {\n  SETUP,                               /* Search index setup */\n  READY,                               /* Search index ready */\n  QUERY,                               /* Search query */\n  RESULT                               /* Search results */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Message containing the data necessary to setup the search index\n */\nexport interface SearchSetupMessage {\n  type: SearchMessageType.SETUP        /* Message type */\n  data: SearchIndex                    /* Message data */\n}\n\n/**\n * Message indicating the search index is ready\n */\nexport interface SearchReadyMessage {\n  type: SearchMessageType.READY        /* Message type */\n}\n\n/**\n * Message containing a search query\n */\nexport interface SearchQueryMessage {\n  type: SearchMessageType.QUERY        /* Message type */\n  data: string                         /* Message data */\n}\n\n/**\n * Message containing results for a search query\n */\nexport interface SearchResultMessage {\n  type: SearchMessageType.RESULT       /* Message type */\n  data: SearchResult                   /* Message data */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Message exchanged with the search worker\n */\nexport type SearchMessage =\n  | SearchSetupMessage\n  | SearchReadyMessage\n  | SearchQueryMessage\n  | SearchResultMessage\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Type guard for search ready messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchReadyMessage(\n  message: SearchMessage\n): message is SearchReadyMessage {\n  return message.type === SearchMessageType.READY\n}\n\n/**\n * Type guard for search result messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchResultMessage(\n  message: SearchMessage\n): message is SearchResultMessage {\n  return message.type === SearchMessageType.RESULT\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  ObservableInput,\n  Subject,\n  first,\n  merge,\n  of,\n  switchMap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport { watchToggle, watchWorker } from \"~/browser\"\n\nimport { SearchIndex } from \"../../config\"\nimport {\n  SearchMessage,\n  SearchMessageType\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up search worker\n *\n * This function creates and initializes a web worker that is used for search,\n * so that the user interface doesn't freeze. In general, the application does\n * not care how search is implemented, as long as the web worker conforms to\n * the format expected by the application as defined in `SearchMessage`. This\n * allows the author to implement custom search functionality, by providing a\n * custom web worker via configuration.\n *\n * Material for MkDocs' built-in search implementation makes use of Lunr.js, an\n * efficient and fast implementation for client-side search. Leveraging a tiny\n * iframe-based web worker shim, search is even supported for the `file://`\n * protocol, enabling search for local non-hosted builds.\n *\n * If the protocol is `file://`, search initialization is deferred to mitigate\n * freezing, as it's now synchronous by design - see https://bit.ly/3C521EO\n *\n * @see https://bit.ly/3igvtQv - How to implement custom search\n *\n * @param url - Worker URL\n * @param index$ - Search index observable input\n *\n * @returns Search worker\n */\nexport function setupSearchWorker(\n  url: string, index$: ObservableInput<SearchIndex>\n): Subject<SearchMessage> {\n  const worker$ = watchWorker<SearchMessage>(url)\n  merge(\n    of(location.protocol !== \"file:\"),\n    watchToggle(\"search\")\n  )\n    .pipe(\n      first(active => active),\n      switchMap(() => index$)\n    )\n      .subscribe(({ config, docs }) => worker$.next({\n        type: SearchMessageType.SETUP,\n        data: {\n          config,\n          docs,\n          options: {\n            suggest: feature(\"search.suggest\")\n          }\n        }\n      }))\n\n  /* Return search worker */\n  return worker$\n}\n", "import { Sitemap } from \"../../sitemap\"\n\n/** See docstring for `selectedVersionCorrespondingURL` for the meaning of these fields. */\ntype CorrespondingURLParams = {\n  selectedVersionSitemap: Sitemap\n  selectedVersionBaseURL: URL\n  currentLocation: URL\n  currentBaseURL: string\n}\n\n/**\n * Choose a URL to navigate to when the user chooses a version in the version\n * selector.\n *\n * The parameters in `params` are named as follows, in order to make it clearer\n * which parameter means what when invoking the function:\n *\n *  - selectedVersionSitemap: Sitemap - as obtained by fetchSitemap from `${selectedVersionBaseURL}/sitemap.xml`\n *\n *  - selectedVersionBaseURL: URL - usually `${currentBaseURL}/../selectedVersion`\n *\n *  - currentLocation: URL - current web browser location\n *\n *  - currentBaseURL: string - as obtained from `config.base`\n *\n * @param params - arguments with the meanings explained above.\n * @returns the URL to navigate to or null if we can't be sure that the\n * corresponding page to the current page exists in the selected version\n */\nexport function selectedVersionCorrespondingURL(\n  params: CorrespondingURLParams\n): URL | undefined {\n  const {selectedVersionSitemap,\n    selectedVersionBaseURL,\n    currentLocation,\n    currentBaseURL} = params\n  const current_path = safeURLParse(currentBaseURL)?.pathname\n  if (current_path === undefined) {\n    return\n  }\n  const currentRelativePath = stripPrefix(currentLocation.pathname, current_path)\n  if (currentRelativePath === undefined) {\n    return\n  }\n  const sitemapCommonPrefix = shortestCommonPrefix(selectedVersionSitemap.keys())\n  if (!selectedVersionSitemap.has(sitemapCommonPrefix)) {\n    // We could also check that `commonSitemapPrefix` ends in the canonical version,\n    // similarly to https://github.com/squidfunk/mkdocs-material/pull/7227. However,\n    // I don't believe that Mike/MkDocs ever generate sitemaps where it would matter\n    return\n  }\n\n  const potentialSitemapURL = safeURLParse(currentRelativePath, sitemapCommonPrefix)\n  if (!potentialSitemapURL || !selectedVersionSitemap.has(potentialSitemapURL.href)) {\n    return\n  }\n\n  const result = safeURLParse(currentRelativePath, selectedVersionBaseURL)\n  if (!result) {\n    return\n  }\n  result.hash = currentLocation.hash\n  result.search = currentLocation.search\n  return result\n}\n\n/**\n * A version of `new URL` that never throws. A polyfill for URL.parse() which is\n * not yet ubuquitous.\n *\n * @param url - passed to `new URL` constructor\n * @param base - passed to `new URL` constructor\n *\n * @returns `new URL(url, base)` or undefined if the URL is invalid.\n */\nfunction safeURLParse(url: string|URL, base?: string|URL): URL | undefined {\n  try {\n    return new URL(url, base)\n  } catch {\n    return\n  }\n}\n\n// Basic string manipulation\n\n/** Strip a given prefix from a function\n *\n * @param s - string\n * @param prefix - prefix to strip\n *\n * @returns either the string with the prefix stripped or undefined if the\n * string did not begin with the prefix.\n */\nexport function stripPrefix(s: string, prefix: string): string | undefined {\n  if (s.startsWith(prefix)) {\n    return s.slice(prefix.length)\n  }\n  return undefined\n}\n\n/** Find the length of the longest common prefix of two strings\n *\n * @param s1 - first string\n * @param s2 - second string\n *\n * @returns - the length of the longest common prefix of the two strings.\n */\nfunction commonPrefixLen(s1: string, s2: string): number {\n  const max = Math.min(s1.length, s2.length)\n  let result\n  for (result = 0; result < max; ++result) {\n    if (s1[result] !== s2[result]) {\n      break\n    }\n  }\n  return result\n}\n\n/** Find the longest common prefix of any number of strings\n *\n * @param strs - an iterable of strings\n *\n * @returns the longest common prefix of all the strings\n */\nexport function shortestCommonPrefix(strs: Iterable<string>): string {\n  let result  // Undefined if no iterations happened\n  for (const s of strs) {\n    if (result === undefined) {\n      result = s\n    } else {\n      result = result.slice(0, commonPrefixLen(result, s))\n    }\n  }\n  return result ?? \"\"\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Subject,\n  catchError,\n  combineLatest,\n  filter,\n  fromEvent,\n  map,\n  of,\n  switchMap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n  getElement,\n  getLocation,\n  requestJSON,\n  setLocation\n} from \"~/browser\"\nimport { getComponentElements } from \"~/components\"\nimport {\n  Version,\n  renderVersionSelector\n} from \"~/templates\"\n\nimport { fetchSitemap } from \"../sitemap\"\n\nimport { selectedVersionCorrespondingURL } from \"./findurl\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n  document$: Subject<Document>         /* Document subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up version selector\n *\n * @param options - Options\n */\nexport function setupVersionSelector(\n  { document$ }: SetupOptions\n): void {\n  const config = configuration()\n  const versions$ = requestJSON<Version[]>(\n    new URL(\"../versions.json\", config.base)\n  )\n    .pipe(\n      catchError(() => EMPTY) // @todo refactor instant loading\n    )\n\n  /* Determine current version */\n  const current$ = versions$\n    .pipe(\n      map(versions => {\n        const [, current] = config.base.match(/([^/]+)\\/?$/)!\n        return versions.find(({ version, aliases }) => (\n          version === current || aliases.includes(current)\n        )) || versions[0]\n      })\n    )\n\n  /* Intercept inter-version navigation */\n  versions$\n    .pipe(\n      map(versions => new Map(versions.map(version => [\n        `${new URL(`../${version.version}/`, config.base)}`,\n        version\n      ]))),\n      switchMap(urls => fromEvent<MouseEvent>(document.body, \"click\")\n        .pipe(\n          filter(ev => !ev.metaKey && !ev.ctrlKey),\n          withLatestFrom(current$),\n          switchMap(([ev, current]) => {\n            if (ev.target instanceof Element) {\n              const el = ev.target.closest(\"a\")\n              if (el && !el.target && urls.has(el.href)) {\n                const url = el.href\n                // This is a temporary hack to detect if a version inside the\n                // version selector or on another part of the site was clicked.\n                // If we're inside the version selector, we definitely want to\n                // find the same page, as we might have different deployments\n                // due to aliases. However, if we're outside the version\n                // selector, we must abort here, because we might otherwise\n                // interfere with instant navigation. We need to refactor this\n                // at some point together with instant navigation.\n                //\n                // See https://github.com/squidfunk/mkdocs-material/issues/4012\n                if (!ev.target.closest(\".md-version\")) {\n                  const version = urls.get(url)!\n                  if (version === current)\n                    return EMPTY\n                }\n                ev.preventDefault()\n                return of(new URL(url))\n              }\n            }\n            return EMPTY\n          }),\n          switchMap(selectedVersionBaseURL => {\n            return fetchSitemap(selectedVersionBaseURL).pipe(\n              map(\n                sitemap =>\n                  selectedVersionCorrespondingURL({\n                    selectedVersionSitemap: sitemap,\n                    selectedVersionBaseURL,\n                    currentLocation: getLocation(),\n                    currentBaseURL: config.base\n                  }) ?? selectedVersionBaseURL,\n              ),\n            )\n          })\n        )\n      )\n    )\n      .subscribe(url => setLocation(url, true))\n\n  /* Render version selector and warning */\n  combineLatest([versions$, current$])\n    .subscribe(([versions, current]) => {\n      const topic = getElement(\".md-header__topic\")\n      topic.appendChild(renderVersionSelector(versions, current))\n    })\n\n  /* Integrate outdated version banner with instant navigation */\n  document$.pipe(switchMap(() => current$))\n    .subscribe(current => {\n\n      /* Check if version state was already determined */\n      let outdated = __md_get(\"__outdated\", sessionStorage)\n      if (outdated === null) {\n        outdated = true\n\n        /* Obtain and normalize default versions */\n        let ignored = config.version?.default || \"latest\"\n        if (!Array.isArray(ignored))\n          ignored = [ignored]\n\n        /* Check if version is considered a default */\n        main: for (const ignore of ignored)\n          for (const version of current.aliases.concat(current.version))\n            if (new RegExp(ignore, \"i\").test(version)) {\n              outdated = false\n              break main\n            }\n\n        /* Persist version state in session storage */\n        __md_set(\"__outdated\", outdated, sessionStorage)\n      }\n\n      /* Unhide outdated version banner */\n      if (outdated)\n        for (const warning of getComponentElements(\"outdated\"))\n          warning.hidden = false\n    })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  combineLatest,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  endWith,\n  finalize,\n  first,\n  fromEvent,\n  ignoreElements,\n  map,\n  merge,\n  shareReplay,\n  takeUntil,\n  tap\n} from \"rxjs\"\n\nimport {\n  getElement,\n  getLocation,\n  setToggle,\n  watchElementFocus,\n  watchToggle\n} from \"~/browser\"\nimport {\n  SearchMessage,\n  SearchMessageType,\n  isSearchReadyMessage\n} from \"~/integrations\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query\n */\nexport interface SearchQuery {\n  value: string                        /* Query value */\n  focus: boolean                       /* Query focus */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  worker$: Subject<SearchMessage>      /* Search worker */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  worker$: Subject<SearchMessage>      /* Search worker */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch search query\n *\n * Note that the focus event which triggers re-reading the current query value\n * is delayed by `1ms` so the input's empty state is allowed to propagate.\n *\n * @param el - Search query element\n * @param options - Options\n *\n * @returns Search query observable\n */\nexport function watchSearchQuery(\n  el: HTMLInputElement, { worker$ }: WatchOptions\n): Observable<SearchQuery> {\n\n  /* Support search deep linking */\n  const { searchParams } = getLocation()\n  if (searchParams.has(\"q\")) {\n    setToggle(\"search\", true)\n\n    /* Set query from parameter */\n    el.value = searchParams.get(\"q\")!\n    el.focus()\n\n    /* Remove query parameter on close */\n    watchToggle(\"search\")\n      .pipe(\n        first(active => !active)\n      )\n        .subscribe(() => {\n          const url = getLocation()\n          url.searchParams.delete(\"q\")\n          history.replaceState({}, \"\", `${url}`)\n        })\n  }\n\n  /* Intercept focus and input events */\n  const focus$ = watchElementFocus(el)\n  const value$ = merge(\n    worker$.pipe(first(isSearchReadyMessage)),\n    fromEvent(el, \"keyup\"),\n    focus$\n  )\n    .pipe(\n      map(() => el.value),\n      distinctUntilChanged()\n    )\n\n  /* Combine into single observable */\n  return combineLatest([value$, focus$])\n    .pipe(\n      map(([value, focus]) => ({ value, focus })),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount search query\n *\n * @param el - Search query element\n * @param options - Options\n *\n * @returns Search query component observable\n */\nexport function mountSearchQuery(\n  el: HTMLInputElement, { worker$ }: MountOptions\n): Observable<Component<SearchQuery, HTMLInputElement>> {\n  const push$ = new Subject<SearchQuery>()\n  const done$ = push$.pipe(ignoreElements(), endWith(true))\n\n  /* Handle value change */\n  combineLatest([\n    worker$.pipe(first(isSearchReadyMessage)),\n    push$\n  ], (_, query) => query)\n    .pipe(\n      distinctUntilKeyChanged(\"value\")\n    )\n      .subscribe(({ value }) => worker$.next({\n        type: SearchMessageType.QUERY,\n        data: value\n      }))\n\n  /* Handle focus change */\n  push$\n    .pipe(\n      distinctUntilKeyChanged(\"focus\")\n    )\n      .subscribe(({ focus }) => {\n        if (focus)\n          setToggle(\"search\", focus)\n      })\n\n  /* Handle reset */\n  fromEvent(el.form!, \"reset\")\n    .pipe(\n      takeUntil(done$)\n    )\n      .subscribe(() => el.focus())\n\n  // Focus search query on label click - note that this is necessary to bring\n  // up the keyboard on iOS and other mobile platforms, as the search dialog is\n  // not visible at first, and programatically focusing an input element must\n  // be triggered by a user interaction - see https://t.ly/Cb30n\n  const label = getElement(\"header [for=__search]\")\n  fromEvent(label, \"click\")\n    .subscribe(() => el.focus())\n\n  /* Create and return component */\n  return watchSearchQuery(el, { worker$ })\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state })),\n      shareReplay(1)\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  bufferCount,\n  filter,\n  finalize,\n  first,\n  fromEvent,\n  map,\n  merge,\n  mergeMap,\n  of,\n  share,\n  skipUntil,\n  switchMap,\n  takeUntil,\n  tap,\n  withLatestFrom,\n  zipWith\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport {\n  getElement,\n  getOptionalElement,\n  watchElementBoundary,\n  watchToggle\n} from \"~/browser\"\nimport {\n  SearchMessage,\n  SearchResult,\n  isSearchReadyMessage,\n  isSearchResultMessage\n} from \"~/integrations\"\nimport { renderSearchResultItem } from \"~/templates\"\nimport { round } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\nimport { SearchQuery } from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  query$: Observable<SearchQuery>      /* Search query observable */\n  worker$: Subject<SearchMessage>      /* Search worker */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search result list\n *\n * This function performs a lazy rendering of the search results, depending on\n * the vertical offset of the search result container.\n *\n * @param el - Search result list element\n * @param options - Options\n *\n * @returns Search result list component observable\n */\nexport function mountSearchResult(\n  el: HTMLElement, { worker$, query$ }: MountOptions\n): Observable<Component<SearchResult>> {\n  const push$ = new Subject<SearchResult>()\n  const boundary$ = watchElementBoundary(el.parentElement!)\n    .pipe(\n      filter(Boolean)\n    )\n\n  /* Retrieve container */\n  const container = el.parentElement!\n\n  /* Retrieve nested components */\n  const meta = getElement(\":scope > :first-child\", el)\n  const list = getElement(\":scope > :last-child\", el)\n\n  /* Reveal to accessibility tree \u2013 see https://bit.ly/3iAA7t8 */\n  watchToggle(\"search\")\n    .subscribe(active => list.setAttribute(\n      \"role\", active ? \"list\" : \"presentation\"\n    ))\n\n  /* Update search result metadata */\n  push$\n    .pipe(\n      withLatestFrom(query$),\n      skipUntil(worker$.pipe(first(isSearchReadyMessage)))\n    )\n      .subscribe(([{ items }, { value }]) => {\n        switch (items.length) {\n\n          /* No results */\n          case 0:\n            meta.textContent = value.length\n              ? translation(\"search.result.none\")\n              : translation(\"search.result.placeholder\")\n            break\n\n          /* One result */\n          case 1:\n            meta.textContent = translation(\"search.result.one\")\n            break\n\n          /* Multiple result */\n          default:\n            const count = round(items.length)\n            meta.textContent = translation(\"search.result.other\", count)\n        }\n      })\n\n  /* Render search result item */\n  const render$ = push$\n    .pipe(\n      tap(() => list.innerHTML = \"\"),\n      switchMap(({ items }) => merge(\n        of(...items.slice(0, 10)),\n        of(...items.slice(10))\n          .pipe(\n            bufferCount(4),\n            zipWith(boundary$),\n            switchMap(([chunk]) => chunk)\n          )\n      )),\n      map(renderSearchResultItem),\n      share()\n    )\n\n  /* Update search result list */\n  render$.subscribe(item => list.appendChild(item))\n  render$\n    .pipe(\n      mergeMap(item => {\n        const details = getOptionalElement(\"details\", item)\n        if (typeof details === \"undefined\")\n          return EMPTY\n\n        /* Keep position of details element stable */\n        return fromEvent(details, \"toggle\")\n          .pipe(\n            takeUntil(push$),\n            map(() => details)\n          )\n      })\n    )\n      .subscribe(details => {\n        if (\n          details.open === false &&\n          details.offsetTop <= container.scrollTop\n        )\n          container.scrollTo({ top: details.offsetTop })\n      })\n\n  /* Filter search result message */\n  const result$ = worker$\n    .pipe(\n      filter(isSearchResultMessage),\n      map(({ data }) => data)\n    )\n\n  /* Create and return component */\n  return result$\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  endWith,\n  finalize,\n  fromEvent,\n  ignoreElements,\n  map,\n  takeUntil,\n  tap\n} from \"rxjs\"\n\nimport { getLocation } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { SearchQuery } from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search sharing\n */\nexport interface SearchShare {\n  url: URL                             /* Deep link for sharing */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  query$: Observable<SearchQuery>      /* Search query observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  query$: Observable<SearchQuery>      /* Search query observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search sharing\n *\n * @param _el - Search sharing element\n * @param options - Options\n *\n * @returns Search sharing observable\n */\nexport function watchSearchShare(\n  _el: HTMLElement, { query$ }: WatchOptions\n): Observable<SearchShare> {\n  return query$\n    .pipe(\n      map(({ value }) => {\n        const url = getLocation()\n        url.hash = \"\"\n\n        /* Compute readable query strings */\n        value = value\n          .replace(/\\s+/g, \"+\")        /* Collapse whitespace */\n          .replace(/&/g, \"%26\")        /* Escape '&' character */\n          .replace(/=/g, \"%3D\")        /* Escape '=' character */\n\n        /* Replace query string */\n        url.search = `q=${value}`\n        return { url }\n      })\n    )\n}\n\n/**\n * Mount search sharing\n *\n * @param el - Search sharing element\n * @param options - Options\n *\n * @returns Search sharing component observable\n */\nexport function mountSearchShare(\n  el: HTMLAnchorElement, options: MountOptions\n): Observable<Component<SearchShare>> {\n  const push$ = new Subject<SearchShare>()\n  const done$ = push$.pipe(ignoreElements(), endWith(true))\n  push$.subscribe(({ url }) => {\n    el.setAttribute(\"data-clipboard-text\", el.href)\n    el.href = `${url}`\n  })\n\n  /* Prevent following of link */\n  fromEvent(el, \"click\")\n    .pipe(\n      takeUntil(done$)\n    )\n      .subscribe(ev => ev.preventDefault())\n\n  /* Create and return component */\n  return watchSearchShare(el, options)\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  asyncScheduler,\n  combineLatestWith,\n  distinctUntilChanged,\n  filter,\n  finalize,\n  fromEvent,\n  map,\n  merge,\n  observeOn,\n  tap\n} from \"rxjs\"\n\nimport { Keyboard } from \"~/browser\"\nimport {\n  SearchMessage,\n  SearchResult,\n  isSearchResultMessage\n} from \"~/integrations\"\n\nimport { Component, getComponentElement } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search suggestions\n */\nexport interface SearchSuggest {}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  keyboard$: Observable<Keyboard>      /* Keyboard observable */\n  worker$: Subject<SearchMessage>      /* Search worker */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search suggestions\n *\n * This function will perform a lazy rendering of the search results, depending\n * on the vertical offset of the search result container.\n *\n * @param el - Search result list element\n * @param options - Options\n *\n * @returns Search result list component observable\n */\nexport function mountSearchSuggest(\n  el: HTMLElement, { worker$, keyboard$ }: MountOptions\n): Observable<Component<SearchSuggest>> {\n  const push$ = new Subject<SearchResult>()\n\n  /* Retrieve query component and track all changes */\n  const query  = getComponentElement(\"search-query\")\n  const query$ = merge(\n    fromEvent(query, \"keydown\"),\n    fromEvent(query, \"focus\")\n  )\n    .pipe(\n      observeOn(asyncScheduler),\n      map(() => query.value),\n      distinctUntilChanged(),\n    )\n\n  /* Update search suggestions */\n  push$\n    .pipe(\n      combineLatestWith(query$),\n      map(([{ suggest }, value]) => {\n        const words = value.split(/([\\s-]+)/)\n        if (suggest?.length && words[words.length - 1]) {\n          const last = suggest[suggest.length - 1]\n          if (last.startsWith(words[words.length - 1]))\n            words[words.length - 1] = last\n        } else {\n          words.length = 0\n        }\n        return words\n      })\n    )\n      .subscribe(words => el.innerHTML = words\n        .join(\"\")\n        .replace(/\\s/g, \"&nbsp;\")\n      )\n\n  /* Set up search keyboard handlers */\n  keyboard$\n    .pipe(\n      filter(({ mode }) => mode === \"search\")\n    )\n      .subscribe(key => {\n        switch (key.type) {\n\n          /* Right arrow: accept current suggestion */\n          case \"ArrowRight\":\n            if (\n              el.innerText.length &&\n              query.selectionStart === query.value.length\n            )\n              query.value = el.innerText\n            break\n        }\n      })\n\n  /* Filter search result message */\n  const result$ = worker$\n    .pipe(\n      filter(isSearchResultMessage),\n      map(({ data }) => data)\n    )\n\n  /* Create and return component */\n  return result$\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(() => ({ ref: el }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  NEVER,\n  Observable,\n  ObservableInput,\n  filter,\n  fromEvent,\n  merge,\n  mergeWith\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n  Keyboard,\n  getActiveElement,\n  getElements,\n  setToggle\n} from \"~/browser\"\nimport {\n  SearchIndex,\n  SearchResult,\n  setupSearchWorker\n} from \"~/integrations\"\n\nimport {\n  Component,\n  getComponentElement,\n  getComponentElements\n} from \"../../_\"\nimport {\n  SearchQuery,\n  mountSearchQuery\n} from \"../query\"\nimport { mountSearchResult } from \"../result\"\nimport {\n  SearchShare,\n  mountSearchShare\n} from \"../share\"\nimport {\n  SearchSuggest,\n  mountSearchSuggest\n} from \"../suggest\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search\n */\nexport type Search =\n  | SearchQuery\n  | SearchResult\n  | SearchShare\n  | SearchSuggest\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  index$: ObservableInput<SearchIndex> /* Search index observable */\n  keyboard$: Observable<Keyboard>      /* Keyboard observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search\n *\n * This function sets up the search functionality, including the underlying\n * web worker and all keyboard bindings.\n *\n * @param el - Search element\n * @param options - Options\n *\n * @returns Search component observable\n */\nexport function mountSearch(\n  el: HTMLElement, { index$, keyboard$ }: MountOptions\n): Observable<Component<Search>> {\n  const config = configuration()\n  try {\n    const worker$ = setupSearchWorker(config.search, index$)\n\n    /* Retrieve query and result components */\n    const query  = getComponentElement(\"search-query\", el)\n    const result = getComponentElement(\"search-result\", el)\n\n    /* Always close search on result selection */\n    fromEvent<PointerEvent>(el, \"click\")\n      .pipe(\n        filter(({ target }) => (\n          target instanceof Element && !!target.closest(\"a\")\n        ))\n      )\n        .subscribe(() => setToggle(\"search\", false))\n\n    /* Set up search keyboard handlers */\n    keyboard$\n      .pipe(\n        filter(({ mode }) => mode === \"search\")\n      )\n        .subscribe(key => {\n          const active = getActiveElement()\n          switch (key.type) {\n\n            /* Enter: go to first (best) result */\n            case \"Enter\":\n              if (active === query) {\n                const anchors = new Map<HTMLAnchorElement, number>()\n                for (const anchor of getElements<HTMLAnchorElement>(\n                  \":first-child [href]\", result\n                )) {\n                  const article = anchor.firstElementChild!\n                  anchors.set(anchor, parseFloat(\n                    article.getAttribute(\"data-md-score\")!\n                  ))\n                }\n\n                /* Go to result with highest score, if any */\n                if (anchors.size) {\n                  const [[best]] = [...anchors].sort(([, a], [, b]) => b - a)\n                  best.click()\n                }\n\n                /* Otherwise omit form submission */\n                key.claim()\n              }\n              break\n\n            /* Escape or Tab: close search */\n            case \"Escape\":\n            case \"Tab\":\n              setToggle(\"search\", false)\n              query.blur()\n              break\n\n            /* Vertical arrows: select previous or next search result */\n            case \"ArrowUp\":\n            case \"ArrowDown\":\n              if (typeof active === \"undefined\") {\n                query.focus()\n              } else {\n                const els = [query, ...getElements(\n                  \":not(details) > [href], summary, details[open] [href]\",\n                  result\n                )]\n                const i = Math.max(0, (\n                  Math.max(0, els.indexOf(active)) + els.length + (\n                    key.type === \"ArrowUp\" ? -1 : +1\n                  )\n                ) % els.length)\n                els[i].focus()\n              }\n\n              /* Prevent scrolling of page */\n              key.claim()\n              break\n\n            /* All other keys: hand to search query */\n            default:\n              if (query !== getActiveElement())\n                query.focus()\n          }\n        })\n\n    /* Set up global keyboard handlers */\n    keyboard$\n      .pipe(\n        filter(({ mode }) => mode === \"global\")\n      )\n        .subscribe(key => {\n          switch (key.type) {\n\n            /* Open search and select query */\n            case \"f\":\n            case \"s\":\n            case \"/\":\n              query.focus()\n              query.select()\n\n              /* Prevent scrolling of page */\n              key.claim()\n              break\n          }\n        })\n\n    /* Create and return component */\n    const query$ = mountSearchQuery(query, { worker$ })\n    return merge(\n      query$,\n      mountSearchResult(result, { worker$, query$ })\n    )\n      .pipe(\n        mergeWith(\n\n          /* Search sharing */\n          ...getComponentElements(\"search-share\", el)\n            .map(child => mountSearchShare(child, { query$ })),\n\n          /* Search suggestions */\n          ...getComponentElements(\"search-suggest\", el)\n            .map(child => mountSearchSuggest(child, { worker$, keyboard$ }))\n        )\n      )\n\n  /* Gracefully handle broken search */\n  } catch (err) {\n    el.hidden = true\n    return NEVER\n  }\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  ObservableInput,\n  combineLatest,\n  filter,\n  map,\n  startWith\n} from \"rxjs\"\n\nimport { getLocation } from \"~/browser\"\nimport {\n  SearchIndex,\n  setupSearchHighlighter\n} from \"~/integrations\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlighting\n */\nexport interface SearchHighlight {\n  nodes: Map<ChildNode, string>        /* Map of replacements */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  index$: ObservableInput<SearchIndex> /* Search index observable */\n  location$: Observable<URL>           /* Location observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search highlighting\n *\n * @param el - Content element\n * @param options - Options\n *\n * @returns Search highlighting component observable\n */\nexport function mountSearchHiglight(\n  el: HTMLElement, { index$, location$ }: MountOptions\n): Observable<Component<SearchHighlight>> {\n  return combineLatest([\n    index$,\n    location$\n      .pipe(\n        startWith(getLocation()),\n        filter(url => !!url.searchParams.get(\"h\"))\n      )\n  ])\n    .pipe(\n      map(([index, url]) => setupSearchHighlighter(index.config)(\n        url.searchParams.get(\"h\")!\n      )),\n      map(fn => {\n        const nodes = new Map<ChildNode, string>()\n\n        /* Traverse text nodes and collect matches */\n        const it = document.createNodeIterator(el, NodeFilter.SHOW_TEXT)\n        for (let node = it.nextNode(); node; node = it.nextNode()) {\n          if (node.parentElement?.offsetHeight) {\n            const original = node.textContent!\n            const replaced = fn(original)\n            if (replaced.length > original.length)\n              nodes.set(node as ChildNode, replaced)\n          }\n        }\n\n        /* Replace original nodes with matches */\n        for (const [node, text] of nodes) {\n          const { childNodes } = h(\"span\", null, text)\n          node.replaceWith(...Array.from(childNodes))\n        }\n\n        /* Return component */\n        return { ref: el, nodes }\n      })\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  asyncScheduler,\n  auditTime,\n  combineLatest,\n  defer,\n  distinctUntilChanged,\n  endWith,\n  finalize,\n  first,\n  from,\n  fromEvent,\n  ignoreElements,\n  map,\n  mergeMap,\n  observeOn,\n  takeUntil,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  getElement,\n  getElementOffset,\n  getElementSize,\n  getElements\n} from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sidebar\n */\nexport interface Sidebar {\n  height: number                       /* Sidebar height */\n  locked: boolean                      /* Sidebar is locked */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  main$: Observable<Main>              /* Main area observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  main$: Observable<Main>              /* Main area observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch sidebar\n *\n * This function returns an observable that computes the visual parameters of\n * the sidebar which depends on the vertical viewport offset, as well as the\n * height of the main area. When the page is scrolled beyond the header, the\n * sidebar is locked and fills the remaining space.\n *\n * @param el - Sidebar element\n * @param options - Options\n *\n * @returns Sidebar observable\n */\nexport function watchSidebar(\n  el: HTMLElement, { viewport$, main$ }: WatchOptions\n): Observable<Sidebar> {\n  const parent = el.closest<HTMLElement>(\".md-grid\")!\n  const adjust =\n    parent.offsetTop -\n    parent.parentElement!.offsetTop\n\n  /* Compute the sidebar's available height and if it should be locked */\n  return combineLatest([main$, viewport$])\n    .pipe(\n      map(([{ offset, height }, { offset: { y } }]) => {\n        height = height\n          + Math.min(adjust, Math.max(0, y - offset))\n          - adjust\n        return {\n          height,\n          locked: y >= offset + adjust\n        }\n      }),\n      distinctUntilChanged((a, b) => (\n        a.height === b.height &&\n        a.locked === b.locked\n      ))\n    )\n}\n\n/**\n * Mount sidebar\n *\n * This function doesn't set the height of the actual sidebar, but of its first\n * child \u2013 the `.md-sidebar__scrollwrap` element in order to mitigiate jittery\n * sidebars when the footer is scrolled into view. At some point we switched\n * from `absolute` / `fixed` positioning to `sticky` positioning, significantly\n * reducing jitter in some browsers (respectively Firefox and Safari) when\n * scrolling from the top. However, top-aligned sticky positioning means that\n * the sidebar snaps to the bottom when the end of the container is reached.\n * This is what leads to the mentioned jitter, as the sidebar's height may be\n * updated too slowly.\n *\n * This behaviour can be mitigiated by setting the height of the sidebar to `0`\n * while preserving the padding, and the height on its first element.\n *\n * @param el - Sidebar element\n * @param options - Options\n *\n * @returns Sidebar component observable\n */\nexport function mountSidebar(\n  el: HTMLElement, { header$, ...options }: MountOptions\n): Observable<Component<Sidebar>> {\n  const inner = getElement(\".md-sidebar__scrollwrap\", el)\n  const { y } = getElementOffset(inner)\n  return defer(() => {\n    const push$ = new Subject<Sidebar>()\n    const done$ = push$.pipe(ignoreElements(), endWith(true))\n    const next$ = push$\n      .pipe(\n        auditTime(0, animationFrameScheduler)\n      )\n\n    /* Update sidebar height and offset */\n    next$.pipe(withLatestFrom(header$))\n      .subscribe({\n\n        /* Handle emission */\n        next([{ height }, { height: offset }]) {\n          inner.style.height = `${height - 2 * y}px`\n          el.style.top       = `${offset}px`\n        },\n\n        /* Handle complete */\n        complete() {\n          inner.style.height = \"\"\n          el.style.top       = \"\"\n        }\n      })\n\n    /* Bring active item into view on initial load */\n    next$.pipe(first())\n      .subscribe(() => {\n        for (const item of getElements(\".md-nav__link--active[href]\", el)) {\n          if (!item.clientHeight) // skip invisible toc in left sidebar\n            continue\n          const container = item.closest<HTMLElement>(\".md-sidebar__scrollwrap\")!\n          if (typeof container !== \"undefined\") {\n            const offset = item.offsetTop - container.offsetTop\n            const { height } = getElementSize(container)\n            container.scrollTo({\n              top: offset - height / 2\n            })\n          }\n        }\n      })\n\n    /* Handle accessibility for expandable items, see https://bit.ly/3jaod9p */\n    from(getElements<HTMLLabelElement>(\"label[tabindex]\", el))\n      .pipe(\n        mergeMap(label => fromEvent(label, \"click\")\n          .pipe(\n            observeOn(asyncScheduler),\n            map(() => label),\n            takeUntil(done$)\n          )\n        )\n      )\n        .subscribe(label => {\n          const input = getElement<HTMLInputElement>(`[id=\"${label.htmlFor}\"]`)\n          const nav = getElement(`[aria-labelledby=\"${label.id}\"]`)\n          nav.setAttribute(\"aria-expanded\", `${input.checked}`)\n        })\n\n    /* Create and return component */\n    return watchSidebar(el, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Repo, User } from \"github-types\"\nimport {\n  EMPTY,\n  Observable,\n  catchError,\n  defaultIfEmpty,\n  map,\n  zip\n} from \"rxjs\"\n\nimport { requestJSON } from \"~/browser\"\n\nimport { SourceFacts } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * GitHub release (partial)\n */\ninterface Release {\n  tag_name: string                     /* Tag name */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch GitHub repository facts\n *\n * @param user - GitHub user or organization\n * @param repo - GitHub repository\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFactsFromGitHub(\n  user: string, repo?: string\n): Observable<SourceFacts> {\n  if (typeof repo !== \"undefined\") {\n    const url = `https://api.github.com/repos/${user}/${repo}`\n    return zip(\n\n      /* Fetch version */\n      requestJSON<Release>(`${url}/releases/latest`)\n        .pipe(\n          catchError(() => EMPTY), // @todo refactor instant loading\n          map(release => ({\n            version: release.tag_name\n          })),\n          defaultIfEmpty({})\n        ),\n\n      /* Fetch stars and forks */\n      requestJSON<Repo>(url)\n        .pipe(\n          catchError(() => EMPTY), // @todo refactor instant loading\n          map(info => ({\n            stars: info.stargazers_count,\n            forks: info.forks_count\n          })),\n          defaultIfEmpty({})\n        )\n    )\n      .pipe(\n        map(([release, info]) => ({ ...release, ...info }))\n      )\n\n  /* User or organization */\n  } else {\n    const url = `https://api.github.com/users/${user}`\n    return requestJSON<User>(url)\n      .pipe(\n        map(info => ({\n          repositories: info.public_repos\n        })),\n        defaultIfEmpty({})\n      )\n  }\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ProjectSchema } from \"gitlab\"\nimport {\n  EMPTY,\n  Observable,\n  catchError,\n  defaultIfEmpty,\n  map,\n  zip\n} from \"rxjs\"\n\nimport { requestJSON } from \"~/browser\"\n\nimport { SourceFacts } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * GitLab release (partial)\n */\ninterface Release { // @todo remove and use the ReleaseSchema type instead after switching from gitlab to @gitbeaker/rest\n  tag_name: string                     /* Tag name */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch GitLab repository facts\n *\n * @param base - GitLab base\n * @param project - GitLab project\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFactsFromGitLab(\n  base: string, project: string\n): Observable<SourceFacts> {\n  const url = `https://${base}/api/v4/projects/${encodeURIComponent(project)}`\n  return zip(\n\n    /* Fetch version */\n    requestJSON<Release>(`${url}/releases/permalink/latest`)\n      .pipe(\n        catchError(() => EMPTY), // @todo refactor instant loading\n        map(({ tag_name }) => ({\n          version: tag_name\n        })),\n        defaultIfEmpty({})\n      ),\n\n    /* Fetch stars and forks */\n    requestJSON<ProjectSchema>(url)\n      .pipe(\n        catchError(() => EMPTY), // @todo refactor instant loading\n        map(({ star_count, forks_count }) => ({\n          stars: star_count,\n          forks: forks_count\n        })),\n        defaultIfEmpty({})\n      )\n  )\n    .pipe(\n      map(([release, info]) => ({ ...release, ...info }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { EMPTY, Observable } from \"rxjs\"\n\nimport { fetchSourceFactsFromGitHub } from \"../github\"\nimport { fetchSourceFactsFromGitLab } from \"../gitlab\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository facts for repositories\n */\nexport interface RepositoryFacts {\n  stars?: number                       /* Number of stars */\n  forks?: number                       /* Number of forks */\n  version?: string                     /* Latest version */\n}\n\n/**\n * Repository facts for organizations\n */\nexport interface OrganizationFacts {\n  repositories?: number                /* Number of repositories */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Repository facts\n */\nexport type SourceFacts =\n  | RepositoryFacts\n  | OrganizationFacts\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch repository facts\n *\n * @param url - Repository URL\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFacts(\n  url: string\n): Observable<SourceFacts> {\n\n  /* Try to match GitHub repository */\n  let match = url.match(/^.+github\\.com\\/([^/]+)\\/?([^/]+)?/i)\n  if (match) {\n    const [, user, repo] = match\n    return fetchSourceFactsFromGitHub(user, repo)\n  }\n\n  /* Try to match GitLab repository */\n  match = url.match(/^.+?([^/]*gitlab[^/]+)\\/(.+?)\\/?$/i)\n  if (match) {\n    const [, base, slug] = match\n    return fetchSourceFactsFromGitLab(base, slug)\n  }\n\n  /* Fallback */\n  return EMPTY\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  catchError,\n  defer,\n  filter,\n  finalize,\n  map,\n  of,\n  shareReplay,\n  tap\n} from \"rxjs\"\n\nimport { getElement } from \"~/browser\"\nimport { ConsentDefaults } from \"~/components/consent\"\nimport { renderSourceFacts } from \"~/templates\"\n\nimport {\n  Component,\n  getComponentElements\n} from \"../../_\"\nimport {\n  SourceFacts,\n  fetchSourceFacts\n} from \"../facts\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository information\n */\nexport interface Source {\n  facts: SourceFacts                   /* Repository facts */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository information observable\n */\nlet fetch$: Observable<Source>\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch repository information\n *\n * This function tries to read the repository facts from session storage, and\n * if unsuccessful, fetches them from the underlying provider.\n *\n * @param el - Repository information element\n *\n * @returns Repository information observable\n */\nexport function watchSource(\n  el: HTMLAnchorElement\n): Observable<Source> {\n  return fetch$ ||= defer(() => {\n    const cached = __md_get<SourceFacts>(\"__source\", sessionStorage)\n    if (cached) {\n      return of(cached)\n    } else {\n\n      /* Check if consent is configured and was given */\n      const els = getComponentElements(\"consent\")\n      if (els.length) {\n        const consent = __md_get<ConsentDefaults>(\"__consent\")\n        if (!(consent && consent.github))\n          return EMPTY\n      }\n\n      /* Fetch repository facts */\n      return fetchSourceFacts(el.href)\n        .pipe(\n          tap(facts => __md_set(\"__source\", facts, sessionStorage))\n        )\n    }\n  })\n    .pipe(\n      catchError(() => EMPTY),\n      filter(facts => Object.keys(facts).length > 0),\n      map(facts => ({ facts })),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount repository information\n *\n * @param el - Repository information element\n *\n * @returns Repository information component observable\n */\nexport function mountSource(\n  el: HTMLAnchorElement\n): Observable<Component<Source>> {\n  const inner = getElement(\":scope > :last-child\", el)\n  return defer(() => {\n    const push$ = new Subject<Source>()\n    push$.subscribe(({ facts }) => {\n      inner.appendChild(renderSourceFacts(facts))\n      inner.classList.add(\"md-source__repository--active\")\n    })\n\n    /* Create and return component */\n    return watchSource(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  defer,\n  distinctUntilKeyChanged,\n  finalize,\n  map,\n  of,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  watchElementSize,\n  watchViewportAt\n} from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Navigation tabs\n */\nexport interface Tabs {\n  hidden: boolean                      /* Navigation tabs are hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch navigation tabs\n *\n * @param el - Navigation tabs element\n * @param options - Options\n *\n * @returns Navigation tabs observable\n */\nexport function watchTabs(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<Tabs> {\n  return watchElementSize(document.body)\n    .pipe(\n      switchMap(() => watchViewportAt(el, { header$, viewport$ })),\n      map(({ offset: { y } }) => {\n        return {\n          hidden: y >= 10\n        }\n      }),\n      distinctUntilKeyChanged(\"hidden\")\n    )\n}\n\n/**\n * Mount navigation tabs\n *\n * This function hides the navigation tabs when scrolling past the threshold\n * and makes them reappear in a nice CSS animation when scrolling back up.\n *\n * @param el - Navigation tabs element\n * @param options - Options\n *\n * @returns Navigation tabs component observable\n */\nexport function mountTabs(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<Tabs>> {\n  return defer(() => {\n    const push$ = new Subject<Tabs>()\n    push$.subscribe({\n\n      /* Handle emission */\n      next({ hidden }) {\n        el.hidden = hidden\n      },\n\n      /* Handle complete */\n      complete() {\n        el.hidden = false\n      }\n    })\n\n    /* Create and return component */\n    return (\n      feature(\"navigation.tabs.sticky\")\n        ? of({ hidden: false })\n        : watchTabs(el, options)\n    )\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  asyncScheduler,\n  bufferCount,\n  combineLatestWith,\n  debounceTime,\n  defer,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  endWith,\n  filter,\n  finalize,\n  ignoreElements,\n  map,\n  merge,\n  observeOn,\n  of,\n  repeat,\n  scan,\n  share,\n  skip,\n  startWith,\n  switchMap,\n  takeUntil,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  getElement,\n  getElementContainer,\n  getElementSize,\n  getElements,\n  getLocation,\n  getOptionalElement,\n  watchElementSize\n} from \"~/browser\"\n\nimport {\n  Component,\n  getComponentElement\n} from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Table of contents\n */\nexport interface TableOfContents {\n  prev: HTMLAnchorElement[][]          /* Anchors (previous) */\n  next: HTMLAnchorElement[][]          /* Anchors (next) */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  main$: Observable<Main>              /* Main area observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch table of contents\n *\n * This is effectively a scroll spy implementation which will account for the\n * fixed header and automatically re-calculate anchor offsets when the viewport\n * is resized. The returned observable will only emit if the table of contents\n * needs to be repainted.\n *\n * This implementation tracks an anchor element's entire path starting from its\n * level up to the top-most anchor element, e.g. `[h3, h2, h1]`. Although the\n * Material theme currently doesn't make use of this information, it enables\n * the styling of the entire hierarchy through customization.\n *\n * Note that the current anchor is the last item of the `prev` anchor list.\n *\n * @param el - Table of contents element\n * @param options - Options\n *\n * @returns Table of contents observable\n */\nexport function watchTableOfContents(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<TableOfContents> {\n  const table = new Map<HTMLAnchorElement, HTMLElement>()\n\n  /* Compute anchor-to-target mapping */\n  const anchors = getElements<HTMLAnchorElement>(\".md-nav__link\", el)\n  for (const anchor of anchors) {\n    const id = decodeURIComponent(anchor.hash.substring(1))\n    const target = getOptionalElement(`[id=\"${id}\"]`)\n    if (typeof target !== \"undefined\")\n      table.set(anchor, target)\n  }\n\n  /* Compute necessary adjustment for header */\n  const adjust$ = header$\n    .pipe(\n      distinctUntilKeyChanged(\"height\"),\n      map(({ height }) => {\n        const main = getComponentElement(\"main\")\n        const grid = getElement(\":scope > :first-child\", main)\n        return height + 0.8 * (\n          grid.offsetTop -\n          main.offsetTop\n        )\n      }),\n      share()\n    )\n\n  /* Compute partition of previous and next anchors */\n  const partition$ = watchElementSize(document.body)\n    .pipe(\n      distinctUntilKeyChanged(\"height\"),\n\n      /* Build index to map anchor paths to vertical offsets */\n      switchMap(body => defer(() => {\n        let path: HTMLAnchorElement[] = []\n        return of([...table].reduce((index, [anchor, target]) => {\n          while (path.length) {\n            const last = table.get(path[path.length - 1])!\n            if (last.tagName >= target.tagName) {\n              path.pop()\n            } else {\n              break\n            }\n          }\n\n          /* If the current anchor is hidden, continue with its parent */\n          let offset = target.offsetTop\n          while (!offset && target.parentElement) {\n            target = target.parentElement\n            offset = target.offsetTop\n          }\n\n          /* Fix anchor offsets in tables - see https://bit.ly/3CUFOcn */\n          let parent = target.offsetParent as HTMLElement\n          for (; parent; parent = parent.offsetParent as HTMLElement)\n            offset += parent.offsetTop\n\n          /* Map reversed anchor path to vertical offset */\n          return index.set(\n            [...path = [...path, anchor]].reverse(),\n            offset\n          )\n        }, new Map<HTMLAnchorElement[], number>()))\n      })\n        .pipe(\n\n          /* Sort index by vertical offset (see https://bit.ly/30z6QSO) */\n          map(index => new Map([...index].sort(([, a], [, b]) => a - b))),\n          combineLatestWith(adjust$),\n\n          /* Re-compute partition when viewport offset changes */\n          switchMap(([index, adjust]) => viewport$\n            .pipe(\n              scan(([prev, next], { offset: { y }, size }) => {\n                const last = y + size.height >= Math.floor(body.height)\n\n                /* Look forward */\n                while (next.length) {\n                  const [, offset] = next[0]\n                  if (offset - adjust < y || last) {\n                    prev = [...prev, next.shift()!]\n                  } else {\n                    break\n                  }\n                }\n\n                /* Look backward */\n                while (prev.length) {\n                  const [, offset] = prev[prev.length - 1]\n                  if (offset - adjust >= y && !last) {\n                    next = [prev.pop()!, ...next]\n                  } else {\n                    break\n                  }\n                }\n\n                /* Return partition */\n                return [prev, next]\n              }, [[], [...index]]),\n              distinctUntilChanged((a, b) => (\n                a[0] === b[0] &&\n                a[1] === b[1]\n              ))\n            )\n          )\n        )\n      )\n    )\n\n  /* Compute and return anchor list migrations */\n  return partition$\n    .pipe(\n      map(([prev, next]) => ({\n        prev: prev.map(([path]) => path),\n        next: next.map(([path]) => path)\n      })),\n\n      /* Extract anchor list migrations */\n      startWith({ prev: [], next: [] }),\n      bufferCount(2, 1),\n      map(([a, b]) => {\n\n        /* Moving down */\n        if (a.prev.length < b.prev.length) {\n          return {\n            prev: b.prev.slice(Math.max(0, a.prev.length - 1), b.prev.length),\n            next: []\n          }\n\n        /* Moving up */\n        } else {\n          return {\n            prev: b.prev.slice(-1),\n            next: b.next.slice(0, b.next.length - a.next.length)\n          }\n        }\n      })\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount table of contents\n *\n * @param el - Table of contents element\n * @param options - Options\n *\n * @returns Table of contents component observable\n */\nexport function mountTableOfContents(\n  el: HTMLElement, { viewport$, header$, main$, target$ }: MountOptions\n): Observable<Component<TableOfContents>> {\n  return defer(() => {\n    const push$ = new Subject<TableOfContents>()\n    const done$ = push$.pipe(ignoreElements(), endWith(true))\n    push$.subscribe(({ prev, next }) => {\n\n      /* Look forward */\n      for (const [anchor] of next) {\n        anchor.classList.remove(\"md-nav__link--passed\")\n        anchor.classList.remove(\"md-nav__link--active\")\n      }\n\n      /* Look backward */\n      for (const [index, [anchor]] of prev.entries()) {\n        anchor.classList.add(\"md-nav__link--passed\")\n        anchor.classList.toggle(\n          \"md-nav__link--active\",\n          index === prev.length - 1\n        )\n      }\n    })\n\n    /* Set up following, if enabled */\n    if (feature(\"toc.follow\")) {\n\n      /* Toggle smooth scrolling only for anchor clicks */\n      const smooth$ = merge(\n        viewport$.pipe(debounceTime(1), map(() => undefined)),\n        viewport$.pipe(debounceTime(250), map(() => \"smooth\" as const))\n      )\n\n      /* Bring active anchor into view */ // @todo: refactor\n      push$\n        .pipe(\n          filter(({ prev }) => prev.length > 0),\n          combineLatestWith(main$.pipe(observeOn(asyncScheduler))),\n          withLatestFrom(smooth$)\n        )\n          .subscribe(([[{ prev }], behavior]) => {\n            const [anchor] = prev[prev.length - 1]\n            if (anchor.offsetHeight) {\n\n              /* Retrieve overflowing container and scroll */\n              const container = getElementContainer(anchor)\n              if (typeof container !== \"undefined\") {\n                const offset = anchor.offsetTop - container.offsetTop\n                const { height } = getElementSize(container)\n                container.scrollTo({\n                  top: offset - height / 2,\n                  behavior\n                })\n              }\n            }\n          })\n    }\n\n    /* Set up anchor tracking, if enabled */\n    if (feature(\"navigation.tracking\"))\n      viewport$\n        .pipe(\n          takeUntil(done$),\n          distinctUntilKeyChanged(\"offset\"),\n          debounceTime(250),\n          skip(1),\n          takeUntil(target$.pipe(skip(1))),\n          repeat({ delay: 250 }),\n          withLatestFrom(push$)\n        )\n          .subscribe(([, { prev }]) => {\n            const url = getLocation()\n\n            /* Set hash fragment to active anchor */\n            const anchor = prev[prev.length - 1]\n            if (anchor && anchor.length) {\n              const [active] = anchor\n              const { hash } = new URL(active.href)\n              if (url.hash !== hash) {\n                url.hash = hash\n                history.replaceState({}, \"\", `${url}`)\n              }\n\n            /* Reset anchor when at the top */\n            } else {\n              url.hash = \"\"\n              history.replaceState({}, \"\", `${url}`)\n            }\n          })\n\n    /* Create and return component */\n    return watchTableOfContents(el, { viewport$, header$ })\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  bufferCount,\n  combineLatest,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  endWith,\n  finalize,\n  fromEvent,\n  ignoreElements,\n  map,\n  repeat,\n  skip,\n  takeUntil,\n  tap\n} from \"rxjs\"\n\nimport { Viewport } from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Back-to-top button\n */\nexport interface BackToTop {\n  hidden: boolean                      /* Back-to-top button is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  main$: Observable<Main>              /* Main area observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  main$: Observable<Main>              /* Main area observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch back-to-top\n *\n * @param _el - Back-to-top element\n * @param options - Options\n *\n * @returns Back-to-top observable\n */\nexport function watchBackToTop(\n  _el: HTMLElement, { viewport$, main$, target$ }: WatchOptions\n): Observable<BackToTop> {\n\n  /* Compute direction */\n  const direction$ = viewport$\n    .pipe(\n      map(({ offset: { y } }) => y),\n      bufferCount(2, 1),\n      map(([a, b]) => a > b && b > 0),\n      distinctUntilChanged()\n    )\n\n  /* Compute whether main area is active */\n  const active$ = main$\n    .pipe(\n      map(({ active }) => active)\n    )\n\n  /* Compute threshold for hiding */\n  return combineLatest([active$, direction$])\n    .pipe(\n      map(([active, direction]) => !(active && direction)),\n      distinctUntilChanged(),\n      takeUntil(target$.pipe(skip(1))),\n      endWith(true),\n      repeat({ delay: 250 }),\n      map(hidden => ({ hidden }))\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount back-to-top\n *\n * @param el - Back-to-top element\n * @param options - Options\n *\n * @returns Back-to-top component observable\n */\nexport function mountBackToTop(\n  el: HTMLElement, { viewport$, header$, main$, target$ }: MountOptions\n): Observable<Component<BackToTop>> {\n  const push$ = new Subject<BackToTop>()\n  const done$ = push$.pipe(ignoreElements(), endWith(true))\n  push$.subscribe({\n\n    /* Handle emission */\n    next({ hidden }) {\n      el.hidden = hidden\n      if (hidden) {\n        el.setAttribute(\"tabindex\", \"-1\")\n        el.blur()\n      } else {\n        el.removeAttribute(\"tabindex\")\n      }\n    },\n\n    /* Handle complete */\n    complete() {\n      el.style.top = \"\"\n      el.hidden = true\n      el.removeAttribute(\"tabindex\")\n    }\n  })\n\n  /* Watch header height */\n  header$\n    .pipe(\n      takeUntil(done$),\n      distinctUntilKeyChanged(\"height\")\n    )\n      .subscribe(({ height }) => {\n        el.style.top = `${height + 16}px`\n      })\n\n  /* Go back to top */\n  fromEvent(el, \"click\")\n    .subscribe(ev => {\n      ev.preventDefault()\n      window.scrollTo({ top: 0 })\n    })\n\n  /* Create and return component */\n  return watchBackToTop(el, { viewport$, main$, target$ })\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  filter,\n  finalize,\n  map,\n  mergeMap,\n  skip,\n  switchMap,\n  take,\n  takeUntil\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  getElements,\n  watchElementVisibility\n} from \"~/browser\"\nimport { mountInlineTooltip2 } from \"~/components/tooltip2\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n  document$: Observable<Document>      /* Document observable */\n  viewport$: Observable<Viewport>      /* Viewport observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch ellipsis\n *\n * This function will fetch all elements that are shortened with ellipsis, and\n * filter those which are visible. Once they become visible, they stay in that\n * state, even though they may be hidden again. This optimization is necessary\n * to reduce pressure on the browser, with elements fading in and out of view.\n *\n * @param options - Options\n */\nexport function patchEllipsis(\n  { document$, viewport$ }: PatchOptions\n): void {\n  document$\n    .pipe(\n      switchMap(() => getElements(\".md-ellipsis\")),\n      mergeMap(el => watchElementVisibility(el)\n        .pipe(\n          takeUntil(document$.pipe(skip(1))),\n          filter(visible => visible),\n          map(() => el),\n          take(1)\n        )\n      ),\n      filter(el => el.offsetWidth < el.scrollWidth),\n      mergeMap(el => {\n        const text = el.innerText\n        const host = el.closest(\"a\") || el\n        host.title = text\n\n        // Do not mount improved tooltip if feature is disabled\n        if (!feature(\"content.tooltips\"))\n          return EMPTY\n\n        /* Mount tooltip */\n        return mountInlineTooltip2(host, { viewport$ })\n          .pipe(\n            takeUntil(document$.pipe(skip(1))),\n            finalize(() => host.removeAttribute(\"title\"))\n          )\n      })\n    )\n      .subscribe()\n\n  // @todo move this outside of here and fix memleaks\n  if (feature(\"content.tooltips\"))\n    document$\n      .pipe(\n        switchMap(() => getElements(\".md-status\")),\n        mergeMap(el => mountInlineTooltip2(el, { viewport$ }))\n      )\n        .subscribe()\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  mergeMap,\n  switchMap,\n  takeWhile,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n  document$: Observable<Document>      /* Document observable */\n  tablet$: Observable<boolean>         /* Media tablet observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch indeterminate checkboxes\n *\n * This function replaces the indeterminate \"pseudo state\" with the actual\n * indeterminate state, which is used to keep navigation always expanded.\n *\n * @param options - Options\n */\nexport function patchIndeterminate(\n  { document$, tablet$ }: PatchOptions\n): void {\n  document$\n    .pipe(\n      switchMap(() => getElements<HTMLInputElement>(\n        \".md-toggle--indeterminate\"\n      )),\n      tap(el => {\n        el.indeterminate = true\n        el.checked = false\n      }),\n      mergeMap(el => fromEvent(el, \"change\")\n        .pipe(\n          takeWhile(() => el.classList.contains(\"md-toggle--indeterminate\")),\n          map(() => el)\n        )\n      ),\n      withLatestFrom(tablet$)\n    )\n      .subscribe(([el, tablet]) => {\n        el.classList.remove(\"md-toggle--indeterminate\")\n        if (tablet)\n          el.checked = false\n      })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  filter,\n  fromEvent,\n  map,\n  mergeMap,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n  document$: Observable<Document>      /* Document observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Check whether the given device is an Apple device\n *\n * @returns Test result\n */\nfunction isAppleDevice(): boolean {\n  return /(iPad|iPhone|iPod)/.test(navigator.userAgent)\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch all elements with `data-md-scrollfix` attributes\n *\n * This is a year-old patch which ensures that overflow scrolling works at the\n * top and bottom of containers on iOS by ensuring a `1px` scroll offset upon\n * the start of a touch event.\n *\n * @see https://bit.ly/2SCtAOO - Original source\n *\n * @param options - Options\n */\nexport function patchScrollfix(\n  { document$ }: PatchOptions\n): void {\n  document$\n    .pipe(\n      switchMap(() => getElements(\"[data-md-scrollfix]\")),\n      tap(el => el.removeAttribute(\"data-md-scrollfix\")),\n      filter(isAppleDevice),\n      mergeMap(el => fromEvent(el, \"touchstart\")\n        .pipe(\n          map(() => el)\n        )\n      )\n    )\n      .subscribe(el => {\n        const top = el.scrollTop\n\n        /* We're at the top of the container */\n        if (top === 0) {\n          el.scrollTop = 1\n\n        /* We're at the bottom of the container */\n        } else if (top + el.offsetHeight === el.scrollHeight) {\n          el.scrollTop = top - 1\n        }\n      })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  delay,\n  map,\n  of,\n  switchMap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  watchToggle\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  tablet$: Observable<boolean>         /* Media tablet observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch the document body to lock when search is open\n *\n * For mobile and tablet viewports, the search is rendered full screen, which\n * leads to scroll leaking when at the top or bottom of the search result. This\n * function locks the body when the search is in full screen mode, and restores\n * the scroll position when leaving.\n *\n * @param options - Options\n */\nexport function patchScrolllock(\n  { viewport$, tablet$ }: PatchOptions\n): void {\n  combineLatest([watchToggle(\"search\"), tablet$])\n    .pipe(\n      map(([active, tablet]) => active && !tablet),\n      switchMap(active => of(active)\n        .pipe(\n          delay(active ? 400 : 100)\n        )\n      ),\n      withLatestFrom(viewport$)\n    )\n      .subscribe(([active, { offset: { y }}]) => {\n        if (active) {\n          document.body.setAttribute(\"data-md-scrolllock\", \"\")\n          document.body.style.top = `-${y}px`\n        } else {\n          const value = -1 * parseInt(document.body.style.top, 10)\n          document.body.removeAttribute(\"data-md-scrolllock\")\n          document.body.style.top = \"\"\n          if (value)\n            window.scrollTo(0, value)\n        }\n      })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n  Object.entries = function (obj: object) {\n    const data: [string, string][] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push([key, obj[key]])\n\n    /* Return entries */\n    return data\n  }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n  Object.values = function (obj: object) {\n    const data: string[] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push(obj[key])\n\n    /* Return values */\n    return data\n  }\n\n/* ------------------------------------------------------------------------- */\n\n/* Polyfills for `Element` */\nif (typeof Element !== \"undefined\") {\n\n  /* Polyfill `Element.scrollTo` */\n  if (!Element.prototype.scrollTo)\n    Element.prototype.scrollTo = function (\n      x?: ScrollToOptions | number, y?: number\n    ): void {\n      if (typeof x === \"object\") {\n        this.scrollLeft = x.left!\n        this.scrollTop = x.top!\n      } else {\n        this.scrollLeft = x!\n        this.scrollTop = y!\n      }\n    }\n\n  /* Polyfill `Element.replaceWith` */\n  if (!Element.prototype.replaceWith)\n    Element.prototype.replaceWith = function (\n      ...nodes: Array<string | Node>\n    ): void {\n      const parent = this.parentNode\n      if (parent) {\n        if (nodes.length === 0)\n          parent.removeChild(this)\n\n        /* Replace children and create text nodes */\n        for (let i = nodes.length - 1; i >= 0; i--) {\n          let node = nodes[i]\n          if (typeof node === \"string\")\n            node = document.createTextNode(node)\n          else if (node.parentNode)\n            node.parentNode.removeChild(node)\n\n          /* Replace child or insert before previous sibling */\n          if (!i)\n            parent.replaceChild(node, this)\n          else\n            parent.insertBefore(this.previousSibling!, node)\n        }\n      }\n    }\n}\n"],
-  "mappings": 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new file mode 100644
index 0000000..cf778d4
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.state-title{fill:var(--md-mermaid-label-fg-color)!important;font-family:var(--md-mermaid-font-family)}g.stateGroup .composit{fill:var(--md-mermaid-label-bg-color)}.nodeLabel,.nodeLabel p{color:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}a .nodeLabel{text-decoration:underline}.node circle.state-end,.node circle.state-start,.start-state{fill:var(--md-mermaid-edge-color);stroke:none}.end-state-inner,.end-state-outer{fill:var(--md-mermaid-edge-color)}.end-state-inner,.node circle.state-end{stroke:var(--md-mermaid-label-bg-color)}.transition{stroke:var(--md-mermaid-edge-color)}[id^=state-fork] rect,[id^=state-join] rect{fill:var(--md-mermaid-edge-color)!important;stroke:none!important}.statediagram-cluster.statediagram-cluster .inner{fill:var(--md-default-bg-color)}.statediagram-cluster rect{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}.statediagram-state rect.divider{fill:var(--md-default-fg-color--lightest);stroke:var(--md-default-fg-color--lighter)}defs #statediagram-barbEnd{stroke:var(--md-mermaid-edge-color)}.attributeBoxEven,.attributeBoxOdd{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}.entityBox{fill:var(--md-mermaid-label-bg-color);stroke:var(--md-mermaid-node-fg-color)}.entityLabel{fill:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.relationshipLabelBox{fill:var(--md-mermaid-label-bg-color);fill-opacity:1;background-color:var(--md-mermaid-label-bg-color);opacity:1}.relationshipLabel{fill:var(--md-mermaid-label-fg-color)}.relationshipLine{stroke:var(--md-mermaid-edge-color)}defs #ONE_OR_MORE_END *,defs #ONE_OR_MORE_START *,defs #ONLY_ONE_END *,defs #ONLY_ONE_START *,defs #ZERO_OR_MORE_END *,defs #ZERO_OR_MORE_START *,defs #ZERO_OR_ONE_END *,defs #ZERO_OR_ONE_START *{stroke:var(--md-mermaid-edge-color)!important}defs #ZERO_OR_MORE_END circle,defs #ZERO_OR_MORE_START circle{fill:var(--md-mermaid-label-bg-color)}.actor{fill:var(--md-mermaid-sequence-actor-bg-color);stroke:var(--md-mermaid-sequence-actor-border-color)}text.actor>tspan{fill:var(--md-mermaid-sequence-actor-fg-color);font-family:var(--md-mermaid-font-family)}line{stroke:var(--md-mermaid-sequence-actor-line-color)}.actor-man circle,.actor-man line{fill:var(--md-mermaid-sequence-actorman-bg-color);stroke:var(--md-mermaid-sequence-actorman-line-color)}.messageLine0,.messageLine1{stroke:var(--md-mermaid-sequence-message-line-color)}.note{fill:var(--md-mermaid-sequence-note-bg-color);stroke:var(--md-mermaid-sequence-note-border-color)}.loopText,.loopText>tspan,.messageText,.noteText>tspan{stroke:none;font-family:var(--md-mermaid-font-family)!important}.messageText{fill:var(--md-mermaid-sequence-message-fg-color)}.loopText,.loopText>tspan{fill:var(--md-mermaid-sequence-loop-fg-color)}.noteText>tspan{fill:var(--md-mermaid-sequence-note-fg-color)}#arrowhead path{fill:var(--md-mermaid-sequence-message-line-color);stroke:none}.loopLine{fill:var(--md-mermaid-sequence-loop-bg-color);stroke:var(--md-mermaid-sequence-loop-border-color)}.labelBox{fill:var(--md-mermaid-sequence-label-bg-color);stroke:none}.labelText,.labelText>span{fill:var(--md-mermaid-sequence-label-fg-color);font-family:var(--md-mermaid-font-family)}.sequenceNumber{fill:var(--md-mermaid-sequence-number-fg-color)}rect.rect{fill:var(--md-mermaid-sequence-box-bg-color);stroke:none}rect.rect+text.text{fill:var(--md-mermaid-sequence-box-fg-color)}defs #sequencenumber{fill:var(--md-mermaid-sequence-number-bg-color)!important}";var Br,Da=0;function Va(){return typeof mermaid=="undefined"||mermaid instanceof Element?wt("https://unpkg.com/mermaid@10/dist/mermaid.min.js"):I(void 0)}function In(e){return e.classList.remove("mermaid"),Br||(Br=Va().pipe(E(()=>mermaid.initialize({startOnLoad:!1,themeCSS:Rn,sequence:{actorFontSize:"16px",messageFontSize:"16px",noteFontSize:"16px"}})),m(()=>{}),G(1))),Br.subscribe(()=>ao(this,null,function*(){e.classList.add("mermaid");let t=`__mermaid_${Da++}`,r=x("div",{class:"mermaid"}),o=e.textContent,{svg:n,fn:i}=yield mermaid.render(t,o),a=r.attachShadow({mode:"closed"});a.innerHTML=n,e.replaceWith(r),i==null||i(a)})),Br.pipe(m(()=>({ref:e})))}var Fn=x("table");function jn(e){return e.replaceWith(Fn),Fn.replaceWith(On(e)),I({ref:e})}function Na(e){let t=e.find(r=>r.checked)||e[0];return S(...e.map(r=>d(r,"change").pipe(m(()=>P(`label[for="${r.id}"]`))))).pipe(Q(P(`label[for="${t.id}"]`)),m(r=>({active:r})))}function Wn(e,{viewport$:t,target$:r}){let o=P(".tabbed-labels",e),n=$(":scope > input",e),i=Qr("prev");e.append(i);let a=Qr("next");return e.append(a),C(()=>{let s=new g,p=s.pipe(X(),ne(!0));z([s,ge(e),tt(e)]).pipe(U(p),Le(1,me)).subscribe({next([{active:c},l]){let f=Ue(c),{width:u}=ce(c);e.style.setProperty("--md-indicator-x",`${f.x}px`),e.style.setProperty("--md-indicator-width",`${u}px`);let h=pr(o);(f.x<h.x||f.x+u>h.x+l.width)&&o.scrollTo({left:Math.max(0,f.x-16),behavior:"smooth"})},complete(){e.style.removeProperty("--md-indicator-x"),e.style.removeProperty("--md-indicator-width")}}),z([De(o),ge(o)]).pipe(U(p)).subscribe(([c,l])=>{let f=Tt(o);i.hidden=c.x<16,a.hidden=c.x>f.width-l.width-16}),S(d(i,"click").pipe(m(()=>-1)),d(a,"click").pipe(m(()=>1))).pipe(U(p)).subscribe(c=>{let{width:l}=ce(o);o.scrollBy({left:l*c,behavior:"smooth"})}),r.pipe(U(p),b(c=>n.includes(c))).subscribe(c=>c.click()),o.classList.add("tabbed-labels--linked");for(let c of n){let l=P(`label[for="${c.id}"]`);l.replaceChildren(x("a",{href:`#${l.htmlFor}`,tabIndex:-1},...Array.from(l.childNodes))),d(l.firstElementChild,"click").pipe(U(p),b(f=>!(f.metaKey||f.ctrlKey)),E(f=>{f.preventDefault(),f.stopPropagation()})).subscribe(()=>{history.replaceState({},"",`#${l.htmlFor}`),l.click()})}return B("content.tabs.link")&&s.pipe(Ce(1),ee(t)).subscribe(([{active:c},{offset:l}])=>{let f=c.innerText.trim();if(c.hasAttribute("data-md-switching"))c.removeAttribute("data-md-switching");else{let u=e.offsetTop-l.y;for(let w of $("[data-tabs]"))for(let A of $(":scope > input",w)){let te=P(`label[for="${A.id}"]`);if(te!==c&&te.innerText.trim()===f){te.setAttribute("data-md-switching",""),A.click();break}}window.scrollTo({top:e.offsetTop-u});let h=__md_get("__tabs")||[];__md_set("__tabs",[...new Set([f,...h])])}}),s.pipe(U(p)).subscribe(()=>{for(let c of $("audio, video",e))c.pause()}),Na(n).pipe(E(c=>s.next(c)),L(()=>s.complete()),m(c=>R({ref:e},c)))}).pipe(Qe(se))}function Un(e,{viewport$:t,target$:r,print$:o}){return S(...$(".annotate:not(.highlight)",e).map(n=>Cn(n,{target$:r,print$:o})),...$("pre:not(.mermaid) > code",e).map(n=>$n(n,{target$:r,print$:o})),...$("pre.mermaid",e).map(n=>In(n)),...$("table:not([class])",e).map(n=>jn(n)),...$("details",e).map(n=>Pn(n,{target$:r,print$:o})),...$("[data-tabs]",e).map(n=>Wn(n,{viewport$:t,target$:r})),...$("[title]",e).filter(()=>B("content.tooltips")).map(n=>lt(n,{viewport$:t})))}function za(e,{alert$:t}){return t.pipe(v(r=>S(I(!0),I(!1).pipe(Ge(2e3))).pipe(m(o=>({message:r,active:o})))))}function Dn(e,t){let r=P(".md-typeset",e);return C(()=>{let o=new g;return o.subscribe(({message:n,active:i})=>{e.classList.toggle("md-dialog--active",i),r.textContent=n}),za(e,t).pipe(E(n=>o.next(n)),L(()=>o.complete()),m(n=>R({ref:e},n)))})}var qa=0;function Qa(e,t){document.body.append(e);let{width:r}=ce(e);e.style.setProperty("--md-tooltip-width",`${r}px`),e.remove();let o=cr(t),n=typeof o!="undefined"?De(o):I({x:0,y:0}),i=S(et(t),kt(t)).pipe(K());return z([i,n]).pipe(m(([a,s])=>{let{x:p,y:c}=Ue(t),l=ce(t),f=t.closest("table");return f&&t.parentElement&&(p+=f.offsetLeft+t.parentElement.offsetLeft,c+=f.offsetTop+t.parentElement.offsetTop),{active:a,offset:{x:p-s.x+l.width/2-r/2,y:c-s.y+l.height+8}}}))}function Vn(e){let t=e.title;if(!t.length)return O;let r=`__tooltip_${qa++}`,o=Pt(r,"inline"),n=P(".md-typeset",o);return n.innerHTML=t,C(()=>{let i=new g;return i.subscribe({next({offset:a}){o.style.setProperty("--md-tooltip-x",`${a.x}px`),o.style.setProperty("--md-tooltip-y",`${a.y}px`)},complete(){o.style.removeProperty("--md-tooltip-x"),o.style.removeProperty("--md-tooltip-y")}}),S(i.pipe(b(({active:a})=>a)),i.pipe(_e(250),b(({active:a})=>!a))).subscribe({next({active:a}){a?(e.insertAdjacentElement("afterend",o),e.setAttribute("aria-describedby",r),e.removeAttribute("title")):(o.remove(),e.removeAttribute("aria-describedby"),e.setAttribute("title",t))},complete(){o.remove(),e.removeAttribute("aria-describedby"),e.setAttribute("title",t)}}),i.pipe(Le(16,me)).subscribe(({active:a})=>{o.classList.toggle("md-tooltip--active",a)}),i.pipe(ct(125,me),b(()=>!!e.offsetParent),m(()=>e.offsetParent.getBoundingClientRect()),m(({x:a})=>a)).subscribe({next(a){a?o.style.setProperty("--md-tooltip-0",`${-a}px`):o.style.removeProperty("--md-tooltip-0")},complete(){o.style.removeProperty("--md-tooltip-0")}}),Qa(o,e).pipe(E(a=>i.next(a)),L(()=>i.complete()),m(a=>R({ref:e},a)))}).pipe(Qe(se))}function Ka({viewport$:e}){if(!B("header.autohide"))return I(!1);let t=e.pipe(m(({offset:{y:n}})=>n),Ye(2,1),m(([n,i])=>[n<i,i]),Z(0)),r=z([e,t]).pipe(b(([{offset:n},[,i]])=>Math.abs(i-n.y)>100),m(([,[n]])=>n),K()),o=Ve("search");return z([e,o]).pipe(m(([{offset:n},i])=>n.y>400&&!i),K(),v(n=>n?r:I(!1)),Q(!1))}function Nn(e,t){return C(()=>z([ge(e),Ka(t)])).pipe(m(([{height:r},o])=>({height:r,hidden:o})),K((r,o)=>r.height===o.height&&r.hidden===o.hidden),G(1))}function zn(e,{header$:t,main$:r}){return C(()=>{let o=new g,n=o.pipe(X(),ne(!0));o.pipe(Z("active"),We(t)).subscribe(([{active:a},{hidden:s}])=>{e.classList.toggle("md-header--shadow",a&&!s),e.hidden=s});let i=ue($("[title]",e)).pipe(b(()=>B("content.tooltips")),oe(a=>Vn(a)));return r.subscribe(o),t.pipe(U(n),m(a=>R({ref:e},a)),Pe(i.pipe(U(n))))})}function Ya(e,{viewport$:t,header$:r}){return mr(e,{viewport$:t,header$:r}).pipe(m(({offset:{y:o}})=>{let{height:n}=ce(e);return{active:o>=n}}),Z("active"))}function qn(e,t){return C(()=>{let r=new g;r.subscribe({next({active:n}){e.classList.toggle("md-header__title--active",n)},complete(){e.classList.remove("md-header__title--active")}});let o=fe(".md-content h1");return typeof o=="undefined"?O:Ya(o,t).pipe(E(n=>r.next(n)),L(()=>r.complete()),m(n=>R({ref:e},n)))})}function Qn(e,{viewport$:t,header$:r}){let o=r.pipe(m(({height:i})=>i),K()),n=o.pipe(v(()=>ge(e).pipe(m(({height:i})=>({top:e.offsetTop,bottom:e.offsetTop+i})),Z("bottom"))));return z([o,n,t]).pipe(m(([i,{top:a,bottom:s},{offset:{y:p},size:{height:c}}])=>(c=Math.max(0,c-Math.max(0,a-p,i)-Math.max(0,c+p-s)),{offset:a-i,height:c,active:a-i<=p})),K((i,a)=>i.offset===a.offset&&i.height===a.height&&i.active===a.active))}function Ba(e){let t=__md_get("__palette")||{index:e.findIndex(o=>matchMedia(o.getAttribute("data-md-color-media")).matches)},r=Math.max(0,Math.min(t.index,e.length-1));return I(...e).pipe(oe(o=>d(o,"change").pipe(m(()=>o))),Q(e[r]),m(o=>({index:e.indexOf(o),color:{media:o.getAttribute("data-md-color-media"),scheme:o.getAttribute("data-md-color-scheme"),primary:o.getAttribute("data-md-color-primary"),accent:o.getAttribute("data-md-color-accent")}})),G(1))}function Kn(e){let t=$("input",e),r=x("meta",{name:"theme-color"});document.head.appendChild(r);let o=x("meta",{name:"color-scheme"});document.head.appendChild(o);let n=$t("(prefers-color-scheme: light)");return C(()=>{let i=new g;return i.subscribe(a=>{if(document.body.setAttribute("data-md-color-switching",""),a.color.media==="(prefers-color-scheme)"){let s=matchMedia("(prefers-color-scheme: light)"),p=document.querySelector(s.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: 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r.subscribe(({value:o})=>{e.style.setProperty("--md-progress-value",`${o}`)}),t.pipe(E(o=>r.next({value:o})),L(()=>r.complete()),m(o=>({ref:e,value:o})))})}var Gr=Vt(Yr());function Ga(e){e.setAttribute("data-md-copying","");let t=e.closest("[data-copy]"),r=t?t.getAttribute("data-copy"):e.innerText;return e.removeAttribute("data-md-copying"),r.trimEnd()}function Bn({alert$:e}){Gr.default.isSupported()&&new F(t=>{new Gr.default("[data-clipboard-target], [data-clipboard-text]",{text:r=>r.getAttribute("data-clipboard-text")||Ga(P(r.getAttribute("data-clipboard-target")))}).on("success",r=>t.next(r))}).pipe(E(t=>{t.trigger.focus()}),m(()=>Ee("clipboard.copied"))).subscribe(e)}function Gn(e,t){return e.protocol=t.protocol,e.hostname=t.hostname,e}function Ja(e,t){let r=new Map;for(let o of $("url",e)){let n=P("loc",o),i=[Gn(new URL(n.textContent),t)];r.set(`${i[0]}`,i);for(let a of $("[rel=alternate]",o)){let s=a.getAttribute("href");s!=null&&i.push(Gn(new URL(s),t))}}return r}function 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t=Jn(document);for(let[o,n]of Jn(e))t.has(o)?t.delete(o):document.head.appendChild(n);for(let o of t.values()){let n=o.getAttribute("name");n!=="theme-color"&&n!=="color-scheme"&&o.remove()}let r=Se("container");return je($("script",r)).pipe(v(o=>{let n=e.createElement("script");if(o.src){for(let i of o.getAttributeNames())n.setAttribute(i,o.getAttribute(i));return o.replaceWith(n),new F(i=>{n.onload=()=>i.complete()})}else return n.textContent=o.textContent,o.replaceWith(n),O}),X(),ne(document))}function Zn({location$:e,viewport$:t,progress$:r}){let o=ye();if(location.protocol==="file:")return O;let n=ur(o.base);I(document).subscribe(Xn);let i=d(document.body,"click").pipe(We(n),v(([p,c])=>Xa(p,c)),pe()),a=d(window,"popstate").pipe(m(xe),pe());i.pipe(ee(t)).subscribe(([p,{offset:c}])=>{history.replaceState(c,""),history.pushState(null,"",p)}),S(i,a).subscribe(e);let s=e.pipe(Z("pathname"),v(p=>ln(p,{progress$:r}).pipe(ve(()=>(pt(p,!0),O)))),v(Xn),v(Za),pe());return 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z([t,r.pipe(Q(xe()),b(o=>!!o.searchParams.get("h")))]).pipe(m(([o,n])=>oi(o.config)(n.searchParams.get("h"))),m(o=>{var a;let n=new Map,i=document.createNodeIterator(e,NodeFilter.SHOW_TEXT);for(let s=i.nextNode();s;s=i.nextNode())if((a=s.parentElement)!=null&&a.offsetHeight){let p=s.textContent,c=o(p);c.length>p.length&&n.set(s,c)}for(let[s,p]of n){let{childNodes:c}=x("span",null,p);s.replaceWith(...Array.from(c))}return{ref:e,nodes:n}}))}function as(e,{viewport$:t,main$:r}){let o=e.closest(".md-grid"),n=o.offsetTop-o.parentElement.offsetTop;return z([r,t]).pipe(m(([{offset:i,height:a},{offset:{y:s}}])=>(a=a+Math.min(n,Math.max(0,s-i))-n,{height:a,locked:s>=i+n})),K((i,a)=>i.height===a.height&&i.locked===a.locked))}function Jr(e,o){var n=o,{header$:t}=n,r=io(n,["header$"]);let i=P(".md-sidebar__scrollwrap",e),{y:a}=Ue(i);return C(()=>{let s=new g,p=s.pipe(X(),ne(!0)),c=s.pipe(Le(0,me));return 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r=`https://api.github.com/users/${e}`;return Ne(r).pipe(m(o=>({repositories:o.public_repos})),Be({}))}}function ui(e,t){let r=`https://${e}/api/v4/projects/${encodeURIComponent(t)}`;return Ne(r).pipe(ve(()=>O),m(({star_count:o,forks_count:n})=>({stars:o,forks:n})),Be({}))}function di(e){let t=e.match(/^.+github\.com\/([^/]+)\/?([^/]+)?/i);if(t){let[,r,o]=t;return fi(r,o)}if(t=e.match(/^.+?([^/]*gitlab[^/]+)\/(.+?)\/?$/i),t){let[,r,o]=t;return ui(r,o)}return O}var ss;function cs(e){return ss||(ss=C(()=>{let t=__md_get("__source",sessionStorage);if(t)return I(t);if(ae("consent").length){let o=__md_get("__consent");if(!(o&&o.github))return O}return di(e.href).pipe(E(o=>__md_set("__source",o,sessionStorage)))}).pipe(ve(()=>O),b(t=>Object.keys(t).length>0),m(t=>({facts:t})),G(1)))}function hi(e){let t=P(":scope > :last-child",e);return C(()=>{let r=new g;return 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i=new g,a=i.pipe(X(),ne(!0));if(i.subscribe(({prev:s,next:p})=>{for(let[c]of p)c.classList.remove("md-nav__link--passed"),c.classList.remove("md-nav__link--active");for(let[c,[l]]of s.entries())l.classList.add("md-nav__link--passed"),l.classList.toggle("md-nav__link--active",c===s.length-1)}),B("toc.follow")){let s=S(t.pipe(_e(1),m(()=>{})),t.pipe(_e(250),m(()=>"smooth")));i.pipe(b(({prev:p})=>p.length>0),We(o.pipe(be(se))),ee(s)).subscribe(([[{prev:p}],c])=>{let[l]=p[p.length-1];if(l.offsetHeight){let f=cr(l);if(typeof f!="undefined"){let u=l.offsetTop-f.offsetTop,{height:h}=ce(f);f.scrollTo({top:u-h/2,behavior:c})}}})}return B("navigation.tracking")&&t.pipe(U(a),Z("offset"),_e(250),Ce(1),U(n.pipe(Ce(1))),st({delay:250}),ee(i)).subscribe(([,{prev:s}])=>{let p=xe(),c=s[s.length-1];if(c&&c.length){let[l]=c,{hash:f}=new URL(l.href);p.hash!==f&&(p.hash=f,history.replaceState({},"",`${p}`))}else 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xi({document$:e,viewport$:t}){e.pipe(v(()=>$(".md-ellipsis")),oe(r=>tt(r).pipe(U(e.pipe(Ce(1))),b(o=>o),m(()=>r),Te(1))),b(r=>r.offsetWidth<r.scrollWidth),oe(r=>{let o=r.innerText,n=r.closest("a")||r;return n.title=o,B("content.tooltips")?lt(n,{viewport$:t}).pipe(U(e.pipe(Ce(1))),L(()=>n.removeAttribute("title"))):O})).subscribe(),B("content.tooltips")&&e.pipe(v(()=>$(".md-status")),oe(r=>lt(r,{viewport$:t}))).subscribe()}function yi({document$:e,tablet$:t}){e.pipe(v(()=>$(".md-toggle--indeterminate")),E(r=>{r.indeterminate=!0,r.checked=!1}),oe(r=>d(r,"change").pipe(Dr(()=>r.classList.contains("md-toggle--indeterminate")),m(()=>r))),ee(t)).subscribe(([r,o])=>{r.classList.remove("md-toggle--indeterminate"),o&&(r.checked=!1)})}function fs(){return/(iPad|iPhone|iPod)/.test(navigator.userAgent)}function Ei({document$:e}){e.pipe(v(()=>$("[data-md-scrollfix]")),E(t=>t.removeAttribute("data-md-scrollfix")),b(fs),oe(t=>d(t,"touchstart").pipe(m(()=>t)))).subscribe(t=>{let r=t.scrollTop;r===0?t.scrollTop=1:r+t.offsetHeight===t.scrollHeight&&(t.scrollTop=r-1)})}function wi({viewport$:e,tablet$:t}){z([Ve("search"),t]).pipe(m(([r,o])=>r&&!o),v(r=>I(r).pipe(Ge(r?400:100))),ee(e)).subscribe(([r,{offset:{y:o}}])=>{if(r)document.body.setAttribute("data-md-scrolllock",""),document.body.style.top=`-${o}px`;else{let n=-1*parseInt(document.body.style.top,10);document.body.removeAttribute("data-md-scrolllock"),document.body.style.top="",n&&window.scrollTo(0,n)}})}Object.entries||(Object.entries=function(e){let t=[];for(let r of Object.keys(e))t.push([r,e[r]]);return t});Object.values||(Object.values=function(e){let t=[];for(let r of Object.keys(e))t.push(e[r]);return t});typeof Element!="undefined"&&(Element.prototype.scrollTo||(Element.prototype.scrollTo=function(e,t){typeof e=="object"?(this.scrollLeft=e.left,this.scrollTop=e.top):(this.scrollLeft=e,this.scrollTop=t)}),Element.prototype.replaceWith||(Element.prototype.replaceWith=function(...e){let t=this.parentNode;if(t){e.length===0&&t.removeChild(this);for(let r=e.length-1;r>=0;r--){let o=e[r];typeof o=="string"?o=document.createTextNode(o):o.parentNode&&o.parentNode.removeChild(o),r?t.insertBefore(this.previousSibling,o):t.replaceChild(o,this)}}}));function us(){return location.protocol==="file:"?wt(`${new URL("search/search_index.js",Xr.base)}`).pipe(m(()=>__index),G(1)):Ne(new URL("search/search_index.json",Xr.base))}document.documentElement.classList.remove("no-js");document.documentElement.classList.add("js");var ot=Yo(),jt=nn(),Ot=cn(jt),Zr=on(),Oe=bn(),hr=$t("(min-width: 960px)"),Si=$t("(min-width: 1220px)"),Oi=pn(),Xr=ye(),Mi=document.forms.namedItem("search")?us():Ke,eo=new g;Bn({alert$:eo});var to=new g;B("navigation.instant")&&Zn({location$:jt,viewport$:Oe,progress$:to}).subscribe(ot);var Ti;((Ti=Xr.version)==null?void 0:Ti.provider)==="mike"&&ii({document$:ot});S(jt,Ot).pipe(Ge(125)).subscribe(()=>{Je("drawer",!1),Je("search",!1)});Zr.pipe(b(({mode:e})=>e==="global")).subscribe(e=>{switch(e.type){case"p":case",":let t=fe("link[rel=prev]");typeof t!="undefined"&&pt(t);break;case"n":case".":let r=fe("link[rel=next]");typeof r!="undefined"&&pt(r);break;case"Enter":let o=Re();o instanceof HTMLLabelElement&&o.click()}});xi({viewport$:Oe,document$:ot});yi({document$:ot,tablet$:hr});Ei({document$:ot});wi({viewport$:Oe,tablet$:hr});var rt=Nn(Se("header"),{viewport$:Oe}),Ft=ot.pipe(m(()=>Se("main")),v(e=>Qn(e,{viewport$:Oe,header$:rt})),G(1)),ds=S(...ae("consent").map(e=>xn(e,{target$:Ot})),...ae("dialog").map(e=>Dn(e,{alert$:eo})),...ae("header").map(e=>zn(e,{viewport$:Oe,header$:rt,main$:Ft})),...ae("palette").map(e=>Kn(e)),...ae("progress").map(e=>Yn(e,{progress$:to})),...ae("search").map(e=>li(e,{index$:Mi,keyboard$:Zr})),...ae("source").map(e=>hi(e))),hs=C(()=>S(...ae("announce").map(e=>gn(e)),...ae("content").map(e=>Un(e,{viewport$:Oe,target$:Ot,print$:Oi})),...ae("content").map(e=>B("search.highlight")?mi(e,{index$:Mi,location$:jt}):O),...ae("header-title").map(e=>qn(e,{viewport$:Oe,header$:rt})),...ae("sidebar").map(e=>e.getAttribute("data-md-type")==="navigation"?Nr(Si,()=>Jr(e,{viewport$:Oe,header$:rt,main$:Ft})):Nr(hr,()=>Jr(e,{viewport$:Oe,header$:rt,main$:Ft}))),...ae("tabs").map(e=>bi(e,{viewport$:Oe,header$:rt})),...ae("toc").map(e=>vi(e,{viewport$:Oe,header$:rt,main$:Ft,target$:Ot})),...ae("top").map(e=>gi(e,{viewport$:Oe,header$:rt,main$:Ft,target$:Ot})))),Li=ot.pipe(v(()=>hs),Pe(ds),G(1));Li.subscribe();window.document$=ot;window.location$=jt;window.target$=Ot;window.keyboard$=Zr;window.viewport$=Oe;window.tablet$=hr;window.screen$=Si;window.print$=Oi;window.alert$=eo;window.progress$=to;window.component$=Li;})();
+//# sourceMappingURL=bundle.fe8b6f2b.min.js.map
+
diff --git a/assets/javascripts/bundle.fe8b6f2b.min.js.map b/assets/javascripts/bundle.fe8b6f2b.min.js.map
new file mode 100644
index 0000000..8263585
--- /dev/null
+++ b/assets/javascripts/bundle.fe8b6f2b.min.js.map
@@ -0,0 +1,7 @@
+{
+  "version": 3,
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+  "sourcesContent": ["(function (global, factory) {\n  typeof exports === 'object' && typeof module !== 'undefined' ? factory() :\n  typeof define === 'function' && define.amd ? define(factory) :\n  (factory());\n}(this, (function () { 'use strict';\n\n  /**\n   * Applies the :focus-visible polyfill at the given scope.\n   * A scope in this case is either the top-level Document or a Shadow Root.\n   *\n   * @param {(Document|ShadowRoot)} scope\n   * @see https://github.com/WICG/focus-visible\n   */\n  function applyFocusVisiblePolyfill(scope) {\n    var hadKeyboardEvent = true;\n    var hadFocusVisibleRecently = false;\n    var hadFocusVisibleRecentlyTimeout = null;\n\n    var inputTypesAllowlist = {\n      text: true,\n      search: true,\n      url: true,\n      tel: true,\n      email: true,\n      password: true,\n      number: true,\n      date: true,\n      month: true,\n      week: true,\n      time: true,\n      datetime: true,\n      'datetime-local': true\n    };\n\n    /**\n     * Helper function for legacy browsers and iframes which sometimes focus\n     * elements like document, body, and non-interactive SVG.\n     * @param {Element} el\n     */\n    function isValidFocusTarget(el) {\n      if (\n        el &&\n        el !== document &&\n        el.nodeName !== 'HTML' &&\n        el.nodeName !== 'BODY' &&\n        'classList' in el &&\n        'contains' in el.classList\n      ) {\n        return true;\n      }\n      return false;\n    }\n\n    /**\n     * Computes whether the given element should automatically trigger the\n     * `focus-visible` class being added, i.e. whether it should always match\n     * `:focus-visible` when focused.\n     * @param {Element} el\n     * @return {boolean}\n     */\n    function focusTriggersKeyboardModality(el) {\n      var type = el.type;\n      var tagName = el.tagName;\n\n      if (tagName === 'INPUT' && inputTypesAllowlist[type] && !el.readOnly) {\n        return true;\n      }\n\n      if (tagName === 'TEXTAREA' && !el.readOnly) {\n        return true;\n      }\n\n      if (el.isContentEditable) {\n        return true;\n      }\n\n      return false;\n    }\n\n    /**\n     * Add the `focus-visible` class to the given element if it was not added by\n     * the author.\n     * @param {Element} el\n     */\n    function addFocusVisibleClass(el) {\n      if (el.classList.contains('focus-visible')) {\n        return;\n      }\n      el.classList.add('focus-visible');\n      el.setAttribute('data-focus-visible-added', '');\n    }\n\n    /**\n     * Remove the `focus-visible` class from the given element if it was not\n     * originally added by the author.\n     * @param {Element} el\n     */\n    function removeFocusVisibleClass(el) {\n      if (!el.hasAttribute('data-focus-visible-added')) {\n        return;\n      }\n      el.classList.remove('focus-visible');\n      el.removeAttribute('data-focus-visible-added');\n    }\n\n    /**\n     * If the most recent user interaction was via the keyboard;\n     * and the key press did not include a meta, alt/option, or control key;\n     * then the modality is keyboard. Otherwise, the modality is not keyboard.\n     * Apply `focus-visible` to any current active element and keep track\n     * of our keyboard modality state with `hadKeyboardEvent`.\n     * @param {KeyboardEvent} e\n     */\n    function onKeyDown(e) {\n      if (e.metaKey || e.altKey || e.ctrlKey) {\n        return;\n      }\n\n      if (isValidFocusTarget(scope.activeElement)) {\n        addFocusVisibleClass(scope.activeElement);\n      }\n\n      hadKeyboardEvent = true;\n    }\n\n    /**\n     * If at any point a user clicks with a pointing device, ensure that we change\n     * the modality away from keyboard.\n     * This avoids the situation where a user presses a key on an already focused\n     * element, and then clicks on a different element, focusing it with a\n     * pointing device, while we still think we're in keyboard modality.\n     * @param {Event} e\n     */\n    function onPointerDown(e) {\n      hadKeyboardEvent = false;\n    }\n\n    /**\n     * On `focus`, add the `focus-visible` class to the target if:\n     * - the target received focus as a result of keyboard navigation, or\n     * - the event target is an element that will likely require interaction\n     *   via the keyboard (e.g. a text box)\n     * @param {Event} e\n     */\n    function onFocus(e) {\n      // Prevent IE from focusing the document or HTML element.\n      if (!isValidFocusTarget(e.target)) {\n        return;\n      }\n\n      if (hadKeyboardEvent || focusTriggersKeyboardModality(e.target)) {\n        addFocusVisibleClass(e.target);\n      }\n    }\n\n    /**\n     * On `blur`, remove the `focus-visible` class from the target.\n     * @param {Event} e\n     */\n    function onBlur(e) {\n      if (!isValidFocusTarget(e.target)) {\n        return;\n      }\n\n      if (\n        e.target.classList.contains('focus-visible') ||\n        e.target.hasAttribute('data-focus-visible-added')\n      ) {\n        // To detect a tab/window switch, we look for a blur event followed\n        // rapidly by a visibility change.\n        // If we don't see a visibility change within 100ms, it's probably a\n        // regular focus change.\n        hadFocusVisibleRecently = true;\n        window.clearTimeout(hadFocusVisibleRecentlyTimeout);\n        hadFocusVisibleRecentlyTimeout = window.setTimeout(function() {\n          hadFocusVisibleRecently = false;\n        }, 100);\n        removeFocusVisibleClass(e.target);\n      }\n    }\n\n    /**\n     * If the user changes tabs, keep track of whether or not the previously\n     * focused element had .focus-visible.\n     * @param {Event} e\n     */\n    function onVisibilityChange(e) {\n      if (document.visibilityState === 'hidden') {\n        // If the tab becomes active again, the browser will handle calling focus\n        // on the element (Safari actually calls it twice).\n        // If this tab change caused a blur on an element with focus-visible,\n        // re-apply the class when the user switches back to the tab.\n        if (hadFocusVisibleRecently) {\n          hadKeyboardEvent = true;\n        }\n        addInitialPointerMoveListeners();\n      }\n    }\n\n    /**\n     * Add a group of listeners to detect usage of any pointing devices.\n     * These listeners will be added when the polyfill first loads, and anytime\n     * the window is blurred, so that they are active when the window regains\n     * focus.\n     */\n    function addInitialPointerMoveListeners() {\n      document.addEventListener('mousemove', onInitialPointerMove);\n      document.addEventListener('mousedown', onInitialPointerMove);\n      document.addEventListener('mouseup', onInitialPointerMove);\n      document.addEventListener('pointermove', onInitialPointerMove);\n      document.addEventListener('pointerdown', onInitialPointerMove);\n      document.addEventListener('pointerup', onInitialPointerMove);\n      document.addEventListener('touchmove', onInitialPointerMove);\n      document.addEventListener('touchstart', onInitialPointerMove);\n      document.addEventListener('touchend', onInitialPointerMove);\n    }\n\n    function removeInitialPointerMoveListeners() {\n      document.removeEventListener('mousemove', onInitialPointerMove);\n      document.removeEventListener('mousedown', onInitialPointerMove);\n      document.removeEventListener('mouseup', onInitialPointerMove);\n      document.removeEventListener('pointermove', onInitialPointerMove);\n      document.removeEventListener('pointerdown', onInitialPointerMove);\n      document.removeEventListener('pointerup', onInitialPointerMove);\n      document.removeEventListener('touchmove', onInitialPointerMove);\n      document.removeEventListener('touchstart', onInitialPointerMove);\n      document.removeEventListener('touchend', onInitialPointerMove);\n    }\n\n    /**\n     * When the polfyill first loads, assume the user is in keyboard modality.\n     * If any event is received from a pointing device (e.g. mouse, pointer,\n     * touch), turn off keyboard modality.\n     * This accounts for situations where focus enters the page from the URL bar.\n     * @param {Event} e\n     */\n    function onInitialPointerMove(e) {\n      // Work around a Safari quirk that fires a mousemove on <html> whenever the\n      // window blurs, even if you're tabbing out of the page. \u00AF\\_(\u30C4)_/\u00AF\n      if (e.target.nodeName && e.target.nodeName.toLowerCase() === 'html') {\n        return;\n      }\n\n      hadKeyboardEvent = false;\n      removeInitialPointerMoveListeners();\n    }\n\n    // For some kinds of state, we are interested in changes at the global scope\n    // only. For example, global pointer input, global key presses and global\n    // visibility change should affect the state at every scope:\n    document.addEventListener('keydown', onKeyDown, true);\n    document.addEventListener('mousedown', onPointerDown, true);\n    document.addEventListener('pointerdown', onPointerDown, true);\n    document.addEventListener('touchstart', onPointerDown, true);\n    document.addEventListener('visibilitychange', onVisibilityChange, true);\n\n    addInitialPointerMoveListeners();\n\n    // For focus and blur, we specifically care about state changes in the local\n    // scope. This is because focus / blur events that originate from within a\n    // shadow root are not re-dispatched from the host element if it was already\n    // the active element in its own scope:\n    scope.addEventListener('focus', onFocus, true);\n    scope.addEventListener('blur', onBlur, true);\n\n    // We detect that a node is a ShadowRoot by ensuring that it is a\n    // DocumentFragment and also has a host property. This check covers native\n    // implementation and polyfill implementation transparently. If we only cared\n    // about the native implementation, we could just check if the scope was\n    // an instance of a ShadowRoot.\n    if (scope.nodeType === Node.DOCUMENT_FRAGMENT_NODE && scope.host) {\n      // Since a ShadowRoot is a special kind of DocumentFragment, it does not\n      // have a root element to add a class to. So, we add this attribute to the\n      // host element instead:\n      scope.host.setAttribute('data-js-focus-visible', '');\n    } else if (scope.nodeType === Node.DOCUMENT_NODE) {\n      document.documentElement.classList.add('js-focus-visible');\n      document.documentElement.setAttribute('data-js-focus-visible', '');\n    }\n  }\n\n  // It is important to wrap all references to global window and document in\n  // these checks to support server-side rendering use cases\n  // @see https://github.com/WICG/focus-visible/issues/199\n  if (typeof window !== 'undefined' && typeof document !== 'undefined') {\n    // Make the polyfill helper globally available. This can be used as a signal\n    // to interested libraries that wish to coordinate with the polyfill for e.g.,\n    // applying the polyfill to a shadow root:\n    window.applyFocusVisiblePolyfill = applyFocusVisiblePolyfill;\n\n    // Notify interested libraries of the polyfill's presence, in case the\n    // polyfill was loaded lazily:\n    var event;\n\n    try {\n      event = new CustomEvent('focus-visible-polyfill-ready');\n    } catch (error) {\n      // IE11 does not support using CustomEvent as a constructor directly:\n      event = document.createEvent('CustomEvent');\n      event.initCustomEvent('focus-visible-polyfill-ready', false, false, {});\n    }\n\n    window.dispatchEvent(event);\n  }\n\n  if (typeof document !== 'undefined') {\n    // Apply the polyfill to the global document, so that no JavaScript\n    // coordination is required to use the polyfill in the top-level document:\n    applyFocusVisiblePolyfill(document);\n  }\n\n})));\n", "/*!\n * clipboard.js v2.0.11\n * https://clipboardjs.com/\n *\n * Licensed MIT \u00A9 Zeno Rocha\n */\n(function webpackUniversalModuleDefinition(root, factory) {\n\tif(typeof exports === 'object' && typeof module === 'object')\n\t\tmodule.exports = factory();\n\telse if(typeof define === 'function' && define.amd)\n\t\tdefine([], factory);\n\telse if(typeof exports === 'object')\n\t\texports[\"ClipboardJS\"] = factory();\n\telse\n\t\troot[\"ClipboardJS\"] = factory();\n})(this, function() {\nreturn /******/ (function() { // webpackBootstrap\n/******/ \tvar __webpack_modules__ = ({\n\n/***/ 686:\n/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {\n\n\"use strict\";\n\n// EXPORTS\n__webpack_require__.d(__webpack_exports__, {\n  \"default\": function() { return /* binding */ clipboard; }\n});\n\n// EXTERNAL MODULE: ./node_modules/tiny-emitter/index.js\nvar tiny_emitter = __webpack_require__(279);\nvar tiny_emitter_default = /*#__PURE__*/__webpack_require__.n(tiny_emitter);\n// EXTERNAL MODULE: ./node_modules/good-listener/src/listen.js\nvar listen = __webpack_require__(370);\nvar listen_default = /*#__PURE__*/__webpack_require__.n(listen);\n// EXTERNAL MODULE: ./node_modules/select/src/select.js\nvar src_select = __webpack_require__(817);\nvar select_default = /*#__PURE__*/__webpack_require__.n(src_select);\n;// CONCATENATED MODULE: ./src/common/command.js\n/**\n * Executes a given operation type.\n * @param {String} type\n * @return {Boolean}\n */\nfunction command(type) {\n  try {\n    return document.execCommand(type);\n  } catch (err) {\n    return false;\n  }\n}\n;// CONCATENATED MODULE: ./src/actions/cut.js\n\n\n/**\n * Cut action wrapper.\n * @param {String|HTMLElement} target\n * @return {String}\n */\n\nvar ClipboardActionCut = function ClipboardActionCut(target) {\n  var selectedText = select_default()(target);\n  command('cut');\n  return selectedText;\n};\n\n/* harmony default export */ var actions_cut = (ClipboardActionCut);\n;// CONCATENATED MODULE: ./src/common/create-fake-element.js\n/**\n * Creates a fake textarea element with a value.\n * @param {String} value\n * @return {HTMLElement}\n */\nfunction createFakeElement(value) {\n  var isRTL = document.documentElement.getAttribute('dir') === 'rtl';\n  var fakeElement = document.createElement('textarea'); // Prevent zooming on iOS\n\n  fakeElement.style.fontSize = '12pt'; // Reset box model\n\n  fakeElement.style.border = '0';\n  fakeElement.style.padding = '0';\n  fakeElement.style.margin = '0'; // Move element out of screen horizontally\n\n  fakeElement.style.position = 'absolute';\n  fakeElement.style[isRTL ? 'right' : 'left'] = '-9999px'; // Move element to the same position vertically\n\n  var yPosition = window.pageYOffset || document.documentElement.scrollTop;\n  fakeElement.style.top = \"\".concat(yPosition, \"px\");\n  fakeElement.setAttribute('readonly', '');\n  fakeElement.value = value;\n  return fakeElement;\n}\n;// CONCATENATED MODULE: ./src/actions/copy.js\n\n\n\n/**\n * Create fake copy action wrapper using a fake element.\n * @param {String} target\n * @param {Object} options\n * @return {String}\n */\n\nvar fakeCopyAction = function fakeCopyAction(value, options) {\n  var fakeElement = createFakeElement(value);\n  options.container.appendChild(fakeElement);\n  var selectedText = select_default()(fakeElement);\n  command('copy');\n  fakeElement.remove();\n  return selectedText;\n};\n/**\n * Copy action wrapper.\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @return {String}\n */\n\n\nvar ClipboardActionCopy = function ClipboardActionCopy(target) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n    container: document.body\n  };\n  var selectedText = '';\n\n  if (typeof target === 'string') {\n    selectedText = fakeCopyAction(target, options);\n  } else if (target instanceof HTMLInputElement && !['text', 'search', 'url', 'tel', 'password'].includes(target === null || target === void 0 ? void 0 : target.type)) {\n    // If input type doesn't support `setSelectionRange`. Simulate it. https://developer.mozilla.org/en-US/docs/Web/API/HTMLInputElement/setSelectionRange\n    selectedText = fakeCopyAction(target.value, options);\n  } else {\n    selectedText = select_default()(target);\n    command('copy');\n  }\n\n  return selectedText;\n};\n\n/* harmony default export */ var actions_copy = (ClipboardActionCopy);\n;// CONCATENATED MODULE: ./src/actions/default.js\nfunction _typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return _typeof(obj); }\n\n\n\n/**\n * Inner function which performs selection from either `text` or `target`\n * properties and then executes copy or cut operations.\n * @param {Object} options\n */\n\nvar ClipboardActionDefault = function ClipboardActionDefault() {\n  var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n  // Defines base properties passed from constructor.\n  var _options$action = options.action,\n      action = _options$action === void 0 ? 'copy' : _options$action,\n      container = options.container,\n      target = options.target,\n      text = options.text; // Sets the `action` to be performed which can be either 'copy' or 'cut'.\n\n  if (action !== 'copy' && action !== 'cut') {\n    throw new Error('Invalid \"action\" value, use either \"copy\" or \"cut\"');\n  } // Sets the `target` property using an element that will be have its content copied.\n\n\n  if (target !== undefined) {\n    if (target && _typeof(target) === 'object' && target.nodeType === 1) {\n      if (action === 'copy' && target.hasAttribute('disabled')) {\n        throw new Error('Invalid \"target\" attribute. Please use \"readonly\" instead of \"disabled\" attribute');\n      }\n\n      if (action === 'cut' && (target.hasAttribute('readonly') || target.hasAttribute('disabled'))) {\n        throw new Error('Invalid \"target\" attribute. You can\\'t cut text from elements with \"readonly\" or \"disabled\" attributes');\n      }\n    } else {\n      throw new Error('Invalid \"target\" value, use a valid Element');\n    }\n  } // Define selection strategy based on `text` property.\n\n\n  if (text) {\n    return actions_copy(text, {\n      container: container\n    });\n  } // Defines which selection strategy based on `target` property.\n\n\n  if (target) {\n    return action === 'cut' ? actions_cut(target) : actions_copy(target, {\n      container: container\n    });\n  }\n};\n\n/* harmony default export */ var actions_default = (ClipboardActionDefault);\n;// CONCATENATED MODULE: ./src/clipboard.js\nfunction clipboard_typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { clipboard_typeof = function _typeof(obj) { return typeof obj; }; } else { clipboard_typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return clipboard_typeof(obj); }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\nfunction _inherits(subClass, superClass) { if (typeof superClass !== \"function\" && superClass !== null) { throw new TypeError(\"Super expression must either be null or a function\"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }\n\nfunction _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }\n\nfunction _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }\n\nfunction _possibleConstructorReturn(self, call) { if (call && (clipboard_typeof(call) === \"object\" || typeof call === \"function\")) { return call; } return _assertThisInitialized(self); }\n\nfunction _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError(\"this hasn't been initialised - super() hasn't been called\"); } return self; }\n\nfunction _isNativeReflectConstruct() { if (typeof Reflect === \"undefined\" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === \"function\") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }\n\nfunction _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }\n\n\n\n\n\n\n/**\n * Helper function to retrieve attribute value.\n * @param {String} suffix\n * @param {Element} element\n */\n\nfunction getAttributeValue(suffix, element) {\n  var attribute = \"data-clipboard-\".concat(suffix);\n\n  if (!element.hasAttribute(attribute)) {\n    return;\n  }\n\n  return element.getAttribute(attribute);\n}\n/**\n * Base class which takes one or more elements, adds event listeners to them,\n * and instantiates a new `ClipboardAction` on each click.\n */\n\n\nvar Clipboard = /*#__PURE__*/function (_Emitter) {\n  _inherits(Clipboard, _Emitter);\n\n  var _super = _createSuper(Clipboard);\n\n  /**\n   * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n   * @param {Object} options\n   */\n  function Clipboard(trigger, options) {\n    var _this;\n\n    _classCallCheck(this, Clipboard);\n\n    _this = _super.call(this);\n\n    _this.resolveOptions(options);\n\n    _this.listenClick(trigger);\n\n    return _this;\n  }\n  /**\n   * Defines if attributes would be resolved using internal setter functions\n   * or custom functions that were passed in the constructor.\n   * @param {Object} options\n   */\n\n\n  _createClass(Clipboard, [{\n    key: \"resolveOptions\",\n    value: function resolveOptions() {\n      var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n      this.action = typeof options.action === 'function' ? options.action : this.defaultAction;\n      this.target = typeof options.target === 'function' ? options.target : this.defaultTarget;\n      this.text = typeof options.text === 'function' ? options.text : this.defaultText;\n      this.container = clipboard_typeof(options.container) === 'object' ? options.container : document.body;\n    }\n    /**\n     * Adds a click event listener to the passed trigger.\n     * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n     */\n\n  }, {\n    key: \"listenClick\",\n    value: function listenClick(trigger) {\n      var _this2 = this;\n\n      this.listener = listen_default()(trigger, 'click', function (e) {\n        return _this2.onClick(e);\n      });\n    }\n    /**\n     * Defines a new `ClipboardAction` on each click event.\n     * @param {Event} e\n     */\n\n  }, {\n    key: \"onClick\",\n    value: function onClick(e) {\n      var trigger = e.delegateTarget || e.currentTarget;\n      var action = this.action(trigger) || 'copy';\n      var text = actions_default({\n        action: action,\n        container: this.container,\n        target: this.target(trigger),\n        text: this.text(trigger)\n      }); // Fires an event based on the copy operation result.\n\n      this.emit(text ? 'success' : 'error', {\n        action: action,\n        text: text,\n        trigger: trigger,\n        clearSelection: function clearSelection() {\n          if (trigger) {\n            trigger.focus();\n          }\n\n          window.getSelection().removeAllRanges();\n        }\n      });\n    }\n    /**\n     * Default `action` lookup function.\n     * @param {Element} trigger\n     */\n\n  }, {\n    key: \"defaultAction\",\n    value: function defaultAction(trigger) {\n      return getAttributeValue('action', trigger);\n    }\n    /**\n     * Default `target` lookup function.\n     * @param {Element} trigger\n     */\n\n  }, {\n    key: \"defaultTarget\",\n    value: function defaultTarget(trigger) {\n      var selector = getAttributeValue('target', trigger);\n\n      if (selector) {\n        return document.querySelector(selector);\n      }\n    }\n    /**\n     * Allow fire programmatically a copy action\n     * @param {String|HTMLElement} target\n     * @param {Object} options\n     * @returns Text copied.\n     */\n\n  }, {\n    key: \"defaultText\",\n\n    /**\n     * Default `text` lookup function.\n     * @param {Element} trigger\n     */\n    value: function defaultText(trigger) {\n      return getAttributeValue('text', trigger);\n    }\n    /**\n     * Destroy lifecycle.\n     */\n\n  }, {\n    key: \"destroy\",\n    value: function destroy() {\n      this.listener.destroy();\n    }\n  }], [{\n    key: \"copy\",\n    value: function copy(target) {\n      var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n        container: document.body\n      };\n      return actions_copy(target, options);\n    }\n    /**\n     * Allow fire programmatically a cut action\n     * @param {String|HTMLElement} target\n     * @returns Text cutted.\n     */\n\n  }, {\n    key: \"cut\",\n    value: function cut(target) {\n      return actions_cut(target);\n    }\n    /**\n     * Returns the support of the given action, or all actions if no action is\n     * given.\n     * @param {String} [action]\n     */\n\n  }, {\n    key: \"isSupported\",\n    value: function isSupported() {\n      var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ['copy', 'cut'];\n      var actions = typeof action === 'string' ? [action] : action;\n      var support = !!document.queryCommandSupported;\n      actions.forEach(function (action) {\n        support = support && !!document.queryCommandSupported(action);\n      });\n      return support;\n    }\n  }]);\n\n  return Clipboard;\n}((tiny_emitter_default()));\n\n/* harmony default export */ var clipboard = (Clipboard);\n\n/***/ }),\n\n/***/ 828:\n/***/ (function(module) {\n\nvar DOCUMENT_NODE_TYPE = 9;\n\n/**\n * A polyfill for Element.matches()\n */\nif (typeof Element !== 'undefined' && !Element.prototype.matches) {\n    var proto = Element.prototype;\n\n    proto.matches = proto.matchesSelector ||\n                    proto.mozMatchesSelector ||\n                    proto.msMatchesSelector ||\n                    proto.oMatchesSelector ||\n                    proto.webkitMatchesSelector;\n}\n\n/**\n * Finds the closest parent that matches a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @return {Function}\n */\nfunction closest (element, selector) {\n    while (element && element.nodeType !== DOCUMENT_NODE_TYPE) {\n        if (typeof element.matches === 'function' &&\n            element.matches(selector)) {\n          return element;\n        }\n        element = element.parentNode;\n    }\n}\n\nmodule.exports = closest;\n\n\n/***/ }),\n\n/***/ 438:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar closest = __webpack_require__(828);\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction _delegate(element, selector, type, callback, useCapture) {\n    var listenerFn = listener.apply(this, arguments);\n\n    element.addEventListener(type, listenerFn, useCapture);\n\n    return {\n        destroy: function() {\n            element.removeEventListener(type, listenerFn, useCapture);\n        }\n    }\n}\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element|String|Array} [elements]\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction delegate(elements, selector, type, callback, useCapture) {\n    // Handle the regular Element usage\n    if (typeof elements.addEventListener === 'function') {\n        return _delegate.apply(null, arguments);\n    }\n\n    // Handle Element-less usage, it defaults to global delegation\n    if (typeof type === 'function') {\n        // Use `document` as the first parameter, then apply arguments\n        // This is a short way to .unshift `arguments` without running into deoptimizations\n        return _delegate.bind(null, document).apply(null, arguments);\n    }\n\n    // Handle Selector-based usage\n    if (typeof elements === 'string') {\n        elements = document.querySelectorAll(elements);\n    }\n\n    // Handle Array-like based usage\n    return Array.prototype.map.call(elements, function (element) {\n        return _delegate(element, selector, type, callback, useCapture);\n    });\n}\n\n/**\n * Finds closest match and invokes callback.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Function}\n */\nfunction listener(element, selector, type, callback) {\n    return function(e) {\n        e.delegateTarget = closest(e.target, selector);\n\n        if (e.delegateTarget) {\n            callback.call(element, e);\n        }\n    }\n}\n\nmodule.exports = delegate;\n\n\n/***/ }),\n\n/***/ 879:\n/***/ (function(__unused_webpack_module, exports) {\n\n/**\n * Check if argument is a HTML element.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.node = function(value) {\n    return value !== undefined\n        && value instanceof HTMLElement\n        && value.nodeType === 1;\n};\n\n/**\n * Check if argument is a list of HTML elements.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.nodeList = function(value) {\n    var type = Object.prototype.toString.call(value);\n\n    return value !== undefined\n        && (type === '[object NodeList]' || type === '[object HTMLCollection]')\n        && ('length' in value)\n        && (value.length === 0 || exports.node(value[0]));\n};\n\n/**\n * Check if argument is a string.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.string = function(value) {\n    return typeof value === 'string'\n        || value instanceof String;\n};\n\n/**\n * Check if argument is a function.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.fn = function(value) {\n    var type = Object.prototype.toString.call(value);\n\n    return type === '[object Function]';\n};\n\n\n/***/ }),\n\n/***/ 370:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar is = __webpack_require__(879);\nvar delegate = __webpack_require__(438);\n\n/**\n * Validates all params and calls the right\n * listener function based on its target type.\n *\n * @param {String|HTMLElement|HTMLCollection|NodeList} target\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listen(target, type, callback) {\n    if (!target && !type && !callback) {\n        throw new Error('Missing required arguments');\n    }\n\n    if (!is.string(type)) {\n        throw new TypeError('Second argument must be a String');\n    }\n\n    if (!is.fn(callback)) {\n        throw new TypeError('Third argument must be a Function');\n    }\n\n    if (is.node(target)) {\n        return listenNode(target, type, callback);\n    }\n    else if (is.nodeList(target)) {\n        return listenNodeList(target, type, callback);\n    }\n    else if (is.string(target)) {\n        return listenSelector(target, type, callback);\n    }\n    else {\n        throw new TypeError('First argument must be a String, HTMLElement, HTMLCollection, or NodeList');\n    }\n}\n\n/**\n * Adds an event listener to a HTML element\n * and returns a remove listener function.\n *\n * @param {HTMLElement} node\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNode(node, type, callback) {\n    node.addEventListener(type, callback);\n\n    return {\n        destroy: function() {\n            node.removeEventListener(type, callback);\n        }\n    }\n}\n\n/**\n * Add an event listener to a list of HTML elements\n * and returns a remove listener function.\n *\n * @param {NodeList|HTMLCollection} nodeList\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNodeList(nodeList, type, callback) {\n    Array.prototype.forEach.call(nodeList, function(node) {\n        node.addEventListener(type, callback);\n    });\n\n    return {\n        destroy: function() {\n            Array.prototype.forEach.call(nodeList, function(node) {\n                node.removeEventListener(type, callback);\n            });\n        }\n    }\n}\n\n/**\n * Add an event listener to a selector\n * and returns a remove listener function.\n *\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenSelector(selector, type, callback) {\n    return delegate(document.body, selector, type, callback);\n}\n\nmodule.exports = listen;\n\n\n/***/ }),\n\n/***/ 817:\n/***/ (function(module) {\n\nfunction select(element) {\n    var selectedText;\n\n    if (element.nodeName === 'SELECT') {\n        element.focus();\n\n        selectedText = element.value;\n    }\n    else if (element.nodeName === 'INPUT' || element.nodeName === 'TEXTAREA') {\n        var isReadOnly = element.hasAttribute('readonly');\n\n        if (!isReadOnly) {\n            element.setAttribute('readonly', '');\n        }\n\n        element.select();\n        element.setSelectionRange(0, element.value.length);\n\n        if (!isReadOnly) {\n            element.removeAttribute('readonly');\n        }\n\n        selectedText = element.value;\n    }\n    else {\n        if (element.hasAttribute('contenteditable')) {\n            element.focus();\n        }\n\n        var selection = window.getSelection();\n        var range = document.createRange();\n\n        range.selectNodeContents(element);\n        selection.removeAllRanges();\n        selection.addRange(range);\n\n        selectedText = selection.toString();\n    }\n\n    return selectedText;\n}\n\nmodule.exports = select;\n\n\n/***/ }),\n\n/***/ 279:\n/***/ (function(module) {\n\nfunction E () {\n  // Keep this empty so it's easier to inherit from\n  // (via https://github.com/lipsmack from https://github.com/scottcorgan/tiny-emitter/issues/3)\n}\n\nE.prototype = {\n  on: function (name, callback, ctx) {\n    var e = this.e || (this.e = {});\n\n    (e[name] || (e[name] = [])).push({\n      fn: callback,\n      ctx: ctx\n    });\n\n    return this;\n  },\n\n  once: function (name, callback, ctx) {\n    var self = this;\n    function listener () {\n      self.off(name, listener);\n      callback.apply(ctx, arguments);\n    };\n\n    listener._ = callback\n    return this.on(name, listener, ctx);\n  },\n\n  emit: function (name) {\n    var data = [].slice.call(arguments, 1);\n    var evtArr = ((this.e || (this.e = {}))[name] || []).slice();\n    var i = 0;\n    var len = evtArr.length;\n\n    for (i; i < len; i++) {\n      evtArr[i].fn.apply(evtArr[i].ctx, data);\n    }\n\n    return this;\n  },\n\n  off: function (name, callback) {\n    var e = this.e || (this.e = {});\n    var evts = e[name];\n    var liveEvents = [];\n\n    if (evts && callback) {\n      for (var i = 0, len = evts.length; i < len; i++) {\n        if (evts[i].fn !== callback && evts[i].fn._ !== callback)\n          liveEvents.push(evts[i]);\n      }\n    }\n\n    // Remove event from queue to prevent memory leak\n    // Suggested by https://github.com/lazd\n    // Ref: https://github.com/scottcorgan/tiny-emitter/commit/c6ebfaa9bc973b33d110a84a307742b7cf94c953#commitcomment-5024910\n\n    (liveEvents.length)\n      ? e[name] = liveEvents\n      : delete e[name];\n\n    return this;\n  }\n};\n\nmodule.exports = E;\nmodule.exports.TinyEmitter = E;\n\n\n/***/ })\n\n/******/ \t});\n/************************************************************************/\n/******/ \t// The module cache\n/******/ \tvar __webpack_module_cache__ = {};\n/******/ \t\n/******/ \t// The require function\n/******/ \tfunction __webpack_require__(moduleId) {\n/******/ \t\t// Check if module is in cache\n/******/ \t\tif(__webpack_module_cache__[moduleId]) {\n/******/ \t\t\treturn __webpack_module_cache__[moduleId].exports;\n/******/ \t\t}\n/******/ \t\t// Create a new module (and put it into the cache)\n/******/ \t\tvar module = __webpack_module_cache__[moduleId] = {\n/******/ \t\t\t// no module.id needed\n/******/ \t\t\t// no module.loaded needed\n/******/ \t\t\texports: {}\n/******/ \t\t};\n/******/ \t\n/******/ \t\t// Execute the module function\n/******/ \t\t__webpack_modules__[moduleId](module, module.exports, __webpack_require__);\n/******/ \t\n/******/ \t\t// Return the exports of the module\n/******/ \t\treturn module.exports;\n/******/ \t}\n/******/ \t\n/************************************************************************/\n/******/ \t/* webpack/runtime/compat get default export */\n/******/ \t!function() {\n/******/ \t\t// getDefaultExport function for compatibility with non-harmony modules\n/******/ \t\t__webpack_require__.n = function(module) {\n/******/ \t\t\tvar getter = module && module.__esModule ?\n/******/ \t\t\t\tfunction() { return module['default']; } :\n/******/ \t\t\t\tfunction() { return module; };\n/******/ \t\t\t__webpack_require__.d(getter, { a: getter });\n/******/ \t\t\treturn getter;\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/define property getters */\n/******/ \t!function() {\n/******/ \t\t// define getter functions for harmony exports\n/******/ \t\t__webpack_require__.d = function(exports, definition) {\n/******/ \t\t\tfor(var key in definition) {\n/******/ \t\t\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n/******/ \t\t\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n/******/ \t\t\t\t}\n/******/ \t\t\t}\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/hasOwnProperty shorthand */\n/******/ \t!function() {\n/******/ \t\t__webpack_require__.o = function(obj, prop) { return Object.prototype.hasOwnProperty.call(obj, prop); }\n/******/ \t}();\n/******/ \t\n/************************************************************************/\n/******/ \t// module exports must be returned from runtime so entry inlining is disabled\n/******/ \t// startup\n/******/ \t// Load entry module and return exports\n/******/ \treturn __webpack_require__(686);\n/******/ })()\n.default;\n});", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param  {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n  var str = '' + string;\n  var match = matchHtmlRegExp.exec(str);\n\n  if (!match) {\n    return str;\n  }\n\n  var escape;\n  var html = '';\n  var index = 0;\n  var lastIndex = 0;\n\n  for (index = match.index; index < str.length; index++) {\n    switch (str.charCodeAt(index)) {\n      case 34: // \"\n        escape = '&quot;';\n        break;\n      case 38: // &\n        escape = '&amp;';\n        break;\n      case 39: // '\n        escape = '&#39;';\n        break;\n      case 60: // <\n        escape = '&lt;';\n        break;\n      case 62: // >\n        escape = '&gt;';\n        break;\n      default:\n        continue;\n    }\n\n    if (lastIndex !== index) {\n      html += str.substring(lastIndex, index);\n    }\n\n    lastIndex = index + 1;\n    html += escape;\n  }\n\n  return lastIndex !== index\n    ? html + str.substring(lastIndex, index)\n    : html;\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport \"focus-visible\"\n\nimport {\n  EMPTY,\n  NEVER,\n  Observable,\n  Subject,\n  defer,\n  delay,\n  filter,\n  map,\n  merge,\n  mergeWith,\n  shareReplay,\n  switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"./_\"\nimport {\n  at,\n  getActiveElement,\n  getOptionalElement,\n  requestJSON,\n  setLocation,\n  setToggle,\n  watchDocument,\n  watchKeyboard,\n  watchLocation,\n  watchLocationTarget,\n  watchMedia,\n  watchPrint,\n  watchScript,\n  watchViewport\n} from \"./browser\"\nimport {\n  getComponentElement,\n  getComponentElements,\n  mountAnnounce,\n  mountBackToTop,\n  mountConsent,\n  mountContent,\n  mountDialog,\n  mountHeader,\n  mountHeaderTitle,\n  mountPalette,\n  mountProgress,\n  mountSearch,\n  mountSearchHiglight,\n  mountSidebar,\n  mountSource,\n  mountTableOfContents,\n  mountTabs,\n  watchHeader,\n  watchMain\n} from \"./components\"\nimport {\n  SearchIndex,\n  setupClipboardJS,\n  setupInstantNavigation,\n  setupVersionSelector\n} from \"./integrations\"\nimport {\n  patchEllipsis,\n  patchIndeterminate,\n  patchScrollfix,\n  patchScrolllock\n} from \"./patches\"\nimport \"./polyfills\"\n\n/* ----------------------------------------------------------------------------\n * Functions - @todo refactor\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch search index\n *\n * @returns Search index observable\n */\nfunction fetchSearchIndex(): Observable<SearchIndex> {\n  if (location.protocol === \"file:\") {\n    return watchScript(\n      `${new URL(\"search/search_index.js\", config.base)}`\n    )\n      .pipe(\n        // @ts-ignore - @todo fix typings\n        map(() => __index),\n        shareReplay(1)\n      )\n  } else {\n    return requestJSON<SearchIndex>(\n      new URL(\"search/search_index.json\", config.base)\n    )\n  }\n}\n\n/* ----------------------------------------------------------------------------\n * Application\n * ------------------------------------------------------------------------- */\n\n/* Yay, JavaScript is available */\ndocument.documentElement.classList.remove(\"no-js\")\ndocument.documentElement.classList.add(\"js\")\n\n/* Set up navigation observables and subjects */\nconst document$ = watchDocument()\nconst location$ = watchLocation()\nconst target$   = watchLocationTarget(location$)\nconst keyboard$ = watchKeyboard()\n\n/* Set up media observables */\nconst viewport$ = watchViewport()\nconst tablet$   = watchMedia(\"(min-width: 960px)\")\nconst screen$   = watchMedia(\"(min-width: 1220px)\")\nconst print$    = watchPrint()\n\n/* Retrieve search index, if search is enabled */\nconst config = configuration()\nconst index$ = document.forms.namedItem(\"search\")\n  ? fetchSearchIndex()\n  : NEVER\n\n/* Set up Clipboard.js integration */\nconst alert$ = new Subject<string>()\nsetupClipboardJS({ alert$ })\n\n/* Set up progress indicator */\nconst progress$ = new Subject<number>()\n\n/* Set up instant navigation, if enabled */\nif (feature(\"navigation.instant\"))\n  setupInstantNavigation({ location$, viewport$, progress$ })\n    .subscribe(document$)\n\n/* Set up version selector */\nif (config.version?.provider === \"mike\")\n  setupVersionSelector({ document$ })\n\n/* Always close drawer and search on navigation */\nmerge(location$, target$)\n  .pipe(\n    delay(125)\n  )\n    .subscribe(() => {\n      setToggle(\"drawer\", false)\n      setToggle(\"search\", false)\n    })\n\n/* Set up global keyboard handlers */\nkeyboard$\n  .pipe(\n    filter(({ mode }) => mode === \"global\")\n  )\n    .subscribe(key => {\n      switch (key.type) {\n\n        /* Go to previous page */\n        case \"p\":\n        case \",\":\n          const prev = getOptionalElement<HTMLLinkElement>(\"link[rel=prev]\")\n          if (typeof prev !== \"undefined\")\n            setLocation(prev)\n          break\n\n        /* Go to next page */\n        case \"n\":\n        case \".\":\n          const next = getOptionalElement<HTMLLinkElement>(\"link[rel=next]\")\n          if (typeof next !== \"undefined\")\n            setLocation(next)\n          break\n\n        /* Expand navigation, see https://bit.ly/3ZjG5io */\n        case \"Enter\":\n          const active = getActiveElement()\n          if (active instanceof HTMLLabelElement)\n            active.click()\n      }\n    })\n\n/* Set up patches */\npatchEllipsis({ viewport$, document$ })\npatchIndeterminate({ document$, tablet$ })\npatchScrollfix({ document$ })\npatchScrolllock({ viewport$, tablet$ })\n\n/* Set up header and main area observable */\nconst header$ = watchHeader(getComponentElement(\"header\"), { viewport$ })\nconst main$ = document$\n  .pipe(\n    map(() => getComponentElement(\"main\")),\n    switchMap(el => watchMain(el, { viewport$, header$ })),\n    shareReplay(1)\n  )\n\n/* Set up control component observables */\nconst control$ = merge(\n\n  /* Consent */\n  ...getComponentElements(\"consent\")\n    .map(el => mountConsent(el, { target$ })),\n\n  /* Dialog */\n  ...getComponentElements(\"dialog\")\n    .map(el => mountDialog(el, { alert$ })),\n\n  /* Header */\n  ...getComponentElements(\"header\")\n    .map(el => mountHeader(el, { viewport$, header$, main$ })),\n\n  /* Color palette */\n  ...getComponentElements(\"palette\")\n    .map(el => mountPalette(el)),\n\n  /* Progress bar */\n  ...getComponentElements(\"progress\")\n    .map(el => mountProgress(el, { progress$ })),\n\n  /* Search */\n  ...getComponentElements(\"search\")\n    .map(el => mountSearch(el, { index$, keyboard$ })),\n\n  /* Repository information */\n  ...getComponentElements(\"source\")\n    .map(el => mountSource(el))\n)\n\n/* Set up content component observables */\nconst content$ = defer(() => merge(\n\n  /* Announcement bar */\n  ...getComponentElements(\"announce\")\n    .map(el => mountAnnounce(el)),\n\n  /* Content */\n  ...getComponentElements(\"content\")\n    .map(el => mountContent(el, { viewport$, target$, print$ })),\n\n  /* Search highlighting */\n  ...getComponentElements(\"content\")\n    .map(el => feature(\"search.highlight\")\n      ? mountSearchHiglight(el, { index$, location$ })\n      : EMPTY\n    ),\n\n  /* Header title */\n  ...getComponentElements(\"header-title\")\n    .map(el => mountHeaderTitle(el, { viewport$, header$ })),\n\n  /* Sidebar */\n  ...getComponentElements(\"sidebar\")\n    .map(el => el.getAttribute(\"data-md-type\") === \"navigation\"\n      ? at(screen$, () => mountSidebar(el, { viewport$, header$, main$ }))\n      : at(tablet$, () => mountSidebar(el, { viewport$, header$, main$ }))\n    ),\n\n  /* Navigation tabs */\n  ...getComponentElements(\"tabs\")\n    .map(el => mountTabs(el, { viewport$, header$ })),\n\n  /* Table of contents */\n  ...getComponentElements(\"toc\")\n    .map(el => mountTableOfContents(el, {\n      viewport$, header$, main$, target$\n    })),\n\n  /* Back-to-top button */\n  ...getComponentElements(\"top\")\n    .map(el => mountBackToTop(el, { viewport$, header$, main$, target$ }))\n))\n\n/* Set up component observables */\nconst component$ = document$\n  .pipe(\n    switchMap(() => content$),\n    mergeWith(control$),\n    shareReplay(1)\n  )\n\n/* Subscribe to all components */\ncomponent$.subscribe()\n\n/* ----------------------------------------------------------------------------\n * Exports\n * ------------------------------------------------------------------------- */\n\nwindow.document$  = document$          /* Document observable */\nwindow.location$  = location$          /* Location subject */\nwindow.target$    = target$            /* Location target observable */\nwindow.keyboard$  = keyboard$          /* Keyboard observable */\nwindow.viewport$  = viewport$          /* Viewport observable */\nwindow.tablet$    = tablet$            /* Media tablet observable */\nwindow.screen$    = screen$            /* Media screen observable */\nwindow.print$     = print$             /* Media print observable */\nwindow.alert$     = alert$             /* Alert subject */\nwindow.progress$  = progress$          /* Progress indicator subject */\nwindow.component$ = component$         /* Component observable */\n", "/*! *****************************************************************************\r\nCopyright (c) Microsoft Corporation.\r\n\r\nPermission to use, copy, modify, and/or distribute this software for any\r\npurpose with or without fee is hereby granted.\r\n\r\nTHE SOFTWARE IS PROVIDED \"AS IS\" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH\r\nREGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY\r\nAND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,\r\nINDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM\r\nLOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR\r\nOTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR\r\nPERFORMANCE OF THIS SOFTWARE.\r\n***************************************************************************** */\r\n/* global Reflect, Promise */\r\n\r\nvar extendStatics = function(d, b) {\r\n    extendStatics = Object.setPrototypeOf ||\r\n        ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||\r\n        function (d, b) { for (var p in b) if (Object.prototype.hasOwnProperty.call(b, p)) d[p] = b[p]; };\r\n    return extendStatics(d, b);\r\n};\r\n\r\nexport function __extends(d, b) {\r\n    if (typeof b !== \"function\" && b !== null)\r\n        throw new TypeError(\"Class extends value \" + String(b) + \" is not a constructor or null\");\r\n    extendStatics(d, b);\r\n    function __() { this.constructor = d; }\r\n    d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());\r\n}\r\n\r\nexport var __assign = function() {\r\n    __assign = Object.assign || function __assign(t) {\r\n        for (var s, i = 1, n = arguments.length; i < n; i++) {\r\n            s = arguments[i];\r\n            for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];\r\n        }\r\n        return t;\r\n    }\r\n    return __assign.apply(this, arguments);\r\n}\r\n\r\nexport function __rest(s, e) {\r\n    var t = {};\r\n    for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)\r\n        t[p] = s[p];\r\n    if (s != null && typeof Object.getOwnPropertySymbols === \"function\")\r\n        for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {\r\n            if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i]))\r\n                t[p[i]] = s[p[i]];\r\n        }\r\n    return t;\r\n}\r\n\r\nexport function __decorate(decorators, target, key, desc) {\r\n    var c = arguments.length, r = c < 3 ? target : desc === null ? desc = Object.getOwnPropertyDescriptor(target, key) : desc, d;\r\n    if (typeof Reflect === \"object\" && typeof Reflect.decorate === \"function\") r = Reflect.decorate(decorators, target, key, desc);\r\n    else for (var i = decorators.length - 1; i >= 0; i--) if (d = decorators[i]) r = (c < 3 ? d(r) : c > 3 ? d(target, key, r) : d(target, key)) || r;\r\n    return c > 3 && r && Object.defineProperty(target, key, r), r;\r\n}\r\n\r\nexport function __param(paramIndex, decorator) {\r\n    return function (target, key) { decorator(target, key, paramIndex); }\r\n}\r\n\r\nexport function __metadata(metadataKey, metadataValue) {\r\n    if (typeof Reflect === \"object\" && typeof Reflect.metadata === \"function\") return Reflect.metadata(metadataKey, metadataValue);\r\n}\r\n\r\nexport function __awaiter(thisArg, _arguments, P, generator) {\r\n    function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }\r\n    return new (P || (P = Promise))(function (resolve, reject) {\r\n        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }\r\n        function rejected(value) { try { step(generator[\"throw\"](value)); } catch (e) { reject(e); } }\r\n        function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }\r\n        step((generator = generator.apply(thisArg, _arguments || [])).next());\r\n    });\r\n}\r\n\r\nexport function __generator(thisArg, body) {\r\n    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;\r\n    return g = { next: verb(0), \"throw\": verb(1), \"return\": verb(2) }, typeof Symbol === \"function\" && (g[Symbol.iterator] = function() { return this; }), g;\r\n    function verb(n) { return function (v) { return step([n, v]); }; }\r\n    function step(op) {\r\n        if (f) throw new TypeError(\"Generator is already executing.\");\r\n        while (_) try {\r\n            if (f = 1, y && (t = op[0] & 2 ? y[\"return\"] : op[0] ? y[\"throw\"] || ((t = y[\"return\"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;\r\n            if (y = 0, t) op = [op[0] & 2, t.value];\r\n            switch (op[0]) {\r\n                case 0: case 1: t = op; break;\r\n                case 4: _.label++; return { value: op[1], done: false };\r\n                case 5: _.label++; y = op[1]; op = [0]; continue;\r\n                case 7: op = _.ops.pop(); _.trys.pop(); continue;\r\n                default:\r\n                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }\r\n                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }\r\n                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }\r\n                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }\r\n                    if (t[2]) _.ops.pop();\r\n                    _.trys.pop(); continue;\r\n            }\r\n            op = body.call(thisArg, _);\r\n        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }\r\n        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };\r\n    }\r\n}\r\n\r\nexport var __createBinding = Object.create ? (function(o, m, k, k2) {\r\n    if (k2 === undefined) k2 = k;\r\n    Object.defineProperty(o, k2, { enumerable: true, get: function() { return m[k]; } });\r\n}) : (function(o, m, k, k2) {\r\n    if (k2 === undefined) k2 = k;\r\n    o[k2] = m[k];\r\n});\r\n\r\nexport function __exportStar(m, o) {\r\n    for (var p in m) if (p !== \"default\" && !Object.prototype.hasOwnProperty.call(o, p)) __createBinding(o, m, p);\r\n}\r\n\r\nexport function __values(o) {\r\n    var s = typeof Symbol === \"function\" && Symbol.iterator, m = s && o[s], i = 0;\r\n    if (m) return m.call(o);\r\n    if (o && typeof o.length === \"number\") return {\r\n        next: function () {\r\n            if (o && i >= o.length) o = void 0;\r\n            return { value: o && o[i++], done: !o };\r\n        }\r\n    };\r\n    throw new TypeError(s ? \"Object is not iterable.\" : \"Symbol.iterator is not defined.\");\r\n}\r\n\r\nexport function __read(o, n) {\r\n    var m = typeof Symbol === \"function\" && o[Symbol.iterator];\r\n    if (!m) return o;\r\n    var i = m.call(o), r, ar = [], e;\r\n    try {\r\n        while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value);\r\n    }\r\n    catch (error) { e = { error: error }; }\r\n    finally {\r\n        try {\r\n            if (r && !r.done && (m = i[\"return\"])) m.call(i);\r\n        }\r\n        finally { if (e) throw e.error; }\r\n    }\r\n    return ar;\r\n}\r\n\r\n/** @deprecated */\r\nexport function __spread() {\r\n    for (var ar = [], i = 0; i < arguments.length; i++)\r\n        ar = ar.concat(__read(arguments[i]));\r\n    return ar;\r\n}\r\n\r\n/** @deprecated */\r\nexport function __spreadArrays() {\r\n    for (var s = 0, i = 0, il = arguments.length; i < il; i++) s += arguments[i].length;\r\n    for (var r = Array(s), k = 0, i = 0; i < il; i++)\r\n        for (var a = arguments[i], j = 0, jl = a.length; j < jl; j++, k++)\r\n            r[k] = a[j];\r\n    return r;\r\n}\r\n\r\nexport function __spreadArray(to, from, pack) {\r\n    if (pack || arguments.length === 2) for (var i = 0, l = from.length, ar; i < l; i++) {\r\n        if (ar || !(i in from)) {\r\n            if (!ar) ar = Array.prototype.slice.call(from, 0, i);\r\n            ar[i] = from[i];\r\n        }\r\n    }\r\n    return to.concat(ar || Array.prototype.slice.call(from));\r\n}\r\n\r\nexport function __await(v) {\r\n    return this instanceof __await ? (this.v = v, this) : new __await(v);\r\n}\r\n\r\nexport function __asyncGenerator(thisArg, _arguments, generator) {\r\n    if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\r\n    var g = generator.apply(thisArg, _arguments || []), i, q = [];\r\n    return i = {}, verb(\"next\"), verb(\"throw\"), verb(\"return\"), i[Symbol.asyncIterator] = function () { return this; }, i;\r\n    function verb(n) { if (g[n]) i[n] = function (v) { return new Promise(function (a, b) { q.push([n, v, a, b]) > 1 || resume(n, v); }); }; }\r\n    function resume(n, v) { try { step(g[n](v)); } catch (e) { settle(q[0][3], e); } }\r\n    function step(r) { r.value instanceof __await ? Promise.resolve(r.value.v).then(fulfill, reject) : settle(q[0][2], r); }\r\n    function fulfill(value) { resume(\"next\", value); }\r\n    function reject(value) { resume(\"throw\", value); }\r\n    function settle(f, v) { if (f(v), q.shift(), q.length) resume(q[0][0], q[0][1]); }\r\n}\r\n\r\nexport function __asyncDelegator(o) {\r\n    var i, p;\r\n    return i = {}, verb(\"next\"), verb(\"throw\", function (e) { throw e; }), verb(\"return\"), i[Symbol.iterator] = function () { return this; }, i;\r\n    function verb(n, f) { i[n] = o[n] ? function (v) { return (p = !p) ? { value: __await(o[n](v)), done: n === \"return\" } : f ? f(v) : v; } : f; }\r\n}\r\n\r\nexport function __asyncValues(o) {\r\n    if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\r\n    var m = o[Symbol.asyncIterator], i;\r\n    return m ? m.call(o) : (o = typeof __values === \"function\" ? __values(o) : o[Symbol.iterator](), i = {}, verb(\"next\"), verb(\"throw\"), verb(\"return\"), i[Symbol.asyncIterator] = function () { return this; }, i);\r\n    function verb(n) { i[n] = o[n] && function (v) { return new Promise(function (resolve, reject) { v = o[n](v), settle(resolve, reject, v.done, v.value); }); }; }\r\n    function settle(resolve, reject, d, v) { Promise.resolve(v).then(function(v) { resolve({ value: v, done: d }); }, reject); }\r\n}\r\n\r\nexport function __makeTemplateObject(cooked, raw) {\r\n    if (Object.defineProperty) { Object.defineProperty(cooked, \"raw\", { value: raw }); } else { cooked.raw = raw; }\r\n    return cooked;\r\n};\r\n\r\nvar __setModuleDefault = Object.create ? (function(o, v) {\r\n    Object.defineProperty(o, \"default\", { enumerable: true, value: v });\r\n}) : function(o, v) {\r\n    o[\"default\"] = v;\r\n};\r\n\r\nexport function __importStar(mod) {\r\n    if (mod && mod.__esModule) return mod;\r\n    var result = {};\r\n    if (mod != null) for (var k in mod) if (k !== \"default\" && Object.prototype.hasOwnProperty.call(mod, k)) __createBinding(result, mod, k);\r\n    __setModuleDefault(result, mod);\r\n    return result;\r\n}\r\n\r\nexport function __importDefault(mod) {\r\n    return (mod && mod.__esModule) ? mod : { default: mod };\r\n}\r\n\r\nexport function __classPrivateFieldGet(receiver, state, kind, f) {\r\n    if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a getter\");\r\n    if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot read private member from an object whose class did not declare it\");\r\n    return kind === \"m\" ? f : kind === \"a\" ? f.call(receiver) : f ? f.value : state.get(receiver);\r\n}\r\n\r\nexport function __classPrivateFieldSet(receiver, state, value, kind, f) {\r\n    if (kind === \"m\") throw new TypeError(\"Private method is not writable\");\r\n    if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a setter\");\r\n    if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot write private member to an object whose class did not declare it\");\r\n    return (kind === \"a\" ? f.call(receiver, value) : f ? f.value = value : state.set(receiver, value)), value;\r\n}\r\n", "/**\n * Returns true if the object is a function.\n * @param value The value to check\n */\nexport function isFunction(value: any): value is (...args: any[]) => any {\n  return typeof value === 'function';\n}\n", "/**\n * Used to create Error subclasses until the community moves away from ES5.\n *\n * This is because compiling from TypeScript down to ES5 has issues with subclassing Errors\n * as well as other built-in types: https://github.com/Microsoft/TypeScript/issues/12123\n *\n * @param createImpl A factory function to create the actual constructor implementation. The returned\n * function should be a named function that calls `_super` internally.\n */\nexport function createErrorClass<T>(createImpl: (_super: any) => any): T {\n  const _super = (instance: any) => {\n    Error.call(instance);\n    instance.stack = new Error().stack;\n  };\n\n  const ctorFunc = createImpl(_super);\n  ctorFunc.prototype = Object.create(Error.prototype);\n  ctorFunc.prototype.constructor = ctorFunc;\n  return ctorFunc;\n}\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface UnsubscriptionError extends Error {\n  readonly errors: any[];\n}\n\nexport interface UnsubscriptionErrorCtor {\n  /**\n   * @deprecated Internal implementation detail. Do not construct error instances.\n   * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n   */\n  new (errors: any[]): UnsubscriptionError;\n}\n\n/**\n * An error thrown when one or more errors have occurred during the\n * `unsubscribe` of a {@link Subscription}.\n */\nexport const UnsubscriptionError: UnsubscriptionErrorCtor = createErrorClass(\n  (_super) =>\n    function UnsubscriptionErrorImpl(this: any, errors: (Error | string)[]) {\n      _super(this);\n      this.message = errors\n        ? `${errors.length} errors occurred during unsubscription:\n${errors.map((err, i) => `${i + 1}) ${err.toString()}`).join('\\n  ')}`\n        : '';\n      this.name = 'UnsubscriptionError';\n      this.errors = errors;\n    }\n);\n", "/**\n * Removes an item from an array, mutating it.\n * @param arr The array to remove the item from\n * @param item The item to remove\n */\nexport function arrRemove<T>(arr: T[] | undefined | null, item: T) {\n  if (arr) {\n    const index = arr.indexOf(item);\n    0 <= index && arr.splice(index, 1);\n  }\n}\n", "import { isFunction } from './util/isFunction';\nimport { UnsubscriptionError } from './util/UnsubscriptionError';\nimport { SubscriptionLike, TeardownLogic, Unsubscribable } from './types';\nimport { arrRemove } from './util/arrRemove';\n\n/**\n * Represents a disposable resource, such as the execution of an Observable. A\n * Subscription has one important method, `unsubscribe`, that takes no argument\n * and just disposes the resource held by the subscription.\n *\n * Additionally, subscriptions may be grouped together through the `add()`\n * method, which will attach a child Subscription to the current Subscription.\n * When a Subscription is unsubscribed, all its children (and its grandchildren)\n * will be unsubscribed as well.\n *\n * @class Subscription\n */\nexport class Subscription implements SubscriptionLike {\n  /** @nocollapse */\n  public static EMPTY = (() => {\n    const empty = new Subscription();\n    empty.closed = true;\n    return empty;\n  })();\n\n  /**\n   * A flag to indicate whether this Subscription has already been unsubscribed.\n   */\n  public closed = false;\n\n  private _parentage: Subscription[] | Subscription | null = null;\n\n  /**\n   * The list of registered finalizers to execute upon unsubscription. Adding and removing from this\n   * list occurs in the {@link #add} and {@link #remove} methods.\n   */\n  private _finalizers: Exclude<TeardownLogic, void>[] | null = null;\n\n  /**\n   * @param initialTeardown A function executed first as part of the finalization\n   * process that is kicked off when {@link #unsubscribe} is called.\n   */\n  constructor(private initialTeardown?: () => void) {}\n\n  /**\n   * Disposes the resources held by the subscription. May, for instance, cancel\n   * an ongoing Observable execution or cancel any other type of work that\n   * started when the Subscription was created.\n   * @return {void}\n   */\n  unsubscribe(): void {\n    let errors: any[] | undefined;\n\n    if (!this.closed) {\n      this.closed = true;\n\n      // Remove this from it's parents.\n      const { _parentage } = this;\n      if (_parentage) {\n        this._parentage = null;\n        if (Array.isArray(_parentage)) {\n          for (const parent of _parentage) {\n            parent.remove(this);\n          }\n        } else {\n          _parentage.remove(this);\n        }\n      }\n\n      const { initialTeardown: initialFinalizer } = this;\n      if (isFunction(initialFinalizer)) {\n        try {\n          initialFinalizer();\n        } catch (e) {\n          errors = e instanceof UnsubscriptionError ? e.errors : [e];\n        }\n      }\n\n      const { _finalizers } = this;\n      if (_finalizers) {\n        this._finalizers = null;\n        for (const finalizer of _finalizers) {\n          try {\n            execFinalizer(finalizer);\n          } catch (err) {\n            errors = errors ?? [];\n            if (err instanceof UnsubscriptionError) {\n              errors = [...errors, ...err.errors];\n            } else {\n              errors.push(err);\n            }\n          }\n        }\n      }\n\n      if (errors) {\n        throw new UnsubscriptionError(errors);\n      }\n    }\n  }\n\n  /**\n   * Adds a finalizer to this subscription, so that finalization will be unsubscribed/called\n   * when this subscription is unsubscribed. If this subscription is already {@link #closed},\n   * because it has already been unsubscribed, then whatever finalizer is passed to it\n   * will automatically be executed (unless the finalizer itself is also a closed subscription).\n   *\n   * Closed Subscriptions cannot be added as finalizers to any subscription. Adding a closed\n   * subscription to a any subscription will result in no operation. (A noop).\n   *\n   * Adding a subscription to itself, or adding `null` or `undefined` will not perform any\n   * operation at all. (A noop).\n   *\n   * `Subscription` instances that are added to this instance will automatically remove themselves\n   * if they are unsubscribed. Functions and {@link Unsubscribable} objects that you wish to remove\n   * will need to be removed manually with {@link #remove}\n   *\n   * @param teardown The finalization logic to add to this subscription.\n   */\n  add(teardown: TeardownLogic): void {\n    // Only add the finalizer if it's not undefined\n    // and don't add a subscription to itself.\n    if (teardown && teardown !== this) {\n      if (this.closed) {\n        // If this subscription is already closed,\n        // execute whatever finalizer is handed to it automatically.\n        execFinalizer(teardown);\n      } else {\n        if (teardown instanceof Subscription) {\n          // We don't add closed subscriptions, and we don't add the same subscription\n          // twice. Subscription unsubscribe is idempotent.\n          if (teardown.closed || teardown._hasParent(this)) {\n            return;\n          }\n          teardown._addParent(this);\n        }\n        (this._finalizers = this._finalizers ?? []).push(teardown);\n      }\n    }\n  }\n\n  /**\n   * Checks to see if a this subscription already has a particular parent.\n   * This will signal that this subscription has already been added to the parent in question.\n   * @param parent the parent to check for\n   */\n  private _hasParent(parent: Subscription) {\n    const { _parentage } = this;\n    return _parentage === parent || (Array.isArray(_parentage) && _parentage.includes(parent));\n  }\n\n  /**\n   * Adds a parent to this subscription so it can be removed from the parent if it\n   * unsubscribes on it's own.\n   *\n   * NOTE: THIS ASSUMES THAT {@link _hasParent} HAS ALREADY BEEN CHECKED.\n   * @param parent The parent subscription to add\n   */\n  private _addParent(parent: Subscription) {\n    const { _parentage } = this;\n    this._parentage = Array.isArray(_parentage) ? (_parentage.push(parent), _parentage) : _parentage ? [_parentage, parent] : parent;\n  }\n\n  /**\n   * Called on a child when it is removed via {@link #remove}.\n   * @param parent The parent to remove\n   */\n  private _removeParent(parent: Subscription) {\n    const { _parentage } = this;\n    if (_parentage === parent) {\n      this._parentage = null;\n    } else if (Array.isArray(_parentage)) {\n      arrRemove(_parentage, parent);\n    }\n  }\n\n  /**\n   * Removes a finalizer from this subscription that was previously added with the {@link #add} method.\n   *\n   * Note that `Subscription` instances, when unsubscribed, will automatically remove themselves\n   * from every other `Subscription` they have been added to. This means that using the `remove` method\n   * is not a common thing and should be used thoughtfully.\n   *\n   * If you add the same finalizer instance of a function or an unsubscribable object to a `Subscription` instance\n   * more than once, you will need to call `remove` the same number of times to remove all instances.\n   *\n   * All finalizer instances are removed to free up memory upon unsubscription.\n   *\n   * @param teardown The finalizer to remove from this subscription\n   */\n  remove(teardown: Exclude<TeardownLogic, void>): void {\n    const { _finalizers } = this;\n    _finalizers && arrRemove(_finalizers, teardown);\n\n    if (teardown instanceof Subscription) {\n      teardown._removeParent(this);\n    }\n  }\n}\n\nexport const EMPTY_SUBSCRIPTION = Subscription.EMPTY;\n\nexport function isSubscription(value: any): value is Subscription {\n  return (\n    value instanceof Subscription ||\n    (value && 'closed' in value && isFunction(value.remove) && isFunction(value.add) && isFunction(value.unsubscribe))\n  );\n}\n\nfunction execFinalizer(finalizer: Unsubscribable | (() => void)) {\n  if (isFunction(finalizer)) {\n    finalizer();\n  } else {\n    finalizer.unsubscribe();\n  }\n}\n", "import { Subscriber } from './Subscriber';\nimport { ObservableNotification } from './types';\n\n/**\n * The {@link GlobalConfig} object for RxJS. It is used to configure things\n * like how to react on unhandled errors.\n */\nexport const config: GlobalConfig = {\n  onUnhandledError: null,\n  onStoppedNotification: null,\n  Promise: undefined,\n  useDeprecatedSynchronousErrorHandling: false,\n  useDeprecatedNextContext: false,\n};\n\n/**\n * The global configuration object for RxJS, used to configure things\n * like how to react on unhandled errors. Accessible via {@link config}\n * object.\n */\nexport interface GlobalConfig {\n  /**\n   * A registration point for unhandled errors from RxJS. These are errors that\n   * cannot were not handled by consuming code in the usual subscription path. For\n   * example, if you have this configured, and you subscribe to an observable without\n   * providing an error handler, errors from that subscription will end up here. This\n   * will _always_ be called asynchronously on another job in the runtime. This is because\n   * we do not want errors thrown in this user-configured handler to interfere with the\n   * behavior of the library.\n   */\n  onUnhandledError: ((err: any) => void) | null;\n\n  /**\n   * A registration point for notifications that cannot be sent to subscribers because they\n   * have completed, errored or have been explicitly unsubscribed. By default, next, complete\n   * and error notifications sent to stopped subscribers are noops. However, sometimes callers\n   * might want a different behavior. For example, with sources that attempt to report errors\n   * to stopped subscribers, a caller can configure RxJS to throw an unhandled error instead.\n   * This will _always_ be called asynchronously on another job in the runtime. This is because\n   * we do not want errors thrown in this user-configured handler to interfere with the\n   * behavior of the library.\n   */\n  onStoppedNotification: ((notification: ObservableNotification<any>, subscriber: Subscriber<any>) => void) | null;\n\n  /**\n   * The promise constructor used by default for {@link Observable#toPromise toPromise} and {@link Observable#forEach forEach}\n   * methods.\n   *\n   * @deprecated As of version 8, RxJS will no longer support this sort of injection of a\n   * Promise constructor. If you need a Promise implementation other than native promises,\n   * please polyfill/patch Promise as you see appropriate. Will be removed in v8.\n   */\n  Promise?: PromiseConstructorLike;\n\n  /**\n   * If true, turns on synchronous error rethrowing, which is a deprecated behavior\n   * in v6 and higher. This behavior enables bad patterns like wrapping a subscribe\n   * call in a try/catch block. It also enables producer interference, a nasty bug\n   * where a multicast can be broken for all observers by a downstream consumer with\n   * an unhandled error. DO NOT USE THIS FLAG UNLESS IT'S NEEDED TO BUY TIME\n   * FOR MIGRATION REASONS.\n   *\n   * @deprecated As of version 8, RxJS will no longer support synchronous throwing\n   * of unhandled errors. All errors will be thrown on a separate call stack to prevent bad\n   * behaviors described above. Will be removed in v8.\n   */\n  useDeprecatedSynchronousErrorHandling: boolean;\n\n  /**\n   * If true, enables an as-of-yet undocumented feature from v5: The ability to access\n   * `unsubscribe()` via `this` context in `next` functions created in observers passed\n   * to `subscribe`.\n   *\n   * This is being removed because the performance was severely problematic, and it could also cause\n   * issues when types other than POJOs are passed to subscribe as subscribers, as they will likely have\n   * their `this` context overwritten.\n   *\n   * @deprecated As of version 8, RxJS will no longer support altering the\n   * context of next functions provided as part of an observer to Subscribe. Instead,\n   * you will have access to a subscription or a signal or token that will allow you to do things like\n   * unsubscribe and test closed status. Will be removed in v8.\n   */\n  useDeprecatedNextContext: boolean;\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetTimeoutFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearTimeoutFunction = (handle: TimerHandle) => void;\n\ninterface TimeoutProvider {\n  setTimeout: SetTimeoutFunction;\n  clearTimeout: ClearTimeoutFunction;\n  delegate:\n    | {\n        setTimeout: SetTimeoutFunction;\n        clearTimeout: ClearTimeoutFunction;\n      }\n    | undefined;\n}\n\nexport const timeoutProvider: TimeoutProvider = {\n  // When accessing the delegate, use the variable rather than `this` so that\n  // the functions can be called without being bound to the provider.\n  setTimeout(handler: () => void, timeout?: number, ...args) {\n    const { delegate } = timeoutProvider;\n    if (delegate?.setTimeout) {\n      return delegate.setTimeout(handler, timeout, ...args);\n    }\n    return setTimeout(handler, timeout, ...args);\n  },\n  clearTimeout(handle) {\n    const { delegate } = timeoutProvider;\n    return (delegate?.clearTimeout || clearTimeout)(handle as any);\n  },\n  delegate: undefined,\n};\n", "import { config } from '../config';\nimport { timeoutProvider } from '../scheduler/timeoutProvider';\n\n/**\n * Handles an error on another job either with the user-configured {@link onUnhandledError},\n * or by throwing it on that new job so it can be picked up by `window.onerror`, `process.on('error')`, etc.\n *\n * This should be called whenever there is an error that is out-of-band with the subscription\n * or when an error hits a terminal boundary of the subscription and no error handler was provided.\n *\n * @param err the error to report\n */\nexport function reportUnhandledError(err: any) {\n  timeoutProvider.setTimeout(() => {\n    const { onUnhandledError } = config;\n    if (onUnhandledError) {\n      // Execute the user-configured error handler.\n      onUnhandledError(err);\n    } else {\n      // Throw so it is picked up by the runtime's uncaught error mechanism.\n      throw err;\n    }\n  });\n}\n", "/* tslint:disable:no-empty */\nexport function noop() { }\n", "import { CompleteNotification, NextNotification, ErrorNotification } from './types';\n\n/**\n * A completion object optimized for memory use and created to be the\n * same \"shape\" as other notifications in v8.\n * @internal\n */\nexport const COMPLETE_NOTIFICATION = (() => createNotification('C', undefined, undefined) as CompleteNotification)();\n\n/**\n * Internal use only. Creates an optimized error notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function errorNotification(error: any): ErrorNotification {\n  return createNotification('E', undefined, error) as any;\n}\n\n/**\n * Internal use only. Creates an optimized next notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function nextNotification<T>(value: T) {\n  return createNotification('N', value, undefined) as NextNotification<T>;\n}\n\n/**\n * Ensures that all notifications created internally have the same \"shape\" in v8.\n *\n * TODO: This is only exported to support a crazy legacy test in `groupBy`.\n * @internal\n */\nexport function createNotification(kind: 'N' | 'E' | 'C', value: any, error: any) {\n  return {\n    kind,\n    value,\n    error,\n  };\n}\n", "import { config } from '../config';\n\nlet context: { errorThrown: boolean; error: any } | null = null;\n\n/**\n * Handles dealing with errors for super-gross mode. Creates a context, in which\n * any synchronously thrown errors will be passed to {@link captureError}. Which\n * will record the error such that it will be rethrown after the call back is complete.\n * TODO: Remove in v8\n * @param cb An immediately executed function.\n */\nexport function errorContext(cb: () => void) {\n  if (config.useDeprecatedSynchronousErrorHandling) {\n    const isRoot = !context;\n    if (isRoot) {\n      context = { errorThrown: false, error: null };\n    }\n    cb();\n    if (isRoot) {\n      const { errorThrown, error } = context!;\n      context = null;\n      if (errorThrown) {\n        throw error;\n      }\n    }\n  } else {\n    // This is the general non-deprecated path for everyone that\n    // isn't crazy enough to use super-gross mode (useDeprecatedSynchronousErrorHandling)\n    cb();\n  }\n}\n\n/**\n * Captures errors only in super-gross mode.\n * @param err the error to capture\n */\nexport function captureError(err: any) {\n  if (config.useDeprecatedSynchronousErrorHandling && context) {\n    context.errorThrown = true;\n    context.error = err;\n  }\n}\n", "import { isFunction } from './util/isFunction';\nimport { Observer, ObservableNotification } from './types';\nimport { isSubscription, Subscription } from './Subscription';\nimport { config } from './config';\nimport { reportUnhandledError } from './util/reportUnhandledError';\nimport { noop } from './util/noop';\nimport { nextNotification, errorNotification, COMPLETE_NOTIFICATION } from './NotificationFactories';\nimport { timeoutProvider } from './scheduler/timeoutProvider';\nimport { captureError } from './util/errorContext';\n\n/**\n * Implements the {@link Observer} interface and extends the\n * {@link Subscription} class. While the {@link Observer} is the public API for\n * consuming the values of an {@link Observable}, all Observers get converted to\n * a Subscriber, in order to provide Subscription-like capabilities such as\n * `unsubscribe`. Subscriber is a common type in RxJS, and crucial for\n * implementing operators, but it is rarely used as a public API.\n *\n * @class Subscriber<T>\n */\nexport class Subscriber<T> extends Subscription implements Observer<T> {\n  /**\n   * A static factory for a Subscriber, given a (potentially partial) definition\n   * of an Observer.\n   * @param next The `next` callback of an Observer.\n   * @param error The `error` callback of an\n   * Observer.\n   * @param complete The `complete` callback of an\n   * Observer.\n   * @return A Subscriber wrapping the (partially defined)\n   * Observer represented by the given arguments.\n   * @nocollapse\n   * @deprecated Do not use. Will be removed in v8. There is no replacement for this\n   * method, and there is no reason to be creating instances of `Subscriber` directly.\n   * If you have a specific use case, please file an issue.\n   */\n  static create<T>(next?: (x?: T) => void, error?: (e?: any) => void, complete?: () => void): Subscriber<T> {\n    return new SafeSubscriber(next, error, complete);\n  }\n\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  protected isStopped: boolean = false;\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  protected destination: Subscriber<any> | Observer<any>; // this `any` is the escape hatch to erase extra type param (e.g. R)\n\n  /**\n   * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n   * There is no reason to directly create an instance of Subscriber. This type is exported for typings reasons.\n   */\n  constructor(destination?: Subscriber<any> | Observer<any>) {\n    super();\n    if (destination) {\n      this.destination = destination;\n      // Automatically chain subscriptions together here.\n      // if destination is a Subscription, then it is a Subscriber.\n      if (isSubscription(destination)) {\n        destination.add(this);\n      }\n    } else {\n      this.destination = EMPTY_OBSERVER;\n    }\n  }\n\n  /**\n   * The {@link Observer} callback to receive notifications of type `next` from\n   * the Observable, with a value. The Observable may call this method 0 or more\n   * times.\n   * @param {T} [value] The `next` value.\n   * @return {void}\n   */\n  next(value?: T): void {\n    if (this.isStopped) {\n      handleStoppedNotification(nextNotification(value), this);\n    } else {\n      this._next(value!);\n    }\n  }\n\n  /**\n   * The {@link Observer} callback to receive notifications of type `error` from\n   * the Observable, with an attached `Error`. Notifies the Observer that\n   * the Observable has experienced an error condition.\n   * @param {any} [err] The `error` exception.\n   * @return {void}\n   */\n  error(err?: any): void {\n    if (this.isStopped) {\n      handleStoppedNotification(errorNotification(err), this);\n    } else {\n      this.isStopped = true;\n      this._error(err);\n    }\n  }\n\n  /**\n   * The {@link Observer} callback to receive a valueless notification of type\n   * `complete` from the Observable. Notifies the Observer that the Observable\n   * has finished sending push-based notifications.\n   * @return {void}\n   */\n  complete(): void {\n    if (this.isStopped) {\n      handleStoppedNotification(COMPLETE_NOTIFICATION, this);\n    } else {\n      this.isStopped = true;\n      this._complete();\n    }\n  }\n\n  unsubscribe(): void {\n    if (!this.closed) {\n      this.isStopped = true;\n      super.unsubscribe();\n      this.destination = null!;\n    }\n  }\n\n  protected _next(value: T): void {\n    this.destination.next(value);\n  }\n\n  protected _error(err: any): void {\n    try {\n      this.destination.error(err);\n    } finally {\n      this.unsubscribe();\n    }\n  }\n\n  protected _complete(): void {\n    try {\n      this.destination.complete();\n    } finally {\n      this.unsubscribe();\n    }\n  }\n}\n\n/**\n * This bind is captured here because we want to be able to have\n * compatibility with monoid libraries that tend to use a method named\n * `bind`. In particular, a library called Monio requires this.\n */\nconst _bind = Function.prototype.bind;\n\nfunction bind<Fn extends (...args: any[]) => any>(fn: Fn, thisArg: any): Fn {\n  return _bind.call(fn, thisArg);\n}\n\n/**\n * Internal optimization only, DO NOT EXPOSE.\n * @internal\n */\nclass ConsumerObserver<T> implements Observer<T> {\n  constructor(private partialObserver: Partial<Observer<T>>) {}\n\n  next(value: T): void {\n    const { partialObserver } = this;\n    if (partialObserver.next) {\n      try {\n        partialObserver.next(value);\n      } catch (error) {\n        handleUnhandledError(error);\n      }\n    }\n  }\n\n  error(err: any): void {\n    const { partialObserver } = this;\n    if (partialObserver.error) {\n      try {\n        partialObserver.error(err);\n      } catch (error) {\n        handleUnhandledError(error);\n      }\n    } else {\n      handleUnhandledError(err);\n    }\n  }\n\n  complete(): void {\n    const { partialObserver } = this;\n    if (partialObserver.complete) {\n      try {\n        partialObserver.complete();\n      } catch (error) {\n        handleUnhandledError(error);\n      }\n    }\n  }\n}\n\nexport class SafeSubscriber<T> extends Subscriber<T> {\n  constructor(\n    observerOrNext?: Partial<Observer<T>> | ((value: T) => void) | null,\n    error?: ((e?: any) => void) | null,\n    complete?: (() => void) | null\n  ) {\n    super();\n\n    let partialObserver: Partial<Observer<T>>;\n    if (isFunction(observerOrNext) || !observerOrNext) {\n      // The first argument is a function, not an observer. The next\n      // two arguments *could* be observers, or they could be empty.\n      partialObserver = {\n        next: (observerOrNext ?? undefined) as (((value: T) => void) | undefined),\n        error: error ?? undefined,\n        complete: complete ?? undefined,\n      };\n    } else {\n      // The first argument is a partial observer.\n      let context: any;\n      if (this && config.useDeprecatedNextContext) {\n        // This is a deprecated path that made `this.unsubscribe()` available in\n        // next handler functions passed to subscribe. This only exists behind a flag\n        // now, as it is *very* slow.\n        context = Object.create(observerOrNext);\n        context.unsubscribe = () => this.unsubscribe();\n        partialObserver = {\n          next: observerOrNext.next && bind(observerOrNext.next, context),\n          error: observerOrNext.error && bind(observerOrNext.error, context),\n          complete: observerOrNext.complete && bind(observerOrNext.complete, context),\n        };\n      } else {\n        // The \"normal\" path. Just use the partial observer directly.\n        partialObserver = observerOrNext;\n      }\n    }\n\n    // Wrap the partial observer to ensure it's a full observer, and\n    // make sure proper error handling is accounted for.\n    this.destination = new ConsumerObserver(partialObserver);\n  }\n}\n\nfunction handleUnhandledError(error: any) {\n  if (config.useDeprecatedSynchronousErrorHandling) {\n    captureError(error);\n  } else {\n    // Ideal path, we report this as an unhandled error,\n    // which is thrown on a new call stack.\n    reportUnhandledError(error);\n  }\n}\n\n/**\n * An error handler used when no error handler was supplied\n * to the SafeSubscriber -- meaning no error handler was supplied\n * do the `subscribe` call on our observable.\n * @param err The error to handle\n */\nfunction defaultErrorHandler(err: any) {\n  throw err;\n}\n\n/**\n * A handler for notifications that cannot be sent to a stopped subscriber.\n * @param notification The notification being sent\n * @param subscriber The stopped subscriber\n */\nfunction handleStoppedNotification(notification: ObservableNotification<any>, subscriber: Subscriber<any>) {\n  const { onStoppedNotification } = config;\n  onStoppedNotification && timeoutProvider.setTimeout(() => onStoppedNotification(notification, subscriber));\n}\n\n/**\n * The observer used as a stub for subscriptions where the user did not\n * pass any arguments to `subscribe`. Comes with the default error handling\n * behavior.\n */\nexport const EMPTY_OBSERVER: Readonly<Observer<any>> & { closed: true } = {\n  closed: true,\n  next: noop,\n  error: defaultErrorHandler,\n  complete: noop,\n};\n", "/**\n * Symbol.observable or a string \"@@observable\". Used for interop\n *\n * @deprecated We will no longer be exporting this symbol in upcoming versions of RxJS.\n * Instead polyfill and use Symbol.observable directly *or* use https://www.npmjs.com/package/symbol-observable\n */\nexport const observable: string | symbol = (() => (typeof Symbol === 'function' && Symbol.observable) || '@@observable')();\n", "/**\n * This function takes one parameter and just returns it. Simply put,\n * this is like `<T>(x: T): T => x`.\n *\n * ## Examples\n *\n * This is useful in some cases when using things like `mergeMap`\n *\n * ```ts\n * import { interval, take, map, range, mergeMap, identity } from 'rxjs';\n *\n * const source$ = interval(1000).pipe(take(5));\n *\n * const result$ = source$.pipe(\n *   map(i => range(i)),\n *   mergeMap(identity) // same as mergeMap(x => x)\n * );\n *\n * result$.subscribe({\n *   next: console.log\n * });\n * ```\n *\n * Or when you want to selectively apply an operator\n *\n * ```ts\n * import { interval, take, identity } from 'rxjs';\n *\n * const shouldLimit = () => Math.random() < 0.5;\n *\n * const source$ = interval(1000);\n *\n * const result$ = source$.pipe(shouldLimit() ? take(5) : identity);\n *\n * result$.subscribe({\n *   next: console.log\n * });\n * ```\n *\n * @param x Any value that is returned by this function\n * @returns The value passed as the first parameter to this function\n */\nexport function identity<T>(x: T): T {\n  return x;\n}\n", "import { identity } from './identity';\nimport { UnaryFunction } from '../types';\n\nexport function pipe(): typeof identity;\nexport function pipe<T, A>(fn1: UnaryFunction<T, A>): UnaryFunction<T, A>;\nexport function pipe<T, A, B>(fn1: UnaryFunction<T, A>, fn2: UnaryFunction<A, B>): UnaryFunction<T, B>;\nexport function pipe<T, A, B, C>(fn1: UnaryFunction<T, A>, fn2: UnaryFunction<A, B>, fn3: UnaryFunction<B, C>): UnaryFunction<T, C>;\nexport function pipe<T, A, B, C, D>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>\n): UnaryFunction<T, D>;\nexport function pipe<T, A, B, C, D, E>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>,\n  fn5: UnaryFunction<D, E>\n): UnaryFunction<T, E>;\nexport function pipe<T, A, B, C, D, E, F>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>,\n  fn5: UnaryFunction<D, E>,\n  fn6: UnaryFunction<E, F>\n): UnaryFunction<T, F>;\nexport function pipe<T, A, B, C, D, E, F, G>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>,\n  fn5: UnaryFunction<D, E>,\n  fn6: UnaryFunction<E, F>,\n  fn7: UnaryFunction<F, G>\n): UnaryFunction<T, G>;\nexport function pipe<T, A, B, C, D, E, F, G, H>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>,\n  fn5: UnaryFunction<D, E>,\n  fn6: UnaryFunction<E, F>,\n  fn7: UnaryFunction<F, G>,\n  fn8: UnaryFunction<G, H>\n): UnaryFunction<T, H>;\nexport function pipe<T, A, B, C, D, E, F, G, H, I>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>,\n  fn5: UnaryFunction<D, E>,\n  fn6: UnaryFunction<E, F>,\n  fn7: UnaryFunction<F, G>,\n  fn8: UnaryFunction<G, H>,\n  fn9: UnaryFunction<H, I>\n): UnaryFunction<T, I>;\nexport function pipe<T, A, B, C, D, E, F, G, H, I>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>,\n  fn5: UnaryFunction<D, E>,\n  fn6: UnaryFunction<E, F>,\n  fn7: UnaryFunction<F, G>,\n  fn8: UnaryFunction<G, H>,\n  fn9: UnaryFunction<H, I>,\n  ...fns: UnaryFunction<any, any>[]\n): UnaryFunction<T, unknown>;\n\n/**\n * pipe() can be called on one or more functions, each of which can take one argument (\"UnaryFunction\")\n * and uses it to return a value.\n * It returns a function that takes one argument, passes it to the first UnaryFunction, and then\n * passes the result to the next one, passes that result to the next one, and so on.  \n */\nexport function pipe(...fns: Array<UnaryFunction<any, any>>): UnaryFunction<any, any> {\n  return pipeFromArray(fns);\n}\n\n/** @internal */\nexport function pipeFromArray<T, R>(fns: Array<UnaryFunction<T, R>>): UnaryFunction<T, R> {\n  if (fns.length === 0) {\n    return identity as UnaryFunction<any, any>;\n  }\n\n  if (fns.length === 1) {\n    return fns[0];\n  }\n\n  return function piped(input: T): R {\n    return fns.reduce((prev: any, fn: UnaryFunction<T, R>) => fn(prev), input as any);\n  };\n}\n", "import { Operator } from './Operator';\nimport { SafeSubscriber, Subscriber } from './Subscriber';\nimport { isSubscription, Subscription } from './Subscription';\nimport { TeardownLogic, OperatorFunction, Subscribable, Observer } from './types';\nimport { observable as Symbol_observable } from './symbol/observable';\nimport { pipeFromArray } from './util/pipe';\nimport { config } from './config';\nimport { isFunction } from './util/isFunction';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A representation of any set of values over any amount of time. This is the most basic building block\n * of RxJS.\n *\n * @class Observable<T>\n */\nexport class Observable<T> implements Subscribable<T> {\n  /**\n   * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n   */\n  source: Observable<any> | undefined;\n\n  /**\n   * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n   */\n  operator: Operator<any, T> | undefined;\n\n  /**\n   * @constructor\n   * @param {Function} subscribe the function that is called when the Observable is\n   * initially subscribed to. This function is given a Subscriber, to which new values\n   * can be `next`ed, or an `error` method can be called to raise an error, or\n   * `complete` can be called to notify of a successful completion.\n   */\n  constructor(subscribe?: (this: Observable<T>, subscriber: Subscriber<T>) => TeardownLogic) {\n    if (subscribe) {\n      this._subscribe = subscribe;\n    }\n  }\n\n  // HACK: Since TypeScript inherits static properties too, we have to\n  // fight against TypeScript here so Subject can have a different static create signature\n  /**\n   * Creates a new Observable by calling the Observable constructor\n   * @owner Observable\n   * @method create\n   * @param {Function} subscribe? the subscriber function to be passed to the Observable constructor\n   * @return {Observable} a new observable\n   * @nocollapse\n   * @deprecated Use `new Observable()` instead. Will be removed in v8.\n   */\n  static create: (...args: any[]) => any = <T>(subscribe?: (subscriber: Subscriber<T>) => TeardownLogic) => {\n    return new Observable<T>(subscribe);\n  };\n\n  /**\n   * Creates a new Observable, with this Observable instance as the source, and the passed\n   * operator defined as the new observable's operator.\n   * @method lift\n   * @param operator the operator defining the operation to take on the observable\n   * @return a new observable with the Operator applied\n   * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n   * If you have implemented an operator using `lift`, it is recommended that you create an\n   * operator by simply returning `new Observable()` directly. See \"Creating new operators from\n   * scratch\" section here: https://rxjs.dev/guide/operators\n   */\n  lift<R>(operator?: Operator<T, R>): Observable<R> {\n    const observable = new Observable<R>();\n    observable.source = this;\n    observable.operator = operator;\n    return observable;\n  }\n\n  subscribe(observerOrNext?: Partial<Observer<T>> | ((value: T) => void)): Subscription;\n  /** @deprecated Instead of passing separate callback arguments, use an observer argument. Signatures taking separate callback arguments will be removed in v8. Details: https://rxjs.dev/deprecations/subscribe-arguments */\n  subscribe(next?: ((value: T) => void) | null, error?: ((error: any) => void) | null, complete?: (() => void) | null): Subscription;\n  /**\n   * Invokes an execution of an Observable and registers Observer handlers for notifications it will emit.\n   *\n   * <span class=\"informal\">Use it when you have all these Observables, but still nothing is happening.</span>\n   *\n   * `subscribe` is not a regular operator, but a method that calls Observable's internal `subscribe` function. It\n   * might be for example a function that you passed to Observable's constructor, but most of the time it is\n   * a library implementation, which defines what will be emitted by an Observable, and when it be will emitted. This means\n   * that calling `subscribe` is actually the moment when Observable starts its work, not when it is created, as it is often\n   * the thought.\n   *\n   * Apart from starting the execution of an Observable, this method allows you to listen for values\n   * that an Observable emits, as well as for when it completes or errors. You can achieve this in two\n   * of the following ways.\n   *\n   * The first way is creating an object that implements {@link Observer} interface. It should have methods\n   * defined by that interface, but note that it should be just a regular JavaScript object, which you can create\n   * yourself in any way you want (ES6 class, classic function constructor, object literal etc.). In particular, do\n   * not attempt to use any RxJS implementation details to create Observers - you don't need them. Remember also\n   * that your object does not have to implement all methods. If you find yourself creating a method that doesn't\n   * do anything, you can simply omit it. Note however, if the `error` method is not provided and an error happens,\n   * it will be thrown asynchronously. Errors thrown asynchronously cannot be caught using `try`/`catch`. Instead,\n   * use the {@link onUnhandledError} configuration option or use a runtime handler (like `window.onerror` or\n   * `process.on('error)`) to be notified of unhandled errors. Because of this, it's recommended that you provide\n   * an `error` method to avoid missing thrown errors.\n   *\n   * The second way is to give up on Observer object altogether and simply provide callback functions in place of its methods.\n   * This means you can provide three functions as arguments to `subscribe`, where the first function is equivalent\n   * of a `next` method, the second of an `error` method and the third of a `complete` method. Just as in case of an Observer,\n   * if you do not need to listen for something, you can omit a function by passing `undefined` or `null`,\n   * since `subscribe` recognizes these functions by where they were placed in function call. When it comes\n   * to the `error` function, as with an Observer, if not provided, errors emitted by an Observable will be thrown asynchronously.\n   *\n   * You can, however, subscribe with no parameters at all. This may be the case where you're not interested in terminal events\n   * and you also handled emissions internally by using operators (e.g. using `tap`).\n   *\n   * Whichever style of calling `subscribe` you use, in both cases it returns a Subscription object.\n   * This object allows you to call `unsubscribe` on it, which in turn will stop the work that an Observable does and will clean\n   * up all resources that an Observable used. Note that cancelling a subscription will not call `complete` callback\n   * provided to `subscribe` function, which is reserved for a regular completion signal that comes from an Observable.\n   *\n   * Remember that callbacks provided to `subscribe` are not guaranteed to be called asynchronously.\n   * It is an Observable itself that decides when these functions will be called. For example {@link of}\n   * by default emits all its values synchronously. Always check documentation for how given Observable\n   * will behave when subscribed and if its default behavior can be modified with a `scheduler`.\n   *\n   * #### Examples\n   *\n   * Subscribe with an {@link guide/observer Observer}\n   *\n   * ```ts\n   * import { of } from 'rxjs';\n   *\n   * const sumObserver = {\n   *   sum: 0,\n   *   next(value) {\n   *     console.log('Adding: ' + value);\n   *     this.sum = this.sum + value;\n   *   },\n   *   error() {\n   *     // We actually could just remove this method,\n   *     // since we do not really care about errors right now.\n   *   },\n   *   complete() {\n   *     console.log('Sum equals: ' + this.sum);\n   *   }\n   * };\n   *\n   * of(1, 2, 3) // Synchronously emits 1, 2, 3 and then completes.\n   *   .subscribe(sumObserver);\n   *\n   * // Logs:\n   * // 'Adding: 1'\n   * // 'Adding: 2'\n   * // 'Adding: 3'\n   * // 'Sum equals: 6'\n   * ```\n   *\n   * Subscribe with functions ({@link deprecations/subscribe-arguments deprecated})\n   *\n   * ```ts\n   * import { of } from 'rxjs'\n   *\n   * let sum = 0;\n   *\n   * of(1, 2, 3).subscribe(\n   *   value => {\n   *     console.log('Adding: ' + value);\n   *     sum = sum + value;\n   *   },\n   *   undefined,\n   *   () => console.log('Sum equals: ' + sum)\n   * );\n   *\n   * // Logs:\n   * // 'Adding: 1'\n   * // 'Adding: 2'\n   * // 'Adding: 3'\n   * // 'Sum equals: 6'\n   * ```\n   *\n   * Cancel a subscription\n   *\n   * ```ts\n   * import { interval } from 'rxjs';\n   *\n   * const subscription = interval(1000).subscribe({\n   *   next(num) {\n   *     console.log(num)\n   *   },\n   *   complete() {\n   *     // Will not be called, even when cancelling subscription.\n   *     console.log('completed!');\n   *   }\n   * });\n   *\n   * setTimeout(() => {\n   *   subscription.unsubscribe();\n   *   console.log('unsubscribed!');\n   * }, 2500);\n   *\n   * // Logs:\n   * // 0 after 1s\n   * // 1 after 2s\n   * // 'unsubscribed!' after 2.5s\n   * ```\n   *\n   * @param {Observer|Function} observerOrNext (optional) Either an observer with methods to be called,\n   * or the first of three possible handlers, which is the handler for each value emitted from the subscribed\n   * Observable.\n   * @param {Function} error (optional) A handler for a terminal event resulting from an error. If no error handler is provided,\n   * the error will be thrown asynchronously as unhandled.\n   * @param {Function} complete (optional) A handler for a terminal event resulting from successful completion.\n   * @return {Subscription} a subscription reference to the registered handlers\n   * @method subscribe\n   */\n  subscribe(\n    observerOrNext?: Partial<Observer<T>> | ((value: T) => void) | null,\n    error?: ((error: any) => void) | null,\n    complete?: (() => void) | null\n  ): Subscription {\n    const subscriber = isSubscriber(observerOrNext) ? observerOrNext : new SafeSubscriber(observerOrNext, error, complete);\n\n    errorContext(() => {\n      const { operator, source } = this;\n      subscriber.add(\n        operator\n          ? // We're dealing with a subscription in the\n            // operator chain to one of our lifted operators.\n            operator.call(subscriber, source)\n          : source\n          ? // If `source` has a value, but `operator` does not, something that\n            // had intimate knowledge of our API, like our `Subject`, must have\n            // set it. We're going to just call `_subscribe` directly.\n            this._subscribe(subscriber)\n          : // In all other cases, we're likely wrapping a user-provided initializer\n            // function, so we need to catch errors and handle them appropriately.\n            this._trySubscribe(subscriber)\n      );\n    });\n\n    return subscriber;\n  }\n\n  /** @internal */\n  protected _trySubscribe(sink: Subscriber<T>): TeardownLogic {\n    try {\n      return this._subscribe(sink);\n    } catch (err) {\n      // We don't need to return anything in this case,\n      // because it's just going to try to `add()` to a subscription\n      // above.\n      sink.error(err);\n    }\n  }\n\n  /**\n   * Used as a NON-CANCELLABLE means of subscribing to an observable, for use with\n   * APIs that expect promises, like `async/await`. You cannot unsubscribe from this.\n   *\n   * **WARNING**: Only use this with observables you *know* will complete. If the source\n   * observable does not complete, you will end up with a promise that is hung up, and\n   * potentially all of the state of an async function hanging out in memory. To avoid\n   * this situation, look into adding something like {@link timeout}, {@link take},\n   * {@link takeWhile}, or {@link takeUntil} amongst others.\n   *\n   * #### Example\n   *\n   * ```ts\n   * import { interval, take } from 'rxjs';\n   *\n   * const source$ = interval(1000).pipe(take(4));\n   *\n   * async function getTotal() {\n   *   let total = 0;\n   *\n   *   await source$.forEach(value => {\n   *     total += value;\n   *     console.log('observable -> ' + value);\n   *   });\n   *\n   *   return total;\n   * }\n   *\n   * getTotal().then(\n   *   total => console.log('Total: ' + total)\n   * );\n   *\n   * // Expected:\n   * // 'observable -> 0'\n   * // 'observable -> 1'\n   * // 'observable -> 2'\n   * // 'observable -> 3'\n   * // 'Total: 6'\n   * ```\n   *\n   * @param next a handler for each value emitted by the observable\n   * @return a promise that either resolves on observable completion or\n   *  rejects with the handled error\n   */\n  forEach(next: (value: T) => void): Promise<void>;\n\n  /**\n   * @param next a handler for each value emitted by the observable\n   * @param promiseCtor a constructor function used to instantiate the Promise\n   * @return a promise that either resolves on observable completion or\n   *  rejects with the handled error\n   * @deprecated Passing a Promise constructor will no longer be available\n   * in upcoming versions of RxJS. This is because it adds weight to the library, for very\n   * little benefit. If you need this functionality, it is recommended that you either\n   * polyfill Promise, or you create an adapter to convert the returned native promise\n   * to whatever promise implementation you wanted. Will be removed in v8.\n   */\n  forEach(next: (value: T) => void, promiseCtor: PromiseConstructorLike): Promise<void>;\n\n  forEach(next: (value: T) => void, promiseCtor?: PromiseConstructorLike): Promise<void> {\n    promiseCtor = getPromiseCtor(promiseCtor);\n\n    return new promiseCtor<void>((resolve, reject) => {\n      const subscriber = new SafeSubscriber<T>({\n        next: (value) => {\n          try {\n            next(value);\n          } catch (err) {\n            reject(err);\n            subscriber.unsubscribe();\n          }\n        },\n        error: reject,\n        complete: resolve,\n      });\n      this.subscribe(subscriber);\n    }) as Promise<void>;\n  }\n\n  /** @internal */\n  protected _subscribe(subscriber: Subscriber<any>): TeardownLogic {\n    return this.source?.subscribe(subscriber);\n  }\n\n  /**\n   * An interop point defined by the es7-observable spec https://github.com/zenparsing/es-observable\n   * @method Symbol.observable\n   * @return {Observable} this instance of the observable\n   */\n  [Symbol_observable]() {\n    return this;\n  }\n\n  /* tslint:disable:max-line-length */\n  pipe(): Observable<T>;\n  pipe<A>(op1: OperatorFunction<T, A>): Observable<A>;\n  pipe<A, B>(op1: OperatorFunction<T, A>, op2: OperatorFunction<A, B>): Observable<B>;\n  pipe<A, B, C>(op1: OperatorFunction<T, A>, op2: OperatorFunction<A, B>, op3: OperatorFunction<B, C>): Observable<C>;\n  pipe<A, B, C, D>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>\n  ): Observable<D>;\n  pipe<A, B, C, D, E>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>,\n    op5: OperatorFunction<D, E>\n  ): Observable<E>;\n  pipe<A, B, C, D, E, F>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>,\n    op5: OperatorFunction<D, E>,\n    op6: OperatorFunction<E, F>\n  ): Observable<F>;\n  pipe<A, B, C, D, E, F, G>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>,\n    op5: OperatorFunction<D, E>,\n    op6: OperatorFunction<E, F>,\n    op7: OperatorFunction<F, G>\n  ): Observable<G>;\n  pipe<A, B, C, D, E, F, G, H>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>,\n    op5: OperatorFunction<D, E>,\n    op6: OperatorFunction<E, F>,\n    op7: OperatorFunction<F, G>,\n    op8: OperatorFunction<G, H>\n  ): Observable<H>;\n  pipe<A, B, C, D, E, F, G, H, I>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>,\n    op5: OperatorFunction<D, E>,\n    op6: OperatorFunction<E, F>,\n    op7: OperatorFunction<F, G>,\n    op8: OperatorFunction<G, H>,\n    op9: OperatorFunction<H, I>\n  ): Observable<I>;\n  pipe<A, B, C, D, E, F, G, H, I>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>,\n    op5: OperatorFunction<D, E>,\n    op6: OperatorFunction<E, F>,\n    op7: OperatorFunction<F, G>,\n    op8: OperatorFunction<G, H>,\n    op9: OperatorFunction<H, I>,\n    ...operations: OperatorFunction<any, any>[]\n  ): Observable<unknown>;\n  /* tslint:enable:max-line-length */\n\n  /**\n   * Used to stitch together functional operators into a chain.\n   * @method pipe\n   * @return {Observable} the Observable result of all of the operators having\n   * been called in the order they were passed in.\n   *\n   * ## Example\n   *\n   * ```ts\n   * import { interval, filter, map, scan } from 'rxjs';\n   *\n   * interval(1000)\n   *   .pipe(\n   *     filter(x => x % 2 === 0),\n   *     map(x => x + x),\n   *     scan((acc, x) => acc + x)\n   *   )\n   *   .subscribe(x => console.log(x));\n   * ```\n   */\n  pipe(...operations: OperatorFunction<any, any>[]): Observable<any> {\n    return pipeFromArray(operations)(this);\n  }\n\n  /* tslint:disable:max-line-length */\n  /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n  toPromise(): Promise<T | undefined>;\n  /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n  toPromise(PromiseCtor: typeof Promise): Promise<T | undefined>;\n  /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n  toPromise(PromiseCtor: PromiseConstructorLike): Promise<T | undefined>;\n  /* tslint:enable:max-line-length */\n\n  /**\n   * Subscribe to this Observable and get a Promise resolving on\n   * `complete` with the last emission (if any).\n   *\n   * **WARNING**: Only use this with observables you *know* will complete. If the source\n   * observable does not complete, you will end up with a promise that is hung up, and\n   * potentially all of the state of an async function hanging out in memory. To avoid\n   * this situation, look into adding something like {@link timeout}, {@link take},\n   * {@link takeWhile}, or {@link takeUntil} amongst others.\n   *\n   * @method toPromise\n   * @param [promiseCtor] a constructor function used to instantiate\n   * the Promise\n   * @return A Promise that resolves with the last value emit, or\n   * rejects on an error. If there were no emissions, Promise\n   * resolves with undefined.\n   * @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise\n   */\n  toPromise(promiseCtor?: PromiseConstructorLike): Promise<T | undefined> {\n    promiseCtor = getPromiseCtor(promiseCtor);\n\n    return new promiseCtor((resolve, reject) => {\n      let value: T | undefined;\n      this.subscribe(\n        (x: T) => (value = x),\n        (err: any) => reject(err),\n        () => resolve(value)\n      );\n    }) as Promise<T | undefined>;\n  }\n}\n\n/**\n * Decides between a passed promise constructor from consuming code,\n * A default configured promise constructor, and the native promise\n * constructor and returns it. If nothing can be found, it will throw\n * an error.\n * @param promiseCtor The optional promise constructor to passed by consuming code\n */\nfunction getPromiseCtor(promiseCtor: PromiseConstructorLike | undefined) {\n  return promiseCtor ?? config.Promise ?? Promise;\n}\n\nfunction isObserver<T>(value: any): value is Observer<T> {\n  return value && isFunction(value.next) && isFunction(value.error) && isFunction(value.complete);\n}\n\nfunction isSubscriber<T>(value: any): value is Subscriber<T> {\n  return (value && value instanceof Subscriber) || (isObserver(value) && isSubscription(value));\n}\n", "import { Observable } from '../Observable';\nimport { Subscriber } from '../Subscriber';\nimport { OperatorFunction } from '../types';\nimport { isFunction } from './isFunction';\n\n/**\n * Used to determine if an object is an Observable with a lift function.\n */\nexport function hasLift(source: any): source is { lift: InstanceType<typeof Observable>['lift'] } {\n  return isFunction(source?.lift);\n}\n\n/**\n * Creates an `OperatorFunction`. Used to define operators throughout the library in a concise way.\n * @param init The logic to connect the liftedSource to the subscriber at the moment of subscription.\n */\nexport function operate<T, R>(\n  init: (liftedSource: Observable<T>, subscriber: Subscriber<R>) => (() => void) | void\n): OperatorFunction<T, R> {\n  return (source: Observable<T>) => {\n    if (hasLift(source)) {\n      return source.lift(function (this: Subscriber<R>, liftedSource: Observable<T>) {\n        try {\n          return init(liftedSource, this);\n        } catch (err) {\n          this.error(err);\n        }\n      });\n    }\n    throw new TypeError('Unable to lift unknown Observable type');\n  };\n}\n", "import { Subscriber } from '../Subscriber';\n\n/**\n * Creates an instance of an `OperatorSubscriber`.\n * @param destination The downstream subscriber.\n * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n * and send to the `destination` error handler.\n * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n * this handler are sent to the `destination` error handler.\n * @param onFinalize Additional teardown logic here. This will only be called on teardown if the\n * subscriber itself is not already closed. This is called after all other teardown logic is executed.\n */\nexport function createOperatorSubscriber<T>(\n  destination: Subscriber<any>,\n  onNext?: (value: T) => void,\n  onComplete?: () => void,\n  onError?: (err: any) => void,\n  onFinalize?: () => void\n): Subscriber<T> {\n  return new OperatorSubscriber(destination, onNext, onComplete, onError, onFinalize);\n}\n\n/**\n * A generic helper for allowing operators to be created with a Subscriber and\n * use closures to capture necessary state from the operator function itself.\n */\nexport class OperatorSubscriber<T> extends Subscriber<T> {\n  /**\n   * Creates an instance of an `OperatorSubscriber`.\n   * @param destination The downstream subscriber.\n   * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n   * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n   * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n   * and send to the `destination` error handler.\n   * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n   * this handler are sent to the `destination` error handler.\n   * @param onFinalize Additional finalization logic here. This will only be called on finalization if the\n   * subscriber itself is not already closed. This is called after all other finalization logic is executed.\n   * @param shouldUnsubscribe An optional check to see if an unsubscribe call should truly unsubscribe.\n   * NOTE: This currently **ONLY** exists to support the strange behavior of {@link groupBy}, where unsubscription\n   * to the resulting observable does not actually disconnect from the source if there are active subscriptions\n   * to any grouped observable. (DO NOT EXPOSE OR USE EXTERNALLY!!!)\n   */\n  constructor(\n    destination: Subscriber<any>,\n    onNext?: (value: T) => void,\n    onComplete?: () => void,\n    onError?: (err: any) => void,\n    private onFinalize?: () => void,\n    private shouldUnsubscribe?: () => boolean\n  ) {\n    // It's important - for performance reasons - that all of this class's\n    // members are initialized and that they are always initialized in the same\n    // order. This will ensure that all OperatorSubscriber instances have the\n    // same hidden class in V8. This, in turn, will help keep the number of\n    // hidden classes involved in property accesses within the base class as\n    // low as possible. If the number of hidden classes involved exceeds four,\n    // the property accesses will become megamorphic and performance penalties\n    // will be incurred - i.e. inline caches won't be used.\n    //\n    // The reasons for ensuring all instances have the same hidden class are\n    // further discussed in this blog post from Benedikt Meurer:\n    // https://benediktmeurer.de/2018/03/23/impact-of-polymorphism-on-component-based-frameworks-like-react/\n    super(destination);\n    this._next = onNext\n      ? function (this: OperatorSubscriber<T>, value: T) {\n          try {\n            onNext(value);\n          } catch (err) {\n            destination.error(err);\n          }\n        }\n      : super._next;\n    this._error = onError\n      ? function (this: OperatorSubscriber<T>, err: any) {\n          try {\n            onError(err);\n          } catch (err) {\n            // Send any errors that occur down stream.\n            destination.error(err);\n          } finally {\n            // Ensure finalization.\n            this.unsubscribe();\n          }\n        }\n      : super._error;\n    this._complete = onComplete\n      ? function (this: OperatorSubscriber<T>) {\n          try {\n            onComplete();\n          } catch (err) {\n            // Send any errors that occur down stream.\n            destination.error(err);\n          } finally {\n            // Ensure finalization.\n            this.unsubscribe();\n          }\n        }\n      : super._complete;\n  }\n\n  unsubscribe() {\n    if (!this.shouldUnsubscribe || this.shouldUnsubscribe()) {\n      const { closed } = this;\n      super.unsubscribe();\n      // Execute additional teardown if we have any and we didn't already do so.\n      !closed && this.onFinalize?.();\n    }\n  }\n}\n", "import { Subscription } from '../Subscription';\n\ninterface AnimationFrameProvider {\n  schedule(callback: FrameRequestCallback): Subscription;\n  requestAnimationFrame: typeof requestAnimationFrame;\n  cancelAnimationFrame: typeof cancelAnimationFrame;\n  delegate:\n    | {\n        requestAnimationFrame: typeof requestAnimationFrame;\n        cancelAnimationFrame: typeof cancelAnimationFrame;\n      }\n    | undefined;\n}\n\nexport const animationFrameProvider: AnimationFrameProvider = {\n  // When accessing the delegate, use the variable rather than `this` so that\n  // the functions can be called without being bound to the provider.\n  schedule(callback) {\n    let request = requestAnimationFrame;\n    let cancel: typeof cancelAnimationFrame | undefined = cancelAnimationFrame;\n    const { delegate } = animationFrameProvider;\n    if (delegate) {\n      request = delegate.requestAnimationFrame;\n      cancel = delegate.cancelAnimationFrame;\n    }\n    const handle = request((timestamp) => {\n      // Clear the cancel function. The request has been fulfilled, so\n      // attempting to cancel the request upon unsubscription would be\n      // pointless.\n      cancel = undefined;\n      callback(timestamp);\n    });\n    return new Subscription(() => cancel?.(handle));\n  },\n  requestAnimationFrame(...args) {\n    const { delegate } = animationFrameProvider;\n    return (delegate?.requestAnimationFrame || requestAnimationFrame)(...args);\n  },\n  cancelAnimationFrame(...args) {\n    const { delegate } = animationFrameProvider;\n    return (delegate?.cancelAnimationFrame || cancelAnimationFrame)(...args);\n  },\n  delegate: undefined,\n};\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface ObjectUnsubscribedError extends Error {}\n\nexport interface ObjectUnsubscribedErrorCtor {\n  /**\n   * @deprecated Internal implementation detail. Do not construct error instances.\n   * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n   */\n  new (): ObjectUnsubscribedError;\n}\n\n/**\n * An error thrown when an action is invalid because the object has been\n * unsubscribed.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n *\n * @class ObjectUnsubscribedError\n */\nexport const ObjectUnsubscribedError: ObjectUnsubscribedErrorCtor = createErrorClass(\n  (_super) =>\n    function ObjectUnsubscribedErrorImpl(this: any) {\n      _super(this);\n      this.name = 'ObjectUnsubscribedError';\n      this.message = 'object unsubscribed';\n    }\n);\n", "import { Operator } from './Operator';\nimport { Observable } from './Observable';\nimport { Subscriber } from './Subscriber';\nimport { Subscription, EMPTY_SUBSCRIPTION } from './Subscription';\nimport { Observer, SubscriptionLike, TeardownLogic } from './types';\nimport { ObjectUnsubscribedError } from './util/ObjectUnsubscribedError';\nimport { arrRemove } from './util/arrRemove';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A Subject is a special type of Observable that allows values to be\n * multicasted to many Observers. Subjects are like EventEmitters.\n *\n * Every Subject is an Observable and an Observer. You can subscribe to a\n * Subject, and you can call next to feed values as well as error and complete.\n */\nexport class Subject<T> extends Observable<T> implements SubscriptionLike {\n  closed = false;\n\n  private currentObservers: Observer<T>[] | null = null;\n\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  observers: Observer<T>[] = [];\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  isStopped = false;\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  hasError = false;\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  thrownError: any = null;\n\n  /**\n   * Creates a \"subject\" by basically gluing an observer to an observable.\n   *\n   * @nocollapse\n   * @deprecated Recommended you do not use. Will be removed at some point in the future. Plans for replacement still under discussion.\n   */\n  static create: (...args: any[]) => any = <T>(destination: Observer<T>, source: Observable<T>): AnonymousSubject<T> => {\n    return new AnonymousSubject<T>(destination, source);\n  };\n\n  constructor() {\n    // NOTE: This must be here to obscure Observable's constructor.\n    super();\n  }\n\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  lift<R>(operator: Operator<T, R>): Observable<R> {\n    const subject = new AnonymousSubject(this, this);\n    subject.operator = operator as any;\n    return subject as any;\n  }\n\n  /** @internal */\n  protected _throwIfClosed() {\n    if (this.closed) {\n      throw new ObjectUnsubscribedError();\n    }\n  }\n\n  next(value: T) {\n    errorContext(() => {\n      this._throwIfClosed();\n      if (!this.isStopped) {\n        if (!this.currentObservers) {\n          this.currentObservers = Array.from(this.observers);\n        }\n        for (const observer of this.currentObservers) {\n          observer.next(value);\n        }\n      }\n    });\n  }\n\n  error(err: any) {\n    errorContext(() => {\n      this._throwIfClosed();\n      if (!this.isStopped) {\n        this.hasError = this.isStopped = true;\n        this.thrownError = err;\n        const { observers } = this;\n        while (observers.length) {\n          observers.shift()!.error(err);\n        }\n      }\n    });\n  }\n\n  complete() {\n    errorContext(() => {\n      this._throwIfClosed();\n      if (!this.isStopped) {\n        this.isStopped = true;\n        const { observers } = this;\n        while (observers.length) {\n          observers.shift()!.complete();\n        }\n      }\n    });\n  }\n\n  unsubscribe() {\n    this.isStopped = this.closed = true;\n    this.observers = this.currentObservers = null!;\n  }\n\n  get observed() {\n    return this.observers?.length > 0;\n  }\n\n  /** @internal */\n  protected _trySubscribe(subscriber: Subscriber<T>): TeardownLogic {\n    this._throwIfClosed();\n    return super._trySubscribe(subscriber);\n  }\n\n  /** @internal */\n  protected _subscribe(subscriber: Subscriber<T>): Subscription {\n    this._throwIfClosed();\n    this._checkFinalizedStatuses(subscriber);\n    return this._innerSubscribe(subscriber);\n  }\n\n  /** @internal */\n  protected _innerSubscribe(subscriber: Subscriber<any>) {\n    const { hasError, isStopped, observers } = this;\n    if (hasError || isStopped) {\n      return EMPTY_SUBSCRIPTION;\n    }\n    this.currentObservers = null;\n    observers.push(subscriber);\n    return new Subscription(() => {\n      this.currentObservers = null;\n      arrRemove(observers, subscriber);\n    });\n  }\n\n  /** @internal */\n  protected _checkFinalizedStatuses(subscriber: Subscriber<any>) {\n    const { hasError, thrownError, isStopped } = this;\n    if (hasError) {\n      subscriber.error(thrownError);\n    } else if (isStopped) {\n      subscriber.complete();\n    }\n  }\n\n  /**\n   * Creates a new Observable with this Subject as the source. You can do this\n   * to create custom Observer-side logic of the Subject and conceal it from\n   * code that uses the Observable.\n   * @return {Observable} Observable that the Subject casts to\n   */\n  asObservable(): Observable<T> {\n    const observable: any = new Observable<T>();\n    observable.source = this;\n    return observable;\n  }\n}\n\n/**\n * @class AnonymousSubject<T>\n */\nexport class AnonymousSubject<T> extends Subject<T> {\n  constructor(\n    /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n    public destination?: Observer<T>,\n    source?: Observable<T>\n  ) {\n    super();\n    this.source = source;\n  }\n\n  next(value: T) {\n    this.destination?.next?.(value);\n  }\n\n  error(err: any) {\n    this.destination?.error?.(err);\n  }\n\n  complete() {\n    this.destination?.complete?.();\n  }\n\n  /** @internal */\n  protected _subscribe(subscriber: Subscriber<T>): Subscription {\n    return this.source?.subscribe(subscriber) ?? EMPTY_SUBSCRIPTION;\n  }\n}\n", "import { Subject } from './Subject';\nimport { Subscriber } from './Subscriber';\nimport { Subscription } from './Subscription';\n\n/**\n * A variant of Subject that requires an initial value and emits its current\n * value whenever it is subscribed to.\n *\n * @class BehaviorSubject<T>\n */\nexport class BehaviorSubject<T> extends Subject<T> {\n  constructor(private _value: T) {\n    super();\n  }\n\n  get value(): T {\n    return this.getValue();\n  }\n\n  /** @internal */\n  protected _subscribe(subscriber: Subscriber<T>): Subscription {\n    const subscription = super._subscribe(subscriber);\n    !subscription.closed && subscriber.next(this._value);\n    return subscription;\n  }\n\n  getValue(): T {\n    const { hasError, thrownError, _value } = this;\n    if (hasError) {\n      throw thrownError;\n    }\n    this._throwIfClosed();\n    return _value;\n  }\n\n  next(value: T): void {\n    super.next((this._value = value));\n  }\n}\n", "import { TimestampProvider } from '../types';\n\ninterface DateTimestampProvider extends TimestampProvider {\n  delegate: TimestampProvider | undefined;\n}\n\nexport const dateTimestampProvider: DateTimestampProvider = {\n  now() {\n    // Use the variable rather than `this` so that the function can be called\n    // without being bound to the provider.\n    return (dateTimestampProvider.delegate || Date).now();\n  },\n  delegate: undefined,\n};\n", "import { Subject } from './Subject';\nimport { TimestampProvider } from './types';\nimport { Subscriber } from './Subscriber';\nimport { Subscription } from './Subscription';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * A variant of {@link Subject} that \"replays\" old values to new subscribers by emitting them when they first subscribe.\n *\n * `ReplaySubject` has an internal buffer that will store a specified number of values that it has observed. Like `Subject`,\n * `ReplaySubject` \"observes\" values by having them passed to its `next` method. When it observes a value, it will store that\n * value for a time determined by the configuration of the `ReplaySubject`, as passed to its constructor.\n *\n * When a new subscriber subscribes to the `ReplaySubject` instance, it will synchronously emit all values in its buffer in\n * a First-In-First-Out (FIFO) manner. The `ReplaySubject` will also complete, if it has observed completion; and it will\n * error if it has observed an error.\n *\n * There are two main configuration items to be concerned with:\n *\n * 1. `bufferSize` - This will determine how many items are stored in the buffer, defaults to infinite.\n * 2. `windowTime` - The amount of time to hold a value in the buffer before removing it from the buffer.\n *\n * Both configurations may exist simultaneously. So if you would like to buffer a maximum of 3 values, as long as the values\n * are less than 2 seconds old, you could do so with a `new ReplaySubject(3, 2000)`.\n *\n * ### Differences with BehaviorSubject\n *\n * `BehaviorSubject` is similar to `new ReplaySubject(1)`, with a couple of exceptions:\n *\n * 1. `BehaviorSubject` comes \"primed\" with a single value upon construction.\n * 2. `ReplaySubject` will replay values, even after observing an error, where `BehaviorSubject` will not.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n * @see {@link shareReplay}\n */\nexport class ReplaySubject<T> extends Subject<T> {\n  private _buffer: (T | number)[] = [];\n  private _infiniteTimeWindow = true;\n\n  /**\n   * @param bufferSize The size of the buffer to replay on subscription\n   * @param windowTime The amount of time the buffered items will stay buffered\n   * @param timestampProvider An object with a `now()` method that provides the current timestamp. This is used to\n   * calculate the amount of time something has been buffered.\n   */\n  constructor(\n    private _bufferSize = Infinity,\n    private _windowTime = Infinity,\n    private _timestampProvider: TimestampProvider = dateTimestampProvider\n  ) {\n    super();\n    this._infiniteTimeWindow = _windowTime === Infinity;\n    this._bufferSize = Math.max(1, _bufferSize);\n    this._windowTime = Math.max(1, _windowTime);\n  }\n\n  next(value: T): void {\n    const { isStopped, _buffer, _infiniteTimeWindow, _timestampProvider, _windowTime } = this;\n    if (!isStopped) {\n      _buffer.push(value);\n      !_infiniteTimeWindow && _buffer.push(_timestampProvider.now() + _windowTime);\n    }\n    this._trimBuffer();\n    super.next(value);\n  }\n\n  /** @internal */\n  protected _subscribe(subscriber: Subscriber<T>): Subscription {\n    this._throwIfClosed();\n    this._trimBuffer();\n\n    const subscription = this._innerSubscribe(subscriber);\n\n    const { _infiniteTimeWindow, _buffer } = this;\n    // We use a copy here, so reentrant code does not mutate our array while we're\n    // emitting it to a new subscriber.\n    const copy = _buffer.slice();\n    for (let i = 0; i < copy.length && !subscriber.closed; i += _infiniteTimeWindow ? 1 : 2) {\n      subscriber.next(copy[i] as T);\n    }\n\n    this._checkFinalizedStatuses(subscriber);\n\n    return subscription;\n  }\n\n  private _trimBuffer() {\n    const { _bufferSize, _timestampProvider, _buffer, _infiniteTimeWindow } = this;\n    // If we don't have an infinite buffer size, and we're over the length,\n    // use splice to truncate the old buffer values off. Note that we have to\n    // double the size for instances where we're not using an infinite time window\n    // because we're storing the values and the timestamps in the same array.\n    const adjustedBufferSize = (_infiniteTimeWindow ? 1 : 2) * _bufferSize;\n    _bufferSize < Infinity && adjustedBufferSize < _buffer.length && _buffer.splice(0, _buffer.length - adjustedBufferSize);\n\n    // Now, if we're not in an infinite time window, remove all values where the time is\n    // older than what is allowed.\n    if (!_infiniteTimeWindow) {\n      const now = _timestampProvider.now();\n      let last = 0;\n      // Search the array for the first timestamp that isn't expired and\n      // truncate the buffer up to that point.\n      for (let i = 1; i < _buffer.length && (_buffer[i] as number) <= now; i += 2) {\n        last = i;\n      }\n      last && _buffer.splice(0, last + 1);\n    }\n  }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Subscription } from '../Subscription';\nimport { SchedulerAction } from '../types';\n\n/**\n * A unit of work to be executed in a `scheduler`. An action is typically\n * created from within a {@link SchedulerLike} and an RxJS user does not need to concern\n * themselves about creating and manipulating an Action.\n *\n * ```ts\n * class Action<T> extends Subscription {\n *   new (scheduler: Scheduler, work: (state?: T) => void);\n *   schedule(state?: T, delay: number = 0): Subscription;\n * }\n * ```\n *\n * @class Action<T>\n */\nexport class Action<T> extends Subscription {\n  constructor(scheduler: Scheduler, work: (this: SchedulerAction<T>, state?: T) => void) {\n    super();\n  }\n  /**\n   * Schedules this action on its parent {@link SchedulerLike} for execution. May be passed\n   * some context object, `state`. May happen at some point in the future,\n   * according to the `delay` parameter, if specified.\n   * @param {T} [state] Some contextual data that the `work` function uses when\n   * called by the Scheduler.\n   * @param {number} [delay] Time to wait before executing the work, where the\n   * time unit is implicit and defined by the Scheduler.\n   * @return {void}\n   */\n  public schedule(state?: T, delay: number = 0): Subscription {\n    return this;\n  }\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetIntervalFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearIntervalFunction = (handle: TimerHandle) => void;\n\ninterface IntervalProvider {\n  setInterval: SetIntervalFunction;\n  clearInterval: ClearIntervalFunction;\n  delegate:\n    | {\n        setInterval: SetIntervalFunction;\n        clearInterval: ClearIntervalFunction;\n      }\n    | undefined;\n}\n\nexport const intervalProvider: IntervalProvider = {\n  // When accessing the delegate, use the variable rather than `this` so that\n  // the functions can be called without being bound to the provider.\n  setInterval(handler: () => void, timeout?: number, ...args) {\n    const { delegate } = intervalProvider;\n    if (delegate?.setInterval) {\n      return delegate.setInterval(handler, timeout, ...args);\n    }\n    return setInterval(handler, timeout, ...args);\n  },\n  clearInterval(handle) {\n    const { delegate } = intervalProvider;\n    return (delegate?.clearInterval || clearInterval)(handle as any);\n  },\n  delegate: undefined,\n};\n", "import { Action } from './Action';\nimport { SchedulerAction } from '../types';\nimport { Subscription } from '../Subscription';\nimport { AsyncScheduler } from './AsyncScheduler';\nimport { intervalProvider } from './intervalProvider';\nimport { arrRemove } from '../util/arrRemove';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncAction<T> extends Action<T> {\n  public id: TimerHandle | undefined;\n  public state?: T;\n  // @ts-ignore: Property has no initializer and is not definitely assigned\n  public delay: number;\n  protected pending: boolean = false;\n\n  constructor(protected scheduler: AsyncScheduler, protected work: (this: SchedulerAction<T>, state?: T) => void) {\n    super(scheduler, work);\n  }\n\n  public schedule(state?: T, delay: number = 0): Subscription {\n    if (this.closed) {\n      return this;\n    }\n\n    // Always replace the current state with the new state.\n    this.state = state;\n\n    const id = this.id;\n    const scheduler = this.scheduler;\n\n    //\n    // Important implementation note:\n    //\n    // Actions only execute once by default, unless rescheduled from within the\n    // scheduled callback. This allows us to implement single and repeat\n    // actions via the same code path, without adding API surface area, as well\n    // as mimic traditional recursion but across asynchronous boundaries.\n    //\n    // However, JS runtimes and timers distinguish between intervals achieved by\n    // serial `setTimeout` calls vs. a single `setInterval` call. An interval of\n    // serial `setTimeout` calls can be individually delayed, which delays\n    // scheduling the next `setTimeout`, and so on. `setInterval` attempts to\n    // guarantee the interval callback will be invoked more precisely to the\n    // interval period, regardless of load.\n    //\n    // Therefore, we use `setInterval` to schedule single and repeat actions.\n    // If the action reschedules itself with the same delay, the interval is not\n    // canceled. If the action doesn't reschedule, or reschedules with a\n    // different delay, the interval will be canceled after scheduled callback\n    // execution.\n    //\n    if (id != null) {\n      this.id = this.recycleAsyncId(scheduler, id, delay);\n    }\n\n    // Set the pending flag indicating that this action has been scheduled, or\n    // has recursively rescheduled itself.\n    this.pending = true;\n\n    this.delay = delay;\n    // If this action has already an async Id, don't request a new one.\n    this.id = this.id ?? this.requestAsyncId(scheduler, this.id, delay);\n\n    return this;\n  }\n\n  protected requestAsyncId(scheduler: AsyncScheduler, _id?: TimerHandle, delay: number = 0): TimerHandle {\n    return intervalProvider.setInterval(scheduler.flush.bind(scheduler, this), delay);\n  }\n\n  protected recycleAsyncId(_scheduler: AsyncScheduler, id?: TimerHandle, delay: number | null = 0): TimerHandle | undefined {\n    // If this action is rescheduled with the same delay time, don't clear the interval id.\n    if (delay != null && this.delay === delay && this.pending === false) {\n      return id;\n    }\n    // Otherwise, if the action's delay time is different from the current delay,\n    // or the action has been rescheduled before it's executed, clear the interval id\n    if (id != null) {\n      intervalProvider.clearInterval(id);\n    }\n\n    return undefined;\n  }\n\n  /**\n   * Immediately executes this action and the `work` it contains.\n   * @return {any}\n   */\n  public execute(state: T, delay: number): any {\n    if (this.closed) {\n      return new Error('executing a cancelled action');\n    }\n\n    this.pending = false;\n    const error = this._execute(state, delay);\n    if (error) {\n      return error;\n    } else if (this.pending === false && this.id != null) {\n      // Dequeue if the action didn't reschedule itself. Don't call\n      // unsubscribe(), because the action could reschedule later.\n      // For example:\n      // ```\n      // scheduler.schedule(function doWork(counter) {\n      //   /* ... I'm a busy worker bee ... */\n      //   var originalAction = this;\n      //   /* wait 100ms before rescheduling the action */\n      //   setTimeout(function () {\n      //     originalAction.schedule(counter + 1);\n      //   }, 100);\n      // }, 1000);\n      // ```\n      this.id = this.recycleAsyncId(this.scheduler, this.id, null);\n    }\n  }\n\n  protected _execute(state: T, _delay: number): any {\n    let errored: boolean = false;\n    let errorValue: any;\n    try {\n      this.work(state);\n    } catch (e) {\n      errored = true;\n      // HACK: Since code elsewhere is relying on the \"truthiness\" of the\n      // return here, we can't have it return \"\" or 0 or false.\n      // TODO: Clean this up when we refactor schedulers mid-version-8 or so.\n      errorValue = e ? e : new Error('Scheduled action threw falsy error');\n    }\n    if (errored) {\n      this.unsubscribe();\n      return errorValue;\n    }\n  }\n\n  unsubscribe() {\n    if (!this.closed) {\n      const { id, scheduler } = this;\n      const { actions } = scheduler;\n\n      this.work = this.state = this.scheduler = null!;\n      this.pending = false;\n\n      arrRemove(actions, this);\n      if (id != null) {\n        this.id = this.recycleAsyncId(scheduler, id, null);\n      }\n\n      this.delay = null!;\n      super.unsubscribe();\n    }\n  }\n}\n", "import { Action } from './scheduler/Action';\nimport { Subscription } from './Subscription';\nimport { SchedulerLike, SchedulerAction } from './types';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * An execution context and a data structure to order tasks and schedule their\n * execution. Provides a notion of (potentially virtual) time, through the\n * `now()` getter method.\n *\n * Each unit of work in a Scheduler is called an `Action`.\n *\n * ```ts\n * class Scheduler {\n *   now(): number;\n *   schedule(work, delay?, state?): Subscription;\n * }\n * ```\n *\n * @class Scheduler\n * @deprecated Scheduler is an internal implementation detail of RxJS, and\n * should not be used directly. Rather, create your own class and implement\n * {@link SchedulerLike}. Will be made internal in v8.\n */\nexport class Scheduler implements SchedulerLike {\n  public static now: () => number = dateTimestampProvider.now;\n\n  constructor(private schedulerActionCtor: typeof Action, now: () => number = Scheduler.now) {\n    this.now = now;\n  }\n\n  /**\n   * A getter method that returns a number representing the current time\n   * (at the time this function was called) according to the scheduler's own\n   * internal clock.\n   * @return {number} A number that represents the current time. May or may not\n   * have a relation to wall-clock time. May or may not refer to a time unit\n   * (e.g. milliseconds).\n   */\n  public now: () => number;\n\n  /**\n   * Schedules a function, `work`, for execution. May happen at some point in\n   * the future, according to the `delay` parameter, if specified. May be passed\n   * some context object, `state`, which will be passed to the `work` function.\n   *\n   * The given arguments will be processed an stored as an Action object in a\n   * queue of actions.\n   *\n   * @param {function(state: ?T): ?Subscription} work A function representing a\n   * task, or some unit of work to be executed by the Scheduler.\n   * @param {number} [delay] Time to wait before executing the work, where the\n   * time unit is implicit and defined by the Scheduler itself.\n   * @param {T} [state] Some contextual data that the `work` function uses when\n   * called by the Scheduler.\n   * @return {Subscription} A subscription in order to be able to unsubscribe\n   * the scheduled work.\n   */\n  public schedule<T>(work: (this: SchedulerAction<T>, state?: T) => void, delay: number = 0, state?: T): Subscription {\n    return new this.schedulerActionCtor<T>(this, work).schedule(state, delay);\n  }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Action } from './Action';\nimport { AsyncAction } from './AsyncAction';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncScheduler extends Scheduler {\n  public actions: Array<AsyncAction<any>> = [];\n  /**\n   * A flag to indicate whether the Scheduler is currently executing a batch of\n   * queued actions.\n   * @type {boolean}\n   * @internal\n   */\n  public _active: boolean = false;\n  /**\n   * An internal ID used to track the latest asynchronous task such as those\n   * coming from `setTimeout`, `setInterval`, `requestAnimationFrame`, and\n   * others.\n   * @type {any}\n   * @internal\n   */\n  public _scheduled: TimerHandle | undefined;\n\n  constructor(SchedulerAction: typeof Action, now: () => number = Scheduler.now) {\n    super(SchedulerAction, now);\n  }\n\n  public flush(action: AsyncAction<any>): void {\n    const { actions } = this;\n\n    if (this._active) {\n      actions.push(action);\n      return;\n    }\n\n    let error: any;\n    this._active = true;\n\n    do {\n      if ((error = action.execute(action.state, action.delay))) {\n        break;\n      }\n    } while ((action = actions.shift()!)); // exhaust the scheduler queue\n\n    this._active = false;\n\n    if (error) {\n      while ((action = actions.shift()!)) {\n        action.unsubscribe();\n      }\n      throw error;\n    }\n  }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\n/**\n *\n * Async Scheduler\n *\n * <span class=\"informal\">Schedule task as if you used setTimeout(task, duration)</span>\n *\n * `async` scheduler schedules tasks asynchronously, by putting them on the JavaScript\n * event loop queue. It is best used to delay tasks in time or to schedule tasks repeating\n * in intervals.\n *\n * If you just want to \"defer\" task, that is to perform it right after currently\n * executing synchronous code ends (commonly achieved by `setTimeout(deferredTask, 0)`),\n * better choice will be the {@link asapScheduler} scheduler.\n *\n * ## Examples\n * Use async scheduler to delay task\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * const task = () => console.log('it works!');\n *\n * asyncScheduler.schedule(task, 2000);\n *\n * // After 2 seconds logs:\n * // \"it works!\"\n * ```\n *\n * Use async scheduler to repeat task in intervals\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * function task(state) {\n *   console.log(state);\n *   this.schedule(state + 1, 1000); // `this` references currently executing Action,\n *                                   // which we reschedule with new state and delay\n * }\n *\n * asyncScheduler.schedule(task, 3000, 0);\n *\n * // Logs:\n * // 0 after 3s\n * // 1 after 4s\n * // 2 after 5s\n * // 3 after 6s\n * ```\n */\n\nexport const asyncScheduler = new AsyncScheduler(AsyncAction);\n\n/**\n * @deprecated Renamed to {@link asyncScheduler}. Will be removed in v8.\n */\nexport const async = asyncScheduler;\n", "import { AsyncAction } from './AsyncAction';\nimport { Subscription } from '../Subscription';\nimport { QueueScheduler } from './QueueScheduler';\nimport { SchedulerAction } from '../types';\nimport { TimerHandle } from './timerHandle';\n\nexport class QueueAction<T> extends AsyncAction<T> {\n  constructor(protected scheduler: QueueScheduler, protected work: (this: SchedulerAction<T>, state?: T) => void) {\n    super(scheduler, work);\n  }\n\n  public schedule(state?: T, delay: number = 0): Subscription {\n    if (delay > 0) {\n      return super.schedule(state, delay);\n    }\n    this.delay = delay;\n    this.state = state;\n    this.scheduler.flush(this);\n    return this;\n  }\n\n  public execute(state: T, delay: number): any {\n    return delay > 0 || this.closed ? super.execute(state, delay) : this._execute(state, delay);\n  }\n\n  protected requestAsyncId(scheduler: QueueScheduler, id?: TimerHandle, delay: number = 0): TimerHandle {\n    // If delay exists and is greater than 0, or if the delay is null (the\n    // action wasn't rescheduled) but was originally scheduled as an async\n    // action, then recycle as an async action.\n\n    if ((delay != null && delay > 0) || (delay == null && this.delay > 0)) {\n      return super.requestAsyncId(scheduler, id, delay);\n    }\n\n    // Otherwise flush the scheduler starting with this action.\n    scheduler.flush(this);\n\n    // HACK: In the past, this was returning `void`. However, `void` isn't a valid\n    // `TimerHandle`, and generally the return value here isn't really used. So the\n    // compromise is to return `0` which is both \"falsy\" and a valid `TimerHandle`,\n    // as opposed to refactoring every other instanceo of `requestAsyncId`.\n    return 0;\n  }\n}\n", "import { AsyncScheduler } from './AsyncScheduler';\n\nexport class QueueScheduler extends AsyncScheduler {\n}\n", "import { QueueAction } from './QueueAction';\nimport { QueueScheduler } from './QueueScheduler';\n\n/**\n *\n * Queue Scheduler\n *\n * <span class=\"informal\">Put every next task on a queue, instead of executing it immediately</span>\n *\n * `queue` scheduler, when used with delay, behaves the same as {@link asyncScheduler} scheduler.\n *\n * When used without delay, it schedules given task synchronously - executes it right when\n * it is scheduled. However when called recursively, that is when inside the scheduled task,\n * another task is scheduled with queue scheduler, instead of executing immediately as well,\n * that task will be put on a queue and wait for current one to finish.\n *\n * This means that when you execute task with `queue` scheduler, you are sure it will end\n * before any other task scheduled with that scheduler will start.\n *\n * ## Examples\n * Schedule recursively first, then do something\n * ```ts\n * import { queueScheduler } from 'rxjs';\n *\n * queueScheduler.schedule(() => {\n *   queueScheduler.schedule(() => console.log('second')); // will not happen now, but will be put on a queue\n *\n *   console.log('first');\n * });\n *\n * // Logs:\n * // \"first\"\n * // \"second\"\n * ```\n *\n * Reschedule itself recursively\n * ```ts\n * import { queueScheduler } from 'rxjs';\n *\n * queueScheduler.schedule(function(state) {\n *   if (state !== 0) {\n *     console.log('before', state);\n *     this.schedule(state - 1); // `this` references currently executing Action,\n *                               // which we reschedule with new state\n *     console.log('after', state);\n *   }\n * }, 0, 3);\n *\n * // In scheduler that runs recursively, you would expect:\n * // \"before\", 3\n * // \"before\", 2\n * // \"before\", 1\n * // \"after\", 1\n * // \"after\", 2\n * // \"after\", 3\n *\n * // But with queue it logs:\n * // \"before\", 3\n * // \"after\", 3\n * // \"before\", 2\n * // \"after\", 2\n * // \"before\", 1\n * // \"after\", 1\n * ```\n */\n\nexport const queueScheduler = new QueueScheduler(QueueAction);\n\n/**\n * @deprecated Renamed to {@link queueScheduler}. Will be removed in v8.\n */\nexport const queue = queueScheduler;\n", "import { AsyncAction } from './AsyncAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\nimport { SchedulerAction } from '../types';\nimport { animationFrameProvider } from './animationFrameProvider';\nimport { TimerHandle } from './timerHandle';\n\nexport class AnimationFrameAction<T> extends AsyncAction<T> {\n  constructor(protected scheduler: AnimationFrameScheduler, protected work: (this: SchedulerAction<T>, state?: T) => void) {\n    super(scheduler, work);\n  }\n\n  protected requestAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle {\n    // If delay is greater than 0, request as an async action.\n    if (delay !== null && delay > 0) {\n      return super.requestAsyncId(scheduler, id, delay);\n    }\n    // Push the action to the end of the scheduler queue.\n    scheduler.actions.push(this);\n    // If an animation frame has already been requested, don't request another\n    // one. If an animation frame hasn't been requested yet, request one. Return\n    // the current animation frame request id.\n    return scheduler._scheduled || (scheduler._scheduled = animationFrameProvider.requestAnimationFrame(() => scheduler.flush(undefined)));\n  }\n\n  protected recycleAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle | undefined {\n    // If delay exists and is greater than 0, or if the delay is null (the\n    // action wasn't rescheduled) but was originally scheduled as an async\n    // action, then recycle as an async action.\n    if (delay != null ? delay > 0 : this.delay > 0) {\n      return super.recycleAsyncId(scheduler, id, delay);\n    }\n    // If the scheduler queue has no remaining actions with the same async id,\n    // cancel the requested animation frame and set the scheduled flag to\n    // undefined so the next AnimationFrameAction will request its own.\n    const { actions } = scheduler;\n    if (id != null && actions[actions.length - 1]?.id !== id) {\n      animationFrameProvider.cancelAnimationFrame(id as number);\n      scheduler._scheduled = undefined;\n    }\n    // Return undefined so the action knows to request a new async id if it's rescheduled.\n    return undefined;\n  }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\nexport class AnimationFrameScheduler extends AsyncScheduler {\n  public flush(action?: AsyncAction<any>): void {\n    this._active = true;\n    // The async id that effects a call to flush is stored in _scheduled.\n    // Before executing an action, it's necessary to check the action's async\n    // id to determine whether it's supposed to be executed in the current\n    // flush.\n    // Previous implementations of this method used a count to determine this,\n    // but that was unsound, as actions that are unsubscribed - i.e. cancelled -\n    // are removed from the actions array and that can shift actions that are\n    // scheduled to be executed in a subsequent flush into positions at which\n    // they are executed within the current flush.\n    const flushId = this._scheduled;\n    this._scheduled = undefined;\n\n    const { actions } = this;\n    let error: any;\n    action = action || actions.shift()!;\n\n    do {\n      if ((error = action.execute(action.state, action.delay))) {\n        break;\n      }\n    } while ((action = actions[0]) && action.id === flushId && actions.shift());\n\n    this._active = false;\n\n    if (error) {\n      while ((action = actions[0]) && action.id === flushId && actions.shift()) {\n        action.unsubscribe();\n      }\n      throw error;\n    }\n  }\n}\n", "import { AnimationFrameAction } from './AnimationFrameAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\n\n/**\n *\n * Animation Frame Scheduler\n *\n * <span class=\"informal\">Perform task when `window.requestAnimationFrame` would fire</span>\n *\n * When `animationFrame` scheduler is used with delay, it will fall back to {@link asyncScheduler} scheduler\n * behaviour.\n *\n * Without delay, `animationFrame` scheduler can be used to create smooth browser animations.\n * It makes sure scheduled task will happen just before next browser content repaint,\n * thus performing animations as efficiently as possible.\n *\n * ## Example\n * Schedule div height animation\n * ```ts\n * // html: <div style=\"background: #0ff;\"></div>\n * import { animationFrameScheduler } from 'rxjs';\n *\n * const div = document.querySelector('div');\n *\n * animationFrameScheduler.schedule(function(height) {\n *   div.style.height = height + \"px\";\n *\n *   this.schedule(height + 1);  // `this` references currently executing Action,\n *                               // which we reschedule with new state\n * }, 0, 0);\n *\n * // You will see a div element growing in height\n * ```\n */\n\nexport const animationFrameScheduler = new AnimationFrameScheduler(AnimationFrameAction);\n\n/**\n * @deprecated Renamed to {@link animationFrameScheduler}. Will be removed in v8.\n */\nexport const animationFrame = animationFrameScheduler;\n", "import { Observable } from '../Observable';\nimport { SchedulerLike } from '../types';\n\n/**\n * A simple Observable that emits no items to the Observer and immediately\n * emits a complete notification.\n *\n * <span class=\"informal\">Just emits 'complete', and nothing else.</span>\n *\n * ![](empty.png)\n *\n * A simple Observable that only emits the complete notification. It can be used\n * for composing with other Observables, such as in a {@link mergeMap}.\n *\n * ## Examples\n *\n * Log complete notification\n *\n * ```ts\n * import { EMPTY } from 'rxjs';\n *\n * EMPTY.subscribe({\n *   next: () => console.log('Next'),\n *   complete: () => console.log('Complete!')\n * });\n *\n * // Outputs\n * // Complete!\n * ```\n *\n * Emit the number 7, then complete\n *\n * ```ts\n * import { EMPTY, startWith } from 'rxjs';\n *\n * const result = EMPTY.pipe(startWith(7));\n * result.subscribe(x => console.log(x));\n *\n * // Outputs\n * // 7\n * ```\n *\n * Map and flatten only odd numbers to the sequence `'a'`, `'b'`, `'c'`\n *\n * ```ts\n * import { interval, mergeMap, of, EMPTY } from 'rxjs';\n *\n * const interval$ = interval(1000);\n * const result = interval$.pipe(\n *   mergeMap(x => x % 2 === 1 ? of('a', 'b', 'c') : EMPTY),\n * );\n * result.subscribe(x => console.log(x));\n *\n * // Results in the following to the console:\n * // x is equal to the count on the interval, e.g. (0, 1, 2, 3, ...)\n * // x will occur every 1000ms\n * // if x % 2 is equal to 1, print a, b, c (each on its own)\n * // if x % 2 is not equal to 1, nothing will be output\n * ```\n *\n * @see {@link Observable}\n * @see {@link NEVER}\n * @see {@link of}\n * @see {@link throwError}\n */\nexport const EMPTY = new Observable<never>((subscriber) => subscriber.complete());\n\n/**\n * @param scheduler A {@link SchedulerLike} to use for scheduling\n * the emission of the complete notification.\n * @deprecated Replaced with the {@link EMPTY} constant or {@link scheduled} (e.g. `scheduled([], scheduler)`). Will be removed in v8.\n */\nexport function empty(scheduler?: SchedulerLike) {\n  return scheduler ? emptyScheduled(scheduler) : EMPTY;\n}\n\nfunction emptyScheduled(scheduler: SchedulerLike) {\n  return new Observable<never>((subscriber) => scheduler.schedule(() => subscriber.complete()));\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport function isScheduler(value: any): value is SchedulerLike {\n  return value && isFunction(value.schedule);\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\nimport { isScheduler } from './isScheduler';\n\nfunction last<T>(arr: T[]): T | undefined {\n  return arr[arr.length - 1];\n}\n\nexport function popResultSelector(args: any[]): ((...args: unknown[]) => unknown) | undefined {\n  return isFunction(last(args)) ? args.pop() : undefined;\n}\n\nexport function popScheduler(args: any[]): SchedulerLike | undefined {\n  return isScheduler(last(args)) ? args.pop() : undefined;\n}\n\nexport function popNumber(args: any[], defaultValue: number): number {\n  return typeof last(args) === 'number' ? args.pop()! : defaultValue;\n}\n", "export const isArrayLike = (<T>(x: any): x is ArrayLike<T> => x && typeof x.length === 'number' && typeof x !== 'function');", "import { isFunction } from \"./isFunction\";\n\n/**\n * Tests to see if the object is \"thennable\".\n * @param value the object to test\n */\nexport function isPromise(value: any): value is PromiseLike<any> {\n  return isFunction(value?.then);\n}\n", "import { InteropObservable } from '../types';\nimport { observable as Symbol_observable } from '../symbol/observable';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being Observable (but not necessary an Rx Observable) */\nexport function isInteropObservable(input: any): input is InteropObservable<any> {\n  return isFunction(input[Symbol_observable]);\n}\n", "import { isFunction } from './isFunction';\n\nexport function isAsyncIterable<T>(obj: any): obj is AsyncIterable<T> {\n  return Symbol.asyncIterator && isFunction(obj?.[Symbol.asyncIterator]);\n}\n", "/**\n * Creates the TypeError to throw if an invalid object is passed to `from` or `scheduled`.\n * @param input The object that was passed.\n */\nexport function createInvalidObservableTypeError(input: any) {\n  // TODO: We should create error codes that can be looked up, so this can be less verbose.\n  return new TypeError(\n    `You provided ${\n      input !== null && typeof input === 'object' ? 'an invalid object' : `'${input}'`\n    } where a stream was expected. You can provide an Observable, Promise, ReadableStream, Array, AsyncIterable, or Iterable.`\n  );\n}\n", "export function getSymbolIterator(): symbol {\n  if (typeof Symbol !== 'function' || !Symbol.iterator) {\n    return '@@iterator' as any;\n  }\n\n  return Symbol.iterator;\n}\n\nexport const iterator = getSymbolIterator();\n", "import { iterator as Symbol_iterator } from '../symbol/iterator';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being an Iterable */\nexport function isIterable(input: any): input is Iterable<any> {\n  return isFunction(input?.[Symbol_iterator]);\n}\n", "import { ReadableStreamLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport async function* readableStreamLikeToAsyncGenerator<T>(readableStream: ReadableStreamLike<T>): AsyncGenerator<T> {\n  const reader = readableStream.getReader();\n  try {\n    while (true) {\n      const { value, done } = await reader.read();\n      if (done) {\n        return;\n      }\n      yield value!;\n    }\n  } finally {\n    reader.releaseLock();\n  }\n}\n\nexport function isReadableStreamLike<T>(obj: any): obj is ReadableStreamLike<T> {\n  // We don't want to use instanceof checks because they would return\n  // false for instances from another Realm, like an <iframe>.\n  return isFunction(obj?.getReader);\n}\n", "import { isArrayLike } from '../util/isArrayLike';\nimport { isPromise } from '../util/isPromise';\nimport { Observable } from '../Observable';\nimport { ObservableInput, ObservedValueOf, ReadableStreamLike } from '../types';\nimport { isInteropObservable } from '../util/isInteropObservable';\nimport { isAsyncIterable } from '../util/isAsyncIterable';\nimport { createInvalidObservableTypeError } from '../util/throwUnobservableError';\nimport { isIterable } from '../util/isIterable';\nimport { isReadableStreamLike, readableStreamLikeToAsyncGenerator } from '../util/isReadableStreamLike';\nimport { Subscriber } from '../Subscriber';\nimport { isFunction } from '../util/isFunction';\nimport { reportUnhandledError } from '../util/reportUnhandledError';\nimport { observable as Symbol_observable } from '../symbol/observable';\n\nexport function innerFrom<O extends ObservableInput<any>>(input: O): Observable<ObservedValueOf<O>>;\nexport function innerFrom<T>(input: ObservableInput<T>): Observable<T> {\n  if (input instanceof Observable) {\n    return input;\n  }\n  if (input != null) {\n    if (isInteropObservable(input)) {\n      return fromInteropObservable(input);\n    }\n    if (isArrayLike(input)) {\n      return fromArrayLike(input);\n    }\n    if (isPromise(input)) {\n      return fromPromise(input);\n    }\n    if (isAsyncIterable(input)) {\n      return fromAsyncIterable(input);\n    }\n    if (isIterable(input)) {\n      return fromIterable(input);\n    }\n    if (isReadableStreamLike(input)) {\n      return fromReadableStreamLike(input);\n    }\n  }\n\n  throw createInvalidObservableTypeError(input);\n}\n\n/**\n * Creates an RxJS Observable from an object that implements `Symbol.observable`.\n * @param obj An object that properly implements `Symbol.observable`.\n */\nexport function fromInteropObservable<T>(obj: any) {\n  return new Observable((subscriber: Subscriber<T>) => {\n    const obs = obj[Symbol_observable]();\n    if (isFunction(obs.subscribe)) {\n      return obs.subscribe(subscriber);\n    }\n    // Should be caught by observable subscribe function error handling.\n    throw new TypeError('Provided object does not correctly implement Symbol.observable');\n  });\n}\n\n/**\n * Synchronously emits the values of an array like and completes.\n * This is exported because there are creation functions and operators that need to\n * make direct use of the same logic, and there's no reason to make them run through\n * `from` conditionals because we *know* they're dealing with an array.\n * @param array The array to emit values from\n */\nexport function fromArrayLike<T>(array: ArrayLike<T>) {\n  return new Observable((subscriber: Subscriber<T>) => {\n    // Loop over the array and emit each value. Note two things here:\n    // 1. We're making sure that the subscriber is not closed on each loop.\n    //    This is so we don't continue looping over a very large array after\n    //    something like a `take`, `takeWhile`, or other synchronous unsubscription\n    //    has already unsubscribed.\n    // 2. In this form, reentrant code can alter that array we're looping over.\n    //    This is a known issue, but considered an edge case. The alternative would\n    //    be to copy the array before executing the loop, but this has\n    //    performance implications.\n    for (let i = 0; i < array.length && !subscriber.closed; i++) {\n      subscriber.next(array[i]);\n    }\n    subscriber.complete();\n  });\n}\n\nexport function fromPromise<T>(promise: PromiseLike<T>) {\n  return new Observable((subscriber: Subscriber<T>) => {\n    promise\n      .then(\n        (value) => {\n          if (!subscriber.closed) {\n            subscriber.next(value);\n            subscriber.complete();\n          }\n        },\n        (err: any) => subscriber.error(err)\n      )\n      .then(null, reportUnhandledError);\n  });\n}\n\nexport function fromIterable<T>(iterable: Iterable<T>) {\n  return new Observable((subscriber: Subscriber<T>) => {\n    for (const value of iterable) {\n      subscriber.next(value);\n      if (subscriber.closed) {\n        return;\n      }\n    }\n    subscriber.complete();\n  });\n}\n\nexport function fromAsyncIterable<T>(asyncIterable: AsyncIterable<T>) {\n  return new Observable((subscriber: Subscriber<T>) => {\n    process(asyncIterable, subscriber).catch((err) => subscriber.error(err));\n  });\n}\n\nexport function fromReadableStreamLike<T>(readableStream: ReadableStreamLike<T>) {\n  return fromAsyncIterable(readableStreamLikeToAsyncGenerator(readableStream));\n}\n\nasync function process<T>(asyncIterable: AsyncIterable<T>, subscriber: Subscriber<T>) {\n  for await (const value of asyncIterable) {\n    subscriber.next(value);\n    // A side-effect may have closed our subscriber,\n    // check before the next iteration.\n    if (subscriber.closed) {\n      return;\n    }\n  }\n  subscriber.complete();\n}\n", "import { Subscription } from '../Subscription';\nimport { SchedulerAction, SchedulerLike } from '../types';\n\nexport function executeSchedule(\n  parentSubscription: Subscription,\n  scheduler: SchedulerLike,\n  work: () => void,\n  delay: number,\n  repeat: true\n): void;\nexport function executeSchedule(\n  parentSubscription: Subscription,\n  scheduler: SchedulerLike,\n  work: () => void,\n  delay?: number,\n  repeat?: false\n): Subscription;\n\nexport function executeSchedule(\n  parentSubscription: Subscription,\n  scheduler: SchedulerLike,\n  work: () => void,\n  delay = 0,\n  repeat = false\n): Subscription | void {\n  const scheduleSubscription = scheduler.schedule(function (this: SchedulerAction<any>) {\n    work();\n    if (repeat) {\n      parentSubscription.add(this.schedule(null, delay));\n    } else {\n      this.unsubscribe();\n    }\n  }, delay);\n\n  parentSubscription.add(scheduleSubscription);\n\n  if (!repeat) {\n    // Because user-land scheduler implementations are unlikely to properly reuse\n    // Actions for repeat scheduling, we can't trust that the returned subscription\n    // will control repeat subscription scenarios. So we're trying to avoid using them\n    // incorrectly within this library.\n    return scheduleSubscription;\n  }\n}\n", "/** @prettier */\nimport { MonoTypeOperatorFunction, SchedulerLike } from '../types';\nimport { executeSchedule } from '../util/executeSchedule';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * Re-emits all notifications from source Observable with specified scheduler.\n *\n * <span class=\"informal\">Ensure a specific scheduler is used, from outside of an Observable.</span>\n *\n * `observeOn` is an operator that accepts a scheduler as a first parameter, which will be used to reschedule\n * notifications emitted by the source Observable. It might be useful, if you do not have control over\n * internal scheduler of a given Observable, but want to control when its values are emitted nevertheless.\n *\n * Returned Observable emits the same notifications (nexted values, complete and error events) as the source Observable,\n * but rescheduled with provided scheduler. Note that this doesn't mean that source Observables internal\n * scheduler will be replaced in any way. Original scheduler still will be used, but when the source Observable emits\n * notification, it will be immediately scheduled again - this time with scheduler passed to `observeOn`.\n * An anti-pattern would be calling `observeOn` on Observable that emits lots of values synchronously, to split\n * that emissions into asynchronous chunks. For this to happen, scheduler would have to be passed into the source\n * Observable directly (usually into the operator that creates it). `observeOn` simply delays notifications a\n * little bit more, to ensure that they are emitted at expected moments.\n *\n * As a matter of fact, `observeOn` accepts second parameter, which specifies in milliseconds with what delay notifications\n * will be emitted. The main difference between {@link delay} operator and `observeOn` is that `observeOn`\n * will delay all notifications - including error notifications - while `delay` will pass through error\n * from source Observable immediately when it is emitted. In general it is highly recommended to use `delay` operator\n * for any kind of delaying of values in the stream, while using `observeOn` to specify which scheduler should be used\n * for notification emissions in general.\n *\n * ## Example\n *\n * Ensure values in subscribe are called just before browser repaint\n *\n * ```ts\n * import { interval, observeOn, animationFrameScheduler } from 'rxjs';\n *\n * const someDiv = document.createElement('div');\n * someDiv.style.cssText = 'width: 200px;background: #09c';\n * document.body.appendChild(someDiv);\n * const intervals = interval(10);      // Intervals are scheduled\n *                                      // with async scheduler by default...\n * intervals.pipe(\n *   observeOn(animationFrameScheduler) // ...but we will observe on animationFrame\n * )                                    // scheduler to ensure smooth animation.\n * .subscribe(val => {\n *   someDiv.style.height = val + 'px';\n * });\n * ```\n *\n * @see {@link delay}\n *\n * @param scheduler Scheduler that will be used to reschedule notifications from source Observable.\n * @param delay Number of milliseconds that states with what delay every notification should be rescheduled.\n * @return A function that returns an Observable that emits the same\n * notifications as the source Observable, but with provided scheduler.\n */\nexport function observeOn<T>(scheduler: SchedulerLike, delay = 0): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => executeSchedule(subscriber, scheduler, () => subscriber.next(value), delay),\n        () => executeSchedule(subscriber, scheduler, () => subscriber.complete(), delay),\n        (err) => executeSchedule(subscriber, scheduler, () => subscriber.error(err), delay)\n      )\n    );\n  });\n}\n", "import { MonoTypeOperatorFunction, SchedulerLike } from '../types';\nimport { operate } from '../util/lift';\n\n/**\n * Asynchronously subscribes Observers to this Observable on the specified {@link SchedulerLike}.\n *\n * With `subscribeOn` you can decide what type of scheduler a specific Observable will be using when it is subscribed to.\n *\n * Schedulers control the speed and order of emissions to observers from an Observable stream.\n *\n * ![](subscribeOn.png)\n *\n * ## Example\n *\n * Given the following code:\n *\n * ```ts\n * import { of, merge } from 'rxjs';\n *\n * const a = of(1, 2, 3);\n * const b = of(4, 5, 6);\n *\n * merge(a, b).subscribe(console.log);\n *\n * // Outputs\n * // 1\n * // 2\n * // 3\n * // 4\n * // 5\n * // 6\n * ```\n *\n * Both Observable `a` and `b` will emit their values directly and synchronously once they are subscribed to.\n *\n * If we instead use the `subscribeOn` operator declaring that we want to use the {@link asyncScheduler} for values emitted by Observable `a`:\n *\n * ```ts\n * import { of, subscribeOn, asyncScheduler, merge } from 'rxjs';\n *\n * const a = of(1, 2, 3).pipe(subscribeOn(asyncScheduler));\n * const b = of(4, 5, 6);\n *\n * merge(a, b).subscribe(console.log);\n *\n * // Outputs\n * // 4\n * // 5\n * // 6\n * // 1\n * // 2\n * // 3\n * ```\n *\n * The reason for this is that Observable `b` emits its values directly and synchronously like before\n * but the emissions from `a` are scheduled on the event loop because we are now using the {@link asyncScheduler} for that specific Observable.\n *\n * @param scheduler The {@link SchedulerLike} to perform subscription actions on.\n * @param delay A delay to pass to the scheduler to delay subscriptions\n * @return A function that returns an Observable modified so that its\n * subscriptions happen on the specified {@link SchedulerLike}.\n */\nexport function subscribeOn<T>(scheduler: SchedulerLike, delay: number = 0): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    subscriber.add(scheduler.schedule(() => source.subscribe(subscriber), delay));\n  });\n}\n", "import { innerFrom } from '../observable/innerFrom';\nimport { observeOn } from '../operators/observeOn';\nimport { subscribeOn } from '../operators/subscribeOn';\nimport { InteropObservable, SchedulerLike } from '../types';\n\nexport function scheduleObservable<T>(input: InteropObservable<T>, scheduler: SchedulerLike) {\n  return innerFrom(input).pipe(subscribeOn(scheduler), observeOn(scheduler));\n}\n", "import { innerFrom } from '../observable/innerFrom';\nimport { observeOn } from '../operators/observeOn';\nimport { subscribeOn } from '../operators/subscribeOn';\nimport { SchedulerLike } from '../types';\n\nexport function schedulePromise<T>(input: PromiseLike<T>, scheduler: SchedulerLike) {\n  return innerFrom(input).pipe(subscribeOn(scheduler), observeOn(scheduler));\n}\n", "import { Observable } from '../Observable';\nimport { SchedulerLike } from '../types';\n\nexport function scheduleArray<T>(input: ArrayLike<T>, scheduler: SchedulerLike) {\n  return new Observable<T>((subscriber) => {\n    // The current array index.\n    let i = 0;\n    // Start iterating over the array like on a schedule.\n    return scheduler.schedule(function () {\n      if (i === input.length) {\n        // If we have hit the end of the array like in the\n        // previous job, we can complete.\n        subscriber.complete();\n      } else {\n        // Otherwise let's next the value at the current index,\n        // then increment our index.\n        subscriber.next(input[i++]);\n        // If the last emission didn't cause us to close the subscriber\n        // (via take or some side effect), reschedule the job and we'll\n        // make another pass.\n        if (!subscriber.closed) {\n          this.schedule();\n        }\n      }\n    });\n  });\n}\n", "import { Observable } from '../Observable';\nimport { SchedulerLike } from '../types';\nimport { iterator as Symbol_iterator } from '../symbol/iterator';\nimport { isFunction } from '../util/isFunction';\nimport { executeSchedule } from '../util/executeSchedule';\n\n/**\n * Used in {@link scheduled} to create an observable from an Iterable.\n * @param input The iterable to create an observable from\n * @param scheduler The scheduler to use\n */\nexport function scheduleIterable<T>(input: Iterable<T>, scheduler: SchedulerLike) {\n  return new Observable<T>((subscriber) => {\n    let iterator: Iterator<T, T>;\n\n    // Schedule the initial creation of the iterator from\n    // the iterable. This is so the code in the iterable is\n    // not called until the scheduled job fires.\n    executeSchedule(subscriber, scheduler, () => {\n      // Create the iterator.\n      iterator = (input as any)[Symbol_iterator]();\n\n      executeSchedule(\n        subscriber,\n        scheduler,\n        () => {\n          let value: T;\n          let done: boolean | undefined;\n          try {\n            // Pull the value out of the iterator\n            ({ value, done } = iterator.next());\n          } catch (err) {\n            // We got an error while pulling from the iterator\n            subscriber.error(err);\n            return;\n          }\n\n          if (done) {\n            // If it is \"done\" we just complete. This mimics the\n            // behavior of JavaScript's `for..of` consumption of\n            // iterables, which will not emit the value from an iterator\n            // result of `{ done: true: value: 'here' }`.\n            subscriber.complete();\n          } else {\n            // The iterable is not done, emit the value.\n            subscriber.next(value);\n          }\n        },\n        0,\n        true\n      );\n    });\n\n    // During finalization, if we see this iterator has a `return` method,\n    // then we know it is a Generator, and not just an Iterator. So we call\n    // the `return()` function. This will ensure that any `finally { }` blocks\n    // inside of the generator we can hit will be hit properly.\n    return () => isFunction(iterator?.return) && iterator.return();\n  });\n}\n", "import { SchedulerLike } from '../types';\nimport { Observable } from '../Observable';\nimport { executeSchedule } from '../util/executeSchedule';\n\nexport function scheduleAsyncIterable<T>(input: AsyncIterable<T>, scheduler: SchedulerLike) {\n  if (!input) {\n    throw new Error('Iterable cannot be null');\n  }\n  return new Observable<T>((subscriber) => {\n    executeSchedule(subscriber, scheduler, () => {\n      const iterator = input[Symbol.asyncIterator]();\n      executeSchedule(\n        subscriber,\n        scheduler,\n        () => {\n          iterator.next().then((result) => {\n            if (result.done) {\n              // This will remove the subscriptions from\n              // the parent subscription.\n              subscriber.complete();\n            } else {\n              subscriber.next(result.value);\n            }\n          });\n        },\n        0,\n        true\n      );\n    });\n  });\n}\n", "import { SchedulerLike, ReadableStreamLike } from '../types';\nimport { Observable } from '../Observable';\nimport { scheduleAsyncIterable } from './scheduleAsyncIterable';\nimport { readableStreamLikeToAsyncGenerator } from '../util/isReadableStreamLike';\n\nexport function scheduleReadableStreamLike<T>(input: ReadableStreamLike<T>, scheduler: SchedulerLike): Observable<T> {\n  return scheduleAsyncIterable(readableStreamLikeToAsyncGenerator(input), scheduler);\n}\n", "import { scheduleObservable } from './scheduleObservable';\nimport { schedulePromise } from './schedulePromise';\nimport { scheduleArray } from './scheduleArray';\nimport { scheduleIterable } from './scheduleIterable';\nimport { scheduleAsyncIterable } from './scheduleAsyncIterable';\nimport { isInteropObservable } from '../util/isInteropObservable';\nimport { isPromise } from '../util/isPromise';\nimport { isArrayLike } from '../util/isArrayLike';\nimport { isIterable } from '../util/isIterable';\nimport { ObservableInput, SchedulerLike } from '../types';\nimport { Observable } from '../Observable';\nimport { isAsyncIterable } from '../util/isAsyncIterable';\nimport { createInvalidObservableTypeError } from '../util/throwUnobservableError';\nimport { isReadableStreamLike } from '../util/isReadableStreamLike';\nimport { scheduleReadableStreamLike } from './scheduleReadableStreamLike';\n\n/**\n * Converts from a common {@link ObservableInput} type to an observable where subscription and emissions\n * are scheduled on the provided scheduler.\n *\n * @see {@link from}\n * @see {@link of}\n *\n * @param input The observable, array, promise, iterable, etc you would like to schedule\n * @param scheduler The scheduler to use to schedule the subscription and emissions from\n * the returned observable.\n */\nexport function scheduled<T>(input: ObservableInput<T>, scheduler: SchedulerLike): Observable<T> {\n  if (input != null) {\n    if (isInteropObservable(input)) {\n      return scheduleObservable(input, scheduler);\n    }\n    if (isArrayLike(input)) {\n      return scheduleArray(input, scheduler);\n    }\n    if (isPromise(input)) {\n      return schedulePromise(input, scheduler);\n    }\n    if (isAsyncIterable(input)) {\n      return scheduleAsyncIterable(input, scheduler);\n    }\n    if (isIterable(input)) {\n      return scheduleIterable(input, scheduler);\n    }\n    if (isReadableStreamLike(input)) {\n      return scheduleReadableStreamLike(input, scheduler);\n    }\n  }\n  throw createInvalidObservableTypeError(input);\n}\n", "import { Observable } from '../Observable';\nimport { ObservableInput, SchedulerLike, ObservedValueOf } from '../types';\nimport { scheduled } from '../scheduled/scheduled';\nimport { innerFrom } from './innerFrom';\n\nexport function from<O extends ObservableInput<any>>(input: O): Observable<ObservedValueOf<O>>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function from<O extends ObservableInput<any>>(input: O, scheduler: SchedulerLike | undefined): Observable<ObservedValueOf<O>>;\n\n/**\n * Creates an Observable from an Array, an array-like object, a Promise, an iterable object, or an Observable-like object.\n *\n * <span class=\"informal\">Converts almost anything to an Observable.</span>\n *\n * ![](from.png)\n *\n * `from` converts various other objects and data types into Observables. It also converts a Promise, an array-like, or an\n * <a href=\"https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Iteration_protocols#iterable\" target=\"_blank\">iterable</a>\n * object into an Observable that emits the items in that promise, array, or iterable. A String, in this context, is treated\n * as an array of characters. Observable-like objects (contains a function named with the ES2015 Symbol for Observable) can also be\n * converted through this operator.\n *\n * ## Examples\n *\n * Converts an array to an Observable\n *\n * ```ts\n * import { from } from 'rxjs';\n *\n * const array = [10, 20, 30];\n * const result = from(array);\n *\n * result.subscribe(x => console.log(x));\n *\n * // Logs:\n * // 10\n * // 20\n * // 30\n * ```\n *\n * Convert an infinite iterable (from a generator) to an Observable\n *\n * ```ts\n * import { from, take } from 'rxjs';\n *\n * function* generateDoubles(seed) {\n *    let i = seed;\n *    while (true) {\n *      yield i;\n *      i = 2 * i; // double it\n *    }\n * }\n *\n * const iterator = generateDoubles(3);\n * const result = from(iterator).pipe(take(10));\n *\n * result.subscribe(x => console.log(x));\n *\n * // Logs:\n * // 3\n * // 6\n * // 12\n * // 24\n * // 48\n * // 96\n * // 192\n * // 384\n * // 768\n * // 1536\n * ```\n *\n * With `asyncScheduler`\n *\n * ```ts\n * import { from, asyncScheduler } from 'rxjs';\n *\n * console.log('start');\n *\n * const array = [10, 20, 30];\n * const result = from(array, asyncScheduler);\n *\n * result.subscribe(x => console.log(x));\n *\n * console.log('end');\n *\n * // Logs:\n * // 'start'\n * // 'end'\n * // 10\n * // 20\n * // 30\n * ```\n *\n * @see {@link fromEvent}\n * @see {@link fromEventPattern}\n *\n * @param {ObservableInput<T>} A subscription object, a Promise, an Observable-like,\n * an Array, an iterable, or an array-like object to be converted.\n * @param {SchedulerLike} An optional {@link SchedulerLike} on which to schedule the emission of values.\n * @return {Observable<T>}\n */\nexport function from<T>(input: ObservableInput<T>, scheduler?: SchedulerLike): Observable<T> {\n  return scheduler ? scheduled(input, scheduler) : innerFrom(input);\n}\n", "import { SchedulerLike, ValueFromArray } from '../types';\nimport { Observable } from '../Observable';\nimport { popScheduler } from '../util/args';\nimport { from } from './from';\n\n// Devs are more likely to pass null or undefined than they are a scheduler\n// without accompanying values. To make things easier for (naughty) devs who\n// use the `strictNullChecks: false` TypeScript compiler option, these\n// overloads with explicit null and undefined values are included.\n\nexport function of(value: null): Observable<null>;\nexport function of(value: undefined): Observable<undefined>;\n\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function of(scheduler: SchedulerLike): Observable<never>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function of<A extends readonly unknown[]>(...valuesAndScheduler: [...A, SchedulerLike]): Observable<ValueFromArray<A>>;\n\nexport function of(): Observable<never>;\n/** @deprecated Do not specify explicit type parameters. Signatures with type parameters that cannot be inferred will be removed in v8. */\nexport function of<T>(): Observable<T>;\nexport function of<T>(value: T): Observable<T>;\nexport function of<A extends readonly unknown[]>(...values: A): Observable<ValueFromArray<A>>;\n\n/**\n * Converts the arguments to an observable sequence.\n *\n * <span class=\"informal\">Each argument becomes a `next` notification.</span>\n *\n * ![](of.png)\n *\n * Unlike {@link from}, it does not do any flattening and emits each argument in whole\n * as a separate `next` notification.\n *\n * ## Examples\n *\n * Emit the values `10, 20, 30`\n *\n * ```ts\n * import { of } from 'rxjs';\n *\n * of(10, 20, 30)\n *   .subscribe({\n *     next: value => console.log('next:', value),\n *     error: err => console.log('error:', err),\n *     complete: () => console.log('the end'),\n *   });\n *\n * // Outputs\n * // next: 10\n * // next: 20\n * // next: 30\n * // the end\n * ```\n *\n * Emit the array `[1, 2, 3]`\n *\n * ```ts\n * import { of } from 'rxjs';\n *\n * of([1, 2, 3])\n *   .subscribe({\n *     next: value => console.log('next:', value),\n *     error: err => console.log('error:', err),\n *     complete: () => console.log('the end'),\n *   });\n *\n * // Outputs\n * // next: [1, 2, 3]\n * // the end\n * ```\n *\n * @see {@link from}\n * @see {@link range}\n *\n * @param {...T} values A comma separated list of arguments you want to be emitted\n * @return {Observable} An Observable that emits the arguments\n * described above and then completes.\n */\nexport function of<T>(...args: Array<T | SchedulerLike>): Observable<T> {\n  const scheduler = popScheduler(args);\n  return from(args as T[], scheduler);\n}\n", "import { Observable } from '../Observable';\nimport { Subscriber } from '../Subscriber';\nimport { SchedulerLike } from '../types';\nimport { isFunction } from '../util/isFunction';\n\n/**\n * Creates an observable that will create an error instance and push it to the consumer as an error\n * immediately upon subscription.\n *\n * <span class=\"informal\">Just errors and does nothing else</span>\n *\n * ![](throw.png)\n *\n * This creation function is useful for creating an observable that will create an error and error every\n * time it is subscribed to. Generally, inside of most operators when you might want to return an errored\n * observable, this is unnecessary. In most cases, such as in the inner return of {@link concatMap},\n * {@link mergeMap}, {@link defer}, and many others, you can simply throw the error, and RxJS will pick\n * that up and notify the consumer of the error.\n *\n * ## Example\n *\n * Create a simple observable that will create a new error with a timestamp and log it\n * and the message every time you subscribe to it\n *\n * ```ts\n * import { throwError } from 'rxjs';\n *\n * let errorCount = 0;\n *\n * const errorWithTimestamp$ = throwError(() => {\n *   const error: any = new Error(`This is error number ${ ++errorCount }`);\n *   error.timestamp = Date.now();\n *   return error;\n * });\n *\n * errorWithTimestamp$.subscribe({\n *   error: err => console.log(err.timestamp, err.message)\n * });\n *\n * errorWithTimestamp$.subscribe({\n *   error: err => console.log(err.timestamp, err.message)\n * });\n *\n * // Logs the timestamp and a new error message for each subscription\n * ```\n *\n * ### Unnecessary usage\n *\n * Using `throwError` inside of an operator or creation function\n * with a callback, is usually not necessary\n *\n * ```ts\n * import { of, concatMap, timer, throwError } from 'rxjs';\n *\n * const delays$ = of(1000, 2000, Infinity, 3000);\n *\n * delays$.pipe(\n *   concatMap(ms => {\n *     if (ms < 10000) {\n *       return timer(ms);\n *     } else {\n *       // This is probably overkill.\n *       return throwError(() => new Error(`Invalid time ${ ms }`));\n *     }\n *   })\n * )\n * .subscribe({\n *   next: console.log,\n *   error: console.error\n * });\n * ```\n *\n * You can just throw the error instead\n *\n * ```ts\n * import { of, concatMap, timer } from 'rxjs';\n *\n * const delays$ = of(1000, 2000, Infinity, 3000);\n *\n * delays$.pipe(\n *   concatMap(ms => {\n *     if (ms < 10000) {\n *       return timer(ms);\n *     } else {\n *       // Cleaner and easier to read for most folks.\n *       throw new Error(`Invalid time ${ ms }`);\n *     }\n *   })\n * )\n * .subscribe({\n *   next: console.log,\n *   error: console.error\n * });\n * ```\n *\n * @param errorFactory A factory function that will create the error instance that is pushed.\n */\nexport function throwError(errorFactory: () => any): Observable<never>;\n\n/**\n * Returns an observable that will error with the specified error immediately upon subscription.\n *\n * @param error The error instance to emit\n * @deprecated Support for passing an error value will be removed in v8. Instead, pass a factory function to `throwError(() => new Error('test'))`. This is\n * because it will create the error at the moment it should be created and capture a more appropriate stack trace. If\n * for some reason you need to create the error ahead of time, you can still do that: `const err = new Error('test'); throwError(() => err);`.\n */\nexport function throwError(error: any): Observable<never>;\n\n/**\n * Notifies the consumer of an error using a given scheduler by scheduling it at delay `0` upon subscription.\n *\n * @param errorOrErrorFactory An error instance or error factory\n * @param scheduler A scheduler to use to schedule the error notification\n * @deprecated The `scheduler` parameter will be removed in v8.\n * Use `throwError` in combination with {@link observeOn}: `throwError(() => new Error('test')).pipe(observeOn(scheduler));`.\n * Details: https://rxjs.dev/deprecations/scheduler-argument\n */\nexport function throwError(errorOrErrorFactory: any, scheduler: SchedulerLike): Observable<never>;\n\nexport function throwError(errorOrErrorFactory: any, scheduler?: SchedulerLike): Observable<never> {\n  const errorFactory = isFunction(errorOrErrorFactory) ? errorOrErrorFactory : () => errorOrErrorFactory;\n  const init = (subscriber: Subscriber<never>) => subscriber.error(errorFactory());\n  return new Observable(scheduler ? (subscriber) => scheduler.schedule(init as any, 0, subscriber) : init);\n}\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface EmptyError extends Error {}\n\nexport interface EmptyErrorCtor {\n  /**\n   * @deprecated Internal implementation detail. Do not construct error instances.\n   * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n   */\n  new (): EmptyError;\n}\n\n/**\n * An error thrown when an Observable or a sequence was queried but has no\n * elements.\n *\n * @see {@link first}\n * @see {@link last}\n * @see {@link single}\n * @see {@link firstValueFrom}\n * @see {@link lastValueFrom}\n *\n * @class EmptyError\n */\nexport const EmptyError: EmptyErrorCtor = createErrorClass((_super) => function EmptyErrorImpl(this: any) {\n  _super(this);\n  this.name = 'EmptyError';\n  this.message = 'no elements in sequence';\n});\n", "/**\n * Checks to see if a value is not only a `Date` object,\n * but a *valid* `Date` object that can be converted to a\n * number. For example, `new Date('blah')` is indeed an\n * `instanceof Date`, however it cannot be converted to a\n * number.\n */\nexport function isValidDate(value: any): value is Date {\n  return value instanceof Date && !isNaN(value as any);\n}\n", "import { OperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\nexport function map<T, R>(project: (value: T, index: number) => R): OperatorFunction<T, R>;\n/** @deprecated Use a closure instead of a `thisArg`. Signatures accepting a `thisArg` will be removed in v8. */\nexport function map<T, R, A>(project: (this: A, value: T, index: number) => R, thisArg: A): OperatorFunction<T, R>;\n\n/**\n * Applies a given `project` function to each value emitted by the source\n * Observable, and emits the resulting values as an Observable.\n *\n * <span class=\"informal\">Like [Array.prototype.map()](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/map),\n * it passes each source value through a transformation function to get\n * corresponding output values.</span>\n *\n * ![](map.png)\n *\n * Similar to the well known `Array.prototype.map` function, this operator\n * applies a projection to each value and emits that projection in the output\n * Observable.\n *\n * ## Example\n *\n * Map every click to the `clientX` position of that click\n *\n * ```ts\n * import { fromEvent, map } from 'rxjs';\n *\n * const clicks = fromEvent<PointerEvent>(document, 'click');\n * const positions = clicks.pipe(map(ev => ev.clientX));\n *\n * positions.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link mapTo}\n * @see {@link pluck}\n *\n * @param {function(value: T, index: number): R} project The function to apply\n * to each `value` emitted by the source Observable. The `index` parameter is\n * the number `i` for the i-th emission that has happened since the\n * subscription, starting from the number `0`.\n * @param {any} [thisArg] An optional argument to define what `this` is in the\n * `project` function.\n * @return A function that returns an Observable that emits the values from the\n * source Observable transformed by the given `project` function.\n */\nexport function map<T, R>(project: (value: T, index: number) => R, thisArg?: any): OperatorFunction<T, R> {\n  return operate((source, subscriber) => {\n    // The index of the value from the source. Used with projection.\n    let index = 0;\n    // Subscribe to the source, all errors and completions are sent along\n    // to the consumer.\n    source.subscribe(\n      createOperatorSubscriber(subscriber, (value: T) => {\n        // Call the projection function with the appropriate this context,\n        // and send the resulting value to the consumer.\n        subscriber.next(project.call(thisArg, value, index++));\n      })\n    );\n  });\n}\n", "import { OperatorFunction } from \"../types\";\nimport { map } from \"../operators/map\";\n\nconst { isArray } = Array;\n\nfunction callOrApply<T, R>(fn: ((...values: T[]) => R), args: T|T[]): R {\n    return isArray(args) ? fn(...args) : fn(args);\n}\n\n/**\n * Used in several -- mostly deprecated -- situations where we need to \n * apply a list of arguments or a single argument to a result selector.\n */\nexport function mapOneOrManyArgs<T, R>(fn: ((...values: T[]) => R)): OperatorFunction<T|T[], R> {\n    return map(args => callOrApply(fn, args))\n}", "const { isArray } = Array;\nconst { getPrototypeOf, prototype: objectProto, keys: getKeys } = Object;\n\n/**\n * Used in functions where either a list of arguments, a single array of arguments, or a\n * dictionary of arguments can be returned. Returns an object with an `args` property with\n * the arguments in an array, if it is a dictionary, it will also return the `keys` in another\n * property.\n */\nexport function argsArgArrayOrObject<T, O extends Record<string, T>>(args: T[] | [O] | [T[]]): { args: T[]; keys: string[] | null } {\n  if (args.length === 1) {\n    const first = args[0];\n    if (isArray(first)) {\n      return { args: first, keys: null };\n    }\n    if (isPOJO(first)) {\n      const keys = getKeys(first);\n      return {\n        args: keys.map((key) => first[key]),\n        keys,\n      };\n    }\n  }\n\n  return { args: args as T[], keys: null };\n}\n\nfunction isPOJO(obj: any): obj is object {\n  return obj && typeof obj === 'object' && getPrototypeOf(obj) === objectProto;\n}\n", "export function createObject(keys: string[], values: any[]) {\n  return keys.reduce((result, key, i) => ((result[key] = values[i]), result), {} as any);\n}\n", "import { Observable } from '../Observable';\nimport { ObservableInput, SchedulerLike, ObservedValueOf, ObservableInputTuple } from '../types';\nimport { argsArgArrayOrObject } from '../util/argsArgArrayOrObject';\nimport { Subscriber } from '../Subscriber';\nimport { from } from './from';\nimport { identity } from '../util/identity';\nimport { Subscription } from '../Subscription';\nimport { mapOneOrManyArgs } from '../util/mapOneOrManyArgs';\nimport { popResultSelector, popScheduler } from '../util/args';\nimport { createObject } from '../util/createObject';\nimport { createOperatorSubscriber } from '../operators/OperatorSubscriber';\nimport { AnyCatcher } from '../AnyCatcher';\nimport { executeSchedule } from '../util/executeSchedule';\n\n// combineLatest(any)\n// We put this first because we need to catch cases where the user has supplied\n// _exactly `any`_ as the argument. Since `any` literally matches _anything_,\n// we don't want it to randomly hit one of the other type signatures below,\n// as we have no idea at build-time what type we should be returning when given an any.\n\n/**\n * You have passed `any` here, we can't figure out if it is\n * an array or an object, so you're getting `unknown`. Use better types.\n * @param arg Something typed as `any`\n */\nexport function combineLatest<T extends AnyCatcher>(arg: T): Observable<unknown>;\n\n// combineLatest([a, b, c])\nexport function combineLatest(sources: []): Observable<never>;\nexport function combineLatest<A extends readonly unknown[]>(sources: readonly [...ObservableInputTuple<A>]): Observable<A>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `combineLatestAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function combineLatest<A extends readonly unknown[], R>(\n  sources: readonly [...ObservableInputTuple<A>],\n  resultSelector: (...values: A) => R,\n  scheduler: SchedulerLike\n): Observable<R>;\nexport function combineLatest<A extends readonly unknown[], R>(\n  sources: readonly [...ObservableInputTuple<A>],\n  resultSelector: (...values: A) => R\n): Observable<R>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `combineLatestAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function combineLatest<A extends readonly unknown[]>(\n  sources: readonly [...ObservableInputTuple<A>],\n  scheduler: SchedulerLike\n): Observable<A>;\n\n// combineLatest(a, b, c)\n/** @deprecated Pass an array of sources instead. The rest-parameters signature will be removed in v8. Details: https://rxjs.dev/deprecations/array-argument */\nexport function combineLatest<A extends readonly unknown[]>(...sources: [...ObservableInputTuple<A>]): Observable<A>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `combineLatestAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function combineLatest<A extends readonly unknown[], R>(\n  ...sourcesAndResultSelectorAndScheduler: [...ObservableInputTuple<A>, (...values: A) => R, SchedulerLike]\n): Observable<R>;\n/** @deprecated Pass an array of sources instead. The rest-parameters signature will be removed in v8. Details: https://rxjs.dev/deprecations/array-argument */\nexport function combineLatest<A extends readonly unknown[], R>(\n  ...sourcesAndResultSelector: [...ObservableInputTuple<A>, (...values: A) => R]\n): Observable<R>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `combineLatestAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function combineLatest<A extends readonly unknown[]>(\n  ...sourcesAndScheduler: [...ObservableInputTuple<A>, SchedulerLike]\n): Observable<A>;\n\n// combineLatest({a, b, c})\nexport function combineLatest(sourcesObject: { [K in any]: never }): Observable<never>;\nexport function combineLatest<T extends Record<string, ObservableInput<any>>>(\n  sourcesObject: T\n): Observable<{ [K in keyof T]: ObservedValueOf<T[K]> }>;\n\n/**\n * Combines multiple Observables to create an Observable whose values are\n * calculated from the latest values of each of its input Observables.\n *\n * <span class=\"informal\">Whenever any input Observable emits a value, it\n * computes a formula using the latest values from all the inputs, then emits\n * the output of that formula.</span>\n *\n * ![](combineLatest.png)\n *\n * `combineLatest` combines the values from all the Observables passed in the\n * observables array. This is done by subscribing to each Observable in order and,\n * whenever any Observable emits, collecting an array of the most recent\n * values from each Observable. So if you pass `n` Observables to this operator,\n * the returned Observable will always emit an array of `n` values, in an order\n * corresponding to the order of the passed Observables (the value from the first Observable\n * will be at index 0 of the array and so on).\n *\n * Static version of `combineLatest` accepts an array of Observables. Note that an array of\n * Observables is a good choice, if you don't know beforehand how many Observables\n * you will combine. Passing an empty array will result in an Observable that\n * completes immediately.\n *\n * To ensure the output array always has the same length, `combineLatest` will\n * actually wait for all input Observables to emit at least once,\n * before it starts emitting results. This means if some Observable emits\n * values before other Observables started emitting, all these values but the last\n * will be lost. On the other hand, if some Observable does not emit a value but\n * completes, resulting Observable will complete at the same moment without\n * emitting anything, since it will now be impossible to include a value from the\n * completed Observable in the resulting array. Also, if some input Observable does\n * not emit any value and never completes, `combineLatest` will also never emit\n * and never complete, since, again, it will wait for all streams to emit some\n * value.\n *\n * If at least one Observable was passed to `combineLatest` and all passed Observables\n * emitted something, the resulting Observable will complete when all combined\n * streams complete. So even if some Observable completes, the result of\n * `combineLatest` will still emit values when other Observables do. In case\n * of a completed Observable, its value from now on will always be the last\n * emitted value. On the other hand, if any Observable errors, `combineLatest`\n * will error immediately as well, and all other Observables will be unsubscribed.\n *\n * ## Examples\n *\n * Combine two timer Observables\n *\n * ```ts\n * import { timer, combineLatest } from 'rxjs';\n *\n * const firstTimer = timer(0, 1000); // emit 0, 1, 2... after every second, starting from now\n * const secondTimer = timer(500, 1000); // emit 0, 1, 2... after every second, starting 0,5s from now\n * const combinedTimers = combineLatest([firstTimer, secondTimer]);\n * combinedTimers.subscribe(value => console.log(value));\n * // Logs\n * // [0, 0] after 0.5s\n * // [1, 0] after 1s\n * // [1, 1] after 1.5s\n * // [2, 1] after 2s\n * ```\n *\n * Combine a dictionary of Observables\n *\n * ```ts\n * import { of, delay, startWith, combineLatest } from 'rxjs';\n *\n * const observables = {\n *   a: of(1).pipe(delay(1000), startWith(0)),\n *   b: of(5).pipe(delay(5000), startWith(0)),\n *   c: of(10).pipe(delay(10000), startWith(0))\n * };\n * const combined = combineLatest(observables);\n * combined.subscribe(value => console.log(value));\n * // Logs\n * // { a: 0, b: 0, c: 0 } immediately\n * // { a: 1, b: 0, c: 0 } after 1s\n * // { a: 1, b: 5, c: 0 } after 5s\n * // { a: 1, b: 5, c: 10 } after 10s\n * ```\n *\n * Combine an array of Observables\n *\n * ```ts\n * import { of, delay, startWith, combineLatest } from 'rxjs';\n *\n * const observables = [1, 5, 10].map(\n *   n => of(n).pipe(\n *     delay(n * 1000), // emit 0 and then emit n after n seconds\n *     startWith(0)\n *   )\n * );\n * const combined = combineLatest(observables);\n * combined.subscribe(value => console.log(value));\n * // Logs\n * // [0, 0, 0] immediately\n * // [1, 0, 0] after 1s\n * // [1, 5, 0] after 5s\n * // [1, 5, 10] after 10s\n * ```\n *\n * Use map operator to dynamically calculate the Body-Mass Index\n *\n * ```ts\n * import { of, combineLatest, map } from 'rxjs';\n *\n * const weight = of(70, 72, 76, 79, 75);\n * const height = of(1.76, 1.77, 1.78);\n * const bmi = combineLatest([weight, height]).pipe(\n *   map(([w, h]) => w / (h * h)),\n * );\n * bmi.subscribe(x => console.log('BMI is ' + x));\n *\n * // With output to console:\n * // BMI is 24.212293388429753\n * // BMI is 23.93948099205209\n * // BMI is 23.671253629592222\n * ```\n *\n * @see {@link combineLatestAll}\n * @see {@link merge}\n * @see {@link withLatestFrom}\n *\n * @param {ObservableInput} [observables] An array of input Observables to combine with each other.\n * An array of Observables must be given as the first argument.\n * @param {function} [project] An optional function to project the values from\n * the combined latest values into a new value on the output Observable.\n * @param {SchedulerLike} [scheduler=null] The {@link SchedulerLike} to use for subscribing to\n * each input Observable.\n * @return {Observable} An Observable of projected values from the most recent\n * values from each input Observable, or an array of the most recent values from\n * each input Observable.\n */\nexport function combineLatest<O extends ObservableInput<any>, R>(...args: any[]): Observable<R> | Observable<ObservedValueOf<O>[]> {\n  const scheduler = popScheduler(args);\n  const resultSelector = popResultSelector(args);\n\n  const { args: observables, keys } = argsArgArrayOrObject(args);\n\n  if (observables.length === 0) {\n    // If no observables are passed, or someone has passed an empty array\n    // of observables, or even an empty object POJO, we need to just\n    // complete (EMPTY), but we have to honor the scheduler provided if any.\n    return from([], scheduler as any);\n  }\n\n  const result = new Observable<ObservedValueOf<O>[]>(\n    combineLatestInit(\n      observables as ObservableInput<ObservedValueOf<O>>[],\n      scheduler,\n      keys\n        ? // A handler for scrubbing the array of args into a dictionary.\n          (values) => createObject(keys, values)\n        : // A passthrough to just return the array\n          identity\n    )\n  );\n\n  return resultSelector ? (result.pipe(mapOneOrManyArgs(resultSelector)) as Observable<R>) : result;\n}\n\nexport function combineLatestInit(\n  observables: ObservableInput<any>[],\n  scheduler?: SchedulerLike,\n  valueTransform: (values: any[]) => any = identity\n) {\n  return (subscriber: Subscriber<any>) => {\n    // The outer subscription. We're capturing this in a function\n    // because we may have to schedule it.\n    maybeSchedule(\n      scheduler,\n      () => {\n        const { length } = observables;\n        // A store for the values each observable has emitted so far. We match observable to value on index.\n        const values = new Array(length);\n        // The number of currently active subscriptions, as they complete, we decrement this number to see if\n        // we are all done combining values, so we can complete the result.\n        let active = length;\n        // The number of inner sources that still haven't emitted the first value\n        // We need to track this because all sources need to emit one value in order\n        // to start emitting values.\n        let remainingFirstValues = length;\n        // The loop to kick off subscription. We're keying everything on index `i` to relate the observables passed\n        // in to the slot in the output array or the key in the array of keys in the output dictionary.\n        for (let i = 0; i < length; i++) {\n          maybeSchedule(\n            scheduler,\n            () => {\n              const source = from(observables[i], scheduler as any);\n              let hasFirstValue = false;\n              source.subscribe(\n                createOperatorSubscriber(\n                  subscriber,\n                  (value) => {\n                    // When we get a value, record it in our set of values.\n                    values[i] = value;\n                    if (!hasFirstValue) {\n                      // If this is our first value, record that.\n                      hasFirstValue = true;\n                      remainingFirstValues--;\n                    }\n                    if (!remainingFirstValues) {\n                      // We're not waiting for any more\n                      // first values, so we can emit!\n                      subscriber.next(valueTransform(values.slice()));\n                    }\n                  },\n                  () => {\n                    if (!--active) {\n                      // We only complete the result if we have no more active\n                      // inner observables.\n                      subscriber.complete();\n                    }\n                  }\n                )\n              );\n            },\n            subscriber\n          );\n        }\n      },\n      subscriber\n    );\n  };\n}\n\n/**\n * A small utility to handle the couple of locations where we want to schedule if a scheduler was provided,\n * but we don't if there was no scheduler.\n */\nfunction maybeSchedule(scheduler: SchedulerLike | undefined, execute: () => void, subscription: Subscription) {\n  if (scheduler) {\n    executeSchedule(subscription, scheduler, execute);\n  } else {\n    execute();\n  }\n}\n", "import { Observable } from '../Observable';\nimport { innerFrom } from '../observable/innerFrom';\nimport { Subscriber } from '../Subscriber';\nimport { ObservableInput, SchedulerLike } from '../types';\nimport { executeSchedule } from '../util/executeSchedule';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * A process embodying the general \"merge\" strategy. This is used in\n * `mergeMap` and `mergeScan` because the logic is otherwise nearly identical.\n * @param source The original source observable\n * @param subscriber The consumer subscriber\n * @param project The projection function to get our inner sources\n * @param concurrent The number of concurrent inner subscriptions\n * @param onBeforeNext Additional logic to apply before nexting to our consumer\n * @param expand If `true` this will perform an \"expand\" strategy, which differs only\n * in that it recurses, and the inner subscription must be schedule-able.\n * @param innerSubScheduler A scheduler to use to schedule inner subscriptions,\n * this is to support the expand strategy, mostly, and should be deprecated\n */\nexport function mergeInternals<T, R>(\n  source: Observable<T>,\n  subscriber: Subscriber<R>,\n  project: (value: T, index: number) => ObservableInput<R>,\n  concurrent: number,\n  onBeforeNext?: (innerValue: R) => void,\n  expand?: boolean,\n  innerSubScheduler?: SchedulerLike,\n  additionalFinalizer?: () => void\n) {\n  // Buffered values, in the event of going over our concurrency limit\n  const buffer: T[] = [];\n  // The number of active inner subscriptions.\n  let active = 0;\n  // An index to pass to our accumulator function\n  let index = 0;\n  // Whether or not the outer source has completed.\n  let isComplete = false;\n\n  /**\n   * Checks to see if we can complete our result or not.\n   */\n  const checkComplete = () => {\n    // If the outer has completed, and nothing is left in the buffer,\n    // and we don't have any active inner subscriptions, then we can\n    // Emit the state and complete.\n    if (isComplete && !buffer.length && !active) {\n      subscriber.complete();\n    }\n  };\n\n  // If we're under our concurrency limit, just start the inner subscription, otherwise buffer and wait.\n  const outerNext = (value: T) => (active < concurrent ? doInnerSub(value) : buffer.push(value));\n\n  const doInnerSub = (value: T) => {\n    // If we're expanding, we need to emit the outer values and the inner values\n    // as the inners will \"become outers\" in a way as they are recursively fed\n    // back to the projection mechanism.\n    expand && subscriber.next(value as any);\n\n    // Increment the number of active subscriptions so we can track it\n    // against our concurrency limit later.\n    active++;\n\n    // A flag used to show that the inner observable completed.\n    // This is checked during finalization to see if we should\n    // move to the next item in the buffer, if there is on.\n    let innerComplete = false;\n\n    // Start our inner subscription.\n    innerFrom(project(value, index++)).subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (innerValue) => {\n          // `mergeScan` has additional handling here. For example\n          // taking the inner value and updating state.\n          onBeforeNext?.(innerValue);\n\n          if (expand) {\n            // If we're expanding, then just recurse back to our outer\n            // handler. It will emit the value first thing.\n            outerNext(innerValue as any);\n          } else {\n            // Otherwise, emit the inner value.\n            subscriber.next(innerValue);\n          }\n        },\n        () => {\n          // Flag that we have completed, so we know to check the buffer\n          // during finalization.\n          innerComplete = true;\n        },\n        // Errors are passed to the destination.\n        undefined,\n        () => {\n          // During finalization, if the inner completed (it wasn't errored or\n          // cancelled), then we want to try the next item in the buffer if\n          // there is one.\n          if (innerComplete) {\n            // We have to wrap this in a try/catch because it happens during\n            // finalization, possibly asynchronously, and we want to pass\n            // any errors that happen (like in a projection function) to\n            // the outer Subscriber.\n            try {\n              // INNER SOURCE COMPLETE\n              // Decrement the active count to ensure that the next time\n              // we try to call `doInnerSub`, the number is accurate.\n              active--;\n              // If we have more values in the buffer, try to process those\n              // Note that this call will increment `active` ahead of the\n              // next conditional, if there were any more inner subscriptions\n              // to start.\n              while (buffer.length && active < concurrent) {\n                const bufferedValue = buffer.shift()!;\n                // Particularly for `expand`, we need to check to see if a scheduler was provided\n                // for when we want to start our inner subscription. Otherwise, we just start\n                // are next inner subscription.\n                if (innerSubScheduler) {\n                  executeSchedule(subscriber, innerSubScheduler, () => doInnerSub(bufferedValue));\n                } else {\n                  doInnerSub(bufferedValue);\n                }\n              }\n              // Check to see if we can complete, and complete if so.\n              checkComplete();\n            } catch (err) {\n              subscriber.error(err);\n            }\n          }\n        }\n      )\n    );\n  };\n\n  // Subscribe to our source observable.\n  source.subscribe(\n    createOperatorSubscriber(subscriber, outerNext, () => {\n      // Outer completed, make a note of it, and check to see if we can complete everything.\n      isComplete = true;\n      checkComplete();\n    })\n  );\n\n  // Additional finalization (for when the destination is torn down).\n  // Other finalization is added implicitly via subscription above.\n  return () => {\n    additionalFinalizer?.();\n  };\n}\n", "import { ObservableInput, OperatorFunction, ObservedValueOf } from '../types';\nimport { map } from './map';\nimport { innerFrom } from '../observable/innerFrom';\nimport { operate } from '../util/lift';\nimport { mergeInternals } from './mergeInternals';\nimport { isFunction } from '../util/isFunction';\n\n/* tslint:disable:max-line-length */\nexport function mergeMap<T, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  concurrent?: number\n): OperatorFunction<T, ObservedValueOf<O>>;\n/** @deprecated The `resultSelector` parameter will be removed in v8. Use an inner `map` instead. Details: https://rxjs.dev/deprecations/resultSelector */\nexport function mergeMap<T, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  resultSelector: undefined,\n  concurrent?: number\n): OperatorFunction<T, ObservedValueOf<O>>;\n/** @deprecated The `resultSelector` parameter will be removed in v8. Use an inner `map` instead. Details: https://rxjs.dev/deprecations/resultSelector */\nexport function mergeMap<T, R, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  resultSelector: (outerValue: T, innerValue: ObservedValueOf<O>, outerIndex: number, innerIndex: number) => R,\n  concurrent?: number\n): OperatorFunction<T, R>;\n/* tslint:enable:max-line-length */\n\n/**\n * Projects each source value to an Observable which is merged in the output\n * Observable.\n *\n * <span class=\"informal\">Maps each value to an Observable, then flattens all of\n * these inner Observables using {@link mergeAll}.</span>\n *\n * ![](mergeMap.png)\n *\n * Returns an Observable that emits items based on applying a function that you\n * supply to each item emitted by the source Observable, where that function\n * returns an Observable, and then merging those resulting Observables and\n * emitting the results of this merger.\n *\n * ## Example\n *\n * Map and flatten each letter to an Observable ticking every 1 second\n *\n * ```ts\n * import { of, mergeMap, interval, map } from 'rxjs';\n *\n * const letters = of('a', 'b', 'c');\n * const result = letters.pipe(\n *   mergeMap(x => interval(1000).pipe(map(i => x + i)))\n * );\n *\n * result.subscribe(x => console.log(x));\n *\n * // Results in the following:\n * // a0\n * // b0\n * // c0\n * // a1\n * // b1\n * // c1\n * // continues to list a, b, c every second with respective ascending integers\n * ```\n *\n * @see {@link concatMap}\n * @see {@link exhaustMap}\n * @see {@link merge}\n * @see {@link mergeAll}\n * @see {@link mergeMapTo}\n * @see {@link mergeScan}\n * @see {@link switchMap}\n *\n * @param {function(value: T, ?index: number): ObservableInput} project A function\n * that, when applied to an item emitted by the source Observable, returns an\n * Observable.\n * @param {number} [concurrent=Infinity] Maximum number of input\n * Observables being subscribed to concurrently.\n * @return A function that returns an Observable that emits the result of\n * applying the projection function (and the optional deprecated\n * `resultSelector`) to each item emitted by the source Observable and merging\n * the results of the Observables obtained from this transformation.\n */\nexport function mergeMap<T, R, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  resultSelector?: ((outerValue: T, innerValue: ObservedValueOf<O>, outerIndex: number, innerIndex: number) => R) | number,\n  concurrent: number = Infinity\n): OperatorFunction<T, ObservedValueOf<O> | R> {\n  if (isFunction(resultSelector)) {\n    // DEPRECATED PATH\n    return mergeMap((a, i) => map((b: any, ii: number) => resultSelector(a, b, i, ii))(innerFrom(project(a, i))), concurrent);\n  } else if (typeof resultSelector === 'number') {\n    concurrent = resultSelector;\n  }\n\n  return operate((source, subscriber) => mergeInternals(source, subscriber, project, concurrent));\n}\n", "import { mergeMap } from './mergeMap';\nimport { identity } from '../util/identity';\nimport { OperatorFunction, ObservableInput, ObservedValueOf } from '../types';\n\n/**\n * Converts a higher-order Observable into a first-order Observable which\n * concurrently delivers all values that are emitted on the inner Observables.\n *\n * <span class=\"informal\">Flattens an Observable-of-Observables.</span>\n *\n * ![](mergeAll.png)\n *\n * `mergeAll` subscribes to an Observable that emits Observables, also known as\n * a higher-order Observable. Each time it observes one of these emitted inner\n * Observables, it subscribes to that and delivers all the values from the\n * inner Observable on the output Observable. The output Observable only\n * completes once all inner Observables have completed. Any error delivered by\n * a inner Observable will be immediately emitted on the output Observable.\n *\n * ## Examples\n *\n * Spawn a new interval Observable for each click event, and blend their outputs as one Observable\n *\n * ```ts\n * import { fromEvent, map, interval, mergeAll } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const higherOrder = clicks.pipe(map(() => interval(1000)));\n * const firstOrder = higherOrder.pipe(mergeAll());\n *\n * firstOrder.subscribe(x => console.log(x));\n * ```\n *\n * Count from 0 to 9 every second for each click, but only allow 2 concurrent timers\n *\n * ```ts\n * import { fromEvent, map, interval, take, mergeAll } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const higherOrder = clicks.pipe(\n *   map(() => interval(1000).pipe(take(10)))\n * );\n * const firstOrder = higherOrder.pipe(mergeAll(2));\n *\n * firstOrder.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link combineLatestAll}\n * @see {@link concatAll}\n * @see {@link exhaustAll}\n * @see {@link merge}\n * @see {@link mergeMap}\n * @see {@link mergeMapTo}\n * @see {@link mergeScan}\n * @see {@link switchAll}\n * @see {@link switchMap}\n * @see {@link zipAll}\n *\n * @param {number} [concurrent=Infinity] Maximum number of inner\n * Observables being subscribed to concurrently.\n * @return A function that returns an Observable that emits values coming from\n * all the inner Observables emitted by the source Observable.\n */\nexport function mergeAll<O extends ObservableInput<any>>(concurrent: number = Infinity): OperatorFunction<O, ObservedValueOf<O>> {\n  return mergeMap(identity, concurrent);\n}\n", "import { mergeAll } from './mergeAll';\nimport { OperatorFunction, ObservableInput, ObservedValueOf } from '../types';\n\n/**\n * Converts a higher-order Observable into a first-order Observable by\n * concatenating the inner Observables in order.\n *\n * <span class=\"informal\">Flattens an Observable-of-Observables by putting one\n * inner Observable after the other.</span>\n *\n * ![](concatAll.svg)\n *\n * Joins every Observable emitted by the source (a higher-order Observable), in\n * a serial fashion. It subscribes to each inner Observable only after the\n * previous inner Observable has completed, and merges all of their values into\n * the returned observable.\n *\n * __Warning:__ If the source Observable emits Observables quickly and\n * endlessly, and the inner Observables it emits generally complete slower than\n * the source emits, you can run into memory issues as the incoming Observables\n * collect in an unbounded buffer.\n *\n * Note: `concatAll` is equivalent to `mergeAll` with concurrency parameter set\n * to `1`.\n *\n * ## Example\n *\n * For each click event, tick every second from 0 to 3, with no concurrency\n *\n * ```ts\n * import { fromEvent, map, interval, take, concatAll } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const higherOrder = clicks.pipe(\n *   map(() => interval(1000).pipe(take(4)))\n * );\n * const firstOrder = higherOrder.pipe(concatAll());\n * firstOrder.subscribe(x => console.log(x));\n *\n * // Results in the following:\n * // (results are not concurrent)\n * // For every click on the \"document\" it will emit values 0 to 3 spaced\n * // on a 1000ms interval\n * // one click = 1000ms-> 0 -1000ms-> 1 -1000ms-> 2 -1000ms-> 3\n * ```\n *\n * @see {@link combineLatestAll}\n * @see {@link concat}\n * @see {@link concatMap}\n * @see {@link concatMapTo}\n * @see {@link exhaustAll}\n * @see {@link mergeAll}\n * @see {@link switchAll}\n * @see {@link switchMap}\n * @see {@link zipAll}\n *\n * @return A function that returns an Observable emitting values from all the\n * inner Observables concatenated.\n */\nexport function concatAll<O extends ObservableInput<any>>(): OperatorFunction<O, ObservedValueOf<O>> {\n  return mergeAll(1);\n}\n", "import { Observable } from '../Observable';\nimport { ObservableInputTuple, SchedulerLike } from '../types';\nimport { concatAll } from '../operators/concatAll';\nimport { popScheduler } from '../util/args';\nimport { from } from './from';\n\nexport function concat<T extends readonly unknown[]>(...inputs: [...ObservableInputTuple<T>]): Observable<T[number]>;\nexport function concat<T extends readonly unknown[]>(\n  ...inputsAndScheduler: [...ObservableInputTuple<T>, SchedulerLike]\n): Observable<T[number]>;\n\n/**\n * Creates an output Observable which sequentially emits all values from the first given\n * Observable and then moves on to the next.\n *\n * <span class=\"informal\">Concatenates multiple Observables together by\n * sequentially emitting their values, one Observable after the other.</span>\n *\n * ![](concat.png)\n *\n * `concat` joins multiple Observables together, by subscribing to them one at a time and\n * merging their results into the output Observable. You can pass either an array of\n * Observables, or put them directly as arguments. Passing an empty array will result\n * in Observable that completes immediately.\n *\n * `concat` will subscribe to first input Observable and emit all its values, without\n * changing or affecting them in any way. When that Observable completes, it will\n * subscribe to then next Observable passed and, again, emit its values. This will be\n * repeated, until the operator runs out of Observables. When last input Observable completes,\n * `concat` will complete as well. At any given moment only one Observable passed to operator\n * emits values. If you would like to emit values from passed Observables concurrently, check out\n * {@link merge} instead, especially with optional `concurrent` parameter. As a matter of fact,\n * `concat` is an equivalent of `merge` operator with `concurrent` parameter set to `1`.\n *\n * Note that if some input Observable never completes, `concat` will also never complete\n * and Observables following the one that did not complete will never be subscribed. On the other\n * hand, if some Observable simply completes immediately after it is subscribed, it will be\n * invisible for `concat`, which will just move on to the next Observable.\n *\n * If any Observable in chain errors, instead of passing control to the next Observable,\n * `concat` will error immediately as well. Observables that would be subscribed after\n * the one that emitted error, never will.\n *\n * If you pass to `concat` the same Observable many times, its stream of values\n * will be \"replayed\" on every subscription, which means you can repeat given Observable\n * as many times as you like. If passing the same Observable to `concat` 1000 times becomes tedious,\n * you can always use {@link repeat}.\n *\n * ## Examples\n *\n * Concatenate a timer counting from 0 to 3 with a synchronous sequence from 1 to 10\n *\n * ```ts\n * import { interval, take, range, concat } from 'rxjs';\n *\n * const timer = interval(1000).pipe(take(4));\n * const sequence = range(1, 10);\n * const result = concat(timer, sequence);\n * result.subscribe(x => console.log(x));\n *\n * // results in:\n * // 0 -1000ms-> 1 -1000ms-> 2 -1000ms-> 3 -immediate-> 1 ... 10\n * ```\n *\n * Concatenate 3 Observables\n *\n * ```ts\n * import { interval, take, concat } from 'rxjs';\n *\n * const timer1 = interval(1000).pipe(take(10));\n * const timer2 = interval(2000).pipe(take(6));\n * const timer3 = interval(500).pipe(take(10));\n *\n * const result = concat(timer1, timer2, timer3);\n * result.subscribe(x => console.log(x));\n *\n * // results in the following:\n * // (Prints to console sequentially)\n * // -1000ms-> 0 -1000ms-> 1 -1000ms-> ... 9\n * // -2000ms-> 0 -2000ms-> 1 -2000ms-> ... 5\n * // -500ms-> 0 -500ms-> 1 -500ms-> ... 9\n * ```\n *\n * Concatenate the same Observable to repeat it\n *\n * ```ts\n * import { interval, take, concat } from 'rxjs';\n *\n * const timer = interval(1000).pipe(take(2));\n *\n * concat(timer, timer) // concatenating the same Observable!\n *   .subscribe({\n *     next: value => console.log(value),\n *     complete: () => console.log('...and it is done!')\n *   });\n *\n * // Logs:\n * // 0 after 1s\n * // 1 after 2s\n * // 0 after 3s\n * // 1 after 4s\n * // '...and it is done!' also after 4s\n * ```\n *\n * @see {@link concatAll}\n * @see {@link concatMap}\n * @see {@link concatMapTo}\n * @see {@link startWith}\n * @see {@link endWith}\n *\n * @param args Input Observables to concatenate.\n */\nexport function concat(...args: any[]): Observable<unknown> {\n  return concatAll()(from(args, popScheduler(args)));\n}\n", "import { Observable } from '../Observable';\nimport { ObservedValueOf, ObservableInput } from '../types';\nimport { innerFrom } from './innerFrom';\n\n/**\n * Creates an Observable that, on subscribe, calls an Observable factory to\n * make an Observable for each new Observer.\n *\n * <span class=\"informal\">Creates the Observable lazily, that is, only when it\n * is subscribed.\n * </span>\n *\n * ![](defer.png)\n *\n * `defer` allows you to create an Observable only when the Observer\n * subscribes. It waits until an Observer subscribes to it, calls the given\n * factory function to get an Observable -- where a factory function typically\n * generates a new Observable -- and subscribes the Observer to this Observable.\n * In case the factory function returns a falsy value, then EMPTY is used as\n * Observable instead. Last but not least, an exception during the factory\n * function call is transferred to the Observer by calling `error`.\n *\n * ## Example\n *\n * Subscribe to either an Observable of clicks or an Observable of interval, at random\n *\n * ```ts\n * import { defer, fromEvent, interval } from 'rxjs';\n *\n * const clicksOrInterval = defer(() => {\n *   return Math.random() > 0.5\n *     ? fromEvent(document, 'click')\n *     : interval(1000);\n * });\n * clicksOrInterval.subscribe(x => console.log(x));\n *\n * // Results in the following behavior:\n * // If the result of Math.random() is greater than 0.5 it will listen\n * // for clicks anywhere on the \"document\"; when document is clicked it\n * // will log a MouseEvent object to the console. If the result is less\n * // than 0.5 it will emit ascending numbers, one every second(1000ms).\n * ```\n *\n * @see {@link Observable}\n *\n * @param {function(): ObservableInput} observableFactory The Observable\n * factory function to invoke for each Observer that subscribes to the output\n * Observable. May also return a Promise, which will be converted on the fly\n * to an Observable.\n * @return {Observable} An Observable whose Observers' subscriptions trigger\n * an invocation of the given Observable factory function.\n */\nexport function defer<R extends ObservableInput<any>>(observableFactory: () => R): Observable<ObservedValueOf<R>> {\n  return new Observable<ObservedValueOf<R>>((subscriber) => {\n    innerFrom(observableFactory()).subscribe(subscriber);\n  });\n}\n", "import { innerFrom } from '../observable/innerFrom';\nimport { Observable } from '../Observable';\nimport { mergeMap } from '../operators/mergeMap';\nimport { isArrayLike } from '../util/isArrayLike';\nimport { isFunction } from '../util/isFunction';\nimport { mapOneOrManyArgs } from '../util/mapOneOrManyArgs';\n\n// These constants are used to create handler registry functions using array mapping below.\nconst nodeEventEmitterMethods = ['addListener', 'removeListener'] as const;\nconst eventTargetMethods = ['addEventListener', 'removeEventListener'] as const;\nconst jqueryMethods = ['on', 'off'] as const;\n\nexport interface NodeStyleEventEmitter {\n  addListener(eventName: string | symbol, handler: NodeEventHandler): this;\n  removeListener(eventName: string | symbol, handler: NodeEventHandler): this;\n}\n\nexport type NodeEventHandler = (...args: any[]) => void;\n\n// For APIs that implement `addListener` and `removeListener` methods that may\n// not use the same arguments or return EventEmitter values\n// such as React Native\nexport interface NodeCompatibleEventEmitter {\n  addListener(eventName: string, handler: NodeEventHandler): void | {};\n  removeListener(eventName: string, handler: NodeEventHandler): void | {};\n}\n\n// Use handler types like those in @types/jquery. See:\n// https://github.com/DefinitelyTyped/DefinitelyTyped/blob/847731ba1d7fa6db6b911c0e43aa0afe596e7723/types/jquery/misc.d.ts#L6395\nexport interface JQueryStyleEventEmitter<TContext, T> {\n  on(eventName: string, handler: (this: TContext, t: T, ...args: any[]) => any): void;\n  off(eventName: string, handler: (this: TContext, t: T, ...args: any[]) => any): void;\n}\n\nexport interface EventListenerObject<E> {\n  handleEvent(evt: E): void;\n}\n\nexport interface HasEventTargetAddRemove<E> {\n  addEventListener(\n    type: string,\n    listener: ((evt: E) => void) | EventListenerObject<E> | null,\n    options?: boolean | AddEventListenerOptions\n  ): void;\n  removeEventListener(\n    type: string,\n    listener: ((evt: E) => void) | EventListenerObject<E> | null,\n    options?: EventListenerOptions | boolean\n  ): void;\n}\n\nexport interface EventListenerOptions {\n  capture?: boolean;\n  passive?: boolean;\n  once?: boolean;\n}\n\nexport interface AddEventListenerOptions extends EventListenerOptions {\n  once?: boolean;\n  passive?: boolean;\n}\n\nexport function fromEvent<T>(target: HasEventTargetAddRemove<T> | ArrayLike<HasEventTargetAddRemove<T>>, eventName: string): Observable<T>;\nexport function fromEvent<T, R>(\n  target: HasEventTargetAddRemove<T> | ArrayLike<HasEventTargetAddRemove<T>>,\n  eventName: string,\n  resultSelector: (event: T) => R\n): Observable<R>;\nexport function fromEvent<T>(\n  target: HasEventTargetAddRemove<T> | ArrayLike<HasEventTargetAddRemove<T>>,\n  eventName: string,\n  options: EventListenerOptions\n): Observable<T>;\nexport function fromEvent<T, R>(\n  target: HasEventTargetAddRemove<T> | ArrayLike<HasEventTargetAddRemove<T>>,\n  eventName: string,\n  options: EventListenerOptions,\n  resultSelector: (event: T) => R\n): Observable<R>;\n\nexport function fromEvent(target: NodeStyleEventEmitter | ArrayLike<NodeStyleEventEmitter>, eventName: string): Observable<unknown>;\n/** @deprecated Do not specify explicit type parameters. Signatures with type parameters that cannot be inferred will be removed in v8. */\nexport function fromEvent<T>(target: NodeStyleEventEmitter | ArrayLike<NodeStyleEventEmitter>, eventName: string): Observable<T>;\nexport function fromEvent<R>(\n  target: NodeStyleEventEmitter | ArrayLike<NodeStyleEventEmitter>,\n  eventName: string,\n  resultSelector: (...args: any[]) => R\n): Observable<R>;\n\nexport function fromEvent(\n  target: NodeCompatibleEventEmitter | ArrayLike<NodeCompatibleEventEmitter>,\n  eventName: string\n): Observable<unknown>;\n/** @deprecated Do not specify explicit type parameters. Signatures with type parameters that cannot be inferred will be removed in v8. */\nexport function fromEvent<T>(target: NodeCompatibleEventEmitter | ArrayLike<NodeCompatibleEventEmitter>, eventName: string): Observable<T>;\nexport function fromEvent<R>(\n  target: NodeCompatibleEventEmitter | ArrayLike<NodeCompatibleEventEmitter>,\n  eventName: string,\n  resultSelector: (...args: any[]) => R\n): Observable<R>;\n\nexport function fromEvent<T>(\n  target: JQueryStyleEventEmitter<any, T> | ArrayLike<JQueryStyleEventEmitter<any, T>>,\n  eventName: string\n): Observable<T>;\nexport function fromEvent<T, R>(\n  target: JQueryStyleEventEmitter<any, T> | ArrayLike<JQueryStyleEventEmitter<any, T>>,\n  eventName: string,\n  resultSelector: (value: T, ...args: any[]) => R\n): Observable<R>;\n\n/**\n * Creates an Observable that emits events of a specific type coming from the\n * given event target.\n *\n * <span class=\"informal\">Creates an Observable from DOM events, or Node.js\n * EventEmitter events or others.</span>\n *\n * ![](fromEvent.png)\n *\n * `fromEvent` accepts as a first argument event target, which is an object with methods\n * for registering event handler functions. As a second argument it takes string that indicates\n * type of event we want to listen for. `fromEvent` supports selected types of event targets,\n * which are described in detail below. If your event target does not match any of the ones listed,\n * you should use {@link fromEventPattern}, which can be used on arbitrary APIs.\n * When it comes to APIs supported by `fromEvent`, their methods for adding and removing event\n * handler functions have different names, but they all accept a string describing event type\n * and function itself, which will be called whenever said event happens.\n *\n * Every time resulting Observable is subscribed, event handler function will be registered\n * to event target on given event type. When that event fires, value\n * passed as a first argument to registered function will be emitted by output Observable.\n * When Observable is unsubscribed, function will be unregistered from event target.\n *\n * Note that if event target calls registered function with more than one argument, second\n * and following arguments will not appear in resulting stream. In order to get access to them,\n * you can pass to `fromEvent` optional project function, which will be called with all arguments\n * passed to event handler. Output Observable will then emit value returned by project function,\n * instead of the usual value.\n *\n * Remember that event targets listed below are checked via duck typing. It means that\n * no matter what kind of object you have and no matter what environment you work in,\n * you can safely use `fromEvent` on that object if it exposes described methods (provided\n * of course they behave as was described above). So for example if Node.js library exposes\n * event target which has the same method names as DOM EventTarget, `fromEvent` is still\n * a good choice.\n *\n * If the API you use is more callback then event handler oriented (subscribed\n * callback function fires only once and thus there is no need to manually\n * unregister it), you should use {@link bindCallback} or {@link bindNodeCallback}\n * instead.\n *\n * `fromEvent` supports following types of event targets:\n *\n * **DOM EventTarget**\n *\n * This is an object with `addEventListener` and `removeEventListener` methods.\n *\n * In the browser, `addEventListener` accepts - apart from event type string and event\n * handler function arguments - optional third parameter, which is either an object or boolean,\n * both used for additional configuration how and when passed function will be called. When\n * `fromEvent` is used with event target of that type, you can provide this values\n * as third parameter as well.\n *\n * **Node.js EventEmitter**\n *\n * An object with `addListener` and `removeListener` methods.\n *\n * **JQuery-style event target**\n *\n * An object with `on` and `off` methods\n *\n * **DOM NodeList**\n *\n * List of DOM Nodes, returned for example by `document.querySelectorAll` or `Node.childNodes`.\n *\n * Although this collection is not event target in itself, `fromEvent` will iterate over all Nodes\n * it contains and install event handler function in every of them. When returned Observable\n * is unsubscribed, function will be removed from all Nodes.\n *\n * **DOM HtmlCollection**\n *\n * Just as in case of NodeList it is a collection of DOM nodes. Here as well event handler function is\n * installed and removed in each of elements.\n *\n *\n * ## Examples\n *\n * Emit clicks happening on the DOM document\n *\n * ```ts\n * import { fromEvent } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * clicks.subscribe(x => console.log(x));\n *\n * // Results in:\n * // MouseEvent object logged to console every time a click\n * // occurs on the document.\n * ```\n *\n * Use `addEventListener` with capture option\n *\n * ```ts\n * import { fromEvent } from 'rxjs';\n *\n * const div = document.createElement('div');\n * div.style.cssText = 'width: 200px; height: 200px; background: #09c;';\n * document.body.appendChild(div);\n *\n * // note optional configuration parameter which will be passed to addEventListener\n * const clicksInDocument = fromEvent(document, 'click', { capture: true });\n * const clicksInDiv = fromEvent(div, 'click');\n *\n * clicksInDocument.subscribe(() => console.log('document'));\n * clicksInDiv.subscribe(() => console.log('div'));\n *\n * // By default events bubble UP in DOM tree, so normally\n * // when we would click on div in document\n * // \"div\" would be logged first and then \"document\".\n * // Since we specified optional `capture` option, document\n * // will catch event when it goes DOWN DOM tree, so console\n * // will log \"document\" and then \"div\".\n * ```\n *\n * @see {@link bindCallback}\n * @see {@link bindNodeCallback}\n * @see {@link fromEventPattern}\n *\n * @param {FromEventTarget<T>} target The DOM EventTarget, Node.js\n * EventEmitter, JQuery-like event target, NodeList or HTMLCollection to attach the event handler to.\n * @param {string} eventName The event name of interest, being emitted by the\n * `target`.\n * @param {EventListenerOptions} [options] Options to pass through to addEventListener\n * @return {Observable<T>}\n */\nexport function fromEvent<T>(\n  target: any,\n  eventName: string,\n  options?: EventListenerOptions | ((...args: any[]) => T),\n  resultSelector?: (...args: any[]) => T\n): Observable<T> {\n  if (isFunction(options)) {\n    resultSelector = options;\n    options = undefined;\n  }\n  if (resultSelector) {\n    return fromEvent<T>(target, eventName, options as EventListenerOptions).pipe(mapOneOrManyArgs(resultSelector));\n  }\n\n  // Figure out our add and remove methods. In order to do this,\n  // we are going to analyze the target in a preferred order, if\n  // the target matches a given signature, we take the two \"add\" and \"remove\"\n  // method names and apply them to a map to create opposite versions of the\n  // same function. This is because they all operate in duplicate pairs,\n  // `addListener(name, handler)`, `removeListener(name, handler)`, for example.\n  // The call only differs by method name, as to whether or not you're adding or removing.\n  const [add, remove] =\n    // If it is an EventTarget, we need to use a slightly different method than the other two patterns.\n    isEventTarget(target)\n      ? eventTargetMethods.map((methodName) => (handler: any) => target[methodName](eventName, handler, options as EventListenerOptions))\n      : // In all other cases, the call pattern is identical with the exception of the method names.\n      isNodeStyleEventEmitter(target)\n      ? nodeEventEmitterMethods.map(toCommonHandlerRegistry(target, eventName))\n      : isJQueryStyleEventEmitter(target)\n      ? jqueryMethods.map(toCommonHandlerRegistry(target, eventName))\n      : [];\n\n  // If add is falsy, it's because we didn't match a pattern above.\n  // Check to see if it is an ArrayLike, because if it is, we want to\n  // try to apply fromEvent to all of it's items. We do this check last,\n  // because there are may be some types that are both ArrayLike *and* implement\n  // event registry points, and we'd rather delegate to that when possible.\n  if (!add) {\n    if (isArrayLike(target)) {\n      return mergeMap((subTarget: any) => fromEvent(subTarget, eventName, options as EventListenerOptions))(\n        innerFrom(target)\n      ) as Observable<T>;\n    }\n  }\n\n  // If add is falsy and we made it here, it's because we didn't\n  // match any valid target objects above.\n  if (!add) {\n    throw new TypeError('Invalid event target');\n  }\n\n  return new Observable<T>((subscriber) => {\n    // The handler we are going to register. Forwards the event object, by itself, or\n    // an array of arguments to the event handler, if there is more than one argument,\n    // to the consumer.\n    const handler = (...args: any[]) => subscriber.next(1 < args.length ? args : args[0]);\n    // Do the work of adding the handler to the target.\n    add(handler);\n    // When we finalize, we want to remove the handler and free up memory.\n    return () => remove!(handler);\n  });\n}\n\n/**\n * Used to create `add` and `remove` functions to register and unregister event handlers\n * from a target in the most common handler pattern, where there are only two arguments.\n * (e.g.  `on(name, fn)`, `off(name, fn)`, `addListener(name, fn)`, or `removeListener(name, fn)`)\n * @param target The target we're calling methods on\n * @param eventName The event name for the event we're creating register or unregister functions for\n */\nfunction toCommonHandlerRegistry(target: any, eventName: string) {\n  return (methodName: string) => (handler: any) => target[methodName](eventName, handler);\n}\n\n/**\n * Checks to see if the target implements the required node-style EventEmitter methods\n * for adding and removing event handlers.\n * @param target the object to check\n */\nfunction isNodeStyleEventEmitter(target: any): target is NodeStyleEventEmitter {\n  return isFunction(target.addListener) && isFunction(target.removeListener);\n}\n\n/**\n * Checks to see if the target implements the required jQuery-style EventEmitter methods\n * for adding and removing event handlers.\n * @param target the object to check\n */\nfunction isJQueryStyleEventEmitter(target: any): target is JQueryStyleEventEmitter<any, any> {\n  return isFunction(target.on) && isFunction(target.off);\n}\n\n/**\n * Checks to see if the target implements the required EventTarget methods\n * for adding and removing event handlers.\n * @param target the object to check\n */\nfunction isEventTarget(target: any): target is HasEventTargetAddRemove<any> {\n  return isFunction(target.addEventListener) && isFunction(target.removeEventListener);\n}\n", "import { Observable } from '../Observable';\nimport { isFunction } from '../util/isFunction';\nimport { NodeEventHandler } from './fromEvent';\nimport { mapOneOrManyArgs } from '../util/mapOneOrManyArgs';\n\n/* tslint:disable:max-line-length */\nexport function fromEventPattern<T>(\n  addHandler: (handler: NodeEventHandler) => any,\n  removeHandler?: (handler: NodeEventHandler, signal?: any) => void\n): Observable<T>;\nexport function fromEventPattern<T>(\n  addHandler: (handler: NodeEventHandler) => any,\n  removeHandler?: (handler: NodeEventHandler, signal?: any) => void,\n  resultSelector?: (...args: any[]) => T\n): Observable<T>;\n/* tslint:enable:max-line-length */\n\n/**\n * Creates an Observable from an arbitrary API for registering event handlers.\n *\n * <span class=\"informal\">When that method for adding event handler was something {@link fromEvent}\n * was not prepared for.</span>\n *\n * ![](fromEventPattern.png)\n *\n * `fromEventPattern` allows you to convert into an Observable any API that supports registering handler functions\n * for events. It is similar to {@link fromEvent}, but far\n * more flexible. In fact, all use cases of {@link fromEvent} could be easily handled by\n * `fromEventPattern` (although in slightly more verbose way).\n *\n * This operator accepts as a first argument an `addHandler` function, which will be injected with\n * handler parameter. That handler is actually an event handler function that you now can pass\n * to API expecting it. `addHandler` will be called whenever Observable\n * returned by the operator is subscribed, so registering handler in API will not\n * necessarily happen when `fromEventPattern` is called.\n *\n * After registration, every time an event that we listen to happens,\n * Observable returned by `fromEventPattern` will emit value that event handler\n * function was called with. Note that if event handler was called with more\n * than one argument, second and following arguments will not appear in the Observable.\n *\n * If API you are using allows to unregister event handlers as well, you can pass to `fromEventPattern`\n * another function - `removeHandler` - as a second parameter. It will be injected\n * with the same handler function as before, which now you can use to unregister\n * it from the API. `removeHandler` will be called when consumer of resulting Observable\n * unsubscribes from it.\n *\n * In some APIs unregistering is actually handled differently. Method registering an event handler\n * returns some kind of token, which is later used to identify which function should\n * be unregistered or it itself has method that unregisters event handler.\n * If that is the case with your API, make sure token returned\n * by registering method is returned by `addHandler`. Then it will be passed\n * as a second argument to `removeHandler`, where you will be able to use it.\n *\n * If you need access to all event handler parameters (not only the first one),\n * or you need to transform them in any way, you can call `fromEventPattern` with optional\n * third parameter - project function which will accept all arguments passed to\n * event handler when it is called. Whatever is returned from project function will appear on\n * resulting stream instead of usual event handlers first argument. This means\n * that default project can be thought of as function that takes its first parameter\n * and ignores the rest.\n *\n * ## Examples\n *\n * Emits clicks happening on the DOM document\n *\n * ```ts\n * import { fromEventPattern } from 'rxjs';\n *\n * function addClickHandler(handler) {\n *   document.addEventListener('click', handler);\n * }\n *\n * function removeClickHandler(handler) {\n *   document.removeEventListener('click', handler);\n * }\n *\n * const clicks = fromEventPattern(\n *   addClickHandler,\n *   removeClickHandler\n * );\n * clicks.subscribe(x => console.log(x));\n *\n * // Whenever you click anywhere in the browser, DOM MouseEvent\n * // object will be logged.\n * ```\n *\n * Use with API that returns cancellation token\n *\n * ```ts\n * import { fromEventPattern } from 'rxjs';\n *\n * const token = someAPI.registerEventHandler(function() {});\n * someAPI.unregisterEventHandler(token); // this APIs cancellation method accepts\n *                                        // not handler itself, but special token.\n *\n * const someAPIObservable = fromEventPattern(\n *   function(handler) { return someAPI.registerEventHandler(handler); }, // Note that we return the token here...\n *   function(handler, token) { someAPI.unregisterEventHandler(token); }  // ...to then use it here.\n * );\n * ```\n *\n * Use with project function\n *\n * ```ts\n * import { fromEventPattern } from 'rxjs';\n *\n * someAPI.registerEventHandler((eventType, eventMessage) => {\n *   console.log(eventType, eventMessage); // Logs 'EVENT_TYPE' 'EVENT_MESSAGE' to console.\n * });\n *\n * const someAPIObservable = fromEventPattern(\n *   handler => someAPI.registerEventHandler(handler),\n *   handler => someAPI.unregisterEventHandler(handler)\n *   (eventType, eventMessage) => eventType + ' --- ' + eventMessage // without that function only 'EVENT_TYPE'\n * );                                                                // would be emitted by the Observable\n *\n * someAPIObservable.subscribe(value => console.log(value));\n *\n * // Logs:\n * // 'EVENT_TYPE --- EVENT_MESSAGE'\n * ```\n *\n * @see {@link fromEvent}\n * @see {@link bindCallback}\n * @see {@link bindNodeCallback}\n *\n * @param {function(handler: Function): any} addHandler A function that takes\n * a `handler` function as argument and attaches it somehow to the actual\n * source of events.\n * @param {function(handler: Function, token?: any): void} [removeHandler] A function that\n * takes a `handler` function as an argument and removes it from the event source. If `addHandler`\n * returns some kind of token, `removeHandler` function will have it as a second parameter.\n * @param {function(...args: any): T} [project] A function to\n * transform results. It takes the arguments from the event handler and\n * should return a single value.\n * @return {Observable<T>} Observable which, when an event happens, emits first parameter\n * passed to registered event handler. Alternatively it emits whatever project function returns\n * at that moment.\n */\nexport function fromEventPattern<T>(\n  addHandler: (handler: NodeEventHandler) => any,\n  removeHandler?: (handler: NodeEventHandler, signal?: any) => void,\n  resultSelector?: (...args: any[]) => T\n): Observable<T | T[]> {\n  if (resultSelector) {\n    return fromEventPattern<T>(addHandler, removeHandler).pipe(mapOneOrManyArgs(resultSelector));\n  }\n\n  return new Observable<T | T[]>((subscriber) => {\n    const handler = (...e: T[]) => subscriber.next(e.length === 1 ? e[0] : e);\n    const retValue = addHandler(handler);\n    return isFunction(removeHandler) ? () => removeHandler(handler, retValue) : undefined;\n  });\n}\n", "import { Observable } from '../Observable';\nimport { SchedulerLike } from '../types';\nimport { async as asyncScheduler } from '../scheduler/async';\nimport { isScheduler } from '../util/isScheduler';\nimport { isValidDate } from '../util/isDate';\n\n/**\n * Creates an observable that will wait for a specified time period, or exact date, before\n * emitting the number 0.\n *\n * <span class=\"informal\">Used to emit a notification after a delay.</span>\n *\n * This observable is useful for creating delays in code, or racing against other values\n * for ad-hoc timeouts.\n *\n * The `delay` is specified by default in milliseconds, however providing a custom scheduler could\n * create a different behavior.\n *\n * ## Examples\n *\n * Wait 3 seconds and start another observable\n *\n * You might want to use `timer` to delay subscription to an\n * observable by a set amount of time. Here we use a timer with\n * {@link concatMapTo} or {@link concatMap} in order to wait\n * a few seconds and start a subscription to a source.\n *\n * ```ts\n * import { of, timer, concatMap } from 'rxjs';\n *\n * // This could be any observable\n * const source = of(1, 2, 3);\n *\n * timer(3000)\n *   .pipe(concatMap(() => source))\n *   .subscribe(console.log);\n * ```\n *\n * Take all values until the start of the next minute\n *\n * Using a `Date` as the trigger for the first emission, you can\n * do things like wait until midnight to fire an event, or in this case,\n * wait until a new minute starts (chosen so the example wouldn't take\n * too long to run) in order to stop watching a stream. Leveraging\n * {@link takeUntil}.\n *\n * ```ts\n * import { interval, takeUntil, timer } from 'rxjs';\n *\n * // Build a Date object that marks the\n * // next minute.\n * const currentDate = new Date();\n * const startOfNextMinute = new Date(\n *   currentDate.getFullYear(),\n *   currentDate.getMonth(),\n *   currentDate.getDate(),\n *   currentDate.getHours(),\n *   currentDate.getMinutes() + 1\n * );\n *\n * // This could be any observable stream\n * const source = interval(1000);\n *\n * const result = source.pipe(\n *   takeUntil(timer(startOfNextMinute))\n * );\n *\n * result.subscribe(console.log);\n * ```\n *\n * ### Known Limitations\n *\n * - The {@link asyncScheduler} uses `setTimeout` which has limitations for how far in the future it can be scheduled.\n *\n * - If a `scheduler` is provided that returns a timestamp other than an epoch from `now()`, and\n * a `Date` object is passed to the `dueTime` argument, the calculation for when the first emission\n * should occur will be incorrect. In this case, it would be best to do your own calculations\n * ahead of time, and pass a `number` in as the `dueTime`.\n *\n * @param due If a `number`, the amount of time in milliseconds to wait before emitting.\n * If a `Date`, the exact time at which to emit.\n * @param scheduler The scheduler to use to schedule the delay. Defaults to {@link asyncScheduler}.\n */\nexport function timer(due: number | Date, scheduler?: SchedulerLike): Observable<0>;\n\n/**\n * Creates an observable that starts an interval after a specified delay, emitting incrementing numbers -- starting at `0` --\n * on each interval after words.\n *\n * The `delay` and `intervalDuration` are specified by default in milliseconds, however providing a custom scheduler could\n * create a different behavior.\n *\n * ## Example\n *\n * ### Start an interval that starts right away\n *\n * Since {@link interval} waits for the passed delay before starting,\n * sometimes that's not ideal. You may want to start an interval immediately.\n * `timer` works well for this. Here we have both side-by-side so you can\n * see them in comparison.\n *\n * Note that this observable will never complete.\n *\n * ```ts\n * import { timer, interval } from 'rxjs';\n *\n * timer(0, 1000).subscribe(n => console.log('timer', n));\n * interval(1000).subscribe(n => console.log('interval', n));\n * ```\n *\n * ### Known Limitations\n *\n * - The {@link asyncScheduler} uses `setTimeout` which has limitations for how far in the future it can be scheduled.\n *\n * - If a `scheduler` is provided that returns a timestamp other than an epoch from `now()`, and\n * a `Date` object is passed to the `dueTime` argument, the calculation for when the first emission\n * should occur will be incorrect. In this case, it would be best to do your own calculations\n * ahead of time, and pass a `number` in as the `startDue`.\n * @param startDue If a `number`, is the time to wait before starting the interval.\n * If a `Date`, is the exact time at which to start the interval.\n * @param intervalDuration The delay between each value emitted in the interval. Passing a\n * negative number here will result in immediate completion after the first value is emitted, as though\n * no `intervalDuration` was passed at all.\n * @param scheduler The scheduler to use to schedule the delay. Defaults to {@link asyncScheduler}.\n */\nexport function timer(startDue: number | Date, intervalDuration: number, scheduler?: SchedulerLike): Observable<number>;\n\n/**\n * @deprecated The signature allowing `undefined` to be passed for `intervalDuration` will be removed in v8. Use the `timer(dueTime, scheduler?)` signature instead.\n */\nexport function timer(dueTime: number | Date, unused: undefined, scheduler?: SchedulerLike): Observable<0>;\n\nexport function timer(\n  dueTime: number | Date = 0,\n  intervalOrScheduler?: number | SchedulerLike,\n  scheduler: SchedulerLike = asyncScheduler\n): Observable<number> {\n  // Since negative intervalDuration is treated as though no\n  // interval was specified at all, we start with a negative number.\n  let intervalDuration = -1;\n\n  if (intervalOrScheduler != null) {\n    // If we have a second argument, and it's a scheduler,\n    // override the scheduler we had defaulted. Otherwise,\n    // it must be an interval.\n    if (isScheduler(intervalOrScheduler)) {\n      scheduler = intervalOrScheduler;\n    } else {\n      // Note that this *could* be negative, in which case\n      // it's like not passing an intervalDuration at all.\n      intervalDuration = intervalOrScheduler;\n    }\n  }\n\n  return new Observable((subscriber) => {\n    // If a valid date is passed, calculate how long to wait before\n    // executing the first value... otherwise, if it's a number just schedule\n    // that many milliseconds (or scheduler-specified unit size) in the future.\n    let due = isValidDate(dueTime) ? +dueTime - scheduler!.now() : dueTime;\n\n    if (due < 0) {\n      // Ensure we don't schedule in the future.\n      due = 0;\n    }\n\n    // The incrementing value we emit.\n    let n = 0;\n\n    // Start the timer.\n    return scheduler.schedule(function () {\n      if (!subscriber.closed) {\n        // Emit the next value and increment.\n        subscriber.next(n++);\n\n        if (0 <= intervalDuration) {\n          // If we have a interval after the initial timer,\n          // reschedule with the period.\n          this.schedule(undefined, intervalDuration);\n        } else {\n          // We didn't have an interval. So just complete.\n          subscriber.complete();\n        }\n      }\n    }, due);\n  });\n}\n", "import { Observable } from '../Observable';\nimport { ObservableInput, ObservableInputTuple, SchedulerLike } from '../types';\nimport { mergeAll } from '../operators/mergeAll';\nimport { innerFrom } from './innerFrom';\nimport { EMPTY } from './empty';\nimport { popNumber, popScheduler } from '../util/args';\nimport { from } from './from';\n\nexport function merge<A extends readonly unknown[]>(...sources: [...ObservableInputTuple<A>]): Observable<A[number]>;\nexport function merge<A extends readonly unknown[]>(...sourcesAndConcurrency: [...ObservableInputTuple<A>, number?]): Observable<A[number]>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `mergeAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function merge<A extends readonly unknown[]>(\n  ...sourcesAndScheduler: [...ObservableInputTuple<A>, SchedulerLike?]\n): Observable<A[number]>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `mergeAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function merge<A extends readonly unknown[]>(\n  ...sourcesAndConcurrencyAndScheduler: [...ObservableInputTuple<A>, number?, SchedulerLike?]\n): Observable<A[number]>;\n\n/**\n * Creates an output Observable which concurrently emits all values from every\n * given input Observable.\n *\n * <span class=\"informal\">Flattens multiple Observables together by blending\n * their values into one Observable.</span>\n *\n * ![](merge.png)\n *\n * `merge` subscribes to each given input Observable (as arguments), and simply\n * forwards (without doing any transformation) all the values from all the input\n * Observables to the output Observable. The output Observable only completes\n * once all input Observables have completed. Any error delivered by an input\n * Observable will be immediately emitted on the output Observable.\n *\n * ## Examples\n *\n * Merge together two Observables: 1s interval and clicks\n *\n * ```ts\n * import { merge, fromEvent, interval } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const timer = interval(1000);\n * const clicksOrTimer = merge(clicks, timer);\n * clicksOrTimer.subscribe(x => console.log(x));\n *\n * // Results in the following:\n * // timer will emit ascending values, one every second(1000ms) to console\n * // clicks logs MouseEvents to console every time the \"document\" is clicked\n * // Since the two streams are merged you see these happening\n * // as they occur.\n * ```\n *\n * Merge together 3 Observables, but run only 2 concurrently\n *\n * ```ts\n * import { interval, take, merge } from 'rxjs';\n *\n * const timer1 = interval(1000).pipe(take(10));\n * const timer2 = interval(2000).pipe(take(6));\n * const timer3 = interval(500).pipe(take(10));\n *\n * const concurrent = 2; // the argument\n * const merged = merge(timer1, timer2, timer3, concurrent);\n * merged.subscribe(x => console.log(x));\n *\n * // Results in the following:\n * // - First timer1 and timer2 will run concurrently\n * // - timer1 will emit a value every 1000ms for 10 iterations\n * // - timer2 will emit a value every 2000ms for 6 iterations\n * // - after timer1 hits its max iteration, timer2 will\n * //   continue, and timer3 will start to run concurrently with timer2\n * // - when timer2 hits its max iteration it terminates, and\n * //   timer3 will continue to emit a value every 500ms until it is complete\n * ```\n *\n * @see {@link mergeAll}\n * @see {@link mergeMap}\n * @see {@link mergeMapTo}\n * @see {@link mergeScan}\n *\n * @param {...ObservableInput} observables Input Observables to merge together.\n * @param {number} [concurrent=Infinity] Maximum number of input\n * Observables being subscribed to concurrently.\n * @param {SchedulerLike} [scheduler=null] The {@link SchedulerLike} to use for managing\n * concurrency of input Observables.\n * @return {Observable} an Observable that emits items that are the result of\n * every input Observable.\n */\nexport function merge(...args: (ObservableInput<unknown> | number | SchedulerLike)[]): Observable<unknown> {\n  const scheduler = popScheduler(args);\n  const concurrent = popNumber(args, Infinity);\n  const sources = args as ObservableInput<unknown>[];\n  return !sources.length\n    ? // No source provided\n      EMPTY\n    : sources.length === 1\n    ? // One source? Just return it.\n      innerFrom(sources[0])\n    : // Merge all sources\n      mergeAll(concurrent)(from(sources, scheduler));\n}\n", "import { Observable } from '../Observable';\nimport { noop } from '../util/noop';\n\n/**\n * An Observable that emits no items to the Observer and never completes.\n *\n * ![](never.png)\n *\n * A simple Observable that emits neither values nor errors nor the completion\n * notification. It can be used for testing purposes or for composing with other\n * Observables. Please note that by never emitting a complete notification, this\n * Observable keeps the subscription from being disposed automatically.\n * Subscriptions need to be manually disposed.\n *\n * ##  Example\n *\n * Emit the number 7, then never emit anything else (not even complete)\n *\n * ```ts\n * import { NEVER, startWith } from 'rxjs';\n *\n * const info = () => console.log('Will not be called');\n *\n * const result = NEVER.pipe(startWith(7));\n * result.subscribe({\n *   next: x => console.log(x),\n *   error: info,\n *   complete: info\n * });\n * ```\n *\n * @see {@link Observable}\n * @see {@link EMPTY}\n * @see {@link of}\n * @see {@link throwError}\n */\nexport const NEVER = new Observable<never>(noop);\n\n/**\n * @deprecated Replaced with the {@link NEVER} constant. Will be removed in v8.\n */\nexport function never() {\n  return NEVER;\n}\n", "const { isArray } = Array;\n\n/**\n * Used in operators and functions that accept either a list of arguments, or an array of arguments\n * as a single argument.\n */\nexport function argsOrArgArray<T>(args: (T | T[])[]): T[] {\n  return args.length === 1 && isArray(args[0]) ? args[0] : (args as T[]);\n}\n", "import { OperatorFunction, MonoTypeOperatorFunction, TruthyTypesOf } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/** @deprecated Use a closure instead of a `thisArg`. Signatures accepting a `thisArg` will be removed in v8. */\nexport function filter<T, S extends T, A>(predicate: (this: A, value: T, index: number) => value is S, thisArg: A): OperatorFunction<T, S>;\nexport function filter<T, S extends T>(predicate: (value: T, index: number) => value is S): OperatorFunction<T, S>;\nexport function filter<T>(predicate: BooleanConstructor): OperatorFunction<T, TruthyTypesOf<T>>;\n/** @deprecated Use a closure instead of a `thisArg`. Signatures accepting a `thisArg` will be removed in v8. */\nexport function filter<T, A>(predicate: (this: A, value: T, index: number) => boolean, thisArg: A): MonoTypeOperatorFunction<T>;\nexport function filter<T>(predicate: (value: T, index: number) => boolean): MonoTypeOperatorFunction<T>;\n\n/**\n * Filter items emitted by the source Observable by only emitting those that\n * satisfy a specified predicate.\n *\n * <span class=\"informal\">Like\n * [Array.prototype.filter()](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/filter),\n * it only emits a value from the source if it passes a criterion function.</span>\n *\n * ![](filter.png)\n *\n * Similar to the well-known `Array.prototype.filter` method, this operator\n * takes values from the source Observable, passes them through a `predicate`\n * function and only emits those values that yielded `true`.\n *\n * ## Example\n *\n * Emit only click events whose target was a DIV element\n *\n * ```ts\n * import { fromEvent, filter } from 'rxjs';\n *\n * const div = document.createElement('div');\n * div.style.cssText = 'width: 200px; height: 200px; background: #09c;';\n * document.body.appendChild(div);\n *\n * const clicks = fromEvent(document, 'click');\n * const clicksOnDivs = clicks.pipe(filter(ev => (<HTMLElement>ev.target).tagName === 'DIV'));\n * clicksOnDivs.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link distinct}\n * @see {@link distinctUntilChanged}\n * @see {@link distinctUntilKeyChanged}\n * @see {@link ignoreElements}\n * @see {@link partition}\n * @see {@link skip}\n *\n * @param predicate A function that\n * evaluates each value emitted by the source Observable. If it returns `true`,\n * the value is emitted, if `false` the value is not passed to the output\n * Observable. The `index` parameter is the number `i` for the i-th source\n * emission that has happened since the subscription, starting from the number\n * `0`.\n * @param thisArg An optional argument to determine the value of `this`\n * in the `predicate` function.\n * @return A function that returns an Observable that emits items from the\n * source Observable that satisfy the specified `predicate`.\n */\nexport function filter<T>(predicate: (value: T, index: number) => boolean, thisArg?: any): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    // An index passed to our predicate function on each call.\n    let index = 0;\n\n    // Subscribe to the source, all errors and completions are\n    // forwarded to the consumer.\n    source.subscribe(\n      // Call the predicate with the appropriate `this` context,\n      // if the predicate returns `true`, then send the value\n      // to the consumer.\n      createOperatorSubscriber(subscriber, (value) => predicate.call(thisArg, value, index++) && subscriber.next(value))\n    );\n  });\n}\n", "import { Observable } from '../Observable';\nimport { ObservableInputTuple } from '../types';\nimport { innerFrom } from './innerFrom';\nimport { argsOrArgArray } from '../util/argsOrArgArray';\nimport { EMPTY } from './empty';\nimport { createOperatorSubscriber } from '../operators/OperatorSubscriber';\nimport { popResultSelector } from '../util/args';\n\nexport function zip<A extends readonly unknown[]>(sources: [...ObservableInputTuple<A>]): Observable<A>;\nexport function zip<A extends readonly unknown[], R>(\n  sources: [...ObservableInputTuple<A>],\n  resultSelector: (...values: A) => R\n): Observable<R>;\nexport function zip<A extends readonly unknown[]>(...sources: [...ObservableInputTuple<A>]): Observable<A>;\nexport function zip<A extends readonly unknown[], R>(\n  ...sourcesAndResultSelector: [...ObservableInputTuple<A>, (...values: A) => R]\n): Observable<R>;\n\n/**\n * Combines multiple Observables to create an Observable whose values are calculated from the values, in order, of each\n * of its input Observables.\n *\n * If the last parameter is a function, this function is used to compute the created value from the input values.\n * Otherwise, an array of the input values is returned.\n *\n * ## Example\n *\n * Combine age and name from different sources\n *\n * ```ts\n * import { of, zip, map } from 'rxjs';\n *\n * const age$ = of(27, 25, 29);\n * const name$ = of('Foo', 'Bar', 'Beer');\n * const isDev$ = of(true, true, false);\n *\n * zip(age$, name$, isDev$).pipe(\n *   map(([age, name, isDev]) => ({ age, name, isDev }))\n * )\n * .subscribe(x => console.log(x));\n *\n * // Outputs\n * // { age: 27, name: 'Foo', isDev: true }\n * // { age: 25, name: 'Bar', isDev: true }\n * // { age: 29, name: 'Beer', isDev: false }\n * ```\n *\n * @param sources\n * @return {Observable<R>}\n */\nexport function zip(...args: unknown[]): Observable<unknown> {\n  const resultSelector = popResultSelector(args);\n\n  const sources = argsOrArgArray(args) as Observable<unknown>[];\n\n  return sources.length\n    ? new Observable<unknown[]>((subscriber) => {\n        // A collection of buffers of values from each source.\n        // Keyed by the same index with which the sources were passed in.\n        let buffers: unknown[][] = sources.map(() => []);\n\n        // An array of flags of whether or not the sources have completed.\n        // This is used to check to see if we should complete the result.\n        // Keyed by the same index with which the sources were passed in.\n        let completed = sources.map(() => false);\n\n        // When everything is done, release the arrays above.\n        subscriber.add(() => {\n          buffers = completed = null!;\n        });\n\n        // Loop over our sources and subscribe to each one. The index `i` is\n        // especially important here, because we use it in closures below to\n        // access the related buffers and completion properties\n        for (let sourceIndex = 0; !subscriber.closed && sourceIndex < sources.length; sourceIndex++) {\n          innerFrom(sources[sourceIndex]).subscribe(\n            createOperatorSubscriber(\n              subscriber,\n              (value) => {\n                buffers[sourceIndex].push(value);\n                // if every buffer has at least one value in it, then we\n                // can shift out the oldest value from each buffer and emit\n                // them as an array.\n                if (buffers.every((buffer) => buffer.length)) {\n                  const result: any = buffers.map((buffer) => buffer.shift()!);\n                  // Emit the array. If theres' a result selector, use that.\n                  subscriber.next(resultSelector ? resultSelector(...result) : result);\n                  // If any one of the sources is both complete and has an empty buffer\n                  // then we complete the result. This is because we cannot possibly have\n                  // any more values to zip together.\n                  if (buffers.some((buffer, i) => !buffer.length && completed[i])) {\n                    subscriber.complete();\n                  }\n                }\n              },\n              () => {\n                // This source completed. Mark it as complete so we can check it later\n                // if we have to.\n                completed[sourceIndex] = true;\n                // But, if this complete source has nothing in its buffer, then we\n                // can complete the result, because we can't possibly have any more\n                // values from this to zip together with the other values.\n                !buffers[sourceIndex].length && subscriber.complete();\n              }\n            )\n          );\n        }\n\n        // When everything is done, release the arrays above.\n        return () => {\n          buffers = completed = null!;\n        };\n      })\n    : EMPTY;\n}\n", "import { Subscriber } from '../Subscriber';\nimport { MonoTypeOperatorFunction, ObservableInput } from '../types';\n\nimport { operate } from '../util/lift';\nimport { innerFrom } from '../observable/innerFrom';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * Ignores source values for a duration determined by another Observable, then\n * emits the most recent value from the source Observable, then repeats this\n * process.\n *\n * <span class=\"informal\">It's like {@link auditTime}, but the silencing\n * duration is determined by a second Observable.</span>\n *\n * ![](audit.svg)\n *\n * `audit` is similar to `throttle`, but emits the last value from the silenced\n * time window, instead of the first value. `audit` emits the most recent value\n * from the source Observable on the output Observable as soon as its internal\n * timer becomes disabled, and ignores source values while the timer is enabled.\n * Initially, the timer is disabled. As soon as the first source value arrives,\n * the timer is enabled by calling the `durationSelector` function with the\n * source value, which returns the \"duration\" Observable. When the duration\n * Observable emits a value, the timer is disabled, then the most\n * recent source value is emitted on the output Observable, and this process\n * repeats for the next source value.\n *\n * ## Example\n *\n * Emit clicks at a rate of at most one click per second\n *\n * ```ts\n * import { fromEvent, audit, interval } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(audit(ev => interval(1000)));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link auditTime}\n * @see {@link debounce}\n * @see {@link delayWhen}\n * @see {@link sample}\n * @see {@link throttle}\n *\n * @param durationSelector A function\n * that receives a value from the source Observable, for computing the silencing\n * duration, returned as an Observable or a Promise.\n * @return A function that returns an Observable that performs rate-limiting of\n * emissions from the source Observable.\n */\nexport function audit<T>(durationSelector: (value: T) => ObservableInput<any>): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    let hasValue = false;\n    let lastValue: T | null = null;\n    let durationSubscriber: Subscriber<any> | null = null;\n    let isComplete = false;\n\n    const endDuration = () => {\n      durationSubscriber?.unsubscribe();\n      durationSubscriber = null;\n      if (hasValue) {\n        hasValue = false;\n        const value = lastValue!;\n        lastValue = null;\n        subscriber.next(value);\n      }\n      isComplete && subscriber.complete();\n    };\n\n    const cleanupDuration = () => {\n      durationSubscriber = null;\n      isComplete && subscriber.complete();\n    };\n\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => {\n          hasValue = true;\n          lastValue = value;\n          if (!durationSubscriber) {\n            innerFrom(durationSelector(value)).subscribe(\n              (durationSubscriber = createOperatorSubscriber(subscriber, endDuration, cleanupDuration))\n            );\n          }\n        },\n        () => {\n          isComplete = true;\n          (!hasValue || !durationSubscriber || durationSubscriber.closed) && subscriber.complete();\n        }\n      )\n    );\n  });\n}\n", "import { asyncScheduler } from '../scheduler/async';\nimport { audit } from './audit';\nimport { timer } from '../observable/timer';\nimport { MonoTypeOperatorFunction, SchedulerLike } from '../types';\n\n/**\n * Ignores source values for `duration` milliseconds, then emits the most recent\n * value from the source Observable, then repeats this process.\n *\n * <span class=\"informal\">When it sees a source value, it ignores that plus\n * the next ones for `duration` milliseconds, and then it emits the most recent\n * value from the source.</span>\n *\n * ![](auditTime.png)\n *\n * `auditTime` is similar to `throttleTime`, but emits the last value from the\n * silenced time window, instead of the first value. `auditTime` emits the most\n * recent value from the source Observable on the output Observable as soon as\n * its internal timer becomes disabled, and ignores source values while the\n * timer is enabled. Initially, the timer is disabled. As soon as the first\n * source value arrives, the timer is enabled. After `duration` milliseconds (or\n * the time unit determined internally by the optional `scheduler`) has passed,\n * the timer is disabled, then the most recent source value is emitted on the\n * output Observable, and this process repeats for the next source value.\n * Optionally takes a {@link SchedulerLike} for managing timers.\n *\n * ## Example\n *\n * Emit clicks at a rate of at most one click per second\n *\n * ```ts\n * import { fromEvent, auditTime } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(auditTime(1000));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link audit}\n * @see {@link debounceTime}\n * @see {@link delay}\n * @see {@link sampleTime}\n * @see {@link throttleTime}\n *\n * @param {number} duration Time to wait before emitting the most recent source\n * value, measured in milliseconds or the time unit determined internally\n * by the optional `scheduler`.\n * @param {SchedulerLike} [scheduler=async] The {@link SchedulerLike} to use for\n * managing the timers that handle the rate-limiting behavior.\n * @return A function that returns an Observable that performs rate-limiting of\n * emissions from the source Observable.\n */\nexport function auditTime<T>(duration: number, scheduler: SchedulerLike = asyncScheduler): MonoTypeOperatorFunction<T> {\n  return audit(() => timer(duration, scheduler));\n}\n", "import { OperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { arrRemove } from '../util/arrRemove';\n\n/**\n * Buffers the source Observable values until the size hits the maximum\n * `bufferSize` given.\n *\n * <span class=\"informal\">Collects values from the past as an array, and emits\n * that array only when its size reaches `bufferSize`.</span>\n *\n * ![](bufferCount.png)\n *\n * Buffers a number of values from the source Observable by `bufferSize` then\n * emits the buffer and clears it, and starts a new buffer each\n * `startBufferEvery` values. If `startBufferEvery` is not provided or is\n * `null`, then new buffers are started immediately at the start of the source\n * and when each buffer closes and is emitted.\n *\n * ## Examples\n *\n * Emit the last two click events as an array\n *\n * ```ts\n * import { fromEvent, bufferCount } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const buffered = clicks.pipe(bufferCount(2));\n * buffered.subscribe(x => console.log(x));\n * ```\n *\n * On every click, emit the last two click events as an array\n *\n * ```ts\n * import { fromEvent, bufferCount } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const buffered = clicks.pipe(bufferCount(2, 1));\n * buffered.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link buffer}\n * @see {@link bufferTime}\n * @see {@link bufferToggle}\n * @see {@link bufferWhen}\n * @see {@link pairwise}\n * @see {@link windowCount}\n *\n * @param {number} bufferSize The maximum size of the buffer emitted.\n * @param {number} [startBufferEvery] Interval at which to start a new buffer.\n * For example if `startBufferEvery` is `2`, then a new buffer will be started\n * on every other value from the source. A new buffer is started at the\n * beginning of the source by default.\n * @return A function that returns an Observable of arrays of buffered values.\n */\nexport function bufferCount<T>(bufferSize: number, startBufferEvery: number | null = null): OperatorFunction<T, T[]> {\n  // If no `startBufferEvery` value was supplied, then we're\n  // opening and closing on the bufferSize itself.\n  startBufferEvery = startBufferEvery ?? bufferSize;\n\n  return operate((source, subscriber) => {\n    let buffers: T[][] = [];\n    let count = 0;\n\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => {\n          let toEmit: T[][] | null = null;\n\n          // Check to see if we need to start a buffer.\n          // This will start one at the first value, and then\n          // a new one every N after that.\n          if (count++ % startBufferEvery! === 0) {\n            buffers.push([]);\n          }\n\n          // Push our value into our active buffers.\n          for (const buffer of buffers) {\n            buffer.push(value);\n            // Check to see if we're over the bufferSize\n            // if we are, record it so we can emit it later.\n            // If we emitted it now and removed it, it would\n            // mutate the `buffers` array while we're looping\n            // over it.\n            if (bufferSize <= buffer.length) {\n              toEmit = toEmit ?? [];\n              toEmit.push(buffer);\n            }\n          }\n\n          if (toEmit) {\n            // We have found some buffers that are over the\n            // `bufferSize`. Emit them, and remove them from our\n            // buffers list.\n            for (const buffer of toEmit) {\n              arrRemove(buffers, buffer);\n              subscriber.next(buffer);\n            }\n          }\n        },\n        () => {\n          // When the source completes, emit all of our\n          // active buffers.\n          for (const buffer of buffers) {\n            subscriber.next(buffer);\n          }\n          subscriber.complete();\n        },\n        // Pass all errors through to consumer.\n        undefined,\n        () => {\n          // Clean up our memory when we finalize\n          buffers = null!;\n        }\n      )\n    );\n  });\n}\n", "import { Observable } from '../Observable';\n\nimport { ObservableInput, OperatorFunction, ObservedValueOf } from '../types';\nimport { Subscription } from '../Subscription';\nimport { innerFrom } from '../observable/innerFrom';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { operate } from '../util/lift';\n\n/* tslint:disable:max-line-length */\nexport function catchError<T, O extends ObservableInput<any>>(\n  selector: (err: any, caught: Observable<T>) => O\n): OperatorFunction<T, T | ObservedValueOf<O>>;\n/* tslint:enable:max-line-length */\n\n/**\n * Catches errors on the observable to be handled by returning a new observable or throwing an error.\n *\n * <span class=\"informal\">\n * It only listens to the error channel and ignores notifications.\n * Handles errors from the source observable, and maps them to a new observable.\n * The error may also be rethrown, or a new error can be thrown to emit an error from the result.\n * </span>\n *\n * ![](catch.png)\n *\n * This operator handles errors, but forwards along all other events to the resulting observable.\n * If the source observable terminates with an error, it will map that error to a new observable,\n * subscribe to it, and forward all of its events to the resulting observable.\n *\n * ## Examples\n *\n * Continue with a different Observable when there's an error\n *\n * ```ts\n * import { of, map, catchError } from 'rxjs';\n *\n * of(1, 2, 3, 4, 5)\n *   .pipe(\n *     map(n => {\n *       if (n === 4) {\n *         throw 'four!';\n *       }\n *       return n;\n *     }),\n *     catchError(err => of('I', 'II', 'III', 'IV', 'V'))\n *   )\n *   .subscribe(x => console.log(x));\n *   // 1, 2, 3, I, II, III, IV, V\n * ```\n *\n * Retry the caught source Observable again in case of error, similar to `retry()` operator\n *\n * ```ts\n * import { of, map, catchError, take } from 'rxjs';\n *\n * of(1, 2, 3, 4, 5)\n *   .pipe(\n *     map(n => {\n *       if (n === 4) {\n *         throw 'four!';\n *       }\n *       return n;\n *     }),\n *     catchError((err, caught) => caught),\n *     take(30)\n *   )\n *   .subscribe(x => console.log(x));\n *   // 1, 2, 3, 1, 2, 3, ...\n * ```\n *\n * Throw a new error when the source Observable throws an error\n *\n * ```ts\n * import { of, map, catchError } from 'rxjs';\n *\n * of(1, 2, 3, 4, 5)\n *   .pipe(\n *     map(n => {\n *       if (n === 4) {\n *         throw 'four!';\n *       }\n *       return n;\n *     }),\n *     catchError(err => {\n *       throw 'error in source. Details: ' + err;\n *     })\n *   )\n *   .subscribe({\n *     next: x => console.log(x),\n *     error: err => console.log(err)\n *   });\n *   // 1, 2, 3, error in source. Details: four!\n * ```\n *\n * @see {@link onErrorResumeNext}\n * @see {@link repeat}\n * @see {@link repeatWhen}\n * @see {@link retry }\n * @see {@link retryWhen}\n *\n * @param {function} selector a function that takes as arguments `err`, which is the error, and `caught`, which\n * is the source observable, in case you'd like to \"retry\" that observable by returning it again. Whatever observable\n * is returned by the `selector` will be used to continue the observable chain.\n * @return A function that returns an Observable that originates from either\n * the source or the Observable returned by the `selector` function.\n */\nexport function catchError<T, O extends ObservableInput<any>>(\n  selector: (err: any, caught: Observable<T>) => O\n): OperatorFunction<T, T | ObservedValueOf<O>> {\n  return operate((source, subscriber) => {\n    let innerSub: Subscription | null = null;\n    let syncUnsub = false;\n    let handledResult: Observable<ObservedValueOf<O>>;\n\n    innerSub = source.subscribe(\n      createOperatorSubscriber(subscriber, undefined, undefined, (err) => {\n        handledResult = innerFrom(selector(err, catchError(selector)(source)));\n        if (innerSub) {\n          innerSub.unsubscribe();\n          innerSub = null;\n          handledResult.subscribe(subscriber);\n        } else {\n          // We don't have an innerSub yet, that means the error was synchronous\n          // because the subscribe call hasn't returned yet.\n          syncUnsub = true;\n        }\n      })\n    );\n\n    if (syncUnsub) {\n      // We have a synchronous error, we need to make sure to\n      // finalize right away. This ensures that callbacks in the `finalize` operator are called\n      // at the right time, and that finalization occurs at the expected\n      // time between the source error and the subscription to the\n      // next observable.\n      innerSub.unsubscribe();\n      innerSub = null;\n      handledResult!.subscribe(subscriber);\n    }\n  });\n}\n", "import { Observable } from '../Observable';\nimport { Subscriber } from '../Subscriber';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * A basic scan operation. This is used for `scan` and `reduce`.\n * @param accumulator The accumulator to use\n * @param seed The seed value for the state to accumulate\n * @param hasSeed Whether or not a seed was provided\n * @param emitOnNext Whether or not to emit the state on next\n * @param emitBeforeComplete Whether or not to emit the before completion\n */\n\nexport function scanInternals<V, A, S>(\n  accumulator: (acc: V | A | S, value: V, index: number) => A,\n  seed: S,\n  hasSeed: boolean,\n  emitOnNext: boolean,\n  emitBeforeComplete?: undefined | true\n) {\n  return (source: Observable<V>, subscriber: Subscriber<any>) => {\n    // Whether or not we have state yet. This will only be\n    // false before the first value arrives if we didn't get\n    // a seed value.\n    let hasState = hasSeed;\n    // The state that we're tracking, starting with the seed,\n    // if there is one, and then updated by the return value\n    // from the accumulator on each emission.\n    let state: any = seed;\n    // An index to pass to the accumulator function.\n    let index = 0;\n\n    // Subscribe to our source. All errors and completions are passed through.\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => {\n          // Always increment the index.\n          const i = index++;\n          // Set the state\n          state = hasState\n            ? // We already have state, so we can get the new state from the accumulator\n              accumulator(state, value, i)\n            : // We didn't have state yet, a seed value was not provided, so\n\n              // we set the state to the first value, and mark that we have state now\n              ((hasState = true), value);\n\n          // Maybe send it to the consumer.\n          emitOnNext && subscriber.next(state);\n        },\n        // If an onComplete was given, call it, otherwise\n        // just pass through the complete notification to the consumer.\n        emitBeforeComplete &&\n          (() => {\n            hasState && subscriber.next(state);\n            subscriber.complete();\n          })\n      )\n    );\n  };\n}\n", "import { combineLatestInit } from '../observable/combineLatest';\nimport { ObservableInput, ObservableInputTuple, OperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { argsOrArgArray } from '../util/argsOrArgArray';\nimport { mapOneOrManyArgs } from '../util/mapOneOrManyArgs';\nimport { pipe } from '../util/pipe';\nimport { popResultSelector } from '../util/args';\n\n/** @deprecated Replaced with {@link combineLatestWith}. Will be removed in v8. */\nexport function combineLatest<T, A extends readonly unknown[], R>(\n  sources: [...ObservableInputTuple<A>],\n  project: (...values: [T, ...A]) => R\n): OperatorFunction<T, R>;\n/** @deprecated Replaced with {@link combineLatestWith}. Will be removed in v8. */\nexport function combineLatest<T, A extends readonly unknown[], R>(sources: [...ObservableInputTuple<A>]): OperatorFunction<T, [T, ...A]>;\n\n/** @deprecated Replaced with {@link combineLatestWith}. Will be removed in v8. */\nexport function combineLatest<T, A extends readonly unknown[], R>(\n  ...sourcesAndProject: [...ObservableInputTuple<A>, (...values: [T, ...A]) => R]\n): OperatorFunction<T, R>;\n/** @deprecated Replaced with {@link combineLatestWith}. Will be removed in v8. */\nexport function combineLatest<T, A extends readonly unknown[], R>(...sources: [...ObservableInputTuple<A>]): OperatorFunction<T, [T, ...A]>;\n\n/**\n * @deprecated Replaced with {@link combineLatestWith}. Will be removed in v8.\n */\nexport function combineLatest<T, R>(...args: (ObservableInput<any> | ((...values: any[]) => R))[]): OperatorFunction<T, unknown> {\n  const resultSelector = popResultSelector(args);\n  return resultSelector\n    ? pipe(combineLatest(...(args as Array<ObservableInput<any>>)), mapOneOrManyArgs(resultSelector))\n    : operate((source, subscriber) => {\n        combineLatestInit([source, ...argsOrArgArray(args)])(subscriber);\n      });\n}\n", "import { ObservableInputTuple, OperatorFunction, Cons } from '../types';\nimport { combineLatest } from './combineLatest';\n\n/**\n * Create an observable that combines the latest values from all passed observables and the source\n * into arrays and emits them.\n *\n * Returns an observable, that when subscribed to, will subscribe to the source observable and all\n * sources provided as arguments. Once all sources emit at least one value, all of the latest values\n * will be emitted as an array. After that, every time any source emits a value, all of the latest values\n * will be emitted as an array.\n *\n * This is a useful operator for eagerly calculating values based off of changed inputs.\n *\n * ## Example\n *\n * Simple concatenation of values from two inputs\n *\n * ```ts\n * import { fromEvent, combineLatestWith, map } from 'rxjs';\n *\n * // Setup: Add two inputs to the page\n * const input1 = document.createElement('input');\n * document.body.appendChild(input1);\n * const input2 = document.createElement('input');\n * document.body.appendChild(input2);\n *\n * // Get streams of changes\n * const input1Changes$ = fromEvent(input1, 'change');\n * const input2Changes$ = fromEvent(input2, 'change');\n *\n * // Combine the changes by adding them together\n * input1Changes$.pipe(\n *   combineLatestWith(input2Changes$),\n *   map(([e1, e2]) => (<HTMLInputElement>e1.target).value + ' - ' + (<HTMLInputElement>e2.target).value)\n * )\n * .subscribe(x => console.log(x));\n * ```\n *\n * @param otherSources the other sources to subscribe to.\n * @return A function that returns an Observable that emits the latest\n * emissions from both source and provided Observables.\n */\nexport function combineLatestWith<T, A extends readonly unknown[]>(\n  ...otherSources: [...ObservableInputTuple<A>]\n): OperatorFunction<T, Cons<T, A>> {\n  return combineLatest(...otherSources);\n}\n", "import { Subscriber } from '../Subscriber';\nimport { MonoTypeOperatorFunction, ObservableInput } from '../types';\nimport { operate } from '../util/lift';\nimport { noop } from '../util/noop';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { innerFrom } from '../observable/innerFrom';\n\n/**\n * Emits a notification from the source Observable only after a particular time span\n * determined by another Observable has passed without another source emission.\n *\n * <span class=\"informal\">It's like {@link debounceTime}, but the time span of\n * emission silence is determined by a second Observable.</span>\n *\n * ![](debounce.svg)\n *\n * `debounce` delays notifications emitted by the source Observable, but drops previous\n * pending delayed emissions if a new notification arrives on the source Observable.\n * This operator keeps track of the most recent notification from the source\n * Observable, and spawns a duration Observable by calling the\n * `durationSelector` function. The notification is emitted only when the duration\n * Observable emits a next notification, and if no other notification was emitted on\n * the source Observable since the duration Observable was spawned. If a new\n * notification appears before the duration Observable emits, the previous notification will\n * not be emitted and a new duration is scheduled from `durationSelector` is scheduled.\n * If the completing event happens during the scheduled duration the last cached notification\n * is emitted before the completion event is forwarded to the output observable.\n * If the error event happens during the scheduled duration or after it only the error event is\n * forwarded to the output observable. The cache notification is not emitted in this case.\n *\n * Like {@link debounceTime}, this is a rate-limiting operator, and also a\n * delay-like operator since output emissions do not necessarily occur at the\n * same time as they did on the source Observable.\n *\n * ## Example\n *\n * Emit the most recent click after a burst of clicks\n *\n * ```ts\n * import { fromEvent, scan, debounce, interval } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(\n *   scan(i => ++i, 1),\n *   debounce(i => interval(200 * i))\n * );\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link audit}\n * @see {@link auditTime}\n * @see {@link debounceTime}\n * @see {@link delay}\n * @see {@link sample}\n * @see {@link sampleTime}\n * @see {@link throttle}\n * @see {@link throttleTime}\n *\n * @param durationSelector A function\n * that receives a value from the source Observable, for computing the timeout\n * duration for each source value, returned as an Observable or a Promise.\n * @return A function that returns an Observable that delays the emissions of\n * the source Observable by the specified duration Observable returned by\n * `durationSelector`, and may drop some values if they occur too frequently.\n */\nexport function debounce<T>(durationSelector: (value: T) => ObservableInput<any>): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    let hasValue = false;\n    let lastValue: T | null = null;\n    // The subscriber/subscription for the current debounce, if there is one.\n    let durationSubscriber: Subscriber<any> | null = null;\n\n    const emit = () => {\n      // Unsubscribe any current debounce subscription we have,\n      // we only cared about the first notification from it, and we\n      // want to clean that subscription up as soon as possible.\n      durationSubscriber?.unsubscribe();\n      durationSubscriber = null;\n      if (hasValue) {\n        // We have a value! Free up memory first, then emit the value.\n        hasValue = false;\n        const value = lastValue!;\n        lastValue = null;\n        subscriber.next(value);\n      }\n    };\n\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value: T) => {\n          // Cancel any pending debounce duration. We don't\n          // need to null it out here yet tho, because we're just going\n          // to create another one in a few lines.\n          durationSubscriber?.unsubscribe();\n          hasValue = true;\n          lastValue = value;\n          // Capture our duration subscriber, so we can unsubscribe it when we're notified\n          // and we're going to emit the value.\n          durationSubscriber = createOperatorSubscriber(subscriber, emit, noop);\n          // Subscribe to the duration.\n          innerFrom(durationSelector(value)).subscribe(durationSubscriber);\n        },\n        () => {\n          // Source completed.\n          // Emit any pending debounced values then complete\n          emit();\n          subscriber.complete();\n        },\n        // Pass all errors through to consumer\n        undefined,\n        () => {\n          // Finalization.\n          lastValue = durationSubscriber = null;\n        }\n      )\n    );\n  });\n}\n", "import { asyncScheduler } from '../scheduler/async';\nimport { Subscription } from '../Subscription';\nimport { MonoTypeOperatorFunction, SchedulerAction, SchedulerLike } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * Emits a notification from the source Observable only after a particular time span\n * has passed without another source emission.\n *\n * <span class=\"informal\">It's like {@link delay}, but passes only the most\n * recent notification from each burst of emissions.</span>\n *\n * ![](debounceTime.png)\n *\n * `debounceTime` delays notifications emitted by the source Observable, but drops\n * previous pending delayed emissions if a new notification arrives on the source\n * Observable. This operator keeps track of the most recent notification from the\n * source Observable, and emits that only when `dueTime` has passed\n * without any other notification appearing on the source Observable. If a new value\n * appears before `dueTime` silence occurs, the previous notification will be dropped\n * and will not be emitted and a new `dueTime` is scheduled.\n * If the completing event happens during `dueTime` the last cached notification\n * is emitted before the completion event is forwarded to the output observable.\n * If the error event happens during `dueTime` or after it only the error event is\n * forwarded to the output observable. The cache notification is not emitted in this case.\n *\n * This is a rate-limiting operator, because it is impossible for more than one\n * notification to be emitted in any time window of duration `dueTime`, but it is also\n * a delay-like operator since output emissions do not occur at the same time as\n * they did on the source Observable. Optionally takes a {@link SchedulerLike} for\n * managing timers.\n *\n * ## Example\n *\n * Emit the most recent click after a burst of clicks\n *\n * ```ts\n * import { fromEvent, debounceTime } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(debounceTime(1000));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link audit}\n * @see {@link auditTime}\n * @see {@link debounce}\n * @see {@link sample}\n * @see {@link sampleTime}\n * @see {@link throttle}\n * @see {@link throttleTime}\n *\n * @param {number} dueTime The timeout duration in milliseconds (or the time\n * unit determined internally by the optional `scheduler`) for the window of\n * time required to wait for emission silence before emitting the most recent\n * source value.\n * @param {SchedulerLike} [scheduler=async] The {@link SchedulerLike} to use for\n * managing the timers that handle the timeout for each value.\n * @return A function that returns an Observable that delays the emissions of\n * the source Observable by the specified `dueTime`, and may drop some values\n * if they occur too frequently.\n */\nexport function debounceTime<T>(dueTime: number, scheduler: SchedulerLike = asyncScheduler): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    let activeTask: Subscription | null = null;\n    let lastValue: T | null = null;\n    let lastTime: number | null = null;\n\n    const emit = () => {\n      if (activeTask) {\n        // We have a value! Free up memory first, then emit the value.\n        activeTask.unsubscribe();\n        activeTask = null;\n        const value = lastValue!;\n        lastValue = null;\n        subscriber.next(value);\n      }\n    };\n    function emitWhenIdle(this: SchedulerAction<unknown>) {\n      // This is called `dueTime` after the first value\n      // but we might have received new values during this window!\n\n      const targetTime = lastTime! + dueTime;\n      const now = scheduler.now();\n      if (now < targetTime) {\n        // On that case, re-schedule to the new target\n        activeTask = this.schedule(undefined, targetTime - now);\n        subscriber.add(activeTask);\n        return;\n      }\n\n      emit();\n    }\n\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value: T) => {\n          lastValue = value;\n          lastTime = scheduler.now();\n\n          // Only set up a task if it's not already up\n          if (!activeTask) {\n            activeTask = scheduler.schedule(emitWhenIdle, dueTime);\n            subscriber.add(activeTask);\n          }\n        },\n        () => {\n          // Source completed.\n          // Emit any pending debounced values then complete\n          emit();\n          subscriber.complete();\n        },\n        // Pass all errors through to consumer.\n        undefined,\n        () => {\n          // Finalization.\n          lastValue = activeTask = null;\n        }\n      )\n    );\n  });\n}\n", "import { OperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * Emits a given value if the source Observable completes without emitting any\n * `next` value, otherwise mirrors the source Observable.\n *\n * <span class=\"informal\">If the source Observable turns out to be empty, then\n * this operator will emit a default value.</span>\n *\n * ![](defaultIfEmpty.png)\n *\n * `defaultIfEmpty` emits the values emitted by the source Observable or a\n * specified default value if the source Observable is empty (completes without\n * having emitted any `next` value).\n *\n * ## Example\n *\n * If no clicks happen in 5 seconds, then emit 'no clicks'\n *\n * ```ts\n * import { fromEvent, takeUntil, interval, defaultIfEmpty } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const clicksBeforeFive = clicks.pipe(takeUntil(interval(5000)));\n * const result = clicksBeforeFive.pipe(defaultIfEmpty('no clicks'));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link empty}\n * @see {@link last}\n *\n * @param defaultValue The default value used if the source\n * Observable is empty.\n * @return A function that returns an Observable that emits either the\n * specified `defaultValue` if the source Observable emits no items, or the\n * values emitted by the source Observable.\n */\nexport function defaultIfEmpty<T, R>(defaultValue: R): OperatorFunction<T, T | R> {\n  return operate((source, subscriber) => {\n    let hasValue = false;\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => {\n          hasValue = true;\n          subscriber.next(value);\n        },\n        () => {\n          if (!hasValue) {\n            subscriber.next(defaultValue!);\n          }\n          subscriber.complete();\n        }\n      )\n    );\n  });\n}\n", "import { MonoTypeOperatorFunction } from '../types';\nimport { EMPTY } from '../observable/empty';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * Emits only the first `count` values emitted by the source Observable.\n *\n * <span class=\"informal\">Takes the first `count` values from the source, then\n * completes.</span>\n *\n * ![](take.png)\n *\n * `take` returns an Observable that emits only the first `count` values emitted\n * by the source Observable. If the source emits fewer than `count` values then\n * all of its values are emitted. After that, it completes, regardless if the\n * source completes.\n *\n * ## Example\n *\n * Take the first 5 seconds of an infinite 1-second interval Observable\n *\n * ```ts\n * import { interval, take } from 'rxjs';\n *\n * const intervalCount = interval(1000);\n * const takeFive = intervalCount.pipe(take(5));\n * takeFive.subscribe(x => console.log(x));\n *\n * // Logs:\n * // 0\n * // 1\n * // 2\n * // 3\n * // 4\n * ```\n *\n * @see {@link takeLast}\n * @see {@link takeUntil}\n * @see {@link takeWhile}\n * @see {@link skip}\n *\n * @param count The maximum number of `next` values to emit.\n * @return A function that returns an Observable that emits only the first\n * `count` values emitted by the source Observable, or all of the values from\n * the source if the source emits fewer than `count` values.\n */\nexport function take<T>(count: number): MonoTypeOperatorFunction<T> {\n  return count <= 0\n    ? // If we are taking no values, that's empty.\n      () => EMPTY\n    : operate((source, subscriber) => {\n        let seen = 0;\n        source.subscribe(\n          createOperatorSubscriber(subscriber, (value) => {\n            // Increment the number of values we have seen,\n            // then check it against the allowed count to see\n            // if we are still letting values through.\n            if (++seen <= count) {\n              subscriber.next(value);\n              // If we have met or passed our allowed count,\n              // we need to complete. We have to do <= here,\n              // because re-entrant code will increment `seen` twice.\n              if (count <= seen) {\n                subscriber.complete();\n              }\n            }\n          })\n        );\n      });\n}\n", "import { OperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { noop } from '../util/noop';\n\n/**\n * Ignores all items emitted by the source Observable and only passes calls of `complete` or `error`.\n *\n * ![](ignoreElements.png)\n *\n * The `ignoreElements` operator suppresses all items emitted by the source Observable,\n * but allows its termination notification (either `error` or `complete`) to pass through unchanged.\n *\n * If you do not care about the items being emitted by an Observable, but you do want to be notified\n * when it completes or when it terminates with an error, you can apply the `ignoreElements` operator\n * to the Observable, which will ensure that it will never call its observers\u2019 `next` handlers.\n *\n * ## Example\n *\n * Ignore all `next` emissions from the source\n *\n * ```ts\n * import { of, ignoreElements } from 'rxjs';\n *\n * of('you', 'talking', 'to', 'me')\n *   .pipe(ignoreElements())\n *   .subscribe({\n *     next: word => console.log(word),\n *     error: err => console.log('error:', err),\n *     complete: () => console.log('the end'),\n *   });\n *\n * // result:\n * // 'the end'\n * ```\n *\n * @return A function that returns an empty Observable that only calls\n * `complete` or `error`, based on which one is called by the source\n * Observable.\n */\nexport function ignoreElements(): OperatorFunction<unknown, never> {\n  return operate((source, subscriber) => {\n    source.subscribe(createOperatorSubscriber(subscriber, noop));\n  });\n}\n", "import { OperatorFunction } from '../types';\nimport { map } from './map';\n\n/** @deprecated To be removed in v9. Use {@link map} instead: `map(() => value)`. */\nexport function mapTo<R>(value: R): OperatorFunction<unknown, R>;\n/**\n * @deprecated Do not specify explicit type parameters. Signatures with type parameters\n * that cannot be inferred will be removed in v8. `mapTo` itself will be removed in v9,\n * use {@link map} instead: `map(() => value)`.\n * */\nexport function mapTo<T, R>(value: R): OperatorFunction<T, R>;\n\n/**\n * Emits the given constant value on the output Observable every time the source\n * Observable emits a value.\n *\n * <span class=\"informal\">Like {@link map}, but it maps every source value to\n * the same output value every time.</span>\n *\n * ![](mapTo.png)\n *\n * Takes a constant `value` as argument, and emits that whenever the source\n * Observable emits a value. In other words, ignores the actual source value,\n * and simply uses the emission moment to know when to emit the given `value`.\n *\n * ## Example\n *\n * Map every click to the string `'Hi'`\n *\n * ```ts\n * import { fromEvent, mapTo } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const greetings = clicks.pipe(mapTo('Hi'));\n *\n * greetings.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link map}\n *\n * @param value The value to map each source value to.\n * @return A function that returns an Observable that emits the given `value`\n * every time the source Observable emits.\n * @deprecated To be removed in v9. Use {@link map} instead: `map(() => value)`.\n */\nexport function mapTo<R>(value: R): OperatorFunction<unknown, R> {\n  return map(() => value);\n}\n", "import { Observable } from '../Observable';\nimport { MonoTypeOperatorFunction, ObservableInput } from '../types';\nimport { concat } from '../observable/concat';\nimport { take } from './take';\nimport { ignoreElements } from './ignoreElements';\nimport { mapTo } from './mapTo';\nimport { mergeMap } from './mergeMap';\nimport { innerFrom } from '../observable/innerFrom';\n\n/** @deprecated The `subscriptionDelay` parameter will be removed in v8. */\nexport function delayWhen<T>(\n  delayDurationSelector: (value: T, index: number) => ObservableInput<any>,\n  subscriptionDelay: Observable<any>\n): MonoTypeOperatorFunction<T>;\nexport function delayWhen<T>(delayDurationSelector: (value: T, index: number) => ObservableInput<any>): MonoTypeOperatorFunction<T>;\n\n/**\n * Delays the emission of items from the source Observable by a given time span\n * determined by the emissions of another Observable.\n *\n * <span class=\"informal\">It's like {@link delay}, but the time span of the\n * delay duration is determined by a second Observable.</span>\n *\n * ![](delayWhen.png)\n *\n * `delayWhen` operator shifts each emitted value from the source Observable by\n * a time span determined by another Observable. When the source emits a value,\n * the `delayDurationSelector` function is called with the value emitted from\n * the source Observable as the first argument to the `delayDurationSelector`.\n * The `delayDurationSelector` function should return an {@link ObservableInput},\n * that is internally converted to an Observable that is called the \"duration\"\n * Observable.\n *\n * The source value is emitted on the output Observable only when the \"duration\"\n * Observable emits ({@link guide/glossary-and-semantics#next next}s) any value.\n * Upon that, the \"duration\" Observable gets unsubscribed.\n *\n * Before RxJS V7, the {@link guide/glossary-and-semantics#complete completion}\n * of the \"duration\" Observable would have been triggering the emission of the\n * source value to the output Observable, but with RxJS V7, this is not the case\n * anymore.\n *\n * Only next notifications (from the \"duration\" Observable) trigger values from\n * the source Observable to be passed to the output Observable. If the \"duration\"\n * Observable only emits the complete notification (without next), the value\n * emitted by the source Observable will never get to the output Observable - it\n * will be swallowed. If the \"duration\" Observable errors, the error will be\n * propagated to the output Observable.\n *\n * Optionally, `delayWhen` takes a second argument, `subscriptionDelay`, which\n * is an Observable. When `subscriptionDelay` emits its first value or\n * completes, the source Observable is subscribed to and starts behaving like\n * described in the previous paragraph. If `subscriptionDelay` is not provided,\n * `delayWhen` will subscribe to the source Observable as soon as the output\n * Observable is subscribed.\n *\n * ## Example\n *\n * Delay each click by a random amount of time, between 0 and 5 seconds\n *\n * ```ts\n * import { fromEvent, delayWhen, interval } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const delayedClicks = clicks.pipe(\n *   delayWhen(() => interval(Math.random() * 5000))\n * );\n * delayedClicks.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link delay}\n * @see {@link throttle}\n * @see {@link throttleTime}\n * @see {@link debounce}\n * @see {@link debounceTime}\n * @see {@link sample}\n * @see {@link sampleTime}\n * @see {@link audit}\n * @see {@link auditTime}\n *\n * @param delayDurationSelector A function that returns an `ObservableInput` for\n * each `value` emitted by the source Observable, which is then used to delay the\n * emission of that `value` on the output Observable until the `ObservableInput`\n * returned from this function emits a next value. When called, beside `value`,\n * this function receives a zero-based `index` of the emission order.\n * @param subscriptionDelay An Observable that triggers the subscription to the\n * source Observable once it emits any value.\n * @return A function that returns an Observable that delays the emissions of\n * the source Observable by an amount of time specified by the Observable\n * returned by `delayDurationSelector`.\n */\nexport function delayWhen<T>(\n  delayDurationSelector: (value: T, index: number) => ObservableInput<any>,\n  subscriptionDelay?: Observable<any>\n): MonoTypeOperatorFunction<T> {\n  if (subscriptionDelay) {\n    // DEPRECATED PATH\n    return (source: Observable<T>) =>\n      concat(subscriptionDelay.pipe(take(1), ignoreElements()), source.pipe(delayWhen(delayDurationSelector)));\n  }\n\n  return mergeMap((value, index) => innerFrom(delayDurationSelector(value, index)).pipe(take(1), mapTo(value)));\n}\n", "import { asyncScheduler } from '../scheduler/async';\nimport { MonoTypeOperatorFunction, SchedulerLike } from '../types';\nimport { delayWhen } from './delayWhen';\nimport { timer } from '../observable/timer';\n\n/**\n * Delays the emission of items from the source Observable by a given timeout or\n * until a given Date.\n *\n * <span class=\"informal\">Time shifts each item by some specified amount of\n * milliseconds.</span>\n *\n * ![](delay.svg)\n *\n * If the delay argument is a Number, this operator time shifts the source\n * Observable by that amount of time expressed in milliseconds. The relative\n * time intervals between the values are preserved.\n *\n * If the delay argument is a Date, this operator time shifts the start of the\n * Observable execution until the given date occurs.\n *\n * ## Examples\n *\n * Delay each click by one second\n *\n * ```ts\n * import { fromEvent, delay } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const delayedClicks = clicks.pipe(delay(1000)); // each click emitted after 1 second\n * delayedClicks.subscribe(x => console.log(x));\n * ```\n *\n * Delay all clicks until a future date happens\n *\n * ```ts\n * import { fromEvent, delay } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const date = new Date('March 15, 2050 12:00:00'); // in the future\n * const delayedClicks = clicks.pipe(delay(date)); // click emitted only after that date\n * delayedClicks.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link delayWhen}\n * @see {@link throttle}\n * @see {@link throttleTime}\n * @see {@link debounce}\n * @see {@link debounceTime}\n * @see {@link sample}\n * @see {@link sampleTime}\n * @see {@link audit}\n * @see {@link auditTime}\n *\n * @param {number|Date} due The delay duration in milliseconds (a `number`) or\n * a `Date` until which the emission of the source items is delayed.\n * @param {SchedulerLike} [scheduler=async] The {@link SchedulerLike} to use for\n * managing the timers that handle the time-shift for each item.\n * @return A function that returns an Observable that delays the emissions of\n * the source Observable by the specified timeout or Date.\n */\nexport function delay<T>(due: number | Date, scheduler: SchedulerLike = asyncScheduler): MonoTypeOperatorFunction<T> {\n  const duration = timer(due, scheduler);\n  return delayWhen(() => duration);\n}\n", "import { MonoTypeOperatorFunction } from '../types';\nimport { identity } from '../util/identity';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\nexport function distinctUntilChanged<T>(comparator?: (previous: T, current: T) => boolean): MonoTypeOperatorFunction<T>;\nexport function distinctUntilChanged<T, K>(\n  comparator: (previous: K, current: K) => boolean,\n  keySelector: (value: T) => K\n): MonoTypeOperatorFunction<T>;\n\n/**\n * Returns a result {@link Observable} that emits all values pushed by the source observable if they\n * are distinct in comparison to the last value the result observable emitted.\n *\n * When provided without parameters or with the first parameter (`{@link distinctUntilChanged#comparator comparator}`),\n * it behaves like this:\n *\n * 1. It will always emit the first value from the source.\n * 2. For all subsequent values pushed by the source, they will be compared to the previously emitted values\n *    using the provided `comparator` or an `===` equality check.\n * 3. If the value pushed by the source is determined to be unequal by this check, that value is emitted and\n *    becomes the new \"previously emitted value\" internally.\n *\n * When the second parameter (`{@link distinctUntilChanged#keySelector keySelector}`) is provided, the behavior\n * changes:\n *\n * 1. It will always emit the first value from the source.\n * 2. The `keySelector` will be run against all values, including the first value.\n * 3. For all values after the first, the selected key will be compared against the key selected from\n *    the previously emitted value using the `comparator`.\n * 4. If the keys are determined to be unequal by this check, the value (not the key), is emitted\n *    and the selected key from that value is saved for future comparisons against other keys.\n *\n * ## Examples\n *\n * A very basic example with no `{@link distinctUntilChanged#comparator comparator}`. Note that `1` is emitted more than once,\n * because it's distinct in comparison to the _previously emitted_ value,\n * not in comparison to _all other emitted values_.\n *\n * ```ts\n * import { of, distinctUntilChanged } from 'rxjs';\n *\n * of(1, 1, 1, 2, 2, 2, 1, 1, 3, 3)\n *   .pipe(distinctUntilChanged())\n *   .subscribe(console.log);\n * // Logs: 1, 2, 1, 3\n * ```\n *\n * With a `{@link distinctUntilChanged#comparator comparator}`, you can do custom comparisons. Let's say\n * you only want to emit a value when all of its components have\n * changed:\n *\n * ```ts\n * import { of, distinctUntilChanged } from 'rxjs';\n *\n * const totallyDifferentBuilds$ = of(\n *   { engineVersion: '1.1.0', transmissionVersion: '1.2.0' },\n *   { engineVersion: '1.1.0', transmissionVersion: '1.4.0' },\n *   { engineVersion: '1.3.0', transmissionVersion: '1.4.0' },\n *   { engineVersion: '1.3.0', transmissionVersion: '1.5.0' },\n *   { engineVersion: '2.0.0', transmissionVersion: '1.5.0' }\n * ).pipe(\n *   distinctUntilChanged((prev, curr) => {\n *     return (\n *       prev.engineVersion === curr.engineVersion ||\n *       prev.transmissionVersion === curr.transmissionVersion\n *     );\n *   })\n * );\n *\n * totallyDifferentBuilds$.subscribe(console.log);\n *\n * // Logs:\n * // { engineVersion: '1.1.0', transmissionVersion: '1.2.0' }\n * // { engineVersion: '1.3.0', transmissionVersion: '1.4.0' }\n * // { engineVersion: '2.0.0', transmissionVersion: '1.5.0' }\n * ```\n *\n * You can also provide a custom `{@link distinctUntilChanged#comparator comparator}` to check that emitted\n * changes are only in one direction. Let's say you only want to get\n * the next record temperature:\n *\n * ```ts\n * import { of, distinctUntilChanged } from 'rxjs';\n *\n * const temps$ = of(30, 31, 20, 34, 33, 29, 35, 20);\n *\n * const recordHighs$ = temps$.pipe(\n *   distinctUntilChanged((prevHigh, temp) => {\n *     // If the current temp is less than\n *     // or the same as the previous record,\n *     // the record hasn't changed.\n *     return temp <= prevHigh;\n *   })\n * );\n *\n * recordHighs$.subscribe(console.log);\n * // Logs: 30, 31, 34, 35\n * ```\n *\n * Selecting update events only when the `updatedBy` field shows\n * the account changed hands.\n *\n * ```ts\n * import { of, distinctUntilChanged } from 'rxjs';\n *\n * // A stream of updates to a given account\n * const accountUpdates$ = of(\n *   { updatedBy: 'blesh', data: [] },\n *   { updatedBy: 'blesh', data: [] },\n *   { updatedBy: 'ncjamieson', data: [] },\n *   { updatedBy: 'ncjamieson', data: [] },\n *   { updatedBy: 'blesh', data: [] }\n * );\n *\n * // We only want the events where it changed hands\n * const changedHands$ = accountUpdates$.pipe(\n *   distinctUntilChanged(undefined, update => update.updatedBy)\n * );\n *\n * changedHands$.subscribe(console.log);\n * // Logs:\n * // { updatedBy: 'blesh', data: Array[0] }\n * // { updatedBy: 'ncjamieson', data: Array[0] }\n * // { updatedBy: 'blesh', data: Array[0] }\n * ```\n *\n * @see {@link distinct}\n * @see {@link distinctUntilKeyChanged}\n *\n * @param comparator A function used to compare the previous and current keys for\n * equality. Defaults to a `===` check.\n * @param keySelector Used to select a key value to be passed to the `comparator`.\n *\n * @return A function that returns an Observable that emits items from the\n * source Observable with distinct values.\n */\nexport function distinctUntilChanged<T, K>(\n  comparator?: (previous: K, current: K) => boolean,\n  keySelector: (value: T) => K = identity as (value: T) => K\n): MonoTypeOperatorFunction<T> {\n  // We've been allowing `null` do be passed as the `compare`, so we can't do\n  // a default value for the parameter, because that will only work\n  // for `undefined`.\n  comparator = comparator ?? defaultCompare;\n\n  return operate((source, subscriber) => {\n    // The previous key, used to compare against keys selected\n    // from new arrivals to determine \"distinctiveness\".\n    let previousKey: K;\n    // Whether or not this is the first value we've gotten.\n    let first = true;\n\n    source.subscribe(\n      createOperatorSubscriber(subscriber, (value) => {\n        // We always call the key selector.\n        const currentKey = keySelector(value);\n\n        // If it's the first value, we always emit it.\n        // Otherwise, we compare this key to the previous key, and\n        // if the comparer returns false, we emit.\n        if (first || !comparator!(previousKey, currentKey)) {\n          // Update our state *before* we emit the value\n          // as emission can be the source of re-entrant code\n          // in functional libraries like this. We only really\n          // need to do this if it's the first value, or if the\n          // key we're tracking in previous needs to change.\n          first = false;\n          previousKey = currentKey;\n\n          // Emit the value!\n          subscriber.next(value);\n        }\n      })\n    );\n  });\n}\n\nfunction defaultCompare(a: any, b: any) {\n  return a === b;\n}\n", "import { distinctUntilChanged } from './distinctUntilChanged';\nimport { MonoTypeOperatorFunction } from '../types';\n\n/* tslint:disable:max-line-length */\nexport function distinctUntilKeyChanged<T>(key: keyof T): MonoTypeOperatorFunction<T>;\nexport function distinctUntilKeyChanged<T, K extends keyof T>(key: K, compare: (x: T[K], y: T[K]) => boolean): MonoTypeOperatorFunction<T>;\n/* tslint:enable:max-line-length */\n\n/**\n * Returns an Observable that emits all items emitted by the source Observable that are distinct by comparison from the previous item,\n * using a property accessed by using the key provided to check if the two items are distinct.\n *\n * If a comparator function is provided, then it will be called for each item to test for whether or not that value should be emitted.\n *\n * If a comparator function is not provided, an equality check is used by default.\n *\n * ## Examples\n *\n * An example comparing the name of persons\n *\n * ```ts\n * import { of, distinctUntilKeyChanged } from 'rxjs';\n *\n * of(\n *   { age: 4, name: 'Foo' },\n *   { age: 7, name: 'Bar' },\n *   { age: 5, name: 'Foo' },\n *   { age: 6, name: 'Foo' }\n * ).pipe(\n *   distinctUntilKeyChanged('name')\n * )\n * .subscribe(x => console.log(x));\n *\n * // displays:\n * // { age: 4, name: 'Foo' }\n * // { age: 7, name: 'Bar' }\n * // { age: 5, name: 'Foo' }\n * ```\n *\n * An example comparing the first letters of the name\n *\n * ```ts\n * import { of, distinctUntilKeyChanged } from 'rxjs';\n *\n * of(\n *   { age: 4, name: 'Foo1' },\n *   { age: 7, name: 'Bar' },\n *   { age: 5, name: 'Foo2' },\n *   { age: 6, name: 'Foo3' }\n * ).pipe(\n *   distinctUntilKeyChanged('name', (x, y) => x.substring(0, 3) === y.substring(0, 3))\n * )\n * .subscribe(x => console.log(x));\n *\n * // displays:\n * // { age: 4, name: 'Foo1' }\n * // { age: 7, name: 'Bar' }\n * // { age: 5, name: 'Foo2' }\n * ```\n *\n * @see {@link distinct}\n * @see {@link distinctUntilChanged}\n *\n * @param {string} key String key for object property lookup on each item.\n * @param {function} [compare] Optional comparison function called to test if an item is distinct from the previous item in the source.\n * @return A function that returns an Observable that emits items from the\n * source Observable with distinct values based on the key specified.\n */\nexport function distinctUntilKeyChanged<T, K extends keyof T>(key: K, compare?: (x: T[K], y: T[K]) => boolean): MonoTypeOperatorFunction<T> {\n  return distinctUntilChanged((x: T, y: T) => compare ? compare(x[key], y[key]) : x[key] === y[key]);\n}\n", "import { EmptyError } from '../util/EmptyError';\nimport { MonoTypeOperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * If the source observable completes without emitting a value, it will emit\n * an error. The error will be created at that time by the optional\n * `errorFactory` argument, otherwise, the error will be {@link EmptyError}.\n *\n * ![](throwIfEmpty.png)\n *\n * ## Example\n *\n * Throw an error if the document wasn't clicked within 1 second\n *\n * ```ts\n * import { fromEvent, takeUntil, timer, throwIfEmpty } from 'rxjs';\n *\n * const click$ = fromEvent(document, 'click');\n *\n * click$.pipe(\n *   takeUntil(timer(1000)),\n *   throwIfEmpty(() => new Error('The document was not clicked within 1 second'))\n * )\n * .subscribe({\n *   next() {\n *    console.log('The document was clicked');\n *   },\n *   error(err) {\n *     console.error(err.message);\n *   }\n * });\n * ```\n *\n * @param errorFactory A factory function called to produce the\n * error to be thrown when the source observable completes without emitting a\n * value.\n * @return A function that returns an Observable that throws an error if the\n * source Observable completed without emitting.\n */\nexport function throwIfEmpty<T>(errorFactory: () => any = defaultErrorFactory): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    let hasValue = false;\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => {\n          hasValue = true;\n          subscriber.next(value);\n        },\n        () => (hasValue ? subscriber.complete() : subscriber.error(errorFactory()))\n      )\n    );\n  });\n}\n\nfunction defaultErrorFactory() {\n  return new EmptyError();\n}\n", "/** prettier */\nimport { Observable } from '../Observable';\nimport { concat } from '../observable/concat';\nimport { of } from '../observable/of';\nimport { MonoTypeOperatorFunction, SchedulerLike, OperatorFunction, ValueFromArray } from '../types';\n\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `concatAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function endWith<T>(scheduler: SchedulerLike): MonoTypeOperatorFunction<T>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `concatAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function endWith<T, A extends unknown[] = T[]>(\n  ...valuesAndScheduler: [...A, SchedulerLike]\n): OperatorFunction<T, T | ValueFromArray<A>>;\n\nexport function endWith<T, A extends unknown[] = T[]>(...values: A): OperatorFunction<T, T | ValueFromArray<A>>;\n\n/**\n * Returns an observable that will emit all values from the source, then synchronously emit\n * the provided value(s) immediately after the source completes.\n *\n * NOTE: Passing a last argument of a Scheduler is _deprecated_, and may result in incorrect\n * types in TypeScript.\n *\n * This is useful for knowing when an observable ends. Particularly when paired with an\n * operator like {@link takeUntil}\n *\n * ![](endWith.png)\n *\n * ## Example\n *\n * Emit values to know when an interval starts and stops. The interval will\n * stop when a user clicks anywhere on the document.\n *\n * ```ts\n * import { interval, map, fromEvent, startWith, takeUntil, endWith } from 'rxjs';\n *\n * const ticker$ = interval(5000).pipe(\n *   map(() => 'tick')\n * );\n *\n * const documentClicks$ = fromEvent(document, 'click');\n *\n * ticker$.pipe(\n *   startWith('interval started'),\n *   takeUntil(documentClicks$),\n *   endWith('interval ended by click')\n * )\n * .subscribe(x => console.log(x));\n *\n * // Result (assuming a user clicks after 15 seconds)\n * // 'interval started'\n * // 'tick'\n * // 'tick'\n * // 'tick'\n * // 'interval ended by click'\n * ```\n *\n * @see {@link startWith}\n * @see {@link concat}\n * @see {@link takeUntil}\n *\n * @param values Items you want the modified Observable to emit last.\n * @return A function that returns an Observable that emits all values from the\n * source, then synchronously emits the provided value(s) immediately after the\n * source completes.\n */\nexport function endWith<T>(...values: Array<T | SchedulerLike>): MonoTypeOperatorFunction<T> {\n  return (source: Observable<T>) => concat(source, of(...values)) as Observable<T>;\n}\n", "import { MonoTypeOperatorFunction } from '../types';\nimport { operate } from '../util/lift';\n\n/**\n * Returns an Observable that mirrors the source Observable, but will call a specified function when\n * the source terminates on complete or error.\n * The specified function will also be called when the subscriber explicitly unsubscribes.\n *\n * ## Examples\n *\n * Execute callback function when the observable completes\n *\n * ```ts\n * import { interval, take, finalize } from 'rxjs';\n *\n * // emit value in sequence every 1 second\n * const source = interval(1000);\n * const example = source.pipe(\n *   take(5), //take only the first 5 values\n *   finalize(() => console.log('Sequence complete')) // Execute when the observable completes\n * );\n * const subscribe = example.subscribe(val => console.log(val));\n *\n * // results:\n * // 0\n * // 1\n * // 2\n * // 3\n * // 4\n * // 'Sequence complete'\n * ```\n *\n * Execute callback function when the subscriber explicitly unsubscribes\n *\n * ```ts\n * import { interval, finalize, tap, noop, timer } from 'rxjs';\n *\n * const source = interval(100).pipe(\n *   finalize(() => console.log('[finalize] Called')),\n *   tap({\n *     next: () => console.log('[next] Called'),\n *     error: () => console.log('[error] Not called'),\n *     complete: () => console.log('[tap complete] Not called')\n *   })\n * );\n *\n * const sub = source.subscribe({\n *   next: x => console.log(x),\n *   error: noop,\n *   complete: () => console.log('[complete] Not called')\n * });\n *\n * timer(150).subscribe(() => sub.unsubscribe());\n *\n * // results:\n * // '[next] Called'\n * // 0\n * // '[finalize] Called'\n * ```\n *\n * @param {function} callback Function to be called when source terminates.\n * @return A function that returns an Observable that mirrors the source, but\n * will call the specified function on termination.\n */\nexport function finalize<T>(callback: () => void): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    // TODO: This try/finally was only added for `useDeprecatedSynchronousErrorHandling`.\n    // REMOVE THIS WHEN THAT HOT GARBAGE IS REMOVED IN V8.\n    try {\n      source.subscribe(subscriber);\n    } finally {\n      subscriber.add(callback);\n    }\n  });\n}\n", "import { Observable } from '../Observable';\nimport { EmptyError } from '../util/EmptyError';\nimport { OperatorFunction, TruthyTypesOf } from '../types';\nimport { filter } from './filter';\nimport { take } from './take';\nimport { defaultIfEmpty } from './defaultIfEmpty';\nimport { throwIfEmpty } from './throwIfEmpty';\nimport { identity } from '../util/identity';\n\nexport function first<T, D = T>(predicate?: null, defaultValue?: D): OperatorFunction<T, T | D>;\nexport function first<T>(predicate: BooleanConstructor): OperatorFunction<T, TruthyTypesOf<T>>;\nexport function first<T, D>(predicate: BooleanConstructor, defaultValue: D): OperatorFunction<T, TruthyTypesOf<T> | D>;\nexport function first<T, S extends T>(\n  predicate: (value: T, index: number, source: Observable<T>) => value is S,\n  defaultValue?: S\n): OperatorFunction<T, S>;\nexport function first<T, S extends T, D>(\n  predicate: (value: T, index: number, source: Observable<T>) => value is S,\n  defaultValue: D\n): OperatorFunction<T, S | D>;\nexport function first<T, D = T>(\n  predicate: (value: T, index: number, source: Observable<T>) => boolean,\n  defaultValue?: D\n): OperatorFunction<T, T | D>;\n\n/**\n * Emits only the first value (or the first value that meets some condition)\n * emitted by the source Observable.\n *\n * <span class=\"informal\">Emits only the first value. Or emits only the first\n * value that passes some test.</span>\n *\n * ![](first.png)\n *\n * If called with no arguments, `first` emits the first value of the source\n * Observable, then completes. If called with a `predicate` function, `first`\n * emits the first value of the source that matches the specified condition. Throws an error if\n * `defaultValue` was not provided and a matching element is not found.\n *\n * ## Examples\n *\n * Emit only the first click that happens on the DOM\n *\n * ```ts\n * import { fromEvent, first } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(first());\n * result.subscribe(x => console.log(x));\n * ```\n *\n * Emits the first click that happens on a DIV\n *\n * ```ts\n * import { fromEvent, first } from 'rxjs';\n *\n * const div = document.createElement('div');\n * div.style.cssText = 'width: 200px; height: 200px; background: #09c;';\n * document.body.appendChild(div);\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(first(ev => (<HTMLElement>ev.target).tagName === 'DIV'));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link filter}\n * @see {@link find}\n * @see {@link take}\n *\n * @throws {EmptyError} Delivers an EmptyError to the Observer's `error`\n * callback if the Observable completes before any `next` notification was sent.\n * This is how `first()` is different from {@link take}(1) which completes instead.\n *\n * @param {function(value: T, index: number, source: Observable<T>): boolean} [predicate]\n * An optional function called with each item to test for condition matching.\n * @param {D} [defaultValue] The default value emitted in case no valid value\n * was found on the source.\n * @return A function that returns an Observable that emits the first item that\n * matches the condition.\n */\nexport function first<T, D>(\n  predicate?: ((value: T, index: number, source: Observable<T>) => boolean) | null,\n  defaultValue?: D\n): OperatorFunction<T, T | D> {\n  const hasDefaultValue = arguments.length >= 2;\n  return (source: Observable<T>) =>\n    source.pipe(\n      predicate ? filter((v, i) => predicate(v, i, source)) : identity,\n      take(1),\n      hasDefaultValue ? defaultIfEmpty(defaultValue!) : throwIfEmpty(() => new EmptyError())\n    );\n}\n", "import { EMPTY } from '../observable/empty';\nimport { MonoTypeOperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * Waits for the source to complete, then emits the last N values from the source,\n * as specified by the `count` argument.\n *\n * ![](takeLast.png)\n *\n * `takeLast` results in an observable that will hold values up to `count` values in memory,\n * until the source completes. It then pushes all values in memory to the consumer, in the\n * order they were received from the source, then notifies the consumer that it is\n * complete.\n *\n * If for some reason the source completes before the `count` supplied to `takeLast` is reached,\n * all values received until that point are emitted, and then completion is notified.\n *\n * **Warning**: Using `takeLast` with an observable that never completes will result\n * in an observable that never emits a value.\n *\n * ## Example\n *\n * Take the last 3 values of an Observable with many values\n *\n * ```ts\n * import { range, takeLast } from 'rxjs';\n *\n * const many = range(1, 100);\n * const lastThree = many.pipe(takeLast(3));\n * lastThree.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link take}\n * @see {@link takeUntil}\n * @see {@link takeWhile}\n * @see {@link skip}\n *\n * @param count The maximum number of values to emit from the end of\n * the sequence of values emitted by the source Observable.\n * @return A function that returns an Observable that emits at most the last\n * `count` values emitted by the source Observable.\n */\nexport function takeLast<T>(count: number): MonoTypeOperatorFunction<T> {\n  return count <= 0\n    ? () => EMPTY\n    : operate((source, subscriber) => {\n        // This buffer will hold the values we are going to emit\n        // when the source completes. Since we only want to take the\n        // last N values, we can't emit until we're sure we're not getting\n        // any more values.\n        let buffer: T[] = [];\n        source.subscribe(\n          createOperatorSubscriber(\n            subscriber,\n            (value) => {\n              // Add the most recent value onto the end of our buffer.\n              buffer.push(value);\n              // If our buffer is now larger than the number of values we\n              // want to take, we remove the oldest value from the buffer.\n              count < buffer.length && buffer.shift();\n            },\n            () => {\n              // The source completed, we now know what are last values\n              // are, emit them in the order they were received.\n              for (const value of buffer) {\n                subscriber.next(value);\n              }\n              subscriber.complete();\n            },\n            // Errors are passed through to the consumer\n            undefined,\n            () => {\n              // During finalization release the values in our buffer.\n              buffer = null!;\n            }\n          )\n        );\n      });\n}\n", "import { ObservableInput, ObservableInputTuple, OperatorFunction, SchedulerLike } from '../types';\nimport { operate } from '../util/lift';\nimport { argsOrArgArray } from '../util/argsOrArgArray';\nimport { mergeAll } from './mergeAll';\nimport { popNumber, popScheduler } from '../util/args';\nimport { from } from '../observable/from';\n\n/** @deprecated Replaced with {@link mergeWith}. Will be removed in v8. */\nexport function merge<T, A extends readonly unknown[]>(...sources: [...ObservableInputTuple<A>]): OperatorFunction<T, T | A[number]>;\n/** @deprecated Replaced with {@link mergeWith}. Will be removed in v8. */\nexport function merge<T, A extends readonly unknown[]>(\n  ...sourcesAndConcurrency: [...ObservableInputTuple<A>, number]\n): OperatorFunction<T, T | A[number]>;\n/** @deprecated Replaced with {@link mergeWith}. Will be removed in v8. */\nexport function merge<T, A extends readonly unknown[]>(\n  ...sourcesAndScheduler: [...ObservableInputTuple<A>, SchedulerLike]\n): OperatorFunction<T, T | A[number]>;\n/** @deprecated Replaced with {@link mergeWith}. Will be removed in v8. */\nexport function merge<T, A extends readonly unknown[]>(\n  ...sourcesAndConcurrencyAndScheduler: [...ObservableInputTuple<A>, number, SchedulerLike]\n): OperatorFunction<T, T | A[number]>;\n\nexport function merge<T>(...args: unknown[]): OperatorFunction<T, unknown> {\n  const scheduler = popScheduler(args);\n  const concurrent = popNumber(args, Infinity);\n  args = argsOrArgArray(args);\n\n  return operate((source, subscriber) => {\n    mergeAll(concurrent)(from([source, ...(args as ObservableInput<T>[])], scheduler)).subscribe(subscriber);\n  });\n}\n", "import { ObservableInputTuple, OperatorFunction } from '../types';\nimport { merge } from './merge';\n\n/**\n * Merge the values from all observables to a single observable result.\n *\n * Creates an observable, that when subscribed to, subscribes to the source\n * observable, and all other sources provided as arguments. All values from\n * every source are emitted from the resulting subscription.\n *\n * When all sources complete, the resulting observable will complete.\n *\n * When any source errors, the resulting observable will error.\n *\n * ## Example\n *\n * Joining all outputs from multiple user input event streams\n *\n * ```ts\n * import { fromEvent, map, mergeWith } from 'rxjs';\n *\n * const clicks$ = fromEvent(document, 'click').pipe(map(() => 'click'));\n * const mousemoves$ = fromEvent(document, 'mousemove').pipe(map(() => 'mousemove'));\n * const dblclicks$ = fromEvent(document, 'dblclick').pipe(map(() => 'dblclick'));\n *\n * mousemoves$\n *   .pipe(mergeWith(clicks$, dblclicks$))\n *   .subscribe(x => console.log(x));\n *\n * // result (assuming user interactions)\n * // 'mousemove'\n * // 'mousemove'\n * // 'mousemove'\n * // 'click'\n * // 'click'\n * // 'dblclick'\n * ```\n *\n * @see {@link merge}\n *\n * @param otherSources the sources to combine the current source with.\n * @return A function that returns an Observable that merges the values from\n * all given Observables.\n */\nexport function mergeWith<T, A extends readonly unknown[]>(\n  ...otherSources: [...ObservableInputTuple<A>]\n): OperatorFunction<T, T | A[number]> {\n  return merge(...otherSources);\n}\n", "import { Subscription } from '../Subscription';\nimport { EMPTY } from '../observable/empty';\nimport { operate } from '../util/lift';\nimport { MonoTypeOperatorFunction, ObservableInput } from '../types';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { innerFrom } from '../observable/innerFrom';\nimport { timer } from '../observable/timer';\n\nexport interface RepeatConfig {\n  /**\n   * The number of times to repeat the source. Defaults to `Infinity`.\n   */\n  count?: number;\n\n  /**\n   * If a `number`, will delay the repeat of the source by that number of milliseconds.\n   * If a function, it will provide the number of times the source has been subscribed to,\n   * and the return value should be a valid observable input that will notify when the source\n   * should be repeated. If the notifier observable is empty, the result will complete.\n   */\n  delay?: number | ((count: number) => ObservableInput<any>);\n}\n\n/**\n * Returns an Observable that will resubscribe to the source stream when the source stream completes.\n *\n * <span class=\"informal\">Repeats all values emitted on the source. It's like {@link retry}, but for non error cases.</span>\n *\n * ![](repeat.png)\n *\n * Repeat will output values from a source until the source completes, then it will resubscribe to the\n * source a specified number of times, with a specified delay. Repeat can be particularly useful in\n * combination with closing operators like {@link take}, {@link takeUntil}, {@link first}, or {@link takeWhile},\n * as it can be used to restart a source again from scratch.\n *\n * Repeat is very similar to {@link retry}, where {@link retry} will resubscribe to the source in the error case, but\n * `repeat` will resubscribe if the source completes.\n *\n * Note that `repeat` will _not_ catch errors. Use {@link retry} for that.\n *\n * - `repeat(0)` returns an empty observable\n * - `repeat()` will repeat forever\n * - `repeat({ delay: 200 })` will repeat forever, with a delay of 200ms between repetitions.\n * - `repeat({ count: 2, delay: 400 })` will repeat twice, with a delay of 400ms between repetitions.\n * - `repeat({ delay: (count) => timer(count * 1000) })` will repeat forever, but will have a delay that grows by one second for each repetition.\n *\n * ## Example\n *\n * Repeat a message stream\n *\n * ```ts\n * import { of, repeat } from 'rxjs';\n *\n * const source = of('Repeat message');\n * const result = source.pipe(repeat(3));\n *\n * result.subscribe(x => console.log(x));\n *\n * // Results\n * // 'Repeat message'\n * // 'Repeat message'\n * // 'Repeat message'\n * ```\n *\n * Repeat 3 values, 2 times\n *\n * ```ts\n * import { interval, take, repeat } from 'rxjs';\n *\n * const source = interval(1000);\n * const result = source.pipe(take(3), repeat(2));\n *\n * result.subscribe(x => console.log(x));\n *\n * // Results every second\n * // 0\n * // 1\n * // 2\n * // 0\n * // 1\n * // 2\n * ```\n *\n * Defining two complex repeats with delays on the same source.\n * Note that the second repeat cannot be called until the first\n * repeat as exhausted it's count.\n *\n * ```ts\n * import { defer, of, repeat } from 'rxjs';\n *\n * const source = defer(() => {\n *    return of(`Hello, it is ${new Date()}`)\n * });\n *\n * source.pipe(\n *    // Repeat 3 times with a delay of 1 second between repetitions\n *    repeat({\n *      count: 3,\n *      delay: 1000,\n *    }),\n *\n *    // *Then* repeat forever, but with an exponential step-back\n *    // maxing out at 1 minute.\n *    repeat({\n *      delay: (count) => timer(Math.min(60000, 2 ^ count * 1000))\n *    })\n * )\n * ```\n *\n * @see {@link repeatWhen}\n * @see {@link retry}\n *\n * @param count The number of times the source Observable items are repeated, a count of 0 will yield\n * an empty Observable.\n */\nexport function repeat<T>(countOrConfig?: number | RepeatConfig): MonoTypeOperatorFunction<T> {\n  let count = Infinity;\n  let delay: RepeatConfig['delay'];\n\n  if (countOrConfig != null) {\n    if (typeof countOrConfig === 'object') {\n      ({ count = Infinity, delay } = countOrConfig);\n    } else {\n      count = countOrConfig;\n    }\n  }\n\n  return count <= 0\n    ? () => EMPTY\n    : operate((source, subscriber) => {\n        let soFar = 0;\n        let sourceSub: Subscription | null;\n\n        const resubscribe = () => {\n          sourceSub?.unsubscribe();\n          sourceSub = null;\n          if (delay != null) {\n            const notifier = typeof delay === 'number' ? timer(delay) : innerFrom(delay(soFar));\n            const notifierSubscriber = createOperatorSubscriber(subscriber, () => {\n              notifierSubscriber.unsubscribe();\n              subscribeToSource();\n            });\n            notifier.subscribe(notifierSubscriber);\n          } else {\n            subscribeToSource();\n          }\n        };\n\n        const subscribeToSource = () => {\n          let syncUnsub = false;\n          sourceSub = source.subscribe(\n            createOperatorSubscriber(subscriber, undefined, () => {\n              if (++soFar < count) {\n                if (sourceSub) {\n                  resubscribe();\n                } else {\n                  syncUnsub = true;\n                }\n              } else {\n                subscriber.complete();\n              }\n            })\n          );\n\n          if (syncUnsub) {\n            resubscribe();\n          }\n        };\n\n        subscribeToSource();\n      });\n}\n", "import { OperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { scanInternals } from './scanInternals';\n\nexport function scan<V, A = V>(accumulator: (acc: A | V, value: V, index: number) => A): OperatorFunction<V, V | A>;\nexport function scan<V, A>(accumulator: (acc: A, value: V, index: number) => A, seed: A): OperatorFunction<V, A>;\nexport function scan<V, A, S>(accumulator: (acc: A | S, value: V, index: number) => A, seed: S): OperatorFunction<V, A>;\n\n// TODO: link to a \"redux pattern\" section in the guide (location TBD)\n\n/**\n * Useful for encapsulating and managing state. Applies an accumulator (or \"reducer function\")\n * to each value from the source after an initial state is established -- either via\n * a `seed` value (second argument), or from the first value from the source.\n *\n * <span class=\"informal\">It's like {@link reduce}, but emits the current\n * accumulation state after each update</span>\n *\n * ![](scan.png)\n *\n * This operator maintains an internal state and emits it after processing each value as follows:\n *\n * 1. First value arrives\n *   - If a `seed` value was supplied (as the second argument to `scan`), let `state = seed` and `value = firstValue`.\n *   - If NO `seed` value was supplied (no second argument), let `state = firstValue` and go to 3.\n * 2. Let `state = accumulator(state, value)`.\n *   - If an error is thrown by `accumulator`, notify the consumer of an error. The process ends.\n * 3. Emit `state`.\n * 4. Next value arrives, let `value = nextValue`, go to 2.\n *\n * ## Examples\n *\n * An average of previous numbers. This example shows how\n * not providing a `seed` can prime the stream with the\n * first value from the source.\n *\n * ```ts\n * import { of, scan, map } from 'rxjs';\n *\n * const numbers$ = of(1, 2, 3);\n *\n * numbers$\n *   .pipe(\n *     // Get the sum of the numbers coming in.\n *     scan((total, n) => total + n),\n *     // Get the average by dividing the sum by the total number\n *     // received so far (which is 1 more than the zero-based index).\n *     map((sum, index) => sum / (index + 1))\n *   )\n *   .subscribe(console.log);\n * ```\n *\n * The Fibonacci sequence. This example shows how you can use\n * a seed to prime accumulation process. Also... you know... Fibonacci.\n * So important to like, computers and stuff that its whiteboarded\n * in job interviews. Now you can show them the Rx version! (Please don't, haha)\n *\n * ```ts\n * import { interval, scan, map, startWith } from 'rxjs';\n *\n * const firstTwoFibs = [0, 1];\n * // An endless stream of Fibonacci numbers.\n * const fibonacci$ = interval(1000).pipe(\n *   // Scan to get the fibonacci numbers (after 0, 1)\n *   scan(([a, b]) => [b, a + b], firstTwoFibs),\n *   // Get the second number in the tuple, it's the one you calculated\n *   map(([, n]) => n),\n *   // Start with our first two digits :)\n *   startWith(...firstTwoFibs)\n * );\n *\n * fibonacci$.subscribe(console.log);\n * ```\n *\n * @see {@link expand}\n * @see {@link mergeScan}\n * @see {@link reduce}\n * @see {@link switchScan}\n *\n * @param accumulator A \"reducer function\". This will be called for each value after an initial state is\n * acquired.\n * @param seed The initial state. If this is not provided, the first value from the source will\n * be used as the initial state, and emitted without going through the accumulator. All subsequent values\n * will be processed by the accumulator function. If this is provided, all values will go through\n * the accumulator function.\n * @return A function that returns an Observable of the accumulated values.\n */\nexport function scan<V, A, S>(accumulator: (acc: V | A | S, value: V, index: number) => A, seed?: S): OperatorFunction<V, V | A> {\n  // providing a seed of `undefined` *should* be valid and trigger\n  // hasSeed! so don't use `seed !== undefined` checks!\n  // For this reason, we have to check it here at the original call site\n  // otherwise inside Operator/Subscriber we won't know if `undefined`\n  // means they didn't provide anything or if they literally provided `undefined`\n  return operate(scanInternals(accumulator, seed as S, arguments.length >= 2, true));\n}\n", "import { innerFrom } from '../observable/innerFrom';\nimport { Subject } from '../Subject';\nimport { SafeSubscriber } from '../Subscriber';\nimport { Subscription } from '../Subscription';\nimport { MonoTypeOperatorFunction, SubjectLike, ObservableInput } from '../types';\nimport { operate } from '../util/lift';\n\nexport interface ShareConfig<T> {\n  /**\n   * The factory used to create the subject that will connect the source observable to\n   * multicast consumers.\n   */\n  connector?: () => SubjectLike<T>;\n  /**\n   * If `true`, the resulting observable will reset internal state on error from source and return to a \"cold\" state. This\n   * allows the resulting observable to be \"retried\" in the event of an error.\n   * If `false`, when an error comes from the source it will push the error into the connecting subject, and the subject\n   * will remain the connecting subject, meaning the resulting observable will not go \"cold\" again, and subsequent retries\n   * or resubscriptions will resubscribe to that same subject. In all cases, RxJS subjects will emit the same error again, however\n   * {@link ReplaySubject} will also push its buffered values before pushing the error.\n   * It is also possible to pass a notifier factory returning an `ObservableInput` instead which grants more fine-grained\n   * control over how and when the reset should happen. This allows behaviors like conditional or delayed resets.\n   */\n  resetOnError?: boolean | ((error: any) => ObservableInput<any>);\n  /**\n   * If `true`, the resulting observable will reset internal state on completion from source and return to a \"cold\" state. This\n   * allows the resulting observable to be \"repeated\" after it is done.\n   * If `false`, when the source completes, it will push the completion through the connecting subject, and the subject\n   * will remain the connecting subject, meaning the resulting observable will not go \"cold\" again, and subsequent repeats\n   * or resubscriptions will resubscribe to that same subject.\n   * It is also possible to pass a notifier factory returning an `ObservableInput` instead which grants more fine-grained\n   * control over how and when the reset should happen. This allows behaviors like conditional or delayed resets.\n   */\n  resetOnComplete?: boolean | (() => ObservableInput<any>);\n  /**\n   * If `true`, when the number of subscribers to the resulting observable reaches zero due to those subscribers unsubscribing, the\n   * internal state will be reset and the resulting observable will return to a \"cold\" state. This means that the next\n   * time the resulting observable is subscribed to, a new subject will be created and the source will be subscribed to\n   * again.\n   * If `false`, when the number of subscribers to the resulting observable reaches zero due to unsubscription, the subject\n   * will remain connected to the source, and new subscriptions to the result will be connected through that same subject.\n   * It is also possible to pass a notifier factory returning an `ObservableInput` instead which grants more fine-grained\n   * control over how and when the reset should happen. This allows behaviors like conditional or delayed resets.\n   */\n  resetOnRefCountZero?: boolean | (() => ObservableInput<any>);\n}\n\nexport function share<T>(): MonoTypeOperatorFunction<T>;\n\nexport function share<T>(options: ShareConfig<T>): MonoTypeOperatorFunction<T>;\n\n/**\n * Returns a new Observable that multicasts (shares) the original Observable. As long as there is at least one\n * Subscriber this Observable will be subscribed and emitting data. When all subscribers have unsubscribed it will\n * unsubscribe from the source Observable. Because the Observable is multicasting it makes the stream `hot`.\n * This is an alias for `multicast(() => new Subject()), refCount()`.\n *\n * The subscription to the underlying source Observable can be reset (unsubscribe and resubscribe for new subscribers),\n * if the subscriber count to the shared observable drops to 0, or if the source Observable errors or completes. It is\n * possible to use notifier factories for the resets to allow for behaviors like conditional or delayed resets. Please\n * note that resetting on error or complete of the source Observable does not behave like a transparent retry or restart\n * of the source because the error or complete will be forwarded to all subscribers and their subscription will be\n * closed. Only new subscribers after a reset on error or complete happened will cause a fresh subscription to the\n * source. To achieve transparent retries or restarts pipe the source through appropriate operators before sharing.\n *\n * ![](share.png)\n *\n * ## Example\n *\n * Generate new multicast Observable from the `source` Observable value\n *\n * ```ts\n * import { interval, tap, map, take, share } from 'rxjs';\n *\n * const source = interval(1000).pipe(\n *   tap(x => console.log('Processing: ', x)),\n *   map(x => x * x),\n *   take(6),\n *   share()\n * );\n *\n * source.subscribe(x => console.log('subscription 1: ', x));\n * source.subscribe(x => console.log('subscription 2: ', x));\n *\n * // Logs:\n * // Processing: 0\n * // subscription 1: 0\n * // subscription 2: 0\n * // Processing: 1\n * // subscription 1: 1\n * // subscription 2: 1\n * // Processing: 2\n * // subscription 1: 4\n * // subscription 2: 4\n * // Processing: 3\n * // subscription 1: 9\n * // subscription 2: 9\n * // Processing: 4\n * // subscription 1: 16\n * // subscription 2: 16\n * // Processing: 5\n * // subscription 1: 25\n * // subscription 2: 25\n * ```\n *\n * ## Example with notifier factory: Delayed reset\n *\n * ```ts\n * import { interval, take, share, timer } from 'rxjs';\n *\n * const source = interval(1000).pipe(\n *   take(3),\n *   share({\n *     resetOnRefCountZero: () => timer(1000)\n *   })\n * );\n *\n * const subscriptionOne = source.subscribe(x => console.log('subscription 1: ', x));\n * setTimeout(() => subscriptionOne.unsubscribe(), 1300);\n *\n * setTimeout(() => source.subscribe(x => console.log('subscription 2: ', x)), 1700);\n *\n * setTimeout(() => source.subscribe(x => console.log('subscription 3: ', x)), 5000);\n *\n * // Logs:\n * // subscription 1:  0\n * // (subscription 1 unsubscribes here)\n * // (subscription 2 subscribes here ~400ms later, source was not reset)\n * // subscription 2:  1\n * // subscription 2:  2\n * // (subscription 2 unsubscribes here)\n * // (subscription 3 subscribes here ~2000ms later, source did reset before)\n * // subscription 3:  0\n * // subscription 3:  1\n * // subscription 3:  2\n * ```\n *\n * @see {@link shareReplay}\n *\n * @return A function that returns an Observable that mirrors the source.\n */\nexport function share<T>(options: ShareConfig<T> = {}): MonoTypeOperatorFunction<T> {\n  const { connector = () => new Subject<T>(), resetOnError = true, resetOnComplete = true, resetOnRefCountZero = true } = options;\n  // It's necessary to use a wrapper here, as the _operator_ must be\n  // referentially transparent. Otherwise, it cannot be used in calls to the\n  // static `pipe` function - to create a partial pipeline.\n  //\n  // The _operator function_ - the function returned by the _operator_ - will\n  // not be referentially transparent - as it shares its source - but the\n  // _operator function_ is called when the complete pipeline is composed via a\n  // call to a source observable's `pipe` method - not when the static `pipe`\n  // function is called.\n  return (wrapperSource) => {\n    let connection: SafeSubscriber<T> | undefined;\n    let resetConnection: Subscription | undefined;\n    let subject: SubjectLike<T> | undefined;\n    let refCount = 0;\n    let hasCompleted = false;\n    let hasErrored = false;\n\n    const cancelReset = () => {\n      resetConnection?.unsubscribe();\n      resetConnection = undefined;\n    };\n    // Used to reset the internal state to a \"cold\"\n    // state, as though it had never been subscribed to.\n    const reset = () => {\n      cancelReset();\n      connection = subject = undefined;\n      hasCompleted = hasErrored = false;\n    };\n    const resetAndUnsubscribe = () => {\n      // We need to capture the connection before\n      // we reset (if we need to reset).\n      const conn = connection;\n      reset();\n      conn?.unsubscribe();\n    };\n\n    return operate<T, T>((source, subscriber) => {\n      refCount++;\n      if (!hasErrored && !hasCompleted) {\n        cancelReset();\n      }\n\n      // Create the subject if we don't have one yet. Grab a local reference to\n      // it as well, which avoids non-null assertions when using it and, if we\n      // connect to it now, then error/complete need a reference after it was\n      // reset.\n      const dest = (subject = subject ?? connector());\n\n      // Add the finalization directly to the subscriber - instead of returning it -\n      // so that the handling of the subscriber's unsubscription will be wired\n      // up _before_ the subscription to the source occurs. This is done so that\n      // the assignment to the source connection's `closed` property will be seen\n      // by synchronous firehose sources.\n      subscriber.add(() => {\n        refCount--;\n\n        // If we're resetting on refCount === 0, and it's 0, we only want to do\n        // that on \"unsubscribe\", really. Resetting on error or completion is a different\n        // configuration.\n        if (refCount === 0 && !hasErrored && !hasCompleted) {\n          resetConnection = handleReset(resetAndUnsubscribe, resetOnRefCountZero);\n        }\n      });\n\n      // The following line adds the subscription to the subscriber passed.\n      // Basically, `subscriber === dest.subscribe(subscriber)` is `true`.\n      dest.subscribe(subscriber);\n\n      if (\n        !connection &&\n        // Check this shareReplay is still activate - it can be reset to 0\n        // and be \"unsubscribed\" _before_ it actually subscribes.\n        // If we were to subscribe then, it'd leak and get stuck.\n        refCount > 0\n      ) {\n        // We need to create a subscriber here - rather than pass an observer and\n        // assign the returned subscription to connection - because it's possible\n        // for reentrant subscriptions to the shared observable to occur and in\n        // those situations we want connection to be already-assigned so that we\n        // don't create another connection to the source.\n        connection = new SafeSubscriber({\n          next: (value) => dest.next(value),\n          error: (err) => {\n            hasErrored = true;\n            cancelReset();\n            resetConnection = handleReset(reset, resetOnError, err);\n            dest.error(err);\n          },\n          complete: () => {\n            hasCompleted = true;\n            cancelReset();\n            resetConnection = handleReset(reset, resetOnComplete);\n            dest.complete();\n          },\n        });\n        innerFrom(source).subscribe(connection);\n      }\n    })(wrapperSource);\n  };\n}\n\nfunction handleReset<T extends unknown[] = never[]>(\n  reset: () => void,\n  on: boolean | ((...args: T) => ObservableInput<any>),\n  ...args: T\n): Subscription | undefined {\n  if (on === true) {\n    reset();\n    return;\n  }\n\n  if (on === false) {\n    return;\n  }\n\n  const onSubscriber = new SafeSubscriber({\n    next: () => {\n      onSubscriber.unsubscribe();\n      reset();\n    },\n  });\n\n  return innerFrom(on(...args)).subscribe(onSubscriber);\n}\n", "import { ReplaySubject } from '../ReplaySubject';\nimport { MonoTypeOperatorFunction, SchedulerLike } from '../types';\nimport { share } from './share';\n\nexport interface ShareReplayConfig {\n  bufferSize?: number;\n  windowTime?: number;\n  refCount: boolean;\n  scheduler?: SchedulerLike;\n}\n\nexport function shareReplay<T>(config: ShareReplayConfig): MonoTypeOperatorFunction<T>;\nexport function shareReplay<T>(bufferSize?: number, windowTime?: number, scheduler?: SchedulerLike): MonoTypeOperatorFunction<T>;\n\n/**\n * Share source and replay specified number of emissions on subscription.\n *\n * This operator is a specialization of `replay` that connects to a source observable\n * and multicasts through a `ReplaySubject` constructed with the specified arguments.\n * A successfully completed source will stay cached in the `shareReplay`ed observable forever,\n * but an errored source can be retried.\n *\n * ## Why use `shareReplay`?\n *\n * You generally want to use `shareReplay` when you have side-effects or taxing computations\n * that you do not wish to be executed amongst multiple subscribers.\n * It may also be valuable in situations where you know you will have late subscribers to\n * a stream that need access to previously emitted values.\n * This ability to replay values on subscription is what differentiates {@link share} and `shareReplay`.\n *\n * ## Reference counting\n *\n * By default `shareReplay` will use `refCount` of false, meaning that it will _not_ unsubscribe the\n * source when the reference counter drops to zero, i.e. the inner `ReplaySubject` will _not_ be unsubscribed\n * (and potentially run for ever).\n * This is the default as it is expected that `shareReplay` is often used to keep around expensive to setup\n * observables which we want to keep running instead of having to do the expensive setup again.\n *\n * As of RXJS version 6.4.0 a new overload signature was added to allow for manual control over what\n * happens when the operators internal reference counter drops to zero.\n * If `refCount` is true, the source will be unsubscribed from once the reference count drops to zero, i.e.\n * the inner `ReplaySubject` will be unsubscribed. All new subscribers will receive value emissions from a\n * new `ReplaySubject` which in turn will cause a new subscription to the source observable.\n *\n * ## Examples\n *\n * Example with a third subscriber coming late to the party\n *\n * ```ts\n * import { interval, take, shareReplay } from 'rxjs';\n *\n * const shared$ = interval(2000).pipe(\n *   take(6),\n *   shareReplay(3)\n * );\n *\n * shared$.subscribe(x => console.log('sub A: ', x));\n * shared$.subscribe(y => console.log('sub B: ', y));\n *\n * setTimeout(() => {\n *   shared$.subscribe(y => console.log('sub C: ', y));\n * }, 11000);\n *\n * // Logs:\n * // (after ~2000 ms)\n * // sub A: 0\n * // sub B: 0\n * // (after ~4000 ms)\n * // sub A: 1\n * // sub B: 1\n * // (after ~6000 ms)\n * // sub A: 2\n * // sub B: 2\n * // (after ~8000 ms)\n * // sub A: 3\n * // sub B: 3\n * // (after ~10000 ms)\n * // sub A: 4\n * // sub B: 4\n * // (after ~11000 ms, sub C gets the last 3 values)\n * // sub C: 2\n * // sub C: 3\n * // sub C: 4\n * // (after ~12000 ms)\n * // sub A: 5\n * // sub B: 5\n * // sub C: 5\n * ```\n *\n * Example for `refCount` usage\n *\n * ```ts\n * import { Observable, tap, interval, shareReplay, take } from 'rxjs';\n *\n * const log = <T>(name: string, source: Observable<T>) => source.pipe(\n *   tap({\n *     subscribe: () => console.log(`${ name }: subscribed`),\n *     next: value => console.log(`${ name }: ${ value }`),\n *     complete: () => console.log(`${ name }: completed`),\n *     finalize: () => console.log(`${ name }: unsubscribed`)\n *   })\n * );\n *\n * const obs$ = log('source', interval(1000));\n *\n * const shared$ = log('shared', obs$.pipe(\n *   shareReplay({ bufferSize: 1, refCount: true }),\n *   take(2)\n * ));\n *\n * shared$.subscribe(x => console.log('sub A: ', x));\n * shared$.subscribe(y => console.log('sub B: ', y));\n *\n * // PRINTS:\n * // shared: subscribed <-- reference count = 1\n * // source: subscribed\n * // shared: subscribed <-- reference count = 2\n * // source: 0\n * // shared: 0\n * // sub A: 0\n * // shared: 0\n * // sub B: 0\n * // source: 1\n * // shared: 1\n * // sub A: 1\n * // shared: completed <-- take(2) completes the subscription for sub A\n * // shared: unsubscribed <-- reference count = 1\n * // shared: 1\n * // sub B: 1\n * // shared: completed <-- take(2) completes the subscription for sub B\n * // shared: unsubscribed <-- reference count = 0\n * // source: unsubscribed <-- replaySubject unsubscribes from source observable because the reference count dropped to 0 and refCount is true\n *\n * // In case of refCount being false, the unsubscribe is never called on the source and the source would keep on emitting, even if no subscribers\n * // are listening.\n * // source: 2\n * // source: 3\n * // source: 4\n * // ...\n * ```\n *\n * @see {@link publish}\n * @see {@link share}\n * @see {@link publishReplay}\n *\n * @param configOrBufferSize Maximum element count of the replay buffer or {@link ShareReplayConfig configuration}\n * object.\n * @param windowTime Maximum time length of the replay buffer in milliseconds.\n * @param scheduler Scheduler where connected observers within the selector function\n * will be invoked on.\n * @return A function that returns an Observable sequence that contains the\n * elements of a sequence produced by multicasting the source sequence within a\n * selector function.\n */\nexport function shareReplay<T>(\n  configOrBufferSize?: ShareReplayConfig | number,\n  windowTime?: number,\n  scheduler?: SchedulerLike\n): MonoTypeOperatorFunction<T> {\n  let bufferSize: number;\n  let refCount = false;\n  if (configOrBufferSize && typeof configOrBufferSize === 'object') {\n    ({ bufferSize = Infinity, windowTime = Infinity, refCount = false, scheduler } = configOrBufferSize);\n  } else {\n    bufferSize = (configOrBufferSize ?? Infinity) as number;\n  }\n  return share<T>({\n    connector: () => new ReplaySubject(bufferSize, windowTime, scheduler),\n    resetOnError: true,\n    resetOnComplete: false,\n    resetOnRefCountZero: refCount,\n  });\n}\n", "import { MonoTypeOperatorFunction } from '../types';\nimport { filter } from './filter';\n\n/**\n * Returns an Observable that skips the first `count` items emitted by the source Observable.\n *\n * ![](skip.png)\n *\n * Skips the values until the sent notifications are equal or less than provided skip count. It raises\n * an error if skip count is equal or more than the actual number of emits and source raises an error.\n *\n * ## Example\n *\n * Skip the values before the emission\n *\n * ```ts\n * import { interval, skip } from 'rxjs';\n *\n * // emit every half second\n * const source = interval(500);\n * // skip the first 10 emitted values\n * const result = source.pipe(skip(10));\n *\n * result.subscribe(value => console.log(value));\n * // output: 10...11...12...13...\n * ```\n *\n * @see {@link last}\n * @see {@link skipWhile}\n * @see {@link skipUntil}\n * @see {@link skipLast}\n *\n * @param {Number} count - The number of times, items emitted by source Observable should be skipped.\n * @return A function that returns an Observable that skips the first `count`\n * values emitted by the source Observable.\n */\nexport function skip<T>(count: number): MonoTypeOperatorFunction<T> {\n  return filter((_, index) => count <= index);\n}\n", "import { MonoTypeOperatorFunction, ObservableInput } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { innerFrom } from '../observable/innerFrom';\nimport { noop } from '../util/noop';\n\n/**\n * Returns an Observable that skips items emitted by the source Observable until a second Observable emits an item.\n *\n * The `skipUntil` operator causes the observable stream to skip the emission of values until the passed in observable\n * emits the first value. This can be particularly useful in combination with user interactions, responses of HTTP\n * requests or waiting for specific times to pass by.\n *\n * ![](skipUntil.png)\n *\n * Internally, the `skipUntil` operator subscribes to the passed in `notifier` `ObservableInput` (which gets converted\n * to an Observable) in order to recognize the emission of its first value. When `notifier` emits next, the operator\n * unsubscribes from it and starts emitting the values of the *source* observable until it completes or errors. It\n * will never let the *source* observable emit any values if the `notifier` completes or throws an error without\n * emitting a value before.\n *\n * ## Example\n *\n * In the following example, all emitted values of the interval observable are skipped until the user clicks anywhere\n * within the page\n *\n * ```ts\n * import { interval, fromEvent, skipUntil } from 'rxjs';\n *\n * const intervalObservable = interval(1000);\n * const click = fromEvent(document, 'click');\n *\n * const emitAfterClick = intervalObservable.pipe(\n *   skipUntil(click)\n * );\n * // clicked at 4.6s. output: 5...6...7...8........ or\n * // clicked at 7.3s. output: 8...9...10..11.......\n * emitAfterClick.subscribe(value => console.log(value));\n * ```\n *\n * @see {@link last}\n * @see {@link skip}\n * @see {@link skipWhile}\n * @see {@link skipLast}\n *\n * @param notifier An `ObservableInput` that has to emit an item before the source Observable elements begin to\n * be mirrored by the resulting Observable.\n * @return A function that returns an Observable that skips items from the\n * source Observable until the `notifier` Observable emits an item, then emits the\n * remaining items.\n */\nexport function skipUntil<T>(notifier: ObservableInput<any>): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    let taking = false;\n\n    const skipSubscriber = createOperatorSubscriber(\n      subscriber,\n      () => {\n        skipSubscriber?.unsubscribe();\n        taking = true;\n      },\n      noop\n    );\n\n    innerFrom(notifier).subscribe(skipSubscriber);\n\n    source.subscribe(createOperatorSubscriber(subscriber, (value) => taking && subscriber.next(value)));\n  });\n}\n", "import { concat } from '../observable/concat';\nimport { OperatorFunction, SchedulerLike, ValueFromArray } from '../types';\nimport { popScheduler } from '../util/args';\nimport { operate } from '../util/lift';\n\n// Devs are more likely to pass null or undefined than they are a scheduler\n// without accompanying values. To make things easier for (naughty) devs who\n// use the `strictNullChecks: false` TypeScript compiler option, these\n// overloads with explicit null and undefined values are included.\n\nexport function startWith<T>(value: null): OperatorFunction<T, T | null>;\nexport function startWith<T>(value: undefined): OperatorFunction<T, T | undefined>;\n\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `concatAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function startWith<T, A extends readonly unknown[] = T[]>(\n  ...valuesAndScheduler: [...A, SchedulerLike]\n): OperatorFunction<T, T | ValueFromArray<A>>;\nexport function startWith<T, A extends readonly unknown[] = T[]>(...values: A): OperatorFunction<T, T | ValueFromArray<A>>;\n\n/**\n * Returns an observable that, at the moment of subscription, will synchronously emit all\n * values provided to this operator, then subscribe to the source and mirror all of its emissions\n * to subscribers.\n *\n * This is a useful way to know when subscription has occurred on an existing observable.\n *\n * <span class=\"informal\">First emits its arguments in order, and then any\n * emissions from the source.</span>\n *\n * ![](startWith.png)\n *\n * ## Examples\n *\n * Emit a value when a timer starts.\n *\n * ```ts\n * import { timer, map, startWith } from 'rxjs';\n *\n * timer(1000)\n *   .pipe(\n *     map(() => 'timer emit'),\n *     startWith('timer start')\n *   )\n *   .subscribe(x => console.log(x));\n *\n * // results:\n * // 'timer start'\n * // 'timer emit'\n * ```\n *\n * @param values Items you want the modified Observable to emit first.\n * @return A function that returns an Observable that synchronously emits\n * provided values before subscribing to the source Observable.\n *\n * @see {@link endWith}\n * @see {@link finalize}\n * @see {@link concat}\n */\nexport function startWith<T, D>(...values: D[]): OperatorFunction<T, T | D> {\n  const scheduler = popScheduler(values);\n  return operate((source, subscriber) => {\n    // Here we can't pass `undefined` as a scheduler, because if we did, the\n    // code inside of `concat` would be confused by the `undefined`, and treat it\n    // like an invalid observable. So we have to split it two different ways.\n    (scheduler ? concat(values, source, scheduler) : concat(values, source)).subscribe(subscriber);\n  });\n}\n", "import { Subscriber } from '../Subscriber';\nimport { ObservableInput, OperatorFunction, ObservedValueOf } from '../types';\nimport { innerFrom } from '../observable/innerFrom';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/* tslint:disable:max-line-length */\nexport function switchMap<T, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O\n): OperatorFunction<T, ObservedValueOf<O>>;\n/** @deprecated The `resultSelector` parameter will be removed in v8. Use an inner `map` instead. Details: https://rxjs.dev/deprecations/resultSelector */\nexport function switchMap<T, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  resultSelector: undefined\n): OperatorFunction<T, ObservedValueOf<O>>;\n/** @deprecated The `resultSelector` parameter will be removed in v8. Use an inner `map` instead. Details: https://rxjs.dev/deprecations/resultSelector */\nexport function switchMap<T, R, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  resultSelector: (outerValue: T, innerValue: ObservedValueOf<O>, outerIndex: number, innerIndex: number) => R\n): OperatorFunction<T, R>;\n/* tslint:enable:max-line-length */\n\n/**\n * Projects each source value to an Observable which is merged in the output\n * Observable, emitting values only from the most recently projected Observable.\n *\n * <span class=\"informal\">Maps each value to an Observable, then flattens all of\n * these inner Observables using {@link switchAll}.</span>\n *\n * ![](switchMap.png)\n *\n * Returns an Observable that emits items based on applying a function that you\n * supply to each item emitted by the source Observable, where that function\n * returns an (so-called \"inner\") Observable. Each time it observes one of these\n * inner Observables, the output Observable begins emitting the items emitted by\n * that inner Observable. When a new inner Observable is emitted, `switchMap`\n * stops emitting items from the earlier-emitted inner Observable and begins\n * emitting items from the new one. It continues to behave like this for\n * subsequent inner Observables.\n *\n * ## Example\n *\n * Generate new Observable according to source Observable values\n *\n * ```ts\n * import { of, switchMap } from 'rxjs';\n *\n * const switched = of(1, 2, 3).pipe(switchMap(x => of(x, x ** 2, x ** 3)));\n * switched.subscribe(x => console.log(x));\n * // outputs\n * // 1\n * // 1\n * // 1\n * // 2\n * // 4\n * // 8\n * // 3\n * // 9\n * // 27\n * ```\n *\n * Restart an interval Observable on every click event\n *\n * ```ts\n * import { fromEvent, switchMap, interval } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(switchMap(() => interval(1000)));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link concatMap}\n * @see {@link exhaustMap}\n * @see {@link mergeMap}\n * @see {@link switchAll}\n * @see {@link switchMapTo}\n *\n * @param {function(value: T, index: number): ObservableInput} project A function\n * that, when applied to an item emitted by the source Observable, returns an\n * Observable.\n * @return A function that returns an Observable that emits the result of\n * applying the projection function (and the optional deprecated\n * `resultSelector`) to each item emitted by the source Observable and taking\n * only the values from the most recently projected inner Observable.\n */\nexport function switchMap<T, R, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  resultSelector?: (outerValue: T, innerValue: ObservedValueOf<O>, outerIndex: number, innerIndex: number) => R\n): OperatorFunction<T, ObservedValueOf<O> | R> {\n  return operate((source, subscriber) => {\n    let innerSubscriber: Subscriber<ObservedValueOf<O>> | null = null;\n    let index = 0;\n    // Whether or not the source subscription has completed\n    let isComplete = false;\n\n    // We only complete the result if the source is complete AND we don't have an active inner subscription.\n    // This is called both when the source completes and when the inners complete.\n    const checkComplete = () => isComplete && !innerSubscriber && subscriber.complete();\n\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => {\n          // Cancel the previous inner subscription if there was one\n          innerSubscriber?.unsubscribe();\n          let innerIndex = 0;\n          const outerIndex = index++;\n          // Start the next inner subscription\n          innerFrom(project(value, outerIndex)).subscribe(\n            (innerSubscriber = createOperatorSubscriber(\n              subscriber,\n              // When we get a new inner value, next it through. Note that this is\n              // handling the deprecate result selector here. This is because with this architecture\n              // it ends up being smaller than using the map operator.\n              (innerValue) => subscriber.next(resultSelector ? resultSelector(value, innerValue, outerIndex, innerIndex++) : innerValue),\n              () => {\n                // The inner has completed. Null out the inner subscriber to\n                // free up memory and to signal that we have no inner subscription\n                // currently.\n                innerSubscriber = null!;\n                checkComplete();\n              }\n            ))\n          );\n        },\n        () => {\n          isComplete = true;\n          checkComplete();\n        }\n      )\n    );\n  });\n}\n", "import { MonoTypeOperatorFunction, ObservableInput } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { innerFrom } from '../observable/innerFrom';\nimport { noop } from '../util/noop';\n\n/**\n * Emits the values emitted by the source Observable until a `notifier`\n * Observable emits a value.\n *\n * <span class=\"informal\">Lets values pass until a second Observable,\n * `notifier`, emits a value. Then, it completes.</span>\n *\n * ![](takeUntil.png)\n *\n * `takeUntil` subscribes and begins mirroring the source Observable. It also\n * monitors a second Observable, `notifier` that you provide. If the `notifier`\n * emits a value, the output Observable stops mirroring the source Observable\n * and completes. If the `notifier` doesn't emit any value and completes\n * then `takeUntil` will pass all values.\n *\n * ## Example\n *\n * Tick every second until the first click happens\n *\n * ```ts\n * import { interval, fromEvent, takeUntil } from 'rxjs';\n *\n * const source = interval(1000);\n * const clicks = fromEvent(document, 'click');\n * const result = source.pipe(takeUntil(clicks));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link take}\n * @see {@link takeLast}\n * @see {@link takeWhile}\n * @see {@link skip}\n *\n * @param {Observable} notifier The Observable whose first emitted value will\n * cause the output Observable of `takeUntil` to stop emitting values from the\n * source Observable.\n * @return A function that returns an Observable that emits the values from the\n * source Observable until `notifier` emits its first value.\n */\nexport function takeUntil<T>(notifier: ObservableInput<any>): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    innerFrom(notifier).subscribe(createOperatorSubscriber(subscriber, () => subscriber.complete(), noop));\n    !subscriber.closed && source.subscribe(subscriber);\n  });\n}\n", "import { OperatorFunction, MonoTypeOperatorFunction, TruthyTypesOf } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\nexport function takeWhile<T>(predicate: BooleanConstructor, inclusive: true): MonoTypeOperatorFunction<T>;\nexport function takeWhile<T>(predicate: BooleanConstructor, inclusive: false): OperatorFunction<T, TruthyTypesOf<T>>;\nexport function takeWhile<T>(predicate: BooleanConstructor): OperatorFunction<T, TruthyTypesOf<T>>;\nexport function takeWhile<T, S extends T>(predicate: (value: T, index: number) => value is S): OperatorFunction<T, S>;\nexport function takeWhile<T, S extends T>(predicate: (value: T, index: number) => value is S, inclusive: false): OperatorFunction<T, S>;\nexport function takeWhile<T>(predicate: (value: T, index: number) => boolean, inclusive?: boolean): MonoTypeOperatorFunction<T>;\n\n/**\n * Emits values emitted by the source Observable so long as each value satisfies\n * the given `predicate`, and then completes as soon as this `predicate` is not\n * satisfied.\n *\n * <span class=\"informal\">Takes values from the source only while they pass the\n * condition given. When the first value does not satisfy, it completes.</span>\n *\n * ![](takeWhile.png)\n *\n * `takeWhile` subscribes and begins mirroring the source Observable. Each value\n * emitted on the source is given to the `predicate` function which returns a\n * boolean, representing a condition to be satisfied by the source values. The\n * output Observable emits the source values until such time as the `predicate`\n * returns false, at which point `takeWhile` stops mirroring the source\n * Observable and completes the output Observable.\n *\n * ## Example\n *\n * Emit click events only while the clientX property is greater than 200\n *\n * ```ts\n * import { fromEvent, takeWhile } from 'rxjs';\n *\n * const clicks = fromEvent<PointerEvent>(document, 'click');\n * const result = clicks.pipe(takeWhile(ev => ev.clientX > 200));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link take}\n * @see {@link takeLast}\n * @see {@link takeUntil}\n * @see {@link skip}\n *\n * @param {function(value: T, index: number): boolean} predicate A function that\n * evaluates a value emitted by the source Observable and returns a boolean.\n * Also takes the (zero-based) index as the second argument.\n * @param {boolean} inclusive When set to `true` the value that caused\n * `predicate` to return `false` will also be emitted.\n * @return A function that returns an Observable that emits values from the\n * source Observable so long as each value satisfies the condition defined by\n * the `predicate`, then completes.\n */\nexport function takeWhile<T>(predicate: (value: T, index: number) => boolean, inclusive = false): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    let index = 0;\n    source.subscribe(\n      createOperatorSubscriber(subscriber, (value) => {\n        const result = predicate(value, index++);\n        (result || inclusive) && subscriber.next(value);\n        !result && subscriber.complete();\n      })\n    );\n  });\n}\n", "import { MonoTypeOperatorFunction, Observer } from '../types';\nimport { isFunction } from '../util/isFunction';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { identity } from '../util/identity';\n\n/**\n * An extension to the {@link Observer} interface used only by the {@link tap} operator.\n *\n * It provides a useful set of callbacks a user can register to do side-effects in\n * cases other than what the usual {@link Observer} callbacks are\n * ({@link guide/glossary-and-semantics#next next},\n * {@link guide/glossary-and-semantics#error error} and/or\n * {@link guide/glossary-and-semantics#complete complete}).\n *\n * ## Example\n *\n * ```ts\n * import { fromEvent, switchMap, tap, interval, take } from 'rxjs';\n *\n * const source$ = fromEvent(document, 'click');\n * const result$ = source$.pipe(\n *   switchMap((_, i) => i % 2 === 0\n *     ? fromEvent(document, 'mousemove').pipe(\n *         tap({\n *           subscribe: () => console.log('Subscribed to the mouse move events after click #' + i),\n *           unsubscribe: () => console.log('Mouse move events #' + i + ' unsubscribed'),\n *           finalize: () => console.log('Mouse move events #' + i + ' finalized')\n *         })\n *       )\n *     : interval(1_000).pipe(\n *         take(5),\n *         tap({\n *           subscribe: () => console.log('Subscribed to the 1-second interval events after click #' + i),\n *           unsubscribe: () => console.log('1-second interval events #' + i + ' unsubscribed'),\n *           finalize: () => console.log('1-second interval events #' + i + ' finalized')\n *         })\n *       )\n *   )\n * );\n *\n * const subscription = result$.subscribe({\n *   next: console.log\n * });\n *\n * setTimeout(() => {\n *   console.log('Unsubscribe after 60 seconds');\n *   subscription.unsubscribe();\n * }, 60_000);\n * ```\n */\nexport interface TapObserver<T> extends Observer<T> {\n  /**\n   * The callback that `tap` operator invokes at the moment when the source Observable\n   * gets subscribed to.\n   */\n  subscribe: () => void;\n  /**\n   * The callback that `tap` operator invokes when an explicit\n   * {@link guide/glossary-and-semantics#unsubscription unsubscribe} happens. It won't get invoked on\n   * `error` or `complete` events.\n   */\n  unsubscribe: () => void;\n  /**\n   * The callback that `tap` operator invokes when any kind of\n   * {@link guide/glossary-and-semantics#finalization finalization} happens - either when\n   * the source Observable `error`s or `complete`s or when it gets explicitly unsubscribed\n   * by the user. There is no difference in using this callback or the {@link finalize}\n   * operator, but if you're already using `tap` operator, you can use this callback\n   * instead. You'd get the same result in either case.\n   */\n  finalize: () => void;\n}\nexport function tap<T>(observerOrNext?: Partial<TapObserver<T>> | ((value: T) => void)): MonoTypeOperatorFunction<T>;\n/** @deprecated Instead of passing separate callback arguments, use an observer argument. Signatures taking separate callback arguments will be removed in v8. Details: https://rxjs.dev/deprecations/subscribe-arguments */\nexport function tap<T>(\n  next?: ((value: T) => void) | null,\n  error?: ((error: any) => void) | null,\n  complete?: (() => void) | null\n): MonoTypeOperatorFunction<T>;\n\n/**\n * Used to perform side-effects for notifications from the source observable\n *\n * <span class=\"informal\">Used when you want to affect outside state with a notification without altering the notification</span>\n *\n * ![](tap.png)\n *\n * Tap is designed to allow the developer a designated place to perform side effects. While you _could_ perform side-effects\n * inside of a `map` or a `mergeMap`, that would make their mapping functions impure, which isn't always a big deal, but will\n * make it so you can't do things like memoize those functions. The `tap` operator is designed solely for such side-effects to\n * help you remove side-effects from other operations.\n *\n * For any notification, next, error, or complete, `tap` will call the appropriate callback you have provided to it, via a function\n * reference, or a partial observer, then pass that notification down the stream.\n *\n * The observable returned by `tap` is an exact mirror of the source, with one exception: Any error that occurs -- synchronously -- in a handler\n * provided to `tap` will be emitted as an error from the returned observable.\n *\n * > Be careful! You can mutate objects as they pass through the `tap` operator's handlers.\n *\n * The most common use of `tap` is actually for debugging. You can place a `tap(console.log)` anywhere\n * in your observable `pipe`, log out the notifications as they are emitted by the source returned by the previous\n * operation.\n *\n * ## Examples\n *\n * Check a random number before it is handled. Below is an observable that will use a random number between 0 and 1,\n * and emit `'big'` or `'small'` depending on the size of that number. But we wanted to log what the original number\n * was, so we have added a `tap(console.log)`.\n *\n * ```ts\n * import { of, tap, map } from 'rxjs';\n *\n * of(Math.random()).pipe(\n *   tap(console.log),\n *   map(n => n > 0.5 ? 'big' : 'small')\n * ).subscribe(console.log);\n * ```\n *\n * Using `tap` to analyze a value and force an error. Below is an observable where in our system we only\n * want to emit numbers 3 or less we get from another source. We can force our observable to error\n * using `tap`.\n *\n * ```ts\n * import { of, tap } from 'rxjs';\n *\n * const source = of(1, 2, 3, 4, 5);\n *\n * source.pipe(\n *   tap(n => {\n *     if (n > 3) {\n *       throw new TypeError(`Value ${ n } is greater than 3`);\n *     }\n *   })\n * )\n * .subscribe({ next: console.log, error: err => console.log(err.message) });\n * ```\n *\n * We want to know when an observable completes before moving on to the next observable. The system\n * below will emit a random series of `'X'` characters from 3 different observables in sequence. The\n * only way we know when one observable completes and moves to the next one, in this case, is because\n * we have added a `tap` with the side effect of logging to console.\n *\n * ```ts\n * import { of, concatMap, interval, take, map, tap } from 'rxjs';\n *\n * of(1, 2, 3).pipe(\n *   concatMap(n => interval(1000).pipe(\n *     take(Math.round(Math.random() * 10)),\n *     map(() => 'X'),\n *     tap({ complete: () => console.log(`Done with ${ n }`) })\n *   ))\n * )\n * .subscribe(console.log);\n * ```\n *\n * @see {@link finalize}\n * @see {@link TapObserver}\n *\n * @param observerOrNext A next handler or partial observer\n * @param error An error handler\n * @param complete A completion handler\n * @return A function that returns an Observable identical to the source, but\n * runs the specified Observer or callback(s) for each item.\n */\nexport function tap<T>(\n  observerOrNext?: Partial<TapObserver<T>> | ((value: T) => void) | null,\n  error?: ((e: any) => void) | null,\n  complete?: (() => void) | null\n): MonoTypeOperatorFunction<T> {\n  // We have to check to see not only if next is a function,\n  // but if error or complete were passed. This is because someone\n  // could technically call tap like `tap(null, fn)` or `tap(null, null, fn)`.\n  const tapObserver =\n    isFunction(observerOrNext) || error || complete\n      ? // tslint:disable-next-line: no-object-literal-type-assertion\n        ({ next: observerOrNext as Exclude<typeof observerOrNext, Partial<TapObserver<T>>>, error, complete } as Partial<TapObserver<T>>)\n      : observerOrNext;\n\n  return tapObserver\n    ? operate((source, subscriber) => {\n        tapObserver.subscribe?.();\n        let isUnsub = true;\n        source.subscribe(\n          createOperatorSubscriber(\n            subscriber,\n            (value) => {\n              tapObserver.next?.(value);\n              subscriber.next(value);\n            },\n            () => {\n              isUnsub = false;\n              tapObserver.complete?.();\n              subscriber.complete();\n            },\n            (err) => {\n              isUnsub = false;\n              tapObserver.error?.(err);\n              subscriber.error(err);\n            },\n            () => {\n              if (isUnsub) {\n                tapObserver.unsubscribe?.();\n              }\n              tapObserver.finalize?.();\n            }\n          )\n        );\n      })\n    : // Tap was called with no valid tap observer or handler\n      // (e.g. `tap(null, null, null)` or `tap(null)` or `tap()`)\n      // so we're going to just mirror the source.\n      identity;\n}\n", "import { Subscription } from '../Subscription';\n\nimport { MonoTypeOperatorFunction, ObservableInput } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { innerFrom } from '../observable/innerFrom';\n\n/**\n * An object interface used by {@link throttle} or {@link throttleTime} that ensure\n * configuration options of these operators.\n *\n * @see {@link throttle}\n * @see {@link throttleTime}\n */\nexport interface ThrottleConfig {\n  /**\n   * If `true`, the resulting Observable will emit the first value from the source\n   * Observable at the **start** of the \"throttling\" process (when starting an\n   * internal timer that prevents other emissions from the source to pass through).\n   * If `false`, it will not emit the first value from the source Observable at the\n   * start of the \"throttling\" process.\n   *\n   * If not provided, defaults to: `true`.\n   */\n  leading?: boolean;\n  /**\n   * If `true`, the resulting Observable will emit the last value from the source\n   * Observable at the **end** of the \"throttling\" process (when ending an internal\n   * timer that prevents other emissions from the source to pass through).\n   * If `false`, it will not emit the last value from the source Observable at the\n   * end of the \"throttling\" process.\n   *\n   * If not provided, defaults to: `false`.\n   */\n  trailing?: boolean;\n}\n\n/**\n * Emits a value from the source Observable, then ignores subsequent source\n * values for a duration determined by another Observable, then repeats this\n * process.\n *\n * <span class=\"informal\">It's like {@link throttleTime}, but the silencing\n * duration is determined by a second Observable.</span>\n *\n * ![](throttle.svg)\n *\n * `throttle` emits the source Observable values on the output Observable\n * when its internal timer is disabled, and ignores source values when the timer\n * is enabled. Initially, the timer is disabled. As soon as the first source\n * value arrives, it is forwarded to the output Observable, and then the timer\n * is enabled by calling the `durationSelector` function with the source value,\n * which returns the \"duration\" Observable. When the duration Observable emits a\n * value, the timer is disabled, and this process repeats for the\n * next source value.\n *\n * ## Example\n *\n * Emit clicks at a rate of at most one click per second\n *\n * ```ts\n * import { fromEvent, throttle, interval } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(throttle(() => interval(1000)));\n *\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link audit}\n * @see {@link debounce}\n * @see {@link delayWhen}\n * @see {@link sample}\n * @see {@link throttleTime}\n *\n * @param durationSelector A function that receives a value from the source\n * Observable, for computing the silencing duration for each source value,\n * returned as an `ObservableInput`.\n * @param config A configuration object to define `leading` and `trailing`\n * behavior. Defaults to `{ leading: true, trailing: false }`.\n * @return A function that returns an Observable that performs the throttle\n * operation to limit the rate of emissions from the source.\n */\nexport function throttle<T>(durationSelector: (value: T) => ObservableInput<any>, config?: ThrottleConfig): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    const { leading = true, trailing = false } = config ?? {};\n    let hasValue = false;\n    let sendValue: T | null = null;\n    let throttled: Subscription | null = null;\n    let isComplete = false;\n\n    const endThrottling = () => {\n      throttled?.unsubscribe();\n      throttled = null;\n      if (trailing) {\n        send();\n        isComplete && subscriber.complete();\n      }\n    };\n\n    const cleanupThrottling = () => {\n      throttled = null;\n      isComplete && subscriber.complete();\n    };\n\n    const startThrottle = (value: T) =>\n      (throttled = innerFrom(durationSelector(value)).subscribe(createOperatorSubscriber(subscriber, endThrottling, cleanupThrottling)));\n\n    const send = () => {\n      if (hasValue) {\n        // Ensure we clear out our value and hasValue flag\n        // before we emit, otherwise reentrant code can cause\n        // issues here.\n        hasValue = false;\n        const value = sendValue!;\n        sendValue = null;\n        // Emit the value.\n        subscriber.next(value);\n        !isComplete && startThrottle(value);\n      }\n    };\n\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        // Regarding the presence of throttled.closed in the following\n        // conditions, if a synchronous duration selector is specified - weird,\n        // but legal - an already-closed subscription will be assigned to\n        // throttled, so the subscription's closed property needs to be checked,\n        // too.\n        (value) => {\n          hasValue = true;\n          sendValue = value;\n          !(throttled && !throttled.closed) && (leading ? send() : startThrottle(value));\n        },\n        () => {\n          isComplete = true;\n          !(trailing && hasValue && throttled && !throttled.closed) && subscriber.complete();\n        }\n      )\n    );\n  });\n}\n", "import { asyncScheduler } from '../scheduler/async';\nimport { throttle, ThrottleConfig } from './throttle';\nimport { MonoTypeOperatorFunction, SchedulerLike } from '../types';\nimport { timer } from '../observable/timer';\n\n/**\n * Emits a value from the source Observable, then ignores subsequent source\n * values for `duration` milliseconds, then repeats this process.\n *\n * <span class=\"informal\">Lets a value pass, then ignores source values for the\n * next `duration` milliseconds.</span>\n *\n * ![](throttleTime.png)\n *\n * `throttleTime` emits the source Observable values on the output Observable\n * when its internal timer is disabled, and ignores source values when the timer\n * is enabled. Initially, the timer is disabled. As soon as the first source\n * value arrives, it is forwarded to the output Observable, and then the timer\n * is enabled. After `duration` milliseconds (or the time unit determined\n * internally by the optional `scheduler`) has passed, the timer is disabled,\n * and this process repeats for the next source value. Optionally takes a\n * {@link SchedulerLike} for managing timers.\n *\n * ## Examples\n *\n * ### Limit click rate\n *\n * Emit clicks at a rate of at most one click per second\n *\n * ```ts\n * import { fromEvent, throttleTime } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(throttleTime(1000));\n *\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link auditTime}\n * @see {@link debounceTime}\n * @see {@link delay}\n * @see {@link sampleTime}\n * @see {@link throttle}\n *\n * @param duration Time to wait before emitting another value after\n * emitting the last value, measured in milliseconds or the time unit determined\n * internally by the optional `scheduler`.\n * @param scheduler The {@link SchedulerLike} to use for\n * managing the timers that handle the throttling. Defaults to {@link asyncScheduler}.\n * @param config A configuration object to define `leading` and\n * `trailing` behavior. Defaults to `{ leading: true, trailing: false }`.\n * @return A function that returns an Observable that performs the throttle\n * operation to limit the rate of emissions from the source.\n */\nexport function throttleTime<T>(\n  duration: number,\n  scheduler: SchedulerLike = asyncScheduler,\n  config?: ThrottleConfig\n): MonoTypeOperatorFunction<T> {\n  const duration$ = timer(duration, scheduler);\n  return throttle(() => duration$, config);\n}\n", "import { OperatorFunction, ObservableInputTuple } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { innerFrom } from '../observable/innerFrom';\nimport { identity } from '../util/identity';\nimport { noop } from '../util/noop';\nimport { popResultSelector } from '../util/args';\n\nexport function withLatestFrom<T, O extends unknown[]>(...inputs: [...ObservableInputTuple<O>]): OperatorFunction<T, [T, ...O]>;\n\nexport function withLatestFrom<T, O extends unknown[], R>(\n  ...inputs: [...ObservableInputTuple<O>, (...value: [T, ...O]) => R]\n): OperatorFunction<T, R>;\n\n/**\n * Combines the source Observable with other Observables to create an Observable\n * whose values are calculated from the latest values of each, only when the\n * source emits.\n *\n * <span class=\"informal\">Whenever the source Observable emits a value, it\n * computes a formula using that value plus the latest values from other input\n * Observables, then emits the output of that formula.</span>\n *\n * ![](withLatestFrom.png)\n *\n * `withLatestFrom` combines each value from the source Observable (the\n * instance) with the latest values from the other input Observables only when\n * the source emits a value, optionally using a `project` function to determine\n * the value to be emitted on the output Observable. All input Observables must\n * emit at least one value before the output Observable will emit a value.\n *\n * ## Example\n *\n * On every click event, emit an array with the latest timer event plus the click event\n *\n * ```ts\n * import { fromEvent, interval, withLatestFrom } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const timer = interval(1000);\n * const result = clicks.pipe(withLatestFrom(timer));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link combineLatest}\n *\n * @param {ObservableInput} other An input Observable to combine with the source\n * Observable. More than one input Observables may be given as argument.\n * @param {Function} [project] Projection function for combining values\n * together. Receives all values in order of the Observables passed, where the\n * first parameter is a value from the source Observable. (e.g.\n * `a.pipe(withLatestFrom(b, c), map(([a1, b1, c1]) => a1 + b1 + c1))`). If this is not\n * passed, arrays will be emitted on the output Observable.\n * @return A function that returns an Observable of projected values from the\n * most recent values from each input Observable, or an array of the most\n * recent values from each input Observable.\n */\nexport function withLatestFrom<T, R>(...inputs: any[]): OperatorFunction<T, R | any[]> {\n  const project = popResultSelector(inputs) as ((...args: any[]) => R) | undefined;\n\n  return operate((source, subscriber) => {\n    const len = inputs.length;\n    const otherValues = new Array(len);\n    // An array of whether or not the other sources have emitted. Matched with them by index.\n    // TODO: At somepoint, we should investigate the performance implications here, and look\n    // into using a `Set()` and checking the `size` to see if we're ready.\n    let hasValue = inputs.map(() => false);\n    // Flipped true when we have at least one value from all other sources and\n    // we are ready to start emitting values.\n    let ready = false;\n\n    // Other sources. Note that here we are not checking `subscriber.closed`,\n    // this causes all inputs to be subscribed to, even if nothing can be emitted\n    // from them. This is an important distinction because subscription constitutes\n    // a side-effect.\n    for (let i = 0; i < len; i++) {\n      innerFrom(inputs[i]).subscribe(\n        createOperatorSubscriber(\n          subscriber,\n          (value) => {\n            otherValues[i] = value;\n            if (!ready && !hasValue[i]) {\n              // If we're not ready yet, flag to show this observable has emitted.\n              hasValue[i] = true;\n              // Intentionally terse code.\n              // If all of our other observables have emitted, set `ready` to `true`,\n              // so we know we can start emitting values, then clean up the `hasValue` array,\n              // because we don't need it anymore.\n              (ready = hasValue.every(identity)) && (hasValue = null!);\n            }\n          },\n          // Completing one of the other sources has\n          // no bearing on the completion of our result.\n          noop\n        )\n      );\n    }\n\n    // Source subscription\n    source.subscribe(\n      createOperatorSubscriber(subscriber, (value) => {\n        if (ready) {\n          // We have at least one value from the other sources. Go ahead and emit.\n          const values = [value, ...otherValues];\n          subscriber.next(project ? project(...values) : values);\n        }\n      })\n    );\n  });\n}\n", "import { zip as zipStatic } from '../observable/zip';\nimport { ObservableInput, ObservableInputTuple, OperatorFunction, Cons } from '../types';\nimport { operate } from '../util/lift';\n\n/** @deprecated Replaced with {@link zipWith}. Will be removed in v8. */\nexport function zip<T, A extends readonly unknown[]>(otherInputs: [...ObservableInputTuple<A>]): OperatorFunction<T, Cons<T, A>>;\n/** @deprecated Replaced with {@link zipWith}. Will be removed in v8. */\nexport function zip<T, A extends readonly unknown[], R>(\n  otherInputsAndProject: [...ObservableInputTuple<A>],\n  project: (...values: Cons<T, A>) => R\n): OperatorFunction<T, R>;\n/** @deprecated Replaced with {@link zipWith}. Will be removed in v8. */\nexport function zip<T, A extends readonly unknown[]>(...otherInputs: [...ObservableInputTuple<A>]): OperatorFunction<T, Cons<T, A>>;\n/** @deprecated Replaced with {@link zipWith}. Will be removed in v8. */\nexport function zip<T, A extends readonly unknown[], R>(\n  ...otherInputsAndProject: [...ObservableInputTuple<A>, (...values: Cons<T, A>) => R]\n): OperatorFunction<T, R>;\n\n/**\n * @deprecated Replaced with {@link zipWith}. Will be removed in v8.\n */\nexport function zip<T, R>(...sources: Array<ObservableInput<any> | ((...values: Array<any>) => R)>): OperatorFunction<T, any> {\n  return operate((source, subscriber) => {\n    zipStatic(source as ObservableInput<any>, ...(sources as Array<ObservableInput<any>>)).subscribe(subscriber);\n  });\n}\n", "import { ObservableInputTuple, OperatorFunction, Cons } from '../types';\nimport { zip } from './zip';\n\n/**\n * Subscribes to the source, and the observable inputs provided as arguments, and combines their values, by index, into arrays.\n *\n * What is meant by \"combine by index\": The first value from each will be made into a single array, then emitted,\n * then the second value from each will be combined into a single array and emitted, then the third value\n * from each will be combined into a single array and emitted, and so on.\n *\n * This will continue until it is no longer able to combine values of the same index into an array.\n *\n * After the last value from any one completed source is emitted in an array, the resulting observable will complete,\n * as there is no way to continue \"zipping\" values together by index.\n *\n * Use-cases for this operator are limited. There are memory concerns if one of the streams is emitting\n * values at a much faster rate than the others. Usage should likely be limited to streams that emit\n * at a similar pace, or finite streams of known length.\n *\n * In many cases, authors want `combineLatestWith` and not `zipWith`.\n *\n * @param otherInputs other observable inputs to collate values from.\n * @return A function that returns an Observable that emits items by index\n * combined from the source Observable and provided Observables, in form of an\n * array.\n */\nexport function zipWith<T, A extends readonly unknown[]>(...otherInputs: [...ObservableInputTuple<A>]): OperatorFunction<T, Cons<T, A>> {\n  return zip(...otherInputs);\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  ReplaySubject,\n  Subject,\n  fromEvent\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch document\n *\n * Documents are implemented as subjects, so all downstream observables are\n * automatically updated when a new document is emitted.\n *\n * @returns Document subject\n */\nexport function watchDocument(): Subject<Document> {\n  const document$ = new ReplaySubject<Document>(1)\n  fromEvent(document, \"DOMContentLoaded\", { once: true })\n    .subscribe(() => document$.next(document))\n\n  /* Return document */\n  return document$\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve all elements matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getElements<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T][]\n\nexport function getElements<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T[]\n\nexport function getElements<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T[] {\n  return Array.from(node.querySelectorAll<T>(selector))\n}\n\n/**\n * Retrieve an element matching a query selector or throw a reference error\n *\n * Note that this function assumes that the element is present. If unsure if an\n * element is existent, use the `getOptionalElement` function instead.\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getElement<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T]\n\nexport function getElement<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T\n\nexport function getElement<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T {\n  const el = getOptionalElement<T>(selector, node)\n  if (typeof el === \"undefined\")\n    throw new ReferenceError(\n      `Missing element: expected \"${selector}\" to be present`\n    )\n\n  /* Return element */\n  return el\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Retrieve an optional element matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element or nothing\n */\nexport function getOptionalElement<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T] | undefined\n\nexport function getOptionalElement<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T | undefined\n\nexport function getOptionalElement<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T | undefined {\n  return node.querySelector<T>(selector) || undefined\n}\n\n/**\n * Retrieve the currently active element\n *\n * @returns Element or nothing\n */\nexport function getActiveElement(): HTMLElement | undefined {\n  return (\n    document.activeElement?.shadowRoot?.activeElement as HTMLElement ??\n    document.activeElement as HTMLElement ??\n    undefined\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  debounceTime,\n  distinctUntilChanged,\n  fromEvent,\n  map,\n  merge,\n  shareReplay,\n  startWith\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Focus observable\n *\n * Previously, this observer used `focus` and `blur` events to determine whether\n * an element is focused, but this doesn't work if there are focusable elements\n * within the elements itself. A better solutions are `focusin` and `focusout`\n * events, which bubble up the tree and allow for more fine-grained control.\n *\n * `debounceTime` is necessary, because when a focus change happens inside an\n * element, the observable would first emit `false` and then `true` again.\n */\nconst observer$ = merge(\n  fromEvent(document.body, \"focusin\"),\n  fromEvent(document.body, \"focusout\")\n)\n  .pipe(\n    debounceTime(1),\n    startWith(undefined),\n    map(() => getActiveElement() || document.body),\n    shareReplay(1)\n  )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element focus\n *\n * @param el - Element\n *\n * @returns Element focus observable\n */\nexport function watchElementFocus(\n  el: HTMLElement\n): Observable<boolean> {\n  return observer$\n    .pipe(\n      map(active => el.contains(active)),\n      distinctUntilChanged()\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  debounce,\n  defer,\n  fromEvent,\n  identity,\n  map,\n  merge,\n  startWith,\n  timer\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element hover\n *\n * The second parameter allows to specify a timeout in milliseconds after which\n * the hover state will be reset to `false`. This is useful for tooltips which\n * should disappear after a certain amount of time, in order to allow the user\n * to move the cursor from the host to the tooltip.\n *\n * @param el - Element\n * @param timeout - Timeout\n *\n * @returns Element hover observable\n */\nexport function watchElementHover(\n  el: HTMLElement, timeout?: number\n): Observable<boolean> {\n  return defer(() => merge(\n    fromEvent(el, \"mouseenter\").pipe(map(() => true)),\n    fromEvent(el, \"mouseleave\").pipe(map(() => false))\n  )\n    .pipe(\n      timeout ? debounce(active => timer(+!active * timeout)) : identity,\n      startWith(el.matches(\":hover\"))\n    )\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { JSX as JSXInternal } from \"preact\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * HTML attributes\n */\ntype Attributes =\n  & JSXInternal.HTMLAttributes\n  & JSXInternal.SVGAttributes\n  & Record<string, any>\n\n/**\n * Child element\n */\ntype Child =\n  | ChildNode\n  | HTMLElement\n  | Text\n  | string\n  | number\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Append a child node to an element\n *\n * @param el - Element\n * @param child - Child node(s)\n */\nfunction appendChild(el: HTMLElement, child: Child | Child[]): void {\n\n  /* Handle primitive types (including raw HTML) */\n  if (typeof child === \"string\" || typeof child === \"number\") {\n    el.innerHTML += child.toString()\n\n  /* Handle nodes */\n  } else if (child instanceof Node) {\n    el.appendChild(child)\n\n  /* Handle nested children */\n  } else if (Array.isArray(child)) {\n    for (const node of child)\n      appendChild(el, node)\n  }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * JSX factory\n *\n * @template T - Element type\n *\n * @param tag - HTML tag\n * @param attributes - HTML attributes\n * @param children - Child elements\n *\n * @returns Element\n */\nexport function h<T extends keyof HTMLElementTagNameMap>(\n  tag: T, attributes?: Attributes | null, ...children: Child[]\n): HTMLElementTagNameMap[T]\n\nexport function h<T extends h.JSX.Element>(\n  tag: string, attributes?: Attributes | null, ...children: Child[]\n): T\n\nexport function h<T extends h.JSX.Element>(\n  tag: string, attributes?: Attributes | null, ...children: Child[]\n): T {\n  const el = document.createElement(tag)\n\n  /* Set attributes, if any */\n  if (attributes)\n    for (const attr of Object.keys(attributes)) {\n      if (typeof attributes[attr] === \"undefined\")\n        continue\n\n      /* Set default attribute or boolean */\n      if (typeof attributes[attr] !== \"boolean\")\n        el.setAttribute(attr, attributes[attr])\n      else\n        el.setAttribute(attr, \"\")\n    }\n\n  /* Append child nodes */\n  for (const child of children)\n    appendChild(el, child)\n\n  /* Return element */\n  return el as T\n}\n\n/* ----------------------------------------------------------------------------\n * Namespace\n * ------------------------------------------------------------------------- */\n\nexport declare namespace h {\n  namespace JSX {\n    type Element = HTMLElement\n    type IntrinsicElements = JSXInternal.IntrinsicElements\n  }\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Round a number for display with repository facts\n *\n * This is a reverse-engineered version of GitHub's weird rounding algorithm\n * for stars, forks and all other numbers. While all numbers below `1,000` are\n * returned as-is, bigger numbers are converted to fixed numbers:\n *\n * - `1,049` => `1k`\n * - `1,050` => `1.1k`\n * - `1,949` => `1.9k`\n * - `1,950` => `2k`\n *\n * @param value - Original value\n *\n * @returns Rounded value\n */\nexport function round(value: number): string {\n  if (value > 999) {\n    const digits = +((value - 950) % 1000 > 99)\n    return `${((value + 0.000001) / 1000).toFixed(digits)}k`\n  } else {\n    return value.toString()\n  }\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  defer,\n  finalize,\n  fromEvent,\n  map,\n  merge,\n  switchMap,\n  take,\n  throwError\n} from \"rxjs\"\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create and load a `script` element\n *\n * This function returns an observable that will emit when the script was\n * successfully loaded, or throw an error if it wasn't.\n *\n * @param src - Script URL\n *\n * @returns Script observable\n */\nexport function watchScript(src: string): Observable<void> {\n  const script = h(\"script\", { src })\n  return defer(() => {\n    document.head.appendChild(script)\n    return merge(\n      fromEvent(script, \"load\"),\n      fromEvent(script, \"error\")\n        .pipe(\n          switchMap(() => (\n            throwError(() => new ReferenceError(`Invalid script: ${src}`))\n          ))\n        )\n    )\n      .pipe(\n        map(() => undefined),\n        finalize(() => document.head.removeChild(script)),\n        take(1)\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  NEVER,\n  Observable,\n  Subject,\n  defer,\n  filter,\n  finalize,\n  map,\n  merge,\n  of,\n  shareReplay,\n  startWith,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport { watchScript } from \"../../../script\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Element offset\n */\nexport interface ElementSize {\n  width: number                        /* Element width */\n  height: number                       /* Element height */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Resize observer entry subject\n */\nconst entry$ = new Subject<ResizeObserverEntry>()\n\n/**\n * Resize observer observable\n *\n * This observable will create a `ResizeObserver` on the first subscription\n * and will automatically terminate it when there are no more subscribers.\n * It's quite important to centralize observation in a single `ResizeObserver`,\n * as the performance difference can be quite dramatic, as the link shows.\n *\n * If the browser doesn't have a `ResizeObserver` implementation available, a\n * polyfill is automatically downloaded from unpkg.com. This is also compatible\n * with the built-in privacy plugin, which will download the polyfill and put\n * it alongside the built site for self-hosting.\n *\n * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => (\n  typeof ResizeObserver === \"undefined\"\n    ? watchScript(\"https://unpkg.com/resize-observer-polyfill\")\n    : of(undefined)\n))\n  .pipe(\n    map(() => new ResizeObserver(entries => (\n      entries.forEach(entry => entry$.next(entry))\n    ))),\n    switchMap(observer => merge(NEVER, of(observer)).pipe(\n      finalize(() => observer.disconnect())\n    )),\n    shareReplay(1)\n  )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element size\n *\n * @param el - Element\n *\n * @returns Element size\n */\nexport function getElementSize(\n  el: HTMLElement\n): ElementSize {\n  return {\n    width:  el.offsetWidth,\n    height: el.offsetHeight\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element size\n *\n * This function returns an observable that subscribes to a single internal\n * instance of `ResizeObserver` upon subscription, and emit resize events until\n * termination. Note that this function should not be called with the same\n * element twice, as the first unsubscription will terminate observation.\n *\n * Sadly, we can't use the `DOMRect` objects returned by the observer, because\n * we need the emitted values to be consistent with `getElementSize`, which will\n * return the used values (rounded) and not actual values (unrounded). Thus, we\n * use the `offset*` properties. See the linked GitHub issue.\n *\n * @see https://bit.ly/3m0k3he - GitHub issue\n *\n * @param el - Element\n *\n * @returns Element size observable\n */\nexport function watchElementSize(\n  el: HTMLElement\n): Observable<ElementSize> {\n\n  // Compute target element - since inline elements cannot be observed by the\n  // current `ResizeObserver` implementation as provided by browsers, we need\n  // to determine the first containing parent element and use that one as a\n  // target, while we always compute the actual size from the element.\n  let target = el\n  while (target.clientWidth === 0)\n    if (target.parentElement)\n      target = target.parentElement\n    else\n      break\n\n  // Observe target element and recompute element size on resize - as described\n  // above, the target element is not necessarily the element of interest\n  return observer$.pipe(\n    tap(observer => observer.observe(target)),\n    switchMap(observer => entry$.pipe(\n      filter(entry => entry.target === target),\n      finalize(() => observer.unobserve(target))\n    )),\n    map(() => getElementSize(el)),\n    startWith(getElementSize(el))\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ElementSize } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element content size (= scroll width and height)\n *\n * @param el - Element\n *\n * @returns Element content size\n */\nexport function getElementContentSize(\n  el: HTMLElement\n): ElementSize {\n  return {\n    width:  el.scrollWidth,\n    height: el.scrollHeight\n  }\n}\n\n/**\n * Retrieve the overflowing container of an element, if any\n *\n * @param el - Element\n *\n * @returns Overflowing container or nothing\n */\nexport function getElementContainer(\n  el: HTMLElement\n): HTMLElement | undefined {\n  let parent = el.parentElement\n  while (parent)\n    if (\n      el.scrollWidth  <= parent.scrollWidth &&\n      el.scrollHeight <= parent.scrollHeight\n    )\n      parent = (el = parent).parentElement\n    else\n      break\n\n  /* Return overflowing container */\n  return parent ? el : undefined\n}\n\n/**\n * Retrieve all overflowing containers of an element, if any\n *\n * Note that this function has a slightly different behavior, so we should at\n * some point consider refactoring how overflowing containers are handled.\n *\n * @param el - Element\n *\n * @returns Overflowing containers\n */\nexport function getElementContainers(\n  el: HTMLElement\n): HTMLElement[] {\n  const containers: HTMLElement[] = []\n\n  // Walk up the DOM tree until we find an overflowing container\n  let parent = el.parentElement\n  while (parent) {\n    if (\n      el.clientWidth  > parent.clientWidth ||\n      el.clientHeight > parent.clientHeight\n    )\n      containers.push(parent)\n\n    // Continue with parent element\n    parent = (el = parent).parentElement\n  }\n\n  // If the page is short, the body might not be overflowing and there might be\n  // no other containers, which is why we need to make sure the body is present\n  if (containers.length === 0)\n    containers.push(document.documentElement)\n\n  // Return overflowing containers\n  return containers\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  animationFrameScheduler,\n  auditTime,\n  fromEvent,\n  map,\n  merge,\n  startWith\n} from \"rxjs\"\n\nimport { watchElementSize } from \"../../size\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Element offset\n */\nexport interface ElementOffset {\n  x: number                            /* Horizontal offset */\n  y: number                            /* Vertical offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element offset\n *\n * @param el - Element\n *\n * @returns Element offset\n */\nexport function getElementOffset(\n  el: HTMLElement\n): ElementOffset {\n  return {\n    x: el.offsetLeft,\n    y: el.offsetTop\n  }\n}\n\n/**\n * Retrieve absolute element offset\n *\n * @param el - Element\n *\n * @returns Element offset\n */\nexport function getElementOffsetAbsolute(\n  el: HTMLElement\n): ElementOffset {\n  const rect = el.getBoundingClientRect()\n  return {\n    x: rect.x + window.scrollX,\n    y: rect.y + window.scrollY\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element offset\n *\n * @param el - Element\n *\n * @returns Element offset observable\n */\nexport function watchElementOffset(\n  el: HTMLElement\n): Observable<ElementOffset> {\n  return merge(\n    fromEvent(window, \"load\"),\n    fromEvent(window, \"resize\")\n  )\n    .pipe(\n      auditTime(0, animationFrameScheduler),\n      map(() => getElementOffset(el)),\n      startWith(getElementOffset(el))\n    )\n}\n\n/**\n * Watch absolute element offset\n *\n * @param el - Element\n *\n * @returns Element offset observable\n */\nexport function watchElementOffsetAbsolute(\n  el: HTMLElement\n): Observable<ElementOffset> {\n  return merge(\n    watchElementOffset(el),\n    watchElementSize(document.body) // @todo find a better way for this\n  )\n    .pipe(\n      map(() => getElementOffsetAbsolute(el)),\n      startWith(getElementOffsetAbsolute(el))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  animationFrameScheduler,\n  auditTime,\n  fromEvent,\n  map,\n  merge,\n  startWith\n} from \"rxjs\"\n\nimport { ElementOffset } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element content offset (= scroll offset)\n *\n * @param el - Element\n *\n * @returns Element content offset\n */\nexport function getElementContentOffset(\n  el: HTMLElement\n): ElementOffset {\n  return {\n    x: el.scrollLeft,\n    y: el.scrollTop\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element content offset\n *\n * @param el - Element\n *\n * @returns Element content offset observable\n */\nexport function watchElementContentOffset(\n  el: HTMLElement\n): Observable<ElementOffset> {\n  return merge(\n    fromEvent(el, \"scroll\"),\n    fromEvent(window, \"scroll\"),\n    fromEvent(window, \"resize\")\n  )\n    .pipe(\n      auditTime(0, animationFrameScheduler),\n      map(() => getElementContentOffset(el)),\n      startWith(getElementContentOffset(el))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  NEVER,\n  Observable,\n  Subject,\n  defer,\n  distinctUntilChanged,\n  filter,\n  finalize,\n  map,\n  merge,\n  of,\n  shareReplay,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport {\n  getElementContentSize,\n  getElementSize,\n  watchElementContentOffset\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Intersection observer entry subject\n */\nconst entry$ = new Subject<IntersectionObserverEntry>()\n\n/**\n * Intersection observer observable\n *\n * This observable will create an `IntersectionObserver` on first subscription\n * and will automatically terminate it when there are no more subscribers.\n *\n * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => of(\n  new IntersectionObserver(entries => {\n    for (const entry of entries)\n      entry$.next(entry)\n  }, {\n    threshold: 0\n  })\n))\n  .pipe(\n    switchMap(observer => merge(NEVER, of(observer))\n      .pipe(\n        finalize(() => observer.disconnect())\n      )\n    ),\n    shareReplay(1)\n  )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element visibility\n *\n * @param el - Element\n *\n * @returns Element visibility observable\n */\nexport function watchElementVisibility(\n  el: HTMLElement\n): Observable<boolean> {\n  return observer$\n    .pipe(\n      tap(observer => observer.observe(el)),\n      switchMap(observer => entry$\n        .pipe(\n          filter(({ target }) => target === el),\n          finalize(() => observer.unobserve(el)),\n          map(({ isIntersecting }) => isIntersecting)\n        )\n      )\n    )\n}\n\n/**\n * Watch element boundary\n *\n * This function returns an observable which emits whether the bottom content\n * boundary (= scroll offset) of an element is within a certain threshold.\n *\n * @param el - Element\n * @param threshold - Threshold\n *\n * @returns Element boundary observable\n */\nexport function watchElementBoundary(\n  el: HTMLElement, threshold = 16\n): Observable<boolean> {\n  return watchElementContentOffset(el)\n    .pipe(\n      map(({ y }) => {\n        const visible = getElementSize(el)\n        const content = getElementContentSize(el)\n        return y >= (\n          content.height - visible.height - threshold\n        )\n      }),\n      distinctUntilChanged()\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  startWith\n} from \"rxjs\"\n\nimport { getElement } from \"../element\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Toggle\n */\nexport type Toggle =\n  | \"drawer\"                           /* Toggle for drawer */\n  | \"search\"                           /* Toggle for search */\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Toggle map\n */\nconst toggles: Record<Toggle, HTMLInputElement> = {\n  drawer: getElement(\"[data-md-toggle=drawer]\"),\n  search: getElement(\"[data-md-toggle=search]\")\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve the value of a toggle\n *\n * @param name - Toggle\n *\n * @returns Toggle value\n */\nexport function getToggle(name: Toggle): boolean {\n  return toggles[name].checked\n}\n\n/**\n * Set toggle\n *\n * Simulating a click event seems to be the most cross-browser compatible way\n * of changing the value while also emitting a `change` event. Before, Material\n * used `CustomEvent` to programmatically change the value of a toggle, but this\n * is a much simpler and cleaner solution which doesn't require a polyfill.\n *\n * @param name - Toggle\n * @param value - Toggle value\n */\nexport function setToggle(name: Toggle, value: boolean): void {\n  if (toggles[name].checked !== value)\n    toggles[name].click()\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch toggle\n *\n * @param name - Toggle\n *\n * @returns Toggle value observable\n */\nexport function watchToggle(name: Toggle): Observable<boolean> {\n  const el = toggles[name]\n  return fromEvent(el, \"change\")\n    .pipe(\n      map(() => el.checked),\n      startWith(el.checked)\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  filter,\n  fromEvent,\n  map,\n  merge,\n  share,\n  startWith,\n  switchMap\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../element\"\nimport { getToggle } from \"../toggle\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Keyboard mode\n */\nexport type KeyboardMode =\n  | \"global\"                           /* Global */\n  | \"search\"                           /* Search is open */\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Keyboard\n */\nexport interface Keyboard {\n  mode: KeyboardMode                   /* Keyboard mode */\n  type: string                         /* Key type */\n  claim(): void                        /* Key claim */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Check whether an element may receive keyboard input\n *\n * @param el - Element\n * @param type - Key type\n *\n * @returns Test result\n */\nfunction isSusceptibleToKeyboard(\n  el: HTMLElement, type: string\n): boolean {\n  switch (el.constructor) {\n\n    /* Input elements */\n    case HTMLInputElement:\n      /* @ts-expect-error - omit unnecessary type cast */\n      if (el.type === \"radio\")\n        return /^Arrow/.test(type)\n      else\n        return true\n\n    /* Select element and textarea */\n    case HTMLSelectElement:\n    case HTMLTextAreaElement:\n      return true\n\n    /* Everything else */\n    default:\n      return el.isContentEditable\n  }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch composition events\n *\n * @returns Composition observable\n */\nexport function watchComposition(): Observable<boolean> {\n  return merge(\n    fromEvent(window, \"compositionstart\").pipe(map(() => true)),\n    fromEvent(window, \"compositionend\").pipe(map(() => false))\n  )\n    .pipe(\n      startWith(false)\n    )\n}\n\n/**\n * Watch keyboard\n *\n * @returns Keyboard observable\n */\nexport function watchKeyboard(): Observable<Keyboard> {\n  const keyboard$ = fromEvent<KeyboardEvent>(window, \"keydown\")\n    .pipe(\n      filter(ev => !(ev.metaKey || ev.ctrlKey)),\n      map(ev => ({\n        mode: getToggle(\"search\") ? \"search\" : \"global\",\n        type: ev.key,\n        claim() {\n          ev.preventDefault()\n          ev.stopPropagation()\n        }\n      } as Keyboard)),\n      filter(({ mode, type }) => {\n        if (mode === \"global\") {\n          const active = getActiveElement()\n          if (typeof active !== \"undefined\")\n            return !isSusceptibleToKeyboard(active, type)\n        }\n        return true\n      }),\n      share()\n    )\n\n  /* Don't emit during composition events - see https://bit.ly/3te3Wl8 */\n  return watchComposition()\n    .pipe(\n      switchMap(active => !active ? keyboard$ : EMPTY)\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Subject } from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve location\n *\n * This function returns a `URL` object (and not `Location`) to normalize the\n * typings across the application. Furthermore, locations need to be tracked\n * without setting them and `Location` is a singleton which represents the\n * current location.\n *\n * @returns URL\n */\nexport function getLocation(): URL {\n  return new URL(location.href)\n}\n\n/**\n * Set location\n *\n * If instant navigation is enabled, this function creates a temporary anchor\n * element, sets the `href` attribute, appends it to the body, clicks it, and\n * then removes it again. The event will bubble up the DOM and trigger be\n * intercepted by the instant loading business logic.\n *\n * Note that we must append and remove the anchor element, or the event will\n * not bubble up the DOM, making it impossible to intercept it.\n *\n * @param url - URL to navigate to\n * @param navigate - Force navigation\n */\nexport function setLocation(\n  url: URL | HTMLLinkElement, navigate = false\n): void {\n  if (feature(\"navigation.instant\") && !navigate) {\n    const el = h(\"a\", { href: url.href })\n    document.body.appendChild(el)\n    el.click()\n    el.remove()\n\n  // If we're not using instant navigation, and the page should not be reloaded\n  // just instruct the browser to navigate to the given URL\n  } else {\n    location.href = url.href\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch location\n *\n * @returns Location subject\n */\nexport function watchLocation(): Subject<URL> {\n  return new Subject<URL>()\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  filter,\n  fromEvent,\n  map,\n  merge,\n  shareReplay,\n  startWith\n} from \"rxjs\"\n\nimport { getOptionalElement } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve location hash\n *\n * @returns Location hash\n */\nexport function getLocationHash(): string {\n  return location.hash.slice(1)\n}\n\n/**\n * Set location hash\n *\n * Setting a new fragment identifier via `location.hash` will have no effect\n * if the value doesn't change. When a new fragment identifier is set, we want\n * the browser to target the respective element at all times, which is why we\n * use this dirty little trick.\n *\n * @param hash - Location hash\n */\nexport function setLocationHash(hash: string): void {\n  const el = h(\"a\", { href: hash })\n  el.addEventListener(\"click\", ev => ev.stopPropagation())\n  el.click()\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch location hash\n *\n * @param location$ - Location observable\n *\n * @returns Location hash observable\n */\nexport function watchLocationHash(\n  location$: Observable<URL>\n): Observable<string> {\n  return merge(\n    fromEvent<HashChangeEvent>(window, \"hashchange\"),\n    location$\n  )\n    .pipe(\n      map(getLocationHash),\n      startWith(getLocationHash()),\n      filter(hash => hash.length > 0),\n      shareReplay(1)\n    )\n}\n\n/**\n * Watch location target\n *\n * @param location$ - Location observable\n *\n * @returns Location target observable\n */\nexport function watchLocationTarget(\n  location$: Observable<URL>\n): Observable<HTMLElement> {\n  return watchLocationHash(location$)\n    .pipe(\n      map(id => getOptionalElement(`[id=\"${id}\"]`)!),\n      filter(el => typeof el !== \"undefined\")\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  fromEvent,\n  fromEventPattern,\n  map,\n  merge,\n  startWith,\n  switchMap\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch media query\n *\n * Note that although `MediaQueryList.addListener` is deprecated we have to\n * use it, because it's the only way to ensure proper downward compatibility.\n *\n * @see https://bit.ly/3dUBH2m - GitHub issue\n *\n * @param query - Media query\n *\n * @returns Media observable\n */\nexport function watchMedia(query: string): Observable<boolean> {\n  const media = matchMedia(query)\n  return fromEventPattern<boolean>(next => (\n    media.addListener(() => next(media.matches))\n  ))\n    .pipe(\n      startWith(media.matches)\n    )\n}\n\n/**\n * Watch print mode\n *\n * @returns Print observable\n */\nexport function watchPrint(): Observable<boolean> {\n  const media = matchMedia(\"print\")\n  return merge(\n    fromEvent(window, \"beforeprint\").pipe(map(() => true)),\n    fromEvent(window, \"afterprint\").pipe(map(() => false))\n  )\n    .pipe(\n      startWith(media.matches)\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Toggle an observable with a media observable\n *\n * @template T - Data type\n *\n * @param query$ - Media observable\n * @param factory - Observable factory\n *\n * @returns Toggled observable\n */\nexport function at<T>(\n  query$: Observable<boolean>, factory: () => Observable<T>\n): Observable<T> {\n  return query$\n    .pipe(\n      switchMap(active => active ? factory() : EMPTY)\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  map,\n  shareReplay,\n  switchMap\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Options\n */\ninterface Options {\n  progress$?: Subject<number>          // Progress subject\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the given URL\n *\n * This function returns an observable that emits the response as a blob and\n * completes, or emits an error if the request failed. The caller can cancel\n * the request by unsubscribing at any time, which will automatically abort\n * the inflight request and complete the observable.\n *\n * Note that we use `XMLHTTPRequest` not because we're nostalgic, but because\n * it's the only way to get progress events for downloads and also allow for\n * cancellation of requests, as the official Fetch API does not support this\n * yet, even though we're already in 2024.\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function request(\n  url: URL | string, options?: Options\n): Observable<Blob> {\n  return new Observable<Blob>(observer => {\n    const req = new XMLHttpRequest()\n    req.open(\"GET\", `${url}`)\n    req.responseType = \"blob\"\n\n    // Handle response\n    req.addEventListener(\"load\", () => {\n      if (req.status >= 200 && req.status < 300) {\n        observer.next(req.response)\n        observer.complete()\n\n      // Every response that is not in the 2xx range is considered an error\n      } else {\n        observer.error(new Error(req.statusText))\n      }\n    })\n\n    // Handle network errors\n    req.addEventListener(\"error\", () => {\n      observer.error(new Error(\"Network error\"))\n    })\n\n    // Handle aborted requests\n    req.addEventListener(\"abort\", () => {\n      observer.complete()\n    })\n\n    // Handle download progress\n    if (typeof options?.progress$ !== \"undefined\") {\n      req.addEventListener(\"progress\", event => {\n        if (event.lengthComputable) {\n          options.progress$!.next((event.loaded / event.total) * 100)\n\n        // Hack: Chromium doesn't report the total number of bytes if content\n        // is compressed, so we need this fallback - see https://t.ly/ZXofI\n        } else {\n          const length = req.getResponseHeader(\"Content-Length\") ?? 0\n          options.progress$!.next((event.loaded / +length) * 100)\n        }\n      })\n\n      // Immediately set progress to 5% to indicate that we're loading\n      options.progress$.next(5)\n    }\n\n    // Send request and automatically abort request upon unsubscription\n    req.send()\n    return () => req.abort()\n  })\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Fetch JSON from the given URL\n *\n * @template T - Data type\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestJSON<T>(\n  url: URL | string, options?: Options\n): Observable<T> {\n  return request(url, options)\n    .pipe(\n      switchMap(res => res.text()),\n      map(body => JSON.parse(body) as T),\n      shareReplay(1)\n    )\n}\n\n/**\n * Fetch HTML from the given URL\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestHTML(\n  url: URL | string, options?: Options\n): Observable<Document> {\n  const dom = new DOMParser()\n  return request(url, options)\n    .pipe(\n      switchMap(res => res.text()),\n      map(res => dom.parseFromString(res, \"text/html\")),\n      shareReplay(1)\n    )\n}\n\n/**\n * Fetch XML from the given URL\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestXML(\n  url: URL | string, options?: Options\n): Observable<Document> {\n  const dom = new DOMParser()\n  return request(url, options)\n    .pipe(\n      switchMap(res => res.text()),\n      map(res => dom.parseFromString(res, \"text/xml\")),\n      shareReplay(1)\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  merge,\n  startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport offset\n */\nexport interface ViewportOffset {\n  x: number                            /* Horizontal offset */\n  y: number                            /* Vertical offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve viewport offset\n *\n * On iOS Safari, viewport offset can be negative due to overflow scrolling.\n * As this may induce strange behaviors downstream, we'll just limit it to 0.\n *\n * @returns Viewport offset\n */\nexport function getViewportOffset(): ViewportOffset {\n  return {\n    x: Math.max(0, scrollX),\n    y: Math.max(0, scrollY)\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport offset\n *\n * @returns Viewport offset observable\n */\nexport function watchViewportOffset(): Observable<ViewportOffset> {\n  return merge(\n    fromEvent(window, \"scroll\", { passive: true }),\n    fromEvent(window, \"resize\", { passive: true })\n  )\n    .pipe(\n      map(getViewportOffset),\n      startWith(getViewportOffset())\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport size\n */\nexport interface ViewportSize {\n  width: number                        /* Viewport width */\n  height: number                       /* Viewport height */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve viewport size\n *\n * @returns Viewport size\n */\nexport function getViewportSize(): ViewportSize {\n  return {\n    width:  innerWidth,\n    height: innerHeight\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport size\n *\n * @returns Viewport size observable\n */\nexport function watchViewportSize(): Observable<ViewportSize> {\n  return fromEvent(window, \"resize\", { passive: true })\n    .pipe(\n      map(getViewportSize),\n      startWith(getViewportSize())\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  map,\n  shareReplay\n} from \"rxjs\"\n\nimport {\n  ViewportOffset,\n  watchViewportOffset\n} from \"../offset\"\nimport {\n  ViewportSize,\n  watchViewportSize\n} from \"../size\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport\n */\nexport interface Viewport {\n  offset: ViewportOffset               /* Viewport offset */\n  size: ViewportSize                   /* Viewport size */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport\n *\n * @returns Viewport observable\n */\nexport function watchViewport(): Observable<Viewport> {\n  return combineLatest([\n    watchViewportOffset(),\n    watchViewportSize()\n  ])\n    .pipe(\n      map(([offset, size]) => ({ offset, size })),\n      shareReplay(1)\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  distinctUntilKeyChanged,\n  map\n} from \"rxjs\"\n\nimport { Header } from \"~/components\"\n\nimport { getElementOffset } from \"../../element\"\nimport { Viewport } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport relative to element\n *\n * @param el - Element\n * @param options - Options\n *\n * @returns Viewport observable\n */\nexport function watchViewportAt(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<Viewport> {\n  const size$ = viewport$\n    .pipe(\n      distinctUntilKeyChanged(\"size\")\n    )\n\n  /* Compute element offset */\n  const offset$ = combineLatest([size$, header$])\n    .pipe(\n      map(() => getElementOffset(el))\n    )\n\n  /* Compute relative viewport, return hot observable */\n  return combineLatest([header$, viewport$, offset$])\n    .pipe(\n      map(([{ height }, { offset, size }, { x, y }]) => ({\n        offset: {\n          x: offset.x - x,\n          y: offset.y - y + height\n        },\n        size\n      }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  endWith,\n  fromEvent,\n  ignoreElements,\n  mergeWith,\n  share,\n  takeUntil\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Worker message\n */\nexport interface WorkerMessage {\n  type: unknown                        /* Message type */\n  data?: unknown                       /* Message data */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create an observable for receiving from a web worker\n *\n * @template T - Data type\n *\n * @param worker - Web worker\n *\n * @returns Message observable\n */\nfunction recv<T>(worker: Worker): Observable<T> {\n  return fromEvent<MessageEvent<T>, T>(worker, \"message\", ev => ev.data)\n}\n\n/**\n * Create a subject for sending to a web worker\n *\n * @template T - Data type\n *\n * @param worker - Web worker\n *\n * @returns Message subject\n */\nfunction send<T>(worker: Worker): Subject<T> {\n  const send$ = new Subject<T>()\n  send$.subscribe(data => worker.postMessage(data))\n\n  /* Return message subject */\n  return send$\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a bidirectional communication channel to a web worker\n *\n * @template T - Data type\n *\n * @param url - Worker URL\n * @param worker - Worker\n *\n * @returns Worker subject\n */\nexport function watchWorker<T extends WorkerMessage>(\n  url: string, worker = new Worker(url)\n): Subject<T> {\n  const recv$ = recv<T>(worker)\n  const send$ = send<T>(worker)\n\n  /* Create worker subject and forward messages */\n  const worker$ = new Subject<T>()\n  worker$.subscribe(send$)\n\n  /* Return worker subject */\n  const done$ = send$.pipe(ignoreElements(), endWith(true))\n  return worker$\n    .pipe(\n      ignoreElements(),\n      mergeWith(recv$.pipe(takeUntil(done$))),\n      share()\n    ) as Subject<T>\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { getElement, getLocation } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Feature flag\n */\nexport type Flag =\n  | \"announce.dismiss\"                 /* Dismissable announcement bar */\n  | \"content.code.annotate\"            /* Code annotations */\n  | \"content.code.copy\"                /* Code copy button */\n  | \"content.lazy\"                     /* Lazy content elements */\n  | \"content.tabs.link\"                /* Link content tabs */\n  | \"content.tooltips\"                 /* Tooltips */\n  | \"header.autohide\"                  /* Hide header */\n  | \"navigation.expand\"                /* Automatic expansion */\n  | \"navigation.indexes\"               /* Section pages */\n  | \"navigation.instant\"               /* Instant navigation */\n  | \"navigation.instant.progress\"      /* Instant navigation progress */\n  | \"navigation.sections\"              /* Section navigation */\n  | \"navigation.tabs\"                  /* Tabs navigation */\n  | \"navigation.tabs.sticky\"           /* Tabs navigation (sticky) */\n  | \"navigation.top\"                   /* Back-to-top button */\n  | \"navigation.tracking\"              /* Anchor tracking */\n  | \"search.highlight\"                 /* Search highlighting */\n  | \"search.share\"                     /* Search sharing */\n  | \"search.suggest\"                   /* Search suggestions */\n  | \"toc.follow\"                       /* Following table of contents */\n  | \"toc.integrate\"                    /* Integrated table of contents */\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Translation\n */\nexport type Translation =\n  | \"clipboard.copy\"                   /* Copy to clipboard */\n  | \"clipboard.copied\"                 /* Copied to clipboard */\n  | \"search.result.placeholder\"        /* Type to start searching */\n  | \"search.result.none\"               /* No matching documents */\n  | \"search.result.one\"                /* 1 matching document */\n  | \"search.result.other\"              /* # matching documents */\n  | \"search.result.more.one\"           /* 1 more on this page */\n  | \"search.result.more.other\"         /* # more on this page */\n  | \"search.result.term.missing\"       /* Missing */\n  | \"select.version\"                   /* Version selector */\n\n/**\n * Translations\n */\nexport type Translations =\n  Record<Translation, string>\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Versioning\n */\nexport interface Versioning {\n  provider: \"mike\"                     /* Version provider */\n  default?: string | string[]          /* Default version */\n  alias?: boolean                      /* Show alias */\n}\n\n/**\n * Configuration\n */\nexport interface Config {\n  base: string                         /* Base URL */\n  features: Flag[]                     /* Feature flags */\n  translations: Translations           /* Translations */\n  search: string                       /* Search worker URL */\n  tags?: Record<string, string>        /* Tags mapping */\n  version?: Versioning                 /* Versioning */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve global configuration and make base URL absolute\n */\nconst script = getElement(\"#__config\")\nconst config: Config = JSON.parse(script.textContent!)\nconfig.base = `${new URL(config.base, getLocation())}`\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve global configuration\n *\n * @returns Global configuration\n */\nexport function configuration(): Config {\n  return config\n}\n\n/**\n * Check whether a feature flag is enabled\n *\n * @param flag - Feature flag\n *\n * @returns Test result\n */\nexport function feature(flag: Flag): boolean {\n  return config.features.includes(flag)\n}\n\n/**\n * Retrieve the translation for the given key\n *\n * @param key - Key to be translated\n * @param value - Positional value, if any\n *\n * @returns Translation\n */\nexport function translation(\n  key: Translation, value?: string | number\n): string {\n  return typeof value !== \"undefined\"\n    ? config.translations[key].replace(\"#\", value.toString())\n    : config.translations[key]\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { getElement, getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Component type\n */\nexport type ComponentType =\n  | \"announce\"                         /* Announcement bar */\n  | \"container\"                        /* Container */\n  | \"consent\"                          /* Consent */\n  | \"content\"                          /* Content */\n  | \"dialog\"                           /* Dialog */\n  | \"header\"                           /* Header */\n  | \"header-title\"                     /* Header title */\n  | \"header-topic\"                     /* Header topic */\n  | \"main\"                             /* Main area */\n  | \"outdated\"                         /* Version warning */\n  | \"palette\"                          /* Color palette */\n  | \"progress\"                         /* Progress indicator */\n  | \"search\"                           /* Search */\n  | \"search-query\"                     /* Search input */\n  | \"search-result\"                    /* Search results */\n  | \"search-share\"                     /* Search sharing */\n  | \"search-suggest\"                   /* Search suggestions */\n  | \"sidebar\"                          /* Sidebar */\n  | \"skip\"                             /* Skip link */\n  | \"source\"                           /* Repository information */\n  | \"tabs\"                             /* Navigation tabs */\n  | \"toc\"                              /* Table of contents */\n  | \"top\"                              /* Back-to-top button */\n\n/**\n * Component\n *\n * @template T - Component type\n * @template U - Reference type\n */\nexport type Component<\n  T extends {} = {},\n  U extends HTMLElement = HTMLElement\n> =\n  T & {\n    ref: U                             /* Component reference */\n  }\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Component type map\n */\ninterface ComponentTypeMap {\n  \"announce\": HTMLElement              /* Announcement bar */\n  \"container\": HTMLElement             /* Container */\n  \"consent\": HTMLElement               /* Consent */\n  \"content\": HTMLElement               /* Content */\n  \"dialog\": HTMLElement                /* Dialog */\n  \"header\": HTMLElement                /* Header */\n  \"header-title\": HTMLElement          /* Header title */\n  \"header-topic\": HTMLElement          /* Header topic */\n  \"main\": HTMLElement                  /* Main area */\n  \"outdated\": HTMLElement              /* Version warning */\n  \"palette\": HTMLElement               /* Color palette */\n  \"progress\": HTMLElement              /* Progress indicator */\n  \"search\": HTMLElement                /* Search */\n  \"search-query\": HTMLInputElement     /* Search input */\n  \"search-result\": HTMLElement         /* Search results */\n  \"search-share\": HTMLAnchorElement    /* Search sharing */\n  \"search-suggest\": HTMLElement        /* Search suggestions */\n  \"sidebar\": HTMLElement               /* Sidebar */\n  \"skip\": HTMLAnchorElement            /* Skip link */\n  \"source\": HTMLAnchorElement          /* Repository information */\n  \"tabs\": HTMLElement                  /* Navigation tabs */\n  \"toc\": HTMLElement                   /* Table of contents */\n  \"top\": HTMLAnchorElement             /* Back-to-top button */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve the element for a given component or throw a reference error\n *\n * @template T - Component type\n *\n * @param type - Component type\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getComponentElement<T extends ComponentType>(\n  type: T, node: ParentNode = document\n): ComponentTypeMap[T] {\n  return getElement(`[data-md-component=${type}]`, node)\n}\n\n/**\n * Retrieve all elements for a given component\n *\n * @template T - Component type\n *\n * @param type - Component type\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getComponentElements<T extends ComponentType>(\n  type: T, node: ParentNode = document\n): ComponentTypeMap[T][] {\n  return getElements(`[data-md-component=${type}]`, node)\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  finalize,\n  fromEvent,\n  map,\n  tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport { getElement } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Announcement bar\n */\nexport interface Announce {\n  hash: number                        /* Content hash */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch announcement bar\n *\n * @param el - Announcement bar element\n *\n * @returns Announcement bar observable\n */\nexport function watchAnnounce(\n  el: HTMLElement\n): Observable<Announce> {\n  const button = getElement(\".md-typeset > :first-child\", el)\n  return fromEvent(button, \"click\", { once: true })\n    .pipe(\n      map(() => getElement(\".md-typeset\", el)),\n      map(content => ({ hash: __md_hash(content.innerHTML) }))\n    )\n}\n\n/**\n * Mount announcement bar\n *\n * @param el - Announcement bar element\n *\n * @returns Announcement bar component observable\n */\nexport function mountAnnounce(\n  el: HTMLElement\n): Observable<Component<Announce>> {\n  if (!feature(\"announce.dismiss\") || !el.childElementCount)\n    return EMPTY\n\n  /* Support instant navigation - see https://t.ly/3FTme */\n  if (!el.hidden) {\n    const content = getElement(\".md-typeset\", el)\n    if (__md_hash(content.innerHTML) === __md_get(\"__announce\"))\n      el.hidden = true\n  }\n\n  /* Mount component on subscription */\n  return defer(() => {\n    const push$ = new Subject<Announce>()\n    push$.subscribe(({ hash }) => {\n      el.hidden = true\n\n      /* Persist preference in local storage */\n      __md_set<number>(\"__announce\", hash)\n    })\n\n    /* Create and return component */\n    return watchAnnounce(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  finalize,\n  map,\n  tap\n} from \"rxjs\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Consent\n */\nexport interface Consent {\n  hidden: boolean                      /* Consent is hidden */\n}\n\n/**\n * Consent defaults\n */\nexport interface ConsentDefaults {\n  analytics?: boolean                  /* Consent for Analytics */\n  github?: boolean                     /* Consent for GitHub */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  target$: Observable<HTMLElement>     /* Target observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch consent\n *\n * @param el - Consent element\n * @param options - Options\n *\n * @returns Consent observable\n */\nexport function watchConsent(\n  el: HTMLElement, { target$ }: WatchOptions\n): Observable<Consent> {\n  return target$\n    .pipe(\n      map(target => ({ hidden: target !== el }))\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount consent\n *\n * @param el - Consent element\n * @param options - Options\n *\n * @returns Consent component observable\n */\nexport function mountConsent(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<Consent>> {\n  const internal$ = new Subject<Consent>()\n  internal$.subscribe(({ hidden }) => {\n    el.hidden = hidden\n  })\n\n  /* Create and return component */\n  return watchConsent(el, options)\n    .pipe(\n      tap(state => internal$.next(state)),\n      finalize(() => internal$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ComponentChild } from \"preact\"\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Tooltip style\n */\nexport type TooltipStyle =\n  | \"inline\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a tooltip\n *\n * @param id - Tooltip identifier\n * @param style - Tooltip style\n *\n * @returns Element\n */\nexport function renderTooltip(\n  id?: string, style?: TooltipStyle\n): HTMLElement {\n  if (style === \"inline\") { // @todo refactor control flow\n    return (\n      <div class=\"md-tooltip md-tooltip--inline\" id={id} role=\"tooltip\">\n        <div class=\"md-tooltip__inner md-typeset\"></div>\n      </div>\n    )\n  } else {\n    return (\n      <div class=\"md-tooltip\" id={id} role=\"tooltip\">\n        <div class=\"md-tooltip__inner md-typeset\"></div>\n      </div>\n    )\n  }\n}\n\n// @todo: rename\nexport function renderInlineTooltip2(\n  ...children: ComponentChild[]\n): HTMLElement {\n  return (\n    <div class=\"md-tooltip2\" role=\"tooltip\">\n      <div class=\"md-tooltip2__inner md-typeset\">\n        {children}\n      </div>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\nimport { renderTooltip } from \"../tooltip\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render an annotation\n *\n * @param id - Annotation identifier\n * @param prefix - Tooltip identifier prefix\n *\n * @returns Element\n */\nexport function renderAnnotation(\n  id: string | number, prefix?: string\n): HTMLElement {\n  prefix = prefix ? `${prefix}_annotation_${id}` : undefined\n\n  /* Render tooltip with anchor, if given */\n  if (prefix) {\n    const anchor = prefix ? `#${prefix}` : undefined\n    return (\n      <aside class=\"md-annotation\" tabIndex={0}>\n        {renderTooltip(prefix)}\n        <a href={anchor} class=\"md-annotation__index\" tabIndex={-1}>\n          <span data-md-annotation-id={id}></span>\n        </a>\n      </aside>\n    )\n  } else {\n    return (\n      <aside class=\"md-annotation\" tabIndex={0}>\n        {renderTooltip(prefix)}\n        <span class=\"md-annotation__index\" tabIndex={-1}>\n          <span data-md-annotation-id={id}></span>\n        </span>\n      </aside>\n    )\n  }\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { translation } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a 'copy-to-clipboard' button\n *\n * @param id - Unique identifier\n *\n * @returns Element\n */\nexport function renderClipboardButton(id: string): HTMLElement {\n  return (\n    <button\n      class=\"md-clipboard md-icon\"\n      title={translation(\"clipboard.copy\")}\n      data-clipboard-target={`#${id} > code`}\n    ></button>\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ComponentChild } from \"preact\"\n\nimport { configuration, feature, translation } from \"~/_\"\nimport { SearchItem } from \"~/integrations/search\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Render flag\n */\nconst enum Flag {\n  TEASER = 1,                          /* Render teaser */\n  PARENT = 2                           /* Render as parent */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper function\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a search document\n *\n * @param document - Search document\n * @param flag - Render flags\n *\n * @returns Element\n */\nfunction renderSearchDocument(\n  document: SearchItem, flag: Flag\n): HTMLElement {\n  const parent = flag & Flag.PARENT\n  const teaser = flag & Flag.TEASER\n\n  /* Render missing query terms */\n  const missing = Object.keys(document.terms)\n    .filter(key => !document.terms[key])\n    .reduce<ComponentChild[]>((list, key) => [\n      ...list, <del>{key}</del>, \" \"\n    ], [])\n    .slice(0, -1)\n\n  /* Assemble query string for highlighting */\n  const config = configuration()\n  const url = new URL(document.location, config.base)\n  if (feature(\"search.highlight\"))\n    url.searchParams.set(\"h\", Object.entries(document.terms)\n      .filter(([, match]) => match)\n      .reduce((highlight, [value]) => `${highlight} ${value}`.trim(), \"\")\n    )\n\n  /* Render article or section, depending on flags */\n  const { tags } = configuration()\n  return (\n    <a href={`${url}`} class=\"md-search-result__link\" tabIndex={-1}>\n      <article\n        class=\"md-search-result__article md-typeset\"\n        data-md-score={document.score.toFixed(2)}\n      >\n        {parent > 0 && <div class=\"md-search-result__icon md-icon\"></div>}\n        {parent > 0 && <h1>{document.title}</h1>}\n        {parent <= 0 && <h2>{document.title}</h2>}\n        {teaser > 0 && document.text.length > 0 &&\n          document.text\n        }\n        {document.tags && document.tags.map(tag => {\n          const type = tags\n            ? tag in tags\n              ? `md-tag-icon md-tag--${tags[tag]}`\n              : \"md-tag-icon\"\n            : \"\"\n          return (\n            <span class={`md-tag ${type}`}>{tag}</span>\n          )\n        })}\n        {teaser > 0 && missing.length > 0 &&\n          <p class=\"md-search-result__terms\">\n            {translation(\"search.result.term.missing\")}: {...missing}\n          </p>\n        }\n      </article>\n    </a>\n  )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a search result\n *\n * @param result - Search result\n *\n * @returns Element\n */\nexport function renderSearchResultItem(\n  result: SearchItem[]\n): HTMLElement {\n  const threshold = result[0].score\n  const docs = [...result]\n\n  const config = configuration()\n\n  /* Find and extract parent article */\n  const parent = docs.findIndex(doc => {\n    const l = `${new URL(doc.location, config.base)}` // @todo hacky\n    return !l.includes(\"#\")\n  })\n  const [article] = docs.splice(parent, 1)\n\n  /* Determine last index above threshold */\n  let index = docs.findIndex(doc => doc.score < threshold)\n  if (index === -1)\n    index = docs.length\n\n  /* Partition sections */\n  const best = docs.slice(0, index)\n  const more = docs.slice(index)\n\n  /* Render children */\n  const children = [\n    renderSearchDocument(article, Flag.PARENT | +(!parent && index === 0)),\n    ...best.map(section => renderSearchDocument(section, Flag.TEASER)),\n    ...more.length ? [\n      <details class=\"md-search-result__more\">\n        <summary tabIndex={-1}>\n          <div>\n            {more.length > 0 && more.length === 1\n              ? translation(\"search.result.more.one\")\n              : translation(\"search.result.more.other\", more.length)\n            }\n          </div>\n        </summary>\n        {...more.map(section => renderSearchDocument(section, Flag.TEASER))}\n      </details>\n    ] : []\n  ]\n\n  /* Render search result */\n  return (\n    <li class=\"md-search-result__item\">\n      {children}\n    </li>\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { SourceFacts } from \"~/components\"\nimport { h, round } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render repository facts\n *\n * @param facts - Repository facts\n *\n * @returns Element\n */\nexport function renderSourceFacts(facts: SourceFacts): HTMLElement {\n  return (\n    <ul class=\"md-source__facts\">\n      {Object.entries(facts).map(([key, value]) => (\n        <li class={`md-source__fact md-source__fact--${key}`}>\n          {typeof value === \"number\" ? round(value) : value}\n        </li>\n      ))}\n    </ul>\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Tabbed control type\n */\ntype TabbedControlType =\n  | \"prev\"\n  | \"next\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render control for content tabs\n *\n * @param type - Control type\n *\n * @returns Element\n */\nexport function renderTabbedControl(\n  type: TabbedControlType\n): HTMLElement {\n  const classes = `tabbed-control tabbed-control--${type}`\n  return (\n    <div class={classes} hidden>\n      <button class=\"tabbed-button\" tabIndex={-1} aria-hidden=\"true\"></button>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a table inside a wrapper to improve scrolling on mobile\n *\n * @param table - Table element\n *\n * @returns Element\n */\nexport function renderTable(table: HTMLElement): HTMLElement {\n  return (\n    <div class=\"md-typeset__scrollwrap\">\n      <div class=\"md-typeset__table\">\n        {table}\n      </div>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { configuration, translation } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Version properties\n */\nexport interface VersionProperties {\n  hidden?: boolean                     /* Version is hidden */\n}\n\n/**\n * Version\n */\nexport interface Version {\n  version: string                      /* Version identifier */\n  title: string                        /* Version title */\n  aliases: string[]                    /* Version aliases */\n  properties?: VersionProperties       /* Version properties */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a version\n *\n * @param version - Version\n *\n * @returns Element\n */\nfunction renderVersion(version: Version): HTMLElement {\n  const config = configuration()\n\n  /* Ensure trailing slash - see https://bit.ly/3rL5u3f */\n  const url = new URL(`../${version.version}/`, config.base)\n  return (\n    <li class=\"md-version__item\">\n      <a href={`${url}`} class=\"md-version__link\">\n        {version.title}\n        {config.version?.alias && version.aliases.length > 0 && (\n          <span class=\"md-version__alias\">\n            {version.aliases[0]}\n          </span>\n        )}\n      </a>\n    </li>\n  )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a version selector\n *\n * @param versions - Versions\n * @param active - Active version\n *\n * @returns Element\n */\nexport function renderVersionSelector(\n  versions: Version[], active: Version\n): HTMLElement {\n  const config = configuration()\n  versions = versions.filter(version => !version.properties?.hidden)\n  return (\n    <div class=\"md-version\">\n      <button\n        class=\"md-version__current\"\n        aria-label={translation(\"select.version\")}\n      >\n        {active.title}\n        {config.version?.alias && active.aliases.length > 0 && (\n          <span class=\"md-version__alias\">\n            {active.aliases[0]}\n          </span>\n        )}\n      </button>\n      <ul class=\"md-version__list\">\n        {versions.map(renderVersion)}\n      </ul>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  BehaviorSubject,\n  EMPTY,\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  combineLatest,\n  debounce,\n  defer,\n  distinctUntilChanged,\n  endWith,\n  filter,\n  finalize,\n  first,\n  ignoreElements,\n  map,\n  mergeMap,\n  observeOn,\n  queueScheduler,\n  share,\n  startWith,\n  switchMap,\n  tap,\n  throttleTime,\n  timer,\n  withLatestFrom\n} from \"rxjs\"\n\nimport {\n  ElementOffset,\n  Viewport,\n  getElement,\n  getElementContainers,\n  getElementOffsetAbsolute,\n  getElementSize,\n  watchElementContentOffset,\n  watchElementFocus,\n  watchElementHover\n} from \"~/browser\"\nimport { renderInlineTooltip2 } from \"~/templates\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Tooltip\n */\nexport interface Tooltip {\n  active: boolean                      // Tooltip is active\n  offset: ElementOffset                // Tooltip offset\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Dependencies\n */\ninterface Dependencies {\n  content$: Observable<HTMLElement>    // Tooltip content observable\n  viewport$: Observable<Viewport>      // Viewport observable\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Global sequence number for tooltips\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch tooltip\n *\n * This function tracks the tooltip host element, and deduces the active state\n * and offset of the tooltip from it. The active state is determined by whether\n * the host element is focused or hovered, and the offset is determined by the\n * host element's absolute position in the document.\n *\n * @param el - Tooltip host element\n *\n * @returns Tooltip observable\n */\nexport function watchTooltip2(\n  el: HTMLElement\n): Observable<Tooltip> {\n\n  // Compute whether tooltip should be shown - we need to watch both focus and\n  // hover events on the host element and emit if one of them is active. In case\n  // of a hover event, we keep the element visible for a short amount of time\n  // after the pointer left the host element for a better user experience.\n  const active$ =\n    combineLatest([\n      watchElementFocus(el),\n      watchElementHover(el)\n    ])\n      .pipe(\n        map(([focus, hover]) => focus || hover),\n        distinctUntilChanged()\n      )\n\n  // We need to determine all parent elements of the host element that are\n  // currently scrollable, as they might affect the position of the tooltip\n  // depending on their horizontal of vertical offset. We must track all of\n  // them and recompute the position of the tooltip if they change.\n  const offset$ =\n    defer(() => getElementContainers(el)).pipe(\n      mergeMap(watchElementContentOffset),\n      throttleTime(1),\n      map(() => getElementOffsetAbsolute(el))\n    )\n\n  // Only track parent elements and compute offset of the tooltip host if the\n  // tooltip should be shown - we defer the computation of the offset until the\n  // tooltip becomes active for the first time. This is necessary, because we\n  // must also keep the tooltip active as long as it is focused or hovered.\n  return active$.pipe(\n    first(active => active),\n    switchMap(() => combineLatest([active$, offset$])),\n    map(([active, offset]) => ({ active, offset })),\n    share()\n  )\n}\n\n/**\n * Mount tooltip\n *\n * This function renders a tooltip with the content from the provided `content$`\n * observable as passed via the dependencies. If the returned element has a role\n * of type `dialog`, the tooltip is considered to be interactive, and rendered\n * either above or below the host element, depending on the available space.\n *\n * If the returned element has a role of type `tooltip`, the tooltip is always\n * rendered below the host element and considered to be non-interactive. This\n * allows us to reuse the same positioning logic for both interactive and\n * non-interactive tooltips, as it is largely the same.\n *\n * @param el - Tooltip host element\n * @param dependencies - Dependencies\n *\n * @returns Tooltip component observable\n */\nexport function mountTooltip2(\n  el: HTMLElement, dependencies: Dependencies\n): Observable<Component<Tooltip>> {\n  const { content$, viewport$ } = dependencies\n\n  // Compute unique tooltip id - this is necessary to associate the tooltip host\n  // element with the tooltip element for ARIA purposes\n  const id = `__tooltip2_${sequence++}`\n\n  // Create component on subscription\n  return defer(() => {\n    const push$ = new Subject<Tooltip>()\n\n    // Create subject to track tooltip presence and visibility - we use another\n    // purely internal subject to track the tooltip's presence and visibility,\n    // as the tooltip should be visible if the host element or tooltip itself\n    // is focused or hovered to allow for smooth pointer migration\n    const show$ = new BehaviorSubject(false)\n    push$.pipe(ignoreElements(), endWith(false))\n      .subscribe(show$)\n\n    // Create observable controlling tooltip element - we create and attach the\n    // tooltip only if it is actually present, in order to keep the number of\n    // elements low. We need to keep the tooltip visible for a short time after\n    // the pointer left the host element or tooltip itself. For this, we use an\n    // inner subscription to the tooltip observable, which we terminate when the\n    // tooltip should not be shown, automatically removing the element. Moreover\n    // we use the queue scheduler, which will schedule synchronously in case the\n    // tooltip should be shown, and asynchronously if it should be hidden.\n    const node$ = show$.pipe(\n      debounce(active => timer(+!active * 250, queueScheduler)),\n      distinctUntilChanged(),\n      switchMap(active => active ? content$ : EMPTY),\n      tap(node => node.id = id),\n      share()\n    )\n\n    // Compute tooltip presence and visibility - the tooltip should be shown if\n    // the host element or the tooltip itself is focused or hovered\n    combineLatest([\n      push$.pipe(map(({ active }) => active)),\n      node$.pipe(\n        switchMap(node => watchElementHover(node, 250)),\n        startWith(false)\n      )\n    ])\n      .pipe(map(states => states.some(active => active)))\n      .subscribe(show$)\n\n    // Compute tooltip origin - we need to compute the tooltip origin depending\n    // on the position of the host element, the viewport size, as well as the\n    // actual size of the tooltip, if positioned above. The tooltip must about\n    // to be rendered for this to be correct, which is why we do it here.\n    const origin$ = show$.pipe(\n      filter(active => active),\n      withLatestFrom(node$, viewport$),\n      map(([_, node, { size }]) => {\n        const host = el.getBoundingClientRect()\n        const x = host.width / 2\n\n        // If the tooltip is non-interactive, we always render it below the\n        // actual element because all operating systems do it that way\n        if (node.role === \"tooltip\") {\n          return { x, y: 8 + host.height }\n\n        // Otherwise, we determine where there is more space, and render the\n        // tooltip either above or below the host element\n        } else if (host.y >= size.height / 2) {\n          const { height } = getElementSize(node)\n          return { x, y: -16 - height }\n        } else {\n          return { x, y: +16 + host.height }\n        }\n      })\n    )\n\n    // Update tooltip position - we always need to update the position of the\n    // tooltip, as it might change depending on the viewport offset of the host\n    combineLatest([node$, push$, origin$])\n      .subscribe(([node, { offset }, origin]) => {\n        node.style.setProperty(\"--md-tooltip-host-x\", `${offset.x}px`)\n        node.style.setProperty(\"--md-tooltip-host-y\", `${offset.y}px`)\n\n        // Update tooltip origin - this is mainly set to determine the position\n        // of the tooltip tail, to show the direction it is originating from\n        node.style.setProperty(\"--md-tooltip-x\", `${origin.x}px`)\n        node.style.setProperty(\"--md-tooltip-y\", `${origin.y}px`)\n\n        // Update tooltip render location, i.e., whether the tooltip is shown\n        // above or below the host element, depending on the available space\n        node.classList.toggle(\"md-tooltip2--top\",    origin.y <  0)\n        node.classList.toggle(\"md-tooltip2--bottom\", origin.y >= 0)\n      })\n\n    // Update tooltip width - we only explicitly set the width of the tooltip\n    // if it is non-interactive, in case it should always be rendered centered\n    show$.pipe(\n      filter(active => active),\n      withLatestFrom(node$, (_, node) => node),\n      filter(node => node.role === \"tooltip\")\n    )\n      .subscribe(node => {\n        const size = getElementSize(getElement(\":scope > *\", node))\n\n        // Set tooltip width and remove tail by setting it to a width of zero -\n        // if authors want to keep the tail, we can move this to CSS later\n        node.style.setProperty(\"--md-tooltip-width\", `${size.width}px`)\n        node.style.setProperty(\"--md-tooltip-tail\",  `${0}px`)\n      })\n\n    // Update tooltip visibility - we defer to the next animation frame, because\n    // the tooltip must first be added to the document before we make it appear,\n    // or it will appear instantly without delay. Additionally, we need to keep\n    // the tooltip visible for a short time after the pointer left the host.\n    show$.pipe(\n      distinctUntilChanged(),\n      observeOn(animationFrameScheduler),\n      withLatestFrom(node$)\n    )\n      .subscribe(([active, node]) => {\n        node.classList.toggle(\"md-tooltip2--active\", active)\n      })\n\n    // Set up ARIA attributes when tooltip is visible\n    combineLatest([\n      show$.pipe(filter(active => active)),\n      node$\n    ])\n      .subscribe(([_, node]) => {\n        if (node.role === \"dialog\") {\n          el.setAttribute(\"aria-controls\", id)\n          el.setAttribute(\"aria-haspopup\", \"dialog\")\n        } else {\n          el.setAttribute(\"aria-describedby\", id)\n        }\n      })\n\n    // Remove ARIA attributes when tooltip is hidden\n    show$.pipe(filter(active => !active))\n      .subscribe(() => {\n        el.removeAttribute(\"aria-controls\")\n        el.removeAttribute(\"aria-describedby\")\n        el.removeAttribute(\"aria-haspopup\")\n      })\n\n    // Create and return component\n    return watchTooltip2(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n\n// ----------------------------------------------------------------------------\n\n/**\n * Mount inline tooltip\n *\n * @todo refactor this function\n *\n * @param el - Tooltip host element\n * @param dependencies - Dependencies\n * @param container - Container\n *\n * @returns Tooltip component observable\n */\nexport function mountInlineTooltip2(\n  el: HTMLElement, { viewport$ }: { viewport$: Observable<Viewport> },\n  container = document.body\n): Observable<Component<Tooltip>> {\n  return mountTooltip2(el, {\n    content$: new Observable<HTMLElement>(observer => {\n      const title = el.title\n      const node = renderInlineTooltip2(title)\n      observer.next(node)\n      el.removeAttribute(\"title\")\n      // Append tooltip and remove on unsubscription\n      container.append(node)\n      return () => {\n        node.remove()\n        el.setAttribute(\"title\", title)\n      }\n    }),\n    viewport$\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  auditTime,\n  combineLatest,\n  debounceTime,\n  defer,\n  delay,\n  endWith,\n  filter,\n  finalize,\n  fromEvent,\n  ignoreElements,\n  map,\n  merge,\n  switchMap,\n  take,\n  takeUntil,\n  tap,\n  throttleTime,\n  withLatestFrom\n} from \"rxjs\"\n\nimport {\n  ElementOffset,\n  getActiveElement,\n  getElementSize,\n  watchElementContentOffset,\n  watchElementFocus,\n  watchElementOffset,\n  watchElementVisibility\n} from \"~/browser\"\n\nimport { Component } from \"../../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Annotation\n */\nexport interface Annotation {\n  active: boolean                      /* Annotation is active */\n  offset: ElementOffset                /* Annotation offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch annotation\n *\n * @param el - Annotation element\n * @param container - Containing element\n *\n * @returns Annotation observable\n */\nexport function watchAnnotation(\n  el: HTMLElement, container: HTMLElement\n): Observable<Annotation> {\n  const offset$ = defer(() => combineLatest([\n    watchElementOffset(el),\n    watchElementContentOffset(container)\n  ]))\n    .pipe(\n      map(([{ x, y }, scroll]): ElementOffset => {\n        const { width, height } = getElementSize(el)\n        return ({\n          x: x - scroll.x + width  / 2,\n          y: y - scroll.y + height / 2\n        })\n      })\n    )\n\n  /* Actively watch annotation on focus */\n  return watchElementFocus(el)\n    .pipe(\n      switchMap(active => offset$\n        .pipe(\n          map(offset => ({ active, offset })),\n          take(+!active || Infinity)\n        )\n      )\n    )\n}\n\n/**\n * Mount annotation\n *\n * @param el - Annotation element\n * @param container - Containing element\n * @param options - Options\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotation(\n  el: HTMLElement, container: HTMLElement, { target$ }: MountOptions\n): Observable<Component<Annotation>> {\n  const [tooltip, index] = Array.from(el.children)\n\n  /* Mount component on subscription */\n  return defer(() => {\n    const push$ = new Subject<Annotation>()\n    const done$ = push$.pipe(ignoreElements(), endWith(true))\n    push$.subscribe({\n\n      /* Handle emission */\n      next({ offset }) {\n        el.style.setProperty(\"--md-tooltip-x\", `${offset.x}px`)\n        el.style.setProperty(\"--md-tooltip-y\", `${offset.y}px`)\n      },\n\n      /* Handle complete */\n      complete() {\n        el.style.removeProperty(\"--md-tooltip-x\")\n        el.style.removeProperty(\"--md-tooltip-y\")\n      }\n    })\n\n    /* Start animation only when annotation is visible */\n    watchElementVisibility(el)\n      .pipe(\n        takeUntil(done$)\n      )\n        .subscribe(visible => {\n          el.toggleAttribute(\"data-md-visible\", visible)\n        })\n\n    /* Toggle tooltip presence to mitigate empty lines when copying */\n    merge(\n      push$.pipe(filter(({ active }) => active)),\n      push$.pipe(debounceTime(250), filter(({ active }) => !active))\n    )\n      .subscribe({\n\n        /* Handle emission */\n        next({ active }) {\n          if (active)\n            el.prepend(tooltip)\n          else\n            tooltip.remove()\n        },\n\n        /* Handle complete */\n        complete() {\n          el.prepend(tooltip)\n        }\n      })\n\n    /* Toggle tooltip visibility */\n    push$\n      .pipe(\n        auditTime(16, animationFrameScheduler)\n      )\n        .subscribe(({ active }) => {\n          tooltip.classList.toggle(\"md-tooltip--active\", active)\n        })\n\n    /* Track relative origin of tooltip */\n    push$\n      .pipe(\n        throttleTime(125, animationFrameScheduler),\n        filter(() => !!el.offsetParent),\n        map(() => el.offsetParent!.getBoundingClientRect()),\n        map(({ x }) => x)\n      )\n        .subscribe({\n\n          /* Handle emission */\n          next(origin) {\n            if (origin)\n              el.style.setProperty(\"--md-tooltip-0\", `${-origin}px`)\n            else\n              el.style.removeProperty(\"--md-tooltip-0\")\n          },\n\n          /* Handle complete */\n          complete() {\n            el.style.removeProperty(\"--md-tooltip-0\")\n          }\n        })\n\n    /* Allow to copy link without scrolling to anchor */\n    fromEvent<MouseEvent>(index, \"click\")\n      .pipe(\n        takeUntil(done$),\n        filter(ev => !(ev.metaKey || ev.ctrlKey))\n      )\n        .subscribe(ev => {\n          ev.stopPropagation()\n          ev.preventDefault()\n        })\n\n    /* Allow to open link in new tab or blur on close */\n    fromEvent<MouseEvent>(index, \"mousedown\")\n      .pipe(\n        takeUntil(done$),\n        withLatestFrom(push$)\n      )\n        .subscribe(([ev, { active }]) => {\n\n          /* Open in new tab */\n          if (ev.button !== 0 || ev.metaKey || ev.ctrlKey) {\n            ev.preventDefault()\n\n          /* Close annotation */\n          } else if (active) {\n            ev.preventDefault()\n\n            /* Focus parent annotation, if any */\n            const parent = el.parentElement!.closest(\".md-annotation\")\n            if (parent instanceof HTMLElement)\n              parent.focus()\n            else\n              getActiveElement()?.blur()\n          }\n        })\n\n    /* Open and focus annotation on location target */\n    target$\n      .pipe(\n        takeUntil(done$),\n        filter(target => target === tooltip),\n        delay(125)\n      )\n        .subscribe(() => el.focus())\n\n    /* Create and return component */\n    return watchAnnotation(el, container)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  endWith,\n  finalize,\n  ignoreElements,\n  merge,\n  share,\n  takeUntil\n} from \"rxjs\"\n\nimport {\n  getElement,\n  getElements,\n  getOptionalElement\n} from \"~/browser\"\nimport { renderAnnotation } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n  Annotation,\n  mountAnnotation\n} from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find all annotation hosts in the containing element\n *\n * @param container - Containing element\n *\n * @returns Annotation hosts\n */\nfunction findHosts(container: HTMLElement): HTMLElement[] {\n  return container.tagName === \"CODE\"\n    ? getElements(\".c, .c1, .cm\", container)\n    : [container]\n}\n\n/**\n * Find all annotation markers in the containing element\n *\n * @param container - Containing element\n *\n * @returns Annotation markers\n */\nfunction findMarkers(container: HTMLElement): Text[] {\n  const markers: Text[] = []\n  for (const el of findHosts(container)) {\n    const nodes: Text[] = []\n\n    /* Find all text nodes in current element */\n    const it = document.createNodeIterator(el, NodeFilter.SHOW_TEXT)\n    for (let node = it.nextNode(); node; node = it.nextNode())\n      nodes.push(node as Text)\n\n    /* Find all markers in each text node */\n    for (let text of nodes) {\n      let match: RegExpExecArray | null\n\n      /* Split text at marker and add to list */\n      while ((match = /(\\(\\d+\\))(!)?/.exec(text.textContent!))) {\n        const [, id, force] = match\n        if (typeof force === \"undefined\") {\n          const marker = text.splitText(match.index)\n          text = marker.splitText(id.length)\n          markers.push(marker)\n\n        /* Replace entire text with marker */\n        } else {\n          text.textContent = id\n          markers.push(text)\n          break\n        }\n      }\n    }\n  }\n  return markers\n}\n\n/**\n * Swap the child nodes of two elements\n *\n * @param source - Source element\n * @param target - Target element\n */\nfunction swap(source: HTMLElement, target: HTMLElement): void {\n  target.append(...Array.from(source.childNodes))\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount annotation list\n *\n * This function analyzes the containing code block and checks for markers\n * referring to elements in the given annotation list. If no markers are found,\n * the list is left untouched. Otherwise, list elements are rendered as\n * annotations inside the code block.\n *\n * @param el - Annotation list element\n * @param container - Containing element\n * @param options - Options\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotationList(\n  el: HTMLElement, container: HTMLElement, { target$, print$ }: MountOptions\n): Observable<Component<Annotation>> {\n\n  /* Compute prefix for tooltip anchors */\n  const parent = container.closest(\"[id]\")\n  const prefix = parent?.id\n\n  /* Find and replace all markers with empty annotations */\n  const annotations = new Map<string, HTMLElement>()\n  for (const marker of findMarkers(container)) {\n    const [, id] = marker.textContent!.match(/\\((\\d+)\\)/)!\n    if (getOptionalElement(`:scope > li:nth-child(${id})`, el)) {\n      annotations.set(id, renderAnnotation(id, prefix))\n      marker.replaceWith(annotations.get(id)!)\n    }\n  }\n\n  /* Keep list if there are no annotations to render */\n  if (annotations.size === 0)\n    return EMPTY\n\n  /* Mount component on subscription */\n  return defer(() => {\n    const push$ = new Subject()\n    const done$ = push$.pipe(ignoreElements(), endWith(true))\n\n    /* Retrieve container pairs for swapping */\n    const pairs: [HTMLElement, HTMLElement][] = []\n    for (const [id, annotation] of annotations)\n      pairs.push([\n        getElement(\".md-typeset\", annotation),\n        getElement(`:scope > li:nth-child(${id})`, el)\n      ])\n\n    /* Handle print mode - see https://bit.ly/3rgPdpt */\n    print$.pipe(takeUntil(done$))\n      .subscribe(active => {\n        el.hidden = !active\n\n        /* Add class to discern list element */\n        el.classList.toggle(\"md-annotation-list\", active)\n\n        /* Show annotations in code block or list (print) */\n        for (const [inner, child] of pairs)\n          if (!active)\n            swap(child, inner)\n          else\n            swap(inner, child)\n      })\n\n    /* Create and return component */\n    return merge(...[...annotations]\n      .map(([, annotation]) => (\n        mountAnnotation(annotation, container, { target$ })\n      ))\n    )\n      .pipe(\n        finalize(() => push$.complete()),\n        share()\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { EMPTY, Observable, defer } from \"rxjs\"\n\nimport { Component } from \"../../../_\"\nimport { Annotation } from \"../_\"\nimport { mountAnnotationList } from \"../list\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find list element directly following a block\n *\n * @param el - Annotation block element\n *\n * @returns List element or nothing\n */\nfunction findList(el: HTMLElement): HTMLElement | undefined {\n  if (el.nextElementSibling) {\n    const sibling = el.nextElementSibling as HTMLElement\n    if (sibling.tagName === \"OL\")\n      return sibling\n\n    /* Skip empty paragraphs - see https://bit.ly/3r4ZJ2O */\n    else if (sibling.tagName === \"P\" && !sibling.children.length)\n      return findList(sibling)\n  }\n\n  /* Everything else */\n  return undefined\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount annotation block\n *\n * @param el - Annotation block element\n * @param options - Options\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotationBlock(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<Annotation>> {\n  return defer(() => {\n    const list = findList(el)\n    return typeof list !== \"undefined\"\n      ? mountAnnotationList(list, el, options)\n      : EMPTY\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  filter,\n  finalize,\n  map,\n  mergeWith,\n  switchMap,\n  take,\n  takeLast,\n  takeUntil,\n  tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  getElementContentSize,\n  getElements,\n  watchElementSize,\n  watchElementVisibility\n} from \"~/browser\"\nimport {\n  Tooltip,\n  mountInlineTooltip2\n} from \"~/components/tooltip2\"\nimport { renderClipboardButton } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n  Annotation,\n  mountAnnotationList\n} from \"../../annotation\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Code block overflow\n */\nexport interface Overflow {\n  scrollable: boolean                  /* Code block overflows */\n}\n\n/**\n * Code block\n */\nexport type CodeBlock =\n  | Overflow\n  | Annotation\n  | Tooltip\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Global sequence number for code blocks\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find candidate list element directly following a code block\n *\n * @param el - Code block element\n *\n * @returns List element or nothing\n */\nfunction findCandidateList(el: HTMLElement): HTMLElement | undefined {\n  if (el.nextElementSibling) {\n    const sibling = el.nextElementSibling as HTMLElement\n    if (sibling.tagName === \"OL\")\n      return sibling\n\n    /* Skip empty paragraphs - see https://bit.ly/3r4ZJ2O */\n    else if (sibling.tagName === \"P\" && !sibling.children.length)\n      return findCandidateList(sibling)\n  }\n\n  /* Everything else */\n  return undefined\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch code block\n *\n * This function monitors size changes of the viewport, as well as switches of\n * content tabs with embedded code blocks, as both may trigger overflow.\n *\n * @param el - Code block element\n *\n * @returns Code block observable\n */\nexport function watchCodeBlock(\n  el: HTMLElement\n): Observable<Overflow> {\n  return watchElementSize(el)\n    .pipe(\n      map(({ width }) => {\n        const content = getElementContentSize(el)\n        return {\n          scrollable: content.width > width\n        }\n      }),\n      distinctUntilKeyChanged(\"scrollable\")\n    )\n}\n\n/**\n * Mount code block\n *\n * This function ensures that an overflowing code block is focusable through\n * keyboard, so it can be scrolled without a mouse to improve on accessibility.\n * Furthermore, if code annotations are enabled, they are mounted if and only\n * if the code block is currently visible, e.g., not in a hidden content tab.\n *\n * Note that code blocks may be mounted eagerly or lazily. If they're mounted\n * lazily (on first visibility), code annotation anchor links will not work,\n * as they are evaluated on initial page load, and code annotations in general\n * might feel a little bumpier.\n *\n * @param el - Code block element\n * @param options - Options\n *\n * @returns Code block and annotation component observable\n */\nexport function mountCodeBlock(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<CodeBlock>> {\n  const { matches: hover } = matchMedia(\"(hover)\")\n\n  /* Defer mounting of code block - see https://bit.ly/3vHVoVD */\n  const factory$ = defer(() => {\n    const push$ = new Subject<Overflow>()\n    const done$ = push$.pipe(takeLast(1))\n    push$.subscribe(({ scrollable }) => {\n      if (scrollable && hover)\n        el.setAttribute(\"tabindex\", \"0\")\n      else\n        el.removeAttribute(\"tabindex\")\n    })\n\n    /* Render button for Clipboard.js integration */\n    const content$: Array<Observable<Component<CodeBlock>>> = []\n    if (ClipboardJS.isSupported()) {\n      if (el.closest(\".copy\") || (\n        feature(\"content.code.copy\") && !el.closest(\".no-copy\")\n      )) {\n        const parent = el.closest(\"pre\")!\n        parent.id = `__code_${sequence++}`\n\n        /* Mount tooltip, if enabled */\n        const button = renderClipboardButton(parent.id)\n        parent.insertBefore(button, el)\n        if (feature(\"content.tooltips\"))\n          content$.push(mountInlineTooltip2(button, { viewport$ }))\n      }\n    }\n\n    /* Handle code annotations */\n    const container = el.closest(\".highlight\")\n    if (container instanceof HTMLElement) {\n      const list = findCandidateList(container)\n\n      /* Mount code annotations, if enabled */\n      if (typeof list !== \"undefined\" && (\n        container.classList.contains(\"annotate\") ||\n        feature(\"content.code.annotate\")\n      )) {\n        const annotations$ = mountAnnotationList(list, el, options)\n        content$.push(\n          watchElementSize(container)\n            .pipe(\n              takeUntil(done$),\n              map(({ width, height }) => width && height),\n              distinctUntilChanged(),\n              switchMap(active => active ? annotations$ : EMPTY)\n            )\n        )\n      }\n    }\n\n    // If the code block has line spans, we can add this additional class to\n    // the code block element, which fixes the problem for highlighted code\n    // lines not stretching to the entirety of the screen when the code block\n    // overflows, e.g., on mobile - see\n    const spans = getElements(\":scope > span[id]\", el)\n    if (spans.length)\n      el.classList.add(\"md-code__content\")\n\n    /* Create and return component */\n    return watchCodeBlock(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state })),\n        mergeWith(...content$)\n      )\n  })\n\n  /* Mount code block lazily */\n  if (feature(\"content.lazy\"))\n    return watchElementVisibility(el)\n      .pipe(\n        filter(visible => visible),\n        take(1),\n        switchMap(() => factory$)\n      )\n\n  /* Mount code block */\n  return factory$\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  defer,\n  filter,\n  finalize,\n  map,\n  merge,\n  tap\n} from \"rxjs\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Details\n */\nexport interface Details {\n  action: \"open\" | \"close\"             /* Details state */\n  reveal?: boolean                     /* Details is revealed */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch details\n *\n * @param el - Details element\n * @param options - Options\n *\n * @returns Details observable\n */\nexport function watchDetails(\n  el: HTMLDetailsElement, { target$, print$ }: WatchOptions\n): Observable<Details> {\n  let open = true\n  return merge(\n\n    /* Open and focus details on location target */\n    target$\n      .pipe(\n        map(target => target.closest(\"details:not([open])\")!),\n        filter(details => el === details),\n        map(() => ({\n          action: \"open\", reveal: true\n        }) as Details)\n      ),\n\n    /* Open details on print and close afterwards */\n    print$\n      .pipe(\n        filter(active => active || !open),\n        tap(() => open = el.open),\n        map(active => ({\n          action: active ? \"open\" : \"close\"\n        }) as Details)\n      )\n  )\n}\n\n/**\n * Mount details\n *\n * This function ensures that `details` tags are opened on anchor jumps and\n * prior to printing, so the whole content of the page is visible.\n *\n * @param el - Details element\n * @param options - Options\n *\n * @returns Details component observable\n */\nexport function mountDetails(\n  el: HTMLDetailsElement, options: MountOptions\n): Observable<Component<Details>> {\n  return defer(() => {\n    const push$ = new Subject<Details>()\n    push$.subscribe(({ action, reveal }) => {\n      el.toggleAttribute(\"open\", action === \"open\")\n      if (reveal)\n        el.scrollIntoView()\n    })\n\n    /* Create and return component */\n    return watchDetails(el, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", ".node circle,.node ellipse,.node path,.node polygon,.node rect{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}marker{fill:var(--md-mermaid-edge-color)!important}.edgeLabel .label rect{fill:#0000}.label{color:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.label foreignObject{line-height:normal;overflow:visible}.label div .edgeLabel{color:var(--md-mermaid-label-fg-color)}.edgeLabel,.edgeLabel rect,.label div .edgeLabel{background-color:var(--md-mermaid-label-bg-color)}.edgeLabel,.edgeLabel rect{fill:var(--md-mermaid-label-bg-color);color:var(--md-mermaid-edge-color)}.edgePath .path,.flowchart-link{stroke:var(--md-mermaid-edge-color);stroke-width:.05rem}.edgePath .arrowheadPath{fill:var(--md-mermaid-edge-color);stroke:none}.cluster rect{fill:var(--md-default-fg-color--lightest);stroke:var(--md-default-fg-color--lighter)}.cluster span{color:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}g #flowchart-circleEnd,g #flowchart-circleStart,g #flowchart-crossEnd,g #flowchart-crossStart,g #flowchart-pointEnd,g #flowchart-pointStart{stroke:none}g.classGroup line,g.classGroup rect{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}g.classGroup text{fill:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.classLabel .box{fill:var(--md-mermaid-label-bg-color);background-color:var(--md-mermaid-label-bg-color);opacity:1}.classLabel .label{fill:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.node .divider{stroke:var(--md-mermaid-node-fg-color)}.relation{stroke:var(--md-mermaid-edge-color)}.cardinality{fill:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.cardinality text{fill:inherit!important}defs #classDiagram-compositionEnd,defs #classDiagram-compositionStart,defs #classDiagram-dependencyEnd,defs #classDiagram-dependencyStart,defs #classDiagram-extensionEnd,defs #classDiagram-extensionStart{fill:var(--md-mermaid-edge-color)!important;stroke:var(--md-mermaid-edge-color)!important}defs #classDiagram-aggregationEnd,defs #classDiagram-aggregationStart{fill:var(--md-mermaid-label-bg-color)!important;stroke:var(--md-mermaid-edge-color)!important}g.stateGroup rect{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}g.stateGroup .state-title{fill:var(--md-mermaid-label-fg-color)!important;font-family:var(--md-mermaid-font-family)}g.stateGroup .composit{fill:var(--md-mermaid-label-bg-color)}.nodeLabel,.nodeLabel p{color:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}a .nodeLabel{text-decoration:underline}.node circle.state-end,.node circle.state-start,.start-state{fill:var(--md-mermaid-edge-color);stroke:none}.end-state-inner,.end-state-outer{fill:var(--md-mermaid-edge-color)}.end-state-inner,.node circle.state-end{stroke:var(--md-mermaid-label-bg-color)}.transition{stroke:var(--md-mermaid-edge-color)}[id^=state-fork] rect,[id^=state-join] rect{fill:var(--md-mermaid-edge-color)!important;stroke:none!important}.statediagram-cluster.statediagram-cluster .inner{fill:var(--md-default-bg-color)}.statediagram-cluster rect{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}.statediagram-state rect.divider{fill:var(--md-default-fg-color--lightest);stroke:var(--md-default-fg-color--lighter)}defs #statediagram-barbEnd{stroke:var(--md-mermaid-edge-color)}.attributeBoxEven,.attributeBoxOdd{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}.entityBox{fill:var(--md-mermaid-label-bg-color);stroke:var(--md-mermaid-node-fg-color)}.entityLabel{fill:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.relationshipLabelBox{fill:var(--md-mermaid-label-bg-color);fill-opacity:1;background-color:var(--md-mermaid-label-bg-color);opacity:1}.relationshipLabel{fill:var(--md-mermaid-label-fg-color)}.relationshipLine{stroke:var(--md-mermaid-edge-color)}defs #ONE_OR_MORE_END *,defs #ONE_OR_MORE_START *,defs #ONLY_ONE_END *,defs #ONLY_ONE_START *,defs #ZERO_OR_MORE_END *,defs #ZERO_OR_MORE_START *,defs #ZERO_OR_ONE_END *,defs #ZERO_OR_ONE_START *{stroke:var(--md-mermaid-edge-color)!important}defs #ZERO_OR_MORE_END circle,defs #ZERO_OR_MORE_START circle{fill:var(--md-mermaid-label-bg-color)}.actor{fill:var(--md-mermaid-sequence-actor-bg-color);stroke:var(--md-mermaid-sequence-actor-border-color)}text.actor>tspan{fill:var(--md-mermaid-sequence-actor-fg-color);font-family:var(--md-mermaid-font-family)}line{stroke:var(--md-mermaid-sequence-actor-line-color)}.actor-man circle,.actor-man line{fill:var(--md-mermaid-sequence-actorman-bg-color);stroke:var(--md-mermaid-sequence-actorman-line-color)}.messageLine0,.messageLine1{stroke:var(--md-mermaid-sequence-message-line-color)}.note{fill:var(--md-mermaid-sequence-note-bg-color);stroke:var(--md-mermaid-sequence-note-border-color)}.loopText,.loopText>tspan,.messageText,.noteText>tspan{stroke:none;font-family:var(--md-mermaid-font-family)!important}.messageText{fill:var(--md-mermaid-sequence-message-fg-color)}.loopText,.loopText>tspan{fill:var(--md-mermaid-sequence-loop-fg-color)}.noteText>tspan{fill:var(--md-mermaid-sequence-note-fg-color)}#arrowhead path{fill:var(--md-mermaid-sequence-message-line-color);stroke:none}.loopLine{fill:var(--md-mermaid-sequence-loop-bg-color);stroke:var(--md-mermaid-sequence-loop-border-color)}.labelBox{fill:var(--md-mermaid-sequence-label-bg-color);stroke:none}.labelText,.labelText>span{fill:var(--md-mermaid-sequence-label-fg-color);font-family:var(--md-mermaid-font-family)}.sequenceNumber{fill:var(--md-mermaid-sequence-number-fg-color)}rect.rect{fill:var(--md-mermaid-sequence-box-bg-color);stroke:none}rect.rect+text.text{fill:var(--md-mermaid-sequence-box-fg-color)}defs #sequencenumber{fill:var(--md-mermaid-sequence-number-bg-color)!important}", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  map,\n  of,\n  shareReplay,\n  tap\n} from \"rxjs\"\n\nimport { watchScript } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\nimport themeCSS from \"./index.css\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid diagram\n */\nexport interface Mermaid {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid instance observable\n */\nlet mermaid$: Observable<void>\n\n/**\n * Global sequence number for diagrams\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch Mermaid script\n *\n * @returns Mermaid scripts observable\n */\nfunction fetchScripts(): Observable<void> {\n  return typeof mermaid === \"undefined\" || mermaid instanceof Element\n    ? watchScript(\"https://unpkg.com/mermaid@10/dist/mermaid.min.js\")\n    : of(undefined)\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount Mermaid diagram\n *\n * @param el - Code block element\n *\n * @returns Mermaid diagram component observable\n */\nexport function mountMermaid(\n  el: HTMLElement\n): Observable<Component<Mermaid>> {\n  el.classList.remove(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n  mermaid$ ||= fetchScripts()\n    .pipe(\n      tap(() => mermaid.initialize({\n        startOnLoad: false,\n        themeCSS,\n        sequence: {\n          actorFontSize: \"16px\", // Hack: mitigate https://bit.ly/3y0NEi3\n          messageFontSize: \"16px\",\n          noteFontSize: \"16px\"\n        }\n      })),\n      map(() => undefined),\n      shareReplay(1)\n    )\n\n  /* Render diagram */\n  mermaid$.subscribe(async () => {\n    el.classList.add(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n    const id = `__mermaid_${sequence++}`\n\n    /* Create host element to replace code block */\n    const host = h(\"div\", { class: \"mermaid\" })\n    const text = el.textContent\n\n    /* Render and inject diagram */\n    const { svg, fn } = await mermaid.render(id, text)\n\n    /* Create a shadow root and inject diagram */\n    const shadow = host.attachShadow({ mode: \"closed\" })\n    shadow.innerHTML = svg\n\n    /* Replace code block with diagram and bind functions */\n    el.replaceWith(host)\n    fn?.(shadow)\n  })\n\n  /* Create and return component */\n  return mermaid$\n    .pipe(\n      map(() => ({ ref: el }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Observable, of } from \"rxjs\"\n\nimport { renderTable } from \"~/templates\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Data table\n */\nexport interface DataTable {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Sentinel for replacement\n */\nconst sentinel = h(\"table\")\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount data table\n *\n * This function wraps a data table in another scrollable container, so it can\n * be smoothly scrolled on smaller screen sizes and won't break the layout.\n *\n * @param el - Data table element\n *\n * @returns Data table component observable\n */\nexport function mountDataTable(\n  el: HTMLElement\n): Observable<Component<DataTable>> {\n  el.replaceWith(sentinel)\n  sentinel.replaceWith(renderTable(el))\n\n  /* Create and return component */\n  return of({ ref: el })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  asyncScheduler,\n  auditTime,\n  combineLatest,\n  defer,\n  endWith,\n  filter,\n  finalize,\n  fromEvent,\n  ignoreElements,\n  map,\n  merge,\n  skip,\n  startWith,\n  subscribeOn,\n  takeUntil,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  getElement,\n  getElementContentOffset,\n  getElementContentSize,\n  getElementOffset,\n  getElementSize,\n  getElements,\n  watchElementContentOffset,\n  watchElementSize,\n  watchElementVisibility\n} from \"~/browser\"\nimport { renderTabbedControl } from \"~/templates\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Content tabs\n */\nexport interface ContentTabs {\n  active: HTMLLabelElement             /* Active tab label */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch content tabs\n *\n * @param inputs - Content tabs input elements\n *\n * @returns Content tabs observable\n */\nexport function watchContentTabs(\n  inputs: HTMLInputElement[]\n): Observable<ContentTabs> {\n  const initial = inputs.find(input => input.checked) || inputs[0]\n  return merge(...inputs.map(input => fromEvent(input, \"change\")\n    .pipe(\n      map(() => getElement<HTMLLabelElement>(`label[for=\"${input.id}\"]`))\n    )\n  ))\n    .pipe(\n      startWith(getElement<HTMLLabelElement>(`label[for=\"${initial.id}\"]`)),\n      map(active => ({ active }))\n    )\n}\n\n/**\n * Mount content tabs\n *\n * @param el - Content tabs element\n * @param options - Options\n *\n * @returns Content tabs component observable\n */\nexport function mountContentTabs(\n  el: HTMLElement, { viewport$, target$ }: MountOptions\n): Observable<Component<ContentTabs>> {\n  const container = getElement(\".tabbed-labels\", el)\n  const inputs = getElements<HTMLInputElement>(\":scope > input\", el)\n\n  /* Render content tab previous button for pagination */\n  const prev = renderTabbedControl(\"prev\")\n  el.append(prev)\n\n  /* Render content tab next button for pagination */\n  const next = renderTabbedControl(\"next\")\n  el.append(next)\n\n  /* Mount component on subscription */\n  return defer(() => {\n    const push$ = new Subject<ContentTabs>()\n    const done$ = push$.pipe(ignoreElements(), endWith(true))\n    combineLatest([push$, watchElementSize(el), watchElementVisibility(el)])\n      .pipe(\n        takeUntil(done$),\n        auditTime(1, animationFrameScheduler)\n      )\n        .subscribe({\n\n          /* Handle emission */\n          next([{ active }, size]) {\n            const offset = getElementOffset(active)\n            const { width } = getElementSize(active)\n\n            /* Set tab indicator offset and width */\n            el.style.setProperty(\"--md-indicator-x\", `${offset.x}px`)\n            el.style.setProperty(\"--md-indicator-width\", `${width}px`)\n\n            /* Scroll container to active content tab */\n            const content = getElementContentOffset(container)\n            if (\n              offset.x         < content.x              ||\n              offset.x + width > content.x + size.width\n            )\n              container.scrollTo({\n                left: Math.max(0, offset.x - 16),\n                behavior: \"smooth\"\n              })\n          },\n\n          /* Handle complete */\n          complete() {\n            el.style.removeProperty(\"--md-indicator-x\")\n            el.style.removeProperty(\"--md-indicator-width\")\n          }\n        })\n\n    /* Hide content tab buttons on borders */\n    combineLatest([\n      watchElementContentOffset(container),\n      watchElementSize(container)\n    ])\n      .pipe(\n        takeUntil(done$)\n      )\n        .subscribe(([offset, size]) => {\n          const content = getElementContentSize(container)\n          prev.hidden = offset.x < 16\n          next.hidden = offset.x > content.width - size.width - 16\n        })\n\n    /* Paginate content tab container on click */\n    merge(\n      fromEvent(prev, \"click\").pipe(map(() => -1)),\n      fromEvent(next, \"click\").pipe(map(() => +1))\n    )\n      .pipe(\n        takeUntil(done$)\n      )\n        .subscribe(direction => {\n          const { width } = getElementSize(container)\n          container.scrollBy({\n            left: width * direction,\n            behavior: \"smooth\"\n          })\n        })\n\n    /* Switch to content tab target */\n    target$\n      .pipe(\n        takeUntil(done$),\n        filter(input => inputs.includes(input as HTMLInputElement))\n      )\n        .subscribe(input => input.click())\n\n    /* Add link to each content tab label */\n    container.classList.add(\"tabbed-labels--linked\")\n    for (const input of inputs) {\n      const label = getElement<HTMLLabelElement>(`label[for=\"${input.id}\"]`)\n      label.replaceChildren(h(\"a\", {\n        href: `#${label.htmlFor}`,\n        tabIndex: -1\n      }, ...Array.from(label.childNodes)))\n\n      /* Allow to copy link without scrolling to anchor */\n      fromEvent<MouseEvent>(label.firstElementChild!, \"click\")\n        .pipe(\n          takeUntil(done$),\n          filter(ev => !(ev.metaKey || ev.ctrlKey)),\n          tap(ev => {\n            ev.preventDefault()\n            ev.stopPropagation()\n          })\n        )\n          // @todo we might need to remove the anchor link on complete\n          .subscribe(() => {\n            history.replaceState({}, \"\", `#${label.htmlFor}`)\n            label.click()\n          })\n    }\n\n    /* Set up linking of content tabs, if enabled */\n    if (feature(\"content.tabs.link\"))\n      push$.pipe(\n        skip(1),\n        withLatestFrom(viewport$)\n      )\n        .subscribe(([{ active }, { offset }]) => {\n          const tab = active.innerText.trim()\n          if (active.hasAttribute(\"data-md-switching\")) {\n            active.removeAttribute(\"data-md-switching\")\n\n          /* Determine viewport offset of active tab */\n          } else {\n            const y = el.offsetTop - offset.y\n\n            /* Passively activate other tabs */\n            for (const set of getElements(\"[data-tabs]\"))\n              for (const input of getElements<HTMLInputElement>(\n                \":scope > input\", set\n              )) {\n                const label = getElement(`label[for=\"${input.id}\"]`)\n                if (\n                  label !== active &&\n                  label.innerText.trim() === tab\n                ) {\n                  label.setAttribute(\"data-md-switching\", \"\")\n                  input.click()\n                  break\n                }\n              }\n\n            /* Bring active tab into view */\n            window.scrollTo({\n              top: el.offsetTop - y\n            })\n\n            /* Persist active tabs in local storage */\n            const tabs = __md_get<string[]>(\"__tabs\") || []\n            __md_set(\"__tabs\", [...new Set([tab, ...tabs])])\n          }\n        })\n\n    /* Pause media (audio, video) on switch - see https://bit.ly/3Bk6cel */\n    push$.pipe(takeUntil(done$))\n      .subscribe(() => {\n        for (const media of getElements<HTMLAudioElement>(\"audio, video\", el))\n          media.pause()\n      })\n\n    /* Create and return component */\n    return watchContentTabs(inputs)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n    .pipe(\n      subscribeOn(asyncScheduler)\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Observable, merge } from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport { Viewport, getElements } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport {\n  Tooltip,\n  mountInlineTooltip2\n} from \"../../tooltip2\"\nimport {\n  Annotation,\n  mountAnnotationBlock\n} from \"../annotation\"\nimport {\n  CodeBlock,\n  mountCodeBlock\n} from \"../code\"\nimport {\n  Details,\n  mountDetails\n} from \"../details\"\nimport {\n  Mermaid,\n  mountMermaid\n} from \"../mermaid\"\nimport {\n  DataTable,\n  mountDataTable\n} from \"../table\"\nimport {\n  ContentTabs,\n  mountContentTabs\n} from \"../tabs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Content\n */\nexport type Content =\n  | Annotation\n  | CodeBlock\n  | ContentTabs\n  | DataTable\n  | Details\n  | Mermaid\n  | Tooltip\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount content\n *\n * This function mounts all components that are found in the content of the\n * actual article, including code blocks, data tables and details.\n *\n * @param el - Content element\n * @param options - Options\n *\n * @returns Content component observable\n */\nexport function mountContent(\n  el: HTMLElement, { viewport$, target$, print$ }: MountOptions\n): Observable<Component<Content>> {\n  return merge(\n\n    /* Annotations */\n    ...getElements(\".annotate:not(.highlight)\", el)\n      .map(child => mountAnnotationBlock(child, { target$, print$ })),\n\n    /* Code blocks */\n    ...getElements(\"pre:not(.mermaid) > code\", el)\n      .map(child => mountCodeBlock(child, { target$, print$ })),\n\n    /* Mermaid diagrams */\n    ...getElements(\"pre.mermaid\", el)\n      .map(child => mountMermaid(child)),\n\n    /* Data tables */\n    ...getElements(\"table:not([class])\", el)\n      .map(child => mountDataTable(child)),\n\n    /* Details */\n    ...getElements(\"details\", el)\n      .map(child => mountDetails(child, { target$, print$ })),\n\n    /* Content tabs */\n    ...getElements(\"[data-tabs]\", el)\n      .map(child => mountContentTabs(child, { viewport$, target$ })),\n\n    /* Tooltips */\n    ...getElements(\"[title]\", el)\n      .filter(() => feature(\"content.tooltips\"))\n      .map(child => mountInlineTooltip2(child, { viewport$ }))\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  defer,\n  delay,\n  finalize,\n  map,\n  merge,\n  of,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport { getElement } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Dialog\n */\nexport interface Dialog {\n  message: string                      /* Dialog message */\n  active: boolean                      /* Dialog is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  alert$: Subject<string>              /* Alert subject */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  alert$: Subject<string>              /* Alert subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch dialog\n *\n * @param _el - Dialog element\n * @param options - Options\n *\n * @returns Dialog observable\n */\nexport function watchDialog(\n  _el: HTMLElement, { alert$ }: WatchOptions\n): Observable<Dialog> {\n  return alert$\n    .pipe(\n      switchMap(message => merge(\n        of(true),\n        of(false).pipe(delay(2000))\n      )\n        .pipe(\n          map(active => ({ message, active }))\n        )\n      )\n    )\n}\n\n/**\n * Mount dialog\n *\n * This function reveals the dialog in the right corner when a new alert is\n * emitted through the subject that is passed as part of the options.\n *\n * @param el - Dialog element\n * @param options - Options\n *\n * @returns Dialog component observable\n */\nexport function mountDialog(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<Dialog>> {\n  const inner = getElement(\".md-typeset\", el)\n  return defer(() => {\n    const push$ = new Subject<Dialog>()\n    push$.subscribe(({ message, active }) => {\n      el.classList.toggle(\"md-dialog--active\", active)\n      inner.textContent = message\n    })\n\n    /* Create and return component */\n    return watchDialog(el, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  asyncScheduler,\n  auditTime,\n  combineLatest,\n  debounceTime,\n  defer,\n  distinctUntilChanged,\n  filter,\n  finalize,\n  map,\n  merge,\n  of,\n  subscribeOn,\n  tap,\n  throttleTime\n} from \"rxjs\"\n\nimport {\n  ElementOffset,\n  getElement,\n  getElementContainer,\n  getElementOffset,\n  getElementSize,\n  watchElementContentOffset,\n  watchElementFocus,\n  watchElementHover\n} from \"~/browser\"\nimport { renderTooltip } from \"~/templates\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Tooltip\n */\nexport interface Tooltip {\n  active: boolean                      /* Tooltip is active */\n  offset: ElementOffset                /* Tooltip offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Global sequence number for tooltips\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch tooltip\n *\n * This function will append the tooltip temporarily to compute its width,\n * which is necessary for correct centering, and then removing it again.\n *\n * @param el - Tooltip element\n * @param host - Host element\n *\n * @returns Tooltip observable\n */\nexport function watchTooltip(\n  el: HTMLElement, host: HTMLElement\n): Observable<Tooltip> {\n  document.body.append(el)\n\n  /* Compute width and remove tooltip immediately */\n  const { width } = getElementSize(el)\n  el.style.setProperty(\"--md-tooltip-width\", `${width}px`)\n  el.remove()\n\n  /* Retrieve and watch containing element */\n  const container = getElementContainer(host)\n  const scroll$ =\n    typeof container !== \"undefined\"\n      ? watchElementContentOffset(container)\n      : of({ x: 0, y: 0 })\n\n  /* Compute tooltip visibility */\n  const active$ = merge(\n    watchElementFocus(host),\n    watchElementHover(host)\n  )\n    .pipe(\n      distinctUntilChanged()\n    )\n\n  /* Compute tooltip offset */\n  return combineLatest([active$, scroll$])\n    .pipe(\n      map(([active, scroll]) => {\n        let { x, y } = getElementOffset(host)\n        const size = getElementSize(host)\n\n        /**\n         * Experimental: fix handling of tables - see https://bit.ly/3TQEj5O\n         *\n         * If this proves to be a viable fix, we should refactor tooltip\n         * positioning and somehow streamline the current process. This might\n         * also fix positioning for annotations inside tables, which is another\n         * limitation.\n         */\n        const table = host.closest(\"table\")\n        if (table && host.parentElement) {\n          x += table.offsetLeft + host.parentElement.offsetLeft\n          y += table.offsetTop  + host.parentElement.offsetTop\n        }\n        return {\n          active,\n          offset: {\n            x: x - scroll.x + size.width  / 2 - width / 2,\n            y: y - scroll.y + size.height + 8\n          }\n        }\n      })\n    )\n}\n\n/**\n * Mount tooltip\n *\n * @param el - Host element\n *\n * @returns Tooltip component observable\n */\nexport function mountTooltip(\n  el: HTMLElement\n): Observable<Component<Tooltip>> {\n  const title = el.title\n  if (!title.length)\n    return EMPTY\n\n  /* Render tooltip and set title from host element */\n  const id = `__tooltip_${sequence++}`\n  const tooltip = renderTooltip(id, \"inline\")\n  const typeset = getElement(\".md-typeset\", tooltip)\n  typeset.innerHTML = title\n\n  /* Mount component on subscription */\n  return defer(() => {\n    const push$ = new Subject<Tooltip>()\n    push$.subscribe({\n\n      /* Handle emission */\n      next({ offset }) {\n        tooltip.style.setProperty(\"--md-tooltip-x\", `${offset.x}px`)\n        tooltip.style.setProperty(\"--md-tooltip-y\", `${offset.y}px`)\n      },\n\n      /* Handle complete */\n      complete() {\n        tooltip.style.removeProperty(\"--md-tooltip-x\")\n        tooltip.style.removeProperty(\"--md-tooltip-y\")\n      }\n    })\n\n    /* Toggle tooltip presence to mitigate empty lines when copying */\n    merge(\n      push$.pipe(filter(({ active }) => active)),\n      push$.pipe(debounceTime(250), filter(({ active }) => !active))\n    )\n      .subscribe({\n\n        /* Handle emission */\n        next({ active }) {\n          if (active) {\n            el.insertAdjacentElement(\"afterend\", tooltip)\n            el.setAttribute(\"aria-describedby\", id)\n            el.removeAttribute(\"title\")\n          } else {\n            tooltip.remove()\n            el.removeAttribute(\"aria-describedby\")\n            el.setAttribute(\"title\", title)\n          }\n        },\n\n        /* Handle complete */\n        complete() {\n          tooltip.remove()\n          el.removeAttribute(\"aria-describedby\")\n          el.setAttribute(\"title\", title)\n        }\n      })\n\n    /* Toggle tooltip visibility */\n    push$\n      .pipe(\n        auditTime(16, animationFrameScheduler)\n      )\n        .subscribe(({ active }) => {\n          tooltip.classList.toggle(\"md-tooltip--active\", active)\n        })\n\n    // @todo - refactor positioning together with annotations \u2013 there are\n    // several things that overlap and are identical in handling\n\n    /* Track relative origin of tooltip */\n    push$\n      .pipe(\n        throttleTime(125, animationFrameScheduler),\n        filter(() => !!el.offsetParent),\n        map(() => el.offsetParent!.getBoundingClientRect()),\n        map(({ x }) => x)\n      )\n      .subscribe({\n\n        /* Handle emission */\n        next(origin) {\n          if (origin)\n            tooltip.style.setProperty(\"--md-tooltip-0\", `${-origin}px`)\n          else\n            tooltip.style.removeProperty(\"--md-tooltip-0\")\n        },\n\n        /* Handle complete */\n        complete() {\n          tooltip.style.removeProperty(\"--md-tooltip-0\")\n        }\n      })\n\n    /* Create and return component */\n    return watchTooltip(tooltip, el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n    .pipe(\n      subscribeOn(asyncScheduler)\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  bufferCount,\n  combineLatest,\n  combineLatestWith,\n  defer,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  endWith,\n  filter,\n  from,\n  ignoreElements,\n  map,\n  mergeMap,\n  mergeWith,\n  of,\n  shareReplay,\n  startWith,\n  switchMap,\n  takeUntil\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  getElements,\n  watchElementSize,\n  watchToggle\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Main } from \"../../main\"\nimport {\n  Tooltip,\n  mountTooltip\n} from \"../../tooltip\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Header\n */\nexport interface Header {\n  height: number                       /* Header visible height */\n  hidden: boolean                      /* Header is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  main$: Observable<Main>              /* Main area observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Compute whether the header is hidden\n *\n * If the user scrolls past a certain threshold, the header can be hidden when\n * scrolling down, and shown when scrolling up.\n *\n * @param options - Options\n *\n * @returns Toggle observable\n */\nfunction isHidden({ viewport$ }: WatchOptions): Observable<boolean> {\n  if (!feature(\"header.autohide\"))\n    return of(false)\n\n  /* Compute direction and turning point */\n  const direction$ = viewport$\n    .pipe(\n      map(({ offset: { y } }) => y),\n      bufferCount(2, 1),\n      map(([a, b]) => [a < b, b] as const),\n      distinctUntilKeyChanged(0)\n    )\n\n  /* Compute whether header should be hidden */\n  const hidden$ = combineLatest([viewport$, direction$])\n    .pipe(\n      filter(([{ offset }, [, y]]) => Math.abs(y - offset.y) > 100),\n      map(([, [direction]]) => direction),\n      distinctUntilChanged()\n    )\n\n  /* Compute threshold for hiding */\n  const search$ = watchToggle(\"search\")\n  return combineLatest([viewport$, search$])\n    .pipe(\n      map(([{ offset }, search]) => offset.y > 400 && !search),\n      distinctUntilChanged(),\n      switchMap(active => active ? hidden$ : of(false)),\n      startWith(false)\n    )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch header\n *\n * @param el - Header element\n * @param options - Options\n *\n * @returns Header observable\n */\nexport function watchHeader(\n  el: HTMLElement, options: WatchOptions\n): Observable<Header> {\n  return defer(() => combineLatest([\n    watchElementSize(el),\n    isHidden(options)\n  ]))\n    .pipe(\n      map(([{ height }, hidden]) => ({\n        height,\n        hidden\n      })),\n      distinctUntilChanged((a, b) => (\n        a.height === b.height &&\n        a.hidden === b.hidden\n      )),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount header\n *\n * This function manages the different states of the header, i.e. whether it's\n * hidden or rendered with a shadow. This depends heavily on the main area.\n *\n * @param el - Header element\n * @param options - Options\n *\n * @returns Header component observable\n */\nexport function mountHeader(\n  el: HTMLElement, { header$, main$ }: MountOptions\n): Observable<Component<Header | Tooltip>> {\n  return defer(() => {\n    const push$ = new Subject<Main>()\n    const done$ = push$.pipe(ignoreElements(), endWith(true))\n    push$\n      .pipe(\n        distinctUntilKeyChanged(\"active\"),\n        combineLatestWith(header$)\n      )\n        .subscribe(([{ active }, { hidden }]) => {\n          el.classList.toggle(\"md-header--shadow\", active && !hidden)\n          el.hidden = hidden\n        })\n\n    /* Mount tooltips, if enabled */\n    const tooltips = from(getElements(\"[title]\", el))\n      .pipe(\n        filter(() => feature(\"content.tooltips\")),\n        mergeMap(child => mountTooltip(child))\n      )\n\n    /* Link to main area */\n    main$.subscribe(push$)\n\n    /* Create and return component */\n    return header$\n      .pipe(\n        takeUntil(done$),\n        map(state => ({ ref: el, ...state })),\n        mergeWith(tooltips.pipe(takeUntil(done$)))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  distinctUntilKeyChanged,\n  finalize,\n  map,\n  tap\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  getElementSize,\n  getOptionalElement,\n  watchViewportAt\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Header } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Header\n */\nexport interface HeaderTitle {\n  active: boolean                      /* Header title is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch header title\n *\n * @param el - Heading element\n * @param options - Options\n *\n * @returns Header title observable\n */\nexport function watchHeaderTitle(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<HeaderTitle> {\n  return watchViewportAt(el, { viewport$, header$ })\n    .pipe(\n      map(({ offset: { y } }) => {\n        const { height } = getElementSize(el)\n        return {\n          active: y >= height\n        }\n      }),\n      distinctUntilKeyChanged(\"active\")\n    )\n}\n\n/**\n * Mount header title\n *\n * This function swaps the header title from the site title to the title of the\n * current page when the user scrolls past the first headline.\n *\n * @param el - Header title element\n * @param options - Options\n *\n * @returns Header title component observable\n */\nexport function mountHeaderTitle(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<HeaderTitle>> {\n  return defer(() => {\n    const push$ = new Subject<HeaderTitle>()\n    push$.subscribe({\n\n      /* Handle emission */\n      next({ active }) {\n        el.classList.toggle(\"md-header__title--active\", active)\n      },\n\n      /* Handle complete */\n      complete() {\n        el.classList.remove(\"md-header__title--active\")\n      }\n    })\n\n    /* Obtain headline, if any */\n    const heading = getOptionalElement(\".md-content h1\")\n    if (typeof heading === \"undefined\")\n      return EMPTY\n\n    /* Create and return component */\n    return watchHeaderTitle(heading, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  map,\n  switchMap\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  watchElementSize\n} from \"~/browser\"\n\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Main area\n */\nexport interface Main {\n  offset: number                       /* Main area top offset */\n  height: number                       /* Main area visible height */\n  active: boolean                      /* Main area is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch main area\n *\n * This function returns an observable that computes the visual parameters of\n * the main area which depends on the viewport vertical offset and height, as\n * well as the height of the header element, if the header is fixed.\n *\n * @param el - Main area element\n * @param options - Options\n *\n * @returns Main area observable\n */\nexport function watchMain(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<Main> {\n\n  /* Compute necessary adjustment for header */\n  const adjust$ = header$\n    .pipe(\n      map(({ height }) => height),\n      distinctUntilChanged()\n    )\n\n  /* Compute the main area's top and bottom borders */\n  const border$ = adjust$\n    .pipe(\n      switchMap(() => watchElementSize(el)\n        .pipe(\n          map(({ height }) => ({\n            top:    el.offsetTop,\n            bottom: el.offsetTop + height\n          })),\n          distinctUntilKeyChanged(\"bottom\")\n        )\n      )\n    )\n\n  /* Compute the main area's offset, visible height and if we scrolled past */\n  return combineLatest([adjust$, border$, viewport$])\n    .pipe(\n      map(([header, { top, bottom }, { offset: { y }, size: { height } }]) => {\n        height = Math.max(0, height\n          - Math.max(0, top    - y,  header)\n          - Math.max(0, height + y - bottom)\n        )\n        return {\n          offset: top - header,\n          height,\n          active: top - header <= y\n        }\n      }),\n      distinctUntilChanged((a, b) => (\n        a.offset === b.offset &&\n        a.height === b.height &&\n        a.active === b.active\n      ))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  asyncScheduler,\n  defer,\n  filter,\n  finalize,\n  fromEvent,\n  map,\n  mergeMap,\n  observeOn,\n  of,\n  repeat,\n  shareReplay,\n  skip,\n  startWith,\n  takeUntil,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { getElements, watchMedia } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\nimport {\n  Component,\n  getComponentElement\n} from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Palette colors\n */\nexport interface PaletteColor {\n  media?: string                       /* Media query */\n  scheme?: string                      /* Color scheme */\n  primary?: string                     /* Primary color */\n  accent?: string                      /* Accent color */\n}\n\n/**\n * Palette\n */\nexport interface Palette {\n  index: number                        /* Palette index */\n  color: PaletteColor                  /* Palette colors */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch color palette\n *\n * @param inputs - Color palette element\n *\n * @returns Color palette observable\n */\nexport function watchPalette(\n  inputs: HTMLInputElement[]\n): Observable<Palette> {\n  const current = __md_get<Palette>(\"__palette\") || {\n    index: inputs.findIndex(input => matchMedia(\n      input.getAttribute(\"data-md-color-media\")!\n    ).matches)\n  }\n\n  /* Emit changes in color palette */\n  const index = Math.max(0, Math.min(current.index, inputs.length - 1))\n  return of(...inputs)\n    .pipe(\n      mergeMap(input => fromEvent(input, \"change\").pipe(map(() => input))),\n      startWith(inputs[index]),\n      map(input => ({\n        index: inputs.indexOf(input),\n        color: {\n          media:   input.getAttribute(\"data-md-color-media\"),\n          scheme:  input.getAttribute(\"data-md-color-scheme\"),\n          primary: input.getAttribute(\"data-md-color-primary\"),\n          accent:  input.getAttribute(\"data-md-color-accent\")\n        }\n      } as Palette)),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount color palette\n *\n * @param el - Color palette element\n *\n * @returns Color palette component observable\n */\nexport function mountPalette(\n  el: HTMLElement\n): Observable<Component<Palette>> {\n  const inputs = getElements<HTMLInputElement>(\"input\", el)\n  const meta = h(\"meta\", { name: \"theme-color\" })\n  document.head.appendChild(meta)\n\n  // Add color scheme meta tag\n  const scheme = h(\"meta\", { name: \"color-scheme\" })\n  document.head.appendChild(scheme)\n\n  /* Mount component on subscription */\n  const media$ = watchMedia(\"(prefers-color-scheme: light)\")\n  return defer(() => {\n    const push$ = new Subject<Palette>()\n    push$.subscribe(palette => {\n      document.body.setAttribute(\"data-md-color-switching\", \"\")\n\n      /* Retrieve color palette for system preference */\n      if (palette.color.media === \"(prefers-color-scheme)\") {\n        const media = matchMedia(\"(prefers-color-scheme: light)\")\n        const input = document.querySelector(media.matches\n          ? \"[data-md-color-media='(prefers-color-scheme: light)']\"\n          : \"[data-md-color-media='(prefers-color-scheme: dark)']\"\n        )!\n\n        /* Retrieve colors for system preference */\n        palette.color.scheme  = input.getAttribute(\"data-md-color-scheme\")!\n        palette.color.primary = input.getAttribute(\"data-md-color-primary\")!\n        palette.color.accent  = input.getAttribute(\"data-md-color-accent\")!\n      }\n\n      /* Set color palette */\n      for (const [key, value] of Object.entries(palette.color))\n        document.body.setAttribute(`data-md-color-${key}`, value)\n\n      /* Set toggle visibility */\n      for (let index = 0; index < inputs.length; index++) {\n        const label = inputs[index].nextElementSibling\n        if (label instanceof HTMLElement)\n          label.hidden = palette.index !== index\n      }\n\n      /* Persist preference in local storage */\n      __md_set(\"__palette\", palette)\n    })\n\n    // Handle color switch on Enter or Space - see https://t.ly/YIhVj\n    fromEvent<KeyboardEvent>(el, \"keydown\").pipe(\n      filter(ev => ev.key === \"Enter\"),\n      withLatestFrom(push$, (_, palette) => palette)\n    )\n      .subscribe(({ index }) => {\n        index = (index + 1) % inputs.length\n        inputs[index].click()\n        inputs[index].focus()\n      })\n\n    /* Update theme-color meta tag */\n    push$\n      .pipe(\n        map(() => {\n          const header = getComponentElement(\"header\")\n          const style  = window.getComputedStyle(header)\n\n          // Set color scheme\n          scheme.content = style.colorScheme\n\n          /* Return color in hexadecimal format */\n          return style.backgroundColor.match(/\\d+/g)!\n            .map(value => (+value).toString(16).padStart(2, \"0\"))\n            .join(\"\")\n        })\n      )\n        .subscribe(color => meta.content = `#${color}`)\n\n    /* Revert transition durations after color switch */\n    push$.pipe(observeOn(asyncScheduler))\n      .subscribe(() => {\n        document.body.removeAttribute(\"data-md-color-switching\")\n      })\n\n    /* Create and return component */\n    return watchPalette(inputs)\n      .pipe(\n        takeUntil(media$.pipe(skip(1))),\n        repeat(),\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  defer,\n  finalize,\n  map,\n  tap\n} from \"rxjs\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Progress indicator\n */\nexport interface Progress {\n  value: number                        // Progress value\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  progress$: Subject<number>           // Progress subject\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount progress indicator\n *\n * @param el - Progress indicator element\n * @param options - Options\n *\n * @returns Progress indicator component observable\n */\nexport function mountProgress(\n  el: HTMLElement, { progress$ }: MountOptions\n): Observable<Component<Progress>> {\n\n  // Mount component on subscription\n  return defer(() => {\n    const push$ = new Subject<Progress>()\n    push$.subscribe(({ value }) => {\n      el.style.setProperty(\"--md-progress-value\", `${value}`)\n    })\n\n    // Create and return component\n    return progress$\n      .pipe(\n        tap(value => push$.next({ value })),\n        finalize(() => push$.complete()),\n        map(value => ({ ref: el, value }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n  Observable,\n  Subject,\n  map,\n  tap\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport { getElement } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n  alert$: Subject<string>              /* Alert subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Extract text to copy\n *\n * @param el - HTML element\n *\n * @returns Extracted text\n */\nfunction extract(el: HTMLElement): string {\n  el.setAttribute(\"data-md-copying\", \"\")\n  const copy = el.closest(\"[data-copy]\")\n  const text = copy\n    ? copy.getAttribute(\"data-copy\")!\n    : el.innerText\n  el.removeAttribute(\"data-md-copying\")\n  return text.trimEnd()\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up Clipboard.js integration\n *\n * @param options - Options\n */\nexport function setupClipboardJS(\n  { alert$ }: SetupOptions\n): void {\n  if (ClipboardJS.isSupported()) {\n    new Observable<ClipboardJS.Event>(subscriber => {\n      new ClipboardJS(\"[data-clipboard-target], [data-clipboard-text]\", {\n        text: el => (\n          el.getAttribute(\"data-clipboard-text\")! ||\n          extract(getElement(\n            el.getAttribute(\"data-clipboard-target\")!\n          ))\n        )\n      })\n        .on(\"success\", ev => subscriber.next(ev))\n    })\n      .pipe(\n        tap(ev => {\n          const trigger = ev.trigger as HTMLElement\n          trigger.focus()\n        }),\n        map(() => translation(\"clipboard.copied\"))\n      )\n        .subscribe(alert$)\n  }\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  catchError,\n  map,\n  of\n} from \"rxjs\"\n\nimport {\n  getElement,\n  getElements,\n  requestXML\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sitemap, i.e. a list of URLs\n */\nexport type Sitemap = Map<string, URL[]>\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Resolve URL to the given base URL\n *\n * When serving the site with instant navigation, MkDocs will set the hostname\n * to the value as specified in `dev_addr`, but the browser allows for several\n * hostnames to be used: `localhost`, `127.0.0.1` or even `0.0.0.0`, depending\n * on configuration. This function resolves the URL to the given hostname.\n *\n * @param url - URL\n * @param base - Base URL\n *\n * @returns Resolved URL\n */\nfunction resolve(url: URL, base: URL) {\n  url.protocol = base.protocol\n  url.hostname = base.hostname\n  return url\n}\n\n/**\n * Extract sitemap from document\n *\n * This function extracts the URLs and alternate links from the document, and\n * associates alternate links to the original URL as found in `loc`, allowing\n * the browser to navigate to the correct page when switching languages. The\n * format of the sitemap is expected to adhere to:\n *\n * ``` xml\n * <urlset>\n *   <url>\n *     <loc>...</loc>\n *     <xhtml:link rel=\"alternate\" hreflang=\"en\" href=\"...\"/>\n *     <xhtml:link rel=\"alternate\" hreflang=\"de\" href=\"...\"/>\n *     ...\n *   </url>\n *   ...\n * </urlset>\n * ```\n *\n * @param document - Document\n * @param base - Base URL\n *\n * @returns Sitemap\n */\nfunction extract(document: Document, base: URL): Sitemap {\n  const sitemap: Sitemap = new Map()\n  for (const el of getElements(\"url\", document)) {\n    const url = getElement(\"loc\", el)\n\n    // Create entry for location and add it to the list of links\n    const links = [resolve(new URL(url.textContent!), base)]\n    sitemap.set(`${links[0]}`, links)\n\n    // Attach alternate links to current entry\n    for (const link of getElements(\"[rel=alternate]\", el)) {\n      const href = link.getAttribute(\"href\")\n      if (href != null)\n        links.push(resolve(new URL(href), base))\n    }\n  }\n\n  // Return sitemap\n  return sitemap\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the sitemap for the given base URL\n *\n * If a network or parsing error occurs, we just default to an empty sitemap,\n * which means the caller should fall back to regular navigation.\n *\n * @param base - Base URL\n *\n * @returns Sitemap observable\n */\nexport function fetchSitemap(base: URL | string): Observable<Sitemap> {\n  return requestXML(new URL(\"sitemap.xml\", base))\n    .pipe(\n      map(document => extract(document, new URL(base))),\n      catchError(() => of(new Map())),\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  catchError,\n  combineLatestWith,\n  concat,\n  debounceTime,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  endWith,\n  fromEvent,\n  ignoreElements,\n  map,\n  merge,\n  of,\n  share,\n  switchMap,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { configuration, feature } from \"~/_\"\nimport {\n  Viewport,\n  getElements,\n  getLocation,\n  getOptionalElement,\n  requestHTML,\n  setLocation,\n  setLocationHash\n} from \"~/browser\"\nimport { getComponentElement } from \"~/components\"\n\nimport { Sitemap, fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n  location$: Subject<URL>              // Location subject\n  viewport$: Observable<Viewport>      // Viewport observable\n  progress$: Subject<number>           // Progress subject\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Handle clicks on internal URLs while skipping external URLs\n *\n * @param ev - Mouse event\n * @param sitemap - Sitemap\n *\n * @returns URL observable\n */\nfunction handle(\n  ev: MouseEvent, sitemap: Sitemap\n): Observable<URL> {\n  if (!(ev.target instanceof Element))\n    return EMPTY\n\n  // Skip, as target is not within a link - clicks on non-link elements are\n  // also captured, which we need to exclude from processing\n  const el = ev.target.closest(\"a\")\n  if (el === null)\n    return EMPTY\n\n  // Skip, as link opens in new window - we now know we have captured a click\n  // on a link, but the link either has a `target` property defined, or the\n  // user pressed the `meta` or `ctrl` key to open it in a new window. Thus,\n  // we need to filter this event as well.\n  if (el.target || ev.metaKey || ev.ctrlKey)\n    return EMPTY\n\n  // Next, we must check if the URL is relevant for us, i.e., if it's an\n  // internal link to a page that is managed by MkDocs. Only then we can be\n  // sure that the structure of the page to be loaded adheres to the current\n  // document structure and can subsequently be injected into it without doing\n  // a full reload. For this reason, we must canonicalize the URL by removing\n  // all search parameters and hash fragments.\n  const url = new URL(el.href)\n  url.search = url.hash = \"\"\n\n  // Skip, if URL is not included in the sitemap - this could be the case when\n  // linking between versions or languages, or to another page that the author\n  // included as part of the build, but that is not managed by MkDocs. In that\n  // case we must not continue with instant navigation.\n  if (!sitemap.has(`${url}`))\n    return EMPTY\n\n  // We now know that we have a link to an internal page, so we prevent the\n  // browser from navigation and emit the URL for instant navigation. Note that\n  // this also includes anchor links, which means we need to implement anchor\n  // positioning ourselves. The reason for this is that if we wouldn't manage\n  // anchor links as well, scroll restoration will not work correctly (e.g.\n  // following an anchor link and scrolling).\n  ev.preventDefault()\n  return of(new URL(el.href))\n}\n\n/**\n * Create a map of head elements for lookup and replacement\n *\n * @param document - Document\n *\n * @returns Tag map\n */\nfunction head(document: Document): Map<string, HTMLElement> {\n  const tags = new Map<string, HTMLElement>()\n  for (const el of getElements(\":scope > *\", document.head))\n    tags.set(el.outerHTML, el)\n\n  // Return tag map\n  return tags\n}\n\n/**\n * Resolve relative URLs in the given document\n *\n * This function resolves relative `href` and `src` attributes, which can belong\n * to all sorts of tags, like meta tags, links, images, scripts and more.\n *\n * @param document - Document\n *\n * @returns Document observable\n */\nfunction resolve(document: Document): Observable<Document> {\n  for (const el of getElements(\"[href], [src]\", document))\n    for (const key of [\"href\", \"src\"]) {\n      const value = el.getAttribute(key)\n      if (value && !/^(?:[a-z]+:)?\\/\\//i.test(value)) {\n        // @ts-expect-error - trick: self-assign to resolve URL\n        el[key] = el[key]\n        break\n      }\n    }\n\n  // Return document observable\n  return of(document)\n}\n\n/**\n * Inject the contents of a document into the current one\n *\n * @param next - Next document\n *\n * @returns Document observable\n */\nfunction inject(next: Document): Observable<Document> {\n  for (const selector of [\n    \"[data-md-component=announce]\",\n    \"[data-md-component=container]\",\n    \"[data-md-component=header-topic]\",\n    \"[data-md-component=outdated]\",\n    \"[data-md-component=logo]\",\n    \"[data-md-component=skip]\",\n    ...feature(\"navigation.tabs.sticky\")\n      ? [\"[data-md-component=tabs]\"]\n      : []\n  ]) {\n    const source = getOptionalElement(selector)\n    const target = getOptionalElement(selector, next)\n    if (\n      typeof source !== \"undefined\" &&\n      typeof target !== \"undefined\"\n    ) {\n      source.replaceWith(target)\n    }\n  }\n\n  // Update meta tags\n  const tags = head(document)\n  for (const [html, el] of head(next))\n    if (tags.has(html))\n      tags.delete(html)\n    else\n      document.head.appendChild(el)\n\n  // Remove meta tags that are not present in the new document\n  for (const el of tags.values()) {\n    const name = el.getAttribute(\"name\")\n    // @todo - find a better way to handle attributes we add dynamically in\n    // other components without mounting components on every navigation, as\n    // this might impact overall performance - see https://t.ly/ehp_O\n    if (name !== \"theme-color\" && name !== \"color-scheme\")\n      el.remove()\n  }\n\n  // After components and meta tags were replaced, re-evaluate scripts\n  // that were provided by the author as part of Markdown files\n  const container = getComponentElement(\"container\")\n  return concat(getElements(\"script\", container))\n    .pipe(\n      switchMap(el => {\n        const script = next.createElement(\"script\")\n        if (el.src) {\n          for (const name of el.getAttributeNames())\n            script.setAttribute(name, el.getAttribute(name)!)\n          el.replaceWith(script)\n\n          // Complete when script is loaded\n          return new Observable(observer => {\n            script.onload = () => observer.complete()\n          })\n\n        // Complete immediately\n        } else {\n          script.textContent = el.textContent\n          el.replaceWith(script)\n          return EMPTY\n        }\n      }),\n      ignoreElements(),\n      endWith(document)\n    )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up instant navigation\n *\n * This is a heavily orchestrated operation - see inline comments to learn how\n * this works with Material for MkDocs, and how you can hook into it.\n *\n * @param options - Options\n *\n * @returns Document observable\n */\nexport function setupInstantNavigation(\n  { location$, viewport$, progress$ }: SetupOptions\n): Observable<Document> {\n  const config = configuration()\n  if (location.protocol === \"file:\")\n    return EMPTY\n\n  // Load sitemap immediately, so we have it available when the user initiates\n  // the first navigation request without any perceivable delay\n  const sitemap$ = fetchSitemap(config.base)\n\n  // Since we might be on a slow connection, the user might trigger multiple\n  // instant navigation events that overlap. MkDocs produces relative URLs for\n  // all internal links, which becomes a problem in this case, because we need\n  // to change the base URL the moment the user clicks a link that should be\n  // intercepted in order to be consistent with popstate, which means that the\n  // base URL would now be incorrect when resolving another relative link from\n  // the same site. For this reason we always resolve all relative links to\n  // absolute links, so we can be sure this never happens.\n  of(document)\n    .subscribe(resolve)\n\n  // --------------------------------------------------------------------------\n  // Navigation interception\n  // --------------------------------------------------------------------------\n\n  // Intercept navigation - to keep the number of event listeners down we use\n  // the fact that uncaptured events bubble up to the body. This has the nice\n  // property that we don't need to detach and then re-attach event listeners\n  // when the document is replaced after a navigation event.\n  const instant$ =\n    fromEvent<MouseEvent>(document.body, \"click\")\n      .pipe(\n        combineLatestWith(sitemap$),\n        switchMap(([ev, sitemap]) => handle(ev, sitemap)),\n        share()\n      )\n\n  // Intercept history change events, e.g. when the user uses the browser's\n  // back or forward buttons, and emit new location for fetching and parsing\n  const history$ =\n    fromEvent<PopStateEvent>(window, \"popstate\")\n      .pipe(\n        map(getLocation),\n        share()\n      )\n\n  // While it would be better UX to defer navigation events until the document\n  // is fully fetched and parsed, we must schedule it here to synchronize with\n  // popstate events, as they are emitted immediately. Moreover we need to\n  // store the current viewport offset for scroll restoration later on.\n  instant$.pipe(withLatestFrom(viewport$))\n    .subscribe(([url, { offset }]) => {\n      history.replaceState(offset, \"\")\n      history.pushState(null, \"\", url)\n    })\n\n  // Emit URLs that should be fetched via instant navigation on location subject\n  // which was passed into this function. The state of instant navigation can be\n  // intercepted by other parts of the application, which can synchronously back\n  // up or restore state before or after instant navigation happens.\n  merge(instant$, history$)\n    .subscribe(location$)\n\n  // --------------------------------------------------------------------------\n  // Fetching and parsing\n  // --------------------------------------------------------------------------\n\n  // Fetch document - we deduplicate requests to the same location, so we don't\n  // end up with multiple requests for the same page. We use `switchMap`, since\n  // we want to cancel the previous request when a new one is triggered, which\n  // is automatically handled by the observable returned by `request`. This is\n  // essential to ensure a good user experience, as we don't want to load pages\n  // that are not needed anymore, e.g., when the user clicks multiple links in\n  // quick succession or on slow connections. If the request fails for some\n  // reason, we fall back and use regular navigation, forcing a reload.\n  const document$ =\n    location$.pipe(\n      distinctUntilKeyChanged(\"pathname\"),\n      switchMap(url => requestHTML(url, { progress$ })\n        .pipe(\n          catchError(() => {\n            setLocation(url, true)\n            return EMPTY\n          })\n        )\n      ),\n\n      // The document was successfully fetched and parsed, so we can inject its\n      // contents into the currently active document\n      switchMap(resolve),\n      switchMap(inject),\n      share()\n    )\n\n  // --------------------------------------------------------------------------\n  // Scroll restoration\n  // --------------------------------------------------------------------------\n\n  // Handle scroll restoration - we must restore the viewport offset after the\n  // document has been fetched and injected, and every time the user clicks an\n  // anchor that leads to an element on the same page, which might also happen\n  // when the user uses the back or forward button.\n  merge(\n    document$.pipe(withLatestFrom(location$, (_, url) => url)),\n\n    // Handle instant navigation events that are triggered by the user clicking\n    // on an anchor link with a hash fragment different from the current one, as\n    // well as from popstate events, which are emitted when the user navigates\n    // back and forth between pages. We use a two-layered subscription to scope\n    // the scroll restoration to the current page, as we don't need to restore\n    // the viewport offset when the user navigates to a different page, as this\n    // is already handled by the previous observable.\n    document$.pipe(\n      switchMap(() => location$),\n      distinctUntilKeyChanged(\"pathname\"),\n      switchMap(() => location$),\n      distinctUntilKeyChanged(\"hash\")\n    ),\n\n    // Handle instant navigation events that are triggered by the user clicking\n    // on an anchor link with the same hash fragment as the current one in the\n    // URL. It is essential that we only intercept those from instant navigation\n    // events and not from history change events, or we'll end up in and endless\n    // loop. The top-level history entry must be removed, as it will be replaced\n    // with a new one, which would otherwise lead to a duplicate entry.\n    location$.pipe(\n      distinctUntilChanged((a, b) => (\n        a.pathname === b.pathname &&\n        a.hash     === b.hash\n      )),\n      switchMap(() => instant$),\n      tap(() => history.back())\n    )\n  )\n    .subscribe(url => {\n\n      // Check if the current history entry has a state, which happens when the\n      // user presses the back or forward button to visit a page we've already\n      // seen. If there's no state, it means a new page was visited and we must\n      // scroll to the top, unless an anchor is given.\n      if (history.state !== null || !url.hash) {\n        window.scrollTo(0, history.state?.y ?? 0)\n      } else {\n        history.scrollRestoration = \"auto\"\n        setLocationHash(url.hash)\n        history.scrollRestoration = \"manual\"\n      }\n    })\n\n  // Disable scroll restoration when an instant navigation event occurs, so the\n  // browser does not immediately set the viewport offset to the prior history\n  // entry, scrolling to the position on the same page, which would look odd.\n  // Instead, we manually restore the position once the page has loaded.\n  location$.subscribe(() => {\n    history.scrollRestoration = \"manual\"\n  })\n\n  // Enable scroll restoration before window unloads - this is essential to\n  // ensure that full reloads (F5) restore the viewport offset correctly. If\n  // only popstate events wouldn't reset the viewport offset prior to their\n  // emission, we could just reset this in popstate. Meh.\n  fromEvent(window, \"beforeunload\")\n    .subscribe(() => {\n      history.scrollRestoration = \"auto\"\n    })\n\n  // Track viewport offset, so we can restore it when the user navigates back\n  // and forth between pages. Note that this must be debounced and cannot be\n  // done in popstate, as popstate has already removed the entry from the\n  // history, which means it is too late.\n  viewport$.pipe(\n    distinctUntilKeyChanged(\"offset\"),\n    debounceTime(100)\n  )\n    .subscribe(({ offset }) => {\n      history.replaceState(offset, \"\")\n    })\n\n  // Return document observable\n  return document$\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchConfig } from \"../config\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlight function\n *\n * @param value - Value\n *\n * @returns Highlighted value\n */\nexport type SearchHighlightFn = (value: string) => string\n\n/**\n * Search highlight factory function\n *\n * @param query - Query value\n *\n * @returns Search highlight function\n */\nexport type SearchHighlightFactoryFn = (query: string) => SearchHighlightFn\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search highlighter\n *\n * @param config - Search configuration\n *\n * @returns Search highlight factory function\n */\nexport function setupSearchHighlighter(\n  config: SearchConfig\n): SearchHighlightFactoryFn {\n  // Hack: temporarily remove pure lookaheads and lookbehinds\n  const regex = config.separator.split(\"|\").map(term => {\n    const temp = term.replace(/(\\(\\?[!=<][^)]+\\))/g, \"\")\n    return temp.length === 0 ? \"\uFFFD\" : term\n  })\n    .join(\"|\")\n\n  const separator = new RegExp(regex, \"img\")\n  const highlight = (_: unknown, data: string, term: string) => {\n    return `${data}<mark data-md-highlight>${term}</mark>`\n  }\n\n  /* Return factory function */\n  return (query: string) => {\n    query = query\n      .replace(/[\\s*+\\-:~^]+/g, \" \")\n      .trim()\n\n    /* Create search term match expression */\n    const match = new RegExp(`(^|${config.separator}|)(${\n      query\n        .replace(/[|\\\\{}()[\\]^$+*?.-]/g, \"\\\\$&\")\n        .replace(separator, \"|\")\n    })`, \"img\")\n\n    /* Highlight string value */\n    return value => escapeHTML(value)\n      .replace(match, highlight)\n      .replace(/<\\/mark>(\\s+)<mark[^>]*>/img, \"$1\")\n  }\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { SearchResult } from \"../../_\"\nimport { SearchIndex } from \"../../config\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search message type\n */\nexport const enum SearchMessageType {\n  SETUP,                               /* Search index setup */\n  READY,                               /* Search index ready */\n  QUERY,                               /* Search query */\n  RESULT                               /* Search results */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Message containing the data necessary to setup the search index\n */\nexport interface SearchSetupMessage {\n  type: SearchMessageType.SETUP        /* Message type */\n  data: SearchIndex                    /* Message data */\n}\n\n/**\n * Message indicating the search index is ready\n */\nexport interface SearchReadyMessage {\n  type: SearchMessageType.READY        /* Message type */\n}\n\n/**\n * Message containing a search query\n */\nexport interface SearchQueryMessage {\n  type: SearchMessageType.QUERY        /* Message type */\n  data: string                         /* Message data */\n}\n\n/**\n * Message containing results for a search query\n */\nexport interface SearchResultMessage {\n  type: SearchMessageType.RESULT       /* Message type */\n  data: SearchResult                   /* Message data */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Message exchanged with the search worker\n */\nexport type SearchMessage =\n  | SearchSetupMessage\n  | SearchReadyMessage\n  | SearchQueryMessage\n  | SearchResultMessage\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Type guard for search ready messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchReadyMessage(\n  message: SearchMessage\n): message is SearchReadyMessage {\n  return message.type === SearchMessageType.READY\n}\n\n/**\n * Type guard for search result messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchResultMessage(\n  message: SearchMessage\n): message is SearchResultMessage {\n  return message.type === SearchMessageType.RESULT\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  ObservableInput,\n  Subject,\n  first,\n  merge,\n  of,\n  switchMap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport { watchToggle, watchWorker } from \"~/browser\"\n\nimport { SearchIndex } from \"../../config\"\nimport {\n  SearchMessage,\n  SearchMessageType\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up search worker\n *\n * This function creates and initializes a web worker that is used for search,\n * so that the user interface doesn't freeze. In general, the application does\n * not care how search is implemented, as long as the web worker conforms to\n * the format expected by the application as defined in `SearchMessage`. This\n * allows the author to implement custom search functionality, by providing a\n * custom web worker via configuration.\n *\n * Material for MkDocs' built-in search implementation makes use of Lunr.js, an\n * efficient and fast implementation for client-side search. Leveraging a tiny\n * iframe-based web worker shim, search is even supported for the `file://`\n * protocol, enabling search for local non-hosted builds.\n *\n * If the protocol is `file://`, search initialization is deferred to mitigate\n * freezing, as it's now synchronous by design - see https://bit.ly/3C521EO\n *\n * @see https://bit.ly/3igvtQv - How to implement custom search\n *\n * @param url - Worker URL\n * @param index$ - Search index observable input\n *\n * @returns Search worker\n */\nexport function setupSearchWorker(\n  url: string, index$: ObservableInput<SearchIndex>\n): Subject<SearchMessage> {\n  const worker$ = watchWorker<SearchMessage>(url)\n  merge(\n    of(location.protocol !== \"file:\"),\n    watchToggle(\"search\")\n  )\n    .pipe(\n      first(active => active),\n      switchMap(() => index$)\n    )\n      .subscribe(({ config, docs }) => worker$.next({\n        type: SearchMessageType.SETUP,\n        data: {\n          config,\n          docs,\n          options: {\n            suggest: feature(\"search.suggest\")\n          }\n        }\n      }))\n\n  /* Return search worker */\n  return worker$\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Subject,\n  catchError,\n  combineLatest,\n  filter,\n  fromEvent,\n  map,\n  of,\n  switchMap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n  getElement,\n  getLocation,\n  requestJSON,\n  setLocation\n} from \"~/browser\"\nimport { getComponentElements } from \"~/components\"\nimport {\n  Version,\n  renderVersionSelector\n} from \"~/templates\"\n\nimport { fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n  document$: Subject<Document>         /* Document subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up version selector\n *\n * @param options - Options\n */\nexport function setupVersionSelector(\n  { document$ }: SetupOptions\n): void {\n  const config = configuration()\n  const versions$ = requestJSON<Version[]>(\n    new URL(\"../versions.json\", config.base)\n  )\n    .pipe(\n      catchError(() => EMPTY) // @todo refactor instant loading\n    )\n\n  /* Determine current version */\n  const current$ = versions$\n    .pipe(\n      map(versions => {\n        const [, current] = config.base.match(/([^/]+)\\/?$/)!\n        return versions.find(({ version, aliases }) => (\n          version === current || aliases.includes(current)\n        )) || versions[0]\n      })\n    )\n\n  /* Intercept inter-version navigation */\n  versions$\n    .pipe(\n      map(versions => new Map(versions.map(version => [\n        `${new URL(`../${version.version}/`, config.base)}`,\n        version\n      ]))),\n      switchMap(urls => fromEvent<MouseEvent>(document.body, \"click\")\n        .pipe(\n          filter(ev => !ev.metaKey && !ev.ctrlKey),\n          withLatestFrom(current$),\n          switchMap(([ev, current]) => {\n            if (ev.target instanceof Element) {\n              const el = ev.target.closest(\"a\")\n              if (el && !el.target && urls.has(el.href)) {\n                const url = el.href\n                // This is a temporary hack to detect if a version inside the\n                // version selector or on another part of the site was clicked.\n                // If we're inside the version selector, we definitely want to\n                // find the same page, as we might have different deployments\n                // due to aliases. However, if we're outside the version\n                // selector, we must abort here, because we might otherwise\n                // interfere with instant navigation. We need to refactor this\n                // at some point together with instant navigation.\n                //\n                // See https://github.com/squidfunk/mkdocs-material/issues/4012\n                if (!ev.target.closest(\".md-version\")) {\n                  const version = urls.get(url)!\n                  if (version === current)\n                    return EMPTY\n                }\n                ev.preventDefault()\n                return of(url)\n              }\n            }\n            return EMPTY\n          }),\n          switchMap(url => {\n            return fetchSitemap(new URL(url))\n              .pipe(\n                map(sitemap => {\n                  const location = getLocation()\n                  const path = location.href.replace(config.base, url)\n                  return sitemap.has(path.split(\"#\")[0])\n                    ? new URL(path)\n                    : new URL(url)\n                })\n              )\n          })\n        )\n      )\n    )\n      .subscribe(url => setLocation(url, true))\n\n  /* Render version selector and warning */\n  combineLatest([versions$, current$])\n    .subscribe(([versions, current]) => {\n      const topic = getElement(\".md-header__topic\")\n      topic.appendChild(renderVersionSelector(versions, current))\n    })\n\n  /* Integrate outdated version banner with instant navigation */\n  document$.pipe(switchMap(() => current$))\n    .subscribe(current => {\n\n      /* Check if version state was already determined */\n      let outdated = __md_get(\"__outdated\", sessionStorage)\n      if (outdated === null) {\n        outdated = true\n\n        /* Obtain and normalize default versions */\n        let ignored = config.version?.default || \"latest\"\n        if (!Array.isArray(ignored))\n          ignored = [ignored]\n\n        /* Check if version is considered a default */\n        main: for (const ignore of ignored)\n          for (const version of current.aliases.concat(current.version))\n            if (new RegExp(ignore, \"i\").test(version)) {\n              outdated = false\n              break main\n            }\n\n        /* Persist version state in session storage */\n        __md_set(\"__outdated\", outdated, sessionStorage)\n      }\n\n      /* Unhide outdated version banner */\n      if (outdated)\n        for (const warning of getComponentElements(\"outdated\"))\n          warning.hidden = false\n    })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  combineLatest,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  endWith,\n  finalize,\n  first,\n  fromEvent,\n  ignoreElements,\n  map,\n  merge,\n  shareReplay,\n  takeUntil,\n  tap\n} from \"rxjs\"\n\nimport {\n  getElement,\n  getLocation,\n  setToggle,\n  watchElementFocus,\n  watchToggle\n} from \"~/browser\"\nimport {\n  SearchMessage,\n  SearchMessageType,\n  isSearchReadyMessage\n} from \"~/integrations\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query\n */\nexport interface SearchQuery {\n  value: string                        /* Query value */\n  focus: boolean                       /* Query focus */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  worker$: Subject<SearchMessage>      /* Search worker */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  worker$: Subject<SearchMessage>      /* Search worker */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch search query\n *\n * Note that the focus event which triggers re-reading the current query value\n * is delayed by `1ms` so the input's empty state is allowed to propagate.\n *\n * @param el - Search query element\n * @param options - Options\n *\n * @returns Search query observable\n */\nexport function watchSearchQuery(\n  el: HTMLInputElement, { worker$ }: WatchOptions\n): Observable<SearchQuery> {\n\n  /* Support search deep linking */\n  const { searchParams } = getLocation()\n  if (searchParams.has(\"q\")) {\n    setToggle(\"search\", true)\n\n    /* Set query from parameter */\n    el.value = searchParams.get(\"q\")!\n    el.focus()\n\n    /* Remove query parameter on close */\n    watchToggle(\"search\")\n      .pipe(\n        first(active => !active)\n      )\n        .subscribe(() => {\n          const url = getLocation()\n          url.searchParams.delete(\"q\")\n          history.replaceState({}, \"\", `${url}`)\n        })\n  }\n\n  /* Intercept focus and input events */\n  const focus$ = watchElementFocus(el)\n  const value$ = merge(\n    worker$.pipe(first(isSearchReadyMessage)),\n    fromEvent(el, \"keyup\"),\n    focus$\n  )\n    .pipe(\n      map(() => el.value),\n      distinctUntilChanged()\n    )\n\n  /* Combine into single observable */\n  return combineLatest([value$, focus$])\n    .pipe(\n      map(([value, focus]) => ({ value, focus })),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount search query\n *\n * @param el - Search query element\n * @param options - Options\n *\n * @returns Search query component observable\n */\nexport function mountSearchQuery(\n  el: HTMLInputElement, { worker$ }: MountOptions\n): Observable<Component<SearchQuery, HTMLInputElement>> {\n  const push$ = new Subject<SearchQuery>()\n  const done$ = push$.pipe(ignoreElements(), endWith(true))\n\n  /* Handle value change */\n  combineLatest([\n    worker$.pipe(first(isSearchReadyMessage)),\n    push$\n  ], (_, query) => query)\n    .pipe(\n      distinctUntilKeyChanged(\"value\")\n    )\n      .subscribe(({ value }) => worker$.next({\n        type: SearchMessageType.QUERY,\n        data: value\n      }))\n\n  /* Handle focus change */\n  push$\n    .pipe(\n      distinctUntilKeyChanged(\"focus\")\n    )\n      .subscribe(({ focus }) => {\n        if (focus)\n          setToggle(\"search\", focus)\n      })\n\n  /* Handle reset */\n  fromEvent(el.form!, \"reset\")\n    .pipe(\n      takeUntil(done$)\n    )\n      .subscribe(() => el.focus())\n\n  // Focus search query on label click - note that this is necessary to bring\n  // up the keyboard on iOS and other mobile platforms, as the search dialog is\n  // not visible at first, and programatically focusing an input element must\n  // be triggered by a user interaction - see https://t.ly/Cb30n\n  const label = getElement(\"header [for=__search]\")\n  fromEvent(label, \"click\")\n    .subscribe(() => el.focus())\n\n  /* Create and return component */\n  return watchSearchQuery(el, { worker$ })\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state })),\n      shareReplay(1)\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  bufferCount,\n  filter,\n  finalize,\n  first,\n  fromEvent,\n  map,\n  merge,\n  mergeMap,\n  of,\n  share,\n  skipUntil,\n  switchMap,\n  takeUntil,\n  tap,\n  withLatestFrom,\n  zipWith\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport {\n  getElement,\n  getOptionalElement,\n  watchElementBoundary,\n  watchToggle\n} from \"~/browser\"\nimport {\n  SearchMessage,\n  SearchResult,\n  isSearchReadyMessage,\n  isSearchResultMessage\n} from \"~/integrations\"\nimport { renderSearchResultItem } from \"~/templates\"\nimport { round } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\nimport { SearchQuery } from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  query$: Observable<SearchQuery>      /* Search query observable */\n  worker$: Subject<SearchMessage>      /* Search worker */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search result list\n *\n * This function performs a lazy rendering of the search results, depending on\n * the vertical offset of the search result container.\n *\n * @param el - Search result list element\n * @param options - Options\n *\n * @returns Search result list component observable\n */\nexport function mountSearchResult(\n  el: HTMLElement, { worker$, query$ }: MountOptions\n): Observable<Component<SearchResult>> {\n  const push$ = new Subject<SearchResult>()\n  const boundary$ = watchElementBoundary(el.parentElement!)\n    .pipe(\n      filter(Boolean)\n    )\n\n  /* Retrieve container */\n  const container = el.parentElement!\n\n  /* Retrieve nested components */\n  const meta = getElement(\":scope > :first-child\", el)\n  const list = getElement(\":scope > :last-child\", el)\n\n  /* Reveal to accessibility tree \u2013 see https://bit.ly/3iAA7t8 */\n  watchToggle(\"search\")\n    .subscribe(active => list.setAttribute(\n      \"role\", active ? \"list\" : \"presentation\"\n    ))\n\n  /* Update search result metadata */\n  push$\n    .pipe(\n      withLatestFrom(query$),\n      skipUntil(worker$.pipe(first(isSearchReadyMessage)))\n    )\n      .subscribe(([{ items }, { value }]) => {\n        switch (items.length) {\n\n          /* No results */\n          case 0:\n            meta.textContent = value.length\n              ? translation(\"search.result.none\")\n              : translation(\"search.result.placeholder\")\n            break\n\n          /* One result */\n          case 1:\n            meta.textContent = translation(\"search.result.one\")\n            break\n\n          /* Multiple result */\n          default:\n            const count = round(items.length)\n            meta.textContent = translation(\"search.result.other\", count)\n        }\n      })\n\n  /* Render search result item */\n  const render$ = push$\n    .pipe(\n      tap(() => list.innerHTML = \"\"),\n      switchMap(({ items }) => merge(\n        of(...items.slice(0, 10)),\n        of(...items.slice(10))\n          .pipe(\n            bufferCount(4),\n            zipWith(boundary$),\n            switchMap(([chunk]) => chunk)\n          )\n      )),\n      map(renderSearchResultItem),\n      share()\n    )\n\n  /* Update search result list */\n  render$.subscribe(item => list.appendChild(item))\n  render$\n    .pipe(\n      mergeMap(item => {\n        const details = getOptionalElement(\"details\", item)\n        if (typeof details === \"undefined\")\n          return EMPTY\n\n        /* Keep position of details element stable */\n        return fromEvent(details, \"toggle\")\n          .pipe(\n            takeUntil(push$),\n            map(() => details)\n          )\n      })\n    )\n      .subscribe(details => {\n        if (\n          details.open === false &&\n          details.offsetTop <= container.scrollTop\n        )\n          container.scrollTo({ top: details.offsetTop })\n      })\n\n  /* Filter search result message */\n  const result$ = worker$\n    .pipe(\n      filter(isSearchResultMessage),\n      map(({ data }) => data)\n    )\n\n  /* Create and return component */\n  return result$\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  endWith,\n  finalize,\n  fromEvent,\n  ignoreElements,\n  map,\n  takeUntil,\n  tap\n} from \"rxjs\"\n\nimport { getLocation } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { SearchQuery } from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search sharing\n */\nexport interface SearchShare {\n  url: URL                             /* Deep link for sharing */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  query$: Observable<SearchQuery>      /* Search query observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  query$: Observable<SearchQuery>      /* Search query observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search sharing\n *\n * @param _el - Search sharing element\n * @param options - Options\n *\n * @returns Search sharing observable\n */\nexport function watchSearchShare(\n  _el: HTMLElement, { query$ }: WatchOptions\n): Observable<SearchShare> {\n  return query$\n    .pipe(\n      map(({ value }) => {\n        const url = getLocation()\n        url.hash = \"\"\n\n        /* Compute readable query strings */\n        value = value\n          .replace(/\\s+/g, \"+\")        /* Collapse whitespace */\n          .replace(/&/g, \"%26\")        /* Escape '&' character */\n          .replace(/=/g, \"%3D\")        /* Escape '=' character */\n\n        /* Replace query string */\n        url.search = `q=${value}`\n        return { url }\n      })\n    )\n}\n\n/**\n * Mount search sharing\n *\n * @param el - Search sharing element\n * @param options - Options\n *\n * @returns Search sharing component observable\n */\nexport function mountSearchShare(\n  el: HTMLAnchorElement, options: MountOptions\n): Observable<Component<SearchShare>> {\n  const push$ = new Subject<SearchShare>()\n  const done$ = push$.pipe(ignoreElements(), endWith(true))\n  push$.subscribe(({ url }) => {\n    el.setAttribute(\"data-clipboard-text\", el.href)\n    el.href = `${url}`\n  })\n\n  /* Prevent following of link */\n  fromEvent(el, \"click\")\n    .pipe(\n      takeUntil(done$)\n    )\n      .subscribe(ev => ev.preventDefault())\n\n  /* Create and return component */\n  return watchSearchShare(el, options)\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  asyncScheduler,\n  combineLatestWith,\n  distinctUntilChanged,\n  filter,\n  finalize,\n  fromEvent,\n  map,\n  merge,\n  observeOn,\n  tap\n} from \"rxjs\"\n\nimport { Keyboard } from \"~/browser\"\nimport {\n  SearchMessage,\n  SearchResult,\n  isSearchResultMessage\n} from \"~/integrations\"\n\nimport { Component, getComponentElement } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search suggestions\n */\nexport interface SearchSuggest {}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  keyboard$: Observable<Keyboard>      /* Keyboard observable */\n  worker$: Subject<SearchMessage>      /* Search worker */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search suggestions\n *\n * This function will perform a lazy rendering of the search results, depending\n * on the vertical offset of the search result container.\n *\n * @param el - Search result list element\n * @param options - Options\n *\n * @returns Search result list component observable\n */\nexport function mountSearchSuggest(\n  el: HTMLElement, { worker$, keyboard$ }: MountOptions\n): Observable<Component<SearchSuggest>> {\n  const push$ = new Subject<SearchResult>()\n\n  /* Retrieve query component and track all changes */\n  const query  = getComponentElement(\"search-query\")\n  const query$ = merge(\n    fromEvent(query, \"keydown\"),\n    fromEvent(query, \"focus\")\n  )\n    .pipe(\n      observeOn(asyncScheduler),\n      map(() => query.value),\n      distinctUntilChanged(),\n    )\n\n  /* Update search suggestions */\n  push$\n    .pipe(\n      combineLatestWith(query$),\n      map(([{ suggest }, value]) => {\n        const words = value.split(/([\\s-]+)/)\n        if (suggest?.length && words[words.length - 1]) {\n          const last = suggest[suggest.length - 1]\n          if (last.startsWith(words[words.length - 1]))\n            words[words.length - 1] = last\n        } else {\n          words.length = 0\n        }\n        return words\n      })\n    )\n      .subscribe(words => el.innerHTML = words\n        .join(\"\")\n        .replace(/\\s/g, \"&nbsp;\")\n      )\n\n  /* Set up search keyboard handlers */\n  keyboard$\n    .pipe(\n      filter(({ mode }) => mode === \"search\")\n    )\n      .subscribe(key => {\n        switch (key.type) {\n\n          /* Right arrow: accept current suggestion */\n          case \"ArrowRight\":\n            if (\n              el.innerText.length &&\n              query.selectionStart === query.value.length\n            )\n              query.value = el.innerText\n            break\n        }\n      })\n\n  /* Filter search result message */\n  const result$ = worker$\n    .pipe(\n      filter(isSearchResultMessage),\n      map(({ data }) => data)\n    )\n\n  /* Create and return component */\n  return result$\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(() => ({ ref: el }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  NEVER,\n  Observable,\n  ObservableInput,\n  filter,\n  fromEvent,\n  merge,\n  mergeWith\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n  Keyboard,\n  getActiveElement,\n  getElements,\n  setToggle\n} from \"~/browser\"\nimport {\n  SearchIndex,\n  SearchResult,\n  setupSearchWorker\n} from \"~/integrations\"\n\nimport {\n  Component,\n  getComponentElement,\n  getComponentElements\n} from \"../../_\"\nimport {\n  SearchQuery,\n  mountSearchQuery\n} from \"../query\"\nimport { mountSearchResult } from \"../result\"\nimport {\n  SearchShare,\n  mountSearchShare\n} from \"../share\"\nimport {\n  SearchSuggest,\n  mountSearchSuggest\n} from \"../suggest\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search\n */\nexport type Search =\n  | SearchQuery\n  | SearchResult\n  | SearchShare\n  | SearchSuggest\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  index$: ObservableInput<SearchIndex> /* Search index observable */\n  keyboard$: Observable<Keyboard>      /* Keyboard observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search\n *\n * This function sets up the search functionality, including the underlying\n * web worker and all keyboard bindings.\n *\n * @param el - Search element\n * @param options - Options\n *\n * @returns Search component observable\n */\nexport function mountSearch(\n  el: HTMLElement, { index$, keyboard$ }: MountOptions\n): Observable<Component<Search>> {\n  const config = configuration()\n  try {\n    const worker$ = setupSearchWorker(config.search, index$)\n\n    /* Retrieve query and result components */\n    const query  = getComponentElement(\"search-query\", el)\n    const result = getComponentElement(\"search-result\", el)\n\n    /* Always close search on result selection */\n    fromEvent<PointerEvent>(el, \"click\")\n      .pipe(\n        filter(({ target }) => (\n          target instanceof Element && !!target.closest(\"a\")\n        ))\n      )\n        .subscribe(() => setToggle(\"search\", false))\n\n    /* Set up search keyboard handlers */\n    keyboard$\n      .pipe(\n        filter(({ mode }) => mode === \"search\")\n      )\n        .subscribe(key => {\n          const active = getActiveElement()\n          switch (key.type) {\n\n            /* Enter: go to first (best) result */\n            case \"Enter\":\n              if (active === query) {\n                const anchors = new Map<HTMLAnchorElement, number>()\n                for (const anchor of getElements<HTMLAnchorElement>(\n                  \":first-child [href]\", result\n                )) {\n                  const article = anchor.firstElementChild!\n                  anchors.set(anchor, parseFloat(\n                    article.getAttribute(\"data-md-score\")!\n                  ))\n                }\n\n                /* Go to result with highest score, if any */\n                if (anchors.size) {\n                  const [[best]] = [...anchors].sort(([, a], [, b]) => b - a)\n                  best.click()\n                }\n\n                /* Otherwise omit form submission */\n                key.claim()\n              }\n              break\n\n            /* Escape or Tab: close search */\n            case \"Escape\":\n            case \"Tab\":\n              setToggle(\"search\", false)\n              query.blur()\n              break\n\n            /* Vertical arrows: select previous or next search result */\n            case \"ArrowUp\":\n            case \"ArrowDown\":\n              if (typeof active === \"undefined\") {\n                query.focus()\n              } else {\n                const els = [query, ...getElements(\n                  \":not(details) > [href], summary, details[open] [href]\",\n                  result\n                )]\n                const i = Math.max(0, (\n                  Math.max(0, els.indexOf(active)) + els.length + (\n                    key.type === \"ArrowUp\" ? -1 : +1\n                  )\n                ) % els.length)\n                els[i].focus()\n              }\n\n              /* Prevent scrolling of page */\n              key.claim()\n              break\n\n            /* All other keys: hand to search query */\n            default:\n              if (query !== getActiveElement())\n                query.focus()\n          }\n        })\n\n    /* Set up global keyboard handlers */\n    keyboard$\n      .pipe(\n        filter(({ mode }) => mode === \"global\")\n      )\n        .subscribe(key => {\n          switch (key.type) {\n\n            /* Open search and select query */\n            case \"f\":\n            case \"s\":\n            case \"/\":\n              query.focus()\n              query.select()\n\n              /* Prevent scrolling of page */\n              key.claim()\n              break\n          }\n        })\n\n    /* Create and return component */\n    const query$ = mountSearchQuery(query, { worker$ })\n    return merge(\n      query$,\n      mountSearchResult(result, { worker$, query$ })\n    )\n      .pipe(\n        mergeWith(\n\n          /* Search sharing */\n          ...getComponentElements(\"search-share\", el)\n            .map(child => mountSearchShare(child, { query$ })),\n\n          /* Search suggestions */\n          ...getComponentElements(\"search-suggest\", el)\n            .map(child => mountSearchSuggest(child, { worker$, keyboard$ }))\n        )\n      )\n\n  /* Gracefully handle broken search */\n  } catch (err) {\n    el.hidden = true\n    return NEVER\n  }\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  ObservableInput,\n  combineLatest,\n  filter,\n  map,\n  startWith\n} from \"rxjs\"\n\nimport { getLocation } from \"~/browser\"\nimport {\n  SearchIndex,\n  setupSearchHighlighter\n} from \"~/integrations\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlighting\n */\nexport interface SearchHighlight {\n  nodes: Map<ChildNode, string>        /* Map of replacements */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  index$: ObservableInput<SearchIndex> /* Search index observable */\n  location$: Observable<URL>           /* Location observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search highlighting\n *\n * @param el - Content element\n * @param options - Options\n *\n * @returns Search highlighting component observable\n */\nexport function mountSearchHiglight(\n  el: HTMLElement, { index$, location$ }: MountOptions\n): Observable<Component<SearchHighlight>> {\n  return combineLatest([\n    index$,\n    location$\n      .pipe(\n        startWith(getLocation()),\n        filter(url => !!url.searchParams.get(\"h\"))\n      )\n  ])\n    .pipe(\n      map(([index, url]) => setupSearchHighlighter(index.config)(\n        url.searchParams.get(\"h\")!\n      )),\n      map(fn => {\n        const nodes = new Map<ChildNode, string>()\n\n        /* Traverse text nodes and collect matches */\n        const it = document.createNodeIterator(el, NodeFilter.SHOW_TEXT)\n        for (let node = it.nextNode(); node; node = it.nextNode()) {\n          if (node.parentElement?.offsetHeight) {\n            const original = node.textContent!\n            const replaced = fn(original)\n            if (replaced.length > original.length)\n              nodes.set(node as ChildNode, replaced)\n          }\n        }\n\n        /* Replace original nodes with matches */\n        for (const [node, text] of nodes) {\n          const { childNodes } = h(\"span\", null, text)\n          node.replaceWith(...Array.from(childNodes))\n        }\n\n        /* Return component */\n        return { ref: el, nodes }\n      })\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  asyncScheduler,\n  auditTime,\n  combineLatest,\n  defer,\n  distinctUntilChanged,\n  endWith,\n  finalize,\n  first,\n  from,\n  fromEvent,\n  ignoreElements,\n  map,\n  mergeMap,\n  observeOn,\n  takeUntil,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  getElement,\n  getElementOffset,\n  getElementSize,\n  getElements\n} from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sidebar\n */\nexport interface Sidebar {\n  height: number                       /* Sidebar height */\n  locked: boolean                      /* Sidebar is locked */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  main$: Observable<Main>              /* Main area observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  main$: Observable<Main>              /* Main area observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch sidebar\n *\n * This function returns an observable that computes the visual parameters of\n * the sidebar which depends on the vertical viewport offset, as well as the\n * height of the main area. When the page is scrolled beyond the header, the\n * sidebar is locked and fills the remaining space.\n *\n * @param el - Sidebar element\n * @param options - Options\n *\n * @returns Sidebar observable\n */\nexport function watchSidebar(\n  el: HTMLElement, { viewport$, main$ }: WatchOptions\n): Observable<Sidebar> {\n  const parent = el.closest<HTMLElement>(\".md-grid\")!\n  const adjust =\n    parent.offsetTop -\n    parent.parentElement!.offsetTop\n\n  /* Compute the sidebar's available height and if it should be locked */\n  return combineLatest([main$, viewport$])\n    .pipe(\n      map(([{ offset, height }, { offset: { y } }]) => {\n        height = height\n          + Math.min(adjust, Math.max(0, y - offset))\n          - adjust\n        return {\n          height,\n          locked: y >= offset + adjust\n        }\n      }),\n      distinctUntilChanged((a, b) => (\n        a.height === b.height &&\n        a.locked === b.locked\n      ))\n    )\n}\n\n/**\n * Mount sidebar\n *\n * This function doesn't set the height of the actual sidebar, but of its first\n * child \u2013 the `.md-sidebar__scrollwrap` element in order to mitigiate jittery\n * sidebars when the footer is scrolled into view. At some point we switched\n * from `absolute` / `fixed` positioning to `sticky` positioning, significantly\n * reducing jitter in some browsers (respectively Firefox and Safari) when\n * scrolling from the top. However, top-aligned sticky positioning means that\n * the sidebar snaps to the bottom when the end of the container is reached.\n * This is what leads to the mentioned jitter, as the sidebar's height may be\n * updated too slowly.\n *\n * This behaviour can be mitigiated by setting the height of the sidebar to `0`\n * while preserving the padding, and the height on its first element.\n *\n * @param el - Sidebar element\n * @param options - Options\n *\n * @returns Sidebar component observable\n */\nexport function mountSidebar(\n  el: HTMLElement, { header$, ...options }: MountOptions\n): Observable<Component<Sidebar>> {\n  const inner = getElement(\".md-sidebar__scrollwrap\", el)\n  const { y } = getElementOffset(inner)\n  return defer(() => {\n    const push$ = new Subject<Sidebar>()\n    const done$ = push$.pipe(ignoreElements(), endWith(true))\n    const next$ = push$\n      .pipe(\n        auditTime(0, animationFrameScheduler)\n      )\n\n    /* Update sidebar height and offset */\n    next$.pipe(withLatestFrom(header$))\n      .subscribe({\n\n        /* Handle emission */\n        next([{ height }, { height: offset }]) {\n          inner.style.height = `${height - 2 * y}px`\n          el.style.top       = `${offset}px`\n        },\n\n        /* Handle complete */\n        complete() {\n          inner.style.height = \"\"\n          el.style.top       = \"\"\n        }\n      })\n\n    /* Bring active item into view on initial load */\n    next$.pipe(first())\n      .subscribe(() => {\n        for (const item of getElements(\".md-nav__link--active[href]\", el)) {\n          if (!item.clientHeight) // skip invisible toc in left sidebar\n            continue\n          const container = item.closest<HTMLElement>(\".md-sidebar__scrollwrap\")!\n          if (typeof container !== \"undefined\") {\n            const offset = item.offsetTop - container.offsetTop\n            const { height } = getElementSize(container)\n            container.scrollTo({\n              top: offset - height / 2\n            })\n          }\n        }\n      })\n\n    /* Handle accessibility for expandable items, see https://bit.ly/3jaod9p */\n    from(getElements<HTMLLabelElement>(\"label[tabindex]\", el))\n      .pipe(\n        mergeMap(label => fromEvent(label, \"click\")\n          .pipe(\n            observeOn(asyncScheduler),\n            map(() => label),\n            takeUntil(done$)\n          )\n        )\n      )\n        .subscribe(label => {\n          const input = getElement<HTMLInputElement>(`[id=\"${label.htmlFor}\"]`)\n          const nav = getElement(`[aria-labelledby=\"${label.id}\"]`)\n          nav.setAttribute(\"aria-expanded\", `${input.checked}`)\n        })\n\n    /* Create and return component */\n    return watchSidebar(el, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Repo, User } from \"github-types\"\nimport {\n  EMPTY,\n  Observable,\n  catchError,\n  defaultIfEmpty,\n  map,\n  zip\n} from \"rxjs\"\n\nimport { requestJSON } from \"~/browser\"\n\nimport { SourceFacts } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * GitHub release (partial)\n */\ninterface Release {\n  tag_name: string                     /* Tag name */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch GitHub repository facts\n *\n * @param user - GitHub user or organization\n * @param repo - GitHub repository\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFactsFromGitHub(\n  user: string, repo?: string\n): Observable<SourceFacts> {\n  if (typeof repo !== \"undefined\") {\n    const url = `https://api.github.com/repos/${user}/${repo}`\n    return zip(\n\n      /* Fetch version */\n      requestJSON<Release>(`${url}/releases/latest`)\n        .pipe(\n          catchError(() => EMPTY), // @todo refactor instant loading\n          map(release => ({\n            version: release.tag_name\n          })),\n          defaultIfEmpty({})\n        ),\n\n      /* Fetch stars and forks */\n      requestJSON<Repo>(url)\n        .pipe(\n          catchError(() => EMPTY), // @todo refactor instant loading\n          map(info => ({\n            stars: info.stargazers_count,\n            forks: info.forks_count\n          })),\n          defaultIfEmpty({})\n        )\n    )\n      .pipe(\n        map(([release, info]) => ({ ...release, ...info }))\n      )\n\n  /* User or organization */\n  } else {\n    const url = `https://api.github.com/users/${user}`\n    return requestJSON<User>(url)\n      .pipe(\n        map(info => ({\n          repositories: info.public_repos\n        })),\n        defaultIfEmpty({})\n      )\n  }\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ProjectSchema } from \"gitlab\"\nimport {\n  EMPTY,\n  Observable,\n  catchError,\n  defaultIfEmpty,\n  map\n} from \"rxjs\"\n\nimport { requestJSON } from \"~/browser\"\n\nimport { SourceFacts } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch GitLab repository facts\n *\n * @param base - GitLab base\n * @param project - GitLab project\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFactsFromGitLab(\n  base: string, project: string\n): Observable<SourceFacts> {\n  const url = `https://${base}/api/v4/projects/${encodeURIComponent(project)}`\n  return requestJSON<ProjectSchema>(url)\n    .pipe(\n      catchError(() => EMPTY), // @todo refactor instant loading\n      map(({ star_count, forks_count }) => ({\n        stars: star_count,\n        forks: forks_count\n      })),\n      defaultIfEmpty({})\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { EMPTY, Observable } from \"rxjs\"\n\nimport { fetchSourceFactsFromGitHub } from \"../github\"\nimport { fetchSourceFactsFromGitLab } from \"../gitlab\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository facts for repositories\n */\nexport interface RepositoryFacts {\n  stars?: number                       /* Number of stars */\n  forks?: number                       /* Number of forks */\n  version?: string                     /* Latest version */\n}\n\n/**\n * Repository facts for organizations\n */\nexport interface OrganizationFacts {\n  repositories?: number                /* Number of repositories */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Repository facts\n */\nexport type SourceFacts =\n  | RepositoryFacts\n  | OrganizationFacts\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch repository facts\n *\n * @param url - Repository URL\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFacts(\n  url: string\n): Observable<SourceFacts> {\n\n  /* Try to match GitHub repository */\n  let match = url.match(/^.+github\\.com\\/([^/]+)\\/?([^/]+)?/i)\n  if (match) {\n    const [, user, repo] = match\n    return fetchSourceFactsFromGitHub(user, repo)\n  }\n\n  /* Try to match GitLab repository */\n  match = url.match(/^.+?([^/]*gitlab[^/]+)\\/(.+?)\\/?$/i)\n  if (match) {\n    const [, base, slug] = match\n    return fetchSourceFactsFromGitLab(base, slug)\n  }\n\n  /* Fallback */\n  return EMPTY\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  catchError,\n  defer,\n  filter,\n  finalize,\n  map,\n  of,\n  shareReplay,\n  tap\n} from \"rxjs\"\n\nimport { getElement } from \"~/browser\"\nimport { ConsentDefaults } from \"~/components/consent\"\nimport { renderSourceFacts } from \"~/templates\"\n\nimport {\n  Component,\n  getComponentElements\n} from \"../../_\"\nimport {\n  SourceFacts,\n  fetchSourceFacts\n} from \"../facts\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository information\n */\nexport interface Source {\n  facts: SourceFacts                   /* Repository facts */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository information observable\n */\nlet fetch$: Observable<Source>\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch repository information\n *\n * This function tries to read the repository facts from session storage, and\n * if unsuccessful, fetches them from the underlying provider.\n *\n * @param el - Repository information element\n *\n * @returns Repository information observable\n */\nexport function watchSource(\n  el: HTMLAnchorElement\n): Observable<Source> {\n  return fetch$ ||= defer(() => {\n    const cached = __md_get<SourceFacts>(\"__source\", sessionStorage)\n    if (cached) {\n      return of(cached)\n    } else {\n\n      /* Check if consent is configured and was given */\n      const els = getComponentElements(\"consent\")\n      if (els.length) {\n        const consent = __md_get<ConsentDefaults>(\"__consent\")\n        if (!(consent && consent.github))\n          return EMPTY\n      }\n\n      /* Fetch repository facts */\n      return fetchSourceFacts(el.href)\n        .pipe(\n          tap(facts => __md_set(\"__source\", facts, sessionStorage))\n        )\n    }\n  })\n    .pipe(\n      catchError(() => EMPTY),\n      filter(facts => Object.keys(facts).length > 0),\n      map(facts => ({ facts })),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount repository information\n *\n * @param el - Repository information element\n *\n * @returns Repository information component observable\n */\nexport function mountSource(\n  el: HTMLAnchorElement\n): Observable<Component<Source>> {\n  const inner = getElement(\":scope > :last-child\", el)\n  return defer(() => {\n    const push$ = new Subject<Source>()\n    push$.subscribe(({ facts }) => {\n      inner.appendChild(renderSourceFacts(facts))\n      inner.classList.add(\"md-source__repository--active\")\n    })\n\n    /* Create and return component */\n    return watchSource(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  defer,\n  distinctUntilKeyChanged,\n  finalize,\n  map,\n  of,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  watchElementSize,\n  watchViewportAt\n} from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Navigation tabs\n */\nexport interface Tabs {\n  hidden: boolean                      /* Navigation tabs are hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch navigation tabs\n *\n * @param el - Navigation tabs element\n * @param options - Options\n *\n * @returns Navigation tabs observable\n */\nexport function watchTabs(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<Tabs> {\n  return watchElementSize(document.body)\n    .pipe(\n      switchMap(() => watchViewportAt(el, { header$, viewport$ })),\n      map(({ offset: { y } }) => {\n        return {\n          hidden: y >= 10\n        }\n      }),\n      distinctUntilKeyChanged(\"hidden\")\n    )\n}\n\n/**\n * Mount navigation tabs\n *\n * This function hides the navigation tabs when scrolling past the threshold\n * and makes them reappear in a nice CSS animation when scrolling back up.\n *\n * @param el - Navigation tabs element\n * @param options - Options\n *\n * @returns Navigation tabs component observable\n */\nexport function mountTabs(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<Tabs>> {\n  return defer(() => {\n    const push$ = new Subject<Tabs>()\n    push$.subscribe({\n\n      /* Handle emission */\n      next({ hidden }) {\n        el.hidden = hidden\n      },\n\n      /* Handle complete */\n      complete() {\n        el.hidden = false\n      }\n    })\n\n    /* Create and return component */\n    return (\n      feature(\"navigation.tabs.sticky\")\n        ? of({ hidden: false })\n        : watchTabs(el, options)\n    )\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  asyncScheduler,\n  bufferCount,\n  combineLatestWith,\n  debounceTime,\n  defer,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  endWith,\n  filter,\n  finalize,\n  ignoreElements,\n  map,\n  merge,\n  observeOn,\n  of,\n  repeat,\n  scan,\n  share,\n  skip,\n  startWith,\n  switchMap,\n  takeUntil,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  getElement,\n  getElementContainer,\n  getElementSize,\n  getElements,\n  getLocation,\n  getOptionalElement,\n  watchElementSize\n} from \"~/browser\"\n\nimport {\n  Component,\n  getComponentElement\n} from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Table of contents\n */\nexport interface TableOfContents {\n  prev: HTMLAnchorElement[][]          /* Anchors (previous) */\n  next: HTMLAnchorElement[][]          /* Anchors (next) */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  main$: Observable<Main>              /* Main area observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch table of contents\n *\n * This is effectively a scroll spy implementation which will account for the\n * fixed header and automatically re-calculate anchor offsets when the viewport\n * is resized. The returned observable will only emit if the table of contents\n * needs to be repainted.\n *\n * This implementation tracks an anchor element's entire path starting from its\n * level up to the top-most anchor element, e.g. `[h3, h2, h1]`. Although the\n * Material theme currently doesn't make use of this information, it enables\n * the styling of the entire hierarchy through customization.\n *\n * Note that the current anchor is the last item of the `prev` anchor list.\n *\n * @param el - Table of contents element\n * @param options - Options\n *\n * @returns Table of contents observable\n */\nexport function watchTableOfContents(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<TableOfContents> {\n  const table = new Map<HTMLAnchorElement, HTMLElement>()\n\n  /* Compute anchor-to-target mapping */\n  const anchors = getElements<HTMLAnchorElement>(\".md-nav__link\", el)\n  for (const anchor of anchors) {\n    const id = decodeURIComponent(anchor.hash.substring(1))\n    const target = getOptionalElement(`[id=\"${id}\"]`)\n    if (typeof target !== \"undefined\")\n      table.set(anchor, target)\n  }\n\n  /* Compute necessary adjustment for header */\n  const adjust$ = header$\n    .pipe(\n      distinctUntilKeyChanged(\"height\"),\n      map(({ height }) => {\n        const main = getComponentElement(\"main\")\n        const grid = getElement(\":scope > :first-child\", main)\n        return height + 0.8 * (\n          grid.offsetTop -\n          main.offsetTop\n        )\n      }),\n      share()\n    )\n\n  /* Compute partition of previous and next anchors */\n  const partition$ = watchElementSize(document.body)\n    .pipe(\n      distinctUntilKeyChanged(\"height\"),\n\n      /* Build index to map anchor paths to vertical offsets */\n      switchMap(body => defer(() => {\n        let path: HTMLAnchorElement[] = []\n        return of([...table].reduce((index, [anchor, target]) => {\n          while (path.length) {\n            const last = table.get(path[path.length - 1])!\n            if (last.tagName >= target.tagName) {\n              path.pop()\n            } else {\n              break\n            }\n          }\n\n          /* If the current anchor is hidden, continue with its parent */\n          let offset = target.offsetTop\n          while (!offset && target.parentElement) {\n            target = target.parentElement\n            offset = target.offsetTop\n          }\n\n          /* Fix anchor offsets in tables - see https://bit.ly/3CUFOcn */\n          let parent = target.offsetParent as HTMLElement\n          for (; parent; parent = parent.offsetParent as HTMLElement)\n            offset += parent.offsetTop\n\n          /* Map reversed anchor path to vertical offset */\n          return index.set(\n            [...path = [...path, anchor]].reverse(),\n            offset\n          )\n        }, new Map<HTMLAnchorElement[], number>()))\n      })\n        .pipe(\n\n          /* Sort index by vertical offset (see https://bit.ly/30z6QSO) */\n          map(index => new Map([...index].sort(([, a], [, b]) => a - b))),\n          combineLatestWith(adjust$),\n\n          /* Re-compute partition when viewport offset changes */\n          switchMap(([index, adjust]) => viewport$\n            .pipe(\n              scan(([prev, next], { offset: { y }, size }) => {\n                const last = y + size.height >= Math.floor(body.height)\n\n                /* Look forward */\n                while (next.length) {\n                  const [, offset] = next[0]\n                  if (offset - adjust < y || last) {\n                    prev = [...prev, next.shift()!]\n                  } else {\n                    break\n                  }\n                }\n\n                /* Look backward */\n                while (prev.length) {\n                  const [, offset] = prev[prev.length - 1]\n                  if (offset - adjust >= y && !last) {\n                    next = [prev.pop()!, ...next]\n                  } else {\n                    break\n                  }\n                }\n\n                /* Return partition */\n                return [prev, next]\n              }, [[], [...index]]),\n              distinctUntilChanged((a, b) => (\n                a[0] === b[0] &&\n                a[1] === b[1]\n              ))\n            )\n          )\n        )\n      )\n    )\n\n  /* Compute and return anchor list migrations */\n  return partition$\n    .pipe(\n      map(([prev, next]) => ({\n        prev: prev.map(([path]) => path),\n        next: next.map(([path]) => path)\n      })),\n\n      /* Extract anchor list migrations */\n      startWith({ prev: [], next: [] }),\n      bufferCount(2, 1),\n      map(([a, b]) => {\n\n        /* Moving down */\n        if (a.prev.length < b.prev.length) {\n          return {\n            prev: b.prev.slice(Math.max(0, a.prev.length - 1), b.prev.length),\n            next: []\n          }\n\n        /* Moving up */\n        } else {\n          return {\n            prev: b.prev.slice(-1),\n            next: b.next.slice(0, b.next.length - a.next.length)\n          }\n        }\n      })\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount table of contents\n *\n * @param el - Table of contents element\n * @param options - Options\n *\n * @returns Table of contents component observable\n */\nexport function mountTableOfContents(\n  el: HTMLElement, { viewport$, header$, main$, target$ }: MountOptions\n): Observable<Component<TableOfContents>> {\n  return defer(() => {\n    const push$ = new Subject<TableOfContents>()\n    const done$ = push$.pipe(ignoreElements(), endWith(true))\n    push$.subscribe(({ prev, next }) => {\n\n      /* Look forward */\n      for (const [anchor] of next) {\n        anchor.classList.remove(\"md-nav__link--passed\")\n        anchor.classList.remove(\"md-nav__link--active\")\n      }\n\n      /* Look backward */\n      for (const [index, [anchor]] of prev.entries()) {\n        anchor.classList.add(\"md-nav__link--passed\")\n        anchor.classList.toggle(\n          \"md-nav__link--active\",\n          index === prev.length - 1\n        )\n      }\n    })\n\n    /* Set up following, if enabled */\n    if (feature(\"toc.follow\")) {\n\n      /* Toggle smooth scrolling only for anchor clicks */\n      const smooth$ = merge(\n        viewport$.pipe(debounceTime(1), map(() => undefined)),\n        viewport$.pipe(debounceTime(250), map(() => \"smooth\" as const))\n      )\n\n      /* Bring active anchor into view */ // @todo: refactor\n      push$\n        .pipe(\n          filter(({ prev }) => prev.length > 0),\n          combineLatestWith(main$.pipe(observeOn(asyncScheduler))),\n          withLatestFrom(smooth$)\n        )\n          .subscribe(([[{ prev }], behavior]) => {\n            const [anchor] = prev[prev.length - 1]\n            if (anchor.offsetHeight) {\n\n              /* Retrieve overflowing container and scroll */\n              const container = getElementContainer(anchor)\n              if (typeof container !== \"undefined\") {\n                const offset = anchor.offsetTop - container.offsetTop\n                const { height } = getElementSize(container)\n                container.scrollTo({\n                  top: offset - height / 2,\n                  behavior\n                })\n              }\n            }\n          })\n    }\n\n    /* Set up anchor tracking, if enabled */\n    if (feature(\"navigation.tracking\"))\n      viewport$\n        .pipe(\n          takeUntil(done$),\n          distinctUntilKeyChanged(\"offset\"),\n          debounceTime(250),\n          skip(1),\n          takeUntil(target$.pipe(skip(1))),\n          repeat({ delay: 250 }),\n          withLatestFrom(push$)\n        )\n          .subscribe(([, { prev }]) => {\n            const url = getLocation()\n\n            /* Set hash fragment to active anchor */\n            const anchor = prev[prev.length - 1]\n            if (anchor && anchor.length) {\n              const [active] = anchor\n              const { hash } = new URL(active.href)\n              if (url.hash !== hash) {\n                url.hash = hash\n                history.replaceState({}, \"\", `${url}`)\n              }\n\n            /* Reset anchor when at the top */\n            } else {\n              url.hash = \"\"\n              history.replaceState({}, \"\", `${url}`)\n            }\n          })\n\n    /* Create and return component */\n    return watchTableOfContents(el, { viewport$, header$ })\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  bufferCount,\n  combineLatest,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  endWith,\n  finalize,\n  fromEvent,\n  ignoreElements,\n  map,\n  repeat,\n  skip,\n  takeUntil,\n  tap\n} from \"rxjs\"\n\nimport { Viewport } from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Back-to-top button\n */\nexport interface BackToTop {\n  hidden: boolean                      /* Back-to-top button is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  main$: Observable<Main>              /* Main area observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  main$: Observable<Main>              /* Main area observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch back-to-top\n *\n * @param _el - Back-to-top element\n * @param options - Options\n *\n * @returns Back-to-top observable\n */\nexport function watchBackToTop(\n  _el: HTMLElement, { viewport$, main$, target$ }: WatchOptions\n): Observable<BackToTop> {\n\n  /* Compute direction */\n  const direction$ = viewport$\n    .pipe(\n      map(({ offset: { y } }) => y),\n      bufferCount(2, 1),\n      map(([a, b]) => a > b && b > 0),\n      distinctUntilChanged()\n    )\n\n  /* Compute whether main area is active */\n  const active$ = main$\n    .pipe(\n      map(({ active }) => active)\n    )\n\n  /* Compute threshold for hiding */\n  return combineLatest([active$, direction$])\n    .pipe(\n      map(([active, direction]) => !(active && direction)),\n      distinctUntilChanged(),\n      takeUntil(target$.pipe(skip(1))),\n      endWith(true),\n      repeat({ delay: 250 }),\n      map(hidden => ({ hidden }))\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount back-to-top\n *\n * @param el - Back-to-top element\n * @param options - Options\n *\n * @returns Back-to-top component observable\n */\nexport function mountBackToTop(\n  el: HTMLElement, { viewport$, header$, main$, target$ }: MountOptions\n): Observable<Component<BackToTop>> {\n  const push$ = new Subject<BackToTop>()\n  const done$ = push$.pipe(ignoreElements(), endWith(true))\n  push$.subscribe({\n\n    /* Handle emission */\n    next({ hidden }) {\n      el.hidden = hidden\n      if (hidden) {\n        el.setAttribute(\"tabindex\", \"-1\")\n        el.blur()\n      } else {\n        el.removeAttribute(\"tabindex\")\n      }\n    },\n\n    /* Handle complete */\n    complete() {\n      el.style.top = \"\"\n      el.hidden = true\n      el.removeAttribute(\"tabindex\")\n    }\n  })\n\n  /* Watch header height */\n  header$\n    .pipe(\n      takeUntil(done$),\n      distinctUntilKeyChanged(\"height\")\n    )\n      .subscribe(({ height }) => {\n        el.style.top = `${height + 16}px`\n      })\n\n  /* Go back to top */\n  fromEvent(el, \"click\")\n    .subscribe(ev => {\n      ev.preventDefault()\n      window.scrollTo({ top: 0 })\n    })\n\n  /* Create and return component */\n  return watchBackToTop(el, { viewport$, main$, target$ })\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  filter,\n  finalize,\n  map,\n  mergeMap,\n  skip,\n  switchMap,\n  take,\n  takeUntil\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  getElements,\n  watchElementVisibility\n} from \"~/browser\"\nimport { mountInlineTooltip2 } from \"~/components/tooltip2\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n  document$: Observable<Document>      /* Document observable */\n  viewport$: Observable<Viewport>      /* Viewport observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch ellipsis\n *\n * This function will fetch all elements that are shortened with ellipsis, and\n * filter those which are visible. Once they become visible, they stay in that\n * state, even though they may be hidden again. This optimization is necessary\n * to reduce pressure on the browser, with elements fading in and out of view.\n *\n * @param options - Options\n */\nexport function patchEllipsis(\n  { document$, viewport$ }: PatchOptions\n): void {\n  document$\n    .pipe(\n      switchMap(() => getElements(\".md-ellipsis\")),\n      mergeMap(el => watchElementVisibility(el)\n        .pipe(\n          takeUntil(document$.pipe(skip(1))),\n          filter(visible => visible),\n          map(() => el),\n          take(1)\n        )\n      ),\n      filter(el => el.offsetWidth < el.scrollWidth),\n      mergeMap(el => {\n        const text = el.innerText\n        const host = el.closest(\"a\") || el\n        host.title = text\n\n        // Do not mount improved tooltip if feature is disabled\n        if (!feature(\"content.tooltips\"))\n          return EMPTY\n\n        /* Mount tooltip */\n        return mountInlineTooltip2(host, { viewport$ })\n          .pipe(\n            takeUntil(document$.pipe(skip(1))),\n            finalize(() => host.removeAttribute(\"title\"))\n          )\n      })\n    )\n      .subscribe()\n\n  // @todo move this outside of here and fix memleaks\n  if (feature(\"content.tooltips\"))\n    document$\n      .pipe(\n        switchMap(() => getElements(\".md-status\")),\n        mergeMap(el => mountInlineTooltip2(el, { viewport$ }))\n      )\n        .subscribe()\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  mergeMap,\n  switchMap,\n  takeWhile,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n  document$: Observable<Document>      /* Document observable */\n  tablet$: Observable<boolean>         /* Media tablet observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch indeterminate checkboxes\n *\n * This function replaces the indeterminate \"pseudo state\" with the actual\n * indeterminate state, which is used to keep navigation always expanded.\n *\n * @param options - Options\n */\nexport function patchIndeterminate(\n  { document$, tablet$ }: PatchOptions\n): void {\n  document$\n    .pipe(\n      switchMap(() => getElements<HTMLInputElement>(\n        \".md-toggle--indeterminate\"\n      )),\n      tap(el => {\n        el.indeterminate = true\n        el.checked = false\n      }),\n      mergeMap(el => fromEvent(el, \"change\")\n        .pipe(\n          takeWhile(() => el.classList.contains(\"md-toggle--indeterminate\")),\n          map(() => el)\n        )\n      ),\n      withLatestFrom(tablet$)\n    )\n      .subscribe(([el, tablet]) => {\n        el.classList.remove(\"md-toggle--indeterminate\")\n        if (tablet)\n          el.checked = false\n      })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  filter,\n  fromEvent,\n  map,\n  mergeMap,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n  document$: Observable<Document>      /* Document observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Check whether the given device is an Apple device\n *\n * @returns Test result\n */\nfunction isAppleDevice(): boolean {\n  return /(iPad|iPhone|iPod)/.test(navigator.userAgent)\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch all elements with `data-md-scrollfix` attributes\n *\n * This is a year-old patch which ensures that overflow scrolling works at the\n * top and bottom of containers on iOS by ensuring a `1px` scroll offset upon\n * the start of a touch event.\n *\n * @see https://bit.ly/2SCtAOO - Original source\n *\n * @param options - Options\n */\nexport function patchScrollfix(\n  { document$ }: PatchOptions\n): void {\n  document$\n    .pipe(\n      switchMap(() => getElements(\"[data-md-scrollfix]\")),\n      tap(el => el.removeAttribute(\"data-md-scrollfix\")),\n      filter(isAppleDevice),\n      mergeMap(el => fromEvent(el, \"touchstart\")\n        .pipe(\n          map(() => el)\n        )\n      )\n    )\n      .subscribe(el => {\n        const top = el.scrollTop\n\n        /* We're at the top of the container */\n        if (top === 0) {\n          el.scrollTop = 1\n\n        /* We're at the bottom of the container */\n        } else if (top + el.offsetHeight === el.scrollHeight) {\n          el.scrollTop = top - 1\n        }\n      })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  delay,\n  map,\n  of,\n  switchMap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  watchToggle\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  tablet$: Observable<boolean>         /* Media tablet observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch the document body to lock when search is open\n *\n * For mobile and tablet viewports, the search is rendered full screen, which\n * leads to scroll leaking when at the top or bottom of the search result. This\n * function locks the body when the search is in full screen mode, and restores\n * the scroll position when leaving.\n *\n * @param options - Options\n */\nexport function patchScrolllock(\n  { viewport$, tablet$ }: PatchOptions\n): void {\n  combineLatest([watchToggle(\"search\"), tablet$])\n    .pipe(\n      map(([active, tablet]) => active && !tablet),\n      switchMap(active => of(active)\n        .pipe(\n          delay(active ? 400 : 100)\n        )\n      ),\n      withLatestFrom(viewport$)\n    )\n      .subscribe(([active, { offset: { y }}]) => {\n        if (active) {\n          document.body.setAttribute(\"data-md-scrolllock\", \"\")\n          document.body.style.top = `-${y}px`\n        } else {\n          const value = -1 * parseInt(document.body.style.top, 10)\n          document.body.removeAttribute(\"data-md-scrolllock\")\n          document.body.style.top = \"\"\n          if (value)\n            window.scrollTo(0, value)\n        }\n      })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n  Object.entries = function (obj: object) {\n    const data: [string, string][] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push([key, obj[key]])\n\n    /* Return entries */\n    return data\n  }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n  Object.values = function (obj: object) {\n    const data: string[] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push(obj[key])\n\n    /* Return values */\n    return data\n  }\n\n/* ------------------------------------------------------------------------- */\n\n/* Polyfills for `Element` */\nif (typeof Element !== \"undefined\") {\n\n  /* Polyfill `Element.scrollTo` */\n  if (!Element.prototype.scrollTo)\n    Element.prototype.scrollTo = function (\n      x?: ScrollToOptions | number, y?: number\n    ): void {\n      if (typeof x === \"object\") {\n        this.scrollLeft = x.left!\n        this.scrollTop = x.top!\n      } else {\n        this.scrollLeft = x!\n        this.scrollTop = y!\n      }\n    }\n\n  /* Polyfill `Element.replaceWith` */\n  if (!Element.prototype.replaceWith)\n    Element.prototype.replaceWith = function (\n      ...nodes: Array<string | Node>\n    ): void {\n      const parent = this.parentNode\n      if (parent) {\n        if (nodes.length === 0)\n          parent.removeChild(this)\n\n        /* Replace children and create text nodes */\n        for (let i = nodes.length - 1; i >= 0; i--) {\n          let node = nodes[i]\n          if (typeof node === \"string\")\n            node = document.createTextNode(node)\n          else if (node.parentNode)\n            node.parentNode.removeChild(node)\n\n          /* Replace child or insert before previous sibling */\n          if (!i)\n            parent.replaceChild(node, this)\n          else\n            parent.insertBefore(this.previousSibling!, node)\n        }\n      }\n    }\n}\n"],
+  "mappings": 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"offset", "direction", "distinctUntilChanged", "search$", "watchToggle", "search", "switchMap", "active", "startWith", "watchHeader", "el", "options", "defer", "watchElementSize", "height", "hidden", "shareReplay", "mountHeader", "header$", "main$", "push$", "Subject", "done$", "ignoreElements", "endWith", "combineLatestWith", "tooltips", "from", "getElements", "mergeMap", "child", "mountTooltip", "takeUntil", "state", "__spreadValues", "mergeWith", "watchHeaderTitle", "el", "viewport$", "header$", "watchViewportAt", "map", "y", "height", "getElementSize", "distinctUntilKeyChanged", "mountHeaderTitle", "options", "defer", "push$", "Subject", "active", "heading", "getOptionalElement", "EMPTY", "tap", "state", "finalize", "__spreadValues", "watchMain", "el", "viewport$", "header$", "adjust$", "map", "height", "distinctUntilChanged", "border$", "switchMap", "watchElementSize", "distinctUntilKeyChanged", "combineLatest", "header", "top", "bottom", "y", "a", "b", "watchPalette", "inputs", "current", "input", "index", "of", "mergeMap", "fromEvent", "map", "startWith", "shareReplay", "mountPalette", "el", "getElements", "meta", "h", "scheme", "media$", "watchMedia", "defer", "push$", "Subject", "palette", "media", "key", "value", "label", "filter", "ev", "withLatestFrom", "_", "header", "getComponentElement", "style", "color", "observeOn", "asyncScheduler", "takeUntil", "skip", "repeat", "tap", "state", "finalize", "__spreadValues", "mountProgress", "el", "progress$", "defer", "push$", "Subject", "value", "tap", "finalize", "map", "import_clipboard", "extract", "el", "copy", "text", "setupClipboardJS", "alert$", "ClipboardJS", "Observable", "subscriber", "getElement", "ev", "tap", "map", "translation", "resolve", "url", "base", "extract", "document", "sitemap", "el", "getElements", "getElement", "links", "link", "href", "fetchSitemap", "requestXML", "map", "catchError", "of", "handle", "ev", "sitemap", "EMPTY", "el", "url", "of", "head", "document", "tags", "getElements", "resolve", "key", "value", "inject", "next", "selector", "feature", "source", "getOptionalElement", "target", "html", "name", "container", "getComponentElement", "concat", "switchMap", "script", "Observable", "observer", "ignoreElements", "endWith", "setupInstantNavigation", "location$", "viewport$", "progress$", "config", "configuration", "sitemap$", "fetchSitemap", "instant$", "fromEvent", "combineLatestWith", "share", "history$", "map", "getLocation", "withLatestFrom", "offset", "merge", "document$", "distinctUntilKeyChanged", "requestHTML", "catchError", "setLocation", "_", "distinctUntilChanged", "a", "b", "tap", "_a", "_b", "setLocationHash", "debounceTime", "import_escape_html", "setupSearchHighlighter", "config", "regex", "term", "separator", "highlight", "_", "data", "query", "match", "value", "escapeHTML", "isSearchReadyMessage", "message", "isSearchResultMessage", "setupSearchWorker", "url", "index$", "worker$", "watchWorker", "merge", "of", "watchToggle", "first", "active", "switchMap", "config", "docs", "feature", "setupVersionSelector", "document$", "config", "configuration", "versions$", "requestJSON", "catchError", "EMPTY", "current$", "map", "versions", "current", "version", "aliases", "switchMap", "urls", "fromEvent", "filter", "ev", "withLatestFrom", "el", "url", "of", "fetchSitemap", "sitemap", "path", "getLocation", "setLocation", "combineLatest", "getElement", "renderVersionSelector", "_a", "outdated", "ignored", "main", "ignore", "warning", "getComponentElements", "watchSearchQuery", "el", "worker$", "searchParams", "getLocation", "setToggle", "watchToggle", "first", "active", "url", "focus$", "watchElementFocus", "value$", "merge", "isSearchReadyMessage", "fromEvent", "map", "distinctUntilChanged", "combineLatest", "value", "focus", "shareReplay", "mountSearchQuery", "push$", "Subject", "done$", "ignoreElements", "endWith", "_", "query", "distinctUntilKeyChanged", "takeUntil", "label", "getElement", "tap", "state", "finalize", "__spreadValues", "mountSearchResult", "el", "worker$", "query$", "push$", "Subject", "boundary$", "watchElementBoundary", "filter", "container", "meta", "getElement", "list", "watchToggle", "active", "withLatestFrom", "skipUntil", "first", "isSearchReadyMessage", "items", "value", "translation", "count", "round", "render$", "tap", "switchMap", "merge", "of", "bufferCount", "zipWith", "chunk", "map", "renderSearchResultItem", "share", "item", "mergeMap", "details", "getOptionalElement", "EMPTY", "fromEvent", "takeUntil", "isSearchResultMessage", "data", "state", "finalize", "__spreadValues", "watchSearchShare", "_el", "query$", "map", "value", "url", "getLocation", "mountSearchShare", "el", "options", "push$", "Subject", "done$", "ignoreElements", "endWith", "fromEvent", "takeUntil", "ev", "tap", "state", "finalize", "__spreadValues", "mountSearchSuggest", "el", "worker$", "keyboard$", "push$", "Subject", "query", "getComponentElement", "query$", "merge", "fromEvent", "observeOn", "asyncScheduler", "map", "distinctUntilChanged", "combineLatestWith", "suggest", "value", "words", "last", "filter", "mode", "key", "isSearchResultMessage", "data", "tap", "state", "finalize", "mountSearch", "el", "index$", "keyboard$", "config", "configuration", "worker$", "setupSearchWorker", "query", "getComponentElement", "result", "fromEvent", "filter", "target", "setToggle", "mode", "key", "active", "getActiveElement", "anchors", "anchor", "getElements", "article", "best", "a", "b", "els", "i", "query$", "mountSearchQuery", "merge", "mountSearchResult", "mergeWith", "getComponentElements", "child", "mountSearchShare", "mountSearchSuggest", "err", "NEVER", "mountSearchHiglight", "el", "index$", "location$", "combineLatest", "startWith", "getLocation", "filter", "url", "map", "index", "setupSearchHighlighter", "fn", "_a", "nodes", "it", "node", "original", "replaced", "text", "childNodes", "h", "watchSidebar", "el", "viewport$", "main$", "parent", "adjust", "combineLatest", "map", "offset", "height", "y", "distinctUntilChanged", "a", "b", "mountSidebar", "_a", "_b", "header$", "options", "__objRest", "inner", "getElement", "getElementOffset", "defer", "push$", "Subject", "done$", "ignoreElements", "endWith", "next$", "auditTime", "animationFrameScheduler", "withLatestFrom", "first", "item", "getElements", "container", "getElementSize", "from", "mergeMap", "label", "fromEvent", "observeOn", "asyncScheduler", "takeUntil", "input", "tap", "state", "finalize", "__spreadValues", "fetchSourceFactsFromGitHub", "user", "repo", "url", "zip", "requestJSON", "catchError", "EMPTY", "map", "release", "defaultIfEmpty", "info", "__spreadValues", "fetchSourceFactsFromGitLab", "base", "project", "url", "requestJSON", "catchError", "EMPTY", "map", "star_count", "forks_count", "defaultIfEmpty", "fetchSourceFacts", "url", "match", "user", "repo", "fetchSourceFactsFromGitHub", "base", "slug", "fetchSourceFactsFromGitLab", "EMPTY", "fetch$", "watchSource", "el", "defer", "cached", "of", "getComponentElements", "consent", "EMPTY", "fetchSourceFacts", "tap", "facts", "catchError", "filter", "map", "shareReplay", "mountSource", "inner", "getElement", "push$", "Subject", "renderSourceFacts", "state", "finalize", "__spreadValues", "watchTabs", "el", "viewport$", "header$", "watchElementSize", "switchMap", "watchViewportAt", "map", "y", "distinctUntilKeyChanged", "mountTabs", "options", "defer", "push$", "Subject", "hidden", "feature", "of", "tap", "state", "finalize", "__spreadValues", "watchTableOfContents", "el", "viewport$", "header$", "table", "anchors", "getElements", "anchor", "id", "target", "getOptionalElement", "adjust$", "distinctUntilKeyChanged", "map", "height", "main", "getComponentElement", "grid", "getElement", "share", "watchElementSize", "switchMap", "body", "defer", "path", "of", "index", "offset", "parent", "a", "b", "combineLatestWith", "adjust", "scan", "prev", "next", "y", "size", "last", "distinctUntilChanged", "startWith", "bufferCount", "mountTableOfContents", "main$", "target$", "push$", "Subject", "done$", "ignoreElements", "endWith", "feature", "smooth$", "merge", "debounceTime", "filter", "observeOn", "asyncScheduler", "withLatestFrom", "behavior", "container", "getElementContainer", "getElementSize", "takeUntil", "skip", "repeat", "url", "getLocation", "active", "hash", "tap", "state", "finalize", "__spreadValues", "watchBackToTop", "_el", "viewport$", "main$", "target$", "direction$", "map", "y", "bufferCount", "b", "distinctUntilChanged", "active$", "active", "combineLatest", "direction", "takeUntil", "skip", "endWith", "repeat", "hidden", "mountBackToTop", "el", "header$", "push$", "Subject", "done$", "ignoreElements", "distinctUntilKeyChanged", "height", "fromEvent", "ev", "tap", "state", "finalize", "__spreadValues", "patchEllipsis", "document$", "viewport$", "switchMap", "getElements", "mergeMap", "el", "watchElementVisibility", "takeUntil", "skip", "filter", "visible", "map", "take", "text", "host", "feature", "mountInlineTooltip2", "finalize", "EMPTY", "patchIndeterminate", "document$", "tablet$", "switchMap", "getElements", "tap", "el", "mergeMap", "fromEvent", "takeWhile", "map", "withLatestFrom", "tablet", "isAppleDevice", "patchScrollfix", "document$", "switchMap", "getElements", "tap", "el", "filter", "mergeMap", "fromEvent", "map", "top", "patchScrolllock", "viewport$", "tablet$", "combineLatest", "watchToggle", "map", "active", "tablet", "switchMap", "of", "delay", "withLatestFrom", "y", "value", "obj", "data", "key", "x", "y", "nodes", "parent", "i", "node", "fetchSearchIndex", "watchScript", "config", "map", "shareReplay", "requestJSON", "document$", "watchDocument", "location$", "watchLocation", "target$", "watchLocationTarget", "keyboard$", "watchKeyboard", "viewport$", "watchViewport", "tablet$", "watchMedia", "screen$", "print$", "watchPrint", "configuration", "index$", "NEVER", "alert$", "Subject", "setupClipboardJS", "progress$", "feature", "setupInstantNavigation", "_a", "setupVersionSelector", "merge", "delay", "setToggle", "filter", "mode", "key", "prev", "getOptionalElement", "setLocation", "next", "active", "getActiveElement", "patchEllipsis", "patchIndeterminate", "patchScrollfix", "patchScrolllock", "header$", "watchHeader", "getComponentElement", "main$", "switchMap", "el", "watchMain", "control$", "getComponentElements", "mountConsent", "mountDialog", "mountHeader", "mountPalette", "mountProgress", "mountSearch", "mountSource", "content$", "defer", "mountAnnounce", "mountContent", "mountSearchHiglight", "EMPTY", "mountHeaderTitle", "at", "mountSidebar", "mountTabs", "mountTableOfContents", "mountBackToTop", "component$", "mergeWith"]
+}
diff --git a/assets/javascripts/workers/search.6ce7567c.min.js b/assets/javascripts/workers/search.b8dbb3d2.min.js
similarity index 94%
rename from assets/javascripts/workers/search.6ce7567c.min.js
rename to assets/javascripts/workers/search.b8dbb3d2.min.js
index 8e7e550..c8a1ec8 100644
--- a/assets/javascripts/workers/search.6ce7567c.min.js
+++ b/assets/javascripts/workers/search.b8dbb3d2.min.js
@@ -1,4 +1,4 @@
-"use strict";(()=>{var xe=Object.create;var U=Object.defineProperty,ve=Object.defineProperties,Se=Object.getOwnPropertyDescriptor,Te=Object.getOwnPropertyDescriptors,Qe=Object.getOwnPropertyNames,J=Object.getOwnPropertySymbols,Ee=Object.getPrototypeOf,Z=Object.prototype.hasOwnProperty,be=Object.prototype.propertyIsEnumerable;var K=Math.pow,X=(t,e,r)=>e in t?U(t,e,{enumerable:!0,configurable:!0,writable:!0,value:r}):t[e]=r,A=(t,e)=>{for(var r in e||(e={}))Z.call(e,r)&&X(t,r,e[r]);if(J)for(var r of J(e))be.call(e,r)&&X(t,r,e[r]);return t},G=(t,e)=>ve(t,Te(e));var Le=(t,e)=>()=>(e||t((e={exports:{}}).exports,e),e.exports);var we=(t,e,r,n)=>{if(e&&typeof e=="object"||typeof e=="function")for(let i of Qe(e))!Z.call(t,i)&&i!==r&&U(t,i,{get:()=>e[i],enumerable:!(n=Se(e,i))||n.enumerable});return t};var Pe=(t,e,r)=>(r=t!=null?xe(Ee(t)):{},we(e||!t||!t.__esModule?U(r,"default",{value:t,enumerable:!0}):r,t));var B=(t,e,r)=>new Promise((n,i)=>{var s=u=>{try{a(r.next(u))}catch(c){i(c)}},o=u=>{try{a(r.throw(u))}catch(c){i(c)}},a=u=>u.done?n(u.value):Promise.resolve(u.value).then(s,o);a((r=r.apply(t,e)).next())});var re=Le((ee,te)=>{/**
+"use strict";(()=>{var xe=Object.create;var U=Object.defineProperty,ve=Object.defineProperties,Se=Object.getOwnPropertyDescriptor,Te=Object.getOwnPropertyDescriptors,Qe=Object.getOwnPropertyNames,Y=Object.getOwnPropertySymbols,Ee=Object.getPrototypeOf,X=Object.prototype.hasOwnProperty,be=Object.prototype.propertyIsEnumerable;var Z=Math.pow,J=(t,e,r)=>e in t?U(t,e,{enumerable:!0,configurable:!0,writable:!0,value:r}):t[e]=r,A=(t,e)=>{for(var r in e||(e={}))X.call(e,r)&&J(t,r,e[r]);if(Y)for(var r of Y(e))be.call(e,r)&&J(t,r,e[r]);return t},G=(t,e)=>ve(t,Te(e));var Le=(t,e)=>()=>(e||t((e={exports:{}}).exports,e),e.exports);var we=(t,e,r,n)=>{if(e&&typeof e=="object"||typeof e=="function")for(let i of Qe(e))!X.call(t,i)&&i!==r&&U(t,i,{get:()=>e[i],enumerable:!(n=Se(e,i))||n.enumerable});return t};var Pe=(t,e,r)=>(r=t!=null?xe(Ee(t)):{},we(e||!t||!t.__esModule?U(r,"default",{value:t,enumerable:!0}):r,t));var B=(t,e,r)=>new Promise((n,i)=>{var s=u=>{try{a(r.next(u))}catch(c){i(c)}},o=u=>{try{a(r.throw(u))}catch(c){i(c)}},a=u=>u.done?n(u.value):Promise.resolve(u.value).then(s,o);a((r=r.apply(t,e)).next())});var te=Le((K,ee)=>{/**
  * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9
  * Copyright (C) 2020 Oliver Nightingale
  * @license MIT
@@ -37,6 +37,6 @@
  */t.Index=function(e){this.invertedIndex=e.invertedIndex,this.fieldVectors=e.fieldVectors,this.tokenSet=e.tokenSet,this.fields=e.fields,this.pipeline=e.pipeline},t.Index.prototype.search=function(e){return this.query(function(r){var n=new t.QueryParser(e,r);n.parse()})},t.Index.prototype.query=function(e){for(var r=new t.Query(this.fields),n=Object.create(null),i=Object.create(null),s=Object.create(null),o=Object.create(null),a=Object.create(null),u=0;u<this.fields.length;u++)i[this.fields[u]]=new t.Vector;e.call(r,r);for(var u=0;u<r.clauses.length;u++){var c=r.clauses[u],f=null,g=t.Set.empty;c.usePipeline?f=this.pipeline.runString(c.term,{fields:c.fields}):f=[c.term];for(var l=0;l<f.length;l++){var m=f[l];c.term=m;var x=t.TokenSet.fromClause(c),v=this.tokenSet.intersect(x).toArray();if(v.length===0&&c.presence===t.Query.presence.REQUIRED){for(var d=0;d<c.fields.length;d++){var y=c.fields[d];o[y]=t.Set.empty}break}for(var b=0;b<v.length;b++)for(var E=v[b],w=this.invertedIndex[E],R=w._index,d=0;d<c.fields.length;d++){var y=c.fields[d],j=w[y],_=Object.keys(j),D=E+"/"+y,N=new t.Set(_);if(c.presence==t.Query.presence.REQUIRED&&(g=g.union(N),o[y]===void 0&&(o[y]=t.Set.complete)),c.presence==t.Query.presence.PROHIBITED){a[y]===void 0&&(a[y]=t.Set.empty),a[y]=a[y].union(N);continue}if(i[y].upsert(R,c.boost,function(ye,me){return ye+me}),!s[D]){for(var C=0;C<_.length;C++){var V=_[C],P=new t.FieldRef(V,y),z=j[V],$;($=n[P])===void 0?n[P]=new t.MatchData(E,y,z):$.add(E,y,z)}s[D]=!0}}}if(c.presence===t.Query.presence.REQUIRED)for(var d=0;d<c.fields.length;d++){var y=c.fields[d];o[y]=o[y].intersect(g)}}for(var M=t.Set.complete,I=t.Set.empty,u=0;u<this.fields.length;u++){var y=this.fields[u];o[y]&&(M=M.intersect(o[y])),a[y]&&(I=I.union(a[y]))}var h=Object.keys(n),S=[],k=Object.create(null);if(r.isNegated()){h=Object.keys(this.fieldVectors);for(var u=0;u<h.length;u++){var P=h[u],L=t.FieldRef.fromString(P);n[P]=new t.MatchData}}for(var u=0;u<h.length;u++){var L=t.FieldRef.fromString(h[u]),p=L.docRef;if(M.contains(p)&&!I.contains(p)){var T=this.fieldVectors[L],O=i[L.fieldName].similarity(T),F;if((F=k[p])!==void 0)F.score+=O,F.matchData.combine(n[L]);else{var Q={ref:p,score:O,matchData:n[L]};k[p]=Q,S.push(Q)}}}return S.sort(function(pe,ge){return ge.score-pe.score})},t.Index.prototype.toJSON=function(){var e=Object.keys(this.invertedIndex).sort().map(function(n){return[n,this.invertedIndex[n]]},this),r=Object.keys(this.fieldVectors).map(function(n){return[n,this.fieldVectors[n].toJSON()]},this);return{version:t.version,fields:this.fields,fieldVectors:r,invertedIndex:e,pipeline:this.pipeline.toJSON()}},t.Index.load=function(e){var r={},n={},i=e.fieldVectors,s=Object.create(null),o=e.invertedIndex,a=new t.TokenSet.Builder,u=t.Pipeline.load(e.pipeline);e.version!=t.version&&t.utils.warn("Version mismatch when loading serialised index. Current version of lunr '"+t.version+"' does not match serialized index '"+e.version+"'");for(var c=0;c<i.length;c++){var f=i[c],g=f[0],l=f[1];n[g]=new t.Vector(l)}for(var c=0;c<o.length;c++){var f=o[c],m=f[0],x=f[1];a.insert(m),s[m]=x}return a.finish(),r.fields=e.fields,r.fieldVectors=n,r.invertedIndex=s,r.tokenSet=a.root,r.pipeline=u,new t.Index(r)};/*!
  * lunr.Builder
  * Copyright (C) 2020 Oliver Nightingale
- */t.Builder=function(){this._ref="id",this._fields=Object.create(null),this._documents=Object.create(null),this.invertedIndex=Object.create(null),this.fieldTermFrequencies={},this.fieldLengths={},this.tokenizer=t.tokenizer,this.pipeline=new t.Pipeline,this.searchPipeline=new t.Pipeline,this.documentCount=0,this._b=.75,this._k1=1.2,this.termIndex=0,this.metadataWhitelist=[]},t.Builder.prototype.ref=function(e){this._ref=e},t.Builder.prototype.field=function(e,r){if(/\//.test(e))throw new RangeError("Field '"+e+"' contains illegal character '/'");this._fields[e]=r||{}},t.Builder.prototype.b=function(e){e<0?this._b=0:e>1?this._b=1:this._b=e},t.Builder.prototype.k1=function(e){this._k1=e},t.Builder.prototype.add=function(e,r){var n=e[this._ref],i=Object.keys(this._fields);this._documents[n]=r||{},this.documentCount+=1;for(var s=0;s<i.length;s++){var o=i[s],a=this._fields[o].extractor,u=a?a(e):e[o],c=this.tokenizer(u,{fields:[o]}),f=this.pipeline.run(c),g=new t.FieldRef(n,o),l=Object.create(null);this.fieldTermFrequencies[g]=l,this.fieldLengths[g]=0,this.fieldLengths[g]+=f.length;for(var m=0;m<f.length;m++){var x=f[m];if(l[x]==null&&(l[x]=0),l[x]+=1,this.invertedIndex[x]==null){var v=Object.create(null);v._index=this.termIndex,this.termIndex+=1;for(var d=0;d<i.length;d++)v[i[d]]=Object.create(null);this.invertedIndex[x]=v}this.invertedIndex[x][o][n]==null&&(this.invertedIndex[x][o][n]=Object.create(null));for(var y=0;y<this.metadataWhitelist.length;y++){var b=this.metadataWhitelist[y],E=x.metadata[b];this.invertedIndex[x][o][n][b]==null&&(this.invertedIndex[x][o][n][b]=[]),this.invertedIndex[x][o][n][b].push(E)}}}},t.Builder.prototype.calculateAverageFieldLengths=function(){for(var e=Object.keys(this.fieldLengths),r=e.length,n={},i={},s=0;s<r;s++){var o=t.FieldRef.fromString(e[s]),a=o.fieldName;i[a]||(i[a]=0),i[a]+=1,n[a]||(n[a]=0),n[a]+=this.fieldLengths[o]}for(var u=Object.keys(this._fields),s=0;s<u.length;s++){var c=u[s];n[c]=n[c]/i[c]}this.averageFieldLength=n},t.Builder.prototype.createFieldVectors=function(){for(var e={},r=Object.keys(this.fieldTermFrequencies),n=r.length,i=Object.create(null),s=0;s<n;s++){for(var o=t.FieldRef.fromString(r[s]),a=o.fieldName,u=this.fieldLengths[o],c=new t.Vector,f=this.fieldTermFrequencies[o],g=Object.keys(f),l=g.length,m=this._fields[a].boost||1,x=this._documents[o.docRef].boost||1,v=0;v<l;v++){var d=g[v],y=f[d],b=this.invertedIndex[d]._index,E,w,R;i[d]===void 0?(E=t.idf(this.invertedIndex[d],this.documentCount),i[d]=E):E=i[d],w=E*((this._k1+1)*y)/(this._k1*(1-this._b+this._b*(u/this.averageFieldLength[a]))+y),w*=m,w*=x,R=Math.round(w*1e3)/1e3,c.insert(b,R)}e[o]=c}this.fieldVectors=e},t.Builder.prototype.createTokenSet=function(){this.tokenSet=t.TokenSet.fromArray(Object.keys(this.invertedIndex).sort())},t.Builder.prototype.build=function(){return this.calculateAverageFieldLengths(),this.createFieldVectors(),this.createTokenSet(),new t.Index({invertedIndex:this.invertedIndex,fieldVectors:this.fieldVectors,tokenSet:this.tokenSet,fields:Object.keys(this._fields),pipeline:this.searchPipeline})},t.Builder.prototype.use=function(e){var r=Array.prototype.slice.call(arguments,1);r.unshift(this),e.apply(this,r)},t.MatchData=function(e,r,n){for(var i=Object.create(null),s=Object.keys(n||{}),o=0;o<s.length;o++){var a=s[o];i[a]=n[a].slice()}this.metadata=Object.create(null),e!==void 0&&(this.metadata[e]=Object.create(null),this.metadata[e][r]=i)},t.MatchData.prototype.combine=function(e){for(var r=Object.keys(e.metadata),n=0;n<r.length;n++){var i=r[n],s=Object.keys(e.metadata[i]);this.metadata[i]==null&&(this.metadata[i]=Object.create(null));for(var o=0;o<s.length;o++){var a=s[o],u=Object.keys(e.metadata[i][a]);this.metadata[i][a]==null&&(this.metadata[i][a]=Object.create(null));for(var c=0;c<u.length;c++){var f=u[c];this.metadata[i][a][f]==null?this.metadata[i][a][f]=e.metadata[i][a][f]:this.metadata[i][a][f]=this.metadata[i][a][f].concat(e.metadata[i][a][f])}}}},t.MatchData.prototype.add=function(e,r,n){if(!(e in this.metadata)){this.metadata[e]=Object.create(null),this.metadata[e][r]=n;return}if(!(r in this.metadata[e])){this.metadata[e][r]=n;return}for(var i=Object.keys(n),s=0;s<i.length;s++){var o=i[s];o in this.metadata[e][r]?this.metadata[e][r][o]=this.metadata[e][r][o].concat(n[o]):this.metadata[e][r][o]=n[o]}},t.Query=function(e){this.clauses=[],this.allFields=e},t.Query.wildcard=new String("*"),t.Query.wildcard.NONE=0,t.Query.wildcard.LEADING=1,t.Query.wildcard.TRAILING=2,t.Query.presence={OPTIONAL:1,REQUIRED:2,PROHIBITED:3},t.Query.prototype.clause=function(e){return"fields"in e||(e.fields=this.allFields),"boost"in e||(e.boost=1),"usePipeline"in e||(e.usePipeline=!0),"wildcard"in e||(e.wildcard=t.Query.wildcard.NONE),e.wildcard&t.Query.wildcard.LEADING&&e.term.charAt(0)!=t.Query.wildcard&&(e.term="*"+e.term),e.wildcard&t.Query.wildcard.TRAILING&&e.term.slice(-1)!=t.Query.wildcard&&(e.term=""+e.term+"*"),"presence"in e||(e.presence=t.Query.presence.OPTIONAL),this.clauses.push(e),this},t.Query.prototype.isNegated=function(){for(var e=0;e<this.clauses.length;e++)if(this.clauses[e].presence!=t.Query.presence.PROHIBITED)return!1;return!0},t.Query.prototype.term=function(e,r){if(Array.isArray(e))return e.forEach(function(i){this.term(i,t.utils.clone(r))},this),this;var n=r||{};return n.term=e.toString(),this.clause(n),this},t.QueryParseError=function(e,r,n){this.name="QueryParseError",this.message=e,this.start=r,this.end=n},t.QueryParseError.prototype=new Error,t.QueryLexer=function(e){this.lexemes=[],this.str=e,this.length=e.length,this.pos=0,this.start=0,this.escapeCharPositions=[]},t.QueryLexer.prototype.run=function(){for(var e=t.QueryLexer.lexText;e;)e=e(this)},t.QueryLexer.prototype.sliceString=function(){for(var e=[],r=this.start,n=this.pos,i=0;i<this.escapeCharPositions.length;i++)n=this.escapeCharPositions[i],e.push(this.str.slice(r,n)),r=n+1;return e.push(this.str.slice(r,this.pos)),this.escapeCharPositions.length=0,e.join("")},t.QueryLexer.prototype.emit=function(e){this.lexemes.push({type:e,str:this.sliceString(),start:this.start,end:this.pos}),this.start=this.pos},t.QueryLexer.prototype.escapeCharacter=function(){this.escapeCharPositions.push(this.pos-1),this.pos+=1},t.QueryLexer.prototype.next=function(){if(this.pos>=this.length)return t.QueryLexer.EOS;var e=this.str.charAt(this.pos);return this.pos+=1,e},t.QueryLexer.prototype.width=function(){return this.pos-this.start},t.QueryLexer.prototype.ignore=function(){this.start==this.pos&&(this.pos+=1),this.start=this.pos},t.QueryLexer.prototype.backup=function(){this.pos-=1},t.QueryLexer.prototype.acceptDigitRun=function(){var e,r;do 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e.ignore(),e.acceptDigitRun(),e.emit(t.QueryLexer.BOOST),t.QueryLexer.lexText},t.QueryLexer.lexEOS=function(e){e.width()>0&&e.emit(t.QueryLexer.TERM)},t.QueryLexer.termSeparator=t.tokenizer.separator,t.QueryLexer.lexText=function(e){for(;;){var r=e.next();if(r==t.QueryLexer.EOS)return t.QueryLexer.lexEOS;if(r.charCodeAt(0)==92){e.escapeCharacter();continue}if(r==":")return t.QueryLexer.lexField;if(r=="~")return e.backup(),e.width()>0&&e.emit(t.QueryLexer.TERM),t.QueryLexer.lexEditDistance;if(r=="^")return e.backup(),e.width()>0&&e.emit(t.QueryLexer.TERM),t.QueryLexer.lexBoost;if(r=="+"&&e.width()===1||r=="-"&&e.width()===1)return e.emit(t.QueryLexer.PRESENCE),t.QueryLexer.lexText;if(r.match(t.QueryLexer.termSeparator))return t.QueryLexer.lexTerm}},t.QueryParser=function(e,r){this.lexer=new t.QueryLexer(e),this.query=r,this.currentClause={},this.lexemeIdx=0},t.QueryParser.prototype.parse=function(){this.lexer.run(),this.lexemes=this.lexer.lexemes;for(var 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r=e.consumeLexeme();if(r!=null){switch(r.str){case"-":e.currentClause.presence=t.Query.presence.PROHIBITED;break;case"+":e.currentClause.presence=t.Query.presence.REQUIRED;break;default:var n="unrecognised presence operator'"+r.str+"'";throw new t.QueryParseError(n,r.start,r.end)}var i=e.peekLexeme();if(i==null){var n="expecting term or field, found nothing";throw new t.QueryParseError(n,r.start,r.end)}switch(i.type){case t.QueryLexer.FIELD:return t.QueryParser.parseField;case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var n="expecting term or field, found '"+i.type+"'";throw new t.QueryParseError(n,i.start,i.end)}}},t.QueryParser.parseField=function(e){var r=e.consumeLexeme();if(r!=null){if(e.query.allFields.indexOf(r.str)==-1){var n=e.query.allFields.map(function(o){return"'"+o+"'"}).join(", "),i="unrecognised field '"+r.str+"', possible fields: "+n;throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.fields=[r.str];var s=e.peekLexeme();if(s==null){var i="expecting term, found nothing";throw new t.QueryParseError(i,r.start,r.end)}switch(s.type){case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var i="expecting term, found '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},t.QueryParser.parseTerm=function(e){var r=e.consumeLexeme();if(r!=null){e.currentClause.term=r.str.toLowerCase(),r.str.indexOf("*")!=-1&&(e.currentClause.usePipeline=!1);var n=e.peekLexeme();if(n==null){e.nextClause();return}switch(n.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+n.type+"'";throw new t.QueryParseError(i,n.start,n.end)}}},t.QueryParser.parseEditDistance=function(e){var 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i;if(this.options.suggest){let s=this.index.query(o=>{for(let a of r)o.term(a.term,{fields:["title"],presence:lunr.Query.presence.REQUIRED,wildcard:lunr.Query.wildcard.TRAILING})});i=s.length?Object.keys(s[0].matchData.metadata):[]}return A({items:[...n.values()]},typeof i!="undefined"&&{suggest:i})}};var de;function Ie(t){return B(this,null,function*(){let e="../lunr";if(typeof parent!="undefined"&&"IFrameWorker"in parent){let n=ne("script[src]"),[i]=n.src.split("/worker");e=e.replace("..",i)}let r=[];for(let n of t.lang){switch(n){case"ja":r.push(`${e}/tinyseg.js`);break;case"hi":case"th":r.push(`${e}/wordcut.js`);break}n!=="en"&&r.push(`${e}/min/lunr.${n}.min.js`)}t.lang.length>1&&r.push(`${e}/min/lunr.multi.min.js`),r.length&&(yield importScripts(`${e}/min/lunr.stemmer.support.min.js`,...r))})}function Fe(t){return B(this,null,function*(){switch(t.type){case 0:return yield Ie(t.data.config),de=new H(t.data),{type:1};case 2:let 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+ */t.Builder=function(){this._ref="id",this._fields=Object.create(null),this._documents=Object.create(null),this.invertedIndex=Object.create(null),this.fieldTermFrequencies={},this.fieldLengths={},this.tokenizer=t.tokenizer,this.pipeline=new t.Pipeline,this.searchPipeline=new t.Pipeline,this.documentCount=0,this._b=.75,this._k1=1.2,this.termIndex=0,this.metadataWhitelist=[]},t.Builder.prototype.ref=function(e){this._ref=e},t.Builder.prototype.field=function(e,r){if(/\//.test(e))throw new RangeError("Field '"+e+"' contains illegal character '/'");this._fields[e]=r||{}},t.Builder.prototype.b=function(e){e<0?this._b=0:e>1?this._b=1:this._b=e},t.Builder.prototype.k1=function(e){this._k1=e},t.Builder.prototype.add=function(e,r){var n=e[this._ref],i=Object.keys(this._fields);this._documents[n]=r||{},this.documentCount+=1;for(var s=0;s<i.length;s++){var o=i[s],a=this._fields[o].extractor,u=a?a(e):e[o],c=this.tokenizer(u,{fields:[o]}),f=this.pipeline.run(c),g=new t.FieldRef(n,o),l=Object.create(null);this.fieldTermFrequencies[g]=l,this.fieldLengths[g]=0,this.fieldLengths[g]+=f.length;for(var m=0;m<f.length;m++){var x=f[m];if(l[x]==null&&(l[x]=0),l[x]+=1,this.invertedIndex[x]==null){var v=Object.create(null);v._index=this.termIndex,this.termIndex+=1;for(var d=0;d<i.length;d++)v[i[d]]=Object.create(null);this.invertedIndex[x]=v}this.invertedIndex[x][o][n]==null&&(this.invertedIndex[x][o][n]=Object.create(null));for(var y=0;y<this.metadataWhitelist.length;y++){var b=this.metadataWhitelist[y],E=x.metadata[b];this.invertedIndex[x][o][n][b]==null&&(this.invertedIndex[x][o][n][b]=[]),this.invertedIndex[x][o][n][b].push(E)}}}},t.Builder.prototype.calculateAverageFieldLengths=function(){for(var e=Object.keys(this.fieldLengths),r=e.length,n={},i={},s=0;s<r;s++){var o=t.FieldRef.fromString(e[s]),a=o.fieldName;i[a]||(i[a]=0),i[a]+=1,n[a]||(n[a]=0),n[a]+=this.fieldLengths[o]}for(var u=Object.keys(this._fields),s=0;s<u.length;s++){var c=u[s];n[c]=n[c]/i[c]}this.averageFieldLength=n},t.Builder.prototype.createFieldVectors=function(){for(var e={},r=Object.keys(this.fieldTermFrequencies),n=r.length,i=Object.create(null),s=0;s<n;s++){for(var o=t.FieldRef.fromString(r[s]),a=o.fieldName,u=this.fieldLengths[o],c=new t.Vector,f=this.fieldTermFrequencies[o],g=Object.keys(f),l=g.length,m=this._fields[a].boost||1,x=this._documents[o.docRef].boost||1,v=0;v<l;v++){var d=g[v],y=f[d],b=this.invertedIndex[d]._index,E,w,R;i[d]===void 0?(E=t.idf(this.invertedIndex[d],this.documentCount),i[d]=E):E=i[d],w=E*((this._k1+1)*y)/(this._k1*(1-this._b+this._b*(u/this.averageFieldLength[a]))+y),w*=m,w*=x,R=Math.round(w*1e3)/1e3,c.insert(b,R)}e[o]=c}this.fieldVectors=e},t.Builder.prototype.createTokenSet=function(){this.tokenSet=t.TokenSet.fromArray(Object.keys(this.invertedIndex).sort())},t.Builder.prototype.build=function(){return this.calculateAverageFieldLengths(),this.createFieldVectors(),this.createTokenSet(),new t.Index({invertedIndex:this.invertedIndex,fieldVectors:this.fieldVectors,tokenSet:this.tokenSet,fields:Object.keys(this._fields),pipeline:this.searchPipeline})},t.Builder.prototype.use=function(e){var r=Array.prototype.slice.call(arguments,1);r.unshift(this),e.apply(this,r)},t.MatchData=function(e,r,n){for(var i=Object.create(null),s=Object.keys(n||{}),o=0;o<s.length;o++){var a=s[o];i[a]=n[a].slice()}this.metadata=Object.create(null),e!==void 0&&(this.metadata[e]=Object.create(null),this.metadata[e][r]=i)},t.MatchData.prototype.combine=function(e){for(var r=Object.keys(e.metadata),n=0;n<r.length;n++){var i=r[n],s=Object.keys(e.metadata[i]);this.metadata[i]==null&&(this.metadata[i]=Object.create(null));for(var o=0;o<s.length;o++){var a=s[o],u=Object.keys(e.metadata[i][a]);this.metadata[i][a]==null&&(this.metadata[i][a]=Object.create(null));for(var c=0;c<u.length;c++){var f=u[c];this.metadata[i][a][f]==null?this.metadata[i][a][f]=e.metadata[i][a][f]:this.metadata[i][a][f]=this.metadata[i][a][f].concat(e.metadata[i][a][f])}}}},t.MatchData.prototype.add=function(e,r,n){if(!(e in this.metadata)){this.metadata[e]=Object.create(null),this.metadata[e][r]=n;return}if(!(r in this.metadata[e])){this.metadata[e][r]=n;return}for(var i=Object.keys(n),s=0;s<i.length;s++){var o=i[s];o in this.metadata[e][r]?this.metadata[e][r][o]=this.metadata[e][r][o].concat(n[o]):this.metadata[e][r][o]=n[o]}},t.Query=function(e){this.clauses=[],this.allFields=e},t.Query.wildcard=new String("*"),t.Query.wildcard.NONE=0,t.Query.wildcard.LEADING=1,t.Query.wildcard.TRAILING=2,t.Query.presence={OPTIONAL:1,REQUIRED:2,PROHIBITED:3},t.Query.prototype.clause=function(e){return"fields"in e||(e.fields=this.allFields),"boost"in e||(e.boost=1),"usePipeline"in e||(e.usePipeline=!0),"wildcard"in e||(e.wildcard=t.Query.wildcard.NONE),e.wildcard&t.Query.wildcard.LEADING&&e.term.charAt(0)!=t.Query.wildcard&&(e.term="*"+e.term),e.wildcard&t.Query.wildcard.TRAILING&&e.term.slice(-1)!=t.Query.wildcard&&(e.term=""+e.term+"*"),"presence"in e||(e.presence=t.Query.presence.OPTIONAL),this.clauses.push(e),this},t.Query.prototype.isNegated=function(){for(var e=0;e<this.clauses.length;e++)if(this.clauses[e].presence!=t.Query.presence.PROHIBITED)return!1;return!0},t.Query.prototype.term=function(e,r){if(Array.isArray(e))return e.forEach(function(i){this.term(i,t.utils.clone(r))},this),this;var n=r||{};return n.term=e.toString(),this.clause(n),this},t.QueryParseError=function(e,r,n){this.name="QueryParseError",this.message=e,this.start=r,this.end=n},t.QueryParseError.prototype=new Error,t.QueryLexer=function(e){this.lexemes=[],this.str=e,this.length=e.length,this.pos=0,this.start=0,this.escapeCharPositions=[]},t.QueryLexer.prototype.run=function(){for(var e=t.QueryLexer.lexText;e;)e=e(this)},t.QueryLexer.prototype.sliceString=function(){for(var e=[],r=this.start,n=this.pos,i=0;i<this.escapeCharPositions.length;i++)n=this.escapeCharPositions[i],e.push(this.str.slice(r,n)),r=n+1;return e.push(this.str.slice(r,this.pos)),this.escapeCharPositions.length=0,e.join("")},t.QueryLexer.prototype.emit=function(e){this.lexemes.push({type:e,str:this.sliceString(),start:this.start,end:this.pos}),this.start=this.pos},t.QueryLexer.prototype.escapeCharacter=function(){this.escapeCharPositions.push(this.pos-1),this.pos+=1},t.QueryLexer.prototype.next=function(){if(this.pos>=this.length)return t.QueryLexer.EOS;var e=this.str.charAt(this.pos);return this.pos+=1,e},t.QueryLexer.prototype.width=function(){return this.pos-this.start},t.QueryLexer.prototype.ignore=function(){this.start==this.pos&&(this.pos+=1),this.start=this.pos},t.QueryLexer.prototype.backup=function(){this.pos-=1},t.QueryLexer.prototype.acceptDigitRun=function(){var e,r;do 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e.ignore(),e.acceptDigitRun(),e.emit(t.QueryLexer.BOOST),t.QueryLexer.lexText},t.QueryLexer.lexEOS=function(e){e.width()>0&&e.emit(t.QueryLexer.TERM)},t.QueryLexer.termSeparator=t.tokenizer.separator,t.QueryLexer.lexText=function(e){for(;;){var r=e.next();if(r==t.QueryLexer.EOS)return t.QueryLexer.lexEOS;if(r.charCodeAt(0)==92){e.escapeCharacter();continue}if(r==":")return t.QueryLexer.lexField;if(r=="~")return e.backup(),e.width()>0&&e.emit(t.QueryLexer.TERM),t.QueryLexer.lexEditDistance;if(r=="^")return e.backup(),e.width()>0&&e.emit(t.QueryLexer.TERM),t.QueryLexer.lexBoost;if(r=="+"&&e.width()===1||r=="-"&&e.width()===1)return e.emit(t.QueryLexer.PRESENCE),t.QueryLexer.lexText;if(r.match(t.QueryLexer.termSeparator))return t.QueryLexer.lexTerm}},t.QueryParser=function(e,r){this.lexer=new t.QueryLexer(e),this.query=r,this.currentClause={},this.lexemeIdx=0},t.QueryParser.prototype.parse=function(){this.lexer.run(),this.lexemes=this.lexer.lexemes;for(var e=t.QueryParser.parseClause;e;)e=e(this);return this.query},t.QueryParser.prototype.peekLexeme=function(){return this.lexemes[this.lexemeIdx]},t.QueryParser.prototype.consumeLexeme=function(){var e=this.peekLexeme();return this.lexemeIdx+=1,e},t.QueryParser.prototype.nextClause=function(){var e=this.currentClause;this.query.clause(e),this.currentClause={}},t.QueryParser.parseClause=function(e){var r=e.peekLexeme();if(r!=null)switch(r.type){case t.QueryLexer.PRESENCE:return t.QueryParser.parsePresence;case t.QueryLexer.FIELD:return t.QueryParser.parseField;case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var n="expected either a field or a term, found "+r.type;throw r.str.length>=1&&(n+=" with value '"+r.str+"'"),new t.QueryParseError(n,r.start,r.end)}},t.QueryParser.parsePresence=function(e){var r=e.consumeLexeme();if(r!=null){switch(r.str){case"-":e.currentClause.presence=t.Query.presence.PROHIBITED;break;case"+":e.currentClause.presence=t.Query.presence.REQUIRED;break;default:var n="unrecognised presence operator'"+r.str+"'";throw new t.QueryParseError(n,r.start,r.end)}var i=e.peekLexeme();if(i==null){var n="expecting term or field, found nothing";throw new t.QueryParseError(n,r.start,r.end)}switch(i.type){case t.QueryLexer.FIELD:return t.QueryParser.parseField;case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var n="expecting term or field, found '"+i.type+"'";throw new t.QueryParseError(n,i.start,i.end)}}},t.QueryParser.parseField=function(e){var r=e.consumeLexeme();if(r!=null){if(e.query.allFields.indexOf(r.str)==-1){var n=e.query.allFields.map(function(o){return"'"+o+"'"}).join(", "),i="unrecognised field '"+r.str+"', possible fields: "+n;throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.fields=[r.str];var s=e.peekLexeme();if(s==null){var i="expecting term, found nothing";throw new t.QueryParseError(i,r.start,r.end)}switch(s.type){case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var i="expecting term, found '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},t.QueryParser.parseTerm=function(e){var r=e.consumeLexeme();if(r!=null){e.currentClause.term=r.str.toLowerCase(),r.str.indexOf("*")!=-1&&(e.currentClause.usePipeline=!1);var n=e.peekLexeme();if(n==null){e.nextClause();return}switch(n.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+n.type+"'";throw new t.QueryParseError(i,n.start,n.end)}}},t.QueryParser.parseEditDistance=function(e){var 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i=e(n);return[...r,...Array.isArray(i)?i:[i]]},[]).map(r=>/([~^]$)/.test(r)?`${r}1`:r).map(r=>/(^[+-]|[~^]\d+$)/.test(r)?r:`${r}*`).join(" ")}function ue(t){return ae(t,e=>{let r=[],n=new lunr.QueryLexer(e);n.run();for(let{type:i,str:s,start:o,end:a}of n.lexemes)switch(i){case"FIELD":["title","text","tags"].includes(s)||(e=[e.slice(0,a)," ",e.slice(a+1)].join(""));break;case"TERM":W(s,lunr.tokenizer.separator,(...u)=>{r.push([e.slice(0,o),s.slice(...u),e.slice(a)].join(""))})}return r})}function ce(t){let e=new lunr.Query(["title","text","tags"]);new lunr.QueryParser(t,e).parse();for(let n of e.clauses)n.usePipeline=!0,n.term.startsWith("*")&&(n.wildcard=lunr.Query.wildcard.LEADING,n.term=n.term.slice(1)),n.term.endsWith("*")&&(n.wildcard=lunr.Query.wildcard.TRAILING,n.term=n.term.slice(0,-1));return e.clauses}function le(t,e){var i;let r=new Set(t),n={};for(let s=0;s<e.length;s++)for(let o of r)e[s].startsWith(o.term)&&(n[o.term]=!0,r.delete(o));for(let s of r)(i=lunr.stopWordFilter)!=null&&i.call(lunr,s.term)&&(n[s.term]=!1);return n}function he(t,e){let r=new Set,n=new Uint16Array(t.length);for(let s=0;s<t.length;s++)for(let o=s+1;o<t.length;o++)t.slice(s,o)in e&&(n[s]=o-s);let i=[0];for(let s=i.length;s>0;){let o=i[--s];for(let u=1;u<n[o];u++)n[o+u]>n[o]-u&&(r.add(t.slice(o,o+u)),i[s++]=o+u);let a=o+n[o];n[a]&&a<t.length-1&&(i[s++]=a),r.add(t.slice(o,a))}return r.has("")?new Set([t]):r}function Oe(t){return e=>r=>{if(typeof r[e]=="undefined")return;let n=[r.location,e].join(":");return t.set(n,lunr.tokenizer.table=[]),r[e]}}function Re(t,e){let[r,n]=[new Set(t),new Set(e)];return[...new Set([...r].filter(i=>!n.has(i)))]}var H=class{constructor({config:e,docs:r,options:n}){let i=Oe(this.table=new Map);this.map=ne(r),this.options=n,this.index=lunr(function(){this.metadataWhitelist=["position"],this.b(0),e.lang.length===1&&e.lang[0]!=="en"?this.use(lunr[e.lang[0]]):e.lang.length>1&&this.use(lunr.multiLanguage(...e.lang)),this.tokenizer=oe,lunr.tokenizer.separator=new RegExp(e.separator),lunr.segmenter="TinySegmenter"in lunr?new lunr.TinySegmenter:void 0;let s=Re(["trimmer","stopWordFilter","stemmer"],e.pipeline);for(let o of e.lang.map(a=>a==="en"?lunr:lunr[a]))for(let a of s)this.pipeline.remove(o[a]),this.searchPipeline.remove(o[a]);this.ref("location"),this.field("title",{boost:1e3,extractor:i("title")}),this.field("text",{boost:1,extractor:i("text")}),this.field("tags",{boost:1e6,extractor:i("tags")});for(let o of r)this.add(o,{boost:o.boost})})}search(e){if(e=e.replace(new RegExp("\\p{sc=Han}+","gu"),s=>[...he(s,this.index.invertedIndex)].join("* ")),e=ue(e),!e)return{items:[]};let r=ce(e).filter(s=>s.presence!==lunr.Query.presence.PROHIBITED),n=this.index.search(e).reduce((s,{ref:o,score:a,matchData:u})=>{let c=this.map.get(o);if(typeof c!="undefined"){c=A({},c),c.tags&&(c.tags=[...c.tags]);let f=le(r,Object.keys(u.metadata));for(let l of this.index.fields){if(typeof c[l]=="undefined")continue;let m=[];for(let d of Object.values(u.metadata))typeof d[l]!="undefined"&&m.push(...d[l].position);if(!m.length)continue;let x=this.table.get([c.location,l].join(":")),v=Array.isArray(c[l])?q:se;c[l]=v(c[l],x,m,l!=="text")}let g=+!c.parent+Object.values(f).filter(l=>l).length/Object.keys(f).length;s.push(G(A({},c),{score:a*(1+Z(g,2)),terms:f}))}return s},[]).sort((s,o)=>o.score-s.score).reduce((s,o)=>{let a=this.map.get(o.location);if(typeof a!="undefined"){let u=a.parent?a.parent.location:a.location;s.set(u,[...s.get(u)||[],o])}return s},new Map);for(let[s,o]of n)if(!o.find(a=>a.location===s)){let a=this.map.get(s);o.push(G(A({},a),{score:0,terms:{}}))}let i;if(this.options.suggest){let s=this.index.query(o=>{for(let a of r)o.term(a.term,{fields:["title"],presence:lunr.Query.presence.REQUIRED,wildcard:lunr.Query.wildcard.TRAILING})});i=s.length?Object.keys(s[0].matchData.metadata):[]}return A({items:[...n.values()]},typeof i!="undefined"&&{suggest:i})}};var fe;function Ie(t){return B(this,null,function*(){let e="../lunr";if(typeof parent!="undefined"&&"IFrameWorker"in parent){let n=re("script[src]"),[i]=n.src.split("/worker");e=e.replace("..",i)}let r=[];for(let n of t.lang){switch(n){case"ja":r.push(`${e}/tinyseg.js`);break;case"hi":case"th":r.push(`${e}/wordcut.js`);break}n!=="en"&&r.push(`${e}/min/lunr.${n}.min.js`)}t.lang.length>1&&r.push(`${e}/min/lunr.multi.min.js`),r.length&&(yield importScripts(`${e}/min/lunr.stemmer.support.min.js`,...r))})}function Fe(t){return B(this,null,function*(){switch(t.type){case 0:return yield Ie(t.data.config),fe=new H(t.data),{type:1};case 2:let e=t.data;try{return{type:3,data:fe.search(e)}}catch(r){return console.warn(`Invalid query: ${e} \u2013 see https://bit.ly/2s3ChXG`),console.warn(r),{type:3,data:{items:[]}}}default:throw new TypeError("Invalid message type")}})}self.lunr=de.default;addEventListener("message",t=>B(void 0,null,function*(){postMessage(yield Fe(t.data))}));})();
+//# sourceMappingURL=search.b8dbb3d2.min.js.map
 
diff --git a/assets/javascripts/workers/search.6ce7567c.min.js.map b/assets/javascripts/workers/search.b8dbb3d2.min.js.map
similarity index 76%
rename from assets/javascripts/workers/search.6ce7567c.min.js.map
rename to assets/javascripts/workers/search.b8dbb3d2.min.js.map
index e7c69d2..7ef0846 100644
--- a/assets/javascripts/workers/search.6ce7567c.min.js.map
+++ b/assets/javascripts/workers/search.b8dbb3d2.min.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
   "sources": ["node_modules/lunr/lunr.js", "src/templates/assets/javascripts/integrations/search/worker/main/index.ts", "src/templates/assets/javascripts/browser/element/_/index.ts", "src/templates/assets/javascripts/polyfills/index.ts", "src/templates/assets/javascripts/integrations/search/config/index.ts", "src/templates/assets/javascripts/integrations/search/internal/_/index.ts", "src/templates/assets/javascripts/integrations/search/internal/extract/index.ts", "src/templates/assets/javascripts/integrations/search/internal/highlight/index.ts", "src/templates/assets/javascripts/integrations/search/internal/tokenize/index.ts", "src/templates/assets/javascripts/integrations/search/query/transform/index.ts", "src/templates/assets/javascripts/integrations/search/query/_/index.ts", "src/templates/assets/javascripts/integrations/search/query/segment/index.ts", "src/templates/assets/javascripts/integrations/search/_/index.ts"],
-  "sourcesContent": ["/**\n * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9\n * Copyright (C) 2020 Oliver Nightingale\n * @license MIT\n */\n\n;(function(){\n\n/**\n * A convenience function for configuring and constructing\n * a new lunr Index.\n *\n * A lunr.Builder instance is created and the pipeline setup\n * with a trimmer, stop word filter and stemmer.\n *\n * This builder object is yielded to the configuration function\n * that is passed as a parameter, allowing the list of fields\n * and other builder parameters to be customised.\n *\n * All documents _must_ be added within the passed config function.\n *\n * @example\n * var idx = lunr(function () {\n *   this.field('title')\n *   this.field('body')\n *   this.ref('id')\n *\n *   documents.forEach(function (doc) {\n *     this.add(doc)\n *   }, this)\n * })\n *\n * @see {@link lunr.Builder}\n * @see {@link lunr.Pipeline}\n * @see {@link lunr.trimmer}\n * @see {@link lunr.stopWordFilter}\n * @see {@link lunr.stemmer}\n * @namespace {function} lunr\n */\nvar lunr = function (config) {\n  var builder = new lunr.Builder\n\n  builder.pipeline.add(\n    lunr.trimmer,\n    lunr.stopWordFilter,\n    lunr.stemmer\n  )\n\n  builder.searchPipeline.add(\n    lunr.stemmer\n  )\n\n  config.call(builder, builder)\n  return builder.build()\n}\n\nlunr.version = \"2.3.9\"\n/*!\n * lunr.utils\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A namespace containing utils for the rest of the lunr library\n * @namespace lunr.utils\n */\nlunr.utils = {}\n\n/**\n * Print a warning message to the console.\n *\n * @param {String} message The message to be printed.\n * @memberOf lunr.utils\n * @function\n */\nlunr.utils.warn = (function (global) {\n  /* eslint-disable no-console */\n  return function (message) {\n    if (global.console && console.warn) {\n      console.warn(message)\n    }\n  }\n  /* eslint-enable no-console */\n})(this)\n\n/**\n * Convert an object to a string.\n *\n * In the case of `null` and `undefined` the function returns\n * the empty string, in all other cases the result of calling\n * `toString` on the passed object is returned.\n *\n * @param {Any} obj The object to convert to a string.\n * @return {String} string representation of the passed object.\n * @memberOf lunr.utils\n */\nlunr.utils.asString = function (obj) {\n  if (obj === void 0 || obj === null) {\n    return \"\"\n  } else {\n    return obj.toString()\n  }\n}\n\n/**\n * Clones an object.\n *\n * Will create a copy of an existing object such that any mutations\n * on the copy cannot affect the original.\n *\n * Only shallow objects are supported, passing a nested object to this\n * function will cause a TypeError.\n *\n * Objects with primitives, and arrays of primitives are supported.\n *\n * @param {Object} obj The object to clone.\n * @return {Object} a clone of the passed object.\n * @throws {TypeError} when a nested object is passed.\n * @memberOf Utils\n */\nlunr.utils.clone = function (obj) {\n  if (obj === null || obj === undefined) {\n    return obj\n  }\n\n  var clone = Object.create(null),\n      keys = Object.keys(obj)\n\n  for (var i = 0; i < keys.length; i++) {\n    var key = keys[i],\n        val = obj[key]\n\n    if (Array.isArray(val)) {\n      clone[key] = val.slice()\n      continue\n    }\n\n    if (typeof val === 'string' ||\n        typeof val === 'number' ||\n        typeof val === 'boolean') {\n      clone[key] = val\n      continue\n    }\n\n    throw new TypeError(\"clone is not deep and does not support nested objects\")\n  }\n\n  return clone\n}\nlunr.FieldRef = function (docRef, fieldName, stringValue) {\n  this.docRef = docRef\n  this.fieldName = fieldName\n  this._stringValue = stringValue\n}\n\nlunr.FieldRef.joiner = \"/\"\n\nlunr.FieldRef.fromString = function (s) {\n  var n = s.indexOf(lunr.FieldRef.joiner)\n\n  if (n === -1) {\n    throw \"malformed field ref string\"\n  }\n\n  var fieldRef = s.slice(0, n),\n      docRef = s.slice(n + 1)\n\n  return new lunr.FieldRef (docRef, fieldRef, s)\n}\n\nlunr.FieldRef.prototype.toString = function () {\n  if (this._stringValue == undefined) {\n    this._stringValue = this.fieldName + lunr.FieldRef.joiner + this.docRef\n  }\n\n  return this._stringValue\n}\n/*!\n * lunr.Set\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A lunr set.\n *\n * @constructor\n */\nlunr.Set = function (elements) {\n  this.elements = Object.create(null)\n\n  if (elements) {\n    this.length = elements.length\n\n    for (var i = 0; i < this.length; i++) {\n      this.elements[elements[i]] = true\n    }\n  } else {\n    this.length = 0\n  }\n}\n\n/**\n * A complete set that contains all elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.complete = {\n  intersect: function (other) {\n    return other\n  },\n\n  union: function () {\n    return this\n  },\n\n  contains: function () {\n    return true\n  }\n}\n\n/**\n * An empty set that contains no elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.empty = {\n  intersect: function () {\n    return this\n  },\n\n  union: function (other) {\n    return other\n  },\n\n  contains: function () {\n    return false\n  }\n}\n\n/**\n * Returns true if this set contains the specified object.\n *\n * @param {object} object - Object whose presence in this set is to be tested.\n * @returns {boolean} - True if this set contains the specified object.\n */\nlunr.Set.prototype.contains = function (object) {\n  return !!this.elements[object]\n}\n\n/**\n * Returns a new set containing only the elements that are present in both\n * this set and the specified set.\n *\n * @param {lunr.Set} other - set to intersect with this set.\n * @returns {lunr.Set} a new set that is the intersection of this and the specified set.\n */\n\nlunr.Set.prototype.intersect = function (other) {\n  var a, b, elements, intersection = []\n\n  if (other === lunr.Set.complete) {\n    return this\n  }\n\n  if (other === lunr.Set.empty) {\n    return other\n  }\n\n  if (this.length < other.length) {\n    a = this\n    b = other\n  } else {\n    a = other\n    b = this\n  }\n\n  elements = Object.keys(a.elements)\n\n  for (var i = 0; i < elements.length; i++) {\n    var element = elements[i]\n    if (element in b.elements) {\n      intersection.push(element)\n    }\n  }\n\n  return new lunr.Set (intersection)\n}\n\n/**\n * Returns a new set combining the elements of this and the specified set.\n *\n * @param {lunr.Set} other - set to union with this set.\n * @return {lunr.Set} a new set that is the union of this and the specified set.\n */\n\nlunr.Set.prototype.union = function (other) {\n  if (other === lunr.Set.complete) {\n    return lunr.Set.complete\n  }\n\n  if (other === lunr.Set.empty) {\n    return this\n  }\n\n  return new lunr.Set(Object.keys(this.elements).concat(Object.keys(other.elements)))\n}\n/**\n * A function to calculate the inverse document frequency for\n * a posting. This is shared between the builder and the index\n *\n * @private\n * @param {object} posting - The posting for a given term\n * @param {number} documentCount - The total number of documents.\n */\nlunr.idf = function (posting, documentCount) {\n  var documentsWithTerm = 0\n\n  for (var fieldName in posting) {\n    if (fieldName == '_index') continue // Ignore the term index, its not a field\n    documentsWithTerm += Object.keys(posting[fieldName]).length\n  }\n\n  var x = (documentCount - documentsWithTerm + 0.5) / (documentsWithTerm + 0.5)\n\n  return Math.log(1 + Math.abs(x))\n}\n\n/**\n * A token wraps a string representation of a token\n * as it is passed through the text processing pipeline.\n *\n * @constructor\n * @param {string} [str=''] - The string token being wrapped.\n * @param {object} [metadata={}] - Metadata associated with this token.\n */\nlunr.Token = function (str, metadata) {\n  this.str = str || \"\"\n  this.metadata = metadata || {}\n}\n\n/**\n * Returns the token string that is being wrapped by this object.\n *\n * @returns {string}\n */\nlunr.Token.prototype.toString = function () {\n  return this.str\n}\n\n/**\n * A token update function is used when updating or optionally\n * when cloning a token.\n *\n * @callback lunr.Token~updateFunction\n * @param {string} str - The string representation of the token.\n * @param {Object} metadata - All metadata associated with this token.\n */\n\n/**\n * Applies the given function to the wrapped string token.\n *\n * @example\n * token.update(function (str, metadata) {\n *   return str.toUpperCase()\n * })\n *\n * @param {lunr.Token~updateFunction} fn - A function to apply to the token string.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.update = function (fn) {\n  this.str = fn(this.str, this.metadata)\n  return this\n}\n\n/**\n * Creates a clone of this token. Optionally a function can be\n * applied to the cloned token.\n *\n * @param {lunr.Token~updateFunction} [fn] - An optional function to apply to the cloned token.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.clone = function (fn) {\n  fn = fn || function (s) { return s }\n  return new lunr.Token (fn(this.str, this.metadata), this.metadata)\n}\n/*!\n * lunr.tokenizer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A function for splitting a string into tokens ready to be inserted into\n * the search index. Uses `lunr.tokenizer.separator` to split strings, change\n * the value of this property to change how strings are split into tokens.\n *\n * This tokenizer will convert its parameter to a string by calling `toString` and\n * then will split this string on the character in `lunr.tokenizer.separator`.\n * Arrays will have their elements converted to strings and wrapped in a lunr.Token.\n *\n * Optional metadata can be passed to the tokenizer, this metadata will be cloned and\n * added as metadata to every token that is created from the object to be tokenized.\n *\n * @static\n * @param {?(string|object|object[])} obj - The object to convert into tokens\n * @param {?object} metadata - Optional metadata to associate with every token\n * @returns {lunr.Token[]}\n * @see {@link lunr.Pipeline}\n */\nlunr.tokenizer = function (obj, metadata) {\n  if (obj == null || obj == undefined) {\n    return []\n  }\n\n  if (Array.isArray(obj)) {\n    return obj.map(function (t) {\n      return new lunr.Token(\n        lunr.utils.asString(t).toLowerCase(),\n        lunr.utils.clone(metadata)\n      )\n    })\n  }\n\n  var str = obj.toString().toLowerCase(),\n      len = str.length,\n      tokens = []\n\n  for (var sliceEnd = 0, sliceStart = 0; sliceEnd <= len; sliceEnd++) {\n    var char = str.charAt(sliceEnd),\n        sliceLength = sliceEnd - sliceStart\n\n    if ((char.match(lunr.tokenizer.separator) || sliceEnd == len)) {\n\n      if (sliceLength > 0) {\n        var tokenMetadata = lunr.utils.clone(metadata) || {}\n        tokenMetadata[\"position\"] = [sliceStart, sliceLength]\n        tokenMetadata[\"index\"] = tokens.length\n\n        tokens.push(\n          new lunr.Token (\n            str.slice(sliceStart, sliceEnd),\n            tokenMetadata\n          )\n        )\n      }\n\n      sliceStart = sliceEnd + 1\n    }\n\n  }\n\n  return tokens\n}\n\n/**\n * The separator used to split a string into tokens. Override this property to change the behaviour of\n * `lunr.tokenizer` behaviour when tokenizing strings. By default this splits on whitespace and hyphens.\n *\n * @static\n * @see lunr.tokenizer\n */\nlunr.tokenizer.separator = /[\\s\\-]+/\n/*!\n * lunr.Pipeline\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Pipelines maintain an ordered list of functions to be applied to all\n * tokens in documents entering the search index and queries being ran against\n * the index.\n *\n * An instance of lunr.Index created with the lunr shortcut will contain a\n * pipeline with a stop word filter and an English language stemmer. Extra\n * functions can be added before or after either of these functions or these\n * default functions can be removed.\n *\n * When run the pipeline will call each function in turn, passing a token, the\n * index of that token in the original list of all tokens and finally a list of\n * all the original tokens.\n *\n * The output of functions in the pipeline will be passed to the next function\n * in the pipeline. To exclude a token from entering the index the function\n * should return undefined, the rest of the pipeline will not be called with\n * this token.\n *\n * For serialisation of pipelines to work, all functions used in an instance of\n * a pipeline should be registered with lunr.Pipeline. Registered functions can\n * then be loaded. If trying to load a serialised pipeline that uses functions\n * that are not registered an error will be thrown.\n *\n * If not planning on serialising the pipeline then registering pipeline functions\n * is not necessary.\n *\n * @constructor\n */\nlunr.Pipeline = function () {\n  this._stack = []\n}\n\nlunr.Pipeline.registeredFunctions = Object.create(null)\n\n/**\n * A pipeline function maps lunr.Token to lunr.Token. A lunr.Token contains the token\n * string as well as all known metadata. A pipeline function can mutate the token string\n * or mutate (or add) metadata for a given token.\n *\n * A pipeline function can indicate that the passed token should be discarded by returning\n * null, undefined or an empty string. This token will not be passed to any downstream pipeline\n * functions and will not be added to the index.\n *\n * Multiple tokens can be returned by returning an array of tokens. Each token will be passed\n * to any downstream pipeline functions and all will returned tokens will be added to the index.\n *\n * Any number of pipeline functions may be chained together using a lunr.Pipeline.\n *\n * @interface lunr.PipelineFunction\n * @param {lunr.Token} token - A token from the document being processed.\n * @param {number} i - The index of this token in the complete list of tokens for this document/field.\n * @param {lunr.Token[]} tokens - All tokens for this document/field.\n * @returns {(?lunr.Token|lunr.Token[])}\n */\n\n/**\n * Register a function with the pipeline.\n *\n * Functions that are used in the pipeline should be registered if the pipeline\n * needs to be serialised, or a serialised pipeline needs to be loaded.\n *\n * Registering a function does not add it to a pipeline, functions must still be\n * added to instances of the pipeline for them to be used when running a pipeline.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @param {String} label - The label to register this function with\n */\nlunr.Pipeline.registerFunction = function (fn, label) {\n  if (label in this.registeredFunctions) {\n    lunr.utils.warn('Overwriting existing registered function: ' + label)\n  }\n\n  fn.label = label\n  lunr.Pipeline.registeredFunctions[fn.label] = fn\n}\n\n/**\n * Warns if the function is not registered as a Pipeline function.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @private\n */\nlunr.Pipeline.warnIfFunctionNotRegistered = function (fn) {\n  var isRegistered = fn.label && (fn.label in this.registeredFunctions)\n\n  if (!isRegistered) {\n    lunr.utils.warn('Function is not registered with pipeline. This may cause problems when serialising the index.\\n', fn)\n  }\n}\n\n/**\n * Loads a previously serialised pipeline.\n *\n * All functions to be loaded must already be registered with lunr.Pipeline.\n * If any function from the serialised data has not been registered then an\n * error will be thrown.\n *\n * @param {Object} serialised - The serialised pipeline to load.\n * @returns {lunr.Pipeline}\n */\nlunr.Pipeline.load = function (serialised) {\n  var pipeline = new lunr.Pipeline\n\n  serialised.forEach(function (fnName) {\n    var fn = lunr.Pipeline.registeredFunctions[fnName]\n\n    if (fn) {\n      pipeline.add(fn)\n    } else {\n      throw new Error('Cannot load unregistered function: ' + fnName)\n    }\n  })\n\n  return pipeline\n}\n\n/**\n * Adds new functions to the end of the pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction[]} functions - Any number of functions to add to the pipeline.\n */\nlunr.Pipeline.prototype.add = function () {\n  var fns = Array.prototype.slice.call(arguments)\n\n  fns.forEach(function (fn) {\n    lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n    this._stack.push(fn)\n  }, this)\n}\n\n/**\n * Adds a single function after a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.after = function (existingFn, newFn) {\n  lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n  var pos = this._stack.indexOf(existingFn)\n  if (pos == -1) {\n    throw new Error('Cannot find existingFn')\n  }\n\n  pos = pos + 1\n  this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Adds a single function before a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.before = function (existingFn, newFn) {\n  lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n  var pos = this._stack.indexOf(existingFn)\n  if (pos == -1) {\n    throw new Error('Cannot find existingFn')\n  }\n\n  this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Removes a function from the pipeline.\n *\n * @param {lunr.PipelineFunction} fn The function to remove from the pipeline.\n */\nlunr.Pipeline.prototype.remove = function (fn) {\n  var pos = this._stack.indexOf(fn)\n  if (pos == -1) {\n    return\n  }\n\n  this._stack.splice(pos, 1)\n}\n\n/**\n * Runs the current list of functions that make up the pipeline against the\n * passed tokens.\n *\n * @param {Array} tokens The tokens to run through the pipeline.\n * @returns {Array}\n */\nlunr.Pipeline.prototype.run = function (tokens) {\n  var stackLength = this._stack.length\n\n  for (var i = 0; i < stackLength; i++) {\n    var fn = this._stack[i]\n    var memo = []\n\n    for (var j = 0; j < tokens.length; j++) {\n      var result = fn(tokens[j], j, tokens)\n\n      if (result === null || result === void 0 || result === '') continue\n\n      if (Array.isArray(result)) {\n        for (var k = 0; k < result.length; k++) {\n          memo.push(result[k])\n        }\n      } else {\n        memo.push(result)\n      }\n    }\n\n    tokens = memo\n  }\n\n  return tokens\n}\n\n/**\n * Convenience method for passing a string through a pipeline and getting\n * strings out. This method takes care of wrapping the passed string in a\n * token and mapping the resulting tokens back to strings.\n *\n * @param {string} str - The string to pass through the pipeline.\n * @param {?object} metadata - Optional metadata to associate with the token\n * passed to the pipeline.\n * @returns {string[]}\n */\nlunr.Pipeline.prototype.runString = function (str, metadata) {\n  var token = new lunr.Token (str, metadata)\n\n  return this.run([token]).map(function (t) {\n    return t.toString()\n  })\n}\n\n/**\n * Resets the pipeline by removing any existing processors.\n *\n */\nlunr.Pipeline.prototype.reset = function () {\n  this._stack = []\n}\n\n/**\n * Returns a representation of the pipeline ready for serialisation.\n *\n * Logs a warning if the function has not been registered.\n *\n * @returns {Array}\n */\nlunr.Pipeline.prototype.toJSON = function () {\n  return this._stack.map(function (fn) {\n    lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n\n    return fn.label\n  })\n}\n/*!\n * lunr.Vector\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A vector is used to construct the vector space of documents and queries. These\n * vectors support operations to determine the similarity between two documents or\n * a document and a query.\n *\n * Normally no parameters are required for initializing a vector, but in the case of\n * loading a previously dumped vector the raw elements can be provided to the constructor.\n *\n * For performance reasons vectors are implemented with a flat array, where an elements\n * index is immediately followed by its value. E.g. [index, value, index, value]. This\n * allows the underlying array to be as sparse as possible and still offer decent\n * performance when being used for vector calculations.\n *\n * @constructor\n * @param {Number[]} [elements] - The flat list of element index and element value pairs.\n */\nlunr.Vector = function (elements) {\n  this._magnitude = 0\n  this.elements = elements || []\n}\n\n\n/**\n * Calculates the position within the vector to insert a given index.\n *\n * This is used internally by insert and upsert. If there are duplicate indexes then\n * the position is returned as if the value for that index were to be updated, but it\n * is the callers responsibility to check whether there is a duplicate at that index\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @returns {Number}\n */\nlunr.Vector.prototype.positionForIndex = function (index) {\n  // For an empty vector the tuple can be inserted at the beginning\n  if (this.elements.length == 0) {\n    return 0\n  }\n\n  var start = 0,\n      end = this.elements.length / 2,\n      sliceLength = end - start,\n      pivotPoint = Math.floor(sliceLength / 2),\n      pivotIndex = this.elements[pivotPoint * 2]\n\n  while (sliceLength > 1) {\n    if (pivotIndex < index) {\n      start = pivotPoint\n    }\n\n    if (pivotIndex > index) {\n      end = pivotPoint\n    }\n\n    if (pivotIndex == index) {\n      break\n    }\n\n    sliceLength = end - start\n    pivotPoint = start + Math.floor(sliceLength / 2)\n    pivotIndex = this.elements[pivotPoint * 2]\n  }\n\n  if (pivotIndex == index) {\n    return pivotPoint * 2\n  }\n\n  if (pivotIndex > index) {\n    return pivotPoint * 2\n  }\n\n  if (pivotIndex < index) {\n    return (pivotPoint + 1) * 2\n  }\n}\n\n/**\n * Inserts an element at an index within the vector.\n *\n * Does not allow duplicates, will throw an error if there is already an entry\n * for this index.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n */\nlunr.Vector.prototype.insert = function (insertIdx, val) {\n  this.upsert(insertIdx, val, function () {\n    throw \"duplicate index\"\n  })\n}\n\n/**\n * Inserts or updates an existing index within the vector.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n * @param {function} fn - A function that is called for updates, the existing value and the\n * requested value are passed as arguments\n */\nlunr.Vector.prototype.upsert = function (insertIdx, val, fn) {\n  this._magnitude = 0\n  var position = this.positionForIndex(insertIdx)\n\n  if (this.elements[position] == insertIdx) {\n    this.elements[position + 1] = fn(this.elements[position + 1], val)\n  } else {\n    this.elements.splice(position, 0, insertIdx, val)\n  }\n}\n\n/**\n * Calculates the magnitude of this vector.\n *\n * @returns {Number}\n */\nlunr.Vector.prototype.magnitude = function () {\n  if (this._magnitude) return this._magnitude\n\n  var sumOfSquares = 0,\n      elementsLength = this.elements.length\n\n  for (var i = 1; i < elementsLength; i += 2) {\n    var val = this.elements[i]\n    sumOfSquares += val * val\n  }\n\n  return this._magnitude = Math.sqrt(sumOfSquares)\n}\n\n/**\n * Calculates the dot product of this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The vector to compute the dot product with.\n * @returns {Number}\n */\nlunr.Vector.prototype.dot = function (otherVector) {\n  var dotProduct = 0,\n      a = this.elements, b = otherVector.elements,\n      aLen = a.length, bLen = b.length,\n      aVal = 0, bVal = 0,\n      i = 0, j = 0\n\n  while (i < aLen && j < bLen) {\n    aVal = a[i], bVal = b[j]\n    if (aVal < bVal) {\n      i += 2\n    } else if (aVal > bVal) {\n      j += 2\n    } else if (aVal == bVal) {\n      dotProduct += a[i + 1] * b[j + 1]\n      i += 2\n      j += 2\n    }\n  }\n\n  return dotProduct\n}\n\n/**\n * Calculates the similarity between this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The other vector to calculate the\n * similarity with.\n * @returns {Number}\n */\nlunr.Vector.prototype.similarity = function (otherVector) {\n  return this.dot(otherVector) / this.magnitude() || 0\n}\n\n/**\n * Converts the vector to an array of the elements within the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toArray = function () {\n  var output = new Array (this.elements.length / 2)\n\n  for (var i = 1, j = 0; i < this.elements.length; i += 2, j++) {\n    output[j] = this.elements[i]\n  }\n\n  return output\n}\n\n/**\n * A JSON serializable representation of the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toJSON = function () {\n  return this.elements\n}\n/* eslint-disable */\n/*!\n * lunr.stemmer\n * Copyright (C) 2020 Oliver Nightingale\n * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt\n */\n\n/**\n * lunr.stemmer is an english language stemmer, this is a JavaScript\n * implementation of the PorterStemmer taken from http://tartarus.org/~martin\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token - The string to stem\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n * @function\n */\nlunr.stemmer = (function(){\n  var step2list = {\n      \"ational\" : \"ate\",\n      \"tional\" : \"tion\",\n      \"enci\" : \"ence\",\n      \"anci\" : \"ance\",\n      \"izer\" : \"ize\",\n      \"bli\" : \"ble\",\n      \"alli\" : \"al\",\n      \"entli\" : \"ent\",\n      \"eli\" : \"e\",\n      \"ousli\" : \"ous\",\n      \"ization\" : \"ize\",\n      \"ation\" : \"ate\",\n      \"ator\" : \"ate\",\n      \"alism\" : \"al\",\n      \"iveness\" : \"ive\",\n      \"fulness\" : \"ful\",\n      \"ousness\" : \"ous\",\n      \"aliti\" : \"al\",\n      \"iviti\" : \"ive\",\n      \"biliti\" : \"ble\",\n      \"logi\" : \"log\"\n    },\n\n    step3list = {\n      \"icate\" : \"ic\",\n      \"ative\" : \"\",\n      \"alize\" : \"al\",\n      \"iciti\" : \"ic\",\n      \"ical\" : \"ic\",\n      \"ful\" : \"\",\n      \"ness\" : \"\"\n    },\n\n    c = \"[^aeiou]\",          // consonant\n    v = \"[aeiouy]\",          // vowel\n    C = c + \"[^aeiouy]*\",    // consonant sequence\n    V = v + \"[aeiou]*\",      // vowel sequence\n\n    mgr0 = \"^(\" + C + \")?\" + V + C,               // [C]VC... is m>0\n    meq1 = \"^(\" + C + \")?\" + V + C + \"(\" + V + \")?$\",  // [C]VC[V] is m=1\n    mgr1 = \"^(\" + C + \")?\" + V + C + V + C,       // [C]VCVC... is m>1\n    s_v = \"^(\" + C + \")?\" + v;                   // vowel in stem\n\n  var re_mgr0 = new RegExp(mgr0);\n  var re_mgr1 = new RegExp(mgr1);\n  var re_meq1 = new RegExp(meq1);\n  var re_s_v = new RegExp(s_v);\n\n  var re_1a = /^(.+?)(ss|i)es$/;\n  var re2_1a = /^(.+?)([^s])s$/;\n  var re_1b = /^(.+?)eed$/;\n  var re2_1b = /^(.+?)(ed|ing)$/;\n  var re_1b_2 = /.$/;\n  var re2_1b_2 = /(at|bl|iz)$/;\n  var re3_1b_2 = new RegExp(\"([^aeiouylsz])\\\\1$\");\n  var re4_1b_2 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n  var re_1c = /^(.+?[^aeiou])y$/;\n  var re_2 = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;\n\n  var re_3 = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;\n\n  var re_4 = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;\n  var re2_4 = /^(.+?)(s|t)(ion)$/;\n\n  var re_5 = /^(.+?)e$/;\n  var re_5_1 = /ll$/;\n  var re3_5 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n  var porterStemmer = function porterStemmer(w) {\n    var stem,\n      suffix,\n      firstch,\n      re,\n      re2,\n      re3,\n      re4;\n\n    if (w.length < 3) { return w; }\n\n    firstch = w.substr(0,1);\n    if (firstch == \"y\") {\n      w = firstch.toUpperCase() + w.substr(1);\n    }\n\n    // Step 1a\n    re = re_1a\n    re2 = re2_1a;\n\n    if (re.test(w)) { w = w.replace(re,\"$1$2\"); }\n    else if (re2.test(w)) { w = w.replace(re2,\"$1$2\"); }\n\n    // Step 1b\n    re = re_1b;\n    re2 = re2_1b;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      re = re_mgr0;\n      if (re.test(fp[1])) {\n        re = re_1b_2;\n        w = w.replace(re,\"\");\n      }\n    } else if (re2.test(w)) {\n      var fp = re2.exec(w);\n      stem = fp[1];\n      re2 = re_s_v;\n      if (re2.test(stem)) {\n        w = stem;\n        re2 = re2_1b_2;\n        re3 = re3_1b_2;\n        re4 = re4_1b_2;\n        if (re2.test(w)) { w = w + \"e\"; }\n        else if (re3.test(w)) { re = re_1b_2; w = w.replace(re,\"\"); }\n        else if (re4.test(w)) { w = w + \"e\"; }\n      }\n    }\n\n    // Step 1c - replace suffix y or Y by i if preceded by a non-vowel which is not the first letter of the word (so cry -> cri, by -> by, say -> say)\n    re = re_1c;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      w = stem + \"i\";\n    }\n\n    // Step 2\n    re = re_2;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      suffix = fp[2];\n      re = re_mgr0;\n      if (re.test(stem)) {\n        w = stem + step2list[suffix];\n      }\n    }\n\n    // Step 3\n    re = re_3;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      suffix = fp[2];\n      re = re_mgr0;\n      if (re.test(stem)) {\n        w = stem + step3list[suffix];\n      }\n    }\n\n    // Step 4\n    re = re_4;\n    re2 = re2_4;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      re = re_mgr1;\n      if (re.test(stem)) {\n        w = stem;\n      }\n    } else if (re2.test(w)) {\n      var fp = re2.exec(w);\n      stem = fp[1] + fp[2];\n      re2 = re_mgr1;\n      if (re2.test(stem)) {\n        w = stem;\n      }\n    }\n\n    // Step 5\n    re = re_5;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      re = re_mgr1;\n      re2 = re_meq1;\n      re3 = re3_5;\n      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) {\n        w = stem;\n      }\n    }\n\n    re = re_5_1;\n    re2 = re_mgr1;\n    if (re.test(w) && re2.test(w)) {\n      re = re_1b_2;\n      w = w.replace(re,\"\");\n    }\n\n    // and turn initial Y back to y\n\n    if (firstch == \"y\") {\n      w = firstch.toLowerCase() + w.substr(1);\n    }\n\n    return w;\n  };\n\n  return function (token) {\n    return token.update(porterStemmer);\n  }\n})();\n\nlunr.Pipeline.registerFunction(lunr.stemmer, 'stemmer')\n/*!\n * lunr.stopWordFilter\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.generateStopWordFilter builds a stopWordFilter function from the provided\n * list of stop words.\n *\n * The built in lunr.stopWordFilter is built using this generator and can be used\n * to generate custom stopWordFilters for applications or non English languages.\n *\n * @function\n * @param {Array} token The token to pass through the filter\n * @returns {lunr.PipelineFunction}\n * @see lunr.Pipeline\n * @see lunr.stopWordFilter\n */\nlunr.generateStopWordFilter = function (stopWords) {\n  var words = stopWords.reduce(function (memo, stopWord) {\n    memo[stopWord] = stopWord\n    return memo\n  }, {})\n\n  return function (token) {\n    if (token && words[token.toString()] !== token.toString()) return token\n  }\n}\n\n/**\n * lunr.stopWordFilter is an English language stop word list filter, any words\n * contained in the list will not be passed through the filter.\n *\n * This is intended to be used in the Pipeline. If the token does not pass the\n * filter then undefined will be returned.\n *\n * @function\n * @implements {lunr.PipelineFunction}\n * @params {lunr.Token} token - A token to check for being a stop word.\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n */\nlunr.stopWordFilter = lunr.generateStopWordFilter([\n  'a',\n  'able',\n  'about',\n  'across',\n  'after',\n  'all',\n  'almost',\n  'also',\n  'am',\n  'among',\n  'an',\n  'and',\n  'any',\n  'are',\n  'as',\n  'at',\n  'be',\n  'because',\n  'been',\n  'but',\n  'by',\n  'can',\n  'cannot',\n  'could',\n  'dear',\n  'did',\n  'do',\n  'does',\n  'either',\n  'else',\n  'ever',\n  'every',\n  'for',\n  'from',\n  'get',\n  'got',\n  'had',\n  'has',\n  'have',\n  'he',\n  'her',\n  'hers',\n  'him',\n  'his',\n  'how',\n  'however',\n  'i',\n  'if',\n  'in',\n  'into',\n  'is',\n  'it',\n  'its',\n  'just',\n  'least',\n  'let',\n  'like',\n  'likely',\n  'may',\n  'me',\n  'might',\n  'most',\n  'must',\n  'my',\n  'neither',\n  'no',\n  'nor',\n  'not',\n  'of',\n  'off',\n  'often',\n  'on',\n  'only',\n  'or',\n  'other',\n  'our',\n  'own',\n  'rather',\n  'said',\n  'say',\n  'says',\n  'she',\n  'should',\n  'since',\n  'so',\n  'some',\n  'than',\n  'that',\n  'the',\n  'their',\n  'them',\n  'then',\n  'there',\n  'these',\n  'they',\n  'this',\n  'tis',\n  'to',\n  'too',\n  'twas',\n  'us',\n  'wants',\n  'was',\n  'we',\n  'were',\n  'what',\n  'when',\n  'where',\n  'which',\n  'while',\n  'who',\n  'whom',\n  'why',\n  'will',\n  'with',\n  'would',\n  'yet',\n  'you',\n  'your'\n])\n\nlunr.Pipeline.registerFunction(lunr.stopWordFilter, 'stopWordFilter')\n/*!\n * lunr.trimmer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.trimmer is a pipeline function for trimming non word\n * characters from the beginning and end of tokens before they\n * enter the index.\n *\n * This implementation may not work correctly for non latin\n * characters and should either be removed or adapted for use\n * with languages with non-latin characters.\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token The token to pass through the filter\n * @returns {lunr.Token}\n * @see lunr.Pipeline\n */\nlunr.trimmer = function (token) {\n  return token.update(function (s) {\n    return s.replace(/^\\W+/, '').replace(/\\W+$/, '')\n  })\n}\n\nlunr.Pipeline.registerFunction(lunr.trimmer, 'trimmer')\n/*!\n * lunr.TokenSet\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A token set is used to store the unique list of all tokens\n * within an index. Token sets are also used to represent an\n * incoming query to the index, this query token set and index\n * token set are then intersected to find which tokens to look\n * up in the inverted index.\n *\n * A token set can hold multiple tokens, as in the case of the\n * index token set, or it can hold a single token as in the\n * case of a simple query token set.\n *\n * Additionally token sets are used to perform wildcard matching.\n * Leading, contained and trailing wildcards are supported, and\n * from this edit distance matching can also be provided.\n *\n * Token sets are implemented as a minimal finite state automata,\n * where both common prefixes and suffixes are shared between tokens.\n * This helps to reduce the space used for storing the token set.\n *\n * @constructor\n */\nlunr.TokenSet = function () {\n  this.final = false\n  this.edges = {}\n  this.id = lunr.TokenSet._nextId\n  lunr.TokenSet._nextId += 1\n}\n\n/**\n * Keeps track of the next, auto increment, identifier to assign\n * to a new tokenSet.\n *\n * TokenSets require a unique identifier to be correctly minimised.\n *\n * @private\n */\nlunr.TokenSet._nextId = 1\n\n/**\n * Creates a TokenSet instance from the given sorted array of words.\n *\n * @param {String[]} arr - A sorted array of strings to create the set from.\n * @returns {lunr.TokenSet}\n * @throws Will throw an error if the input array is not sorted.\n */\nlunr.TokenSet.fromArray = function (arr) {\n  var builder = new lunr.TokenSet.Builder\n\n  for (var i = 0, len = arr.length; i < len; i++) {\n    builder.insert(arr[i])\n  }\n\n  builder.finish()\n  return builder.root\n}\n\n/**\n * Creates a token set from a query clause.\n *\n * @private\n * @param {Object} clause - A single clause from lunr.Query.\n * @param {string} clause.term - The query clause term.\n * @param {number} [clause.editDistance] - The optional edit distance for the term.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromClause = function (clause) {\n  if ('editDistance' in clause) {\n    return lunr.TokenSet.fromFuzzyString(clause.term, clause.editDistance)\n  } else {\n    return lunr.TokenSet.fromString(clause.term)\n  }\n}\n\n/**\n * Creates a token set representing a single string with a specified\n * edit distance.\n *\n * Insertions, deletions, substitutions and transpositions are each\n * treated as an edit distance of 1.\n *\n * Increasing the allowed edit distance will have a dramatic impact\n * on the performance of both creating and intersecting these TokenSets.\n * It is advised to keep the edit distance less than 3.\n *\n * @param {string} str - The string to create the token set from.\n * @param {number} editDistance - The allowed edit distance to match.\n * @returns {lunr.Vector}\n */\nlunr.TokenSet.fromFuzzyString = function (str, editDistance) {\n  var root = new lunr.TokenSet\n\n  var stack = [{\n    node: root,\n    editsRemaining: editDistance,\n    str: str\n  }]\n\n  while (stack.length) {\n    var frame = stack.pop()\n\n    // no edit\n    if (frame.str.length > 0) {\n      var char = frame.str.charAt(0),\n          noEditNode\n\n      if (char in frame.node.edges) {\n        noEditNode = frame.node.edges[char]\n      } else {\n        noEditNode = new lunr.TokenSet\n        frame.node.edges[char] = noEditNode\n      }\n\n      if (frame.str.length == 1) {\n        noEditNode.final = true\n      }\n\n      stack.push({\n        node: noEditNode,\n        editsRemaining: frame.editsRemaining,\n        str: frame.str.slice(1)\n      })\n    }\n\n    if (frame.editsRemaining == 0) {\n      continue\n    }\n\n    // insertion\n    if (\"*\" in frame.node.edges) {\n      var insertionNode = frame.node.edges[\"*\"]\n    } else {\n      var insertionNode = new lunr.TokenSet\n      frame.node.edges[\"*\"] = insertionNode\n    }\n\n    if (frame.str.length == 0) {\n      insertionNode.final = true\n    }\n\n    stack.push({\n      node: insertionNode,\n      editsRemaining: frame.editsRemaining - 1,\n      str: frame.str\n    })\n\n    // deletion\n    // can only do a deletion if we have enough edits remaining\n    // and if there are characters left to delete in the string\n    if (frame.str.length > 1) {\n      stack.push({\n        node: frame.node,\n        editsRemaining: frame.editsRemaining - 1,\n        str: frame.str.slice(1)\n      })\n    }\n\n    // deletion\n    // just removing the last character from the str\n    if (frame.str.length == 1) {\n      frame.node.final = true\n    }\n\n    // substitution\n    // can only do a substitution if we have enough edits remaining\n    // and if there are characters left to substitute\n    if (frame.str.length >= 1) {\n      if (\"*\" in frame.node.edges) {\n        var substitutionNode = frame.node.edges[\"*\"]\n      } else {\n        var substitutionNode = new lunr.TokenSet\n        frame.node.edges[\"*\"] = substitutionNode\n      }\n\n      if (frame.str.length == 1) {\n        substitutionNode.final = true\n      }\n\n      stack.push({\n        node: substitutionNode,\n        editsRemaining: frame.editsRemaining - 1,\n        str: frame.str.slice(1)\n      })\n    }\n\n    // transposition\n    // can only do a transposition if there are edits remaining\n    // and there are enough characters to transpose\n    if (frame.str.length > 1) {\n      var charA = frame.str.charAt(0),\n          charB = frame.str.charAt(1),\n          transposeNode\n\n      if (charB in frame.node.edges) {\n        transposeNode = frame.node.edges[charB]\n      } else {\n        transposeNode = new lunr.TokenSet\n        frame.node.edges[charB] = transposeNode\n      }\n\n      if (frame.str.length == 1) {\n        transposeNode.final = true\n      }\n\n      stack.push({\n        node: transposeNode,\n        editsRemaining: frame.editsRemaining - 1,\n        str: charA + frame.str.slice(2)\n      })\n    }\n  }\n\n  return root\n}\n\n/**\n * Creates a TokenSet from a string.\n *\n * The string may contain one or more wildcard characters (*)\n * that will allow wildcard matching when intersecting with\n * another TokenSet.\n *\n * @param {string} str - The string to create a TokenSet from.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromString = function (str) {\n  var node = new lunr.TokenSet,\n      root = node\n\n  /*\n   * Iterates through all characters within the passed string\n   * appending a node for each character.\n   *\n   * When a wildcard character is found then a self\n   * referencing edge is introduced to continually match\n   * any number of any characters.\n   */\n  for (var i = 0, len = str.length; i < len; i++) {\n    var char = str[i],\n        final = (i == len - 1)\n\n    if (char == \"*\") {\n      node.edges[char] = node\n      node.final = final\n\n    } else {\n      var next = new lunr.TokenSet\n      next.final = final\n\n      node.edges[char] = next\n      node = next\n    }\n  }\n\n  return root\n}\n\n/**\n * Converts this TokenSet into an array of strings\n * contained within the TokenSet.\n *\n * This is not intended to be used on a TokenSet that\n * contains wildcards, in these cases the results are\n * undefined and are likely to cause an infinite loop.\n *\n * @returns {string[]}\n */\nlunr.TokenSet.prototype.toArray = function () {\n  var words = []\n\n  var stack = [{\n    prefix: \"\",\n    node: this\n  }]\n\n  while (stack.length) {\n    var frame = stack.pop(),\n        edges = Object.keys(frame.node.edges),\n        len = edges.length\n\n    if (frame.node.final) {\n      /* In Safari, at this point the prefix is sometimes corrupted, see:\n       * https://github.com/olivernn/lunr.js/issues/279 Calling any\n       * String.prototype method forces Safari to \"cast\" this string to what\n       * it's supposed to be, fixing the bug. */\n      frame.prefix.charAt(0)\n      words.push(frame.prefix)\n    }\n\n    for (var i = 0; i < len; i++) {\n      var edge = edges[i]\n\n      stack.push({\n        prefix: frame.prefix.concat(edge),\n        node: frame.node.edges[edge]\n      })\n    }\n  }\n\n  return words\n}\n\n/**\n * Generates a string representation of a TokenSet.\n *\n * This is intended to allow TokenSets to be used as keys\n * in objects, largely to aid the construction and minimisation\n * of a TokenSet. As such it is not designed to be a human\n * friendly representation of the TokenSet.\n *\n * @returns {string}\n */\nlunr.TokenSet.prototype.toString = function () {\n  // NOTE: Using Object.keys here as this.edges is very likely\n  // to enter 'hash-mode' with many keys being added\n  //\n  // avoiding a for-in loop here as it leads to the function\n  // being de-optimised (at least in V8). From some simple\n  // benchmarks the performance is comparable, but allowing\n  // V8 to optimize may mean easy performance wins in the future.\n\n  if (this._str) {\n    return this._str\n  }\n\n  var str = this.final ? '1' : '0',\n      labels = Object.keys(this.edges).sort(),\n      len = labels.length\n\n  for (var i = 0; i < len; i++) {\n    var label = labels[i],\n        node = this.edges[label]\n\n    str = str + label + node.id\n  }\n\n  return str\n}\n\n/**\n * Returns a new TokenSet that is the intersection of\n * this TokenSet and the passed TokenSet.\n *\n * This intersection will take into account any wildcards\n * contained within the TokenSet.\n *\n * @param {lunr.TokenSet} b - An other TokenSet to intersect with.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.prototype.intersect = function (b) {\n  var output = new lunr.TokenSet,\n      frame = undefined\n\n  var stack = [{\n    qNode: b,\n    output: output,\n    node: this\n  }]\n\n  while (stack.length) {\n    frame = stack.pop()\n\n    // NOTE: As with the #toString method, we are using\n    // Object.keys and a for loop instead of a for-in loop\n    // as both of these objects enter 'hash' mode, causing\n    // the function to be de-optimised in V8\n    var qEdges = Object.keys(frame.qNode.edges),\n        qLen = qEdges.length,\n        nEdges = Object.keys(frame.node.edges),\n        nLen = nEdges.length\n\n    for (var q = 0; q < qLen; q++) {\n      var qEdge = qEdges[q]\n\n      for (var n = 0; n < nLen; n++) {\n        var nEdge = nEdges[n]\n\n        if (nEdge == qEdge || qEdge == '*') {\n          var node = frame.node.edges[nEdge],\n              qNode = frame.qNode.edges[qEdge],\n              final = node.final && qNode.final,\n              next = undefined\n\n          if (nEdge in frame.output.edges) {\n            // an edge already exists for this character\n            // no need to create a new node, just set the finality\n            // bit unless this node is already final\n            next = frame.output.edges[nEdge]\n            next.final = next.final || final\n\n          } else {\n            // no edge exists yet, must create one\n            // set the finality bit and insert it\n            // into the output\n            next = new lunr.TokenSet\n            next.final = final\n            frame.output.edges[nEdge] = next\n          }\n\n          stack.push({\n            qNode: qNode,\n            output: next,\n            node: node\n          })\n        }\n      }\n    }\n  }\n\n  return output\n}\nlunr.TokenSet.Builder = function () {\n  this.previousWord = \"\"\n  this.root = new lunr.TokenSet\n  this.uncheckedNodes = []\n  this.minimizedNodes = {}\n}\n\nlunr.TokenSet.Builder.prototype.insert = function (word) {\n  var node,\n      commonPrefix = 0\n\n  if (word < this.previousWord) {\n    throw new Error (\"Out of order word insertion\")\n  }\n\n  for (var i = 0; i < word.length && i < this.previousWord.length; i++) {\n    if (word[i] != this.previousWord[i]) break\n    commonPrefix++\n  }\n\n  this.minimize(commonPrefix)\n\n  if (this.uncheckedNodes.length == 0) {\n    node = this.root\n  } else {\n    node = this.uncheckedNodes[this.uncheckedNodes.length - 1].child\n  }\n\n  for (var i = commonPrefix; i < word.length; i++) {\n    var nextNode = new lunr.TokenSet,\n        char = word[i]\n\n    node.edges[char] = nextNode\n\n    this.uncheckedNodes.push({\n      parent: node,\n      char: char,\n      child: nextNode\n    })\n\n    node = nextNode\n  }\n\n  node.final = true\n  this.previousWord = word\n}\n\nlunr.TokenSet.Builder.prototype.finish = function () {\n  this.minimize(0)\n}\n\nlunr.TokenSet.Builder.prototype.minimize = function (downTo) {\n  for (var i = this.uncheckedNodes.length - 1; i >= downTo; i--) {\n    var node = this.uncheckedNodes[i],\n        childKey = node.child.toString()\n\n    if (childKey in this.minimizedNodes) {\n      node.parent.edges[node.char] = this.minimizedNodes[childKey]\n    } else {\n      // Cache the key for this node since\n      // we know it can't change anymore\n      node.child._str = childKey\n\n      this.minimizedNodes[childKey] = node.child\n    }\n\n    this.uncheckedNodes.pop()\n  }\n}\n/*!\n * lunr.Index\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * An index contains the built index of all documents and provides a query interface\n * to the index.\n *\n * Usually instances of lunr.Index will not be created using this constructor, instead\n * lunr.Builder should be used to construct new indexes, or lunr.Index.load should be\n * used to load previously built and serialized indexes.\n *\n * @constructor\n * @param {Object} attrs - The attributes of the built search index.\n * @param {Object} attrs.invertedIndex - An index of term/field to document reference.\n * @param {Object<string, lunr.Vector>} attrs.fieldVectors - Field vectors\n * @param {lunr.TokenSet} attrs.tokenSet - An set of all corpus tokens.\n * @param {string[]} attrs.fields - The names of indexed document fields.\n * @param {lunr.Pipeline} attrs.pipeline - The pipeline to use for search terms.\n */\nlunr.Index = function (attrs) {\n  this.invertedIndex = attrs.invertedIndex\n  this.fieldVectors = attrs.fieldVectors\n  this.tokenSet = attrs.tokenSet\n  this.fields = attrs.fields\n  this.pipeline = attrs.pipeline\n}\n\n/**\n * A result contains details of a document matching a search query.\n * @typedef {Object} lunr.Index~Result\n * @property {string} ref - The reference of the document this result represents.\n * @property {number} score - A number between 0 and 1 representing how similar this document is to the query.\n * @property {lunr.MatchData} matchData - Contains metadata about this match including which term(s) caused the match.\n */\n\n/**\n * Although lunr provides the ability to create queries using lunr.Query, it also provides a simple\n * query language which itself is parsed into an instance of lunr.Query.\n *\n * For programmatically building queries it is advised to directly use lunr.Query, the query language\n * is best used for human entered text rather than program generated text.\n *\n * At its simplest queries can just be a single term, e.g. `hello`, multiple terms are also supported\n * and will be combined with OR, e.g `hello world` will match documents that contain either 'hello'\n * or 'world', though those that contain both will rank higher in the results.\n *\n * Wildcards can be included in terms to match one or more unspecified characters, these wildcards can\n * be inserted anywhere within the term, and more than one wildcard can exist in a single term. Adding\n * wildcards will increase the number of documents that will be found but can also have a negative\n * impact on query performance, especially with wildcards at the beginning of a term.\n *\n * Terms can be restricted to specific fields, e.g. `title:hello`, only documents with the term\n * hello in the title field will match this query. Using a field not present in the index will lead\n * to an error being thrown.\n *\n * Modifiers can also be added to terms, lunr supports edit distance and boost modifiers on terms. A term\n * boost will make documents matching that term score higher, e.g. `foo^5`. Edit distance is also supported\n * to provide fuzzy matching, e.g. 'hello~2' will match documents with hello with an edit distance of 2.\n * Avoid large values for edit distance to improve query performance.\n *\n * Each term also supports a presence modifier. By default a term's presence in document is optional, however\n * this can be changed to either required or prohibited. For a term's presence to be required in a document the\n * term should be prefixed with a '+', e.g. `+foo bar` is a search for documents that must contain 'foo' and\n * optionally contain 'bar'. Conversely a leading '-' sets the terms presence to prohibited, i.e. it must not\n * appear in a document, e.g. `-foo bar` is a search for documents that do not contain 'foo' but may contain 'bar'.\n *\n * To escape special characters the backslash character '\\' can be used, this allows searches to include\n * characters that would normally be considered modifiers, e.g. `foo\\~2` will search for a term \"foo~2\" instead\n * of attempting to apply a boost of 2 to the search term \"foo\".\n *\n * @typedef {string} lunr.Index~QueryString\n * @example <caption>Simple single term query</caption>\n * hello\n * @example <caption>Multiple term query</caption>\n * hello world\n * @example <caption>term scoped to a field</caption>\n * title:hello\n * @example <caption>term with a boost of 10</caption>\n * hello^10\n * @example <caption>term with an edit distance of 2</caption>\n * hello~2\n * @example <caption>terms with presence modifiers</caption>\n * -foo +bar baz\n */\n\n/**\n * Performs a search against the index using lunr query syntax.\n *\n * Results will be returned sorted by their score, the most relevant results\n * will be returned first.  For details on how the score is calculated, please see\n * the {@link https://lunrjs.com/guides/searching.html#scoring|guide}.\n *\n * For more programmatic querying use lunr.Index#query.\n *\n * @param {lunr.Index~QueryString} queryString - A string containing a lunr query.\n * @throws {lunr.QueryParseError} If the passed query string cannot be parsed.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.search = function (queryString) {\n  return this.query(function (query) {\n    var parser = new lunr.QueryParser(queryString, query)\n    parser.parse()\n  })\n}\n\n/**\n * A query builder callback provides a query object to be used to express\n * the query to perform on the index.\n *\n * @callback lunr.Index~queryBuilder\n * @param {lunr.Query} query - The query object to build up.\n * @this lunr.Query\n */\n\n/**\n * Performs a query against the index using the yielded lunr.Query object.\n *\n * If performing programmatic queries against the index, this method is preferred\n * over lunr.Index#search so as to avoid the additional query parsing overhead.\n *\n * A query object is yielded to the supplied function which should be used to\n * express the query to be run against the index.\n *\n * Note that although this function takes a callback parameter it is _not_ an\n * asynchronous operation, the callback is just yielded a query object to be\n * customized.\n *\n * @param {lunr.Index~queryBuilder} fn - A function that is used to build the query.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.query = function (fn) {\n  // for each query clause\n  // * process terms\n  // * expand terms from token set\n  // * find matching documents and metadata\n  // * get document vectors\n  // * score documents\n\n  var query = new lunr.Query(this.fields),\n      matchingFields = Object.create(null),\n      queryVectors = Object.create(null),\n      termFieldCache = Object.create(null),\n      requiredMatches = Object.create(null),\n      prohibitedMatches = Object.create(null)\n\n  /*\n   * To support field level boosts a query vector is created per\n   * field. An empty vector is eagerly created to support negated\n   * queries.\n   */\n  for (var i = 0; i < this.fields.length; i++) {\n    queryVectors[this.fields[i]] = new lunr.Vector\n  }\n\n  fn.call(query, query)\n\n  for (var i = 0; i < query.clauses.length; i++) {\n    /*\n     * Unless the pipeline has been disabled for this term, which is\n     * the case for terms with wildcards, we need to pass the clause\n     * term through the search pipeline. A pipeline returns an array\n     * of processed terms. Pipeline functions may expand the passed\n     * term, which means we may end up performing multiple index lookups\n     * for a single query term.\n     */\n    var clause = query.clauses[i],\n        terms = null,\n        clauseMatches = lunr.Set.empty\n\n    if (clause.usePipeline) {\n      terms = this.pipeline.runString(clause.term, {\n        fields: clause.fields\n      })\n    } else {\n      terms = [clause.term]\n    }\n\n    for (var m = 0; m < terms.length; m++) {\n      var term = terms[m]\n\n      /*\n       * Each term returned from the pipeline needs to use the same query\n       * clause object, e.g. the same boost and or edit distance. The\n       * simplest way to do this is to re-use the clause object but mutate\n       * its term property.\n       */\n      clause.term = term\n\n      /*\n       * From the term in the clause we create a token set which will then\n       * be used to intersect the indexes token set to get a list of terms\n       * to lookup in the inverted index\n       */\n      var termTokenSet = lunr.TokenSet.fromClause(clause),\n          expandedTerms = this.tokenSet.intersect(termTokenSet).toArray()\n\n      /*\n       * If a term marked as required does not exist in the tokenSet it is\n       * impossible for the search to return any matches. We set all the field\n       * scoped required matches set to empty and stop examining any further\n       * clauses.\n       */\n      if (expandedTerms.length === 0 && clause.presence === lunr.Query.presence.REQUIRED) {\n        for (var k = 0; k < clause.fields.length; k++) {\n          var field = clause.fields[k]\n          requiredMatches[field] = lunr.Set.empty\n        }\n\n        break\n      }\n\n      for (var j = 0; j < expandedTerms.length; j++) {\n        /*\n         * For each term get the posting and termIndex, this is required for\n         * building the query vector.\n         */\n        var expandedTerm = expandedTerms[j],\n            posting = this.invertedIndex[expandedTerm],\n            termIndex = posting._index\n\n        for (var k = 0; k < clause.fields.length; k++) {\n          /*\n           * For each field that this query term is scoped by (by default\n           * all fields are in scope) we need to get all the document refs\n           * that have this term in that field.\n           *\n           * The posting is the entry in the invertedIndex for the matching\n           * term from above.\n           */\n          var field = clause.fields[k],\n              fieldPosting = posting[field],\n              matchingDocumentRefs = Object.keys(fieldPosting),\n              termField = expandedTerm + \"/\" + field,\n              matchingDocumentsSet = new lunr.Set(matchingDocumentRefs)\n\n          /*\n           * if the presence of this term is required ensure that the matching\n           * documents are added to the set of required matches for this clause.\n           *\n           */\n          if (clause.presence == lunr.Query.presence.REQUIRED) {\n            clauseMatches = clauseMatches.union(matchingDocumentsSet)\n\n            if (requiredMatches[field] === undefined) {\n              requiredMatches[field] = lunr.Set.complete\n            }\n          }\n\n          /*\n           * if the presence of this term is prohibited ensure that the matching\n           * documents are added to the set of prohibited matches for this field,\n           * creating that set if it does not yet exist.\n           */\n          if (clause.presence == lunr.Query.presence.PROHIBITED) {\n            if (prohibitedMatches[field] === undefined) {\n              prohibitedMatches[field] = lunr.Set.empty\n            }\n\n            prohibitedMatches[field] = prohibitedMatches[field].union(matchingDocumentsSet)\n\n            /*\n             * Prohibited matches should not be part of the query vector used for\n             * similarity scoring and no metadata should be extracted so we continue\n             * to the next field\n             */\n            continue\n          }\n\n          /*\n           * The query field vector is populated using the termIndex found for\n           * the term and a unit value with the appropriate boost applied.\n           * Using upsert because there could already be an entry in the vector\n           * for the term we are working with. In that case we just add the scores\n           * together.\n           */\n          queryVectors[field].upsert(termIndex, clause.boost, function (a, b) { return a + b })\n\n          /**\n           * If we've already seen this term, field combo then we've already collected\n           * the matching documents and metadata, no need to go through all that again\n           */\n          if (termFieldCache[termField]) {\n            continue\n          }\n\n          for (var l = 0; l < matchingDocumentRefs.length; l++) {\n            /*\n             * All metadata for this term/field/document triple\n             * are then extracted and collected into an instance\n             * of lunr.MatchData ready to be returned in the query\n             * results\n             */\n            var matchingDocumentRef = matchingDocumentRefs[l],\n                matchingFieldRef = new lunr.FieldRef (matchingDocumentRef, field),\n                metadata = fieldPosting[matchingDocumentRef],\n                fieldMatch\n\n            if ((fieldMatch = matchingFields[matchingFieldRef]) === undefined) {\n              matchingFields[matchingFieldRef] = new lunr.MatchData (expandedTerm, field, metadata)\n            } else {\n              fieldMatch.add(expandedTerm, field, metadata)\n            }\n\n          }\n\n          termFieldCache[termField] = true\n        }\n      }\n    }\n\n    /**\n     * If the presence was required we need to update the requiredMatches field sets.\n     * We do this after all fields for the term have collected their matches because\n     * the clause terms presence is required in _any_ of the fields not _all_ of the\n     * fields.\n     */\n    if (clause.presence === lunr.Query.presence.REQUIRED) {\n      for (var k = 0; k < clause.fields.length; k++) {\n        var field = clause.fields[k]\n        requiredMatches[field] = requiredMatches[field].intersect(clauseMatches)\n      }\n    }\n  }\n\n  /**\n   * Need to combine the field scoped required and prohibited\n   * matching documents into a global set of required and prohibited\n   * matches\n   */\n  var allRequiredMatches = lunr.Set.complete,\n      allProhibitedMatches = lunr.Set.empty\n\n  for (var i = 0; i < this.fields.length; i++) {\n    var field = this.fields[i]\n\n    if (requiredMatches[field]) {\n      allRequiredMatches = allRequiredMatches.intersect(requiredMatches[field])\n    }\n\n    if (prohibitedMatches[field]) {\n      allProhibitedMatches = allProhibitedMatches.union(prohibitedMatches[field])\n    }\n  }\n\n  var matchingFieldRefs = Object.keys(matchingFields),\n      results = [],\n      matches = Object.create(null)\n\n  /*\n   * If the query is negated (contains only prohibited terms)\n   * we need to get _all_ fieldRefs currently existing in the\n   * index. This is only done when we know that the query is\n   * entirely prohibited terms to avoid any cost of getting all\n   * fieldRefs unnecessarily.\n   *\n   * Additionally, blank MatchData must be created to correctly\n   * populate the results.\n   */\n  if (query.isNegated()) {\n    matchingFieldRefs = Object.keys(this.fieldVectors)\n\n    for (var i = 0; i < matchingFieldRefs.length; i++) {\n      var matchingFieldRef = matchingFieldRefs[i]\n      var fieldRef = lunr.FieldRef.fromString(matchingFieldRef)\n      matchingFields[matchingFieldRef] = new lunr.MatchData\n    }\n  }\n\n  for (var i = 0; i < matchingFieldRefs.length; i++) {\n    /*\n     * Currently we have document fields that match the query, but we\n     * need to return documents. The matchData and scores are combined\n     * from multiple fields belonging to the same document.\n     *\n     * Scores are calculated by field, using the query vectors created\n     * above, and combined into a final document score using addition.\n     */\n    var fieldRef = lunr.FieldRef.fromString(matchingFieldRefs[i]),\n        docRef = fieldRef.docRef\n\n    if (!allRequiredMatches.contains(docRef)) {\n      continue\n    }\n\n    if (allProhibitedMatches.contains(docRef)) {\n      continue\n    }\n\n    var fieldVector = this.fieldVectors[fieldRef],\n        score = queryVectors[fieldRef.fieldName].similarity(fieldVector),\n        docMatch\n\n    if ((docMatch = matches[docRef]) !== undefined) {\n      docMatch.score += score\n      docMatch.matchData.combine(matchingFields[fieldRef])\n    } else {\n      var match = {\n        ref: docRef,\n        score: score,\n        matchData: matchingFields[fieldRef]\n      }\n      matches[docRef] = match\n      results.push(match)\n    }\n  }\n\n  /*\n   * Sort the results objects by score, highest first.\n   */\n  return results.sort(function (a, b) {\n    return b.score - a.score\n  })\n}\n\n/**\n * Prepares the index for JSON serialization.\n *\n * The schema for this JSON blob will be described in a\n * separate JSON schema file.\n *\n * @returns {Object}\n */\nlunr.Index.prototype.toJSON = function () {\n  var invertedIndex = Object.keys(this.invertedIndex)\n    .sort()\n    .map(function (term) {\n      return [term, this.invertedIndex[term]]\n    }, this)\n\n  var fieldVectors = Object.keys(this.fieldVectors)\n    .map(function (ref) {\n      return [ref, this.fieldVectors[ref].toJSON()]\n    }, this)\n\n  return {\n    version: lunr.version,\n    fields: this.fields,\n    fieldVectors: fieldVectors,\n    invertedIndex: invertedIndex,\n    pipeline: this.pipeline.toJSON()\n  }\n}\n\n/**\n * Loads a previously serialized lunr.Index\n *\n * @param {Object} serializedIndex - A previously serialized lunr.Index\n * @returns {lunr.Index}\n */\nlunr.Index.load = function (serializedIndex) {\n  var attrs = {},\n      fieldVectors = {},\n      serializedVectors = serializedIndex.fieldVectors,\n      invertedIndex = Object.create(null),\n      serializedInvertedIndex = serializedIndex.invertedIndex,\n      tokenSetBuilder = new lunr.TokenSet.Builder,\n      pipeline = lunr.Pipeline.load(serializedIndex.pipeline)\n\n  if (serializedIndex.version != lunr.version) {\n    lunr.utils.warn(\"Version mismatch when loading serialised index. Current version of lunr '\" + lunr.version + \"' does not match serialized index '\" + serializedIndex.version + \"'\")\n  }\n\n  for (var i = 0; i < serializedVectors.length; i++) {\n    var tuple = serializedVectors[i],\n        ref = tuple[0],\n        elements = tuple[1]\n\n    fieldVectors[ref] = new lunr.Vector(elements)\n  }\n\n  for (var i = 0; i < serializedInvertedIndex.length; i++) {\n    var tuple = serializedInvertedIndex[i],\n        term = tuple[0],\n        posting = tuple[1]\n\n    tokenSetBuilder.insert(term)\n    invertedIndex[term] = posting\n  }\n\n  tokenSetBuilder.finish()\n\n  attrs.fields = serializedIndex.fields\n\n  attrs.fieldVectors = fieldVectors\n  attrs.invertedIndex = invertedIndex\n  attrs.tokenSet = tokenSetBuilder.root\n  attrs.pipeline = pipeline\n\n  return new lunr.Index(attrs)\n}\n/*!\n * lunr.Builder\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Builder performs indexing on a set of documents and\n * returns instances of lunr.Index ready for querying.\n *\n * All configuration of the index is done via the builder, the\n * fields to index, the document reference, the text processing\n * pipeline and document scoring parameters are all set on the\n * builder before indexing.\n *\n * @constructor\n * @property {string} _ref - Internal reference to the document reference field.\n * @property {string[]} _fields - Internal reference to the document fields to index.\n * @property {object} invertedIndex - The inverted index maps terms to document fields.\n * @property {object} documentTermFrequencies - Keeps track of document term frequencies.\n * @property {object} documentLengths - Keeps track of the length of documents added to the index.\n * @property {lunr.tokenizer} tokenizer - Function for splitting strings into tokens for indexing.\n * @property {lunr.Pipeline} pipeline - The pipeline performs text processing on tokens before indexing.\n * @property {lunr.Pipeline} searchPipeline - A pipeline for processing search terms before querying the index.\n * @property {number} documentCount - Keeps track of the total number of documents indexed.\n * @property {number} _b - A parameter to control field length normalization, setting this to 0 disabled normalization, 1 fully normalizes field lengths, the default value is 0.75.\n * @property {number} _k1 - A parameter to control how quickly an increase in term frequency results in term frequency saturation, the default value is 1.2.\n * @property {number} termIndex - A counter incremented for each unique term, used to identify a terms position in the vector space.\n * @property {array} metadataWhitelist - A list of metadata keys that have been whitelisted for entry in the index.\n */\nlunr.Builder = function () {\n  this._ref = \"id\"\n  this._fields = Object.create(null)\n  this._documents = Object.create(null)\n  this.invertedIndex = Object.create(null)\n  this.fieldTermFrequencies = {}\n  this.fieldLengths = {}\n  this.tokenizer = lunr.tokenizer\n  this.pipeline = new lunr.Pipeline\n  this.searchPipeline = new lunr.Pipeline\n  this.documentCount = 0\n  this._b = 0.75\n  this._k1 = 1.2\n  this.termIndex = 0\n  this.metadataWhitelist = []\n}\n\n/**\n * Sets the document field used as the document reference. Every document must have this field.\n * The type of this field in the document should be a string, if it is not a string it will be\n * coerced into a string by calling toString.\n *\n * The default ref is 'id'.\n *\n * The ref should _not_ be changed during indexing, it should be set before any documents are\n * added to the index. Changing it during indexing can lead to inconsistent results.\n *\n * @param {string} ref - The name of the reference field in the document.\n */\nlunr.Builder.prototype.ref = function (ref) {\n  this._ref = ref\n}\n\n/**\n * A function that is used to extract a field from a document.\n *\n * Lunr expects a field to be at the top level of a document, if however the field\n * is deeply nested within a document an extractor function can be used to extract\n * the right field for indexing.\n *\n * @callback fieldExtractor\n * @param {object} doc - The document being added to the index.\n * @returns {?(string|object|object[])} obj - The object that will be indexed for this field.\n * @example <caption>Extracting a nested field</caption>\n * function (doc) { return doc.nested.field }\n */\n\n/**\n * Adds a field to the list of document fields that will be indexed. Every document being\n * indexed should have this field. Null values for this field in indexed documents will\n * not cause errors but will limit the chance of that document being retrieved by searches.\n *\n * All fields should be added before adding documents to the index. Adding fields after\n * a document has been indexed will have no effect on already indexed documents.\n *\n * Fields can be boosted at build time. This allows terms within that field to have more\n * importance when ranking search results. Use a field boost to specify that matches within\n * one field are more important than other fields.\n *\n * @param {string} fieldName - The name of a field to index in all documents.\n * @param {object} attributes - Optional attributes associated with this field.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this field.\n * @param {fieldExtractor} [attributes.extractor] - Function to extract a field from a document.\n * @throws {RangeError} fieldName cannot contain unsupported characters '/'\n */\nlunr.Builder.prototype.field = function (fieldName, attributes) {\n  if (/\\//.test(fieldName)) {\n    throw new RangeError (\"Field '\" + fieldName + \"' contains illegal character '/'\")\n  }\n\n  this._fields[fieldName] = attributes || {}\n}\n\n/**\n * A parameter to tune the amount of field length normalisation that is applied when\n * calculating relevance scores. A value of 0 will completely disable any normalisation\n * and a value of 1 will fully normalise field lengths. The default is 0.75. Values of b\n * will be clamped to the range 0 - 1.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.b = function (number) {\n  if (number < 0) {\n    this._b = 0\n  } else if (number > 1) {\n    this._b = 1\n  } else {\n    this._b = number\n  }\n}\n\n/**\n * A parameter that controls the speed at which a rise in term frequency results in term\n * frequency saturation. The default value is 1.2. Setting this to a higher value will give\n * slower saturation levels, a lower value will result in quicker saturation.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.k1 = function (number) {\n  this._k1 = number\n}\n\n/**\n * Adds a document to the index.\n *\n * Before adding fields to the index the index should have been fully setup, with the document\n * ref and all fields to index already having been specified.\n *\n * The document must have a field name as specified by the ref (by default this is 'id') and\n * it should have all fields defined for indexing, though null or undefined values will not\n * cause errors.\n *\n * Entire documents can be boosted at build time. Applying a boost to a document indicates that\n * this document should rank higher in search results than other documents.\n *\n * @param {object} doc - The document to add to the index.\n * @param {object} attributes - Optional attributes associated with this document.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this document.\n */\nlunr.Builder.prototype.add = function (doc, attributes) {\n  var docRef = doc[this._ref],\n      fields = Object.keys(this._fields)\n\n  this._documents[docRef] = attributes || {}\n  this.documentCount += 1\n\n  for (var i = 0; i < fields.length; i++) {\n    var fieldName = fields[i],\n        extractor = this._fields[fieldName].extractor,\n        field = extractor ? extractor(doc) : doc[fieldName],\n        tokens = this.tokenizer(field, {\n          fields: [fieldName]\n        }),\n        terms = this.pipeline.run(tokens),\n        fieldRef = new lunr.FieldRef (docRef, fieldName),\n        fieldTerms = Object.create(null)\n\n    this.fieldTermFrequencies[fieldRef] = fieldTerms\n    this.fieldLengths[fieldRef] = 0\n\n    // store the length of this field for this document\n    this.fieldLengths[fieldRef] += terms.length\n\n    // calculate term frequencies for this field\n    for (var j = 0; j < terms.length; j++) {\n      var term = terms[j]\n\n      if (fieldTerms[term] == undefined) {\n        fieldTerms[term] = 0\n      }\n\n      fieldTerms[term] += 1\n\n      // add to inverted index\n      // create an initial posting if one doesn't exist\n      if (this.invertedIndex[term] == undefined) {\n        var posting = Object.create(null)\n        posting[\"_index\"] = this.termIndex\n        this.termIndex += 1\n\n        for (var k = 0; k < fields.length; k++) {\n          posting[fields[k]] = Object.create(null)\n        }\n\n        this.invertedIndex[term] = posting\n      }\n\n      // add an entry for this term/fieldName/docRef to the invertedIndex\n      if (this.invertedIndex[term][fieldName][docRef] == undefined) {\n        this.invertedIndex[term][fieldName][docRef] = Object.create(null)\n      }\n\n      // store all whitelisted metadata about this token in the\n      // inverted index\n      for (var l = 0; l < this.metadataWhitelist.length; l++) {\n        var metadataKey = this.metadataWhitelist[l],\n            metadata = term.metadata[metadataKey]\n\n        if (this.invertedIndex[term][fieldName][docRef][metadataKey] == undefined) {\n          this.invertedIndex[term][fieldName][docRef][metadataKey] = []\n        }\n\n        this.invertedIndex[term][fieldName][docRef][metadataKey].push(metadata)\n      }\n    }\n\n  }\n}\n\n/**\n * Calculates the average document length for this index\n *\n * @private\n */\nlunr.Builder.prototype.calculateAverageFieldLengths = function () {\n\n  var fieldRefs = Object.keys(this.fieldLengths),\n      numberOfFields = fieldRefs.length,\n      accumulator = {},\n      documentsWithField = {}\n\n  for (var i = 0; i < numberOfFields; i++) {\n    var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n        field = fieldRef.fieldName\n\n    documentsWithField[field] || (documentsWithField[field] = 0)\n    documentsWithField[field] += 1\n\n    accumulator[field] || (accumulator[field] = 0)\n    accumulator[field] += this.fieldLengths[fieldRef]\n  }\n\n  var fields = Object.keys(this._fields)\n\n  for (var i = 0; i < fields.length; i++) {\n    var fieldName = fields[i]\n    accumulator[fieldName] = accumulator[fieldName] / documentsWithField[fieldName]\n  }\n\n  this.averageFieldLength = accumulator\n}\n\n/**\n * Builds a vector space model of every document using lunr.Vector\n *\n * @private\n */\nlunr.Builder.prototype.createFieldVectors = function () {\n  var fieldVectors = {},\n      fieldRefs = Object.keys(this.fieldTermFrequencies),\n      fieldRefsLength = fieldRefs.length,\n      termIdfCache = Object.create(null)\n\n  for (var i = 0; i < fieldRefsLength; i++) {\n    var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n        fieldName = fieldRef.fieldName,\n        fieldLength = this.fieldLengths[fieldRef],\n        fieldVector = new lunr.Vector,\n        termFrequencies = this.fieldTermFrequencies[fieldRef],\n        terms = Object.keys(termFrequencies),\n        termsLength = terms.length\n\n\n    var fieldBoost = this._fields[fieldName].boost || 1,\n        docBoost = this._documents[fieldRef.docRef].boost || 1\n\n    for (var j = 0; j < termsLength; j++) {\n      var term = terms[j],\n          tf = termFrequencies[term],\n          termIndex = this.invertedIndex[term]._index,\n          idf, score, scoreWithPrecision\n\n      if (termIdfCache[term] === undefined) {\n        idf = lunr.idf(this.invertedIndex[term], this.documentCount)\n        termIdfCache[term] = idf\n      } else {\n        idf = termIdfCache[term]\n      }\n\n      score = idf * ((this._k1 + 1) * tf) / (this._k1 * (1 - this._b + this._b * (fieldLength / this.averageFieldLength[fieldName])) + tf)\n      score *= fieldBoost\n      score *= docBoost\n      scoreWithPrecision = Math.round(score * 1000) / 1000\n      // Converts 1.23456789 to 1.234.\n      // Reducing the precision so that the vectors take up less\n      // space when serialised. Doing it now so that they behave\n      // the same before and after serialisation. Also, this is\n      // the fastest approach to reducing a number's precision in\n      // JavaScript.\n\n      fieldVector.insert(termIndex, scoreWithPrecision)\n    }\n\n    fieldVectors[fieldRef] = fieldVector\n  }\n\n  this.fieldVectors = fieldVectors\n}\n\n/**\n * Creates a token set of all tokens in the index using lunr.TokenSet\n *\n * @private\n */\nlunr.Builder.prototype.createTokenSet = function () {\n  this.tokenSet = lunr.TokenSet.fromArray(\n    Object.keys(this.invertedIndex).sort()\n  )\n}\n\n/**\n * Builds the index, creating an instance of lunr.Index.\n *\n * This completes the indexing process and should only be called\n * once all documents have been added to the index.\n *\n * @returns {lunr.Index}\n */\nlunr.Builder.prototype.build = function () {\n  this.calculateAverageFieldLengths()\n  this.createFieldVectors()\n  this.createTokenSet()\n\n  return new lunr.Index({\n    invertedIndex: this.invertedIndex,\n    fieldVectors: this.fieldVectors,\n    tokenSet: this.tokenSet,\n    fields: Object.keys(this._fields),\n    pipeline: this.searchPipeline\n  })\n}\n\n/**\n * Applies a plugin to the index builder.\n *\n * A plugin is a function that is called with the index builder as its context.\n * Plugins can be used to customise or extend the behaviour of the index\n * in some way. A plugin is just a function, that encapsulated the custom\n * behaviour that should be applied when building the index.\n *\n * The plugin function will be called with the index builder as its argument, additional\n * arguments can also be passed when calling use. The function will be called\n * with the index builder as its context.\n *\n * @param {Function} plugin The plugin to apply.\n */\nlunr.Builder.prototype.use = function (fn) {\n  var args = Array.prototype.slice.call(arguments, 1)\n  args.unshift(this)\n  fn.apply(this, args)\n}\n/**\n * Contains and collects metadata about a matching document.\n * A single instance of lunr.MatchData is returned as part of every\n * lunr.Index~Result.\n *\n * @constructor\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n * @property {object} metadata - A cloned collection of metadata associated with this document.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData = function (term, field, metadata) {\n  var clonedMetadata = Object.create(null),\n      metadataKeys = Object.keys(metadata || {})\n\n  // Cloning the metadata to prevent the original\n  // being mutated during match data combination.\n  // Metadata is kept in an array within the inverted\n  // index so cloning the data can be done with\n  // Array#slice\n  for (var i = 0; i < metadataKeys.length; i++) {\n    var key = metadataKeys[i]\n    clonedMetadata[key] = metadata[key].slice()\n  }\n\n  this.metadata = Object.create(null)\n\n  if (term !== undefined) {\n    this.metadata[term] = Object.create(null)\n    this.metadata[term][field] = clonedMetadata\n  }\n}\n\n/**\n * An instance of lunr.MatchData will be created for every term that matches a\n * document. However only one instance is required in a lunr.Index~Result. This\n * method combines metadata from another instance of lunr.MatchData with this\n * objects metadata.\n *\n * @param {lunr.MatchData} otherMatchData - Another instance of match data to merge with this one.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData.prototype.combine = function (otherMatchData) {\n  var terms = Object.keys(otherMatchData.metadata)\n\n  for (var i = 0; i < terms.length; i++) {\n    var term = terms[i],\n        fields = Object.keys(otherMatchData.metadata[term])\n\n    if (this.metadata[term] == undefined) {\n      this.metadata[term] = Object.create(null)\n    }\n\n    for (var j = 0; j < fields.length; j++) {\n      var field = fields[j],\n          keys = Object.keys(otherMatchData.metadata[term][field])\n\n      if (this.metadata[term][field] == undefined) {\n        this.metadata[term][field] = Object.create(null)\n      }\n\n      for (var k = 0; k < keys.length; k++) {\n        var key = keys[k]\n\n        if (this.metadata[term][field][key] == undefined) {\n          this.metadata[term][field][key] = otherMatchData.metadata[term][field][key]\n        } else {\n          this.metadata[term][field][key] = this.metadata[term][field][key].concat(otherMatchData.metadata[term][field][key])\n        }\n\n      }\n    }\n  }\n}\n\n/**\n * Add metadata for a term/field pair to this instance of match data.\n *\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n */\nlunr.MatchData.prototype.add = function (term, field, metadata) {\n  if (!(term in this.metadata)) {\n    this.metadata[term] = Object.create(null)\n    this.metadata[term][field] = metadata\n    return\n  }\n\n  if (!(field in this.metadata[term])) {\n    this.metadata[term][field] = metadata\n    return\n  }\n\n  var metadataKeys = Object.keys(metadata)\n\n  for (var i = 0; i < metadataKeys.length; i++) {\n    var key = metadataKeys[i]\n\n    if (key in this.metadata[term][field]) {\n      this.metadata[term][field][key] = this.metadata[term][field][key].concat(metadata[key])\n    } else {\n      this.metadata[term][field][key] = metadata[key]\n    }\n  }\n}\n/**\n * A lunr.Query provides a programmatic way of defining queries to be performed\n * against a {@link lunr.Index}.\n *\n * Prefer constructing a lunr.Query using the {@link lunr.Index#query} method\n * so the query object is pre-initialized with the right index fields.\n *\n * @constructor\n * @property {lunr.Query~Clause[]} clauses - An array of query clauses.\n * @property {string[]} allFields - An array of all available fields in a lunr.Index.\n */\nlunr.Query = function (allFields) {\n  this.clauses = []\n  this.allFields = allFields\n}\n\n/**\n * Constants for indicating what kind of automatic wildcard insertion will be used when constructing a query clause.\n *\n * This allows wildcards to be added to the beginning and end of a term without having to manually do any string\n * concatenation.\n *\n * The wildcard constants can be bitwise combined to select both leading and trailing wildcards.\n *\n * @constant\n * @default\n * @property {number} wildcard.NONE - The term will have no wildcards inserted, this is the default behaviour\n * @property {number} wildcard.LEADING - Prepend the term with a wildcard, unless a leading wildcard already exists\n * @property {number} wildcard.TRAILING - Append a wildcard to the term, unless a trailing wildcard already exists\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example <caption>query term with trailing wildcard</caption>\n * query.term('foo', { wildcard: lunr.Query.wildcard.TRAILING })\n * @example <caption>query term with leading and trailing wildcard</caption>\n * query.term('foo', {\n *   wildcard: lunr.Query.wildcard.LEADING | lunr.Query.wildcard.TRAILING\n * })\n */\n\nlunr.Query.wildcard = new String (\"*\")\nlunr.Query.wildcard.NONE = 0\nlunr.Query.wildcard.LEADING = 1\nlunr.Query.wildcard.TRAILING = 2\n\n/**\n * Constants for indicating what kind of presence a term must have in matching documents.\n *\n * @constant\n * @enum {number}\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example <caption>query term with required presence</caption>\n * query.term('foo', { presence: lunr.Query.presence.REQUIRED })\n */\nlunr.Query.presence = {\n  /**\n   * Term's presence in a document is optional, this is the default value.\n   */\n  OPTIONAL: 1,\n\n  /**\n   * Term's presence in a document is required, documents that do not contain\n   * this term will not be returned.\n   */\n  REQUIRED: 2,\n\n  /**\n   * Term's presence in a document is prohibited, documents that do contain\n   * this term will not be returned.\n   */\n  PROHIBITED: 3\n}\n\n/**\n * A single clause in a {@link lunr.Query} contains a term and details on how to\n * match that term against a {@link lunr.Index}.\n *\n * @typedef {Object} lunr.Query~Clause\n * @property {string[]} fields - The fields in an index this clause should be matched against.\n * @property {number} [boost=1] - Any boost that should be applied when matching this clause.\n * @property {number} [editDistance] - Whether the term should have fuzzy matching applied, and how fuzzy the match should be.\n * @property {boolean} [usePipeline] - Whether the term should be passed through the search pipeline.\n * @property {number} [wildcard=lunr.Query.wildcard.NONE] - Whether the term should have wildcards appended or prepended.\n * @property {number} [presence=lunr.Query.presence.OPTIONAL] - The terms presence in any matching documents.\n */\n\n/**\n * Adds a {@link lunr.Query~Clause} to this query.\n *\n * Unless the clause contains the fields to be matched all fields will be matched. In addition\n * a default boost of 1 is applied to the clause.\n *\n * @param {lunr.Query~Clause} clause - The clause to add to this query.\n * @see lunr.Query~Clause\n * @returns {lunr.Query}\n */\nlunr.Query.prototype.clause = function (clause) {\n  if (!('fields' in clause)) {\n    clause.fields = this.allFields\n  }\n\n  if (!('boost' in clause)) {\n    clause.boost = 1\n  }\n\n  if (!('usePipeline' in clause)) {\n    clause.usePipeline = true\n  }\n\n  if (!('wildcard' in clause)) {\n    clause.wildcard = lunr.Query.wildcard.NONE\n  }\n\n  if ((clause.wildcard & lunr.Query.wildcard.LEADING) && (clause.term.charAt(0) != lunr.Query.wildcard)) {\n    clause.term = \"*\" + clause.term\n  }\n\n  if ((clause.wildcard & lunr.Query.wildcard.TRAILING) && (clause.term.slice(-1) != lunr.Query.wildcard)) {\n    clause.term = \"\" + clause.term + \"*\"\n  }\n\n  if (!('presence' in clause)) {\n    clause.presence = lunr.Query.presence.OPTIONAL\n  }\n\n  this.clauses.push(clause)\n\n  return this\n}\n\n/**\n * A negated query is one in which every clause has a presence of\n * prohibited. These queries require some special processing to return\n * the expected results.\n *\n * @returns boolean\n */\nlunr.Query.prototype.isNegated = function () {\n  for (var i = 0; i < this.clauses.length; i++) {\n    if (this.clauses[i].presence != lunr.Query.presence.PROHIBITED) {\n      return false\n    }\n  }\n\n  return true\n}\n\n/**\n * Adds a term to the current query, under the covers this will create a {@link lunr.Query~Clause}\n * to the list of clauses that make up this query.\n *\n * The term is used as is, i.e. no tokenization will be performed by this method. Instead conversion\n * to a token or token-like string should be done before calling this method.\n *\n * The term will be converted to a string by calling `toString`. Multiple terms can be passed as an\n * array, each term in the array will share the same options.\n *\n * @param {object|object[]} term - The term(s) to add to the query.\n * @param {object} [options] - Any additional properties to add to the query clause.\n * @returns {lunr.Query}\n * @see lunr.Query#clause\n * @see lunr.Query~Clause\n * @example <caption>adding a single term to a query</caption>\n * query.term(\"foo\")\n * @example <caption>adding a single term to a query and specifying search fields, term boost and automatic trailing wildcard</caption>\n * query.term(\"foo\", {\n *   fields: [\"title\"],\n *   boost: 10,\n *   wildcard: lunr.Query.wildcard.TRAILING\n * })\n * @example <caption>using lunr.tokenizer to convert a string to tokens before using them as terms</caption>\n * query.term(lunr.tokenizer(\"foo bar\"))\n */\nlunr.Query.prototype.term = function (term, options) {\n  if (Array.isArray(term)) {\n    term.forEach(function (t) { this.term(t, lunr.utils.clone(options)) }, this)\n    return this\n  }\n\n  var clause = options || {}\n  clause.term = term.toString()\n\n  this.clause(clause)\n\n  return this\n}\nlunr.QueryParseError = function (message, start, end) {\n  this.name = \"QueryParseError\"\n  this.message = message\n  this.start = start\n  this.end = end\n}\n\nlunr.QueryParseError.prototype = new Error\nlunr.QueryLexer = function (str) {\n  this.lexemes = []\n  this.str = str\n  this.length = str.length\n  this.pos = 0\n  this.start = 0\n  this.escapeCharPositions = []\n}\n\nlunr.QueryLexer.prototype.run = function () {\n  var state = lunr.QueryLexer.lexText\n\n  while (state) {\n    state = state(this)\n  }\n}\n\nlunr.QueryLexer.prototype.sliceString = function () {\n  var subSlices = [],\n      sliceStart = this.start,\n      sliceEnd = this.pos\n\n  for (var i = 0; i < this.escapeCharPositions.length; i++) {\n    sliceEnd = this.escapeCharPositions[i]\n    subSlices.push(this.str.slice(sliceStart, sliceEnd))\n    sliceStart = sliceEnd + 1\n  }\n\n  subSlices.push(this.str.slice(sliceStart, this.pos))\n  this.escapeCharPositions.length = 0\n\n  return subSlices.join('')\n}\n\nlunr.QueryLexer.prototype.emit = function (type) {\n  this.lexemes.push({\n    type: type,\n    str: this.sliceString(),\n    start: this.start,\n    end: this.pos\n  })\n\n  this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.escapeCharacter = function () {\n  this.escapeCharPositions.push(this.pos - 1)\n  this.pos += 1\n}\n\nlunr.QueryLexer.prototype.next = function () {\n  if (this.pos >= this.length) {\n    return lunr.QueryLexer.EOS\n  }\n\n  var char = this.str.charAt(this.pos)\n  this.pos += 1\n  return char\n}\n\nlunr.QueryLexer.prototype.width = function () {\n  return this.pos - this.start\n}\n\nlunr.QueryLexer.prototype.ignore = function () {\n  if (this.start == this.pos) {\n    this.pos += 1\n  }\n\n  this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.backup = function () {\n  this.pos -= 1\n}\n\nlunr.QueryLexer.prototype.acceptDigitRun = function () {\n  var char, charCode\n\n  do {\n    char = this.next()\n    charCode = char.charCodeAt(0)\n  } while (charCode > 47 && charCode < 58)\n\n  if (char != lunr.QueryLexer.EOS) {\n    this.backup()\n  }\n}\n\nlunr.QueryLexer.prototype.more = function () {\n  return this.pos < this.length\n}\n\nlunr.QueryLexer.EOS = 'EOS'\nlunr.QueryLexer.FIELD = 'FIELD'\nlunr.QueryLexer.TERM = 'TERM'\nlunr.QueryLexer.EDIT_DISTANCE = 'EDIT_DISTANCE'\nlunr.QueryLexer.BOOST = 'BOOST'\nlunr.QueryLexer.PRESENCE = 'PRESENCE'\n\nlunr.QueryLexer.lexField = function (lexer) {\n  lexer.backup()\n  lexer.emit(lunr.QueryLexer.FIELD)\n  lexer.ignore()\n  return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexTerm = function (lexer) {\n  if (lexer.width() > 1) {\n    lexer.backup()\n    lexer.emit(lunr.QueryLexer.TERM)\n  }\n\n  lexer.ignore()\n\n  if (lexer.more()) {\n    return lunr.QueryLexer.lexText\n  }\n}\n\nlunr.QueryLexer.lexEditDistance = function (lexer) {\n  lexer.ignore()\n  lexer.acceptDigitRun()\n  lexer.emit(lunr.QueryLexer.EDIT_DISTANCE)\n  return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexBoost = function (lexer) {\n  lexer.ignore()\n  lexer.acceptDigitRun()\n  lexer.emit(lunr.QueryLexer.BOOST)\n  return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexEOS = function (lexer) {\n  if (lexer.width() > 0) {\n    lexer.emit(lunr.QueryLexer.TERM)\n  }\n}\n\n// This matches the separator used when tokenising fields\n// within a document. These should match otherwise it is\n// not possible to search for some tokens within a document.\n//\n// It is possible for the user to change the separator on the\n// tokenizer so it _might_ clash with any other of the special\n// characters already used within the search string, e.g. :.\n//\n// This means that it is possible to change the separator in\n// such a way that makes some words unsearchable using a search\n// string.\nlunr.QueryLexer.termSeparator = lunr.tokenizer.separator\n\nlunr.QueryLexer.lexText = function (lexer) {\n  while (true) {\n    var char = lexer.next()\n\n    if (char == lunr.QueryLexer.EOS) {\n      return lunr.QueryLexer.lexEOS\n    }\n\n    // Escape character is '\\'\n    if (char.charCodeAt(0) == 92) {\n      lexer.escapeCharacter()\n      continue\n    }\n\n    if (char == \":\") {\n      return lunr.QueryLexer.lexField\n    }\n\n    if (char == \"~\") {\n      lexer.backup()\n      if (lexer.width() > 0) {\n        lexer.emit(lunr.QueryLexer.TERM)\n      }\n      return lunr.QueryLexer.lexEditDistance\n    }\n\n    if (char == \"^\") {\n      lexer.backup()\n      if (lexer.width() > 0) {\n        lexer.emit(lunr.QueryLexer.TERM)\n      }\n      return lunr.QueryLexer.lexBoost\n    }\n\n    // \"+\" indicates term presence is required\n    // checking for length to ensure that only\n    // leading \"+\" are considered\n    if (char == \"+\" && lexer.width() === 1) {\n      lexer.emit(lunr.QueryLexer.PRESENCE)\n      return lunr.QueryLexer.lexText\n    }\n\n    // \"-\" indicates term presence is prohibited\n    // checking for length to ensure that only\n    // leading \"-\" are considered\n    if (char == \"-\" && lexer.width() === 1) {\n      lexer.emit(lunr.QueryLexer.PRESENCE)\n      return lunr.QueryLexer.lexText\n    }\n\n    if (char.match(lunr.QueryLexer.termSeparator)) {\n      return lunr.QueryLexer.lexTerm\n    }\n  }\n}\n\nlunr.QueryParser = function (str, query) {\n  this.lexer = new lunr.QueryLexer (str)\n  this.query = query\n  this.currentClause = {}\n  this.lexemeIdx = 0\n}\n\nlunr.QueryParser.prototype.parse = function () {\n  this.lexer.run()\n  this.lexemes = this.lexer.lexemes\n\n  var state = lunr.QueryParser.parseClause\n\n  while (state) {\n    state = state(this)\n  }\n\n  return this.query\n}\n\nlunr.QueryParser.prototype.peekLexeme = function () {\n  return this.lexemes[this.lexemeIdx]\n}\n\nlunr.QueryParser.prototype.consumeLexeme = function () {\n  var lexeme = this.peekLexeme()\n  this.lexemeIdx += 1\n  return lexeme\n}\n\nlunr.QueryParser.prototype.nextClause = function () {\n  var completedClause = this.currentClause\n  this.query.clause(completedClause)\n  this.currentClause = {}\n}\n\nlunr.QueryParser.parseClause = function (parser) {\n  var lexeme = parser.peekLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  switch (lexeme.type) {\n    case lunr.QueryLexer.PRESENCE:\n      return lunr.QueryParser.parsePresence\n    case lunr.QueryLexer.FIELD:\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.TERM:\n      return lunr.QueryParser.parseTerm\n    default:\n      var errorMessage = \"expected either a field or a term, found \" + lexeme.type\n\n      if (lexeme.str.length >= 1) {\n        errorMessage += \" with value '\" + lexeme.str + \"'\"\n      }\n\n      throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n}\n\nlunr.QueryParser.parsePresence = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  switch (lexeme.str) {\n    case \"-\":\n      parser.currentClause.presence = lunr.Query.presence.PROHIBITED\n      break\n    case \"+\":\n      parser.currentClause.presence = lunr.Query.presence.REQUIRED\n      break\n    default:\n      var errorMessage = \"unrecognised presence operator'\" + lexeme.str + \"'\"\n      throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    var errorMessage = \"expecting term or field, found nothing\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.FIELD:\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.TERM:\n      return lunr.QueryParser.parseTerm\n    default:\n      var errorMessage = \"expecting term or field, found '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseField = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  if (parser.query.allFields.indexOf(lexeme.str) == -1) {\n    var possibleFields = parser.query.allFields.map(function (f) { return \"'\" + f + \"'\" }).join(', '),\n        errorMessage = \"unrecognised field '\" + lexeme.str + \"', possible fields: \" + possibleFields\n\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  parser.currentClause.fields = [lexeme.str]\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    var errorMessage = \"expecting term, found nothing\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      return lunr.QueryParser.parseTerm\n    default:\n      var errorMessage = \"expecting term, found '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseTerm = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  parser.currentClause.term = lexeme.str.toLowerCase()\n\n  if (lexeme.str.indexOf(\"*\") != -1) {\n    parser.currentClause.usePipeline = false\n  }\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    parser.nextClause()\n    return\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      parser.nextClause()\n      return lunr.QueryParser.parseTerm\n    case lunr.QueryLexer.FIELD:\n      parser.nextClause()\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.EDIT_DISTANCE:\n      return lunr.QueryParser.parseEditDistance\n    case lunr.QueryLexer.BOOST:\n      return lunr.QueryParser.parseBoost\n    case lunr.QueryLexer.PRESENCE:\n      parser.nextClause()\n      return lunr.QueryParser.parsePresence\n    default:\n      var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseEditDistance = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  var editDistance = parseInt(lexeme.str, 10)\n\n  if (isNaN(editDistance)) {\n    var errorMessage = \"edit distance must be numeric\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  parser.currentClause.editDistance = editDistance\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    parser.nextClause()\n    return\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      parser.nextClause()\n      return lunr.QueryParser.parseTerm\n    case lunr.QueryLexer.FIELD:\n      parser.nextClause()\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.EDIT_DISTANCE:\n      return lunr.QueryParser.parseEditDistance\n    case lunr.QueryLexer.BOOST:\n      return lunr.QueryParser.parseBoost\n    case lunr.QueryLexer.PRESENCE:\n      parser.nextClause()\n      return lunr.QueryParser.parsePresence\n    default:\n      var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseBoost = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  var boost = parseInt(lexeme.str, 10)\n\n  if (isNaN(boost)) {\n    var errorMessage = \"boost must be numeric\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  parser.currentClause.boost = boost\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    parser.nextClause()\n    return\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      parser.nextClause()\n      return lunr.QueryParser.parseTerm\n    case lunr.QueryLexer.FIELD:\n      parser.nextClause()\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.EDIT_DISTANCE:\n      return lunr.QueryParser.parseEditDistance\n    case lunr.QueryLexer.BOOST:\n      return lunr.QueryParser.parseBoost\n    case lunr.QueryLexer.PRESENCE:\n      parser.nextClause()\n      return lunr.QueryParser.parsePresence\n    default:\n      var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\n  /**\n   * export the module via AMD, CommonJS or as a browser global\n   * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js\n   */\n  ;(function (root, factory) {\n    if (typeof define === 'function' && define.amd) {\n      // AMD. Register as an anonymous module.\n      define(factory)\n    } else if (typeof exports === 'object') {\n      /**\n       * Node. Does not work with strict CommonJS, but\n       * only CommonJS-like enviroments that support module.exports,\n       * like Node.\n       */\n      module.exports = factory()\n    } else {\n      // Browser globals (root is window)\n      root.lunr = factory()\n    }\n  }(this, function () {\n    /**\n     * Just return a value to define the module export.\n     * This example returns an object, but the module\n     * can return a function as the exported value.\n     */\n    return lunr\n  }))\n})();\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport lunr from \"lunr\"\n\nimport { getElement } from \"~/browser/element/_\"\nimport \"~/polyfills\"\n\nimport { Search } from \"../../_\"\nimport { SearchConfig } from \"../../config\"\nimport {\n  SearchMessage,\n  SearchMessageType\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Add support for `iframe-worker` shim\n *\n * While `importScripts` is synchronous when executed inside of a web worker,\n * it's not possible to provide a synchronous shim implementation. The cool\n * thing is that awaiting a non-Promise will convert it into a Promise, so\n * extending the type definition to return a `Promise` shouldn't break anything.\n *\n * @see https://bit.ly/2PjDnXi - GitHub comment\n *\n * @param urls - Scripts to load\n *\n * @returns Promise resolving with no result\n */\ndeclare global {\n  function importScripts(...urls: string[]): Promise<void> | void\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nlet index: Search\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch (= import) multi-language support through `lunr-languages`\n *\n * This function automatically imports the stemmers necessary to process the\n * languages which are defined as part of the search configuration.\n *\n * If the worker runs inside of an `iframe` (when using `iframe-worker` as\n * a shim), the base URL for the stemmers to be loaded must be determined by\n * searching for the first `script` element with a `src` attribute, which will\n * contain the contents of this script.\n *\n * @param config - Search configuration\n *\n * @returns Promise resolving with no result\n */\nasync function setupSearchLanguages(\n  config: SearchConfig\n): Promise<void> {\n  let base = \"../lunr\"\n\n  /* Detect `iframe-worker` and fix base URL */\n  if (typeof parent !== \"undefined\" && \"IFrameWorker\" in parent) {\n    const worker = getElement<HTMLScriptElement>(\"script[src]\")\n    const [path] = worker.src.split(\"/worker\")\n\n    /* Prefix base with path */\n    base = base.replace(\"..\", path)\n  }\n\n  /* Add scripts for languages */\n  const scripts = []\n  for (const lang of config.lang) {\n    switch (lang) {\n\n      /* Add segmenter for Japanese */\n      case \"ja\":\n        scripts.push(`${base}/tinyseg.js`)\n        break\n\n      /* Add segmenter for Hindi and Thai */\n      case \"hi\":\n      case \"th\":\n        scripts.push(`${base}/wordcut.js`)\n        break\n    }\n\n    /* Add language support */\n    if (lang !== \"en\")\n      scripts.push(`${base}/min/lunr.${lang}.min.js`)\n  }\n\n  /* Add multi-language support */\n  if (config.lang.length > 1)\n    scripts.push(`${base}/min/lunr.multi.min.js`)\n\n  /* Load scripts synchronously */\n  if (scripts.length)\n    await importScripts(\n      `${base}/min/lunr.stemmer.support.min.js`,\n      ...scripts\n    )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Message handler\n *\n * @param message - Source message\n *\n * @returns Target message\n */\nexport async function handler(\n  message: SearchMessage\n): Promise<SearchMessage> {\n  switch (message.type) {\n\n    /* Search setup message */\n    case SearchMessageType.SETUP:\n      await setupSearchLanguages(message.data.config)\n      index = new Search(message.data)\n      return {\n        type: SearchMessageType.READY\n      }\n\n    /* Search query message */\n    case SearchMessageType.QUERY:\n      const query = message.data\n      try {\n        return {\n          type: SearchMessageType.RESULT,\n          data: index.search(query)\n        }\n\n      /* Return empty result in case of error */\n      } catch (err) {\n        console.warn(`Invalid query: ${query} \u2013 see https://bit.ly/2s3ChXG`)\n        console.warn(err)\n        return {\n          type: SearchMessageType.RESULT,\n          data: { items: [] }\n        }\n      }\n\n    /* All other messages */\n    default:\n      throw new TypeError(\"Invalid message type\")\n  }\n}\n\n/* ----------------------------------------------------------------------------\n * Worker\n * ------------------------------------------------------------------------- */\n\n/* Expose Lunr.js in global scope, or stemmers won't work */\nself.lunr = lunr\n\n/* Monkey-patch Lunr.js to mitigate https://t.ly/68TLq */\nlunr.utils.warn = console.warn\n\n/* Handle messages */\naddEventListener(\"message\", async ev => {\n  postMessage(await handler(ev.data))\n})\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve all elements matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getElements<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T][]\n\nexport function getElements<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T[]\n\nexport function getElements<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T[] {\n  return Array.from(node.querySelectorAll<T>(selector))\n}\n\n/**\n * Retrieve an element matching a query selector or throw a reference error\n *\n * Note that this function assumes that the element is present. If unsure if an\n * element is existent, use the `getOptionalElement` function instead.\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getElement<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T]\n\nexport function getElement<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T\n\nexport function getElement<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T {\n  const el = getOptionalElement<T>(selector, node)\n  if (typeof el === \"undefined\")\n    throw new ReferenceError(\n      `Missing element: expected \"${selector}\" to be present`\n    )\n\n  /* Return element */\n  return el\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Retrieve an optional element matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element or nothing\n */\nexport function getOptionalElement<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T] | undefined\n\nexport function getOptionalElement<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T | undefined\n\nexport function getOptionalElement<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T | undefined {\n  return node.querySelector<T>(selector) || undefined\n}\n\n/**\n * Retrieve the currently active element\n *\n * @returns Element or nothing\n */\nexport function getActiveElement(): HTMLElement | undefined {\n  return (\n    document.activeElement?.shadowRoot?.activeElement as HTMLElement ??\n    document.activeElement as HTMLElement ??\n    undefined\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n  Object.entries = function (obj: object) {\n    const data: [string, string][] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push([key, obj[key]])\n\n    /* Return entries */\n    return data\n  }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n  Object.values = function (obj: object) {\n    const data: string[] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push(obj[key])\n\n    /* Return values */\n    return data\n  }\n\n/* ------------------------------------------------------------------------- */\n\n/* Polyfills for `Element` */\nif (typeof Element !== \"undefined\") {\n\n  /* Polyfill `Element.scrollTo` */\n  if (!Element.prototype.scrollTo)\n    Element.prototype.scrollTo = function (\n      x?: ScrollToOptions | number, y?: number\n    ): void {\n      if (typeof x === \"object\") {\n        this.scrollLeft = x.left!\n        this.scrollTop = x.top!\n      } else {\n        this.scrollLeft = x!\n        this.scrollTop = y!\n      }\n    }\n\n  /* Polyfill `Element.replaceWith` */\n  if (!Element.prototype.replaceWith)\n    Element.prototype.replaceWith = function (\n      ...nodes: Array<string | Node>\n    ): void {\n      const parent = this.parentNode\n      if (parent) {\n        if (nodes.length === 0)\n          parent.removeChild(this)\n\n        /* Replace children and create text nodes */\n        for (let i = nodes.length - 1; i >= 0; i--) {\n          let node = nodes[i]\n          if (typeof node === \"string\")\n            node = document.createTextNode(node)\n          else if (node.parentNode)\n            node.parentNode.removeChild(node)\n\n          /* Replace child or insert before previous sibling */\n          if (!i)\n            parent.replaceChild(node, this)\n          else\n            parent.insertBefore(this.previousSibling!, node)\n        }\n      }\n    }\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search configuration\n */\nexport interface SearchConfig {\n  lang: string[]                       /* Search languages */\n  separator: string                    /* Search separator */\n  pipeline: SearchPipelineFn[]         /* Search pipeline */\n}\n\n/**\n * Search document\n */\nexport interface SearchDocument {\n  location: string                     /* Document location */\n  title: string                        /* Document title */\n  text: string                         /* Document text */\n  tags?: string[]                      /* Document tags */\n  boost?: number                       /* Document boost */\n  parent?: SearchDocument              /* Document parent */\n}\n\n/**\n * Search options\n */\nexport interface SearchOptions {\n  suggest: boolean                     /* Search suggestions */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nexport interface SearchIndex {\n  config: SearchConfig                 /* Search configuration */\n  docs: SearchDocument[]               /* Search documents */\n  options: SearchOptions               /* Search options */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search pipeline function\n */\ntype SearchPipelineFn =\n  | \"trimmer\"                          /* Trimmer */\n  | \"stopWordFilter\"                   /* Stop word filter */\n  | \"stemmer\"                          /* Stemmer */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search document map\n *\n * This function creates a mapping of URLs (including anchors) to the actual\n * articles and sections. It relies on the invariant that the search index is\n * ordered with the main article appearing before all sections with anchors.\n * If this is not the case, the logic music be changed.\n *\n * @param docs - Search documents\n *\n * @returns Search document map\n */\nexport function setupSearchDocumentMap(\n  docs: SearchDocument[]\n): Map<string, SearchDocument> {\n  const map = new Map<string, SearchDocument>()\n  for (const doc of docs) {\n    const [path] = doc.location.split(\"#\")\n\n    /* Add document article */\n    const article = map.get(path)\n    if (typeof article === \"undefined\") {\n      map.set(path, doc)\n\n      /* Add document section */\n    } else {\n      map.set(doc.location, doc)\n      doc.parent = article\n    }\n  }\n\n  /* Return search document map */\n  return map\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Visitor function\n *\n * @param start - Start offset\n * @param end - End offset\n */\ntype VisitorFn = (\n  start: number, end: number\n) => void\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Split a string using the given separator\n *\n * @param input - Input value\n * @param separator - Separator\n * @param fn - Visitor function\n */\nexport function split(\n  input: string, separator: RegExp, fn: VisitorFn\n): void {\n  separator = new RegExp(separator, \"g\")\n\n  /* Split string using separator */\n  let match: RegExpExecArray | null\n  let index = 0\n  do {\n    match = separator.exec(input)\n\n    /* Emit non-empty range */\n    const until = match?.index ?? input.length\n    if (index < until)\n      fn(index, until)\n\n    /* Update last index */\n    if (match) {\n      const [term] = match\n      index = match.index + term.length\n\n      /* Support zero-length lookaheads */\n      if (term.length === 0)\n        separator.lastIndex = match.index + 1\n    }\n  } while (match)\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Extraction type\n *\n * This type defines the possible values that are encoded into the first two\n * bits of a section that is part of the blocks of a tokenization table. There\n * are three types of interest: HTML opening and closing tags, as well as the\n * actual text content we need to extract for indexing.\n */\nexport const enum Extract {\n  TAG_OPEN  = 0,                       /* HTML opening tag */\n  TEXT      = 1,                       /* Text content */\n  TAG_CLOSE = 2                        /* HTML closing tag */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Visitor function\n *\n * @param block - Block index\n * @param type - Extraction type\n * @param start - Start offset\n * @param end - End offset\n */\ntype VisitorFn = (\n  block: number, type: Extract, start: number, end: number\n) => void\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Split a string into markup and text sections\n *\n * This function scans a string and divides it up into sections of markup and\n * text. For each section, it invokes the given visitor function with the block\n * index, extraction type, as well as start and end offsets. Using a visitor\n * function (= streaming data) is ideal for minimizing pressure on the GC.\n *\n * @param input - Input value\n * @param fn - Visitor function\n */\nexport function extract(\n  input: string, fn: VisitorFn\n): void {\n\n  let block = 0                        /* Current block */\n  let start = 0                        /* Current start offset */\n  let end = 0                          /* Current end offset */\n\n  /* Split string into sections */\n  for (let stack = 0; end < input.length; end++) {\n\n    /* Opening tag after non-empty section */\n    if (input.charAt(end) === \"<\" && end > start) {\n      fn(block, Extract.TEXT, start, start = end)\n\n    /* Closing tag */\n    } else if (input.charAt(end) === \">\") {\n      if (input.charAt(start + 1) === \"/\") {\n        if (--stack === 0)\n          fn(block++, Extract.TAG_CLOSE, start, end + 1)\n\n      /* Tag is not self-closing */\n      } else if (input.charAt(end - 1) !== \"/\") {\n        if (stack++ === 0)\n          fn(block, Extract.TAG_OPEN, start, end + 1)\n      }\n\n      /* New section */\n      start = end + 1\n    }\n  }\n\n  /* Add trailing section */\n  if (end > start)\n    fn(block, Extract.TEXT, start, end)\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Position table\n */\nexport type PositionTable = number[][]\n\n/**\n * Position\n */\nexport type Position = number\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Highlight all occurrences in a string\n *\n * This function receives a field's value (e.g. like `title` or `text`), it's\n * position table that was generated during indexing, and the positions found\n * when executing the query. It then highlights all occurrences, and returns\n * their concatenation. In case of multiple blocks, two are returned.\n *\n * @param input - Input value\n * @param table - Table for indexing\n * @param positions - Occurrences\n * @param full - Full results\n *\n * @returns Highlighted string value\n */\nexport function highlight(\n  input: string, table: PositionTable, positions: Position[], full = false\n): string {\n  return highlightAll([input], table, positions, full).pop()!\n}\n\n/**\n * Highlight all occurrences in a set of strings\n *\n * @param inputs - Input values\n * @param table - Table for indexing\n * @param positions - Occurrences\n * @param full - Full results\n *\n * @returns Highlighted string values\n */\nexport function highlightAll(\n  inputs: string[], table: PositionTable, positions: Position[], full = false\n): string[] {\n\n  /* Map blocks to input values */\n  const mapping = [0]\n  for (let t = 1; t < table.length; t++) {\n    const prev = table[t - 1]\n    const next = table[t]\n\n    /* Check if table points to new block */\n    const p = prev[prev.length - 1] >>> 2 & 0x3FF\n    const q = next[0]               >>> 12\n\n    /* Add block to mapping */\n    mapping.push(+(p > q) + mapping[mapping.length - 1])\n  }\n\n  /* Highlight strings one after another */\n  return inputs.map((input, i) => {\n    let cursor = 0\n\n    /* Map occurrences to blocks */\n    const blocks = new Map<number, number[]>()\n    for (const p of positions.sort((a, b) => a - b)) {\n      const index = p & 0xFFFFF\n      const block = p >>> 20\n      if (mapping[block] !== i)\n        continue\n\n      /* Ensure presence of block group */\n      let group = blocks.get(block)\n      if (typeof group === \"undefined\")\n        blocks.set(block, group = [])\n\n      /* Add index to group */\n      group.push(index)\n    }\n\n    /* Just return string, if no occurrences */\n    if (blocks.size === 0)\n      return input\n\n    /* Compute slices */\n    const slices: string[] = []\n    for (const [block, indexes] of blocks) {\n      const t = table[block]\n\n      /* Extract positions and length */\n      const start  = t[0]            >>> 12\n      const end    = t[t.length - 1] >>> 12\n      const length = t[t.length - 1] >>> 2 & 0x3FF\n\n      /* Add prefix, if full results are desired */\n      if (full && start > cursor)\n        slices.push(input.slice(cursor, start))\n\n      /* Extract and highlight slice */\n      let slice = input.slice(start, end + length)\n      for (const j of indexes.sort((a, b) => b - a)) {\n\n        /* Retrieve offset and length of match */\n        const p = (t[j] >>> 12) - start\n        const q = (t[j] >>> 2 & 0x3FF) + p\n\n        /* Wrap occurrence */\n        slice = [\n          slice.slice(0, p),\n          \"<mark>\",\n          slice.slice(p, q),\n          \"</mark>\",\n          slice.slice(q)\n        ].join(\"\")\n      }\n\n      /* Update cursor */\n      cursor = end + length\n\n      /* Append slice and abort if we have two */\n      if (slices.push(slice) === 2)\n        break\n    }\n\n    /* Add suffix, if full results are desired */\n    if (full && cursor < input.length)\n      slices.push(input.slice(cursor))\n\n    /* Return highlighted slices */\n    return slices.join(\"\")\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { split } from \"../_\"\nimport {\n  Extract,\n  extract\n} from \"../extract\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Split a string or set of strings into tokens\n *\n * This tokenizer supersedes the default tokenizer that is provided by Lunr.js,\n * as it is aware of HTML tags and allows for multi-character splitting.\n *\n * It takes the given inputs, splits each of them into markup and text sections,\n * tokenizes and segments (if necessary) each of them, and then indexes them in\n * a table by using a compact bit representation. Bitwise techniques are used\n * to write and read from the table during indexing and querying.\n *\n * @see https://bit.ly/3W3Xw4J - Search: better, faster, smaller\n *\n * @param input - Input value(s)\n *\n * @returns Tokens\n */\nexport function tokenize(\n  input?: string | string[]\n): lunr.Token[] {\n  const tokens: lunr.Token[] = []\n  if (typeof input === \"undefined\")\n    return tokens\n\n  /* Tokenize strings one after another */\n  const inputs = Array.isArray(input) ? input : [input]\n  for (let i = 0; i < inputs.length; i++) {\n    const table = lunr.tokenizer.table\n    const total = table.length\n\n    /* Split string into sections and tokenize content blocks */\n    extract(inputs[i], (block, type, start, end) => {\n      table[block += total] ||= []\n      switch (type) {\n\n        /* Handle markup */\n        case Extract.TAG_OPEN:\n        case Extract.TAG_CLOSE:\n          table[block].push(\n            start       << 12 |\n            end - start <<  2 |\n            type\n          )\n          break\n\n        /* Handle text content */\n        case Extract.TEXT:\n          const section = inputs[i].slice(start, end)\n          split(section, lunr.tokenizer.separator, (index, until) => {\n\n            /**\n             * Apply segmenter after tokenization. Note that the segmenter will\n             * also split words at word boundaries, which is not what we want,\n             * so we need to check if we can somehow mitigate this behavior.\n             */\n            if (typeof lunr.segmenter !== \"undefined\") {\n              const subsection = section.slice(index, until)\n              if (/^[MHIK]$/.test(lunr.segmenter.ctype_(subsection))) {\n                const segments = lunr.segmenter.segment(subsection)\n                for (let s = 0, l = 0; s < segments.length; s++) {\n\n                  /* Add block to section */\n                  table[block] ||= []\n                  table[block].push(\n                    start + index + l  << 12 |\n                    segments[s].length <<  2 |\n                    type\n                  )\n\n                  /* Add token with position */\n                  tokens.push(new lunr.Token(\n                    segments[s].toLowerCase(), {\n                      position: block << 20 | table[block].length - 1\n                    }\n                  ))\n\n                  /* Keep track of length */\n                  l += segments[s].length\n                }\n                return\n              }\n            }\n\n            /* Add block to section */\n            table[block].push(\n              start + index << 12 |\n              until - index <<  2 |\n              type\n            )\n\n            /* Add token with position */\n            tokens.push(new lunr.Token(\n              section.slice(index, until).toLowerCase(), {\n                position: block << 20 | table[block].length - 1\n              }\n            ))\n          })\n      }\n    })\n  }\n\n  /* Return tokens */\n  return tokens\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Visitor function\n *\n * @param value - String value\n *\n * @returns String term(s)\n */\ntype VisitorFn = (\n  value: string\n) => string | string[]\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Default transformation function\n *\n * 1. Trim excess whitespace from left and right.\n *\n * 2. Search for parts in quotation marks and prepend a `+` modifier to denote\n *    that the resulting document must contain all parts, converting the query\n *    to an `AND` query (as opposed to the default `OR` behavior). While users\n *    may expect parts enclosed in quotation marks to map to span queries, i.e.\n *    for which order is important, Lunr.js doesn't support them, so the best\n *    we can do is to convert the parts to an `AND` query.\n *\n * 3. Replace control characters which are not located at the beginning of the\n *    query or preceded by white space, or are not followed by a non-whitespace\n *    character or are at the end of the query string. Furthermore, filter\n *    unmatched quotation marks.\n *\n * 4. Split the query string at whitespace, then pass each part to the visitor\n *    function for tokenization, and append a wildcard to every resulting term\n *    that is not explicitly marked with a `+`, `-`, `~` or `^` modifier, since\n *    it ensures consistent and stable ranking when multiple terms are entered.\n *    Also, if a fuzzy or boost modifier are given, but no numeric value has\n *    been entered, default to 1 to not induce a query error.\n *\n * @param query - Query value\n * @param fn - Visitor function\n *\n * @returns Transformed query value\n */\nexport function transform(\n  query: string, fn: VisitorFn = term => term\n): string {\n  return query\n\n    /* => 1 */\n    .trim()\n\n    /* => 2 */\n    .split(/\"([^\"]+)\"/g)\n      .map((parts, index) => index & 1\n        ? parts.replace(/^\\b|^(?![^\\x00-\\x7F]|$)|\\s+/g, \" +\")\n        : parts\n      )\n      .join(\"\")\n\n    /* => 3 */\n    .replace(/\"|(?:^|\\s+)[*+\\-:^~]+(?=\\s+|$)/g, \"\")\n\n    /* => 4 */\n    .split(/\\s+/g)\n      .reduce((prev, term) => {\n        const next = fn(term)\n        return [...prev, ...Array.isArray(next) ? next : [next]]\n      }, [] as string[])\n      .map(term => /([~^]$)/.test(term) ? `${term}1` : term)\n      .map(term => /(^[+-]|[~^]\\d+$)/.test(term) ? term : `${term}*`)\n      .join(\" \")\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { split } from \"../../internal\"\nimport { transform } from \"../transform\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query clause\n */\nexport interface SearchQueryClause {\n  presence: lunr.Query.presence        /* Clause presence */\n  term: string                         /* Clause term */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search query terms\n */\nexport type SearchQueryTerms = Record<string, boolean>\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Transform search query\n *\n * This function lexes the given search query and applies the transformation\n * function to each term, preserving markup like `+` and `-` modifiers.\n *\n * @param query - Search query\n *\n * @returns Search query\n */\nexport function transformSearchQuery(\n  query: string\n): string {\n\n  /* Split query terms with tokenizer */\n  return transform(query, part => {\n    const terms: string[] = []\n\n    /* Initialize lexer and analyze part */\n    const lexer = new lunr.QueryLexer(part)\n    lexer.run()\n\n    /* Extract and tokenize term from lexeme */\n    for (const { type, str: term, start, end } of lexer.lexemes)\n      switch (type) {\n\n        /* Hack: remove colon - see https://bit.ly/3wD3T3I */\n        case \"FIELD\":\n          if (![\"title\", \"text\", \"tags\"].includes(term))\n            part = [\n              part.slice(0, end),\n              \" \",\n              part.slice(end + 1)\n            ].join(\"\")\n          break\n\n        /* Tokenize term */\n        case \"TERM\":\n          split(term, lunr.tokenizer.separator, (...range) => {\n            terms.push([\n              part.slice(0, start),\n              term.slice(...range),\n              part.slice(end)\n            ].join(\"\"))\n          })\n      }\n\n    /* Return terms */\n    return terms\n  })\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Parse a search query for analysis\n *\n * Lunr.js itself has a bug where it doesn't detect or remove wildcards for\n * query clauses, so we must do this here.\n *\n * @see https://bit.ly/3DpTGtz - GitHub issue\n *\n * @param value - Query value\n *\n * @returns Search query clauses\n */\nexport function parseSearchQuery(\n  value: string\n): SearchQueryClause[] {\n  const query  = new lunr.Query([\"title\", \"text\", \"tags\"])\n  const parser = new lunr.QueryParser(value, query)\n\n  /* Parse Search query */\n  parser.parse()\n  for (const clause of query.clauses) {\n    clause.usePipeline = true\n\n    /* Handle leading wildcard */\n    if (clause.term.startsWith(\"*\")) {\n      clause.wildcard = lunr.Query.wildcard.LEADING\n      clause.term = clause.term.slice(1)\n    }\n\n    /* Handle trailing wildcard */\n    if (clause.term.endsWith(\"*\")) {\n      clause.wildcard = lunr.Query.wildcard.TRAILING\n      clause.term = clause.term.slice(0, -1)\n    }\n  }\n\n  /* Return query clauses */\n  return query.clauses\n}\n\n/**\n * Analyze the search query clauses in regard to the search terms found\n *\n * @param query - Search query clauses\n * @param terms - Search terms\n *\n * @returns Search query terms\n */\nexport function getSearchQueryTerms(\n  query: SearchQueryClause[], terms: string[]\n): SearchQueryTerms {\n  const clauses = new Set<SearchQueryClause>(query)\n\n  /* Match query clauses against terms */\n  const result: SearchQueryTerms = {}\n  for (let t = 0; t < terms.length; t++)\n    for (const clause of clauses)\n      if (terms[t].startsWith(clause.term)) {\n        result[clause.term] = true\n        clauses.delete(clause)\n      }\n\n  /* Annotate unmatched non-stopword query clauses */\n  for (const clause of clauses)\n    if (lunr.stopWordFilter?.(clause.term))\n      result[clause.term] = false\n\n  /* Return query terms */\n  return result\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Segment a search query using the inverted index\n *\n * This function implements a clever approach to text segmentation for Asian\n * languages, as it used the information already available in the search index.\n * The idea is to greedily segment the search query based on the tokens that are\n * already part of the index, as described in the linked issue.\n *\n * @see https://bit.ly/3lwjrk7 - GitHub issue\n *\n * @param query - Query value\n * @param index - Inverted index\n *\n * @returns Segmented query value\n */\nexport function segment(\n  query: string, index: object\n): Iterable<string> {\n  const segments = new Set<string>()\n\n  /* Segment search query */\n  const wordcuts = new Uint16Array(query.length)\n  for (let i = 0; i < query.length; i++)\n    for (let j = i + 1; j < query.length; j++) {\n      const value = query.slice(i, j)\n      if (value in index)\n        wordcuts[i] = j - i\n    }\n\n  /* Compute longest matches with minimum overlap */\n  const stack = [0]\n  for (let s = stack.length; s > 0;) {\n    const p = stack[--s]\n    for (let q = 1; q < wordcuts[p]; q++)\n      if (wordcuts[p + q] > wordcuts[p] - q) {\n        segments.add(query.slice(p, p + q))\n        stack[s++] = p + q\n      }\n\n    /* Continue at end of query string */\n    const q = p + wordcuts[p]\n    if (wordcuts[q] && q < query.length - 1)\n      stack[s++] = q\n\n    /* Add current segment */\n    segments.add(query.slice(p, q))\n  }\n\n  // @todo fix this case in the code block above, this is a hotfix\n  if (segments.has(\"\"))\n    return new Set([query])\n\n  /* Return segmented query value */\n  return segments\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  SearchDocument,\n  SearchIndex,\n  SearchOptions,\n  setupSearchDocumentMap\n} from \"../config\"\nimport {\n  Position,\n  PositionTable,\n  highlight,\n  highlightAll,\n  tokenize\n} from \"../internal\"\nimport {\n  SearchQueryTerms,\n  getSearchQueryTerms,\n  parseSearchQuery,\n  segment,\n  transformSearchQuery\n} from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search item\n */\nexport interface SearchItem\n  extends SearchDocument\n{\n  score: number                        /* Score (relevance) */\n  terms: SearchQueryTerms              /* Search query terms */\n}\n\n/**\n * Search result\n */\nexport interface SearchResult {\n  items: SearchItem[][]                /* Search items */\n  suggest?: string[]                   /* Search suggestions */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create field extractor factory\n *\n * @param table - Position table map\n *\n * @returns Extractor factory\n */\nfunction extractor(table: Map<string, PositionTable>) {\n  return (name: keyof SearchDocument) => {\n    return (doc: SearchDocument) => {\n      if (typeof doc[name] === \"undefined\")\n        return undefined\n\n      /* Compute identifier and initialize table */\n      const id = [doc.location, name].join(\":\")\n      table.set(id, lunr.tokenizer.table = [])\n\n      /* Return field value */\n      return doc[name]\n    }\n  }\n}\n\n/**\n * Compute the difference of two lists of strings\n *\n * @param a - 1st list of strings\n * @param b - 2nd list of strings\n *\n * @returns Difference\n */\nfunction difference(a: string[], b: string[]): string[] {\n  const [x, y] = [new Set(a), new Set(b)]\n  return [\n    ...new Set([...x].filter(value => !y.has(value)))\n  ]\n}\n\n/* ----------------------------------------------------------------------------\n * Class\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nexport class Search {\n\n  /**\n   * Search document map\n   */\n  protected map: Map<string, SearchDocument>\n\n  /**\n   * Search options\n   */\n  protected options: SearchOptions\n\n  /**\n   * The underlying Lunr.js search index\n   */\n  protected index: lunr.Index\n\n  /**\n   * Internal position table map\n   */\n  protected table: Map<string, PositionTable>\n\n  /**\n   * Create the search integration\n   *\n   * @param data - Search index\n   */\n  public constructor({ config, docs, options }: SearchIndex) {\n    const field = extractor(this.table = new Map())\n\n    /* Set up document map and options */\n    this.map = setupSearchDocumentMap(docs)\n    this.options = options\n\n    /* Set up document index */\n    this.index = lunr(function () {\n      this.metadataWhitelist = [\"position\"]\n      this.b(0)\n\n      /* Set up (multi-)language support */\n      if (config.lang.length === 1 && config.lang[0] !== \"en\") {\n        // @ts-expect-error - namespace indexing not supported\n        this.use(lunr[config.lang[0]])\n      } else if (config.lang.length > 1) {\n        this.use(lunr.multiLanguage(...config.lang))\n      }\n\n      /* Set up custom tokenizer (must be after language setup) */\n      this.tokenizer = tokenize as typeof lunr.tokenizer\n      lunr.tokenizer.separator = new RegExp(config.separator)\n\n      /* Set up custom segmenter, if loaded */\n      lunr.segmenter = \"TinySegmenter\" in lunr\n        ? new lunr.TinySegmenter()\n        : undefined\n\n      /* Compute functions to be removed from the pipeline */\n      const fns = difference([\n        \"trimmer\", \"stopWordFilter\", \"stemmer\"\n      ], config.pipeline)\n\n      /* Remove functions from the pipeline for registered languages */\n      for (const lang of config.lang.map(language => (\n        // @ts-expect-error - namespace indexing not supported\n        language === \"en\" ? lunr : lunr[language]\n      )))\n        for (const fn of fns) {\n          this.pipeline.remove(lang[fn])\n          this.searchPipeline.remove(lang[fn])\n        }\n\n      /* Set up index reference */\n      this.ref(\"location\")\n\n      /* Set up index fields */\n      this.field(\"title\", { boost: 1e3, extractor: field(\"title\") })\n      this.field(\"text\",  { boost: 1e0, extractor: field(\"text\") })\n      this.field(\"tags\",  { boost: 1e6, extractor: field(\"tags\") })\n\n      /* Add documents to index */\n      for (const doc of docs)\n        this.add(doc, { boost: doc.boost })\n    })\n  }\n\n  /**\n   * Search for matching documents\n   *\n   * @param query - Search query\n   *\n   * @returns Search result\n   */\n  public search(query: string): SearchResult {\n\n    // Experimental Chinese segmentation\n    query = query.replace(/\\p{sc=Han}+/gu, value => {\n      return [...segment(value, this.index.invertedIndex)]\n        .join(\"* \")\n    })\n\n    // @todo: move segmenter (above) into transformSearchQuery\n    query = transformSearchQuery(query)\n    if (!query)\n      return { items: [] }\n\n    /* Parse query to extract clauses for analysis */\n    const clauses = parseSearchQuery(query)\n      .filter(clause => (\n        clause.presence !== lunr.Query.presence.PROHIBITED\n      ))\n\n    /* Perform search and post-process results */\n    const groups = this.index.search(query)\n\n      /* Apply post-query boosts based on title and search query terms */\n      .reduce<SearchItem[]>((item, { ref, score, matchData }) => {\n        let doc = this.map.get(ref)\n        if (typeof doc !== \"undefined\") {\n\n          /* Shallow copy document */\n          doc = { ...doc }\n          if (doc.tags)\n            doc.tags = [...doc.tags]\n\n          /* Compute and analyze search query terms */\n          const terms = getSearchQueryTerms(\n            clauses,\n            Object.keys(matchData.metadata)\n          )\n\n          /* Highlight matches in fields */\n          for (const field of this.index.fields) {\n            if (typeof doc[field] === \"undefined\")\n              continue\n\n            /* Collect positions from matches */\n            const positions: Position[] = []\n            for (const match of Object.values(matchData.metadata))\n              if (typeof match[field] !== \"undefined\")\n                positions.push(...match[field].position)\n\n            /* Skip highlighting, if no positions were collected */\n            if (!positions.length)\n              continue\n\n            /* Load table and determine highlighting method */\n            const table = this.table.get([doc.location, field].join(\":\"))!\n            const fn = Array.isArray(doc[field])\n              ? highlightAll\n              : highlight\n\n            // @ts-expect-error - stop moaning, TypeScript!\n            doc[field] = fn(doc[field], table, positions, field !== \"text\")\n          }\n\n          /* Highlight title and text and apply post-query boosts */\n          const boost = +!doc.parent +\n            Object.values(terms)\n              .filter(t => t).length /\n            Object.keys(terms).length\n\n          /* Append item */\n          item.push({\n            ...doc,\n            score: score * (1 + boost ** 2),\n            terms\n          })\n        }\n        return item\n      }, [])\n\n      /* Sort search results again after applying boosts */\n      .sort((a, b) => b.score - a.score)\n\n      /* Group search results by article */\n      .reduce((items, result) => {\n        const doc = this.map.get(result.location)\n        if (typeof doc !== \"undefined\") {\n          const ref = doc.parent\n            ? doc.parent.location\n            : doc.location\n          items.set(ref, [...items.get(ref) || [], result])\n        }\n        return items\n      }, new Map<string, SearchItem[]>())\n\n    /* Ensure that every item set has an article */\n    for (const [ref, items] of groups)\n      if (!items.find(item => item.location === ref)) {\n        const doc = this.map.get(ref)!\n        items.push({ ...doc, score: 0, terms: {} })\n      }\n\n    /* Generate search suggestions, if desired */\n    let suggest: string[] | undefined\n    if (this.options.suggest) {\n      const titles = this.index.query(builder => {\n        for (const clause of clauses)\n          builder.term(clause.term, {\n            fields: [\"title\"],\n            presence: lunr.Query.presence.REQUIRED,\n            wildcard: lunr.Query.wildcard.TRAILING\n          })\n      })\n\n      /* Retrieve suggestions for best match */\n      suggest = titles.length\n        ? Object.keys(titles[0].matchData.metadata)\n        : []\n    }\n\n    /* Return search result */\n    return {\n      items: [...groups.values()],\n      ...typeof suggest !== \"undefined\" && { suggest }\n    }\n  }\n}\n"],
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+  "sourcesContent": ["/**\n * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9\n * Copyright (C) 2020 Oliver Nightingale\n * @license MIT\n */\n\n;(function(){\n\n/**\n * A convenience function for configuring and constructing\n * a new lunr Index.\n *\n * A lunr.Builder instance is created and the pipeline setup\n * with a trimmer, stop word filter and stemmer.\n *\n * This builder object is yielded to the configuration function\n * that is passed as a parameter, allowing the list of fields\n * and other builder parameters to be customised.\n *\n * All documents _must_ be added within the passed config function.\n *\n * @example\n * var idx = lunr(function () {\n *   this.field('title')\n *   this.field('body')\n *   this.ref('id')\n *\n *   documents.forEach(function (doc) {\n *     this.add(doc)\n *   }, this)\n * })\n *\n * @see {@link lunr.Builder}\n * @see {@link lunr.Pipeline}\n * @see {@link lunr.trimmer}\n * @see {@link lunr.stopWordFilter}\n * @see {@link lunr.stemmer}\n * @namespace {function} lunr\n */\nvar lunr = function (config) {\n  var builder = new lunr.Builder\n\n  builder.pipeline.add(\n    lunr.trimmer,\n    lunr.stopWordFilter,\n    lunr.stemmer\n  )\n\n  builder.searchPipeline.add(\n    lunr.stemmer\n  )\n\n  config.call(builder, builder)\n  return builder.build()\n}\n\nlunr.version = \"2.3.9\"\n/*!\n * lunr.utils\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A namespace containing utils for the rest of the lunr library\n * @namespace lunr.utils\n */\nlunr.utils = {}\n\n/**\n * Print a warning message to the console.\n *\n * @param {String} message The message to be printed.\n * @memberOf lunr.utils\n * @function\n */\nlunr.utils.warn = (function (global) {\n  /* eslint-disable no-console */\n  return function (message) {\n    if (global.console && console.warn) {\n      console.warn(message)\n    }\n  }\n  /* eslint-enable no-console */\n})(this)\n\n/**\n * Convert an object to a string.\n *\n * In the case of `null` and `undefined` the function returns\n * the empty string, in all other cases the result of calling\n * `toString` on the passed object is returned.\n *\n * @param {Any} obj The object to convert to a string.\n * @return {String} string representation of the passed object.\n * @memberOf lunr.utils\n */\nlunr.utils.asString = function (obj) {\n  if (obj === void 0 || obj === null) {\n    return \"\"\n  } else {\n    return obj.toString()\n  }\n}\n\n/**\n * Clones an object.\n *\n * Will create a copy of an existing object such that any mutations\n * on the copy cannot affect the original.\n *\n * Only shallow objects are supported, passing a nested object to this\n * function will cause a TypeError.\n *\n * Objects with primitives, and arrays of primitives are supported.\n *\n * @param {Object} obj The object to clone.\n * @return {Object} a clone of the passed object.\n * @throws {TypeError} when a nested object is passed.\n * @memberOf Utils\n */\nlunr.utils.clone = function (obj) {\n  if (obj === null || obj === undefined) {\n    return obj\n  }\n\n  var clone = Object.create(null),\n      keys = Object.keys(obj)\n\n  for (var i = 0; i < keys.length; i++) {\n    var key = keys[i],\n        val = obj[key]\n\n    if (Array.isArray(val)) {\n      clone[key] = val.slice()\n      continue\n    }\n\n    if (typeof val === 'string' ||\n        typeof val === 'number' ||\n        typeof val === 'boolean') {\n      clone[key] = val\n      continue\n    }\n\n    throw new TypeError(\"clone is not deep and does not support nested objects\")\n  }\n\n  return clone\n}\nlunr.FieldRef = function (docRef, fieldName, stringValue) {\n  this.docRef = docRef\n  this.fieldName = fieldName\n  this._stringValue = stringValue\n}\n\nlunr.FieldRef.joiner = \"/\"\n\nlunr.FieldRef.fromString = function (s) {\n  var n = s.indexOf(lunr.FieldRef.joiner)\n\n  if (n === -1) {\n    throw \"malformed field ref string\"\n  }\n\n  var fieldRef = s.slice(0, n),\n      docRef = s.slice(n + 1)\n\n  return new lunr.FieldRef (docRef, fieldRef, s)\n}\n\nlunr.FieldRef.prototype.toString = function () {\n  if (this._stringValue == undefined) {\n    this._stringValue = this.fieldName + lunr.FieldRef.joiner + this.docRef\n  }\n\n  return this._stringValue\n}\n/*!\n * lunr.Set\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A lunr set.\n *\n * @constructor\n */\nlunr.Set = function (elements) {\n  this.elements = Object.create(null)\n\n  if (elements) {\n    this.length = elements.length\n\n    for (var i = 0; i < this.length; i++) {\n      this.elements[elements[i]] = true\n    }\n  } else {\n    this.length = 0\n  }\n}\n\n/**\n * A complete set that contains all elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.complete = {\n  intersect: function (other) {\n    return other\n  },\n\n  union: function () {\n    return this\n  },\n\n  contains: function () {\n    return true\n  }\n}\n\n/**\n * An empty set that contains no elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.empty = {\n  intersect: function () {\n    return this\n  },\n\n  union: function (other) {\n    return other\n  },\n\n  contains: function () {\n    return false\n  }\n}\n\n/**\n * Returns true if this set contains the specified object.\n *\n * @param {object} object - Object whose presence in this set is to be tested.\n * @returns {boolean} - True if this set contains the specified object.\n */\nlunr.Set.prototype.contains = function (object) {\n  return !!this.elements[object]\n}\n\n/**\n * Returns a new set containing only the elements that are present in both\n * this set and the specified set.\n *\n * @param {lunr.Set} other - set to intersect with this set.\n * @returns {lunr.Set} a new set that is the intersection of this and the specified set.\n */\n\nlunr.Set.prototype.intersect = function (other) {\n  var a, b, elements, intersection = []\n\n  if (other === lunr.Set.complete) {\n    return this\n  }\n\n  if (other === lunr.Set.empty) {\n    return other\n  }\n\n  if (this.length < other.length) {\n    a = this\n    b = other\n  } else {\n    a = other\n    b = this\n  }\n\n  elements = Object.keys(a.elements)\n\n  for (var i = 0; i < elements.length; i++) {\n    var element = elements[i]\n    if (element in b.elements) {\n      intersection.push(element)\n    }\n  }\n\n  return new lunr.Set (intersection)\n}\n\n/**\n * Returns a new set combining the elements of this and the specified set.\n *\n * @param {lunr.Set} other - set to union with this set.\n * @return {lunr.Set} a new set that is the union of this and the specified set.\n */\n\nlunr.Set.prototype.union = function (other) {\n  if (other === lunr.Set.complete) {\n    return lunr.Set.complete\n  }\n\n  if (other === lunr.Set.empty) {\n    return this\n  }\n\n  return new lunr.Set(Object.keys(this.elements).concat(Object.keys(other.elements)))\n}\n/**\n * A function to calculate the inverse document frequency for\n * a posting. This is shared between the builder and the index\n *\n * @private\n * @param {object} posting - The posting for a given term\n * @param {number} documentCount - The total number of documents.\n */\nlunr.idf = function (posting, documentCount) {\n  var documentsWithTerm = 0\n\n  for (var fieldName in posting) {\n    if (fieldName == '_index') continue // Ignore the term index, its not a field\n    documentsWithTerm += Object.keys(posting[fieldName]).length\n  }\n\n  var x = (documentCount - documentsWithTerm + 0.5) / (documentsWithTerm + 0.5)\n\n  return Math.log(1 + Math.abs(x))\n}\n\n/**\n * A token wraps a string representation of a token\n * as it is passed through the text processing pipeline.\n *\n * @constructor\n * @param {string} [str=''] - The string token being wrapped.\n * @param {object} [metadata={}] - Metadata associated with this token.\n */\nlunr.Token = function (str, metadata) {\n  this.str = str || \"\"\n  this.metadata = metadata || {}\n}\n\n/**\n * Returns the token string that is being wrapped by this object.\n *\n * @returns {string}\n */\nlunr.Token.prototype.toString = function () {\n  return this.str\n}\n\n/**\n * A token update function is used when updating or optionally\n * when cloning a token.\n *\n * @callback lunr.Token~updateFunction\n * @param {string} str - The string representation of the token.\n * @param {Object} metadata - All metadata associated with this token.\n */\n\n/**\n * Applies the given function to the wrapped string token.\n *\n * @example\n * token.update(function (str, metadata) {\n *   return str.toUpperCase()\n * })\n *\n * @param {lunr.Token~updateFunction} fn - A function to apply to the token string.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.update = function (fn) {\n  this.str = fn(this.str, this.metadata)\n  return this\n}\n\n/**\n * Creates a clone of this token. Optionally a function can be\n * applied to the cloned token.\n *\n * @param {lunr.Token~updateFunction} [fn] - An optional function to apply to the cloned token.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.clone = function (fn) {\n  fn = fn || function (s) { return s }\n  return new lunr.Token (fn(this.str, this.metadata), this.metadata)\n}\n/*!\n * lunr.tokenizer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A function for splitting a string into tokens ready to be inserted into\n * the search index. Uses `lunr.tokenizer.separator` to split strings, change\n * the value of this property to change how strings are split into tokens.\n *\n * This tokenizer will convert its parameter to a string by calling `toString` and\n * then will split this string on the character in `lunr.tokenizer.separator`.\n * Arrays will have their elements converted to strings and wrapped in a lunr.Token.\n *\n * Optional metadata can be passed to the tokenizer, this metadata will be cloned and\n * added as metadata to every token that is created from the object to be tokenized.\n *\n * @static\n * @param {?(string|object|object[])} obj - The object to convert into tokens\n * @param {?object} metadata - Optional metadata to associate with every token\n * @returns {lunr.Token[]}\n * @see {@link lunr.Pipeline}\n */\nlunr.tokenizer = function (obj, metadata) {\n  if (obj == null || obj == undefined) {\n    return []\n  }\n\n  if (Array.isArray(obj)) {\n    return obj.map(function (t) {\n      return new lunr.Token(\n        lunr.utils.asString(t).toLowerCase(),\n        lunr.utils.clone(metadata)\n      )\n    })\n  }\n\n  var str = obj.toString().toLowerCase(),\n      len = str.length,\n      tokens = []\n\n  for (var sliceEnd = 0, sliceStart = 0; sliceEnd <= len; sliceEnd++) {\n    var char = str.charAt(sliceEnd),\n        sliceLength = sliceEnd - sliceStart\n\n    if ((char.match(lunr.tokenizer.separator) || sliceEnd == len)) {\n\n      if (sliceLength > 0) {\n        var tokenMetadata = lunr.utils.clone(metadata) || {}\n        tokenMetadata[\"position\"] = [sliceStart, sliceLength]\n        tokenMetadata[\"index\"] = tokens.length\n\n        tokens.push(\n          new lunr.Token (\n            str.slice(sliceStart, sliceEnd),\n            tokenMetadata\n          )\n        )\n      }\n\n      sliceStart = sliceEnd + 1\n    }\n\n  }\n\n  return tokens\n}\n\n/**\n * The separator used to split a string into tokens. Override this property to change the behaviour of\n * `lunr.tokenizer` behaviour when tokenizing strings. By default this splits on whitespace and hyphens.\n *\n * @static\n * @see lunr.tokenizer\n */\nlunr.tokenizer.separator = /[\\s\\-]+/\n/*!\n * lunr.Pipeline\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Pipelines maintain an ordered list of functions to be applied to all\n * tokens in documents entering the search index and queries being ran against\n * the index.\n *\n * An instance of lunr.Index created with the lunr shortcut will contain a\n * pipeline with a stop word filter and an English language stemmer. Extra\n * functions can be added before or after either of these functions or these\n * default functions can be removed.\n *\n * When run the pipeline will call each function in turn, passing a token, the\n * index of that token in the original list of all tokens and finally a list of\n * all the original tokens.\n *\n * The output of functions in the pipeline will be passed to the next function\n * in the pipeline. To exclude a token from entering the index the function\n * should return undefined, the rest of the pipeline will not be called with\n * this token.\n *\n * For serialisation of pipelines to work, all functions used in an instance of\n * a pipeline should be registered with lunr.Pipeline. Registered functions can\n * then be loaded. If trying to load a serialised pipeline that uses functions\n * that are not registered an error will be thrown.\n *\n * If not planning on serialising the pipeline then registering pipeline functions\n * is not necessary.\n *\n * @constructor\n */\nlunr.Pipeline = function () {\n  this._stack = []\n}\n\nlunr.Pipeline.registeredFunctions = Object.create(null)\n\n/**\n * A pipeline function maps lunr.Token to lunr.Token. A lunr.Token contains the token\n * string as well as all known metadata. A pipeline function can mutate the token string\n * or mutate (or add) metadata for a given token.\n *\n * A pipeline function can indicate that the passed token should be discarded by returning\n * null, undefined or an empty string. This token will not be passed to any downstream pipeline\n * functions and will not be added to the index.\n *\n * Multiple tokens can be returned by returning an array of tokens. Each token will be passed\n * to any downstream pipeline functions and all will returned tokens will be added to the index.\n *\n * Any number of pipeline functions may be chained together using a lunr.Pipeline.\n *\n * @interface lunr.PipelineFunction\n * @param {lunr.Token} token - A token from the document being processed.\n * @param {number} i - The index of this token in the complete list of tokens for this document/field.\n * @param {lunr.Token[]} tokens - All tokens for this document/field.\n * @returns {(?lunr.Token|lunr.Token[])}\n */\n\n/**\n * Register a function with the pipeline.\n *\n * Functions that are used in the pipeline should be registered if the pipeline\n * needs to be serialised, or a serialised pipeline needs to be loaded.\n *\n * Registering a function does not add it to a pipeline, functions must still be\n * added to instances of the pipeline for them to be used when running a pipeline.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @param {String} label - The label to register this function with\n */\nlunr.Pipeline.registerFunction = function (fn, label) {\n  if (label in this.registeredFunctions) {\n    lunr.utils.warn('Overwriting existing registered function: ' + label)\n  }\n\n  fn.label = label\n  lunr.Pipeline.registeredFunctions[fn.label] = fn\n}\n\n/**\n * Warns if the function is not registered as a Pipeline function.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @private\n */\nlunr.Pipeline.warnIfFunctionNotRegistered = function (fn) {\n  var isRegistered = fn.label && (fn.label in this.registeredFunctions)\n\n  if (!isRegistered) {\n    lunr.utils.warn('Function is not registered with pipeline. This may cause problems when serialising the index.\\n', fn)\n  }\n}\n\n/**\n * Loads a previously serialised pipeline.\n *\n * All functions to be loaded must already be registered with lunr.Pipeline.\n * If any function from the serialised data has not been registered then an\n * error will be thrown.\n *\n * @param {Object} serialised - The serialised pipeline to load.\n * @returns {lunr.Pipeline}\n */\nlunr.Pipeline.load = function (serialised) {\n  var pipeline = new lunr.Pipeline\n\n  serialised.forEach(function (fnName) {\n    var fn = lunr.Pipeline.registeredFunctions[fnName]\n\n    if (fn) {\n      pipeline.add(fn)\n    } else {\n      throw new Error('Cannot load unregistered function: ' + fnName)\n    }\n  })\n\n  return pipeline\n}\n\n/**\n * Adds new functions to the end of the pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction[]} functions - Any number of functions to add to the pipeline.\n */\nlunr.Pipeline.prototype.add = function () {\n  var fns = Array.prototype.slice.call(arguments)\n\n  fns.forEach(function (fn) {\n    lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n    this._stack.push(fn)\n  }, this)\n}\n\n/**\n * Adds a single function after a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.after = function (existingFn, newFn) {\n  lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n  var pos = this._stack.indexOf(existingFn)\n  if (pos == -1) {\n    throw new Error('Cannot find existingFn')\n  }\n\n  pos = pos + 1\n  this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Adds a single function before a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.before = function (existingFn, newFn) {\n  lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n  var pos = this._stack.indexOf(existingFn)\n  if (pos == -1) {\n    throw new Error('Cannot find existingFn')\n  }\n\n  this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Removes a function from the pipeline.\n *\n * @param {lunr.PipelineFunction} fn The function to remove from the pipeline.\n */\nlunr.Pipeline.prototype.remove = function (fn) {\n  var pos = this._stack.indexOf(fn)\n  if (pos == -1) {\n    return\n  }\n\n  this._stack.splice(pos, 1)\n}\n\n/**\n * Runs the current list of functions that make up the pipeline against the\n * passed tokens.\n *\n * @param {Array} tokens The tokens to run through the pipeline.\n * @returns {Array}\n */\nlunr.Pipeline.prototype.run = function (tokens) {\n  var stackLength = this._stack.length\n\n  for (var i = 0; i < stackLength; i++) {\n    var fn = this._stack[i]\n    var memo = []\n\n    for (var j = 0; j < tokens.length; j++) {\n      var result = fn(tokens[j], j, tokens)\n\n      if (result === null || result === void 0 || result === '') continue\n\n      if (Array.isArray(result)) {\n        for (var k = 0; k < result.length; k++) {\n          memo.push(result[k])\n        }\n      } else {\n        memo.push(result)\n      }\n    }\n\n    tokens = memo\n  }\n\n  return tokens\n}\n\n/**\n * Convenience method for passing a string through a pipeline and getting\n * strings out. This method takes care of wrapping the passed string in a\n * token and mapping the resulting tokens back to strings.\n *\n * @param {string} str - The string to pass through the pipeline.\n * @param {?object} metadata - Optional metadata to associate with the token\n * passed to the pipeline.\n * @returns {string[]}\n */\nlunr.Pipeline.prototype.runString = function (str, metadata) {\n  var token = new lunr.Token (str, metadata)\n\n  return this.run([token]).map(function (t) {\n    return t.toString()\n  })\n}\n\n/**\n * Resets the pipeline by removing any existing processors.\n *\n */\nlunr.Pipeline.prototype.reset = function () {\n  this._stack = []\n}\n\n/**\n * Returns a representation of the pipeline ready for serialisation.\n *\n * Logs a warning if the function has not been registered.\n *\n * @returns {Array}\n */\nlunr.Pipeline.prototype.toJSON = function () {\n  return this._stack.map(function (fn) {\n    lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n\n    return fn.label\n  })\n}\n/*!\n * lunr.Vector\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A vector is used to construct the vector space of documents and queries. These\n * vectors support operations to determine the similarity between two documents or\n * a document and a query.\n *\n * Normally no parameters are required for initializing a vector, but in the case of\n * loading a previously dumped vector the raw elements can be provided to the constructor.\n *\n * For performance reasons vectors are implemented with a flat array, where an elements\n * index is immediately followed by its value. E.g. [index, value, index, value]. This\n * allows the underlying array to be as sparse as possible and still offer decent\n * performance when being used for vector calculations.\n *\n * @constructor\n * @param {Number[]} [elements] - The flat list of element index and element value pairs.\n */\nlunr.Vector = function (elements) {\n  this._magnitude = 0\n  this.elements = elements || []\n}\n\n\n/**\n * Calculates the position within the vector to insert a given index.\n *\n * This is used internally by insert and upsert. If there are duplicate indexes then\n * the position is returned as if the value for that index were to be updated, but it\n * is the callers responsibility to check whether there is a duplicate at that index\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @returns {Number}\n */\nlunr.Vector.prototype.positionForIndex = function (index) {\n  // For an empty vector the tuple can be inserted at the beginning\n  if (this.elements.length == 0) {\n    return 0\n  }\n\n  var start = 0,\n      end = this.elements.length / 2,\n      sliceLength = end - start,\n      pivotPoint = Math.floor(sliceLength / 2),\n      pivotIndex = this.elements[pivotPoint * 2]\n\n  while (sliceLength > 1) {\n    if (pivotIndex < index) {\n      start = pivotPoint\n    }\n\n    if (pivotIndex > index) {\n      end = pivotPoint\n    }\n\n    if (pivotIndex == index) {\n      break\n    }\n\n    sliceLength = end - start\n    pivotPoint = start + Math.floor(sliceLength / 2)\n    pivotIndex = this.elements[pivotPoint * 2]\n  }\n\n  if (pivotIndex == index) {\n    return pivotPoint * 2\n  }\n\n  if (pivotIndex > index) {\n    return pivotPoint * 2\n  }\n\n  if (pivotIndex < index) {\n    return (pivotPoint + 1) * 2\n  }\n}\n\n/**\n * Inserts an element at an index within the vector.\n *\n * Does not allow duplicates, will throw an error if there is already an entry\n * for this index.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n */\nlunr.Vector.prototype.insert = function (insertIdx, val) {\n  this.upsert(insertIdx, val, function () {\n    throw \"duplicate index\"\n  })\n}\n\n/**\n * Inserts or updates an existing index within the vector.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n * @param {function} fn - A function that is called for updates, the existing value and the\n * requested value are passed as arguments\n */\nlunr.Vector.prototype.upsert = function (insertIdx, val, fn) {\n  this._magnitude = 0\n  var position = this.positionForIndex(insertIdx)\n\n  if (this.elements[position] == insertIdx) {\n    this.elements[position + 1] = fn(this.elements[position + 1], val)\n  } else {\n    this.elements.splice(position, 0, insertIdx, val)\n  }\n}\n\n/**\n * Calculates the magnitude of this vector.\n *\n * @returns {Number}\n */\nlunr.Vector.prototype.magnitude = function () {\n  if (this._magnitude) return this._magnitude\n\n  var sumOfSquares = 0,\n      elementsLength = this.elements.length\n\n  for (var i = 1; i < elementsLength; i += 2) {\n    var val = this.elements[i]\n    sumOfSquares += val * val\n  }\n\n  return this._magnitude = Math.sqrt(sumOfSquares)\n}\n\n/**\n * Calculates the dot product of this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The vector to compute the dot product with.\n * @returns {Number}\n */\nlunr.Vector.prototype.dot = function (otherVector) {\n  var dotProduct = 0,\n      a = this.elements, b = otherVector.elements,\n      aLen = a.length, bLen = b.length,\n      aVal = 0, bVal = 0,\n      i = 0, j = 0\n\n  while (i < aLen && j < bLen) {\n    aVal = a[i], bVal = b[j]\n    if (aVal < bVal) {\n      i += 2\n    } else if (aVal > bVal) {\n      j += 2\n    } else if (aVal == bVal) {\n      dotProduct += a[i + 1] * b[j + 1]\n      i += 2\n      j += 2\n    }\n  }\n\n  return dotProduct\n}\n\n/**\n * Calculates the similarity between this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The other vector to calculate the\n * similarity with.\n * @returns {Number}\n */\nlunr.Vector.prototype.similarity = function (otherVector) {\n  return this.dot(otherVector) / this.magnitude() || 0\n}\n\n/**\n * Converts the vector to an array of the elements within the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toArray = function () {\n  var output = new Array (this.elements.length / 2)\n\n  for (var i = 1, j = 0; i < this.elements.length; i += 2, j++) {\n    output[j] = this.elements[i]\n  }\n\n  return output\n}\n\n/**\n * A JSON serializable representation of the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toJSON = function () {\n  return this.elements\n}\n/* eslint-disable */\n/*!\n * lunr.stemmer\n * Copyright (C) 2020 Oliver Nightingale\n * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt\n */\n\n/**\n * lunr.stemmer is an english language stemmer, this is a JavaScript\n * implementation of the PorterStemmer taken from http://tartarus.org/~martin\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token - The string to stem\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n * @function\n */\nlunr.stemmer = (function(){\n  var step2list = {\n      \"ational\" : \"ate\",\n      \"tional\" : \"tion\",\n      \"enci\" : \"ence\",\n      \"anci\" : \"ance\",\n      \"izer\" : \"ize\",\n      \"bli\" : \"ble\",\n      \"alli\" : \"al\",\n      \"entli\" : \"ent\",\n      \"eli\" : \"e\",\n      \"ousli\" : \"ous\",\n      \"ization\" : \"ize\",\n      \"ation\" : \"ate\",\n      \"ator\" : \"ate\",\n      \"alism\" : \"al\",\n      \"iveness\" : \"ive\",\n      \"fulness\" : \"ful\",\n      \"ousness\" : \"ous\",\n      \"aliti\" : \"al\",\n      \"iviti\" : \"ive\",\n      \"biliti\" : \"ble\",\n      \"logi\" : \"log\"\n    },\n\n    step3list = {\n      \"icate\" : \"ic\",\n      \"ative\" : \"\",\n      \"alize\" : \"al\",\n      \"iciti\" : \"ic\",\n      \"ical\" : \"ic\",\n      \"ful\" : \"\",\n      \"ness\" : \"\"\n    },\n\n    c = \"[^aeiou]\",          // consonant\n    v = \"[aeiouy]\",          // vowel\n    C = c + \"[^aeiouy]*\",    // consonant sequence\n    V = v + \"[aeiou]*\",      // vowel sequence\n\n    mgr0 = \"^(\" + C + \")?\" + V + C,               // [C]VC... is m>0\n    meq1 = \"^(\" + C + \")?\" + V + C + \"(\" + V + \")?$\",  // [C]VC[V] is m=1\n    mgr1 = \"^(\" + C + \")?\" + V + C + V + C,       // [C]VCVC... is m>1\n    s_v = \"^(\" + C + \")?\" + v;                   // vowel in stem\n\n  var re_mgr0 = new RegExp(mgr0);\n  var re_mgr1 = new RegExp(mgr1);\n  var re_meq1 = new RegExp(meq1);\n  var re_s_v = new RegExp(s_v);\n\n  var re_1a = /^(.+?)(ss|i)es$/;\n  var re2_1a = /^(.+?)([^s])s$/;\n  var re_1b = /^(.+?)eed$/;\n  var re2_1b = /^(.+?)(ed|ing)$/;\n  var re_1b_2 = /.$/;\n  var re2_1b_2 = /(at|bl|iz)$/;\n  var re3_1b_2 = new RegExp(\"([^aeiouylsz])\\\\1$\");\n  var re4_1b_2 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n  var re_1c = /^(.+?[^aeiou])y$/;\n  var re_2 = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;\n\n  var re_3 = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;\n\n  var re_4 = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;\n  var re2_4 = /^(.+?)(s|t)(ion)$/;\n\n  var re_5 = /^(.+?)e$/;\n  var re_5_1 = /ll$/;\n  var re3_5 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n  var porterStemmer = function porterStemmer(w) {\n    var stem,\n      suffix,\n      firstch,\n      re,\n      re2,\n      re3,\n      re4;\n\n    if (w.length < 3) { return w; }\n\n    firstch = w.substr(0,1);\n    if (firstch == \"y\") {\n      w = firstch.toUpperCase() + w.substr(1);\n    }\n\n    // Step 1a\n    re = re_1a\n    re2 = re2_1a;\n\n    if (re.test(w)) { w = w.replace(re,\"$1$2\"); }\n    else if (re2.test(w)) { w = w.replace(re2,\"$1$2\"); }\n\n    // Step 1b\n    re = re_1b;\n    re2 = re2_1b;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      re = re_mgr0;\n      if (re.test(fp[1])) {\n        re = re_1b_2;\n        w = w.replace(re,\"\");\n      }\n    } else if (re2.test(w)) {\n      var fp = re2.exec(w);\n      stem = fp[1];\n      re2 = re_s_v;\n      if (re2.test(stem)) {\n        w = stem;\n        re2 = re2_1b_2;\n        re3 = re3_1b_2;\n        re4 = re4_1b_2;\n        if (re2.test(w)) { w = w + \"e\"; }\n        else if (re3.test(w)) { re = re_1b_2; w = w.replace(re,\"\"); }\n        else if (re4.test(w)) { w = w + \"e\"; }\n      }\n    }\n\n    // Step 1c - replace suffix y or Y by i if preceded by a non-vowel which is not the first letter of the word (so cry -> cri, by -> by, say -> say)\n    re = re_1c;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      w = stem + \"i\";\n    }\n\n    // Step 2\n    re = re_2;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      suffix = fp[2];\n      re = re_mgr0;\n      if (re.test(stem)) {\n        w = stem + step2list[suffix];\n      }\n    }\n\n    // Step 3\n    re = re_3;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      suffix = fp[2];\n      re = re_mgr0;\n      if (re.test(stem)) {\n        w = stem + step3list[suffix];\n      }\n    }\n\n    // Step 4\n    re = re_4;\n    re2 = re2_4;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      re = re_mgr1;\n      if (re.test(stem)) {\n        w = stem;\n      }\n    } else if (re2.test(w)) {\n      var fp = re2.exec(w);\n      stem = fp[1] + fp[2];\n      re2 = re_mgr1;\n      if (re2.test(stem)) {\n        w = stem;\n      }\n    }\n\n    // Step 5\n    re = re_5;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      re = re_mgr1;\n      re2 = re_meq1;\n      re3 = re3_5;\n      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) {\n        w = stem;\n      }\n    }\n\n    re = re_5_1;\n    re2 = re_mgr1;\n    if (re.test(w) && re2.test(w)) {\n      re = re_1b_2;\n      w = w.replace(re,\"\");\n    }\n\n    // and turn initial Y back to y\n\n    if (firstch == \"y\") {\n      w = firstch.toLowerCase() + w.substr(1);\n    }\n\n    return w;\n  };\n\n  return function (token) {\n    return token.update(porterStemmer);\n  }\n})();\n\nlunr.Pipeline.registerFunction(lunr.stemmer, 'stemmer')\n/*!\n * lunr.stopWordFilter\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.generateStopWordFilter builds a stopWordFilter function from the provided\n * list of stop words.\n *\n * The built in lunr.stopWordFilter is built using this generator and can be used\n * to generate custom stopWordFilters for applications or non English languages.\n *\n * @function\n * @param {Array} token The token to pass through the filter\n * @returns {lunr.PipelineFunction}\n * @see lunr.Pipeline\n * @see lunr.stopWordFilter\n */\nlunr.generateStopWordFilter = function (stopWords) {\n  var words = stopWords.reduce(function (memo, stopWord) {\n    memo[stopWord] = stopWord\n    return memo\n  }, {})\n\n  return function (token) {\n    if (token && words[token.toString()] !== token.toString()) return token\n  }\n}\n\n/**\n * lunr.stopWordFilter is an English language stop word list filter, any words\n * contained in the list will not be passed through the filter.\n *\n * This is intended to be used in the Pipeline. If the token does not pass the\n * filter then undefined will be returned.\n *\n * @function\n * @implements {lunr.PipelineFunction}\n * @params {lunr.Token} token - A token to check for being a stop word.\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n */\nlunr.stopWordFilter = lunr.generateStopWordFilter([\n  'a',\n  'able',\n  'about',\n  'across',\n  'after',\n  'all',\n  'almost',\n  'also',\n  'am',\n  'among',\n  'an',\n  'and',\n  'any',\n  'are',\n  'as',\n  'at',\n  'be',\n  'because',\n  'been',\n  'but',\n  'by',\n  'can',\n  'cannot',\n  'could',\n  'dear',\n  'did',\n  'do',\n  'does',\n  'either',\n  'else',\n  'ever',\n  'every',\n  'for',\n  'from',\n  'get',\n  'got',\n  'had',\n  'has',\n  'have',\n  'he',\n  'her',\n  'hers',\n  'him',\n  'his',\n  'how',\n  'however',\n  'i',\n  'if',\n  'in',\n  'into',\n  'is',\n  'it',\n  'its',\n  'just',\n  'least',\n  'let',\n  'like',\n  'likely',\n  'may',\n  'me',\n  'might',\n  'most',\n  'must',\n  'my',\n  'neither',\n  'no',\n  'nor',\n  'not',\n  'of',\n  'off',\n  'often',\n  'on',\n  'only',\n  'or',\n  'other',\n  'our',\n  'own',\n  'rather',\n  'said',\n  'say',\n  'says',\n  'she',\n  'should',\n  'since',\n  'so',\n  'some',\n  'than',\n  'that',\n  'the',\n  'their',\n  'them',\n  'then',\n  'there',\n  'these',\n  'they',\n  'this',\n  'tis',\n  'to',\n  'too',\n  'twas',\n  'us',\n  'wants',\n  'was',\n  'we',\n  'were',\n  'what',\n  'when',\n  'where',\n  'which',\n  'while',\n  'who',\n  'whom',\n  'why',\n  'will',\n  'with',\n  'would',\n  'yet',\n  'you',\n  'your'\n])\n\nlunr.Pipeline.registerFunction(lunr.stopWordFilter, 'stopWordFilter')\n/*!\n * lunr.trimmer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.trimmer is a pipeline function for trimming non word\n * characters from the beginning and end of tokens before they\n * enter the index.\n *\n * This implementation may not work correctly for non latin\n * characters and should either be removed or adapted for use\n * with languages with non-latin characters.\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token The token to pass through the filter\n * @returns {lunr.Token}\n * @see lunr.Pipeline\n */\nlunr.trimmer = function (token) {\n  return token.update(function (s) {\n    return s.replace(/^\\W+/, '').replace(/\\W+$/, '')\n  })\n}\n\nlunr.Pipeline.registerFunction(lunr.trimmer, 'trimmer')\n/*!\n * lunr.TokenSet\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A token set is used to store the unique list of all tokens\n * within an index. Token sets are also used to represent an\n * incoming query to the index, this query token set and index\n * token set are then intersected to find which tokens to look\n * up in the inverted index.\n *\n * A token set can hold multiple tokens, as in the case of the\n * index token set, or it can hold a single token as in the\n * case of a simple query token set.\n *\n * Additionally token sets are used to perform wildcard matching.\n * Leading, contained and trailing wildcards are supported, and\n * from this edit distance matching can also be provided.\n *\n * Token sets are implemented as a minimal finite state automata,\n * where both common prefixes and suffixes are shared between tokens.\n * This helps to reduce the space used for storing the token set.\n *\n * @constructor\n */\nlunr.TokenSet = function () {\n  this.final = false\n  this.edges = {}\n  this.id = lunr.TokenSet._nextId\n  lunr.TokenSet._nextId += 1\n}\n\n/**\n * Keeps track of the next, auto increment, identifier to assign\n * to a new tokenSet.\n *\n * TokenSets require a unique identifier to be correctly minimised.\n *\n * @private\n */\nlunr.TokenSet._nextId = 1\n\n/**\n * Creates a TokenSet instance from the given sorted array of words.\n *\n * @param {String[]} arr - A sorted array of strings to create the set from.\n * @returns {lunr.TokenSet}\n * @throws Will throw an error if the input array is not sorted.\n */\nlunr.TokenSet.fromArray = function (arr) {\n  var builder = new lunr.TokenSet.Builder\n\n  for (var i = 0, len = arr.length; i < len; i++) {\n    builder.insert(arr[i])\n  }\n\n  builder.finish()\n  return builder.root\n}\n\n/**\n * Creates a token set from a query clause.\n *\n * @private\n * @param {Object} clause - A single clause from lunr.Query.\n * @param {string} clause.term - The query clause term.\n * @param {number} [clause.editDistance] - The optional edit distance for the term.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromClause = function (clause) {\n  if ('editDistance' in clause) {\n    return lunr.TokenSet.fromFuzzyString(clause.term, clause.editDistance)\n  } else {\n    return lunr.TokenSet.fromString(clause.term)\n  }\n}\n\n/**\n * Creates a token set representing a single string with a specified\n * edit distance.\n *\n * Insertions, deletions, substitutions and transpositions are each\n * treated as an edit distance of 1.\n *\n * Increasing the allowed edit distance will have a dramatic impact\n * on the performance of both creating and intersecting these TokenSets.\n * It is advised to keep the edit distance less than 3.\n *\n * @param {string} str - The string to create the token set from.\n * @param {number} editDistance - The allowed edit distance to match.\n * @returns {lunr.Vector}\n */\nlunr.TokenSet.fromFuzzyString = function (str, editDistance) {\n  var root = new lunr.TokenSet\n\n  var stack = [{\n    node: root,\n    editsRemaining: editDistance,\n    str: str\n  }]\n\n  while (stack.length) {\n    var frame = stack.pop()\n\n    // no edit\n    if (frame.str.length > 0) {\n      var char = frame.str.charAt(0),\n          noEditNode\n\n      if (char in frame.node.edges) {\n        noEditNode = frame.node.edges[char]\n      } else {\n        noEditNode = new lunr.TokenSet\n        frame.node.edges[char] = noEditNode\n      }\n\n      if (frame.str.length == 1) {\n        noEditNode.final = true\n      }\n\n      stack.push({\n        node: noEditNode,\n        editsRemaining: frame.editsRemaining,\n        str: frame.str.slice(1)\n      })\n    }\n\n    if (frame.editsRemaining == 0) {\n      continue\n    }\n\n    // insertion\n    if (\"*\" in frame.node.edges) {\n      var insertionNode = frame.node.edges[\"*\"]\n    } else {\n      var insertionNode = new lunr.TokenSet\n      frame.node.edges[\"*\"] = insertionNode\n    }\n\n    if (frame.str.length == 0) {\n      insertionNode.final = true\n    }\n\n    stack.push({\n      node: insertionNode,\n      editsRemaining: frame.editsRemaining - 1,\n      str: frame.str\n    })\n\n    // deletion\n    // can only do a deletion if we have enough edits remaining\n    // and if there are characters left to delete in the string\n    if (frame.str.length > 1) {\n      stack.push({\n        node: frame.node,\n        editsRemaining: frame.editsRemaining - 1,\n        str: frame.str.slice(1)\n      })\n    }\n\n    // deletion\n    // just removing the last character from the str\n    if (frame.str.length == 1) {\n      frame.node.final = true\n    }\n\n    // substitution\n    // can only do a substitution if we have enough edits remaining\n    // and if there are characters left to substitute\n    if (frame.str.length >= 1) {\n      if (\"*\" in frame.node.edges) {\n        var substitutionNode = frame.node.edges[\"*\"]\n      } else {\n        var substitutionNode = new lunr.TokenSet\n        frame.node.edges[\"*\"] = substitutionNode\n      }\n\n      if (frame.str.length == 1) {\n        substitutionNode.final = true\n      }\n\n      stack.push({\n        node: substitutionNode,\n        editsRemaining: frame.editsRemaining - 1,\n        str: frame.str.slice(1)\n      })\n    }\n\n    // transposition\n    // can only do a transposition if there are edits remaining\n    // and there are enough characters to transpose\n    if (frame.str.length > 1) {\n      var charA = frame.str.charAt(0),\n          charB = frame.str.charAt(1),\n          transposeNode\n\n      if (charB in frame.node.edges) {\n        transposeNode = frame.node.edges[charB]\n      } else {\n        transposeNode = new lunr.TokenSet\n        frame.node.edges[charB] = transposeNode\n      }\n\n      if (frame.str.length == 1) {\n        transposeNode.final = true\n      }\n\n      stack.push({\n        node: transposeNode,\n        editsRemaining: frame.editsRemaining - 1,\n        str: charA + frame.str.slice(2)\n      })\n    }\n  }\n\n  return root\n}\n\n/**\n * Creates a TokenSet from a string.\n *\n * The string may contain one or more wildcard characters (*)\n * that will allow wildcard matching when intersecting with\n * another TokenSet.\n *\n * @param {string} str - The string to create a TokenSet from.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromString = function (str) {\n  var node = new lunr.TokenSet,\n      root = node\n\n  /*\n   * Iterates through all characters within the passed string\n   * appending a node for each character.\n   *\n   * When a wildcard character is found then a self\n   * referencing edge is introduced to continually match\n   * any number of any characters.\n   */\n  for (var i = 0, len = str.length; i < len; i++) {\n    var char = str[i],\n        final = (i == len - 1)\n\n    if (char == \"*\") {\n      node.edges[char] = node\n      node.final = final\n\n    } else {\n      var next = new lunr.TokenSet\n      next.final = final\n\n      node.edges[char] = next\n      node = next\n    }\n  }\n\n  return root\n}\n\n/**\n * Converts this TokenSet into an array of strings\n * contained within the TokenSet.\n *\n * This is not intended to be used on a TokenSet that\n * contains wildcards, in these cases the results are\n * undefined and are likely to cause an infinite loop.\n *\n * @returns {string[]}\n */\nlunr.TokenSet.prototype.toArray = function () {\n  var words = []\n\n  var stack = [{\n    prefix: \"\",\n    node: this\n  }]\n\n  while (stack.length) {\n    var frame = stack.pop(),\n        edges = Object.keys(frame.node.edges),\n        len = edges.length\n\n    if (frame.node.final) {\n      /* In Safari, at this point the prefix is sometimes corrupted, see:\n       * https://github.com/olivernn/lunr.js/issues/279 Calling any\n       * String.prototype method forces Safari to \"cast\" this string to what\n       * it's supposed to be, fixing the bug. */\n      frame.prefix.charAt(0)\n      words.push(frame.prefix)\n    }\n\n    for (var i = 0; i < len; i++) {\n      var edge = edges[i]\n\n      stack.push({\n        prefix: frame.prefix.concat(edge),\n        node: frame.node.edges[edge]\n      })\n    }\n  }\n\n  return words\n}\n\n/**\n * Generates a string representation of a TokenSet.\n *\n * This is intended to allow TokenSets to be used as keys\n * in objects, largely to aid the construction and minimisation\n * of a TokenSet. As such it is not designed to be a human\n * friendly representation of the TokenSet.\n *\n * @returns {string}\n */\nlunr.TokenSet.prototype.toString = function () {\n  // NOTE: Using Object.keys here as this.edges is very likely\n  // to enter 'hash-mode' with many keys being added\n  //\n  // avoiding a for-in loop here as it leads to the function\n  // being de-optimised (at least in V8). From some simple\n  // benchmarks the performance is comparable, but allowing\n  // V8 to optimize may mean easy performance wins in the future.\n\n  if (this._str) {\n    return this._str\n  }\n\n  var str = this.final ? '1' : '0',\n      labels = Object.keys(this.edges).sort(),\n      len = labels.length\n\n  for (var i = 0; i < len; i++) {\n    var label = labels[i],\n        node = this.edges[label]\n\n    str = str + label + node.id\n  }\n\n  return str\n}\n\n/**\n * Returns a new TokenSet that is the intersection of\n * this TokenSet and the passed TokenSet.\n *\n * This intersection will take into account any wildcards\n * contained within the TokenSet.\n *\n * @param {lunr.TokenSet} b - An other TokenSet to intersect with.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.prototype.intersect = function (b) {\n  var output = new lunr.TokenSet,\n      frame = undefined\n\n  var stack = [{\n    qNode: b,\n    output: output,\n    node: this\n  }]\n\n  while (stack.length) {\n    frame = stack.pop()\n\n    // NOTE: As with the #toString method, we are using\n    // Object.keys and a for loop instead of a for-in loop\n    // as both of these objects enter 'hash' mode, causing\n    // the function to be de-optimised in V8\n    var qEdges = Object.keys(frame.qNode.edges),\n        qLen = qEdges.length,\n        nEdges = Object.keys(frame.node.edges),\n        nLen = nEdges.length\n\n    for (var q = 0; q < qLen; q++) {\n      var qEdge = qEdges[q]\n\n      for (var n = 0; n < nLen; n++) {\n        var nEdge = nEdges[n]\n\n        if (nEdge == qEdge || qEdge == '*') {\n          var node = frame.node.edges[nEdge],\n              qNode = frame.qNode.edges[qEdge],\n              final = node.final && qNode.final,\n              next = undefined\n\n          if (nEdge in frame.output.edges) {\n            // an edge already exists for this character\n            // no need to create a new node, just set the finality\n            // bit unless this node is already final\n            next = frame.output.edges[nEdge]\n            next.final = next.final || final\n\n          } else {\n            // no edge exists yet, must create one\n            // set the finality bit and insert it\n            // into the output\n            next = new lunr.TokenSet\n            next.final = final\n            frame.output.edges[nEdge] = next\n          }\n\n          stack.push({\n            qNode: qNode,\n            output: next,\n            node: node\n          })\n        }\n      }\n    }\n  }\n\n  return output\n}\nlunr.TokenSet.Builder = function () {\n  this.previousWord = \"\"\n  this.root = new lunr.TokenSet\n  this.uncheckedNodes = []\n  this.minimizedNodes = {}\n}\n\nlunr.TokenSet.Builder.prototype.insert = function (word) {\n  var node,\n      commonPrefix = 0\n\n  if (word < this.previousWord) {\n    throw new Error (\"Out of order word insertion\")\n  }\n\n  for (var i = 0; i < word.length && i < this.previousWord.length; i++) {\n    if (word[i] != this.previousWord[i]) break\n    commonPrefix++\n  }\n\n  this.minimize(commonPrefix)\n\n  if (this.uncheckedNodes.length == 0) {\n    node = this.root\n  } else {\n    node = this.uncheckedNodes[this.uncheckedNodes.length - 1].child\n  }\n\n  for (var i = commonPrefix; i < word.length; i++) {\n    var nextNode = new lunr.TokenSet,\n        char = word[i]\n\n    node.edges[char] = nextNode\n\n    this.uncheckedNodes.push({\n      parent: node,\n      char: char,\n      child: nextNode\n    })\n\n    node = nextNode\n  }\n\n  node.final = true\n  this.previousWord = word\n}\n\nlunr.TokenSet.Builder.prototype.finish = function () {\n  this.minimize(0)\n}\n\nlunr.TokenSet.Builder.prototype.minimize = function (downTo) {\n  for (var i = this.uncheckedNodes.length - 1; i >= downTo; i--) {\n    var node = this.uncheckedNodes[i],\n        childKey = node.child.toString()\n\n    if (childKey in this.minimizedNodes) {\n      node.parent.edges[node.char] = this.minimizedNodes[childKey]\n    } else {\n      // Cache the key for this node since\n      // we know it can't change anymore\n      node.child._str = childKey\n\n      this.minimizedNodes[childKey] = node.child\n    }\n\n    this.uncheckedNodes.pop()\n  }\n}\n/*!\n * lunr.Index\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * An index contains the built index of all documents and provides a query interface\n * to the index.\n *\n * Usually instances of lunr.Index will not be created using this constructor, instead\n * lunr.Builder should be used to construct new indexes, or lunr.Index.load should be\n * used to load previously built and serialized indexes.\n *\n * @constructor\n * @param {Object} attrs - The attributes of the built search index.\n * @param {Object} attrs.invertedIndex - An index of term/field to document reference.\n * @param {Object<string, lunr.Vector>} attrs.fieldVectors - Field vectors\n * @param {lunr.TokenSet} attrs.tokenSet - An set of all corpus tokens.\n * @param {string[]} attrs.fields - The names of indexed document fields.\n * @param {lunr.Pipeline} attrs.pipeline - The pipeline to use for search terms.\n */\nlunr.Index = function (attrs) {\n  this.invertedIndex = attrs.invertedIndex\n  this.fieldVectors = attrs.fieldVectors\n  this.tokenSet = attrs.tokenSet\n  this.fields = attrs.fields\n  this.pipeline = attrs.pipeline\n}\n\n/**\n * A result contains details of a document matching a search query.\n * @typedef {Object} lunr.Index~Result\n * @property {string} ref - The reference of the document this result represents.\n * @property {number} score - A number between 0 and 1 representing how similar this document is to the query.\n * @property {lunr.MatchData} matchData - Contains metadata about this match including which term(s) caused the match.\n */\n\n/**\n * Although lunr provides the ability to create queries using lunr.Query, it also provides a simple\n * query language which itself is parsed into an instance of lunr.Query.\n *\n * For programmatically building queries it is advised to directly use lunr.Query, the query language\n * is best used for human entered text rather than program generated text.\n *\n * At its simplest queries can just be a single term, e.g. `hello`, multiple terms are also supported\n * and will be combined with OR, e.g `hello world` will match documents that contain either 'hello'\n * or 'world', though those that contain both will rank higher in the results.\n *\n * Wildcards can be included in terms to match one or more unspecified characters, these wildcards can\n * be inserted anywhere within the term, and more than one wildcard can exist in a single term. Adding\n * wildcards will increase the number of documents that will be found but can also have a negative\n * impact on query performance, especially with wildcards at the beginning of a term.\n *\n * Terms can be restricted to specific fields, e.g. `title:hello`, only documents with the term\n * hello in the title field will match this query. Using a field not present in the index will lead\n * to an error being thrown.\n *\n * Modifiers can also be added to terms, lunr supports edit distance and boost modifiers on terms. A term\n * boost will make documents matching that term score higher, e.g. `foo^5`. Edit distance is also supported\n * to provide fuzzy matching, e.g. 'hello~2' will match documents with hello with an edit distance of 2.\n * Avoid large values for edit distance to improve query performance.\n *\n * Each term also supports a presence modifier. By default a term's presence in document is optional, however\n * this can be changed to either required or prohibited. For a term's presence to be required in a document the\n * term should be prefixed with a '+', e.g. `+foo bar` is a search for documents that must contain 'foo' and\n * optionally contain 'bar'. Conversely a leading '-' sets the terms presence to prohibited, i.e. it must not\n * appear in a document, e.g. `-foo bar` is a search for documents that do not contain 'foo' but may contain 'bar'.\n *\n * To escape special characters the backslash character '\\' can be used, this allows searches to include\n * characters that would normally be considered modifiers, e.g. `foo\\~2` will search for a term \"foo~2\" instead\n * of attempting to apply a boost of 2 to the search term \"foo\".\n *\n * @typedef {string} lunr.Index~QueryString\n * @example <caption>Simple single term query</caption>\n * hello\n * @example <caption>Multiple term query</caption>\n * hello world\n * @example <caption>term scoped to a field</caption>\n * title:hello\n * @example <caption>term with a boost of 10</caption>\n * hello^10\n * @example <caption>term with an edit distance of 2</caption>\n * hello~2\n * @example <caption>terms with presence modifiers</caption>\n * -foo +bar baz\n */\n\n/**\n * Performs a search against the index using lunr query syntax.\n *\n * Results will be returned sorted by their score, the most relevant results\n * will be returned first.  For details on how the score is calculated, please see\n * the {@link https://lunrjs.com/guides/searching.html#scoring|guide}.\n *\n * For more programmatic querying use lunr.Index#query.\n *\n * @param {lunr.Index~QueryString} queryString - A string containing a lunr query.\n * @throws {lunr.QueryParseError} If the passed query string cannot be parsed.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.search = function (queryString) {\n  return this.query(function (query) {\n    var parser = new lunr.QueryParser(queryString, query)\n    parser.parse()\n  })\n}\n\n/**\n * A query builder callback provides a query object to be used to express\n * the query to perform on the index.\n *\n * @callback lunr.Index~queryBuilder\n * @param {lunr.Query} query - The query object to build up.\n * @this lunr.Query\n */\n\n/**\n * Performs a query against the index using the yielded lunr.Query object.\n *\n * If performing programmatic queries against the index, this method is preferred\n * over lunr.Index#search so as to avoid the additional query parsing overhead.\n *\n * A query object is yielded to the supplied function which should be used to\n * express the query to be run against the index.\n *\n * Note that although this function takes a callback parameter it is _not_ an\n * asynchronous operation, the callback is just yielded a query object to be\n * customized.\n *\n * @param {lunr.Index~queryBuilder} fn - A function that is used to build the query.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.query = function (fn) {\n  // for each query clause\n  // * process terms\n  // * expand terms from token set\n  // * find matching documents and metadata\n  // * get document vectors\n  // * score documents\n\n  var query = new lunr.Query(this.fields),\n      matchingFields = Object.create(null),\n      queryVectors = Object.create(null),\n      termFieldCache = Object.create(null),\n      requiredMatches = Object.create(null),\n      prohibitedMatches = Object.create(null)\n\n  /*\n   * To support field level boosts a query vector is created per\n   * field. An empty vector is eagerly created to support negated\n   * queries.\n   */\n  for (var i = 0; i < this.fields.length; i++) {\n    queryVectors[this.fields[i]] = new lunr.Vector\n  }\n\n  fn.call(query, query)\n\n  for (var i = 0; i < query.clauses.length; i++) {\n    /*\n     * Unless the pipeline has been disabled for this term, which is\n     * the case for terms with wildcards, we need to pass the clause\n     * term through the search pipeline. A pipeline returns an array\n     * of processed terms. Pipeline functions may expand the passed\n     * term, which means we may end up performing multiple index lookups\n     * for a single query term.\n     */\n    var clause = query.clauses[i],\n        terms = null,\n        clauseMatches = lunr.Set.empty\n\n    if (clause.usePipeline) {\n      terms = this.pipeline.runString(clause.term, {\n        fields: clause.fields\n      })\n    } else {\n      terms = [clause.term]\n    }\n\n    for (var m = 0; m < terms.length; m++) {\n      var term = terms[m]\n\n      /*\n       * Each term returned from the pipeline needs to use the same query\n       * clause object, e.g. the same boost and or edit distance. The\n       * simplest way to do this is to re-use the clause object but mutate\n       * its term property.\n       */\n      clause.term = term\n\n      /*\n       * From the term in the clause we create a token set which will then\n       * be used to intersect the indexes token set to get a list of terms\n       * to lookup in the inverted index\n       */\n      var termTokenSet = lunr.TokenSet.fromClause(clause),\n          expandedTerms = this.tokenSet.intersect(termTokenSet).toArray()\n\n      /*\n       * If a term marked as required does not exist in the tokenSet it is\n       * impossible for the search to return any matches. We set all the field\n       * scoped required matches set to empty and stop examining any further\n       * clauses.\n       */\n      if (expandedTerms.length === 0 && clause.presence === lunr.Query.presence.REQUIRED) {\n        for (var k = 0; k < clause.fields.length; k++) {\n          var field = clause.fields[k]\n          requiredMatches[field] = lunr.Set.empty\n        }\n\n        break\n      }\n\n      for (var j = 0; j < expandedTerms.length; j++) {\n        /*\n         * For each term get the posting and termIndex, this is required for\n         * building the query vector.\n         */\n        var expandedTerm = expandedTerms[j],\n            posting = this.invertedIndex[expandedTerm],\n            termIndex = posting._index\n\n        for (var k = 0; k < clause.fields.length; k++) {\n          /*\n           * For each field that this query term is scoped by (by default\n           * all fields are in scope) we need to get all the document refs\n           * that have this term in that field.\n           *\n           * The posting is the entry in the invertedIndex for the matching\n           * term from above.\n           */\n          var field = clause.fields[k],\n              fieldPosting = posting[field],\n              matchingDocumentRefs = Object.keys(fieldPosting),\n              termField = expandedTerm + \"/\" + field,\n              matchingDocumentsSet = new lunr.Set(matchingDocumentRefs)\n\n          /*\n           * if the presence of this term is required ensure that the matching\n           * documents are added to the set of required matches for this clause.\n           *\n           */\n          if (clause.presence == lunr.Query.presence.REQUIRED) {\n            clauseMatches = clauseMatches.union(matchingDocumentsSet)\n\n            if (requiredMatches[field] === undefined) {\n              requiredMatches[field] = lunr.Set.complete\n            }\n          }\n\n          /*\n           * if the presence of this term is prohibited ensure that the matching\n           * documents are added to the set of prohibited matches for this field,\n           * creating that set if it does not yet exist.\n           */\n          if (clause.presence == lunr.Query.presence.PROHIBITED) {\n            if (prohibitedMatches[field] === undefined) {\n              prohibitedMatches[field] = lunr.Set.empty\n            }\n\n            prohibitedMatches[field] = prohibitedMatches[field].union(matchingDocumentsSet)\n\n            /*\n             * Prohibited matches should not be part of the query vector used for\n             * similarity scoring and no metadata should be extracted so we continue\n             * to the next field\n             */\n            continue\n          }\n\n          /*\n           * The query field vector is populated using the termIndex found for\n           * the term and a unit value with the appropriate boost applied.\n           * Using upsert because there could already be an entry in the vector\n           * for the term we are working with. In that case we just add the scores\n           * together.\n           */\n          queryVectors[field].upsert(termIndex, clause.boost, function (a, b) { return a + b })\n\n          /**\n           * If we've already seen this term, field combo then we've already collected\n           * the matching documents and metadata, no need to go through all that again\n           */\n          if (termFieldCache[termField]) {\n            continue\n          }\n\n          for (var l = 0; l < matchingDocumentRefs.length; l++) {\n            /*\n             * All metadata for this term/field/document triple\n             * are then extracted and collected into an instance\n             * of lunr.MatchData ready to be returned in the query\n             * results\n             */\n            var matchingDocumentRef = matchingDocumentRefs[l],\n                matchingFieldRef = new lunr.FieldRef (matchingDocumentRef, field),\n                metadata = fieldPosting[matchingDocumentRef],\n                fieldMatch\n\n            if ((fieldMatch = matchingFields[matchingFieldRef]) === undefined) {\n              matchingFields[matchingFieldRef] = new lunr.MatchData (expandedTerm, field, metadata)\n            } else {\n              fieldMatch.add(expandedTerm, field, metadata)\n            }\n\n          }\n\n          termFieldCache[termField] = true\n        }\n      }\n    }\n\n    /**\n     * If the presence was required we need to update the requiredMatches field sets.\n     * We do this after all fields for the term have collected their matches because\n     * the clause terms presence is required in _any_ of the fields not _all_ of the\n     * fields.\n     */\n    if (clause.presence === lunr.Query.presence.REQUIRED) {\n      for (var k = 0; k < clause.fields.length; k++) {\n        var field = clause.fields[k]\n        requiredMatches[field] = requiredMatches[field].intersect(clauseMatches)\n      }\n    }\n  }\n\n  /**\n   * Need to combine the field scoped required and prohibited\n   * matching documents into a global set of required and prohibited\n   * matches\n   */\n  var allRequiredMatches = lunr.Set.complete,\n      allProhibitedMatches = lunr.Set.empty\n\n  for (var i = 0; i < this.fields.length; i++) {\n    var field = this.fields[i]\n\n    if (requiredMatches[field]) {\n      allRequiredMatches = allRequiredMatches.intersect(requiredMatches[field])\n    }\n\n    if (prohibitedMatches[field]) {\n      allProhibitedMatches = allProhibitedMatches.union(prohibitedMatches[field])\n    }\n  }\n\n  var matchingFieldRefs = Object.keys(matchingFields),\n      results = [],\n      matches = Object.create(null)\n\n  /*\n   * If the query is negated (contains only prohibited terms)\n   * we need to get _all_ fieldRefs currently existing in the\n   * index. This is only done when we know that the query is\n   * entirely prohibited terms to avoid any cost of getting all\n   * fieldRefs unnecessarily.\n   *\n   * Additionally, blank MatchData must be created to correctly\n   * populate the results.\n   */\n  if (query.isNegated()) {\n    matchingFieldRefs = Object.keys(this.fieldVectors)\n\n    for (var i = 0; i < matchingFieldRefs.length; i++) {\n      var matchingFieldRef = matchingFieldRefs[i]\n      var fieldRef = lunr.FieldRef.fromString(matchingFieldRef)\n      matchingFields[matchingFieldRef] = new lunr.MatchData\n    }\n  }\n\n  for (var i = 0; i < matchingFieldRefs.length; i++) {\n    /*\n     * Currently we have document fields that match the query, but we\n     * need to return documents. The matchData and scores are combined\n     * from multiple fields belonging to the same document.\n     *\n     * Scores are calculated by field, using the query vectors created\n     * above, and combined into a final document score using addition.\n     */\n    var fieldRef = lunr.FieldRef.fromString(matchingFieldRefs[i]),\n        docRef = fieldRef.docRef\n\n    if (!allRequiredMatches.contains(docRef)) {\n      continue\n    }\n\n    if (allProhibitedMatches.contains(docRef)) {\n      continue\n    }\n\n    var fieldVector = this.fieldVectors[fieldRef],\n        score = queryVectors[fieldRef.fieldName].similarity(fieldVector),\n        docMatch\n\n    if ((docMatch = matches[docRef]) !== undefined) {\n      docMatch.score += score\n      docMatch.matchData.combine(matchingFields[fieldRef])\n    } else {\n      var match = {\n        ref: docRef,\n        score: score,\n        matchData: matchingFields[fieldRef]\n      }\n      matches[docRef] = match\n      results.push(match)\n    }\n  }\n\n  /*\n   * Sort the results objects by score, highest first.\n   */\n  return results.sort(function (a, b) {\n    return b.score - a.score\n  })\n}\n\n/**\n * Prepares the index for JSON serialization.\n *\n * The schema for this JSON blob will be described in a\n * separate JSON schema file.\n *\n * @returns {Object}\n */\nlunr.Index.prototype.toJSON = function () {\n  var invertedIndex = Object.keys(this.invertedIndex)\n    .sort()\n    .map(function (term) {\n      return [term, this.invertedIndex[term]]\n    }, this)\n\n  var fieldVectors = Object.keys(this.fieldVectors)\n    .map(function (ref) {\n      return [ref, this.fieldVectors[ref].toJSON()]\n    }, this)\n\n  return {\n    version: lunr.version,\n    fields: this.fields,\n    fieldVectors: fieldVectors,\n    invertedIndex: invertedIndex,\n    pipeline: this.pipeline.toJSON()\n  }\n}\n\n/**\n * Loads a previously serialized lunr.Index\n *\n * @param {Object} serializedIndex - A previously serialized lunr.Index\n * @returns {lunr.Index}\n */\nlunr.Index.load = function (serializedIndex) {\n  var attrs = {},\n      fieldVectors = {},\n      serializedVectors = serializedIndex.fieldVectors,\n      invertedIndex = Object.create(null),\n      serializedInvertedIndex = serializedIndex.invertedIndex,\n      tokenSetBuilder = new lunr.TokenSet.Builder,\n      pipeline = lunr.Pipeline.load(serializedIndex.pipeline)\n\n  if (serializedIndex.version != lunr.version) {\n    lunr.utils.warn(\"Version mismatch when loading serialised index. Current version of lunr '\" + lunr.version + \"' does not match serialized index '\" + serializedIndex.version + \"'\")\n  }\n\n  for (var i = 0; i < serializedVectors.length; i++) {\n    var tuple = serializedVectors[i],\n        ref = tuple[0],\n        elements = tuple[1]\n\n    fieldVectors[ref] = new lunr.Vector(elements)\n  }\n\n  for (var i = 0; i < serializedInvertedIndex.length; i++) {\n    var tuple = serializedInvertedIndex[i],\n        term = tuple[0],\n        posting = tuple[1]\n\n    tokenSetBuilder.insert(term)\n    invertedIndex[term] = posting\n  }\n\n  tokenSetBuilder.finish()\n\n  attrs.fields = serializedIndex.fields\n\n  attrs.fieldVectors = fieldVectors\n  attrs.invertedIndex = invertedIndex\n  attrs.tokenSet = tokenSetBuilder.root\n  attrs.pipeline = pipeline\n\n  return new lunr.Index(attrs)\n}\n/*!\n * lunr.Builder\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Builder performs indexing on a set of documents and\n * returns instances of lunr.Index ready for querying.\n *\n * All configuration of the index is done via the builder, the\n * fields to index, the document reference, the text processing\n * pipeline and document scoring parameters are all set on the\n * builder before indexing.\n *\n * @constructor\n * @property {string} _ref - Internal reference to the document reference field.\n * @property {string[]} _fields - Internal reference to the document fields to index.\n * @property {object} invertedIndex - The inverted index maps terms to document fields.\n * @property {object} documentTermFrequencies - Keeps track of document term frequencies.\n * @property {object} documentLengths - Keeps track of the length of documents added to the index.\n * @property {lunr.tokenizer} tokenizer - Function for splitting strings into tokens for indexing.\n * @property {lunr.Pipeline} pipeline - The pipeline performs text processing on tokens before indexing.\n * @property {lunr.Pipeline} searchPipeline - A pipeline for processing search terms before querying the index.\n * @property {number} documentCount - Keeps track of the total number of documents indexed.\n * @property {number} _b - A parameter to control field length normalization, setting this to 0 disabled normalization, 1 fully normalizes field lengths, the default value is 0.75.\n * @property {number} _k1 - A parameter to control how quickly an increase in term frequency results in term frequency saturation, the default value is 1.2.\n * @property {number} termIndex - A counter incremented for each unique term, used to identify a terms position in the vector space.\n * @property {array} metadataWhitelist - A list of metadata keys that have been whitelisted for entry in the index.\n */\nlunr.Builder = function () {\n  this._ref = \"id\"\n  this._fields = Object.create(null)\n  this._documents = Object.create(null)\n  this.invertedIndex = Object.create(null)\n  this.fieldTermFrequencies = {}\n  this.fieldLengths = {}\n  this.tokenizer = lunr.tokenizer\n  this.pipeline = new lunr.Pipeline\n  this.searchPipeline = new lunr.Pipeline\n  this.documentCount = 0\n  this._b = 0.75\n  this._k1 = 1.2\n  this.termIndex = 0\n  this.metadataWhitelist = []\n}\n\n/**\n * Sets the document field used as the document reference. Every document must have this field.\n * The type of this field in the document should be a string, if it is not a string it will be\n * coerced into a string by calling toString.\n *\n * The default ref is 'id'.\n *\n * The ref should _not_ be changed during indexing, it should be set before any documents are\n * added to the index. Changing it during indexing can lead to inconsistent results.\n *\n * @param {string} ref - The name of the reference field in the document.\n */\nlunr.Builder.prototype.ref = function (ref) {\n  this._ref = ref\n}\n\n/**\n * A function that is used to extract a field from a document.\n *\n * Lunr expects a field to be at the top level of a document, if however the field\n * is deeply nested within a document an extractor function can be used to extract\n * the right field for indexing.\n *\n * @callback fieldExtractor\n * @param {object} doc - The document being added to the index.\n * @returns {?(string|object|object[])} obj - The object that will be indexed for this field.\n * @example <caption>Extracting a nested field</caption>\n * function (doc) { return doc.nested.field }\n */\n\n/**\n * Adds a field to the list of document fields that will be indexed. Every document being\n * indexed should have this field. Null values for this field in indexed documents will\n * not cause errors but will limit the chance of that document being retrieved by searches.\n *\n * All fields should be added before adding documents to the index. Adding fields after\n * a document has been indexed will have no effect on already indexed documents.\n *\n * Fields can be boosted at build time. This allows terms within that field to have more\n * importance when ranking search results. Use a field boost to specify that matches within\n * one field are more important than other fields.\n *\n * @param {string} fieldName - The name of a field to index in all documents.\n * @param {object} attributes - Optional attributes associated with this field.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this field.\n * @param {fieldExtractor} [attributes.extractor] - Function to extract a field from a document.\n * @throws {RangeError} fieldName cannot contain unsupported characters '/'\n */\nlunr.Builder.prototype.field = function (fieldName, attributes) {\n  if (/\\//.test(fieldName)) {\n    throw new RangeError (\"Field '\" + fieldName + \"' contains illegal character '/'\")\n  }\n\n  this._fields[fieldName] = attributes || {}\n}\n\n/**\n * A parameter to tune the amount of field length normalisation that is applied when\n * calculating relevance scores. A value of 0 will completely disable any normalisation\n * and a value of 1 will fully normalise field lengths. The default is 0.75. Values of b\n * will be clamped to the range 0 - 1.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.b = function (number) {\n  if (number < 0) {\n    this._b = 0\n  } else if (number > 1) {\n    this._b = 1\n  } else {\n    this._b = number\n  }\n}\n\n/**\n * A parameter that controls the speed at which a rise in term frequency results in term\n * frequency saturation. The default value is 1.2. Setting this to a higher value will give\n * slower saturation levels, a lower value will result in quicker saturation.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.k1 = function (number) {\n  this._k1 = number\n}\n\n/**\n * Adds a document to the index.\n *\n * Before adding fields to the index the index should have been fully setup, with the document\n * ref and all fields to index already having been specified.\n *\n * The document must have a field name as specified by the ref (by default this is 'id') and\n * it should have all fields defined for indexing, though null or undefined values will not\n * cause errors.\n *\n * Entire documents can be boosted at build time. Applying a boost to a document indicates that\n * this document should rank higher in search results than other documents.\n *\n * @param {object} doc - The document to add to the index.\n * @param {object} attributes - Optional attributes associated with this document.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this document.\n */\nlunr.Builder.prototype.add = function (doc, attributes) {\n  var docRef = doc[this._ref],\n      fields = Object.keys(this._fields)\n\n  this._documents[docRef] = attributes || {}\n  this.documentCount += 1\n\n  for (var i = 0; i < fields.length; i++) {\n    var fieldName = fields[i],\n        extractor = this._fields[fieldName].extractor,\n        field = extractor ? extractor(doc) : doc[fieldName],\n        tokens = this.tokenizer(field, {\n          fields: [fieldName]\n        }),\n        terms = this.pipeline.run(tokens),\n        fieldRef = new lunr.FieldRef (docRef, fieldName),\n        fieldTerms = Object.create(null)\n\n    this.fieldTermFrequencies[fieldRef] = fieldTerms\n    this.fieldLengths[fieldRef] = 0\n\n    // store the length of this field for this document\n    this.fieldLengths[fieldRef] += terms.length\n\n    // calculate term frequencies for this field\n    for (var j = 0; j < terms.length; j++) {\n      var term = terms[j]\n\n      if (fieldTerms[term] == undefined) {\n        fieldTerms[term] = 0\n      }\n\n      fieldTerms[term] += 1\n\n      // add to inverted index\n      // create an initial posting if one doesn't exist\n      if (this.invertedIndex[term] == undefined) {\n        var posting = Object.create(null)\n        posting[\"_index\"] = this.termIndex\n        this.termIndex += 1\n\n        for (var k = 0; k < fields.length; k++) {\n          posting[fields[k]] = Object.create(null)\n        }\n\n        this.invertedIndex[term] = posting\n      }\n\n      // add an entry for this term/fieldName/docRef to the invertedIndex\n      if (this.invertedIndex[term][fieldName][docRef] == undefined) {\n        this.invertedIndex[term][fieldName][docRef] = Object.create(null)\n      }\n\n      // store all whitelisted metadata about this token in the\n      // inverted index\n      for (var l = 0; l < this.metadataWhitelist.length; l++) {\n        var metadataKey = this.metadataWhitelist[l],\n            metadata = term.metadata[metadataKey]\n\n        if (this.invertedIndex[term][fieldName][docRef][metadataKey] == undefined) {\n          this.invertedIndex[term][fieldName][docRef][metadataKey] = []\n        }\n\n        this.invertedIndex[term][fieldName][docRef][metadataKey].push(metadata)\n      }\n    }\n\n  }\n}\n\n/**\n * Calculates the average document length for this index\n *\n * @private\n */\nlunr.Builder.prototype.calculateAverageFieldLengths = function () {\n\n  var fieldRefs = Object.keys(this.fieldLengths),\n      numberOfFields = fieldRefs.length,\n      accumulator = {},\n      documentsWithField = {}\n\n  for (var i = 0; i < numberOfFields; i++) {\n    var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n        field = fieldRef.fieldName\n\n    documentsWithField[field] || (documentsWithField[field] = 0)\n    documentsWithField[field] += 1\n\n    accumulator[field] || (accumulator[field] = 0)\n    accumulator[field] += this.fieldLengths[fieldRef]\n  }\n\n  var fields = Object.keys(this._fields)\n\n  for (var i = 0; i < fields.length; i++) {\n    var fieldName = fields[i]\n    accumulator[fieldName] = accumulator[fieldName] / documentsWithField[fieldName]\n  }\n\n  this.averageFieldLength = accumulator\n}\n\n/**\n * Builds a vector space model of every document using lunr.Vector\n *\n * @private\n */\nlunr.Builder.prototype.createFieldVectors = function () {\n  var fieldVectors = {},\n      fieldRefs = Object.keys(this.fieldTermFrequencies),\n      fieldRefsLength = fieldRefs.length,\n      termIdfCache = Object.create(null)\n\n  for (var i = 0; i < fieldRefsLength; i++) {\n    var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n        fieldName = fieldRef.fieldName,\n        fieldLength = this.fieldLengths[fieldRef],\n        fieldVector = new lunr.Vector,\n        termFrequencies = this.fieldTermFrequencies[fieldRef],\n        terms = Object.keys(termFrequencies),\n        termsLength = terms.length\n\n\n    var fieldBoost = this._fields[fieldName].boost || 1,\n        docBoost = this._documents[fieldRef.docRef].boost || 1\n\n    for (var j = 0; j < termsLength; j++) {\n      var term = terms[j],\n          tf = termFrequencies[term],\n          termIndex = this.invertedIndex[term]._index,\n          idf, score, scoreWithPrecision\n\n      if (termIdfCache[term] === undefined) {\n        idf = lunr.idf(this.invertedIndex[term], this.documentCount)\n        termIdfCache[term] = idf\n      } else {\n        idf = termIdfCache[term]\n      }\n\n      score = idf * ((this._k1 + 1) * tf) / (this._k1 * (1 - this._b + this._b * (fieldLength / this.averageFieldLength[fieldName])) + tf)\n      score *= fieldBoost\n      score *= docBoost\n      scoreWithPrecision = Math.round(score * 1000) / 1000\n      // Converts 1.23456789 to 1.234.\n      // Reducing the precision so that the vectors take up less\n      // space when serialised. Doing it now so that they behave\n      // the same before and after serialisation. Also, this is\n      // the fastest approach to reducing a number's precision in\n      // JavaScript.\n\n      fieldVector.insert(termIndex, scoreWithPrecision)\n    }\n\n    fieldVectors[fieldRef] = fieldVector\n  }\n\n  this.fieldVectors = fieldVectors\n}\n\n/**\n * Creates a token set of all tokens in the index using lunr.TokenSet\n *\n * @private\n */\nlunr.Builder.prototype.createTokenSet = function () {\n  this.tokenSet = lunr.TokenSet.fromArray(\n    Object.keys(this.invertedIndex).sort()\n  )\n}\n\n/**\n * Builds the index, creating an instance of lunr.Index.\n *\n * This completes the indexing process and should only be called\n * once all documents have been added to the index.\n *\n * @returns {lunr.Index}\n */\nlunr.Builder.prototype.build = function () {\n  this.calculateAverageFieldLengths()\n  this.createFieldVectors()\n  this.createTokenSet()\n\n  return new lunr.Index({\n    invertedIndex: this.invertedIndex,\n    fieldVectors: this.fieldVectors,\n    tokenSet: this.tokenSet,\n    fields: Object.keys(this._fields),\n    pipeline: this.searchPipeline\n  })\n}\n\n/**\n * Applies a plugin to the index builder.\n *\n * A plugin is a function that is called with the index builder as its context.\n * Plugins can be used to customise or extend the behaviour of the index\n * in some way. A plugin is just a function, that encapsulated the custom\n * behaviour that should be applied when building the index.\n *\n * The plugin function will be called with the index builder as its argument, additional\n * arguments can also be passed when calling use. The function will be called\n * with the index builder as its context.\n *\n * @param {Function} plugin The plugin to apply.\n */\nlunr.Builder.prototype.use = function (fn) {\n  var args = Array.prototype.slice.call(arguments, 1)\n  args.unshift(this)\n  fn.apply(this, args)\n}\n/**\n * Contains and collects metadata about a matching document.\n * A single instance of lunr.MatchData is returned as part of every\n * lunr.Index~Result.\n *\n * @constructor\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n * @property {object} metadata - A cloned collection of metadata associated with this document.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData = function (term, field, metadata) {\n  var clonedMetadata = Object.create(null),\n      metadataKeys = Object.keys(metadata || {})\n\n  // Cloning the metadata to prevent the original\n  // being mutated during match data combination.\n  // Metadata is kept in an array within the inverted\n  // index so cloning the data can be done with\n  // Array#slice\n  for (var i = 0; i < metadataKeys.length; i++) {\n    var key = metadataKeys[i]\n    clonedMetadata[key] = metadata[key].slice()\n  }\n\n  this.metadata = Object.create(null)\n\n  if (term !== undefined) {\n    this.metadata[term] = Object.create(null)\n    this.metadata[term][field] = clonedMetadata\n  }\n}\n\n/**\n * An instance of lunr.MatchData will be created for every term that matches a\n * document. However only one instance is required in a lunr.Index~Result. This\n * method combines metadata from another instance of lunr.MatchData with this\n * objects metadata.\n *\n * @param {lunr.MatchData} otherMatchData - Another instance of match data to merge with this one.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData.prototype.combine = function (otherMatchData) {\n  var terms = Object.keys(otherMatchData.metadata)\n\n  for (var i = 0; i < terms.length; i++) {\n    var term = terms[i],\n        fields = Object.keys(otherMatchData.metadata[term])\n\n    if (this.metadata[term] == undefined) {\n      this.metadata[term] = Object.create(null)\n    }\n\n    for (var j = 0; j < fields.length; j++) {\n      var field = fields[j],\n          keys = Object.keys(otherMatchData.metadata[term][field])\n\n      if (this.metadata[term][field] == undefined) {\n        this.metadata[term][field] = Object.create(null)\n      }\n\n      for (var k = 0; k < keys.length; k++) {\n        var key = keys[k]\n\n        if (this.metadata[term][field][key] == undefined) {\n          this.metadata[term][field][key] = otherMatchData.metadata[term][field][key]\n        } else {\n          this.metadata[term][field][key] = this.metadata[term][field][key].concat(otherMatchData.metadata[term][field][key])\n        }\n\n      }\n    }\n  }\n}\n\n/**\n * Add metadata for a term/field pair to this instance of match data.\n *\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n */\nlunr.MatchData.prototype.add = function (term, field, metadata) {\n  if (!(term in this.metadata)) {\n    this.metadata[term] = Object.create(null)\n    this.metadata[term][field] = metadata\n    return\n  }\n\n  if (!(field in this.metadata[term])) {\n    this.metadata[term][field] = metadata\n    return\n  }\n\n  var metadataKeys = Object.keys(metadata)\n\n  for (var i = 0; i < metadataKeys.length; i++) {\n    var key = metadataKeys[i]\n\n    if (key in this.metadata[term][field]) {\n      this.metadata[term][field][key] = this.metadata[term][field][key].concat(metadata[key])\n    } else {\n      this.metadata[term][field][key] = metadata[key]\n    }\n  }\n}\n/**\n * A lunr.Query provides a programmatic way of defining queries to be performed\n * against a {@link lunr.Index}.\n *\n * Prefer constructing a lunr.Query using the {@link lunr.Index#query} method\n * so the query object is pre-initialized with the right index fields.\n *\n * @constructor\n * @property {lunr.Query~Clause[]} clauses - An array of query clauses.\n * @property {string[]} allFields - An array of all available fields in a lunr.Index.\n */\nlunr.Query = function (allFields) {\n  this.clauses = []\n  this.allFields = allFields\n}\n\n/**\n * Constants for indicating what kind of automatic wildcard insertion will be used when constructing a query clause.\n *\n * This allows wildcards to be added to the beginning and end of a term without having to manually do any string\n * concatenation.\n *\n * The wildcard constants can be bitwise combined to select both leading and trailing wildcards.\n *\n * @constant\n * @default\n * @property {number} wildcard.NONE - The term will have no wildcards inserted, this is the default behaviour\n * @property {number} wildcard.LEADING - Prepend the term with a wildcard, unless a leading wildcard already exists\n * @property {number} wildcard.TRAILING - Append a wildcard to the term, unless a trailing wildcard already exists\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example <caption>query term with trailing wildcard</caption>\n * query.term('foo', { wildcard: lunr.Query.wildcard.TRAILING })\n * @example <caption>query term with leading and trailing wildcard</caption>\n * query.term('foo', {\n *   wildcard: lunr.Query.wildcard.LEADING | lunr.Query.wildcard.TRAILING\n * })\n */\n\nlunr.Query.wildcard = new String (\"*\")\nlunr.Query.wildcard.NONE = 0\nlunr.Query.wildcard.LEADING = 1\nlunr.Query.wildcard.TRAILING = 2\n\n/**\n * Constants for indicating what kind of presence a term must have in matching documents.\n *\n * @constant\n * @enum {number}\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example <caption>query term with required presence</caption>\n * query.term('foo', { presence: lunr.Query.presence.REQUIRED })\n */\nlunr.Query.presence = {\n  /**\n   * Term's presence in a document is optional, this is the default value.\n   */\n  OPTIONAL: 1,\n\n  /**\n   * Term's presence in a document is required, documents that do not contain\n   * this term will not be returned.\n   */\n  REQUIRED: 2,\n\n  /**\n   * Term's presence in a document is prohibited, documents that do contain\n   * this term will not be returned.\n   */\n  PROHIBITED: 3\n}\n\n/**\n * A single clause in a {@link lunr.Query} contains a term and details on how to\n * match that term against a {@link lunr.Index}.\n *\n * @typedef {Object} lunr.Query~Clause\n * @property {string[]} fields - The fields in an index this clause should be matched against.\n * @property {number} [boost=1] - Any boost that should be applied when matching this clause.\n * @property {number} [editDistance] - Whether the term should have fuzzy matching applied, and how fuzzy the match should be.\n * @property {boolean} [usePipeline] - Whether the term should be passed through the search pipeline.\n * @property {number} [wildcard=lunr.Query.wildcard.NONE] - Whether the term should have wildcards appended or prepended.\n * @property {number} [presence=lunr.Query.presence.OPTIONAL] - The terms presence in any matching documents.\n */\n\n/**\n * Adds a {@link lunr.Query~Clause} to this query.\n *\n * Unless the clause contains the fields to be matched all fields will be matched. In addition\n * a default boost of 1 is applied to the clause.\n *\n * @param {lunr.Query~Clause} clause - The clause to add to this query.\n * @see lunr.Query~Clause\n * @returns {lunr.Query}\n */\nlunr.Query.prototype.clause = function (clause) {\n  if (!('fields' in clause)) {\n    clause.fields = this.allFields\n  }\n\n  if (!('boost' in clause)) {\n    clause.boost = 1\n  }\n\n  if (!('usePipeline' in clause)) {\n    clause.usePipeline = true\n  }\n\n  if (!('wildcard' in clause)) {\n    clause.wildcard = lunr.Query.wildcard.NONE\n  }\n\n  if ((clause.wildcard & lunr.Query.wildcard.LEADING) && (clause.term.charAt(0) != lunr.Query.wildcard)) {\n    clause.term = \"*\" + clause.term\n  }\n\n  if ((clause.wildcard & lunr.Query.wildcard.TRAILING) && (clause.term.slice(-1) != lunr.Query.wildcard)) {\n    clause.term = \"\" + clause.term + \"*\"\n  }\n\n  if (!('presence' in clause)) {\n    clause.presence = lunr.Query.presence.OPTIONAL\n  }\n\n  this.clauses.push(clause)\n\n  return this\n}\n\n/**\n * A negated query is one in which every clause has a presence of\n * prohibited. These queries require some special processing to return\n * the expected results.\n *\n * @returns boolean\n */\nlunr.Query.prototype.isNegated = function () {\n  for (var i = 0; i < this.clauses.length; i++) {\n    if (this.clauses[i].presence != lunr.Query.presence.PROHIBITED) {\n      return false\n    }\n  }\n\n  return true\n}\n\n/**\n * Adds a term to the current query, under the covers this will create a {@link lunr.Query~Clause}\n * to the list of clauses that make up this query.\n *\n * The term is used as is, i.e. no tokenization will be performed by this method. Instead conversion\n * to a token or token-like string should be done before calling this method.\n *\n * The term will be converted to a string by calling `toString`. Multiple terms can be passed as an\n * array, each term in the array will share the same options.\n *\n * @param {object|object[]} term - The term(s) to add to the query.\n * @param {object} [options] - Any additional properties to add to the query clause.\n * @returns {lunr.Query}\n * @see lunr.Query#clause\n * @see lunr.Query~Clause\n * @example <caption>adding a single term to a query</caption>\n * query.term(\"foo\")\n * @example <caption>adding a single term to a query and specifying search fields, term boost and automatic trailing wildcard</caption>\n * query.term(\"foo\", {\n *   fields: [\"title\"],\n *   boost: 10,\n *   wildcard: lunr.Query.wildcard.TRAILING\n * })\n * @example <caption>using lunr.tokenizer to convert a string to tokens before using them as terms</caption>\n * query.term(lunr.tokenizer(\"foo bar\"))\n */\nlunr.Query.prototype.term = function (term, options) {\n  if (Array.isArray(term)) {\n    term.forEach(function (t) { this.term(t, lunr.utils.clone(options)) }, this)\n    return this\n  }\n\n  var clause = options || {}\n  clause.term = term.toString()\n\n  this.clause(clause)\n\n  return this\n}\nlunr.QueryParseError = function (message, start, end) {\n  this.name = \"QueryParseError\"\n  this.message = message\n  this.start = start\n  this.end = end\n}\n\nlunr.QueryParseError.prototype = new Error\nlunr.QueryLexer = function (str) {\n  this.lexemes = []\n  this.str = str\n  this.length = str.length\n  this.pos = 0\n  this.start = 0\n  this.escapeCharPositions = []\n}\n\nlunr.QueryLexer.prototype.run = function () {\n  var state = lunr.QueryLexer.lexText\n\n  while (state) {\n    state = state(this)\n  }\n}\n\nlunr.QueryLexer.prototype.sliceString = function () {\n  var subSlices = [],\n      sliceStart = this.start,\n      sliceEnd = this.pos\n\n  for (var i = 0; i < this.escapeCharPositions.length; i++) {\n    sliceEnd = this.escapeCharPositions[i]\n    subSlices.push(this.str.slice(sliceStart, sliceEnd))\n    sliceStart = sliceEnd + 1\n  }\n\n  subSlices.push(this.str.slice(sliceStart, this.pos))\n  this.escapeCharPositions.length = 0\n\n  return subSlices.join('')\n}\n\nlunr.QueryLexer.prototype.emit = function (type) {\n  this.lexemes.push({\n    type: type,\n    str: this.sliceString(),\n    start: this.start,\n    end: this.pos\n  })\n\n  this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.escapeCharacter = function () {\n  this.escapeCharPositions.push(this.pos - 1)\n  this.pos += 1\n}\n\nlunr.QueryLexer.prototype.next = function () {\n  if (this.pos >= this.length) {\n    return lunr.QueryLexer.EOS\n  }\n\n  var char = this.str.charAt(this.pos)\n  this.pos += 1\n  return char\n}\n\nlunr.QueryLexer.prototype.width = function () {\n  return this.pos - this.start\n}\n\nlunr.QueryLexer.prototype.ignore = function () {\n  if (this.start == this.pos) {\n    this.pos += 1\n  }\n\n  this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.backup = function () {\n  this.pos -= 1\n}\n\nlunr.QueryLexer.prototype.acceptDigitRun = function () {\n  var char, charCode\n\n  do {\n    char = this.next()\n    charCode = char.charCodeAt(0)\n  } while (charCode > 47 && charCode < 58)\n\n  if (char != lunr.QueryLexer.EOS) {\n    this.backup()\n  }\n}\n\nlunr.QueryLexer.prototype.more = function () {\n  return this.pos < this.length\n}\n\nlunr.QueryLexer.EOS = 'EOS'\nlunr.QueryLexer.FIELD = 'FIELD'\nlunr.QueryLexer.TERM = 'TERM'\nlunr.QueryLexer.EDIT_DISTANCE = 'EDIT_DISTANCE'\nlunr.QueryLexer.BOOST = 'BOOST'\nlunr.QueryLexer.PRESENCE = 'PRESENCE'\n\nlunr.QueryLexer.lexField = function (lexer) {\n  lexer.backup()\n  lexer.emit(lunr.QueryLexer.FIELD)\n  lexer.ignore()\n  return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexTerm = function (lexer) {\n  if (lexer.width() > 1) {\n    lexer.backup()\n    lexer.emit(lunr.QueryLexer.TERM)\n  }\n\n  lexer.ignore()\n\n  if (lexer.more()) {\n    return lunr.QueryLexer.lexText\n  }\n}\n\nlunr.QueryLexer.lexEditDistance = function (lexer) {\n  lexer.ignore()\n  lexer.acceptDigitRun()\n  lexer.emit(lunr.QueryLexer.EDIT_DISTANCE)\n  return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexBoost = function (lexer) {\n  lexer.ignore()\n  lexer.acceptDigitRun()\n  lexer.emit(lunr.QueryLexer.BOOST)\n  return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexEOS = function (lexer) {\n  if (lexer.width() > 0) {\n    lexer.emit(lunr.QueryLexer.TERM)\n  }\n}\n\n// This matches the separator used when tokenising fields\n// within a document. These should match otherwise it is\n// not possible to search for some tokens within a document.\n//\n// It is possible for the user to change the separator on the\n// tokenizer so it _might_ clash with any other of the special\n// characters already used within the search string, e.g. :.\n//\n// This means that it is possible to change the separator in\n// such a way that makes some words unsearchable using a search\n// string.\nlunr.QueryLexer.termSeparator = lunr.tokenizer.separator\n\nlunr.QueryLexer.lexText = function (lexer) {\n  while (true) {\n    var char = lexer.next()\n\n    if (char == lunr.QueryLexer.EOS) {\n      return lunr.QueryLexer.lexEOS\n    }\n\n    // Escape character is '\\'\n    if (char.charCodeAt(0) == 92) {\n      lexer.escapeCharacter()\n      continue\n    }\n\n    if (char == \":\") {\n      return lunr.QueryLexer.lexField\n    }\n\n    if (char == \"~\") {\n      lexer.backup()\n      if (lexer.width() > 0) {\n        lexer.emit(lunr.QueryLexer.TERM)\n      }\n      return lunr.QueryLexer.lexEditDistance\n    }\n\n    if (char == \"^\") {\n      lexer.backup()\n      if (lexer.width() > 0) {\n        lexer.emit(lunr.QueryLexer.TERM)\n      }\n      return lunr.QueryLexer.lexBoost\n    }\n\n    // \"+\" indicates term presence is required\n    // checking for length to ensure that only\n    // leading \"+\" are considered\n    if (char == \"+\" && lexer.width() === 1) {\n      lexer.emit(lunr.QueryLexer.PRESENCE)\n      return lunr.QueryLexer.lexText\n    }\n\n    // \"-\" indicates term presence is prohibited\n    // checking for length to ensure that only\n    // leading \"-\" are considered\n    if (char == \"-\" && lexer.width() === 1) {\n      lexer.emit(lunr.QueryLexer.PRESENCE)\n      return lunr.QueryLexer.lexText\n    }\n\n    if (char.match(lunr.QueryLexer.termSeparator)) {\n      return lunr.QueryLexer.lexTerm\n    }\n  }\n}\n\nlunr.QueryParser = function (str, query) {\n  this.lexer = new lunr.QueryLexer (str)\n  this.query = query\n  this.currentClause = {}\n  this.lexemeIdx = 0\n}\n\nlunr.QueryParser.prototype.parse = function () {\n  this.lexer.run()\n  this.lexemes = this.lexer.lexemes\n\n  var state = lunr.QueryParser.parseClause\n\n  while (state) {\n    state = state(this)\n  }\n\n  return this.query\n}\n\nlunr.QueryParser.prototype.peekLexeme = function () {\n  return this.lexemes[this.lexemeIdx]\n}\n\nlunr.QueryParser.prototype.consumeLexeme = function () {\n  var lexeme = this.peekLexeme()\n  this.lexemeIdx += 1\n  return lexeme\n}\n\nlunr.QueryParser.prototype.nextClause = function () {\n  var completedClause = this.currentClause\n  this.query.clause(completedClause)\n  this.currentClause = {}\n}\n\nlunr.QueryParser.parseClause = function (parser) {\n  var lexeme = parser.peekLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  switch (lexeme.type) {\n    case lunr.QueryLexer.PRESENCE:\n      return lunr.QueryParser.parsePresence\n    case lunr.QueryLexer.FIELD:\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.TERM:\n      return lunr.QueryParser.parseTerm\n    default:\n      var errorMessage = \"expected either a field or a term, found \" + lexeme.type\n\n      if (lexeme.str.length >= 1) {\n        errorMessage += \" with value '\" + lexeme.str + \"'\"\n      }\n\n      throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n}\n\nlunr.QueryParser.parsePresence = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  switch (lexeme.str) {\n    case \"-\":\n      parser.currentClause.presence = lunr.Query.presence.PROHIBITED\n      break\n    case \"+\":\n      parser.currentClause.presence = lunr.Query.presence.REQUIRED\n      break\n    default:\n      var errorMessage = \"unrecognised presence operator'\" + lexeme.str + \"'\"\n      throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    var errorMessage = \"expecting term or field, found nothing\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.FIELD:\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.TERM:\n      return lunr.QueryParser.parseTerm\n    default:\n      var errorMessage = \"expecting term or field, found '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseField = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  if (parser.query.allFields.indexOf(lexeme.str) == -1) {\n    var possibleFields = parser.query.allFields.map(function (f) { return \"'\" + f + \"'\" }).join(', '),\n        errorMessage = \"unrecognised field '\" + lexeme.str + \"', possible fields: \" + possibleFields\n\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  parser.currentClause.fields = [lexeme.str]\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    var errorMessage = \"expecting term, found nothing\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      return lunr.QueryParser.parseTerm\n    default:\n      var errorMessage = \"expecting term, found '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseTerm = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  parser.currentClause.term = lexeme.str.toLowerCase()\n\n  if (lexeme.str.indexOf(\"*\") != -1) {\n    parser.currentClause.usePipeline = false\n  }\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    parser.nextClause()\n    return\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      parser.nextClause()\n      return lunr.QueryParser.parseTerm\n    case lunr.QueryLexer.FIELD:\n      parser.nextClause()\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.EDIT_DISTANCE:\n      return lunr.QueryParser.parseEditDistance\n    case lunr.QueryLexer.BOOST:\n      return lunr.QueryParser.parseBoost\n    case lunr.QueryLexer.PRESENCE:\n      parser.nextClause()\n      return lunr.QueryParser.parsePresence\n    default:\n      var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseEditDistance = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  var editDistance = parseInt(lexeme.str, 10)\n\n  if (isNaN(editDistance)) {\n    var errorMessage = \"edit distance must be numeric\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  parser.currentClause.editDistance = editDistance\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    parser.nextClause()\n    return\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      parser.nextClause()\n      return lunr.QueryParser.parseTerm\n    case lunr.QueryLexer.FIELD:\n      parser.nextClause()\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.EDIT_DISTANCE:\n      return lunr.QueryParser.parseEditDistance\n    case lunr.QueryLexer.BOOST:\n      return lunr.QueryParser.parseBoost\n    case lunr.QueryLexer.PRESENCE:\n      parser.nextClause()\n      return lunr.QueryParser.parsePresence\n    default:\n      var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseBoost = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  var boost = parseInt(lexeme.str, 10)\n\n  if (isNaN(boost)) {\n    var errorMessage = \"boost must be numeric\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  parser.currentClause.boost = boost\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    parser.nextClause()\n    return\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      parser.nextClause()\n      return lunr.QueryParser.parseTerm\n    case lunr.QueryLexer.FIELD:\n      parser.nextClause()\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.EDIT_DISTANCE:\n      return lunr.QueryParser.parseEditDistance\n    case lunr.QueryLexer.BOOST:\n      return lunr.QueryParser.parseBoost\n    case lunr.QueryLexer.PRESENCE:\n      parser.nextClause()\n      return lunr.QueryParser.parsePresence\n    default:\n      var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\n  /**\n   * export the module via AMD, CommonJS or as a browser global\n   * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js\n   */\n  ;(function (root, factory) {\n    if (typeof define === 'function' && define.amd) {\n      // AMD. Register as an anonymous module.\n      define(factory)\n    } else if (typeof exports === 'object') {\n      /**\n       * Node. Does not work with strict CommonJS, but\n       * only CommonJS-like enviroments that support module.exports,\n       * like Node.\n       */\n      module.exports = factory()\n    } else {\n      // Browser globals (root is window)\n      root.lunr = factory()\n    }\n  }(this, function () {\n    /**\n     * Just return a value to define the module export.\n     * This example returns an object, but the module\n     * can return a function as the exported value.\n     */\n    return lunr\n  }))\n})();\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport lunr from \"lunr\"\n\nimport { getElement } from \"~/browser/element/_\"\nimport \"~/polyfills\"\n\nimport { Search } from \"../../_\"\nimport { SearchConfig } from \"../../config\"\nimport {\n  SearchMessage,\n  SearchMessageType\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Add support for `iframe-worker` shim\n *\n * While `importScripts` is synchronous when executed inside of a web worker,\n * it's not possible to provide a synchronous shim implementation. The cool\n * thing is that awaiting a non-Promise will convert it into a Promise, so\n * extending the type definition to return a `Promise` shouldn't break anything.\n *\n * @see https://bit.ly/2PjDnXi - GitHub comment\n *\n * @param urls - Scripts to load\n *\n * @returns Promise resolving with no result\n */\ndeclare global {\n  function importScripts(...urls: string[]): Promise<void> | void\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nlet index: Search\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch (= import) multi-language support through `lunr-languages`\n *\n * This function automatically imports the stemmers necessary to process the\n * languages which are defined as part of the search configuration.\n *\n * If the worker runs inside of an `iframe` (when using `iframe-worker` as\n * a shim), the base URL for the stemmers to be loaded must be determined by\n * searching for the first `script` element with a `src` attribute, which will\n * contain the contents of this script.\n *\n * @param config - Search configuration\n *\n * @returns Promise resolving with no result\n */\nasync function setupSearchLanguages(\n  config: SearchConfig\n): Promise<void> {\n  let base = \"../lunr\"\n\n  /* Detect `iframe-worker` and fix base URL */\n  if (typeof parent !== \"undefined\" && \"IFrameWorker\" in parent) {\n    const worker = getElement<HTMLScriptElement>(\"script[src]\")!\n    const [path] = worker.src.split(\"/worker\")\n\n    /* Prefix base with path */\n    base = base.replace(\"..\", path)\n  }\n\n  /* Add scripts for languages */\n  const scripts = []\n  for (const lang of config.lang) {\n    switch (lang) {\n\n      /* Add segmenter for Japanese */\n      case \"ja\":\n        scripts.push(`${base}/tinyseg.js`)\n        break\n\n      /* Add segmenter for Hindi and Thai */\n      case \"hi\":\n      case \"th\":\n        scripts.push(`${base}/wordcut.js`)\n        break\n    }\n\n    /* Add language support */\n    if (lang !== \"en\")\n      scripts.push(`${base}/min/lunr.${lang}.min.js`)\n  }\n\n  /* Add multi-language support */\n  if (config.lang.length > 1)\n    scripts.push(`${base}/min/lunr.multi.min.js`)\n\n  /* Load scripts synchronously */\n  if (scripts.length)\n    await importScripts(\n      `${base}/min/lunr.stemmer.support.min.js`,\n      ...scripts\n    )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Message handler\n *\n * @param message - Source message\n *\n * @returns Target message\n */\nexport async function handler(\n  message: SearchMessage\n): Promise<SearchMessage> {\n  switch (message.type) {\n\n    /* Search setup message */\n    case SearchMessageType.SETUP:\n      await setupSearchLanguages(message.data.config)\n      index = new Search(message.data)\n      return {\n        type: SearchMessageType.READY\n      }\n\n    /* Search query message */\n    case SearchMessageType.QUERY:\n      const query = message.data\n      try {\n        return {\n          type: SearchMessageType.RESULT,\n          data: index.search(query)\n        }\n\n      /* Return empty result in case of error */\n      } catch (err) {\n        console.warn(`Invalid query: ${query} \u2013 see https://bit.ly/2s3ChXG`)\n        console.warn(err)\n        return {\n          type: SearchMessageType.RESULT,\n          data: { items: [] }\n        }\n      }\n\n    /* All other messages */\n    default:\n      throw new TypeError(\"Invalid message type\")\n  }\n}\n\n/* ----------------------------------------------------------------------------\n * Worker\n * ------------------------------------------------------------------------- */\n\n/* Expose Lunr.js in global scope, or stemmers won't work */\nself.lunr = lunr\n\n/* Handle messages */\naddEventListener(\"message\", async ev => {\n  postMessage(await handler(ev.data))\n})\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve all elements matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getElements<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T][]\n\nexport function getElements<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T[]\n\nexport function getElements<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T[] {\n  return Array.from(node.querySelectorAll<T>(selector))\n}\n\n/**\n * Retrieve an element matching a query selector or throw a reference error\n *\n * Note that this function assumes that the element is present. If unsure if an\n * element is existent, use the `getOptionalElement` function instead.\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getElement<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T]\n\nexport function getElement<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T\n\nexport function getElement<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T {\n  const el = getOptionalElement<T>(selector, node)\n  if (typeof el === \"undefined\")\n    throw new ReferenceError(\n      `Missing element: expected \"${selector}\" to be present`\n    )\n\n  /* Return element */\n  return el\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Retrieve an optional element matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element or nothing\n */\nexport function getOptionalElement<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T] | undefined\n\nexport function getOptionalElement<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T | undefined\n\nexport function getOptionalElement<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T | undefined {\n  return node.querySelector<T>(selector) || undefined\n}\n\n/**\n * Retrieve the currently active element\n *\n * @returns Element or nothing\n */\nexport function getActiveElement(): HTMLElement | undefined {\n  return (\n    document.activeElement?.shadowRoot?.activeElement as HTMLElement ??\n    document.activeElement as HTMLElement ??\n    undefined\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n  Object.entries = function (obj: object) {\n    const data: [string, string][] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push([key, obj[key]])\n\n    /* Return entries */\n    return data\n  }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n  Object.values = function (obj: object) {\n    const data: string[] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push(obj[key])\n\n    /* Return values */\n    return data\n  }\n\n/* ------------------------------------------------------------------------- */\n\n/* Polyfills for `Element` */\nif (typeof Element !== \"undefined\") {\n\n  /* Polyfill `Element.scrollTo` */\n  if (!Element.prototype.scrollTo)\n    Element.prototype.scrollTo = function (\n      x?: ScrollToOptions | number, y?: number\n    ): void {\n      if (typeof x === \"object\") {\n        this.scrollLeft = x.left!\n        this.scrollTop = x.top!\n      } else {\n        this.scrollLeft = x!\n        this.scrollTop = y!\n      }\n    }\n\n  /* Polyfill `Element.replaceWith` */\n  if (!Element.prototype.replaceWith)\n    Element.prototype.replaceWith = function (\n      ...nodes: Array<string | Node>\n    ): void {\n      const parent = this.parentNode\n      if (parent) {\n        if (nodes.length === 0)\n          parent.removeChild(this)\n\n        /* Replace children and create text nodes */\n        for (let i = nodes.length - 1; i >= 0; i--) {\n          let node = nodes[i]\n          if (typeof node === \"string\")\n            node = document.createTextNode(node)\n          else if (node.parentNode)\n            node.parentNode.removeChild(node)\n\n          /* Replace child or insert before previous sibling */\n          if (!i)\n            parent.replaceChild(node, this)\n          else\n            parent.insertBefore(this.previousSibling!, node)\n        }\n      }\n    }\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search configuration\n */\nexport interface SearchConfig {\n  lang: string[]                       /* Search languages */\n  separator: string                    /* Search separator */\n  pipeline: SearchPipelineFn[]         /* Search pipeline */\n}\n\n/**\n * Search document\n */\nexport interface SearchDocument {\n  location: string                     /* Document location */\n  title: string                        /* Document title */\n  text: string                         /* Document text */\n  tags?: string[]                      /* Document tags */\n  boost?: number                       /* Document boost */\n  parent?: SearchDocument              /* Document parent */\n}\n\n/**\n * Search options\n */\nexport interface SearchOptions {\n  suggest: boolean                     /* Search suggestions */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nexport interface SearchIndex {\n  config: SearchConfig                 /* Search configuration */\n  docs: SearchDocument[]               /* Search documents */\n  options: SearchOptions               /* Search options */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search pipeline function\n */\ntype SearchPipelineFn =\n  | \"trimmer\"                          /* Trimmer */\n  | \"stopWordFilter\"                   /* Stop word filter */\n  | \"stemmer\"                          /* Stemmer */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search document map\n *\n * This function creates a mapping of URLs (including anchors) to the actual\n * articles and sections. It relies on the invariant that the search index is\n * ordered with the main article appearing before all sections with anchors.\n * If this is not the case, the logic music be changed.\n *\n * @param docs - Search documents\n *\n * @returns Search document map\n */\nexport function setupSearchDocumentMap(\n  docs: SearchDocument[]\n): Map<string, SearchDocument> {\n  const map = new Map<string, SearchDocument>()\n  for (const doc of docs) {\n    const [path] = doc.location.split(\"#\")\n\n    /* Add document article */\n    const article = map.get(path)\n    if (typeof article === \"undefined\") {\n      map.set(path, doc)\n\n      /* Add document section */\n    } else {\n      map.set(doc.location, doc)\n      doc.parent = article\n    }\n  }\n\n  /* Return search document map */\n  return map\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Visitor function\n *\n * @param start - Start offset\n * @param end - End offset\n */\ntype VisitorFn = (\n  start: number, end: number\n) => void\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Split a string using the given separator\n *\n * @param input - Input value\n * @param separator - Separator\n * @param fn - Visitor function\n */\nexport function split(\n  input: string, separator: RegExp, fn: VisitorFn\n): void {\n  separator = new RegExp(separator, \"g\")\n\n  /* Split string using separator */\n  let match: RegExpExecArray | null\n  let index = 0\n  do {\n    match = separator.exec(input)\n\n    /* Emit non-empty range */\n    const until = match?.index ?? input.length\n    if (index < until)\n      fn(index, until)\n\n    /* Update last index */\n    if (match) {\n      const [term] = match\n      index = match.index + term.length\n\n      /* Support zero-length lookaheads */\n      if (term.length === 0)\n        separator.lastIndex = match.index + 1\n    }\n  } while (match)\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Extraction type\n *\n * This type defines the possible values that are encoded into the first two\n * bits of a section that is part of the blocks of a tokenization table. There\n * are three types of interest: HTML opening and closing tags, as well as the\n * actual text content we need to extract for indexing.\n */\nexport const enum Extract {\n  TAG_OPEN  = 0,                       /* HTML opening tag */\n  TEXT      = 1,                       /* Text content */\n  TAG_CLOSE = 2                        /* HTML closing tag */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Visitor function\n *\n * @param block - Block index\n * @param type - Extraction type\n * @param start - Start offset\n * @param end - End offset\n */\ntype VisitorFn = (\n  block: number, type: Extract, start: number, end: number\n) => void\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Split a string into markup and text sections\n *\n * This function scans a string and divides it up into sections of markup and\n * text. For each section, it invokes the given visitor function with the block\n * index, extraction type, as well as start and end offsets. Using a visitor\n * function (= streaming data) is ideal for minimizing pressure on the GC.\n *\n * @param input - Input value\n * @param fn - Visitor function\n */\nexport function extract(\n  input: string, fn: VisitorFn\n): void {\n\n  let block = 0                        /* Current block */\n  let start = 0                        /* Current start offset */\n  let end = 0                          /* Current end offset */\n\n  /* Split string into sections */\n  for (let stack = 0; end < input.length; end++) {\n\n    /* Opening tag after non-empty section */\n    if (input.charAt(end) === \"<\" && end > start) {\n      fn(block, Extract.TEXT, start, start = end)\n\n    /* Closing tag */\n    } else if (input.charAt(end) === \">\") {\n      if (input.charAt(start + 1) === \"/\") {\n        if (--stack === 0)\n          fn(block++, Extract.TAG_CLOSE, start, end + 1)\n\n      /* Tag is not self-closing */\n      } else if (input.charAt(end - 1) !== \"/\") {\n        if (stack++ === 0)\n          fn(block, Extract.TAG_OPEN, start, end + 1)\n      }\n\n      /* New section */\n      start = end + 1\n    }\n  }\n\n  /* Add trailing section */\n  if (end > start)\n    fn(block, Extract.TEXT, start, end)\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Position table\n */\nexport type PositionTable = number[][]\n\n/**\n * Position\n */\nexport type Position = number\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Highlight all occurrences in a string\n *\n * This function receives a field's value (e.g. like `title` or `text`), it's\n * position table that was generated during indexing, and the positions found\n * when executing the query. It then highlights all occurrences, and returns\n * their concatenation. In case of multiple blocks, two are returned.\n *\n * @param input - Input value\n * @param table - Table for indexing\n * @param positions - Occurrences\n * @param full - Full results\n *\n * @returns Highlighted string value\n */\nexport function highlight(\n  input: string, table: PositionTable, positions: Position[], full = false\n): string {\n  return highlightAll([input], table, positions, full).pop()!\n}\n\n/**\n * Highlight all occurrences in a set of strings\n *\n * @param inputs - Input values\n * @param table - Table for indexing\n * @param positions - Occurrences\n * @param full - Full results\n *\n * @returns Highlighted string values\n */\nexport function highlightAll(\n  inputs: string[], table: PositionTable, positions: Position[], full = false\n): string[] {\n\n  /* Map blocks to input values */\n  const mapping = [0]\n  for (let t = 1; t < table.length; t++) {\n    const prev = table[t - 1]\n    const next = table[t]\n\n    /* Check if table points to new block */\n    const p = prev[prev.length - 1] >>> 2 & 0x3FF\n    const q = next[0]               >>> 12\n\n    /* Add block to mapping */\n    mapping.push(+(p > q) + mapping[mapping.length - 1])\n  }\n\n  /* Highlight strings one after another */\n  return inputs.map((input, i) => {\n    let cursor = 0\n\n    /* Map occurrences to blocks */\n    const blocks = new Map<number, number[]>()\n    for (const p of positions.sort((a, b) => a - b)) {\n      const index = p & 0xFFFFF\n      const block = p >>> 20\n      if (mapping[block] !== i)\n        continue\n\n      /* Ensure presence of block group */\n      let group = blocks.get(block)\n      if (typeof group === \"undefined\")\n        blocks.set(block, group = [])\n\n      /* Add index to group */\n      group.push(index)\n    }\n\n    /* Just return string, if no occurrences */\n    if (blocks.size === 0)\n      return input\n\n    /* Compute slices */\n    const slices: string[] = []\n    for (const [block, indexes] of blocks) {\n      const t = table[block]\n\n      /* Extract positions and length */\n      const start  = t[0]            >>> 12\n      const end    = t[t.length - 1] >>> 12\n      const length = t[t.length - 1] >>> 2 & 0x3FF\n\n      /* Add prefix, if full results are desired */\n      if (full && start > cursor)\n        slices.push(input.slice(cursor, start))\n\n      /* Extract and highlight slice */\n      let slice = input.slice(start, end + length)\n      for (const j of indexes.sort((a, b) => b - a)) {\n\n        /* Retrieve offset and length of match */\n        const p = (t[j] >>> 12) - start\n        const q = (t[j] >>> 2 & 0x3FF) + p\n\n        /* Wrap occurrence */\n        slice = [\n          slice.slice(0, p),\n          \"<mark>\",\n          slice.slice(p, q),\n          \"</mark>\",\n          slice.slice(q)\n        ].join(\"\")\n      }\n\n      /* Update cursor */\n      cursor = end + length\n\n      /* Append slice and abort if we have two */\n      if (slices.push(slice) === 2)\n        break\n    }\n\n    /* Add suffix, if full results are desired */\n    if (full && cursor < input.length)\n      slices.push(input.slice(cursor))\n\n    /* Return highlighted slices */\n    return slices.join(\"\")\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { split } from \"../_\"\nimport {\n  Extract,\n  extract\n} from \"../extract\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Split a string or set of strings into tokens\n *\n * This tokenizer supersedes the default tokenizer that is provided by Lunr.js,\n * as it is aware of HTML tags and allows for multi-character splitting.\n *\n * It takes the given inputs, splits each of them into markup and text sections,\n * tokenizes and segments (if necessary) each of them, and then indexes them in\n * a table by using a compact bit representation. Bitwise techniques are used\n * to write and read from the table during indexing and querying.\n *\n * @see https://bit.ly/3W3Xw4J - Search: better, faster, smaller\n *\n * @param input - Input value(s)\n *\n * @returns Tokens\n */\nexport function tokenize(\n  input?: string | string[]\n): lunr.Token[] {\n  const tokens: lunr.Token[] = []\n  if (typeof input === \"undefined\")\n    return tokens\n\n  /* Tokenize strings one after another */\n  const inputs = Array.isArray(input) ? input : [input]\n  for (let i = 0; i < inputs.length; i++) {\n    const table = lunr.tokenizer.table\n    const total = table.length\n\n    /* Split string into sections and tokenize content blocks */\n    extract(inputs[i], (block, type, start, end) => {\n      table[block += total] ||= []\n      switch (type) {\n\n        /* Handle markup */\n        case Extract.TAG_OPEN:\n        case Extract.TAG_CLOSE:\n          table[block].push(\n            start       << 12 |\n            end - start <<  2 |\n            type\n          )\n          break\n\n        /* Handle text content */\n        case Extract.TEXT:\n          const section = inputs[i].slice(start, end)\n          split(section, lunr.tokenizer.separator, (index, until) => {\n\n            /**\n             * Apply segmenter after tokenization. Note that the segmenter will\n             * also split words at word boundaries, which is not what we want,\n             * so we need to check if we can somehow mitigate this behavior.\n             */\n            if (typeof lunr.segmenter !== \"undefined\") {\n              const subsection = section.slice(index, until)\n              if (/^[MHIK]$/.test(lunr.segmenter.ctype_(subsection))) {\n                const segments = lunr.segmenter.segment(subsection)\n                for (let s = 0, l = 0; s < segments.length; s++) {\n\n                  /* Add block to section */\n                  table[block] ||= []\n                  table[block].push(\n                    start + index + l  << 12 |\n                    segments[s].length <<  2 |\n                    type\n                  )\n\n                  /* Add token with position */\n                  tokens.push(new lunr.Token(\n                    segments[s].toLowerCase(), {\n                      position: block << 20 | table[block].length - 1\n                    }\n                  ))\n\n                  /* Keep track of length */\n                  l += segments[s].length\n                }\n                return\n              }\n            }\n\n            /* Add block to section */\n            table[block].push(\n              start + index << 12 |\n              until - index <<  2 |\n              type\n            )\n\n            /* Add token with position */\n            tokens.push(new lunr.Token(\n              section.slice(index, until).toLowerCase(), {\n                position: block << 20 | table[block].length - 1\n              }\n            ))\n          })\n      }\n    })\n  }\n\n  /* Return tokens */\n  return tokens\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Visitor function\n *\n * @param value - String value\n *\n * @returns String term(s)\n */\ntype VisitorFn = (\n  value: string\n) => string | string[]\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Default transformation function\n *\n * 1. Trim excess whitespace from left and right.\n *\n * 2. Search for parts in quotation marks and prepend a `+` modifier to denote\n *    that the resulting document must contain all parts, converting the query\n *    to an `AND` query (as opposed to the default `OR` behavior). While users\n *    may expect parts enclosed in quotation marks to map to span queries, i.e.\n *    for which order is important, Lunr.js doesn't support them, so the best\n *    we can do is to convert the parts to an `AND` query.\n *\n * 3. Replace control characters which are not located at the beginning of the\n *    query or preceded by white space, or are not followed by a non-whitespace\n *    character or are at the end of the query string. Furthermore, filter\n *    unmatched quotation marks.\n *\n * 4. Split the query string at whitespace, then pass each part to the visitor\n *    function for tokenization, and append a wildcard to every resulting term\n *    that is not explicitly marked with a `+`, `-`, `~` or `^` modifier, since\n *    it ensures consistent and stable ranking when multiple terms are entered.\n *    Also, if a fuzzy or boost modifier are given, but no numeric value has\n *    been entered, default to 1 to not induce a query error.\n *\n * @param query - Query value\n * @param fn - Visitor function\n *\n * @returns Transformed query value\n */\nexport function transform(\n  query: string, fn: VisitorFn = term => term\n): string {\n  return query\n\n    /* => 1 */\n    .trim()\n\n    /* => 2 */\n    .split(/\"([^\"]+)\"/g)\n      .map((parts, index) => index & 1\n        ? parts.replace(/^\\b|^(?![^\\x00-\\x7F]|$)|\\s+/g, \" +\")\n        : parts\n      )\n      .join(\"\")\n\n    /* => 3 */\n    .replace(/\"|(?:^|\\s+)[*+\\-:^~]+(?=\\s+|$)/g, \"\")\n\n    /* => 4 */\n    .split(/\\s+/g)\n      .reduce((prev, term) => {\n        const next = fn(term)\n        return [...prev, ...Array.isArray(next) ? next : [next]]\n      }, [] as string[])\n      .map(term => /([~^]$)/.test(term) ? `${term}1` : term)\n      .map(term => /(^[+-]|[~^]\\d+$)/.test(term) ? term : `${term}*`)\n      .join(\" \")\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { split } from \"../../internal\"\nimport { transform } from \"../transform\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query clause\n */\nexport interface SearchQueryClause {\n  presence: lunr.Query.presence        /* Clause presence */\n  term: string                         /* Clause term */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search query terms\n */\nexport type SearchQueryTerms = Record<string, boolean>\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Transform search query\n *\n * This function lexes the given search query and applies the transformation\n * function to each term, preserving markup like `+` and `-` modifiers.\n *\n * @param query - Search query\n *\n * @returns Search query\n */\nexport function transformSearchQuery(\n  query: string\n): string {\n\n  /* Split query terms with tokenizer */\n  return transform(query, part => {\n    const terms: string[] = []\n\n    /* Initialize lexer and analyze part */\n    const lexer = new lunr.QueryLexer(part)\n    lexer.run()\n\n    /* Extract and tokenize term from lexeme */\n    for (const { type, str: term, start, end } of lexer.lexemes)\n      switch (type) {\n\n        /* Hack: remove colon - see https://bit.ly/3wD3T3I */\n        case \"FIELD\":\n          if (![\"title\", \"text\", \"tags\"].includes(term))\n            part = [\n              part.slice(0, end),\n              \" \",\n              part.slice(end + 1)\n            ].join(\"\")\n          break\n\n        /* Tokenize term */\n        case \"TERM\":\n          split(term, lunr.tokenizer.separator, (...range) => {\n            terms.push([\n              part.slice(0, start),\n              term.slice(...range),\n              part.slice(end)\n            ].join(\"\"))\n          })\n      }\n\n    /* Return terms */\n    return terms\n  })\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Parse a search query for analysis\n *\n * Lunr.js itself has a bug where it doesn't detect or remove wildcards for\n * query clauses, so we must do this here.\n *\n * @see https://bit.ly/3DpTGtz - GitHub issue\n *\n * @param value - Query value\n *\n * @returns Search query clauses\n */\nexport function parseSearchQuery(\n  value: string\n): SearchQueryClause[] {\n  const query  = new lunr.Query([\"title\", \"text\", \"tags\"])\n  const parser = new lunr.QueryParser(value, query)\n\n  /* Parse Search query */\n  parser.parse()\n  for (const clause of query.clauses) {\n    clause.usePipeline = true\n\n    /* Handle leading wildcard */\n    if (clause.term.startsWith(\"*\")) {\n      clause.wildcard = lunr.Query.wildcard.LEADING\n      clause.term = clause.term.slice(1)\n    }\n\n    /* Handle trailing wildcard */\n    if (clause.term.endsWith(\"*\")) {\n      clause.wildcard = lunr.Query.wildcard.TRAILING\n      clause.term = clause.term.slice(0, -1)\n    }\n  }\n\n  /* Return query clauses */\n  return query.clauses\n}\n\n/**\n * Analyze the search query clauses in regard to the search terms found\n *\n * @param query - Search query clauses\n * @param terms - Search terms\n *\n * @returns Search query terms\n */\nexport function getSearchQueryTerms(\n  query: SearchQueryClause[], terms: string[]\n): SearchQueryTerms {\n  const clauses = new Set<SearchQueryClause>(query)\n\n  /* Match query clauses against terms */\n  const result: SearchQueryTerms = {}\n  for (let t = 0; t < terms.length; t++)\n    for (const clause of clauses)\n      if (terms[t].startsWith(clause.term)) {\n        result[clause.term] = true\n        clauses.delete(clause)\n      }\n\n  /* Annotate unmatched non-stopword query clauses */\n  for (const clause of clauses)\n    if (lunr.stopWordFilter?.(clause.term))\n      result[clause.term] = false\n\n  /* Return query terms */\n  return result\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Segment a search query using the inverted index\n *\n * This function implements a clever approach to text segmentation for Asian\n * languages, as it used the information already available in the search index.\n * The idea is to greedily segment the search query based on the tokens that are\n * already part of the index, as described in the linked issue.\n *\n * @see https://bit.ly/3lwjrk7 - GitHub issue\n *\n * @param query - Query value\n * @param index - Inverted index\n *\n * @returns Segmented query value\n */\nexport function segment(\n  query: string, index: object\n): Iterable<string> {\n  const segments = new Set<string>()\n\n  /* Segment search query */\n  const wordcuts = new Uint16Array(query.length)\n  for (let i = 0; i < query.length; i++)\n    for (let j = i + 1; j < query.length; j++) {\n      const value = query.slice(i, j)\n      if (value in index)\n        wordcuts[i] = j - i\n    }\n\n  /* Compute longest matches with minimum overlap */\n  const stack = [0]\n  for (let s = stack.length; s > 0;) {\n    const p = stack[--s]\n    for (let q = 1; q < wordcuts[p]; q++)\n      if (wordcuts[p + q] > wordcuts[p] - q) {\n        segments.add(query.slice(p, p + q))\n        stack[s++] = p + q\n      }\n\n    /* Continue at end of query string */\n    const q = p + wordcuts[p]\n    if (wordcuts[q] && q < query.length - 1)\n      stack[s++] = q\n\n    /* Add current segment */\n    segments.add(query.slice(p, q))\n  }\n\n  // @todo fix this case in the code block above, this is a hotfix\n  if (segments.has(\"\"))\n    return new Set([query])\n\n  /* Return segmented query value */\n  return segments\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  SearchDocument,\n  SearchIndex,\n  SearchOptions,\n  setupSearchDocumentMap\n} from \"../config\"\nimport {\n  Position,\n  PositionTable,\n  highlight,\n  highlightAll,\n  tokenize\n} from \"../internal\"\nimport {\n  SearchQueryTerms,\n  getSearchQueryTerms,\n  parseSearchQuery,\n  segment,\n  transformSearchQuery\n} from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search item\n */\nexport interface SearchItem\n  extends SearchDocument\n{\n  score: number                        /* Score (relevance) */\n  terms: SearchQueryTerms              /* Search query terms */\n}\n\n/**\n * Search result\n */\nexport interface SearchResult {\n  items: SearchItem[][]                /* Search items */\n  suggest?: string[]                   /* Search suggestions */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create field extractor factory\n *\n * @param table - Position table map\n *\n * @returns Extractor factory\n */\nfunction extractor(table: Map<string, PositionTable>) {\n  return (name: keyof SearchDocument) => {\n    return (doc: SearchDocument) => {\n      if (typeof doc[name] === \"undefined\")\n        return undefined\n\n      /* Compute identifier and initialize table */\n      const id = [doc.location, name].join(\":\")\n      table.set(id, lunr.tokenizer.table = [])\n\n      /* Return field value */\n      return doc[name]\n    }\n  }\n}\n\n/**\n * Compute the difference of two lists of strings\n *\n * @param a - 1st list of strings\n * @param b - 2nd list of strings\n *\n * @returns Difference\n */\nfunction difference(a: string[], b: string[]): string[] {\n  const [x, y] = [new Set(a), new Set(b)]\n  return [\n    ...new Set([...x].filter(value => !y.has(value)))\n  ]\n}\n\n/* ----------------------------------------------------------------------------\n * Class\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nexport class Search {\n\n  /**\n   * Search document map\n   */\n  protected map: Map<string, SearchDocument>\n\n  /**\n   * Search options\n   */\n  protected options: SearchOptions\n\n  /**\n   * The underlying Lunr.js search index\n   */\n  protected index: lunr.Index\n\n  /**\n   * Internal position table map\n   */\n  protected table: Map<string, PositionTable>\n\n  /**\n   * Create the search integration\n   *\n   * @param data - Search index\n   */\n  public constructor({ config, docs, options }: SearchIndex) {\n    const field = extractor(this.table = new Map())\n\n    /* Set up document map and options */\n    this.map = setupSearchDocumentMap(docs)\n    this.options = options\n\n    /* Set up document index */\n    this.index = lunr(function () {\n      this.metadataWhitelist = [\"position\"]\n      this.b(0)\n\n      /* Set up (multi-)language support */\n      if (config.lang.length === 1 && config.lang[0] !== \"en\") {\n        // @ts-expect-error - namespace indexing not supported\n        this.use(lunr[config.lang[0]])\n      } else if (config.lang.length > 1) {\n        this.use(lunr.multiLanguage(...config.lang))\n      }\n\n      /* Set up custom tokenizer (must be after language setup) */\n      this.tokenizer = tokenize as typeof lunr.tokenizer\n      lunr.tokenizer.separator = new RegExp(config.separator)\n\n      /* Set up custom segmenter, if loaded */\n      lunr.segmenter = \"TinySegmenter\" in lunr\n        ? new lunr.TinySegmenter()\n        : undefined\n\n      /* Compute functions to be removed from the pipeline */\n      const fns = difference([\n        \"trimmer\", \"stopWordFilter\", \"stemmer\"\n      ], config.pipeline)\n\n      /* Remove functions from the pipeline for registered languages */\n      for (const lang of config.lang.map(language => (\n        // @ts-expect-error - namespace indexing not supported\n        language === \"en\" ? lunr : lunr[language]\n      )))\n        for (const fn of fns) {\n          this.pipeline.remove(lang[fn])\n          this.searchPipeline.remove(lang[fn])\n        }\n\n      /* Set up index reference */\n      this.ref(\"location\")\n\n      /* Set up index fields */\n      this.field(\"title\", { boost: 1e3, extractor: field(\"title\") })\n      this.field(\"text\",  { boost: 1e0, extractor: field(\"text\") })\n      this.field(\"tags\",  { boost: 1e6, extractor: field(\"tags\") })\n\n      /* Add documents to index */\n      for (const doc of docs)\n        this.add(doc, { boost: doc.boost })\n    })\n  }\n\n  /**\n   * Search for matching documents\n   *\n   * @param query - Search query\n   *\n   * @returns Search result\n   */\n  public search(query: string): SearchResult {\n\n    // Experimental Chinese segmentation\n    query = query.replace(/\\p{sc=Han}+/gu, value => {\n      return [...segment(value, this.index.invertedIndex)]\n        .join(\"* \")\n    })\n\n    // @todo: move segmenter (above) into transformSearchQuery\n    query = transformSearchQuery(query)\n    if (!query)\n      return { items: [] }\n\n    /* Parse query to extract clauses for analysis */\n    const clauses = parseSearchQuery(query)\n      .filter(clause => (\n        clause.presence !== lunr.Query.presence.PROHIBITED\n      ))\n\n    /* Perform search and post-process results */\n    const groups = this.index.search(query)\n\n      /* Apply post-query boosts based on title and search query terms */\n      .reduce<SearchItem[]>((item, { ref, score, matchData }) => {\n        let doc = this.map.get(ref)\n        if (typeof doc !== \"undefined\") {\n\n          /* Shallow copy document */\n          doc = { ...doc }\n          if (doc.tags)\n            doc.tags = [...doc.tags]\n\n          /* Compute and analyze search query terms */\n          const terms = getSearchQueryTerms(\n            clauses,\n            Object.keys(matchData.metadata)\n          )\n\n          /* Highlight matches in fields */\n          for (const field of this.index.fields) {\n            if (typeof doc[field] === \"undefined\")\n              continue\n\n            /* Collect positions from matches */\n            const positions: Position[] = []\n            for (const match of Object.values(matchData.metadata))\n              if (typeof match[field] !== \"undefined\")\n                positions.push(...match[field].position)\n\n            /* Skip highlighting, if no positions were collected */\n            if (!positions.length)\n              continue\n\n            /* Load table and determine highlighting method */\n            const table = this.table.get([doc.location, field].join(\":\"))!\n            const fn = Array.isArray(doc[field])\n              ? highlightAll\n              : highlight\n\n            // @ts-expect-error - stop moaning, TypeScript!\n            doc[field] = fn(doc[field], table, positions, field !== \"text\")\n          }\n\n          /* Highlight title and text and apply post-query boosts */\n          const boost = +!doc.parent +\n            Object.values(terms)\n              .filter(t => t).length /\n            Object.keys(terms).length\n\n          /* Append item */\n          item.push({\n            ...doc,\n            score: score * (1 + boost ** 2),\n            terms\n          })\n        }\n        return item\n      }, [])\n\n      /* Sort search results again after applying boosts */\n      .sort((a, b) => b.score - a.score)\n\n      /* Group search results by article */\n      .reduce((items, result) => {\n        const doc = this.map.get(result.location)\n        if (typeof doc !== \"undefined\") {\n          const ref = doc.parent\n            ? doc.parent.location\n            : doc.location\n          items.set(ref, [...items.get(ref) || [], result])\n        }\n        return items\n      }, new Map<string, SearchItem[]>())\n\n    /* Ensure that every item set has an article */\n    for (const [ref, items] of groups)\n      if (!items.find(item => item.location === ref)) {\n        const doc = this.map.get(ref)!\n        items.push({ ...doc, score: 0, terms: {} })\n      }\n\n    /* Generate search suggestions, if desired */\n    let suggest: string[] | undefined\n    if (this.options.suggest) {\n      const titles = this.index.query(builder => {\n        for (const clause of clauses)\n          builder.term(clause.term, {\n            fields: [\"title\"],\n            presence: lunr.Query.presence.REQUIRED,\n            wildcard: lunr.Query.wildcard.TRAILING\n          })\n      })\n\n      /* Retrieve suggestions for best match */\n      suggest = titles.length\n        ? 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diff --git a/blueos-sgp-tools.html b/blueos-sgp-tools.html
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+    Adaptive-IPP with Non-Point FoV
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+<h1 id="blueos-sgp-tools"><a href="https://github.com/itskalvik/docker-sgp-tools/tree/main/robot-docker">BlueOS SGP-Tools <span class="twemoji"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 .297c-6.63 0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61C4.422 18.07 3.633 17.7 3.633 17.7c-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22 0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22 0 1.606-.015 2.896-.015 3.286 0 .315.21.69.825.57C20.565 22.092 24 17.592 24 12.297c0-6.627-5.373-12-12-12"/></svg></span></a></h1>
+<p>A <a href="https://blueos.cloud/">BlueOS</a> Extension for Autonomous Approximate Bathymetric Surveys</p>
+<div style="text-align:left">
+<img height="60" src="https://raw.githubusercontent.com/itskalvik/docker-sgp-tools/refs/heads/main/.assets/blueos_sgptools.png">
+</div>
+
+<h2 id="what-does-it-do">What Does It Do?</h2>
+<p>Autonomous Surface Vehicles (ASVs), such as the <a href="https://bluerobotics.com/store/boat/blueboat/blueboat/">BlueRobotics BlueBoat</a>, are well-suited for bathymetric surveys. However, it is often the case that an exhaustive survey mapping the depth at every location in an area is infeasible or unnecessary. In such cases, we can leverage variations in the underwater terrain to determine a few critical locations for data collection, which would result in a good approximation of the area's bathymetry.</p>
+<p>The <a href="https://www.itskalvik.com/sgp-tools">SGP-Tools python library</a> provides path planners to address the above problem, known as the informative path planning problem. The BlueOS SGP-Tools extension uses this library to determine ideal locations for the ASV to collect data and controls the ASV to autonomously visit the selected locations.</p>
+<p>The following shows the path planner adaptively planning paths for four robots tasked with surveying a given area:</p>
+<div style="text-align:left">
+<p><a href="http://itskalvik.com/sgp-tools">
+<img width="472" src="https://raw.githubusercontent.com/itskalvik/docker-sgp-tools/refs/heads/main/.assets/AIPP-4R.gif">
+</a></p>
+</div>
+
+<h2 id="usage">Usage</h2>
+<ol>
+<li>
+<p>First, we need to define the survey area and the robot launch location. The extension can read this data from <a href="https://docs.qgroundcontrol.com/Stable_V4.3/en/qgc-user-guide/plan_view/plan_geofence.html">QGC plan files</a>. The survey area and launch position must be defined using a <strong>polygon-shaped</strong> geofence drawn in <a href="https://qgroundcontrol.com/">QGC</a> and saved as <code>mission.plan</code>.</p>
+<p><div style="text-align:left">
+<img width="472" src="https://raw.githubusercontent.com/itskalvik/docker-sgp-tools/refs/heads/main/.assets/generate_plan.gif">
+</a></p>
+</div></p>
+</li>
+<li>
+<p>Once you have the plan file, copy it to the robot using the <code>File Browser</code> feature in BlueOS's <a href="https://blueos.cloud/docs/1.0/usage/advanced/"><code>Pirate Mode</code></a>. The <code>mission.plan</code> file should be uploaded to the following directory: <code>/extensions/sgptools/</code></p>
+<p>Once the <code>mission.plan</code> file is uploaded, restart the extension to ensure that the new file is used for the mission.</p>
+<p><div style="text-align:left">
+<img width="472" src="https://raw.githubusercontent.com/itskalvik/docker-sgp-tools/refs/heads/main/.assets/upload_mission.gif">
+</a></p>
+</div></p>
+</li>
+<li>
+<p>Finally, use the terminal provided by the SGP-Tools extension to start the mission with the following command:
+    <div class="language-text highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>ros2 launch ros_sgp_tools single_robot.launch.py
+</span></code></pre></div></p>
+<p><div style="text-align:left">
+<img width="472" src="https://raw.githubusercontent.com/itskalvik/docker-sgp-tools/refs/heads/main/.assets/start_mission.gif">
+</a></p>
+</div></p>
+</li>
+<li>
+<p>The sensor data, along with the corresponding GPS coordinates, will be logged to an <a href="https://docs.h5py.org/en/stable/">HDF5</a> file in the <code>DATA_FOLDER</code>, where the <code>mission.plan</code> was uploaded. </p>
+<p>We can estimate the bathymetry of the entire survey area using the collected data and visualize a normalized version with the following command (⚠️ Do not run this during the mission, as it will disrupt the path planner):
+<div class="language-text highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>ros2 launch ros_sgp_tools visualize_data.launch.py
+</span></code></pre></div></p>
+<p>The above command will publish a point cloud that can be viewed using <a href="https://foxglove.dev/product">foxglove</a>. You can access it from a web browser at <a href="https://app.foxglove.dev/">https://app.foxglove.dev/</a>. Use the <code>open connection</code> feature and change the address from <code>localhost</code> to the IP address of the ASV.</p>
+<p><div style="text-align:left">
+<img width="472" src="https://raw.githubusercontent.com/itskalvik/docker-sgp-tools/refs/heads/main/.assets/data_viz.gif">
+</a></p>
+</div></p>
+<p>You can control the point cloud density using the <code>num_samples</code> parameter. You can set this from foxglove's <code>Parameters</code> panel.</p>
+<p>By default, the latest mission log will be visualized. You can visualize a specific mission log using the following command (replace <code>&lt;log folder name&gt;</code> with  the log folder name):</p>
+<div class="language-text highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a>ros2 launch ros_sgp_tools visualize_data.launch.py mission_log:=&lt;log folder name&gt;
+</span></code></pre></div>
+</li>
+</ol>
+<h2 id="parameters">Parameters</h2>
+<p>You can control the following extension parameters by running the following command in the terminal provided by the SGP-Tools extension:</p>
+<div class="language-text highlight"><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a>export &lt;parameter_name&gt;=&lt;parameter_value&gt;
+</span></code></pre></div>
+<p>The parameters reset to their default values after rebooting. They can be made permanent by configuring the parameters using the app environment variables on the BlueOS extensions page in pirate mode.</p>
+<h4 id="available-parameters">Available Parameters:</h4>
+<ul>
+<li>
+<p><code>PING2_PORT</code> (<code>default: /dev/ttyUSB0</code>): </p>
+<p>Specifies the device to which the Ping2 sonar is mounted. </p>
+</li>
+<li>
+<p><code>NUM_WAYPOINTS</code> (<code>default: 20</code>): </p>
+<p>The number of waypoints optimized by the path planner. Increasing the number of waypoints gives a more complex path which potentally covers a larger area but also requires more compute and could result in slower online path updates. </p>
+</li>
+<li>
+<p><code>SAMPLING_RATE</code> (<code>default: 2</code>): </p>
+<p>The number of points to sample along each edge of the path during path planning. Increasing the samping rate results in the planner being aware of the data being collected along the whole path instead of assuming that the data is collected only at the vertices. This method requires relatively less compute then increasing the number of waypints, but still adds to the overall compute cost.</p>
+</li>
+<li>
+<p><code>START_FOXGLOVE</code> (<code>default: False</code>): </p>
+<p>Enables <a href="https://foxglove.dev/product">foxglove</a>, a web-based data visualization platform similar to <a href="https://docs.ros.org/en/humble/Tutorials/Intermediate/RViz/RViz-User-Guide/RViz-User-Guide.html">RViz</a>.</p>
+</li>
+<li>
+<p><code>ADAPTIVE_IPP</code> (<code>default: True</code>): </p>
+<p>Enables adaptive IPP, which uses the data streaming from the sonar to learn the correlations in the underwater bathymetry and further optimizes the future waypoints to maximize collecting new informative data.</p>
+</li>
+<li>
+<p><code>NAMESPACE</code> (<code>default: robot_0</code>): </p>
+<p>ROS2 namespace, useful when multiple ROS2 robots are operating on the same network.</p>
+</li>
+<li>
+<p><code>DATA_TYPE</code> (<code>default: Ping2</code>): </p>
+<p>Type of sensor to be used by the path planner. Currently, only the <a href="https://bluerobotics.com/store/sonars/echosounders/ping-sonar-r2-rp/">BlueRobotics Ping Sonar</a> is supported.</p>
+</li>
+<li>
+<p><code>FAKE_DATA</code> (<code>default: False</code>):</p>
+<p>Enables a fake sensor data publisher used for testing. Requires <code>DATA_TYPE=SerialPing2</code>.</p>
+</li>
+</ul>
+<h2 id="hardware-configuration">Hardware Configuration</h2>
+<ul>
+<li>
+<p>This extension works only on 64-bit operating systems. You can install the latest version of <a href="https://github.com/bluerobotics/BlueOS">BlueOS</a> on <a href="https://www.raspberrypi.com/software/operating-systems/">64-bit Raspberry Pi OS Lite</a> by running the following command on the Pi (ensure the username is set to <code>pi</code>):
+    <div class="language-text highlight"><pre><span></span><code><span id="__span-4-1"><a id="__codelineno-4-1" name="__codelineno-4-1" href="#__codelineno-4-1"></a>sudo su -c &#39;curl -fsSL https://raw.githubusercontent.com/bluerobotics/blueos-docker/master/install/install.sh | bash&#39;
+</span></code></pre></div></p>
+</li>
+<li>
+<p>The extension requires over 4GB of memory+swap. Please ensure that the swap size is large enough to accommodate the extension. The extension will copy the shell script <code>config_swap.sh</code> to <code>/usr/blueos/extensions/sgptools/</code> folder on the underlying device. You can use this script to increase the swap size before starting the path planner. </p>
+<p>You will have to use <a href="https://blueos.cloud/docs/1.0/usage/advanced/"><code>Pirate Mode</code></a> to access BlueOS's built-in terminal and run the script on the underlying device via the <code>red-pill</code> utility. Use the following commands to enable <code>red-pill</code> and increase the swap size: 
+<div class="language-text highlight"><pre><span></span><code><span id="__span-5-1"><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a>red-pill
+</span><span id="__span-5-2"><a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a>sudo bash /usr/blueos/extensions/sgptools/config_swap.sh
+</span></code></pre></div></p>
+<p><div style="text-align:left">
+<img width="472" src="https://raw.githubusercontent.com/itskalvik/docker-sgp-tools/refs/heads/main/.assets/upload_mission.gif">
+</a></p>
+</div></p>
+</li>
+</ul>
+<h2 id="disclaimer">Disclaimer ⚠️</h2>
+<p>This extension, when executed properly, will take control of the ASV and could potentially collide the vehicle with obstacles in the environment. Please use it with caution.</p>
+
+
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     <!-- Button to open drawer -->
     <label class="md-header__button md-icon" for="__drawer">
       
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3zm0 5h18v2H3zm0 5h18v2H3z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
 
     <!-- Header title -->
@@ -184,7 +184,7 @@
     <input class="md-option" data-md-color-media="(prefers-color-scheme: light)" data-md-color-scheme="default" data-md-color-primary="custom" data-md-color-accent="custom"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_0">
     
       <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5s-1.65.15-2.39.42zM3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29zm.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14zM20.65 7l-1.77 3.79a7.02 7.02 0 0 0-2.38-4.15zm-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29zM12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5c-.84 0-1.65.15-2.39.42L12 2M3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29L3.34 7m.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14L3.36 17M20.65 7l-1.77 3.79a7.023 7.023 0 0 0-2.38-4.15l4.15.36m-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29L20.64 17M12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44L12 22Z"/></svg>
       </label>
     
   
@@ -194,7 +194,7 @@
     <input class="md-option" data-md-color-media="(prefers-color-scheme: dark)" data-md-color-scheme="slate" data-md-color-primary="custom" data-md-color-accent="custom"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_1">
     
       <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3zm3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95zm-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3 3.19.09m3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95 2.06.05m-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31Z"/></svg>
       </label>
     
   
@@ -204,7 +204,7 @@
 
     <!-- User preference: color palette -->
     
-      <script>var palette=__md_get("__palette");if(palette&&palette.color){if("(prefers-color-scheme)"===palette.color.media){var media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']");palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent")}for(var[key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
     
 
     <!-- Site language selector -->
@@ -214,7 +214,7 @@
     
       <label class="md-header__button md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
 
       <!-- Search interface -->
@@ -225,20 +225,20 @@
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
           <a href="javascript:void(0)" class="md-search__icon md-icon" title="Share" aria-label="Share" data-clipboard data-clipboard-text="" data-md-component="search-share" tabindex="-1">
             
-            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7s-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91s2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08"/></svg>
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7 0-.24-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91 1.61 0 2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08Z"/></svg>
           </a>
         
         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
           
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12z"/></svg>
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
       
@@ -265,7 +265,7 @@
         <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -377,6 +377,26 @@
   
     
     
+      <li class="md-tabs__item">
+        <a href="blueos-sgp-tools.html" class="md-tabs__link">
+          
+  
+    
+  
+  BlueOS
+
+        </a>
+      </li>
+    
+  
+
+      
+        
+  
+  
+  
+    
+    
       <li class="md-tabs__item">
         <a href="examples/AIPP_non_point_FoV.html" class="md-tabs__link">
           
@@ -473,7 +493,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
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     GitHub
@@ -1429,6 +1449,78 @@
   
     
     
+      
+        
+      
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+    
+      
+      
+        
+      
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
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+        
+          
+          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    BlueOS
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_5">
+            <span class="md-nav__icon md-icon"></span>
+            BlueOS
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="blueos-sgp-tools.html" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    blueos-sgp-tools
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    
+    
       
         
       
@@ -1454,10 +1546,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
         
           
-          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+          <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
             
   
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@@ -1468,8 +1560,8 @@
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           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_5">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1626,15 +1718,16 @@
   
 
 
-<p><img alt="Image title" src="assets/SGP-Tools.png#only-light" />
-<img alt="Image title" src="assets/logo_dark.png#only-dark" /></p>
 <h1 id="docker-sgp-tools"><a href="https://github.com/itskalvik/docker-sgp-tools">docker-sgp-tools <span class="twemoji"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 .297c-6.63 0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61C4.422 18.07 3.633 17.7 3.633 17.7c-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22 0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22 0 1.606-.015 2.896-.015 3.286 0 .315.21.69.825.57C20.565 22.092 24 17.592 24 12.297c0-6.627-5.373-12-12-12"/></svg></span></a></h1>
 <p>The <a href="https://github.com/itskalvik/docker-sgp-tools">docker-sgp-tools</a> package provides the following docker-compose scripts:</p>
 <ul>
-<li><code>robot-compose.yml</code>: A minimal docker container used to run SGP-Tools ROS2 package on ArduPilot-based robots.</li>
-<li><code>sitl-compose.yml</code>: A GUI-based docker container with ROS2, Gazebo, ArduPilot SITL, and SGP-Tools used for simulating ArduPilot vehicles and testing SGP-Tools IPP code. </li>
+<li><code>robot-compose.yml</code>: A minimal docker container used to run <a href="http://itskalvik.com/sgp-tools">SGP-Tools</a> <a href="https://github.com/itskalvik/ros_sgp_tools">ROS2 package</a> on ArduPilot-based robots.</li>
+<li><code>sitl-compose.yml</code>: A GUI-based docker container with ROS2, Gazebo, ArduPilot SITL, and <a href="http://itskalvik.com/sgp-tools">SGP-Tools</a> used for simulating ArduPilot vehicles and testing SGP-Tools IPP code. </li>
 </ul>
-<p><img alt="Image title" src="assets/docker_ros2_ardupilot.png" /></p>
+<div style="text-align:left">
+<img height="60" src="assets/docker_ros2_ardupilot.png">
+</div>
+
 <h3 id="prerequisites">Prerequisites</h3>
 <ul>
 <li><a href="https://docs.docker.com/engine/install/">docker</a></li>
@@ -1729,7 +1822,7 @@ <h2 id="references">References</h2>
         
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-  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8z"/></svg>
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg>
   Back to top
 </button>
         
@@ -1759,10 +1852,10 @@ <h2 id="references">References</h2>
     </div>
     
     
-    <script id="__config" type="application/json">{"base": ".", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "assets/javascripts/workers/search.6ce7567c.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": ".", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
   </body>
diff --git a/examples/AIPP_non_point_FoV.html b/examples/AIPP_non_point_FoV.html
index c77c28b..94320ce 100644
--- a/examples/AIPP_non_point_FoV.html
+++ b/examples/AIPP_non_point_FoV.html
@@ -13,14 +13,14 @@
         <link rel="canonical" href="https://www.itskalvik.com/sgp-tools/examples/AIPP_non_point_FoV.html">
       
       
-        <link rel="prev" href="../docker-sgp-tools.html">
+        <link rel="prev" href="../blueos-sgp-tools.html">
       
       
         <link rel="next" href="IPP.html">
       
       
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-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.49">
+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.31">
     
     
       
@@ -28,7 +28,7 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.6f8fc17f.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.3cba04c6.min.css">
       
         
         <link rel="stylesheet" href="../assets/stylesheets/palette.06af60db.min.css">
@@ -56,7 +56,7 @@
     
       <link rel="stylesheet" href="../stylesheets/extra.css">
     
-    <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce(((e,_)=>(e<<5)-e+_.charCodeAt(0)),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
   
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+<script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",function(){document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document$.subscribe(function(){var a=document.forms.feedback;if(void 0!==a)for(var e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}),location$.subscribe(function(e){n("config","G-Z2TVV3MF1S",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
   
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@@ -153,7 +153,7 @@
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       <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3zm3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95zm-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3 3.19.09m3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95 2.06.05m-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31Z"/></svg>
       </label>
     
   
@@ -204,7 +204,7 @@
 
     <!-- User preference: color palette -->
     
-      <script>var palette=__md_get("__palette");if(palette&&palette.color){if("(prefers-color-scheme)"===palette.color.media){var media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']");palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent")}for(var[key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
     
 
     <!-- Site language selector -->
@@ -214,7 +214,7 @@
     
       <label class="md-header__button md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
 
       <!-- Search interface -->
@@ -225,20 +225,20 @@
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
           <a href="javascript:void(0)" class="md-search__icon md-icon" title="Share" aria-label="Share" data-clipboard data-clipboard-text="" data-md-component="search-share" tabindex="-1">
             
-            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7s-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91s2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08"/></svg>
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7 0-.24-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91 1.61 0 2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08Z"/></svg>
           </a>
         
         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
           
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12z"/></svg>
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
       
@@ -265,7 +265,7 @@
         <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -372,6 +372,26 @@
         
   
   
+  
+    
+    
+      <li class="md-tabs__item">
+        <a href="../blueos-sgp-tools.html" class="md-tabs__link">
+          
+  
+    
+  
+  BlueOS
+
+        </a>
+      </li>
+    
+  
+
+      
+        
+  
+  
     
   
   
@@ -473,7 +493,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -1311,6 +1331,78 @@
       
   
   
+  
+  
+    
+    
+      
+        
+      
+    
+    
+      
+      
+        
+      
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        
+          
+          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    BlueOS
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_5">
+            <span class="md-nav__icon md-icon"></span>
+            BlueOS
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../blueos-sgp-tools.html" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    blueos-sgp-tools
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
     
   
   
@@ -1345,10 +1437,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" checked>
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" checked>
         
           
-          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="">
+          <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="">
             
   
   <span class="md-ellipsis">
@@ -1359,8 +1451,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="true">
-          <label class="md-nav__title" for="__nav_5">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1787,7 +1879,7 @@
 .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
   </style>
 <style type="text/css">
-@charset "UTF-8";.jupyter-wrapper{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: #3e2723;--md-grey-50: #fafafa;--md-grey-100: #f5f5f5;--md-grey-200: #eeeeee;--md-grey-300: #e0e0e0;--md-grey-400: #bdbdbd;--md-grey-500: #9e9e9e;--md-grey-600: #757575;--md-grey-700: #616161;--md-grey-800: #424242;--md-grey-900: #212121;--md-blue-grey-50: #eceff1;--md-blue-grey-100: #cfd8dc;--md-blue-grey-200: #b0bec5;--md-blue-grey-300: #90a4ae;--md-blue-grey-400: #78909c;--md-blue-grey-500: #607d8b;--md-blue-grey-600: #546e7a;--md-blue-grey-700: #455a64;--md-blue-grey-800: #37474f;--md-blue-grey-900: #263238}.jupyter-wrapper{--jp-shadow-base-lightness: 0;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-400);--jp-border-color1: var(--md-grey-400);--jp-border-color2: var(--md-grey-300);--jp-border-color3: var(--md-grey-200);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(0, 0, 0, 1);--jp-ui-font-color1: rgba(0, 0, 0, .87);--jp-ui-font-color2: rgba(0, 0, 0, .54);--jp-ui-font-color3: rgba(0, 0, 0, .38);--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color2: rgba(255, 255, 255, .7);--jp-ui-inverse-font-color3: rgba(255, 255, 255, .5);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: white;--jp-layout-color1: white;--jp-layout-color2: var(--md-grey-200);--jp-layout-color3: var(--md-grey-400);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: #111111;--jp-inverse-layout-color1: var(--md-grey-900);--jp-inverse-layout-color2: var(--md-grey-800);--jp-inverse-layout-color3: var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(255, 255, 255, 1);--jp-ui-font-color1: rgba(255, 255, 255, .87);--jp-ui-font-color2: rgba(255, 255, 255, .54);--jp-ui-font-color3: rgba(255, 255, 255, .38);--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);--jp-ui-inverse-font-color1: rgba(0, 0, 0, .8);--jp-ui-inverse-font-color2: rgba(0, 0, 0, .5);--jp-ui-inverse-font-color3: rgba(0, 0, 0, .3);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(255, 255, 255, 1);--jp-content-font-color1: rgba(255, 255, 255, 1);--jp-content-font-color2: rgba(255, 255, 255, .7);--jp-content-font-color3: rgba(255, 255, 255, .5);--jp-content-link-color: var(--md-blue-300);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: #111111;--jp-layout-color1: var(--md-grey-900);--jp-layout-color2: var(--md-grey-800);--jp-layout-color3: var(--md-grey-700);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: white;--jp-inverse-layout-color1: white;--jp-inverse-layout-color2: var(--md-grey-200);--jp-inverse-layout-color3: var(--md-grey-400);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-700);--jp-brand-color1: var(--md-blue-500);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-700);--jp-accent-color1: var(--md-green-500);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-700);--jp-warn-color1: var(--md-orange-500);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-700);--jp-error-color1: var(--md-red-500);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-700);--jp-success-color1: var(--md-green-500);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-700);--jp-info-color1: var(--md-cyan-500);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--jp-layout-color1);--jp-cell-editor-border-color: var(--md-grey-700);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: 1;--jp-cell-prompt-not-active-font-color: var(--md-grey-300);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: rgba(33, 150, 243, .24);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: rgba(244, 67, 54, .28);--jp-rendermime-table-row-background: var(--md-grey-900);--jp-rendermime-table-row-hover-background: rgba(3, 169, 244, .2);--jp-dialog-background: rgba(0, 0, 0, .6);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color2);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .8);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--jp-layout-color0);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color0);--jp-input-hover-background: var(--jp-layout-color2);--jp-input-background: var(--md-grey-800);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: var(--jp-layout-color2);--jp-editor-selected-focused-background: rgba(33, 150, 243, .24);--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: var(--md-green-500);--jp-mirror-editor-atom-color: var(--md-blue-300);--jp-mirror-editor-number-color: var(--md-green-400);--jp-mirror-editor-def-color: var(--md-blue-600);--jp-mirror-editor-variable-color: var(--md-grey-300);--jp-mirror-editor-variable-2-color: var(--md-blue-400);--jp-mirror-editor-variable-3-color: var(--md-green-600);--jp-mirror-editor-punctuation-color: var(--md-blue-400);--jp-mirror-editor-property-color: var(--md-blue-400);--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ff7070;--jp-mirror-editor-string-2-color: var(--md-purple-300);--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: var(--md-green-600);--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: var(--md-green-700);--jp-mirror-editor-attribute-color: var(--md-blue-700);--jp-mirror-editor-header-color: var(--md-blue-500);--jp-mirror-editor-quote-color: var(--md-green-300);--jp-mirror-editor-link-color: var(--md-blue-700);--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ad4a00;--jp-collaborator-color2: #7b6a00;--jp-collaborator-color3: #007e00;--jp-collaborator-color4: #008772;--jp-collaborator-color5: #0079b9;--jp-collaborator-color6: #8b45c6;--jp-collaborator-color7: #be208b;--jp-vega-background: var(--md-grey-400);--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .6;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(255, 225, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-scrollbar-background-color: #3f4244;--jp-scrollbar-thumb-color: 88, 96, 97;--jp-scrollbar-endpad: 3px;--jp-scrollbar-thumb-margin: 3.5px;--jp-scrollbar-thumb-radius: 9px;--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-500);--jp-console-icon-background-color: var(--md-blue-500);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-200);--jp-terminal-icon-color: var(--md-grey-800);--jp-text-editor-icon-color: var(--md-grey-200);--jp-inspector-icon-color: var(--md-grey-200);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-700)}.jupyter-wrapper 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#f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: #3e2723;--md-grey-50: #fafafa;--md-grey-100: #f5f5f5;--md-grey-200: #eeeeee;--md-grey-300: #e0e0e0;--md-grey-400: #bdbdbd;--md-grey-500: #9e9e9e;--md-grey-600: #757575;--md-grey-700: #616161;--md-grey-800: #424242;--md-grey-900: #212121;--md-blue-grey-50: #eceff1;--md-blue-grey-100: #cfd8dc;--md-blue-grey-200: #b0bec5;--md-blue-grey-300: #90a4ae;--md-blue-grey-400: #78909c;--md-blue-grey-500: #607d8b;--md-blue-grey-600: #546e7a;--md-blue-grey-700: #455a64;--md-blue-grey-800: #37474f;--md-blue-grey-900: #263238}.jupyter-wrapper{--jp-shadow-base-lightness: 0;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-400);--jp-border-color1: var(--md-grey-400);--jp-border-color2: var(--md-grey-300);--jp-border-color3: var(--md-grey-200);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(0, 0, 0, 1);--jp-ui-font-color1: rgba(0, 0, 0, .87);--jp-ui-font-color2: rgba(0, 0, 0, .54);--jp-ui-font-color3: rgba(0, 0, 0, .38);--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color2: rgba(255, 255, 255, .7);--jp-ui-inverse-font-color3: rgba(255, 255, 255, .5);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: white;--jp-layout-color1: white;--jp-layout-color2: var(--md-grey-200);--jp-layout-color3: var(--md-grey-400);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: #111111;--jp-inverse-layout-color1: var(--md-grey-900);--jp-inverse-layout-color2: var(--md-grey-800);--jp-inverse-layout-color3: var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(255, 255, 255, 1);--jp-ui-font-color1: rgba(255, 255, 255, .87);--jp-ui-font-color2: rgba(255, 255, 255, .54);--jp-ui-font-color3: rgba(255, 255, 255, .38);--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);--jp-ui-inverse-font-color1: rgba(0, 0, 0, .8);--jp-ui-inverse-font-color2: rgba(0, 0, 0, .5);--jp-ui-inverse-font-color3: rgba(0, 0, 0, .3);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(255, 255, 255, 1);--jp-content-font-color1: 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#05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: 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@@ -2029,7 +2121,7 @@ <h1 id="adaptive-ipp-with-non-point-fov">Adaptive-IPP with Non-Point FoV<a class
 <div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="CodeMirror cm-s-jupyter">
 <div class="zeroclipboard-container">
-<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-1">
+<clipboard-copy for="cell-1">
 <div>
 <span class="notice" hidden="">Copied!</span>
 <svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
@@ -2108,7 +2200,7 @@ <h1 id="adaptive-ipp-with-non-point-fov">Adaptive-IPP with Non-Point FoV<a class
 <div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="CodeMirror cm-s-jupyter">
 <div class="zeroclipboard-container">
-<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-2">
+<clipboard-copy for="cell-2">
 <div>
 <span class="notice" hidden="">Copied!</span>
 <svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
@@ -2305,7 +2397,7 @@ <h1 id="adaptive-ipp-with-non-point-fov">Adaptive-IPP with Non-Point FoV<a class
 <div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="CodeMirror cm-s-jupyter">
 <div class="zeroclipboard-container">
-<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-3">
+<clipboard-copy for="cell-3">
 <div>
 <span class="notice" hidden="">Copied!</span>
 <svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
@@ -2489,7 +2581,7 @@ <h2 id="generate-synthetic-data-and-initial-sgp-hyperparameters">Generate synthe
 <div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="CodeMirror cm-s-jupyter">
 <div class="zeroclipboard-container">
-<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-4">
+<clipboard-copy for="cell-4">
 <div>
 <span class="notice" hidden="">Copied!</span>
 <svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
@@ -2611,7 +2703,7 @@ <h2 id="generate-the-aipp-solution">Generate the AIPP solution<a class="anchor-l
 <div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="CodeMirror cm-s-jupyter">
 <div class="zeroclipboard-container">
-<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-5">
+<clipboard-copy for="cell-5">
 <div>
 <span class="notice" hidden="">Copied!</span>
 <svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
@@ -3002,7 +3094,7 @@ <h2 id="generate-the-aipp-solution">Generate the AIPP solution<a class="anchor-l
         
           <button type="button" class="md-top md-icon" data-md-component="top" hidden>
   
-  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8z"/></svg>
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg>
   Back to top
 </button>
         
@@ -3032,10 +3124,10 @@ <h2 id="generate-the-aipp-solution">Generate the AIPP solution<a class="anchor-l
     </div>
     
     
-    <script id="__config" type="application/json">{"base": "..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../assets/javascripts/workers/search.6ce7567c.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="../assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
   </body>
diff --git a/examples/Baselines.html b/examples/Baselines.html
index 5b761c1..0ad0edb 100644
--- a/examples/Baselines.html
+++ b/examples/Baselines.html
@@ -20,7 +20,7 @@
       
       
       <link rel="icon" href="../assets/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.49">
+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.31">
     
     
       
@@ -28,7 +28,7 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.6f8fc17f.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.3cba04c6.min.css">
       
         
         <link rel="stylesheet" href="../assets/stylesheets/palette.06af60db.min.css">
@@ -56,7 +56,7 @@
     
       <link rel="stylesheet" href="../stylesheets/extra.css">
     
-    <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce(((e,_)=>(e<<5)-e+_.charCodeAt(0)),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
   
@@ -65,7 +65,7 @@
   
   
 
-<script id="__analytics">function __md_analytics(){function e(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],e("js",new Date),e("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",(function(){document.forms.search&&document.forms.search.query.addEventListener("blur",(function(){this.value&&e("event","search",{search_term:this.value})}));document$.subscribe((function(){var t=document.forms.feedback;if(void 0!==t)for(var a of t.querySelectorAll("[type=submit]"))a.addEventListener("click",(function(a){a.preventDefault();var n=document.location.pathname,d=this.getAttribute("data-md-value");e("event","feedback",{page:n,data:d}),t.firstElementChild.disabled=!0;var r=t.querySelector(".md-feedback__note [data-md-value='"+d+"']");r&&(r.hidden=!1)})),t.hidden=!1})),location$.subscribe((function(t){e("config","G-Z2TVV3MF1S",{page_path:t.pathname})}))}));var t=document.createElement("script");t.async=!0,t.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",t)}</script>
+<script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",function(){document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document$.subscribe(function(){var a=document.forms.feedback;if(void 0!==a)for(var e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}),location$.subscribe(function(e){n("config","G-Z2TVV3MF1S",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
   
     <script>"undefined"!=typeof __md_analytics&&__md_analytics()</script>
   
@@ -153,7 +153,7 @@
     <!-- Button to open drawer -->
     <label class="md-header__button md-icon" for="__drawer">
       
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3zm0 5h18v2H3zm0 5h18v2H3z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
 
     <!-- Header title -->
@@ -184,7 +184,7 @@
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       <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5s-1.65.15-2.39.42zM3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29zm.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14zM20.65 7l-1.77 3.79a7.02 7.02 0 0 0-2.38-4.15zm-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29zM12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5c-.84 0-1.65.15-2.39.42L12 2M3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29L3.34 7m.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14L3.36 17M20.65 7l-1.77 3.79a7.023 7.023 0 0 0-2.38-4.15l4.15.36m-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29L20.64 17M12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44L12 22Z"/></svg>
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@@ -194,7 +194,7 @@
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       <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
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+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3 3.19.09m3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95 2.06.05m-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31Z"/></svg>
       </label>
     
   
@@ -204,7 +204,7 @@
 
     <!-- User preference: color palette -->
     
-      <script>var palette=__md_get("__palette");if(palette&&palette.color){if("(prefers-color-scheme)"===palette.color.media){var media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']");palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent")}for(var[key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
     
 
     <!-- Site language selector -->
@@ -214,7 +214,7 @@
     
       <label class="md-header__button md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
 
       <!-- Search interface -->
@@ -225,20 +225,20 @@
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
           <a href="javascript:void(0)" class="md-search__icon md-icon" title="Share" aria-label="Share" data-clipboard data-clipboard-text="" data-md-component="search-share" tabindex="-1">
             
-            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7s-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91s2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08"/></svg>
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7 0-.24-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91 1.61 0 2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08Z"/></svg>
           </a>
         
         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
           
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12z"/></svg>
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
       
@@ -265,7 +265,7 @@
         <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -372,6 +372,26 @@
         
   
   
+  
+    
+    
+      <li class="md-tabs__item">
+        <a href="../blueos-sgp-tools.html" class="md-tabs__link">
+          
+  
+    
+  
+  BlueOS
+
+        </a>
+      </li>
+    
+  
+
+      
+        
+  
+  
     
   
   
@@ -473,7 +493,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -1311,6 +1331,78 @@
       
   
   
+  
+  
+    
+    
+      
+        
+      
+    
+    
+      
+      
+        
+      
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        
+          
+          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    BlueOS
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_5">
+            <span class="md-nav__icon md-icon"></span>
+            BlueOS
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../blueos-sgp-tools.html" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    blueos-sgp-tools
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
     
   
   
@@ -1345,10 +1437,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" checked>
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" checked>
         
           
-          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="">
+          <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="">
             
   
   <span class="md-ellipsis">
@@ -1359,8 +1451,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="true">
-          <label class="md-nav__title" for="__nav_5">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1796,7 +1888,7 @@
 .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
   </style>
 <style type="text/css">
-@charset "UTF-8";.jupyter-wrapper{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: #3e2723;--md-grey-50: #fafafa;--md-grey-100: #f5f5f5;--md-grey-200: #eeeeee;--md-grey-300: #e0e0e0;--md-grey-400: #bdbdbd;--md-grey-500: #9e9e9e;--md-grey-600: #757575;--md-grey-700: #616161;--md-grey-800: #424242;--md-grey-900: #212121;--md-blue-grey-50: #eceff1;--md-blue-grey-100: #cfd8dc;--md-blue-grey-200: #b0bec5;--md-blue-grey-300: #90a4ae;--md-blue-grey-400: #78909c;--md-blue-grey-500: #607d8b;--md-blue-grey-600: #546e7a;--md-blue-grey-700: #455a64;--md-blue-grey-800: #37474f;--md-blue-grey-900: #263238}.jupyter-wrapper{--jp-shadow-base-lightness: 0;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-400);--jp-border-color1: var(--md-grey-400);--jp-border-color2: var(--md-grey-300);--jp-border-color3: var(--md-grey-200);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(0, 0, 0, 1);--jp-ui-font-color1: rgba(0, 0, 0, .87);--jp-ui-font-color2: rgba(0, 0, 0, .54);--jp-ui-font-color3: rgba(0, 0, 0, .38);--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color2: rgba(255, 255, 255, .7);--jp-ui-inverse-font-color3: rgba(255, 255, 255, .5);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: white;--jp-layout-color1: white;--jp-layout-color2: var(--md-grey-200);--jp-layout-color3: var(--md-grey-400);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: #111111;--jp-inverse-layout-color1: var(--md-grey-900);--jp-inverse-layout-color2: var(--md-grey-800);--jp-inverse-layout-color3: var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(255, 255, 255, 1);--jp-ui-font-color1: rgba(255, 255, 255, .87);--jp-ui-font-color2: rgba(255, 255, 255, .54);--jp-ui-font-color3: rgba(255, 255, 255, .38);--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);--jp-ui-inverse-font-color1: rgba(0, 0, 0, .8);--jp-ui-inverse-font-color2: rgba(0, 0, 0, .5);--jp-ui-inverse-font-color3: rgba(0, 0, 0, .3);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(255, 255, 255, 1);--jp-content-font-color1: rgba(255, 255, 255, 1);--jp-content-font-color2: rgba(255, 255, 255, .7);--jp-content-font-color3: rgba(255, 255, 255, .5);--jp-content-link-color: var(--md-blue-300);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: #111111;--jp-layout-color1: var(--md-grey-900);--jp-layout-color2: var(--md-grey-800);--jp-layout-color3: var(--md-grey-700);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: white;--jp-inverse-layout-color1: white;--jp-inverse-layout-color2: var(--md-grey-200);--jp-inverse-layout-color3: var(--md-grey-400);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-700);--jp-brand-color1: var(--md-blue-500);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-700);--jp-accent-color1: var(--md-green-500);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-700);--jp-warn-color1: var(--md-orange-500);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-700);--jp-error-color1: var(--md-red-500);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-700);--jp-success-color1: var(--md-green-500);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-700);--jp-info-color1: var(--md-cyan-500);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--jp-layout-color1);--jp-cell-editor-border-color: var(--md-grey-700);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: 1;--jp-cell-prompt-not-active-font-color: var(--md-grey-300);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: rgba(33, 150, 243, .24);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: rgba(244, 67, 54, .28);--jp-rendermime-table-row-background: var(--md-grey-900);--jp-rendermime-table-row-hover-background: rgba(3, 169, 244, .2);--jp-dialog-background: rgba(0, 0, 0, .6);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color2);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .8);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--jp-layout-color0);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color0);--jp-input-hover-background: var(--jp-layout-color2);--jp-input-background: var(--md-grey-800);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: var(--jp-layout-color2);--jp-editor-selected-focused-background: rgba(33, 150, 243, .24);--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: var(--md-green-500);--jp-mirror-editor-atom-color: var(--md-blue-300);--jp-mirror-editor-number-color: var(--md-green-400);--jp-mirror-editor-def-color: var(--md-blue-600);--jp-mirror-editor-variable-color: var(--md-grey-300);--jp-mirror-editor-variable-2-color: var(--md-blue-400);--jp-mirror-editor-variable-3-color: var(--md-green-600);--jp-mirror-editor-punctuation-color: var(--md-blue-400);--jp-mirror-editor-property-color: var(--md-blue-400);--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ff7070;--jp-mirror-editor-string-2-color: var(--md-purple-300);--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: var(--md-green-600);--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: var(--md-green-700);--jp-mirror-editor-attribute-color: var(--md-blue-700);--jp-mirror-editor-header-color: var(--md-blue-500);--jp-mirror-editor-quote-color: var(--md-green-300);--jp-mirror-editor-link-color: var(--md-blue-700);--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ad4a00;--jp-collaborator-color2: #7b6a00;--jp-collaborator-color3: #007e00;--jp-collaborator-color4: #008772;--jp-collaborator-color5: #0079b9;--jp-collaborator-color6: #8b45c6;--jp-collaborator-color7: #be208b;--jp-vega-background: var(--md-grey-400);--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .6;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(255, 225, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-scrollbar-background-color: #3f4244;--jp-scrollbar-thumb-color: 88, 96, 97;--jp-scrollbar-endpad: 3px;--jp-scrollbar-thumb-margin: 3.5px;--jp-scrollbar-thumb-radius: 9px;--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-500);--jp-console-icon-background-color: var(--md-blue-500);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-200);--jp-terminal-icon-color: var(--md-grey-800);--jp-text-editor-icon-color: var(--md-grey-200);--jp-inspector-icon-color: var(--md-grey-200);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-700)}.jupyter-wrapper 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#f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: #3e2723;--md-grey-50: #fafafa;--md-grey-100: #f5f5f5;--md-grey-200: #eeeeee;--md-grey-300: #e0e0e0;--md-grey-400: #bdbdbd;--md-grey-500: #9e9e9e;--md-grey-600: #757575;--md-grey-700: #616161;--md-grey-800: #424242;--md-grey-900: #212121;--md-blue-grey-50: #eceff1;--md-blue-grey-100: #cfd8dc;--md-blue-grey-200: #b0bec5;--md-blue-grey-300: #90a4ae;--md-blue-grey-400: #78909c;--md-blue-grey-500: #607d8b;--md-blue-grey-600: #546e7a;--md-blue-grey-700: #455a64;--md-blue-grey-800: #37474f;--md-blue-grey-900: #263238}.jupyter-wrapper{--jp-shadow-base-lightness: 0;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-400);--jp-border-color1: var(--md-grey-400);--jp-border-color2: var(--md-grey-300);--jp-border-color3: var(--md-grey-200);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(0, 0, 0, 1);--jp-ui-font-color1: rgba(0, 0, 0, .87);--jp-ui-font-color2: rgba(0, 0, 0, .54);--jp-ui-font-color3: rgba(0, 0, 0, .38);--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color2: rgba(255, 255, 255, .7);--jp-ui-inverse-font-color3: rgba(255, 255, 255, .5);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: white;--jp-layout-color1: white;--jp-layout-color2: var(--md-grey-200);--jp-layout-color3: var(--md-grey-400);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: #111111;--jp-inverse-layout-color1: var(--md-grey-900);--jp-inverse-layout-color2: var(--md-grey-800);--jp-inverse-layout-color3: var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(255, 255, 255, 1);--jp-ui-font-color1: rgba(255, 255, 255, .87);--jp-ui-font-color2: rgba(255, 255, 255, .54);--jp-ui-font-color3: rgba(255, 255, 255, .38);--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);--jp-ui-inverse-font-color1: rgba(0, 0, 0, .8);--jp-ui-inverse-font-color2: rgba(0, 0, 0, .5);--jp-ui-inverse-font-color3: rgba(0, 0, 0, .3);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(255, 255, 255, 1);--jp-content-font-color1: 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var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}
@@ -2038,7 +2130,7 @@ <h1 id="baselines">Baselines<a class="anchor-link" href="#baselines">¶</a></h1>
 <div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="CodeMirror cm-s-jupyter">
 <div class="zeroclipboard-container">
-<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-1">
+<clipboard-copy for="cell-1">
 <div>
 <span class="notice" hidden="">Copied!</span>
 <svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
@@ -2122,7 +2214,7 @@ <h2 id="set-the-initial-parameters-and-generate-synthetic-data">Set the initial
 <div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="CodeMirror cm-s-jupyter">
 <div class="zeroclipboard-container">
-<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-2">
+<clipboard-copy for="cell-2">
 <div>
 <span class="notice" hidden="">Copied!</span>
 <svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
@@ -2241,7 +2333,7 @@ <h2 id="helper-functions">Helper functions<a class="anchor-link" href="#helper-f
 <div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="CodeMirror cm-s-jupyter">
 <div class="zeroclipboard-container">
-<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-3">
+<clipboard-copy for="cell-3">
 <div>
 <span class="notice" hidden="">Copied!</span>
 <svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
@@ -2321,7 +2413,7 @@ <h2 id="continuous-sgp">Continuous-SGP<a class="anchor-link" href="#continuous-s
 <div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="CodeMirror cm-s-jupyter">
 <div class="zeroclipboard-container">
-<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-4">
+<clipboard-copy for="cell-4">
 <div>
 <span class="notice" hidden="">Copied!</span>
 <svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
@@ -2425,7 +2517,7 @@ <h2 id="discrete-sgp">Discrete-SGP<a class="anchor-link" href="#discrete-sgp">¶
 <div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="CodeMirror cm-s-jupyter">
 <div class="zeroclipboard-container">
-<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-5">
+<clipboard-copy for="cell-5">
 <div>
 <span class="notice" hidden="">Copied!</span>
 <svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
@@ -2523,7 +2615,7 @@ <h2 id="greedy-mutual-information">Greedy Mutual Information<a class="anchor-lin
 <div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="CodeMirror cm-s-jupyter">
 <div class="zeroclipboard-container">
-<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-6">
+<clipboard-copy for="cell-6">
 <div>
 <span class="notice" hidden="">Copied!</span>
 <svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
@@ -2621,7 +2713,7 @@ <h2 id="cma-es-mutual-information-genetic-algorithm">CMA-ES Mutual Information (
 <div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="CodeMirror cm-s-jupyter">
 <div class="zeroclipboard-container">
-<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-7">
+<clipboard-copy for="cell-7">
 <div>
 <span class="notice" hidden="">Copied!</span>
 <svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
@@ -2719,7 +2811,7 @@ <h2 id="bayesian-optimization-mutual-information">Bayesian Optimization Mutual I
 <div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="CodeMirror cm-s-jupyter">
 <div class="zeroclipboard-container">
-<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-8">
+<clipboard-copy for="cell-8">
 <div>
 <span class="notice" hidden="">Copied!</span>
 <svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
@@ -2829,7 +2921,7 @@ <h2 id="bayesian-optimization-mutual-information">Bayesian Optimization Mutual I
         
           <button type="button" class="md-top md-icon" data-md-component="top" hidden>
   
-  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8z"/></svg>
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg>
   Back to top
 </button>
         
@@ -2859,10 +2951,10 @@ <h2 id="bayesian-optimization-mutual-information">Bayesian Optimization Mutual I
     </div>
     
     
-    <script id="__config" type="application/json">{"base": "..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../assets/javascripts/workers/search.6ce7567c.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="../assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
   </body>
diff --git a/examples/IPP.html b/examples/IPP.html
index b793644..bf9ed2b 100644
--- a/examples/IPP.html
+++ b/examples/IPP.html
@@ -20,7 +20,7 @@
       
       
       <link rel="icon" href="../assets/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.49">
+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.31">
     
     
       
@@ -28,7 +28,7 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.6f8fc17f.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.3cba04c6.min.css">
       
         
         <link rel="stylesheet" href="../assets/stylesheets/palette.06af60db.min.css">
@@ -56,7 +56,7 @@
     
       <link rel="stylesheet" href="../stylesheets/extra.css">
     
-    <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce(((e,_)=>(e<<5)-e+_.charCodeAt(0)),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
   
@@ -65,7 +65,7 @@
   
   
 
-<script id="__analytics">function __md_analytics(){function e(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],e("js",new Date),e("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",(function(){document.forms.search&&document.forms.search.query.addEventListener("blur",(function(){this.value&&e("event","search",{search_term:this.value})}));document$.subscribe((function(){var t=document.forms.feedback;if(void 0!==t)for(var a of t.querySelectorAll("[type=submit]"))a.addEventListener("click",(function(a){a.preventDefault();var n=document.location.pathname,d=this.getAttribute("data-md-value");e("event","feedback",{page:n,data:d}),t.firstElementChild.disabled=!0;var r=t.querySelector(".md-feedback__note [data-md-value='"+d+"']");r&&(r.hidden=!1)})),t.hidden=!1})),location$.subscribe((function(t){e("config","G-Z2TVV3MF1S",{page_path:t.pathname})}))}));var t=document.createElement("script");t.async=!0,t.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",t)}</script>
+<script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",function(){document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document$.subscribe(function(){var a=document.forms.feedback;if(void 0!==a)for(var e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}),location$.subscribe(function(e){n("config","G-Z2TVV3MF1S",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
   
     <script>"undefined"!=typeof __md_analytics&&__md_analytics()</script>
   
@@ -153,7 +153,7 @@
     <!-- Button to open drawer -->
     <label class="md-header__button md-icon" for="__drawer">
       
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3zm0 5h18v2H3zm0 5h18v2H3z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
 
     <!-- Header title -->
@@ -184,7 +184,7 @@
     <input class="md-option" data-md-color-media="(prefers-color-scheme: light)" data-md-color-scheme="default" data-md-color-primary="custom" data-md-color-accent="custom"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_0">
     
       <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5s-1.65.15-2.39.42zM3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29zm.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14zM20.65 7l-1.77 3.79a7.02 7.02 0 0 0-2.38-4.15zm-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29zM12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5c-.84 0-1.65.15-2.39.42L12 2M3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29L3.34 7m.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14L3.36 17M20.65 7l-1.77 3.79a7.023 7.023 0 0 0-2.38-4.15l4.15.36m-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29L20.64 17M12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44L12 22Z"/></svg>
       </label>
     
   
@@ -194,7 +194,7 @@
     <input class="md-option" data-md-color-media="(prefers-color-scheme: dark)" data-md-color-scheme="slate" data-md-color-primary="custom" data-md-color-accent="custom"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_1">
     
       <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3zm3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95zm-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3 3.19.09m3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95 2.06.05m-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31Z"/></svg>
       </label>
     
   
@@ -204,7 +204,7 @@
 
     <!-- User preference: color palette -->
     
-      <script>var palette=__md_get("__palette");if(palette&&palette.color){if("(prefers-color-scheme)"===palette.color.media){var media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']");palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent")}for(var[key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
     
 
     <!-- Site language selector -->
@@ -214,7 +214,7 @@
     
       <label class="md-header__button md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
 
       <!-- Search interface -->
@@ -225,20 +225,20 @@
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
           <a href="javascript:void(0)" class="md-search__icon md-icon" title="Share" aria-label="Share" data-clipboard data-clipboard-text="" data-md-component="search-share" tabindex="-1">
             
-            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7s-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91s2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08"/></svg>
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7 0-.24-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91 1.61 0 2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08Z"/></svg>
           </a>
         
         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
           
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12z"/></svg>
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
       
@@ -265,7 +265,7 @@
         <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -372,6 +372,26 @@
         
   
   
+  
+    
+    
+      <li class="md-tabs__item">
+        <a href="../blueos-sgp-tools.html" class="md-tabs__link">
+          
+  
+    
+  
+  BlueOS
+
+        </a>
+      </li>
+    
+  
+
+      
+        
+  
+  
     
   
   
@@ -473,7 +493,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -1311,6 +1331,78 @@
       
   
   
+  
+  
+    
+    
+      
+        
+      
+    
+    
+      
+      
+        
+      
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        
+          
+          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    BlueOS
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_5">
+            <span class="md-nav__icon md-icon"></span>
+            BlueOS
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../blueos-sgp-tools.html" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    blueos-sgp-tools
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
     
   
   
@@ -1345,10 +1437,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" checked>
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" checked>
         
           
-          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="">
+          <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="">
             
   
   <span class="md-ellipsis">
@@ -1359,8 +1451,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="true">
-          <label class="md-nav__title" for="__nav_5">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1739,7 +1831,7 @@
 .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
   </style>
 <style type="text/css">
-@charset "UTF-8";.jupyter-wrapper{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: #3e2723;--md-grey-50: #fafafa;--md-grey-100: #f5f5f5;--md-grey-200: #eeeeee;--md-grey-300: #e0e0e0;--md-grey-400: #bdbdbd;--md-grey-500: #9e9e9e;--md-grey-600: #757575;--md-grey-700: #616161;--md-grey-800: #424242;--md-grey-900: #212121;--md-blue-grey-50: #eceff1;--md-blue-grey-100: #cfd8dc;--md-blue-grey-200: #b0bec5;--md-blue-grey-300: #90a4ae;--md-blue-grey-400: #78909c;--md-blue-grey-500: #607d8b;--md-blue-grey-600: #546e7a;--md-blue-grey-700: #455a64;--md-blue-grey-800: #37474f;--md-blue-grey-900: #263238}.jupyter-wrapper{--jp-shadow-base-lightness: 0;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-400);--jp-border-color1: var(--md-grey-400);--jp-border-color2: var(--md-grey-300);--jp-border-color3: var(--md-grey-200);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(0, 0, 0, 1);--jp-ui-font-color1: rgba(0, 0, 0, .87);--jp-ui-font-color2: rgba(0, 0, 0, .54);--jp-ui-font-color3: rgba(0, 0, 0, .38);--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color2: rgba(255, 255, 255, .7);--jp-ui-inverse-font-color3: rgba(255, 255, 255, .5);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: white;--jp-layout-color1: white;--jp-layout-color2: var(--md-grey-200);--jp-layout-color3: var(--md-grey-400);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: #111111;--jp-inverse-layout-color1: var(--md-grey-900);--jp-inverse-layout-color2: var(--md-grey-800);--jp-inverse-layout-color3: var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(255, 255, 255, 1);--jp-ui-font-color1: rgba(255, 255, 255, .87);--jp-ui-font-color2: rgba(255, 255, 255, .54);--jp-ui-font-color3: rgba(255, 255, 255, .38);--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);--jp-ui-inverse-font-color1: rgba(0, 0, 0, .8);--jp-ui-inverse-font-color2: rgba(0, 0, 0, .5);--jp-ui-inverse-font-color3: rgba(0, 0, 0, .3);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(255, 255, 255, 1);--jp-content-font-color1: rgba(255, 255, 255, 1);--jp-content-font-color2: rgba(255, 255, 255, .7);--jp-content-font-color3: rgba(255, 255, 255, .5);--jp-content-link-color: var(--md-blue-300);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: #111111;--jp-layout-color1: var(--md-grey-900);--jp-layout-color2: var(--md-grey-800);--jp-layout-color3: var(--md-grey-700);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: white;--jp-inverse-layout-color1: white;--jp-inverse-layout-color2: var(--md-grey-200);--jp-inverse-layout-color3: var(--md-grey-400);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-700);--jp-brand-color1: var(--md-blue-500);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-700);--jp-accent-color1: var(--md-green-500);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-700);--jp-warn-color1: var(--md-orange-500);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-700);--jp-error-color1: var(--md-red-500);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-700);--jp-success-color1: var(--md-green-500);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-700);--jp-info-color1: var(--md-cyan-500);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--jp-layout-color1);--jp-cell-editor-border-color: var(--md-grey-700);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: 1;--jp-cell-prompt-not-active-font-color: var(--md-grey-300);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: rgba(33, 150, 243, .24);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: rgba(244, 67, 54, .28);--jp-rendermime-table-row-background: var(--md-grey-900);--jp-rendermime-table-row-hover-background: rgba(3, 169, 244, .2);--jp-dialog-background: rgba(0, 0, 0, .6);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color2);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .8);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--jp-layout-color0);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color0);--jp-input-hover-background: var(--jp-layout-color2);--jp-input-background: var(--md-grey-800);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: var(--jp-layout-color2);--jp-editor-selected-focused-background: rgba(33, 150, 243, .24);--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: var(--md-green-500);--jp-mirror-editor-atom-color: var(--md-blue-300);--jp-mirror-editor-number-color: var(--md-green-400);--jp-mirror-editor-def-color: var(--md-blue-600);--jp-mirror-editor-variable-color: var(--md-grey-300);--jp-mirror-editor-variable-2-color: var(--md-blue-400);--jp-mirror-editor-variable-3-color: var(--md-green-600);--jp-mirror-editor-punctuation-color: var(--md-blue-400);--jp-mirror-editor-property-color: var(--md-blue-400);--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ff7070;--jp-mirror-editor-string-2-color: var(--md-purple-300);--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: var(--md-green-600);--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: var(--md-green-700);--jp-mirror-editor-attribute-color: var(--md-blue-700);--jp-mirror-editor-header-color: var(--md-blue-500);--jp-mirror-editor-quote-color: var(--md-green-300);--jp-mirror-editor-link-color: var(--md-blue-700);--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ad4a00;--jp-collaborator-color2: #7b6a00;--jp-collaborator-color3: #007e00;--jp-collaborator-color4: #008772;--jp-collaborator-color5: #0079b9;--jp-collaborator-color6: #8b45c6;--jp-collaborator-color7: #be208b;--jp-vega-background: var(--md-grey-400);--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .6;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(255, 225, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-scrollbar-background-color: #3f4244;--jp-scrollbar-thumb-color: 88, 96, 97;--jp-scrollbar-endpad: 3px;--jp-scrollbar-thumb-margin: 3.5px;--jp-scrollbar-thumb-radius: 9px;--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-500);--jp-console-icon-background-color: var(--md-blue-500);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-200);--jp-terminal-icon-color: var(--md-grey-800);--jp-text-editor-icon-color: var(--md-grey-200);--jp-inspector-icon-color: var(--md-grey-200);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-700)}.jupyter-wrapper 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#f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: #3e2723;--md-grey-50: #fafafa;--md-grey-100: #f5f5f5;--md-grey-200: #eeeeee;--md-grey-300: #e0e0e0;--md-grey-400: #bdbdbd;--md-grey-500: #9e9e9e;--md-grey-600: #757575;--md-grey-700: #616161;--md-grey-800: #424242;--md-grey-900: #212121;--md-blue-grey-50: #eceff1;--md-blue-grey-100: #cfd8dc;--md-blue-grey-200: #b0bec5;--md-blue-grey-300: #90a4ae;--md-blue-grey-400: #78909c;--md-blue-grey-500: #607d8b;--md-blue-grey-600: #546e7a;--md-blue-grey-700: #455a64;--md-blue-grey-800: #37474f;--md-blue-grey-900: #263238}.jupyter-wrapper{--jp-shadow-base-lightness: 0;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-400);--jp-border-color1: var(--md-grey-400);--jp-border-color2: var(--md-grey-300);--jp-border-color3: var(--md-grey-200);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(0, 0, 0, 1);--jp-ui-font-color1: rgba(0, 0, 0, .87);--jp-ui-font-color2: rgba(0, 0, 0, .54);--jp-ui-font-color3: rgba(0, 0, 0, .38);--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color2: rgba(255, 255, 255, .7);--jp-ui-inverse-font-color3: rgba(255, 255, 255, .5);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: white;--jp-layout-color1: white;--jp-layout-color2: var(--md-grey-200);--jp-layout-color3: var(--md-grey-400);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: #111111;--jp-inverse-layout-color1: var(--md-grey-900);--jp-inverse-layout-color2: var(--md-grey-800);--jp-inverse-layout-color3: var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(255, 255, 255, 1);--jp-ui-font-color1: rgba(255, 255, 255, .87);--jp-ui-font-color2: rgba(255, 255, 255, .54);--jp-ui-font-color3: rgba(255, 255, 255, .38);--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);--jp-ui-inverse-font-color1: rgba(0, 0, 0, .8);--jp-ui-inverse-font-color2: rgba(0, 0, 0, .5);--jp-ui-inverse-font-color3: rgba(0, 0, 0, .3);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(255, 255, 255, 1);--jp-content-font-color1: 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@@ -1981,7 +2073,7 @@ <h1 id="sgp-based-ipp">SGP-Based IPP<a class="anchor-link" href="#sgp-based-ipp"
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+<clipboard-copy for="cell-1">
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 <svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
@@ -2053,7 +2145,7 @@ <h1 id="get-the-data-and-initial-sgp-hyperparameters">Get the data and initial S
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+<clipboard-copy for="cell-2">
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@@ -2142,7 +2234,7 @@ <h1 id="single-robot-ipp-with-point-sensing">Single-Robot IPP with Point Sensing
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+<clipboard-copy for="cell-3">
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 <svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
@@ -2244,7 +2336,7 @@ <h1 id="single-robot-ipp-with-continuous-sensing">Single-Robot IPP with Continuo
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-<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-4">
+<clipboard-copy for="cell-4">
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 <span class="notice" hidden="">Copied!</span>
 <svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
@@ -2350,7 +2442,7 @@ <h1 id="single-robot-ipp-with-continuous-non-point-fov-sensor">Single-Robot IPP
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 <div class="CodeMirror cm-s-jupyter">
 <div class="zeroclipboard-container">
-<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-5">
+<clipboard-copy for="cell-5">
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 <span class="notice" hidden="">Copied!</span>
 <svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
@@ -2482,7 +2574,7 @@ <h1 id="multi-robot-ipp-with-continuous-sensing-and-a-distance-budget">Multi-Rob
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 <div class="CodeMirror cm-s-jupyter">
 <div class="zeroclipboard-container">
-<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-6">
+<clipboard-copy for="cell-6">
 <div>
 <span class="notice" hidden="">Copied!</span>
 <svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
@@ -2621,7 +2713,7 @@ <h1 id="multi-robot-ipp-with-continuous-sensing-and-a-distance-budget">Multi-Rob
         
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-  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8z"/></svg>
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg>
   Back to top
 </button>
         
@@ -2651,10 +2743,10 @@ <h1 id="multi-robot-ipp-with-continuous-sensing-and-a-distance-budget">Multi-Rob
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-    <script id="__config" type="application/json">{"base": "..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../assets/javascripts/workers/search.6ce7567c.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="../assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
   </body>
diff --git a/examples/non_point_FoV.html b/examples/non_point_FoV.html
index 68c2b31..e7199da 100644
--- a/examples/non_point_FoV.html
+++ b/examples/non_point_FoV.html
@@ -20,7 +20,7 @@
       
       
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-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.49">
+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.31">
     
     
       
@@ -28,7 +28,7 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.6f8fc17f.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.3cba04c6.min.css">
       
         
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@@ -56,7 +56,7 @@
     
       <link rel="stylesheet" href="../stylesheets/extra.css">
     
-    <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce(((e,_)=>(e<<5)-e+_.charCodeAt(0)),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
   
@@ -65,7 +65,7 @@
   
   
 
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+<script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",function(){document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document$.subscribe(function(){var a=document.forms.feedback;if(void 0!==a)for(var e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}),location$.subscribe(function(e){n("config","G-Z2TVV3MF1S",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
   
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@@ -153,7 +153,7 @@
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       <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3zm3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95zm-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3 3.19.09m3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95 2.06.05m-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31Z"/></svg>
       </label>
     
   
@@ -204,7 +204,7 @@
 
     <!-- User preference: color palette -->
     
-      <script>var palette=__md_get("__palette");if(palette&&palette.color){if("(prefers-color-scheme)"===palette.color.media){var media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']");palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent")}for(var[key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
     
 
     <!-- Site language selector -->
@@ -214,7 +214,7 @@
     
       <label class="md-header__button md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
 
       <!-- Search interface -->
@@ -225,20 +225,20 @@
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
           <a href="javascript:void(0)" class="md-search__icon md-icon" title="Share" aria-label="Share" data-clipboard data-clipboard-text="" data-md-component="search-share" tabindex="-1">
             
-            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7s-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91s2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08"/></svg>
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7 0-.24-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91 1.61 0 2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08Z"/></svg>
           </a>
         
         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
           
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12z"/></svg>
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
       
@@ -265,7 +265,7 @@
         <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -372,6 +372,26 @@
         
   
   
+  
+    
+    
+      <li class="md-tabs__item">
+        <a href="../blueos-sgp-tools.html" class="md-tabs__link">
+          
+  
+    
+  
+  BlueOS
+
+        </a>
+      </li>
+    
+  
+
+      
+        
+  
+  
     
   
   
@@ -473,7 +493,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -1311,6 +1331,78 @@
       
   
   
+  
+  
+    
+    
+      
+        
+      
+    
+    
+      
+      
+        
+      
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        
+          
+          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    BlueOS
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_5">
+            <span class="md-nav__icon md-icon"></span>
+            BlueOS
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../blueos-sgp-tools.html" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    blueos-sgp-tools
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
     
   
   
@@ -1345,10 +1437,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" checked>
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" checked>
         
           
-          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="">
+          <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="">
             
   
   <span class="md-ellipsis">
@@ -1359,8 +1451,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="true">
-          <label class="md-nav__title" for="__nav_5">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1805,7 +1897,7 @@
 .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
   </style>
 <style type="text/css">
-@charset "UTF-8";.jupyter-wrapper{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: #3e2723;--md-grey-50: #fafafa;--md-grey-100: #f5f5f5;--md-grey-200: #eeeeee;--md-grey-300: #e0e0e0;--md-grey-400: #bdbdbd;--md-grey-500: #9e9e9e;--md-grey-600: #757575;--md-grey-700: #616161;--md-grey-800: #424242;--md-grey-900: #212121;--md-blue-grey-50: #eceff1;--md-blue-grey-100: #cfd8dc;--md-blue-grey-200: #b0bec5;--md-blue-grey-300: #90a4ae;--md-blue-grey-400: #78909c;--md-blue-grey-500: #607d8b;--md-blue-grey-600: #546e7a;--md-blue-grey-700: #455a64;--md-blue-grey-800: #37474f;--md-blue-grey-900: #263238}.jupyter-wrapper{--jp-shadow-base-lightness: 0;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-400);--jp-border-color1: var(--md-grey-400);--jp-border-color2: var(--md-grey-300);--jp-border-color3: var(--md-grey-200);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(0, 0, 0, 1);--jp-ui-font-color1: rgba(0, 0, 0, .87);--jp-ui-font-color2: rgba(0, 0, 0, .54);--jp-ui-font-color3: rgba(0, 0, 0, .38);--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color2: rgba(255, 255, 255, .7);--jp-ui-inverse-font-color3: rgba(255, 255, 255, .5);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: white;--jp-layout-color1: white;--jp-layout-color2: var(--md-grey-200);--jp-layout-color3: var(--md-grey-400);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: #111111;--jp-inverse-layout-color1: var(--md-grey-900);--jp-inverse-layout-color2: var(--md-grey-800);--jp-inverse-layout-color3: var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(255, 255, 255, 1);--jp-ui-font-color1: rgba(255, 255, 255, .87);--jp-ui-font-color2: rgba(255, 255, 255, .54);--jp-ui-font-color3: rgba(255, 255, 255, .38);--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);--jp-ui-inverse-font-color1: rgba(0, 0, 0, .8);--jp-ui-inverse-font-color2: rgba(0, 0, 0, .5);--jp-ui-inverse-font-color3: rgba(0, 0, 0, .3);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(255, 255, 255, 1);--jp-content-font-color1: rgba(255, 255, 255, 1);--jp-content-font-color2: rgba(255, 255, 255, .7);--jp-content-font-color3: rgba(255, 255, 255, .5);--jp-content-link-color: var(--md-blue-300);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: #111111;--jp-layout-color1: var(--md-grey-900);--jp-layout-color2: var(--md-grey-800);--jp-layout-color3: var(--md-grey-700);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: white;--jp-inverse-layout-color1: white;--jp-inverse-layout-color2: var(--md-grey-200);--jp-inverse-layout-color3: var(--md-grey-400);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-700);--jp-brand-color1: var(--md-blue-500);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-700);--jp-accent-color1: var(--md-green-500);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-700);--jp-warn-color1: var(--md-orange-500);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-700);--jp-error-color1: var(--md-red-500);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-700);--jp-success-color1: var(--md-green-500);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-700);--jp-info-color1: var(--md-cyan-500);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--jp-layout-color1);--jp-cell-editor-border-color: var(--md-grey-700);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: 1;--jp-cell-prompt-not-active-font-color: var(--md-grey-300);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: rgba(33, 150, 243, .24);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: rgba(244, 67, 54, .28);--jp-rendermime-table-row-background: var(--md-grey-900);--jp-rendermime-table-row-hover-background: rgba(3, 169, 244, .2);--jp-dialog-background: rgba(0, 0, 0, .6);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color2);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .8);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--jp-layout-color0);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color0);--jp-input-hover-background: var(--jp-layout-color2);--jp-input-background: var(--md-grey-800);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: var(--jp-layout-color2);--jp-editor-selected-focused-background: rgba(33, 150, 243, .24);--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: var(--md-green-500);--jp-mirror-editor-atom-color: var(--md-blue-300);--jp-mirror-editor-number-color: var(--md-green-400);--jp-mirror-editor-def-color: var(--md-blue-600);--jp-mirror-editor-variable-color: var(--md-grey-300);--jp-mirror-editor-variable-2-color: var(--md-blue-400);--jp-mirror-editor-variable-3-color: var(--md-green-600);--jp-mirror-editor-punctuation-color: var(--md-blue-400);--jp-mirror-editor-property-color: var(--md-blue-400);--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ff7070;--jp-mirror-editor-string-2-color: var(--md-purple-300);--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: var(--md-green-600);--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: var(--md-green-700);--jp-mirror-editor-attribute-color: var(--md-blue-700);--jp-mirror-editor-header-color: var(--md-blue-500);--jp-mirror-editor-quote-color: var(--md-green-300);--jp-mirror-editor-link-color: var(--md-blue-700);--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ad4a00;--jp-collaborator-color2: #7b6a00;--jp-collaborator-color3: #007e00;--jp-collaborator-color4: #008772;--jp-collaborator-color5: #0079b9;--jp-collaborator-color6: #8b45c6;--jp-collaborator-color7: #be208b;--jp-vega-background: var(--md-grey-400);--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .6;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(255, 225, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-scrollbar-background-color: #3f4244;--jp-scrollbar-thumb-color: 88, 96, 97;--jp-scrollbar-endpad: 3px;--jp-scrollbar-thumb-margin: 3.5px;--jp-scrollbar-thumb-radius: 9px;--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-500);--jp-console-icon-background-color: var(--md-blue-500);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-200);--jp-terminal-icon-color: var(--md-grey-800);--jp-text-editor-icon-color: var(--md-grey-200);--jp-inspector-icon-color: var(--md-grey-200);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-700)}.jupyter-wrapper 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#f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: #3e2723;--md-grey-50: #fafafa;--md-grey-100: #f5f5f5;--md-grey-200: #eeeeee;--md-grey-300: #e0e0e0;--md-grey-400: #bdbdbd;--md-grey-500: #9e9e9e;--md-grey-600: #757575;--md-grey-700: #616161;--md-grey-800: #424242;--md-grey-900: #212121;--md-blue-grey-50: #eceff1;--md-blue-grey-100: #cfd8dc;--md-blue-grey-200: #b0bec5;--md-blue-grey-300: #90a4ae;--md-blue-grey-400: #78909c;--md-blue-grey-500: #607d8b;--md-blue-grey-600: #546e7a;--md-blue-grey-700: #455a64;--md-blue-grey-800: #37474f;--md-blue-grey-900: #263238}.jupyter-wrapper{--jp-shadow-base-lightness: 0;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-400);--jp-border-color1: var(--md-grey-400);--jp-border-color2: var(--md-grey-300);--jp-border-color3: var(--md-grey-200);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(0, 0, 0, 1);--jp-ui-font-color1: rgba(0, 0, 0, .87);--jp-ui-font-color2: rgba(0, 0, 0, .54);--jp-ui-font-color3: rgba(0, 0, 0, .38);--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color2: rgba(255, 255, 255, .7);--jp-ui-inverse-font-color3: rgba(255, 255, 255, .5);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: white;--jp-layout-color1: white;--jp-layout-color2: var(--md-grey-200);--jp-layout-color3: var(--md-grey-400);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: #111111;--jp-inverse-layout-color1: var(--md-grey-900);--jp-inverse-layout-color2: var(--md-grey-800);--jp-inverse-layout-color3: var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(255, 255, 255, 1);--jp-ui-font-color1: rgba(255, 255, 255, .87);--jp-ui-font-color2: rgba(255, 255, 255, .54);--jp-ui-font-color3: rgba(255, 255, 255, .38);--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);--jp-ui-inverse-font-color1: rgba(0, 0, 0, .8);--jp-ui-inverse-font-color2: rgba(0, 0, 0, .5);--jp-ui-inverse-font-color3: rgba(0, 0, 0, .3);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(255, 255, 255, 1);--jp-content-font-color1: 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diff --git a/examples/non_stationary_kernel.html b/examples/non_stationary_kernel.html
index 2cebba8..50295a6 100644
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       <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
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+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3 3.19.09m3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95 2.06.05m-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31Z"/></svg>
       </label>
     
   
@@ -204,7 +204,7 @@
 
     <!-- User preference: color palette -->
     
-      <script>var palette=__md_get("__palette");if(palette&&palette.color){if("(prefers-color-scheme)"===palette.color.media){var media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']");palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent")}for(var[key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
     
 
     <!-- Site language selector -->
@@ -214,7 +214,7 @@
     
       <label class="md-header__button md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
 
       <!-- Search interface -->
@@ -225,20 +225,20 @@
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
           <a href="javascript:void(0)" class="md-search__icon md-icon" title="Share" aria-label="Share" data-clipboard data-clipboard-text="" data-md-component="search-share" tabindex="-1">
             
-            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7s-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91s2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08"/></svg>
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7 0-.24-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91 1.61 0 2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08Z"/></svg>
           </a>
         
         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
           
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12z"/></svg>
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
       
@@ -265,7 +265,7 @@
         <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -372,6 +372,26 @@
         
   
   
+  
+    
+    
+      <li class="md-tabs__item">
+        <a href="../blueos-sgp-tools.html" class="md-tabs__link">
+          
+  
+    
+  
+  BlueOS
+
+        </a>
+      </li>
+    
+  
+
+      
+        
+  
+  
     
   
   
@@ -473,7 +493,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -1311,6 +1331,78 @@
       
   
   
+  
+  
+    
+    
+      
+        
+      
+    
+    
+      
+      
+        
+      
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        
+          
+          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    BlueOS
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_5">
+            <span class="md-nav__icon md-icon"></span>
+            BlueOS
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../blueos-sgp-tools.html" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    blueos-sgp-tools
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
     
   
   
@@ -1345,10 +1437,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" checked>
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" checked>
         
           
-          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="">
+          <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="">
             
   
   <span class="md-ellipsis">
@@ -1359,8 +1451,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="true">
-          <label class="md-nav__title" for="__nav_5">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1796,7 +1888,7 @@
 .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
   </style>
 <style type="text/css">
-@charset "UTF-8";.jupyter-wrapper{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: #3e2723;--md-grey-50: #fafafa;--md-grey-100: #f5f5f5;--md-grey-200: #eeeeee;--md-grey-300: #e0e0e0;--md-grey-400: #bdbdbd;--md-grey-500: #9e9e9e;--md-grey-600: #757575;--md-grey-700: #616161;--md-grey-800: #424242;--md-grey-900: #212121;--md-blue-grey-50: #eceff1;--md-blue-grey-100: #cfd8dc;--md-blue-grey-200: #b0bec5;--md-blue-grey-300: #90a4ae;--md-blue-grey-400: #78909c;--md-blue-grey-500: #607d8b;--md-blue-grey-600: #546e7a;--md-blue-grey-700: #455a64;--md-blue-grey-800: #37474f;--md-blue-grey-900: #263238}.jupyter-wrapper{--jp-shadow-base-lightness: 0;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-400);--jp-border-color1: var(--md-grey-400);--jp-border-color2: var(--md-grey-300);--jp-border-color3: var(--md-grey-200);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(0, 0, 0, 1);--jp-ui-font-color1: rgba(0, 0, 0, .87);--jp-ui-font-color2: rgba(0, 0, 0, .54);--jp-ui-font-color3: rgba(0, 0, 0, .38);--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color2: rgba(255, 255, 255, .7);--jp-ui-inverse-font-color3: rgba(255, 255, 255, .5);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: white;--jp-layout-color1: white;--jp-layout-color2: var(--md-grey-200);--jp-layout-color3: var(--md-grey-400);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: #111111;--jp-inverse-layout-color1: var(--md-grey-900);--jp-inverse-layout-color2: var(--md-grey-800);--jp-inverse-layout-color3: var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(255, 255, 255, 1);--jp-ui-font-color1: rgba(255, 255, 255, .87);--jp-ui-font-color2: rgba(255, 255, 255, .54);--jp-ui-font-color3: rgba(255, 255, 255, .38);--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);--jp-ui-inverse-font-color1: rgba(0, 0, 0, .8);--jp-ui-inverse-font-color2: rgba(0, 0, 0, .5);--jp-ui-inverse-font-color3: rgba(0, 0, 0, .3);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(255, 255, 255, 1);--jp-content-font-color1: rgba(255, 255, 255, 1);--jp-content-font-color2: rgba(255, 255, 255, .7);--jp-content-font-color3: rgba(255, 255, 255, .5);--jp-content-link-color: var(--md-blue-300);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: #111111;--jp-layout-color1: var(--md-grey-900);--jp-layout-color2: var(--md-grey-800);--jp-layout-color3: var(--md-grey-700);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: white;--jp-inverse-layout-color1: white;--jp-inverse-layout-color2: var(--md-grey-200);--jp-inverse-layout-color3: var(--md-grey-400);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-700);--jp-brand-color1: var(--md-blue-500);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-700);--jp-accent-color1: var(--md-green-500);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-700);--jp-warn-color1: var(--md-orange-500);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-700);--jp-error-color1: var(--md-red-500);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-700);--jp-success-color1: var(--md-green-500);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-700);--jp-info-color1: var(--md-cyan-500);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--jp-layout-color1);--jp-cell-editor-border-color: var(--md-grey-700);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: 1;--jp-cell-prompt-not-active-font-color: var(--md-grey-300);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: rgba(33, 150, 243, .24);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: rgba(244, 67, 54, .28);--jp-rendermime-table-row-background: var(--md-grey-900);--jp-rendermime-table-row-hover-background: rgba(3, 169, 244, .2);--jp-dialog-background: rgba(0, 0, 0, .6);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color2);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .8);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--jp-layout-color0);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color0);--jp-input-hover-background: var(--jp-layout-color2);--jp-input-background: var(--md-grey-800);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: var(--jp-layout-color2);--jp-editor-selected-focused-background: rgba(33, 150, 243, .24);--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: var(--md-green-500);--jp-mirror-editor-atom-color: var(--md-blue-300);--jp-mirror-editor-number-color: var(--md-green-400);--jp-mirror-editor-def-color: var(--md-blue-600);--jp-mirror-editor-variable-color: var(--md-grey-300);--jp-mirror-editor-variable-2-color: var(--md-blue-400);--jp-mirror-editor-variable-3-color: var(--md-green-600);--jp-mirror-editor-punctuation-color: var(--md-blue-400);--jp-mirror-editor-property-color: var(--md-blue-400);--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ff7070;--jp-mirror-editor-string-2-color: var(--md-purple-300);--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: var(--md-green-600);--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: var(--md-green-700);--jp-mirror-editor-attribute-color: var(--md-blue-700);--jp-mirror-editor-header-color: var(--md-blue-500);--jp-mirror-editor-quote-color: var(--md-green-300);--jp-mirror-editor-link-color: var(--md-blue-700);--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ad4a00;--jp-collaborator-color2: #7b6a00;--jp-collaborator-color3: #007e00;--jp-collaborator-color4: #008772;--jp-collaborator-color5: #0079b9;--jp-collaborator-color6: #8b45c6;--jp-collaborator-color7: #be208b;--jp-vega-background: var(--md-grey-400);--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .6;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(255, 225, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-scrollbar-background-color: #3f4244;--jp-scrollbar-thumb-color: 88, 96, 97;--jp-scrollbar-endpad: 3px;--jp-scrollbar-thumb-margin: 3.5px;--jp-scrollbar-thumb-radius: 9px;--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-500);--jp-console-icon-background-color: var(--md-blue-500);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-200);--jp-terminal-icon-color: var(--md-grey-800);--jp-text-editor-icon-color: var(--md-grey-200);--jp-inspector-icon-color: var(--md-grey-200);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-700)}.jupyter-wrapper 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#f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: #3e2723;--md-grey-50: #fafafa;--md-grey-100: #f5f5f5;--md-grey-200: #eeeeee;--md-grey-300: #e0e0e0;--md-grey-400: #bdbdbd;--md-grey-500: #9e9e9e;--md-grey-600: #757575;--md-grey-700: #616161;--md-grey-800: #424242;--md-grey-900: #212121;--md-blue-grey-50: #eceff1;--md-blue-grey-100: #cfd8dc;--md-blue-grey-200: #b0bec5;--md-blue-grey-300: #90a4ae;--md-blue-grey-400: #78909c;--md-blue-grey-500: #607d8b;--md-blue-grey-600: #546e7a;--md-blue-grey-700: #455a64;--md-blue-grey-800: #37474f;--md-blue-grey-900: #263238}.jupyter-wrapper{--jp-shadow-base-lightness: 0;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-400);--jp-border-color1: var(--md-grey-400);--jp-border-color2: var(--md-grey-300);--jp-border-color3: var(--md-grey-200);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(0, 0, 0, 1);--jp-ui-font-color1: rgba(0, 0, 0, .87);--jp-ui-font-color2: rgba(0, 0, 0, .54);--jp-ui-font-color3: rgba(0, 0, 0, .38);--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color2: rgba(255, 255, 255, .7);--jp-ui-inverse-font-color3: rgba(255, 255, 255, .5);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: white;--jp-layout-color1: white;--jp-layout-color2: var(--md-grey-200);--jp-layout-color3: var(--md-grey-400);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: #111111;--jp-inverse-layout-color1: var(--md-grey-900);--jp-inverse-layout-color2: var(--md-grey-800);--jp-inverse-layout-color3: var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(255, 255, 255, 1);--jp-ui-font-color1: rgba(255, 255, 255, .87);--jp-ui-font-color2: rgba(255, 255, 255, .54);--jp-ui-font-color3: rgba(255, 255, 255, .38);--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);--jp-ui-inverse-font-color1: rgba(0, 0, 0, .8);--jp-ui-inverse-font-color2: rgba(0, 0, 0, .5);--jp-ui-inverse-font-color3: rgba(0, 0, 0, .3);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(255, 255, 255, 1);--jp-content-font-color1: 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@@ -2106,7 +2198,7 @@ <h2 id="generate-synthetic-non-stationary-data">Generate synthetic non-stationar
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@@ -2278,7 +2370,7 @@ <h2 id="learn-kernel-parameters-from-train-data">Learn kernel parameters from tr
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+<clipboard-copy for="cell-3">
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@@ -2450,7 +2542,7 @@ <h2 id="get-continuous-sgp-solution-with-a-baseline-stationary-kernel-and-the-le
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+<clipboard-copy for="cell-4">
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@@ -2618,7 +2710,7 @@ <h2 id="get-continuous-sgp-solution-with-a-baseline-stationary-kernel-and-the-le
         
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   Back to top
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@@ -2648,10 +2740,10 @@ <h2 id="get-continuous-sgp-solution-with-a-baseline-stationary-kernel-and-the-le
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+    <script id="__config" type="application/json">{"base": "..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="../assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
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diff --git a/examples/obstacles.html b/examples/obstacles.html
index 4b35fe8..79cb567 100644
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+++ b/examples/obstacles.html
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+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.31">
     
     
       
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-      <link rel="stylesheet" href="../assets/stylesheets/main.6f8fc17f.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.3cba04c6.min.css">
       
         
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+    <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
   
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@@ -202,7 +202,7 @@
 
     <!-- User preference: color palette -->
     
-      <script>var palette=__md_get("__palette");if(palette&&palette.color){if("(prefers-color-scheme)"===palette.color.media){var media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']");palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent")}for(var[key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
     
 
     <!-- Site language selector -->
@@ -212,7 +212,7 @@
     
       <label class="md-header__button md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
 
       <!-- Search interface -->
@@ -223,20 +223,20 @@
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
           <a href="javascript:void(0)" class="md-search__icon md-icon" title="Share" aria-label="Share" data-clipboard data-clipboard-text="" data-md-component="search-share" tabindex="-1">
             
-            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7s-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91s2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08"/></svg>
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7 0-.24-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91 1.61 0 2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08Z"/></svg>
           </a>
         
         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
           
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12z"/></svg>
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
       
@@ -263,7 +263,7 @@
         <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -370,6 +370,26 @@
         
   
   
+  
+    
+    
+      <li class="md-tabs__item">
+        <a href="../blueos-sgp-tools.html" class="md-tabs__link">
+          
+  
+    
+  
+  BlueOS
+
+        </a>
+      </li>
+    
+  
+
+      
+        
+  
+  
     
   
   
@@ -471,7 +491,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -1309,6 +1329,78 @@
       
   
   
+  
+  
+    
+    
+      
+        
+      
+    
+    
+      
+      
+        
+      
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        
+          
+          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    BlueOS
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_5">
+            <span class="md-nav__icon md-icon"></span>
+            BlueOS
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../blueos-sgp-tools.html" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    blueos-sgp-tools
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
     
   
   
@@ -1343,10 +1435,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" checked>
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" checked>
         
           
-          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="">
+          <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="">
             
   
   <span class="md-ellipsis">
@@ -1357,8 +1449,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="true">
-          <label class="md-nav__title" for="__nav_5">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1776,7 +1868,7 @@
 .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
   </style>
 <style type="text/css">
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#00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(255, 255, 255, 1);--jp-ui-font-color1: rgba(255, 255, 255, .87);--jp-ui-font-color2: rgba(255, 255, 255, .54);--jp-ui-font-color3: rgba(255, 255, 255, .38);--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);--jp-ui-inverse-font-color1: rgba(0, 0, 0, .8);--jp-ui-inverse-font-color2: rgba(0, 0, 0, .5);--jp-ui-inverse-font-color3: rgba(0, 0, 0, .3);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(255, 255, 255, 1);--jp-content-font-color1: rgba(255, 255, 255, 1);--jp-content-font-color2: rgba(255, 255, 255, .7);--jp-content-font-color3: rgba(255, 255, 255, .5);--jp-content-link-color: var(--md-blue-300);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: #111111;--jp-layout-color1: var(--md-grey-900);--jp-layout-color2: var(--md-grey-800);--jp-layout-color3: var(--md-grey-700);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: white;--jp-inverse-layout-color1: white;--jp-inverse-layout-color2: var(--md-grey-200);--jp-inverse-layout-color3: var(--md-grey-400);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-700);--jp-brand-color1: var(--md-blue-500);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-700);--jp-accent-color1: var(--md-green-500);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-700);--jp-warn-color1: var(--md-orange-500);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-700);--jp-error-color1: var(--md-red-500);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-700);--jp-success-color1: var(--md-green-500);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-700);--jp-info-color1: var(--md-cyan-500);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--jp-layout-color1);--jp-cell-editor-border-color: var(--md-grey-700);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: 1;--jp-cell-prompt-not-active-font-color: var(--md-grey-300);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: rgba(33, 150, 243, .24);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: rgba(244, 67, 54, .28);--jp-rendermime-table-row-background: var(--md-grey-900);--jp-rendermime-table-row-hover-background: rgba(3, 169, 244, .2);--jp-dialog-background: rgba(0, 0, 0, .6);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color2);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .8);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--jp-layout-color0);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color0);--jp-input-hover-background: var(--jp-layout-color2);--jp-input-background: var(--md-grey-800);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: var(--jp-layout-color2);--jp-editor-selected-focused-background: rgba(33, 150, 243, .24);--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: var(--md-green-500);--jp-mirror-editor-atom-color: var(--md-blue-300);--jp-mirror-editor-number-color: var(--md-green-400);--jp-mirror-editor-def-color: var(--md-blue-600);--jp-mirror-editor-variable-color: var(--md-grey-300);--jp-mirror-editor-variable-2-color: var(--md-blue-400);--jp-mirror-editor-variable-3-color: var(--md-green-600);--jp-mirror-editor-punctuation-color: var(--md-blue-400);--jp-mirror-editor-property-color: var(--md-blue-400);--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ff7070;--jp-mirror-editor-string-2-color: var(--md-purple-300);--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: var(--md-green-600);--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: var(--md-green-700);--jp-mirror-editor-attribute-color: var(--md-blue-700);--jp-mirror-editor-header-color: var(--md-blue-500);--jp-mirror-editor-quote-color: var(--md-green-300);--jp-mirror-editor-link-color: var(--md-blue-700);--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ad4a00;--jp-collaborator-color2: #7b6a00;--jp-collaborator-color3: #007e00;--jp-collaborator-color4: #008772;--jp-collaborator-color5: #0079b9;--jp-collaborator-color6: #8b45c6;--jp-collaborator-color7: #be208b;--jp-vega-background: var(--md-grey-400);--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .6;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(255, 225, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-scrollbar-background-color: #3f4244;--jp-scrollbar-thumb-color: 88, 96, 97;--jp-scrollbar-endpad: 3px;--jp-scrollbar-thumb-margin: 3.5px;--jp-scrollbar-thumb-radius: 9px;--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-500);--jp-console-icon-background-color: var(--md-blue-500);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-200);--jp-terminal-icon-color: var(--md-grey-800);--jp-text-editor-icon-color: var(--md-grey-200);--jp-inspector-icon-color: var(--md-grey-200);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-700)}.jupyter-wrapper 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#f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: #3e2723;--md-grey-50: #fafafa;--md-grey-100: #f5f5f5;--md-grey-200: #eeeeee;--md-grey-300: #e0e0e0;--md-grey-400: #bdbdbd;--md-grey-500: #9e9e9e;--md-grey-600: #757575;--md-grey-700: #616161;--md-grey-800: #424242;--md-grey-900: #212121;--md-blue-grey-50: #eceff1;--md-blue-grey-100: #cfd8dc;--md-blue-grey-200: #b0bec5;--md-blue-grey-300: #90a4ae;--md-blue-grey-400: #78909c;--md-blue-grey-500: #607d8b;--md-blue-grey-600: #546e7a;--md-blue-grey-700: #455a64;--md-blue-grey-800: #37474f;--md-blue-grey-900: #263238}.jupyter-wrapper{--jp-shadow-base-lightness: 0;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-400);--jp-border-color1: var(--md-grey-400);--jp-border-color2: var(--md-grey-300);--jp-border-color3: var(--md-grey-200);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(0, 0, 0, 1);--jp-ui-font-color1: rgba(0, 0, 0, .87);--jp-ui-font-color2: rgba(0, 0, 0, .54);--jp-ui-font-color3: rgba(0, 0, 0, .38);--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color2: rgba(255, 255, 255, .7);--jp-ui-inverse-font-color3: rgba(255, 255, 255, .5);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: white;--jp-layout-color1: white;--jp-layout-color2: var(--md-grey-200);--jp-layout-color3: var(--md-grey-400);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: #111111;--jp-inverse-layout-color1: var(--md-grey-900);--jp-inverse-layout-color2: var(--md-grey-800);--jp-inverse-layout-color3: var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(255, 255, 255, 1);--jp-ui-font-color1: rgba(255, 255, 255, .87);--jp-ui-font-color2: rgba(255, 255, 255, .54);--jp-ui-font-color3: rgba(255, 255, 255, .38);--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);--jp-ui-inverse-font-color1: rgba(0, 0, 0, .8);--jp-ui-inverse-font-color2: rgba(0, 0, 0, .5);--jp-ui-inverse-font-color3: rgba(0, 0, 0, .3);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(255, 255, 255, 1);--jp-content-font-color1: 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var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}
@@ -2018,7 +2110,7 @@ <h1 id="environment-with-obstacles">Environment with Obstacles<a class="anchor-l
 <div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="CodeMirror cm-s-jupyter">
 <div class="zeroclipboard-container">
-<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-1">
+<clipboard-copy for="cell-1">
 <div>
 <span class="notice" hidden="">Copied!</span>
 <svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
@@ -2065,7 +2157,7 @@ <h1 id="environment-with-obstacles">Environment with Obstacles<a class="anchor-l
 <div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="CodeMirror cm-s-jupyter">
 <div class="zeroclipboard-container">
-<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-2">
+<clipboard-copy for="cell-2">
 <div>
 <span class="notice" hidden="">Copied!</span>
 <svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
@@ -2177,7 +2269,7 @@ <h1 id="environment-with-obstacles">Environment with Obstacles<a class="anchor-l
 <div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="CodeMirror cm-s-jupyter">
 <div class="zeroclipboard-container">
-<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-3">
+<clipboard-copy for="cell-3">
 <div>
 <span class="notice" hidden="">Copied!</span>
 <svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
@@ -2257,7 +2349,7 @@ <h1 id="environment-with-obstacles">Environment with Obstacles<a class="anchor-l
 <div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="CodeMirror cm-s-jupyter">
 <div class="zeroclipboard-container">
-<clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-4">
+<clipboard-copy for="cell-4">
 <div>
 <span class="notice" hidden="">Copied!</span>
 <svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
@@ -2372,7 +2464,7 @@ <h3 id="note-that-all-the-solution-placements-avoid-the-obstacles">Note that all
         
           <button type="button" class="md-top md-icon" data-md-component="top" hidden>
   
-  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8z"/></svg>
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg>
   Back to top
 </button>
         
@@ -2402,10 +2494,10 @@ <h3 id="note-that-all-the-solution-placements-avoid-the-obstacles">Note that all
     </div>
     
     
-    <script id="__config" type="application/json">{"base": "..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../assets/javascripts/workers/search.6ce7567c.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="../assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
   </body>
diff --git a/index.html b/index.html
index 3f7fa28..4fd0d35 100644
--- a/index.html
+++ b/index.html
@@ -18,7 +18,7 @@
       
       
       <link rel="icon" href="assets/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.49">
+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.31">
     
     
       
@@ -26,7 +26,7 @@
       
     
     
-      <link rel="stylesheet" href="assets/stylesheets/main.6f8fc17f.min.css">
+      <link rel="stylesheet" href="assets/stylesheets/main.3cba04c6.min.css">
       
         
         <link rel="stylesheet" href="assets/stylesheets/palette.06af60db.min.css">
@@ -54,7 +54,7 @@
     
       <link rel="stylesheet" href="stylesheets/extra.css">
     
-    <script>__md_scope=new URL(".",location),__md_hash=e=>[...e].reduce(((e,_)=>(e<<5)-e+_.charCodeAt(0)),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL(".",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
   
@@ -63,7 +63,7 @@
   
   
 
-<script id="__analytics">function __md_analytics(){function e(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],e("js",new Date),e("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",(function(){document.forms.search&&document.forms.search.query.addEventListener("blur",(function(){this.value&&e("event","search",{search_term:this.value})}));document$.subscribe((function(){var t=document.forms.feedback;if(void 0!==t)for(var a of t.querySelectorAll("[type=submit]"))a.addEventListener("click",(function(a){a.preventDefault();var n=document.location.pathname,d=this.getAttribute("data-md-value");e("event","feedback",{page:n,data:d}),t.firstElementChild.disabled=!0;var r=t.querySelector(".md-feedback__note [data-md-value='"+d+"']");r&&(r.hidden=!1)})),t.hidden=!1})),location$.subscribe((function(t){e("config","G-Z2TVV3MF1S",{page_path:t.pathname})}))}));var t=document.createElement("script");t.async=!0,t.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",t)}</script>
+<script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",function(){document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document$.subscribe(function(){var a=document.forms.feedback;if(void 0!==a)for(var e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}),location$.subscribe(function(e){n("config","G-Z2TVV3MF1S",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
   
     <script>"undefined"!=typeof __md_analytics&&__md_analytics()</script>
   
@@ -151,7 +151,7 @@
     <!-- Button to open drawer -->
     <label class="md-header__button md-icon" for="__drawer">
       
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3zm0 5h18v2H3zm0 5h18v2H3z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
 
     <!-- Header title -->
@@ -182,7 +182,7 @@
     <input class="md-option" data-md-color-media="(prefers-color-scheme: light)" data-md-color-scheme="default" data-md-color-primary="custom" data-md-color-accent="custom"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_0">
     
       <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5s-1.65.15-2.39.42zM3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29zm.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14zM20.65 7l-1.77 3.79a7.02 7.02 0 0 0-2.38-4.15zm-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29zM12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5c-.84 0-1.65.15-2.39.42L12 2M3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29L3.34 7m.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14L3.36 17M20.65 7l-1.77 3.79a7.023 7.023 0 0 0-2.38-4.15l4.15.36m-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29L20.64 17M12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44L12 22Z"/></svg>
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@@ -192,7 +192,7 @@
     <input class="md-option" data-md-color-media="(prefers-color-scheme: dark)" data-md-color-scheme="slate" data-md-color-primary="custom" data-md-color-accent="custom"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_1">
     
       <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3zm3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95zm-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3 3.19.09m3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95 2.06.05m-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31Z"/></svg>
       </label>
     
   
@@ -202,7 +202,7 @@
 
     <!-- User preference: color palette -->
     
-      <script>var palette=__md_get("__palette");if(palette&&palette.color){if("(prefers-color-scheme)"===palette.color.media){var media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']");palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent")}for(var[key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
     
 
     <!-- Site language selector -->
@@ -212,7 +212,7 @@
     
       <label class="md-header__button md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
 
       <!-- Search interface -->
@@ -223,20 +223,20 @@
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
           <a href="javascript:void(0)" class="md-search__icon md-icon" title="Share" aria-label="Share" data-clipboard data-clipboard-text="" data-md-component="search-share" tabindex="-1">
             
-            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7s-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91s2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08"/></svg>
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7 0-.24-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91 1.61 0 2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08Z"/></svg>
           </a>
         
         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
           
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12z"/></svg>
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
       
@@ -263,7 +263,7 @@
         <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -375,6 +375,26 @@
   
     
     
+      <li class="md-tabs__item">
+        <a href="blueos-sgp-tools.html" class="md-tabs__link">
+          
+  
+    
+  
+  BlueOS
+
+        </a>
+      </li>
+    
+  
+
+      
+        
+  
+  
+  
+    
+    
       <li class="md-tabs__item">
         <a href="examples/AIPP_non_point_FoV.html" class="md-tabs__link">
           
@@ -471,7 +491,7 @@
       <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.7.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6m-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3m44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9M244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8M97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1m-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7m32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1m-11.4-14.7c-1.6 1-1.6 3.6 0 5.9s4.3 3.3 5.6 2.3c1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -1412,6 +1432,78 @@
   
     
     
+      
+        
+      
+    
+    
+      
+      
+        
+      
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        
+          
+          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    BlueOS
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_5">
+            <span class="md-nav__icon md-icon"></span>
+            BlueOS
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="blueos-sgp-tools.html" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    blueos-sgp-tools
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    
+    
       
         
       
@@ -1437,10 +1529,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
         
           
-          <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+          <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
             
   
   <span class="md-ellipsis">
@@ -1451,8 +1543,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_5">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1697,7 +1789,7 @@ <h2 id="license">License</h2>
         
           <button type="button" class="md-top md-icon" data-md-component="top" hidden>
   
-  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8z"/></svg>
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg>
   Back to top
 </button>
         
@@ -1727,10 +1819,10 @@ <h2 id="license">License</h2>
     </div>
     
     
-    <script id="__config" type="application/json">{"base": ".", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "assets/javascripts/workers/search.6ce7567c.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": ".", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
   </body>
diff --git a/ros_sgp_tools.html b/ros_sgp_tools.html
index dac3af1..38529b7 100644
--- a/ros_sgp_tools.html
+++ b/ros_sgp_tools.html
@@ -20,7 +20,7 @@
       
       
       <link rel="icon" href="assets/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.5.49">
+      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.31">
     
     
       
@@ -28,7 +28,7 @@
       
     
     
-      <link rel="stylesheet" href="assets/stylesheets/main.6f8fc17f.min.css">
+      <link rel="stylesheet" href="assets/stylesheets/main.3cba04c6.min.css">
       
         
         <link rel="stylesheet" href="assets/stylesheets/palette.06af60db.min.css">
@@ -56,7 +56,7 @@
     
       <link rel="stylesheet" href="stylesheets/extra.css">
     
-    <script>__md_scope=new URL(".",location),__md_hash=e=>[...e].reduce(((e,_)=>(e<<5)-e+_.charCodeAt(0)),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL(".",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
   
@@ -65,7 +65,7 @@
   
   
 
-<script id="__analytics">function __md_analytics(){function e(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],e("js",new Date),e("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",(function(){document.forms.search&&document.forms.search.query.addEventListener("blur",(function(){this.value&&e("event","search",{search_term:this.value})}));document$.subscribe((function(){var t=document.forms.feedback;if(void 0!==t)for(var a of t.querySelectorAll("[type=submit]"))a.addEventListener("click",(function(a){a.preventDefault();var n=document.location.pathname,d=this.getAttribute("data-md-value");e("event","feedback",{page:n,data:d}),t.firstElementChild.disabled=!0;var r=t.querySelector(".md-feedback__note [data-md-value='"+d+"']");r&&(r.hidden=!1)})),t.hidden=!1})),location$.subscribe((function(t){e("config","G-Z2TVV3MF1S",{page_path:t.pathname})}))}));var t=document.createElement("script");t.async=!0,t.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",t)}</script>
+<script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-Z2TVV3MF1S"),document.addEventListener("DOMContentLoaded",function(){document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document$.subscribe(function(){var a=document.forms.feedback;if(void 0!==a)for(var e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}),location$.subscribe(function(e){n("config","G-Z2TVV3MF1S",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-Z2TVV3MF1S",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script>
   
     <script>"undefined"!=typeof __md_analytics&&__md_analytics()</script>
   
@@ -153,7 +153,7 @@
     <!-- Button to open drawer -->
     <label class="md-header__button md-icon" for="__drawer">
       
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3zm0 5h18v2H3zm0 5h18v2H3z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
 
     <!-- Header title -->
@@ -184,7 +184,7 @@
     <input class="md-option" data-md-color-media="(prefers-color-scheme: light)" data-md-color-scheme="default" data-md-color-primary="custom" data-md-color-accent="custom"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_0">
     
       <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5s-1.65.15-2.39.42zM3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29zm.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14zM20.65 7l-1.77 3.79a7.02 7.02 0 0 0-2.38-4.15zm-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29zM12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5c-.84 0-1.65.15-2.39.42L12 2M3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29L3.34 7m.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14L3.36 17M20.65 7l-1.77 3.79a7.023 7.023 0 0 0-2.38-4.15l4.15.36m-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29L20.64 17M12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44L12 22Z"/></svg>
       </label>
     
   
@@ -194,7 +194,7 @@
     <input class="md-option" data-md-color-media="(prefers-color-scheme: dark)" data-md-color-scheme="slate" data-md-color-primary="custom" data-md-color-accent="custom"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_1">
     
       <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3zm3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95zm-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m17.75 4.09-2.53 1.94.91 3.06-2.63-1.81-2.63 1.81.91-3.06-2.53-1.94L12.44 4l1.06-3 1.06 3 3.19.09m3.5 6.91-1.64 1.25.59 1.98-1.7-1.17-1.7 1.17.59-1.98L15.75 11l2.06-.05L18.5 9l.69 1.95 2.06.05m-2.28 4.95c.83-.08 1.72 1.1 1.19 1.85-.32.45-.66.87-1.08 1.27C15.17 23 8.84 23 4.94 19.07c-3.91-3.9-3.91-10.24 0-14.14.4-.4.82-.76 1.27-1.08.75-.53 1.93.36 1.85 1.19-.27 2.86.69 5.83 2.89 8.02a9.96 9.96 0 0 0 8.02 2.89m-1.64 2.02a12.08 12.08 0 0 1-7.8-3.47c-2.17-2.19-3.33-5-3.49-7.82-2.81 3.14-2.7 7.96.31 10.98 3.02 3.01 7.84 3.12 10.98.31Z"/></svg>
       </label>
     
   
@@ -204,7 +204,7 @@
 
     <!-- User preference: color palette -->
     
-      <script>var palette=__md_get("__palette");if(palette&&palette.color){if("(prefers-color-scheme)"===palette.color.media){var media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']");palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent")}for(var[key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
     
 
     <!-- Site language selector -->
@@ -214,7 +214,7 @@
     
       <label class="md-header__button md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
 
       <!-- Search interface -->
@@ -225,20 +225,20 @@
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
         
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
           <a href="javascript:void(0)" class="md-search__icon md-icon" title="Share" aria-label="Share" data-clipboard data-clipboard-text="" data-md-component="search-share" tabindex="-1">
             
-            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7s-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91s2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08"/></svg>
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7 0-.24-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91 1.61 0 2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08Z"/></svg>
           </a>
         
         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
           
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12z"/></svg>
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
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@@ -265,7 +265,7 @@
         <a href="https://github.com/itskalvik/SGP-Tools" title="Go to repository" class="md-source" data-md-component="source">
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@@ -1387,6 +1407,78 @@
   
     
     
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+    <li class="md-nav__item">
+      <a href="blueos-sgp-tools.html" class="md-nav__link">
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+    blueos-sgp-tools
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@@ -1426,8 +1518,8 @@
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-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_5">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_6">
             <span class="md-nav__icon md-icon"></span>
             Examples
           </label>
@@ -1584,8 +1676,6 @@
   
 
 
-<p><img alt="Image title" src="assets/SGP-Tools.png#only-light" />
-<img alt="Image title" src="assets/logo_dark.png#only-dark" /></p>
 <h1 id="ros_sgp_tools"><a href="https://github.com/itskalvik/ros_sgp_tools">ros_sgp_tools <span class="twemoji"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 .297c-6.63 0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61C4.422 18.07 3.633 17.7 3.633 17.7c-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22 0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22 0 1.606-.015 2.896-.015 3.286 0 .315.21.69.825.57C20.565 22.092 24 17.592 24 12.297c0-6.627-5.373-12-12-12"/></svg></span></a></h1>
 <p>The <a href="https://github.com/itskalvik/ros_sgp_tools">ros_sgp_tools</a> package provides a <a href="https://github.com/ros2">ROS2</a> companion package for the SGP-Tools python library that can be deployed on <a href="https://ardupilot.org/copter/docs/common-use-cases-and-applications.html">ArduPilot-based vehicles</a>. </p>
 <ul>
@@ -1593,7 +1683,10 @@ <h1 id="ros_sgp_tools"><a href="https://github.com/itskalvik/ros_sgp_tools">ros_
 <li>The package can also be used with Gazebo/Ardupilot SITL.</li>
 <li>To use our Docker container with the preconfigured development environment, please refer to the documentation <a href="docker-sgp-tools.html">here</a>. </li>
 </ul>
-<p><img alt="Image title" src="assets/ros2_ardupilot.png" /></p>
+<div style="text-align:left">
+<img height="60" src="assets/ros2_ardupilot.png">
+</div>
+
 <h3 id="running-sgp-tools-onlineadaptive-ipp-with-gazeboardurover-simulator">Running SGP-Tools Online/Adaptive IPP with Gazebo/ArduRover Simulator</h3>
 <p><img alt="Image title" src="assets/ros_demo.png" /></p>
 <p>Run the following commands in separate terminals:</p>
@@ -1688,7 +1781,7 @@ <h3 id="package-setup">Package setup</h3>
         
           <button type="button" class="md-top md-icon" data-md-component="top" hidden>
   
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   Back to top
 </button>
         
@@ -1718,10 +1811,10 @@ <h3 id="package-setup">Package setup</h3>
     </div>
     
     
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+    <script id="__config" type="application/json">{"base": ".", "features": ["search.suggest", "search.highlight", "search.share", "toc.integrate", "toc.follow", "navigation.top", "navigation.tabs.sticky", "navigation.tabs", "navigation.sections", "navigation.indexes", "content.code.copy"], "search": "assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="assets/javascripts/bundle.88dd0f4e.min.js"></script>
+      <script src="assets/javascripts/bundle.fe8b6f2b.min.js"></script>
       
     
   </body>
diff --git a/search/search_index.js b/search/search_index.js
index 7abae6a..86036dc 100644
--- a/search/search_index.js
+++ b/search/search_index.js
@@ -1 +1 @@
-var __index = {"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"index.html","title":"Home","text":"<p>SGP-Tools is a software suite for Sensor Placement and Informative Path Planning.</p> <p>The library includes python code for the following:</p> <ul> <li>Greedy algorithm-based approaches</li> <li>Bayesian optimization-based approaches</li> <li>Genetic algorithm-based approaches</li> <li>Sparse Gaussian process (SGP)-based approaches</li> </ul> <p> </p>"},{"location":"index.html#installation","title":"Installation","text":"<p>The library is available as a <code>pip</code> package. To install the package, run the following command:</p> <pre><code>python3 -m pip install sgptools\n</code></pre> <p>Installation from source:</p> <pre><code>git clone https://github.com/itskalvik/sgp-tools.git\ncd sgp-tools/\npython3 -m pip install -r requirements.txt\npython3 -m pip install -e .\n</code></pre> <p>Note: The requirements.txt file contains packages and their latest versions that were last verified to be working without any issues.</p>"},{"location":"index.html#quick-start","title":"Quick Start","text":"<p>Please refer to the example Jupyter notebooks demonstrating the methods included in the library \ud83d\ude04</p>"},{"location":"index.html#method-summary","title":"Method Summary","text":""},{"location":"index.html#datasets","title":"Datasets","text":"<ul> <li>High-resolution topography data can be downloaded from NOAA Digital Coast</li> <li>High-resolution bathymetry data can be downloaded from NOAA Digital Coast</li> </ul>"},{"location":"index.html#about","title":"About","text":"<p>Please consider citing the following papers if you use SGP-Tools in your academic work \ud83d\ude04</p> <pre><code>@misc{JakkalaA23SP,\nAUTHOR={Kalvik Jakkala and Srinivas Akella},\nTITLE={Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces},\nNOTE= {Preprint},\nYEAR={2023},\nURL={https://itskalvik.github.io/publication/sgp-sp},\n}\n\n@inproceedings{JakkalaA24IPP,\nAUTHOR={Kalvik Jakkala and Srinivas Akella},\nTITLE={Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes},\nbooktitle={IEEE International Conference on Robotics and Automation, {ICRA}},\nYEAR={2024},\nPUBLISHER = {{IEEE}},\nURL={https://itskalvik.github.io/publication/sgp-ipp}\n}\n</code></pre>"},{"location":"index.html#acknowledgements","title":"Acknowledgements","text":"<p>This work was funded in part by the UNC Charlotte Office of Research and Economic Development and by NSF under Award Number IIP-1919233.</p>"},{"location":"index.html#license","title":"License","text":"<p>The SGP-Tools software suite is licensed under the terms of the Apache License 2.0. See LICENSE for more information.</p>"},{"location":"docker-sgp-tools.html","title":"docker-sgp-tools","text":""},{"location":"docker-sgp-tools.html#docker-sgp-tools","title":"docker-sgp-tools","text":"<p>The docker-sgp-tools package provides the following docker-compose scripts:</p> <ul> <li><code>robot-compose.yml</code>: A minimal docker container used to run SGP-Tools ROS2 package on ArduPilot-based robots.</li> <li><code>sitl-compose.yml</code>: A GUI-based docker container with ROS2, Gazebo, ArduPilot SITL, and SGP-Tools used for simulating ArduPilot vehicles and testing SGP-Tools IPP code. </li> </ul> <p></p>"},{"location":"docker-sgp-tools.html#prerequisites","title":"Prerequisites","text":"<ul> <li>docker</li> <li>docker-compose</li> <li>(Optional) WSLg</li> </ul>"},{"location":"docker-sgp-tools.html#getting-started","title":"Getting Started","text":""},{"location":"docker-sgp-tools.html#starting-the-containers","title":"Starting the Containers","text":"<p>Run the following commands to start the SITL docker container:</p> <pre><code>git clone --recurse-submodules https://github.com/itskalvik/docker-sgp-tools.git\ncd docker-sgp-tools\ndocker-compose -f sitl-compose.yml pull\ndocker-compose -f sitl-compose.yml up -d\ndocker-compose -f sitl-compose.yml exec sgptools bash\n</code></pre> <p>Use <code>robot-compose.yml</code> to run the minimal docker container. </p>"},{"location":"docker-sgp-tools.html#running-sgp-tools-onlineadaptive-ipp-with-gazeboardurover-simulator","title":"Running SGP-Tools Online/Adaptive IPP with Gazebo/ArduRover Simulator","text":"<p>Use <code>docker-compose -f sitl-compose.yml exec sgptools bash</code> to get a new terminal. Run the following commands in separate terminals in the docker container:</p> <ul> <li> <p>Launch Gazebo with the AION R1 UGV:     <pre><code>gz sim -v4 -r r1_rover_runway.sdf\n</code></pre>     To simulate a BlueBoat refer to this documentation. Note that the container already has Wave Sim installed in it.</p> </li> <li> <p>Launch ArduRover SITL:     <pre><code>sim_vehicle.py -v Rover -f rover-skid --model JSON --add-param-file=$HOME/SITL_Models/Gazebo/config/r1_rover.param --console --map -N -l 35.30371178789218,-80.73099267294185,0.,0.\n</code></pre>     Note: Restart sim_vechile.py if you get the following message: <code>paramftp: bad count 1294 should be 1284</code></p> </li> <li> <p>Launch the SGP-Tools Online/Adaptive IPP method:     <pre><code>ros2 launch ros_sgp_tools single_robot.launch.py\n</code></pre></p> </li> </ul>"},{"location":"docker-sgp-tools.html#building-the-docker-containers-from-scratch","title":"Building the Docker Containers from Scratch","text":"<p>First, setup buildx to build the containers for both arm64 and amd64 platforms:  <pre><code>docker buildx create --name multi-arch \\\n  --platform \"linux/arm64,linux/amd64\" \\\n  --driver \"docker-container\"\ndocker buildx use multi-arch\n</code></pre></p> <p>Next, clone the repo and build the container.  <pre><code>git clone --recurse-submodules https://github.com/itskalvik/docker-sgp-tools.git\ncd docker-sgp-tools\ndocker-compose -f sitl-compose.yml build \n</code></pre></p> <p>Use <code>robot-compose.yml</code> to build the minimal docker container.</p>"},{"location":"docker-sgp-tools.html#other-commands","title":"Other commands","text":"<ul> <li> <p>The docker-compose down command stops and removes containers, networks, volumes, and images, making it suitable for completely clearing all resources deployed by an application.</p> <pre><code>docker-compose -f sitl-compose.yml down\n</code></pre> </li> <li> <p>The docker-compose stop command just pauses running containers without removing them, which is ideal for temporary halts.</p> <pre><code>docker-compose -f sitl-compose.yml stop\n</code></pre> </li> </ul>"},{"location":"docker-sgp-tools.html#references","title":"References","text":"<p>Based on Dockerfiles from the following repos:</p> <ul> <li>https://github.com/Robotic-Decision-Making-Lab/blue</li> <li>https://github.com/ryomo/ros2-gazebo-docker</li> </ul>"},{"location":"ros_sgp_tools.html","title":"ros_sgp_tools","text":""},{"location":"ros_sgp_tools.html#ros_sgp_tools","title":"ros_sgp_tools","text":"<p>The ros_sgp_tools package provides a ROS2 companion package for the SGP-Tools python library that can be deployed on ArduPilot-based vehicles. </p> <ul> <li>The package can be used to run online/adaptive IPP on ArduPilot based UGVs and ASVs. </li> <li>The package can also be used with Gazebo/Ardupilot SITL.</li> <li>To use our Docker container with the preconfigured development environment, please refer to the documentation here. </li> </ul> <p></p>"},{"location":"ros_sgp_tools.html#running-sgp-tools-onlineadaptive-ipp-with-gazeboardurover-simulator","title":"Running SGP-Tools Online/Adaptive IPP with Gazebo/ArduRover Simulator","text":"<p>Run the following commands in separate terminals:</p> <ul> <li> <p>Launch Gazebo with the AION R1 UGV:     <pre><code>gz sim -v4 -r r1_rover_runway.sdf\n</code></pre>     To simulate a BlueBoat refer to this documentation.</p> </li> <li> <p>Launch ArduRover SITL:     <pre><code>sim_vehicle.py -v Rover -f rover-skid --model JSON --add-param-file=$HOME/SITL_Models/Gazebo/config/r1_rover.param --console --map -N -l 35.30371178789218,-80.73099267294185,0.,0.\n</code></pre>     Note: </p> <ul> <li>Restart sim_vechile.py if you get the following message: <code>paramftp: bad count</code></li> <li>Ensure the MAV Console shows that the vehicle has a GPS lock before running the next command</li> <li>Ensure the MAV Map shows the vehicle before running the next command</li> </ul> </li> <li> <p>Launch the SGP-Tools Online/Adaptive IPP method:     <pre><code>ros2 launch ros_sgp_tools single_robot.launch.py\n</code></pre></p> </li> </ul>"},{"location":"ros_sgp_tools.html#environment-setup","title":"Environment setup","text":"<ul> <li>To use our Docker container with the preconfigured development environment, please refer to the documentation here. </li> </ul> <p>Alternatively, please follow the following instructions to configure the development envirnoment on your local machine. </p> <ul> <li>Install ROS 2 Humble <pre><code>sudo apt install ros-humble-desktop\necho \"source /opt/ros/humble/setup.bash\" &gt;&gt; ~/.bashrc\nsource ~/.bashrc\n</code></pre></li> <li>Create a ROS 2 workspace   <pre><code>mkdir -p ~/ros2_ws/src\ncd ~/ros2_ws/\ncolcon build --symlink-install\necho \"source $HOME/ros2_ws/install/setup.bash\" &gt;&gt; ~/.bashrc\nsource ~/.bashrc\n</code></pre></li> <li>Install Gazebo Garden</li> <li>Install ArduPilot SITL</li> <li>Install ardupilot_gazebo</li> <li>Get SITL_Models repo</li> <li>Setup environment variables</li> </ul> <pre><code>echo \"export GZ_VERSION=garden\" &gt;&gt; ~/.bashrc\necho \"export GZ_SIM_SYSTEM_PLUGIN_PATH=$HOME/ardupilot_gazebo/build:${GZ_SIM_SYSTEM_PLUGIN_PATH}\" &gt;&gt; ~/.bashrc\necho \"export GZ_SIM_RESOURCE_PATH=$HOME/ardupilot_gazebo/models:$HOME/ardupilot_gazebo/worlds:$HOME/SITL_Models/Gazebo/models:$HOME/SITL_Models/Gazebo/worlds:$GZ_SIM_RESOURCE_PATH\" &gt;&gt; ~/.bashrc\n</code></pre>"},{"location":"ros_sgp_tools.html#package-setup","title":"Package setup","text":"<ul> <li>Install apt dependencies    <pre><code>sudo apt install ros-humble-mavros* ros-$ROS_DISTRO-cv-bridge\n</code></pre></li> <li>Install pip dependencies    <pre><code>pip3 install -r requirements.txt\n</code></pre></li> <li>Clone repo and build package   <pre><code>cd ~/ros2_ws/src\ngit clone https://github.com/itskalvik/ros_sgp_tools.git\ncd ..\ncolcon build --symlink-install\n</code></pre></li> </ul>"},{"location":"api/kernels/index.html","title":"","text":""},{"location":"api/kernels/index.html#sgptools.kernels","title":"<code>sgptools.kernels</code>","text":"<p>Special kernel functions in this package:</p> <ul> <li><code>neural_kernel</code>: Provides a neural spectral kernel function that uses a mixture of multilayer perceptrons</li> </ul>"},{"location":"api/kernels/Neural-Kernel.html","title":"Neural Kernel","text":""},{"location":"api/kernels/Neural-Kernel.html#sgptools.kernels.neural_kernel","title":"<code>sgptools.kernels.neural_kernel</code>","text":"<p>Provides a neural spectral kernel function along with an initialization function</p>"},{"location":"api/kernels/Neural-Kernel.html#sgptools.kernels.neural_kernel.NeuralSpectralKernel","title":"<code>NeuralSpectralKernel</code>","text":"<p>               Bases: <code>Kernel</code></p> <p>Neural Spectral Kernel function (non-stationary kernel function).  Based on the implementation from the following repo</p> Refer to the following papers for more details <ul> <li>Neural Non-Stationary Spectral Kernel [Remes et al., 2018]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>input_dim</code> <code>int</code> <p>Number of data dimensions</p> required <code>active_dims</code> <code>int</code> <p>Number of data dimensions that are used for computing the covariances</p> <code>None</code> <code>Q</code> <code>int</code> <p>Number of MLP mixture components used in the kernel function</p> <code>1</code> <code>hidden_sizes</code> <code>list</code> <p>Number of hidden units in each MLP layer. Length of the list determines the number of layers.</p> <code>[32, 32]</code> Source code in <code>sgptools/kernels/neural_kernel.py</code> <pre><code>class NeuralSpectralKernel(gpflow.kernels.Kernel):\n    \"\"\"Neural Spectral Kernel function (non-stationary kernel function). \n    Based on the implementation from the following [repo](https://github.com/sremes/nssm-gp/tree/master?tab=readme-ov-file)\n\n    Refer to the following papers for more details:\n        - Neural Non-Stationary Spectral Kernel [Remes et al., 2018]\n\n    Args:\n        input_dim (int): Number of data dimensions\n        active_dims (int): Number of data dimensions that are used for computing the covariances\n        Q (int): Number of MLP mixture components used in the kernel function\n        hidden_sizes (list): Number of hidden units in each MLP layer. Length of the list determines the number of layers.\n    \"\"\"\n    def __init__(self, input_dim, active_dims=None, Q=1, hidden_sizes=[32, 32]):\n        super().__init__(active_dims=active_dims)\n\n        self.input_dim = input_dim\n        self.Q = Q\n        self.num_hidden = len(hidden_sizes)\n\n        self.freq = []\n        self.length = []\n        self.var = []\n        for q in range(self.Q):\n            freq = keras.Sequential([layers.Dense(hidden_sizes[i], activation='selu') for i in range(self.num_hidden)] + \n                                    [layers.Dense(input_dim, activation='softplus')])\n            length = keras.Sequential([layers.Dense(hidden_sizes[i], activation='selu') for i in range(self.num_hidden)] +\n                                   [layers.Dense(input_dim, activation='softplus')])\n            var = keras.Sequential([layers.Dense(hidden_sizes[i], activation='selu') for i in range(self.num_hidden)] +\n                                   [layers.Dense(1, activation='softplus')])\n            self.freq.append(freq)\n            self.length.append(length)\n            self.var.append(var)\n\n    def K(self, X, X2=None):\n        \"\"\"Computes the covariances between/amongst the input variables\n\n        Args:\n            X (ndarray): Variables to compute the covariance matrix\n            X2 (ndarray): If passed, the covariance between X and X2 is computed. Otherwise, \n                          the covariance between X and X is computed.\n\n        Returns:\n            cov (ndarray): covariance matrix\n        \"\"\"\n        if X2 is None:\n            X2 = X\n            equal = True\n        else:\n            equal = False\n\n        kern = 0.0\n        for q in range(self.Q):\n            # compute latent function values by the neural network\n            freq, freq2 = self.freq[q](X), self.freq[q](X2)\n            lens, lens2 = self.length[q](X), self.length[q](X2)\n            var, var2 = self.var[q](X), self.var[q](X2)\n\n            # compute length-scale term\n            Xr = tf.expand_dims(X, 1)  # N1 1 D\n            X2r = tf.expand_dims(X2, 0)  # 1 N2 D\n            l1 = tf.expand_dims(lens, 1)  # N1 1 D\n            l2 = tf.expand_dims(lens2, 0)  # 1 N2 D\n            L = tf.square(l1) + tf.square(l2)  # N1 N2 D\n            #D = tf.square((Xr - X2r) / L)  # N1 N2 D\n            D = tf.square(Xr - X2r) / L  # N1 N2 D\n            D = tf.reduce_sum(D, 2)  # N1 N2\n            det = tf.sqrt(2 * l1 * l2 / L)  # N1 N2 D\n            det = tf.reduce_prod(det, 2)  # N1 N2\n            E = det * tf.exp(-D)  # N1 N2\n\n            # compute cosine term\n            muX = (tf.reduce_sum(freq * X, 1, keepdims=True)\n                   - tf.transpose(tf.reduce_sum(freq2 * X2, 1, keepdims=True)))\n            COS = tf.cos(2 * np.pi * muX)\n\n            # compute kernel variance term\n            WW = tf.matmul(var, var2, transpose_b=True)  # w*w'^T\n\n            # compute the q'th kernel component\n            kern += WW * E * COS\n        if equal:\n            return robust_kernel(kern, tf.shape(X)[0])\n        else:\n            return kern\n\n    def K_diag(self, X):\n        kd = default_jitter()\n        for q in range(self.Q):\n            kd += tf.square(self.var[q](X))\n        return tf.squeeze(kd)\n</code></pre>"},{"location":"api/kernels/Neural-Kernel.html#sgptools.kernels.neural_kernel.NeuralSpectralKernel.K","title":"<code>K(X, X2=None)</code>","text":"<p>Computes the covariances between/amongst the input variables</p> <p>Parameters:</p> Name Type Description Default <code>X</code> <code>ndarray</code> <p>Variables to compute the covariance matrix</p> required <code>X2</code> <code>ndarray</code> <p>If passed, the covariance between X and X2 is computed. Otherwise,            the covariance between X and X is computed.</p> <code>None</code> <p>Returns:</p> Name Type Description <code>cov</code> <code>ndarray</code> <p>covariance matrix</p> Source code in <code>sgptools/kernels/neural_kernel.py</code> <pre><code>def K(self, X, X2=None):\n    \"\"\"Computes the covariances between/amongst the input variables\n\n    Args:\n        X (ndarray): Variables to compute the covariance matrix\n        X2 (ndarray): If passed, the covariance between X and X2 is computed. Otherwise, \n                      the covariance between X and X is computed.\n\n    Returns:\n        cov (ndarray): covariance matrix\n    \"\"\"\n    if X2 is None:\n        X2 = X\n        equal = True\n    else:\n        equal = False\n\n    kern = 0.0\n    for q in range(self.Q):\n        # compute latent function values by the neural network\n        freq, freq2 = self.freq[q](X), self.freq[q](X2)\n        lens, lens2 = self.length[q](X), self.length[q](X2)\n        var, var2 = self.var[q](X), self.var[q](X2)\n\n        # compute length-scale term\n        Xr = tf.expand_dims(X, 1)  # N1 1 D\n        X2r = tf.expand_dims(X2, 0)  # 1 N2 D\n        l1 = tf.expand_dims(lens, 1)  # N1 1 D\n        l2 = tf.expand_dims(lens2, 0)  # 1 N2 D\n        L = tf.square(l1) + tf.square(l2)  # N1 N2 D\n        #D = tf.square((Xr - X2r) / L)  # N1 N2 D\n        D = tf.square(Xr - X2r) / L  # N1 N2 D\n        D = tf.reduce_sum(D, 2)  # N1 N2\n        det = tf.sqrt(2 * l1 * l2 / L)  # N1 N2 D\n        det = tf.reduce_prod(det, 2)  # N1 N2\n        E = det * tf.exp(-D)  # N1 N2\n\n        # compute cosine term\n        muX = (tf.reduce_sum(freq * X, 1, keepdims=True)\n               - tf.transpose(tf.reduce_sum(freq2 * X2, 1, keepdims=True)))\n        COS = tf.cos(2 * np.pi * muX)\n\n        # compute kernel variance term\n        WW = tf.matmul(var, var2, transpose_b=True)  # w*w'^T\n\n        # compute the q'th kernel component\n        kern += WW * E * COS\n    if equal:\n        return robust_kernel(kern, tf.shape(X)[0])\n    else:\n        return kern\n</code></pre>"},{"location":"api/kernels/Neural-Kernel.html#sgptools.kernels.neural_kernel.init_neural_kernel","title":"<code>init_neural_kernel(x, y, inducing_variable, Q, n_inits=1, hidden_sizes=None)</code>","text":"<p>Helper function to initialize a Neural Spectral Kernel function (non-stationary kernel function).  Based on the implementation from the following repo</p> Refer to the following papers for more details <ul> <li>Neural Non-Stationary Spectral Kernel [Remes et al., 2018]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>ndarray</code> <p>(n, d); Input training set points</p> required <code>y</code> <code>ndarray</code> <p>(n, 1); Training set labels</p> required <code>inducing_variable</code> <code>ndarray</code> <p>(m, d); Initial inducing points</p> required <code>Q</code> <code>int</code> <p>Number of MLP mixture components used in the kernel function</p> required <code>n_inits</code> <code>int</code> <p>Number of times to initalize the kernel function (returns the best model)</p> <code>1</code> <code>hidden_sizes</code> <code>list</code> <p>Number of hidden units in each MLP layer. Length of the list determines the number of layers.</p> <code>None</code> Source code in <code>sgptools/kernels/neural_kernel.py</code> <pre><code>def init_neural_kernel(x, y, inducing_variable, Q, n_inits=1, hidden_sizes=None):\n    \"\"\"Helper function to initialize a Neural Spectral Kernel function (non-stationary kernel function). \n    Based on the implementation from the following [repo](https://github.com/sremes/nssm-gp/tree/master?tab=readme-ov-file)\n\n    Refer to the following papers for more details:\n        - Neural Non-Stationary Spectral Kernel [Remes et al., 2018]\n\n    Args:\n        x (ndarray): (n, d); Input training set points\n        y (ndarray): (n, 1); Training set labels\n        inducing_variable (ndarray): (m, d); Initial inducing points\n        Q (int): Number of MLP mixture components used in the kernel function\n        n_inits (int): Number of times to initalize the kernel function (returns the best model)\n        hidden_sizes (list): Number of hidden units in each MLP layer. Length of the list determines the number of layers.\n    \"\"\"\n    x, y = data_input_to_tensor((x, y))\n\n    print('Initializing neural spectral kernel...')\n    best_loglik = -np.inf\n    best_m = None\n    N, input_dim = x.shape\n\n    for k in range(n_inits):\n        # gpflow.reset_default_graph_and_session()\n        k = NeuralSpectralKernel(input_dim=input_dim, Q=Q, \n                                    hidden_sizes=hidden_sizes)\n        model = SGPR((x, y), inducing_variable=inducing_variable, \n                        kernel=k)\n        loglik = model.elbo()\n        if loglik &gt; best_loglik:\n            best_loglik = loglik\n            best_m = model\n        del model\n        gc.collect()\n    print('Best init: %f' % best_loglik)\n\n    return best_m\n</code></pre>"},{"location":"api/models/index.html","title":"","text":""},{"location":"api/models/index.html#sgptools.models","title":"<code>sgptools.models</code>","text":"<p>Sensor placement and informative path planning methods in this package:</p> <ul> <li><code>bo</code>: Provides a Bayesian optimization based approach that maximizes mutual-information to get sensor placements</li> <li><code>cma_es</code>: Provides a genetic algorithm (CMA-ES) based approach that maximizes mutual-information to get sensor placements</li> <li><code>continuous_sgp</code>: Provides an SGP-based sensor placement approach that is optimized using gradient descent</li> <li><code>greedy_mi</code>: Provides a greedy algorithm based approach that maximizes mutual-information to get sensor placements</li> <li><code>greedy_sgp</code>: Provides an SGP-based sensor placement approach that is optimized using a greedy algorithm</li> </ul>"},{"location":"api/models/Bayesian-Optimization.html","title":"Bayesian Optimization","text":""},{"location":"api/models/Bayesian-Optimization.html#sgptools.models.bo","title":"<code>sgptools.models.bo</code>","text":""},{"location":"api/models/Bayesian-Optimization.html#sgptools.models.bo.BayesianOpt","title":"<code>BayesianOpt</code>","text":"<p>Class for optimizing sensor placements using Bayesian Optimization</p> Refer to the following papers for more details <ul> <li>UAV route planning for active disease classification [Vivaldini et al., 2019]</li> <li>Occupancy map building through Bayesian exploration [Francis et al., 2019]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>X_train</code> <code>ndarray</code> <p>(n, d); Locations in the environment used to approximate the monitoring regions</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> Source code in <code>sgptools/models/bo.py</code> <pre><code>class BayesianOpt:\n    \"\"\"Class for optimizing sensor placements using Bayesian Optimization\n\n    Refer to the following papers for more details:\n        - UAV route planning for active disease classification [Vivaldini et al., 2019]\n        - Occupancy map building through Bayesian exploration [Francis et al., 2019]\n\n    Args:\n        X_train (ndarray): (n, d); Locations in the environment used to approximate the monitoring regions\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        transform (Transform): Transform object\n    \"\"\"\n    def __init__(self, X_train, noise_variance, kernel,\n                 transform=None):\n        self.X_train = X_train\n        self.noise_variance = noise_variance\n        self.kernel = kernel\n        self.num_dim = X_train.shape[-1]\n        self.transform = transform\n\n        # use the boundaries of the region as the search space\n        self.pbounds_dim = []\n        for i in range(self.num_dim):\n            self.pbounds_dim.append((np.min(X_train[:, i]), np.max(X_train[:, i])))\n\n    def objective(self, **kwargs):\n        \"\"\"Objective function (GP-based Mutual Information)\n\n        Args:\n            x&lt;i&gt; (ndarray): (1, d); Current solution sensor placement location i\n        \"\"\"\n        # MI does not depend on waypoint order (reshape to -1, num_dim)\n        X = []\n        for i in range(len(kwargs)):\n            X.append(kwargs['x{}'.format(i)])\n        X = np.array(X).reshape(-1, self.num_dim)\n        if self.transform is not None:\n            X = self.transform.expand(X)\n            constraints_loss = self.transform.constraints(X)\n\n        try:\n            mi = get_mi(X, self.X_train, self.noise_variance, self.kernel)\n            mi += constraints_loss\n            mi = mi.numpy()\n        except:\n            mi = -1e4 # if the cholskey decomposition fails\n        return mi\n\n    def optimize(self, \n                 num_sensors=10, \n                 max_steps=100,  \n                 X_init=None,\n                 init_points=10,\n                 verbose=0, \n                 seed=1234):\n        \"\"\"Optimizes the sensor placements using Bayesian Optimization without any constraints\n\n        Args:\n            num_sensors (int): Number of sensor locations to optimize.\n            max_steps (int): Maximum number of optimization steps. \n            X_init (ndarray): (m, d); Initial inducing points.\n            init_points (int): Number of random solutions used for initial exploration. \n                               Random exploration can help by diversifying the exploration space. \n            verbose (int): The level of verbosity.\n            seed (int): The algorithm will use it to seed the randomnumber generator, ensuring replicability.\n\n        Returns:\n            Xu (ndarray): (m, d); Solution sensor placement locations\n        \"\"\"\n        if X_init is None:\n            X_init = get_inducing_pts(self.X_train, num_sensors, random=True)\n        else:\n            num_sensors = len(X_init.reshape(-1, self.num_dim))\n        X_init = X_init.reshape(-1)\n\n        pbounds = {}\n        for i in range(self.num_dim*num_sensors):\n            pbounds['x{}'.format(i)] = self.pbounds_dim[i%self.num_dim]\n\n        optimizer = BayesianOptimization(f=self.objective,\n                                         pbounds=pbounds,\n                                         verbose=verbose,\n                                         random_state=seed,\n                                         allow_duplicate_points=True)\n        optimizer.maximize(init_points=init_points,\n                           n_iter=max_steps)\n\n        sol = []\n        for i in range(self.num_dim*num_sensors):\n            sol.append(optimizer.max['params']['x{}'.format(i)])\n        sol = np.array(sol).reshape(-1, self.num_dim)\n        if self.transform is not None:\n            sol = self.transform.expand(sol,\n                                        expand_sensor_model=False)\n            if not isinstance(sol, np.ndarray):\n                sol = sol.numpy()\n        return sol.reshape(-1, self.num_dim)\n</code></pre>"},{"location":"api/models/Bayesian-Optimization.html#sgptools.models.bo.BayesianOpt.objective","title":"<code>objective(**kwargs)</code>","text":"<p>Objective function (GP-based Mutual Information)</p> <p>Parameters:</p> Name Type Description Default <code>x&lt;i&gt;</code> <code>ndarray</code> <p>(1, d); Current solution sensor placement location i</p> required Source code in <code>sgptools/models/bo.py</code> <pre><code>def objective(self, **kwargs):\n    \"\"\"Objective function (GP-based Mutual Information)\n\n    Args:\n        x&lt;i&gt; (ndarray): (1, d); Current solution sensor placement location i\n    \"\"\"\n    # MI does not depend on waypoint order (reshape to -1, num_dim)\n    X = []\n    for i in range(len(kwargs)):\n        X.append(kwargs['x{}'.format(i)])\n    X = np.array(X).reshape(-1, self.num_dim)\n    if self.transform is not None:\n        X = self.transform.expand(X)\n        constraints_loss = self.transform.constraints(X)\n\n    try:\n        mi = get_mi(X, self.X_train, self.noise_variance, self.kernel)\n        mi += constraints_loss\n        mi = mi.numpy()\n    except:\n        mi = -1e4 # if the cholskey decomposition fails\n    return mi\n</code></pre>"},{"location":"api/models/Bayesian-Optimization.html#sgptools.models.bo.BayesianOpt.optimize","title":"<code>optimize(num_sensors=10, max_steps=100, X_init=None, init_points=10, verbose=0, seed=1234)</code>","text":"<p>Optimizes the sensor placements using Bayesian Optimization without any constraints</p> <p>Parameters:</p> Name Type Description Default <code>num_sensors</code> <code>int</code> <p>Number of sensor locations to optimize.</p> <code>10</code> <code>max_steps</code> <code>int</code> <p>Maximum number of optimization steps. </p> <code>100</code> <code>X_init</code> <code>ndarray</code> <p>(m, d); Initial inducing points.</p> <code>None</code> <code>init_points</code> <code>int</code> <p>Number of random solutions used for initial exploration.                 Random exploration can help by diversifying the exploration space. </p> <code>10</code> <code>verbose</code> <code>int</code> <p>The level of verbosity.</p> <code>0</code> <code>seed</code> <code>int</code> <p>The algorithm will use it to seed the randomnumber generator, ensuring replicability.</p> <code>1234</code> <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(m, d); Solution sensor placement locations</p> Source code in <code>sgptools/models/bo.py</code> <pre><code>def optimize(self, \n             num_sensors=10, \n             max_steps=100,  \n             X_init=None,\n             init_points=10,\n             verbose=0, \n             seed=1234):\n    \"\"\"Optimizes the sensor placements using Bayesian Optimization without any constraints\n\n    Args:\n        num_sensors (int): Number of sensor locations to optimize.\n        max_steps (int): Maximum number of optimization steps. \n        X_init (ndarray): (m, d); Initial inducing points.\n        init_points (int): Number of random solutions used for initial exploration. \n                           Random exploration can help by diversifying the exploration space. \n        verbose (int): The level of verbosity.\n        seed (int): The algorithm will use it to seed the randomnumber generator, ensuring replicability.\n\n    Returns:\n        Xu (ndarray): (m, d); Solution sensor placement locations\n    \"\"\"\n    if X_init is None:\n        X_init = get_inducing_pts(self.X_train, num_sensors, random=True)\n    else:\n        num_sensors = len(X_init.reshape(-1, self.num_dim))\n    X_init = X_init.reshape(-1)\n\n    pbounds = {}\n    for i in range(self.num_dim*num_sensors):\n        pbounds['x{}'.format(i)] = self.pbounds_dim[i%self.num_dim]\n\n    optimizer = BayesianOptimization(f=self.objective,\n                                     pbounds=pbounds,\n                                     verbose=verbose,\n                                     random_state=seed,\n                                     allow_duplicate_points=True)\n    optimizer.maximize(init_points=init_points,\n                       n_iter=max_steps)\n\n    sol = []\n    for i in range(self.num_dim*num_sensors):\n        sol.append(optimizer.max['params']['x{}'.format(i)])\n    sol = np.array(sol).reshape(-1, self.num_dim)\n    if self.transform is not None:\n        sol = self.transform.expand(sol,\n                                    expand_sensor_model=False)\n        if not isinstance(sol, np.ndarray):\n            sol = sol.numpy()\n    return sol.reshape(-1, self.num_dim)\n</code></pre>"},{"location":"api/models/Continuous-SGP.html","title":"Continuous SGP","text":""},{"location":"api/models/Continuous-SGP.html#sgptools.models.continuous_sgp","title":"<code>sgptools.models.continuous_sgp</code>","text":""},{"location":"api/models/Continuous-SGP.html#sgptools.models.continuous_sgp.continuous_sgp","title":"<code>continuous_sgp(num_inducing, X_train, noise_variance, kernel, transform=None, Xu_init=None, Xu_time=None, orientation=False, **kwargs)</code>","text":"<p>Get sensor placement solutions using the Continuous-SGP method</p> Refer to the following papers for more details <ul> <li>Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]</li> <li>Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>num_inducing</code> <code>int</code> <p>Number of inducing points</p> required <code>X_train</code> <code>ndarray</code> <p>(n, d); Unlabeled random sampled training points</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> <code>Xu_init</code> <code>ndarray</code> <p>(m, d); Initial inducing points</p> <code>None</code> <code>Xu_time</code> <code>ndarray</code> <p>(t, d); Temporal inducing points used in spatio-temporal models</p> <code>None</code> <code>orientation</code> <code>bool</code> <p>If True, a additionl dimension is added to the                  inducing points to represent the FoV orientation</p> <code>False</code> <p>Returns:</p> Name Type Description <code>sgpr</code> <code>AugmentedSGPR</code> <p>Optimized sparse Gaussian process model</p> <code>loss</code> <code>ndarray</code> <p>Loss values computed during training</p> Source code in <code>sgptools/models/continuous_sgp.py</code> <pre><code>def continuous_sgp(num_inducing, X_train, noise_variance, kernel, \n                   transform=None,\n                   Xu_init=None, \n                   Xu_time=None, \n                   orientation=False,\n                   **kwargs):\n    \"\"\"Get sensor placement solutions using the Continuous-SGP method\n\n    Refer to the following papers for more details:\n        - Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [[Jakkala and Akella, 2023](https://www.itskalvik.com/publication/sgp-sp/)]\n        - Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [[Jakkala and Akella, 2024](https://www.itskalvik.com/publication/sgp-ipp/)]\n\n    Args:\n        num_inducing (int): Number of inducing points\n        X_train (ndarray): (n, d); Unlabeled random sampled training points\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        transform (Transform): Transform object\n        Xu_init (ndarray): (m, d); Initial inducing points\n        Xu_time (ndarray): (t, d); Temporal inducing points used in spatio-temporal models\n        orientation (bool): If True, a additionl dimension is added to the \n                            inducing points to represent the FoV orientation\n\n    Returns:\n        sgpr (AugmentedSGPR): Optimized sparse Gaussian process model\n        loss (ndarray): Loss values computed during training\n    \"\"\"\n    # Generate init inducing points\n    if Xu_init is None:\n        Xu_init = get_inducing_pts(X_train, num_inducing, \n                                   orientation=orientation)\n\n    # Fit spare GP\n    sgpr = AugmentedSGPR((X_train, np.zeros((len(X_train), 1)).astype(X_train.dtype)),\n                         noise_variance=noise_variance,\n                         kernel=kernel, \n                         inducing_variable=Xu_init,\n                         inducing_variable_time=Xu_time,\n                         transform=transform)\n\n    # Train the mode\n    loss = optimize_model(sgpr,\n                          kernel_grad=False, \n                          **kwargs)\n\n    return sgpr, loss\n</code></pre>"},{"location":"api/models/Genetic-Algorithm.html","title":"Genetic Algorithm","text":""},{"location":"api/models/Genetic-Algorithm.html#sgptools.models.cma_es","title":"<code>sgptools.models.cma_es</code>","text":""},{"location":"api/models/Genetic-Algorithm.html#sgptools.models.cma_es.CMA_ES","title":"<code>CMA_ES</code>","text":"<p>Class for optimizing sensor placements using CMA-ES (a genetic algorithm)</p> Refer to the following paper for more details <ul> <li>Adaptive Continuous-Space Informative Path Planning for Online Environmental Monitoring [Hitz et al., 2017]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>X_train</code> <code>ndarray</code> <p>(n, d); Locations in the environment used to approximate the monitoring regions</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>distance_budget</code> <code>float</code> <p>Distance budget for when treating the inducing points                       as waypoints of a path</p> <code>None</code> <code>num_robots</code> <code>int</code> <p>Number of robots, used when modeling                multi-robot IPP with a distance budget</p> <code>1</code> <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> Source code in <code>sgptools/models/cma_es.py</code> <pre><code>class CMA_ES:\n    \"\"\"Class for optimizing sensor placements using CMA-ES (a genetic algorithm)\n\n    Refer to the following paper for more details:\n        - Adaptive Continuous-Space Informative Path Planning for Online Environmental Monitoring [Hitz et al., 2017]\n\n    Args:\n        X_train (ndarray): (n, d); Locations in the environment used to approximate the monitoring regions\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        distance_budget (float): Distance budget for when treating the inducing points \n                                 as waypoints of a path\n        num_robots (int): Number of robots, used when modeling \n                          multi-robot IPP with a distance budget\n        transform (Transform): Transform object\n    \"\"\"\n    def __init__(self, X_train, noise_variance, kernel,\n                 distance_budget=None,\n                 num_robots=1,\n                 transform=None):\n        self.boundaries = geometry.MultiPoint([[p[0], p[1]] for p in X_train]).convex_hull\n        self.X_train = X_train\n        self.noise_variance = noise_variance\n        self.kernel = kernel\n        self.num_dim = X_train.shape[-1]\n        self.distance_budget = distance_budget\n        self.num_robots = num_robots\n        self.transform = transform\n\n    def update(self, noise_variance, kernel):\n        \"\"\"Update GP noise variance and kernel function parameters\n\n        Args:\n            noise_variance (float): data variance\n            kernel (gpflow.kernels.Kernel): gpflow kernel function\n        \"\"\"\n        self.noise_variance = noise_variance\n        self.kernel = kernel\n\n    def objective(self, X):\n        \"\"\"Objective function (GP-based Mutual Information)\n\n        Args:\n            X (ndarray): (n, d); Current solution sensor placement locations\n        \"\"\"\n        # MI does not depend on waypoint order (reshape to -1, num_dim)\n        X = np.array(X).reshape(-1, self.num_dim)\n        constraints_loss = 0.0\n        if self.transform is not None:\n            X = self.transform.expand(X)\n            constraints_loss = self.transform.constraints(X)\n\n        try:\n            mi = -get_mi(X, self.X_train, self.noise_variance, self.kernel)\n            mi -= constraints_loss\n            mi = mi.numpy()\n        except:\n            mi = 0.0 # if the cholskey decomposition fails\n        return mi\n\n    def optimize(self, \n                 num_sensors=10, \n                 max_steps=5000, \n                 tol=1e-6, \n                 X_init=None,\n                 verbose=0,\n                 seed=1234):\n        \"\"\"Optimizes the sensor placements using CMA-ES without any constraints\n\n        Args:\n            num_sensors (int): Number of sensor locations to optimize\n            max_steps (int): Maximum number of optimization steps\n            tol (float): Convergence tolerance to decide when to stop optimization\n            X_init (ndarray): (m, d); Initial inducing points\n            verbose (int): The level of verbosity.\n            seed (int): The algorithm will use it to seed the randomnumber generator, ensuring replicability.\n\n        Returns:\n            Xu (ndarray): (m, d); Solution sensor placement locations\n        \"\"\"\n        sigma0 = 1.0\n\n        if X_init is None:\n            X_init = get_inducing_pts(self.X_train, num_sensors, random=True)\n        X_init = X_init.reshape(-1)\n\n        xopt, _ = cma.fmin2(self.objective, X_init, sigma0, \n                            options={'maxfevals': max_steps,\n                                     'verb_disp': verbose,\n                                     'tolfun': tol,\n                                     'seed': seed},\n                            restarts=5)\n\n        xopt = np.array(xopt).reshape(-1, self.num_dim)\n        if self.transform is not None:\n            xopt = self.transform.expand(xopt, \n                                         expand_sensor_model=False)\n            if not isinstance(xopt, np.ndarray):\n                xopt = xopt.numpy()\n        return xopt.reshape(-1, self.num_dim)\n</code></pre>"},{"location":"api/models/Genetic-Algorithm.html#sgptools.models.cma_es.CMA_ES.objective","title":"<code>objective(X)</code>","text":"<p>Objective function (GP-based Mutual Information)</p> <p>Parameters:</p> Name Type Description Default <code>X</code> <code>ndarray</code> <p>(n, d); Current solution sensor placement locations</p> required Source code in <code>sgptools/models/cma_es.py</code> <pre><code>def objective(self, X):\n    \"\"\"Objective function (GP-based Mutual Information)\n\n    Args:\n        X (ndarray): (n, d); Current solution sensor placement locations\n    \"\"\"\n    # MI does not depend on waypoint order (reshape to -1, num_dim)\n    X = np.array(X).reshape(-1, self.num_dim)\n    constraints_loss = 0.0\n    if self.transform is not None:\n        X = self.transform.expand(X)\n        constraints_loss = self.transform.constraints(X)\n\n    try:\n        mi = -get_mi(X, self.X_train, self.noise_variance, self.kernel)\n        mi -= constraints_loss\n        mi = mi.numpy()\n    except:\n        mi = 0.0 # if the cholskey decomposition fails\n    return mi\n</code></pre>"},{"location":"api/models/Genetic-Algorithm.html#sgptools.models.cma_es.CMA_ES.optimize","title":"<code>optimize(num_sensors=10, max_steps=5000, tol=1e-06, X_init=None, verbose=0, seed=1234)</code>","text":"<p>Optimizes the sensor placements using CMA-ES without any constraints</p> <p>Parameters:</p> Name Type Description Default <code>num_sensors</code> <code>int</code> <p>Number of sensor locations to optimize</p> <code>10</code> <code>max_steps</code> <code>int</code> <p>Maximum number of optimization steps</p> <code>5000</code> <code>tol</code> <code>float</code> <p>Convergence tolerance to decide when to stop optimization</p> <code>1e-06</code> <code>X_init</code> <code>ndarray</code> <p>(m, d); Initial inducing points</p> <code>None</code> <code>verbose</code> <code>int</code> <p>The level of verbosity.</p> <code>0</code> <code>seed</code> <code>int</code> <p>The algorithm will use it to seed the randomnumber generator, ensuring replicability.</p> <code>1234</code> <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(m, d); Solution sensor placement locations</p> Source code in <code>sgptools/models/cma_es.py</code> <pre><code>def optimize(self, \n             num_sensors=10, \n             max_steps=5000, \n             tol=1e-6, \n             X_init=None,\n             verbose=0,\n             seed=1234):\n    \"\"\"Optimizes the sensor placements using CMA-ES without any constraints\n\n    Args:\n        num_sensors (int): Number of sensor locations to optimize\n        max_steps (int): Maximum number of optimization steps\n        tol (float): Convergence tolerance to decide when to stop optimization\n        X_init (ndarray): (m, d); Initial inducing points\n        verbose (int): The level of verbosity.\n        seed (int): The algorithm will use it to seed the randomnumber generator, ensuring replicability.\n\n    Returns:\n        Xu (ndarray): (m, d); Solution sensor placement locations\n    \"\"\"\n    sigma0 = 1.0\n\n    if X_init is None:\n        X_init = get_inducing_pts(self.X_train, num_sensors, random=True)\n    X_init = X_init.reshape(-1)\n\n    xopt, _ = cma.fmin2(self.objective, X_init, sigma0, \n                        options={'maxfevals': max_steps,\n                                 'verb_disp': verbose,\n                                 'tolfun': tol,\n                                 'seed': seed},\n                        restarts=5)\n\n    xopt = np.array(xopt).reshape(-1, self.num_dim)\n    if self.transform is not None:\n        xopt = self.transform.expand(xopt, \n                                     expand_sensor_model=False)\n        if not isinstance(xopt, np.ndarray):\n            xopt = xopt.numpy()\n    return xopt.reshape(-1, self.num_dim)\n</code></pre>"},{"location":"api/models/Genetic-Algorithm.html#sgptools.models.cma_es.CMA_ES.update","title":"<code>update(noise_variance, kernel)</code>","text":"<p>Update GP noise variance and kernel function parameters</p> <p>Parameters:</p> Name Type Description Default <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required Source code in <code>sgptools/models/cma_es.py</code> <pre><code>def update(self, noise_variance, kernel):\n    \"\"\"Update GP noise variance and kernel function parameters\n\n    Args:\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n    \"\"\"\n    self.noise_variance = noise_variance\n    self.kernel = kernel\n</code></pre>"},{"location":"api/models/Greedy-MI.html","title":"Greedy MI","text":""},{"location":"api/models/Greedy-MI.html#sgptools.models.greedy_mi","title":"<code>sgptools.models.greedy_mi</code>","text":""},{"location":"api/models/Greedy-MI.html#sgptools.models.greedy_mi.GreedyMI","title":"<code>GreedyMI</code>","text":"<p>Helper class to compute mutual information using a Gaussian process for a given set of sensor locations. Used by <code>get_greedy_mi_sol</code> function to compute the solution sensor placements using the Greedy-MI method.</p> Refer to the following papers for more details <ul> <li>Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies [Krause et al., 2008]</li> <li>Data-driven learning and planning for environmental sampling [Ma et al., 2018]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>S</code> <code>ndarray</code> <p>(n, d); Candidate sensor placement locations</p> required <code>V</code> <code>ndarray</code> <p>(n, d); Locations in the environment used to approximate the monitoring regions</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> Source code in <code>sgptools/models/greedy_mi.py</code> <pre><code>class GreedyMI:\n    \"\"\"Helper class to compute mutual information using a Gaussian process for a given set of sensor locations.\n    Used by `get_greedy_mi_sol` function to compute the solution sensor placements using the Greedy-MI method.\n\n    Refer to the following papers for more details:\n        - Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies [Krause et al., 2008]\n        - Data-driven learning and planning for environmental sampling [Ma et al., 2018]\n\n    Args:\n        S (ndarray): (n, d); Candidate sensor placement locations\n        V (ndarray): (n, d); Locations in the environment used to approximate the monitoring regions\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        transform (Transform): Transform object\n    \"\"\"\n    def __init__(self, S, V, noise_variance, kernel, \n                 transform=None):\n        self.S = S\n        self.V = V\n        self.kernel = kernel\n        self.input_dim = S.shape[1]\n        self.noise_variance = noise_variance\n        self.transform = transform\n\n    def mutual_info(self, x):\n        \"\"\"Computes mutual information using the points `x` \n\n        Args:\n            x (ndarray): (n); Indices of the solution placement locations\n\n        Returns:\n            MI (float): Mutual information between the placement x and candidate locations\n        \"\"\"\n        x = np.array(x).reshape(-1).astype(int)\n        A = self.S[x[:-1]].reshape(-1, self.input_dim)\n        y = self.S[x[-1]].reshape(-1, self.input_dim)\n\n        if len(A) == 0:\n            sigma_a = 1.0\n        else:\n            if self.transform is not None:\n                A = self.transform.expand(A)\n            a_gp = GPR(data=(A, np.zeros((len(A), 1))),\n                       kernel=self.kernel,\n                       noise_variance=self.noise_variance)\n            _, sigma_a = a_gp.predict_f(y)\n\n        # Remove locations in A\u222ay from V to build A bar (Refer to Krause et al., 2008)\n        V_ = self.V.copy()\n        V_rows = V_.view([('', V_.dtype)] * V_.shape[1])\n\n        if self.transform is not None:\n            solution = self.S[x].reshape(-1, self.input_dim)\n            A_ = self.transform.expand(solution)\n        else:\n            A_ = self.S[x]\n        A_rows = A_.view([('', V_.dtype)] * A_.shape[1])\n\n        V_ = np.setdiff1d(V_rows, A_rows).view(V_.dtype).reshape(-1, V_.shape[1])\n\n        self.v_gp = GPR(data=(V_, np.zeros((len(V_), 1))), \n                        kernel=self.kernel,\n                        noise_variance=self.noise_variance)\n        _, sigma_v = self.v_gp.predict_f(y)\n\n        return (sigma_a/sigma_v).numpy().squeeze()\n</code></pre>"},{"location":"api/models/Greedy-MI.html#sgptools.models.greedy_mi.GreedyMI.mutual_info","title":"<code>mutual_info(x)</code>","text":"<p>Computes mutual information using the points <code>x</code> </p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>ndarray</code> <p>(n); Indices of the solution placement locations</p> required <p>Returns:</p> Name Type Description <code>MI</code> <code>float</code> <p>Mutual information between the placement x and candidate locations</p> Source code in <code>sgptools/models/greedy_mi.py</code> <pre><code>def mutual_info(self, x):\n    \"\"\"Computes mutual information using the points `x` \n\n    Args:\n        x (ndarray): (n); Indices of the solution placement locations\n\n    Returns:\n        MI (float): Mutual information between the placement x and candidate locations\n    \"\"\"\n    x = np.array(x).reshape(-1).astype(int)\n    A = self.S[x[:-1]].reshape(-1, self.input_dim)\n    y = self.S[x[-1]].reshape(-1, self.input_dim)\n\n    if len(A) == 0:\n        sigma_a = 1.0\n    else:\n        if self.transform is not None:\n            A = self.transform.expand(A)\n        a_gp = GPR(data=(A, np.zeros((len(A), 1))),\n                   kernel=self.kernel,\n                   noise_variance=self.noise_variance)\n        _, sigma_a = a_gp.predict_f(y)\n\n    # Remove locations in A\u222ay from V to build A bar (Refer to Krause et al., 2008)\n    V_ = self.V.copy()\n    V_rows = V_.view([('', V_.dtype)] * V_.shape[1])\n\n    if self.transform is not None:\n        solution = self.S[x].reshape(-1, self.input_dim)\n        A_ = self.transform.expand(solution)\n    else:\n        A_ = self.S[x]\n    A_rows = A_.view([('', V_.dtype)] * A_.shape[1])\n\n    V_ = np.setdiff1d(V_rows, A_rows).view(V_.dtype).reshape(-1, V_.shape[1])\n\n    self.v_gp = GPR(data=(V_, np.zeros((len(V_), 1))), \n                    kernel=self.kernel,\n                    noise_variance=self.noise_variance)\n    _, sigma_v = self.v_gp.predict_f(y)\n\n    return (sigma_a/sigma_v).numpy().squeeze()\n</code></pre>"},{"location":"api/models/Greedy-MI.html#sgptools.models.greedy_mi.get_greedy_mi_sol","title":"<code>get_greedy_mi_sol(num_sensors, candidates, X_train, noise_variance, kernel, transform=None, optimizer='naive')</code>","text":"<p>Get sensor placement solutions using the GP-based mutual information approach (submodular objective function).  Uses a greedy algorithm to select sensor placements from a given discrete set of candidates locations.</p> Refer to the following papers for more details <ul> <li>Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies [Krause et al., 2008]</li> <li>Data-driven learning and planning for environmental sampling [Ma et al., 2018]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>num_sensors</code> <code>int</code> <p>Number of sensor locations to optimize</p> required <code>candidates</code> <code>ndarray</code> <p>(n, d); Candidate sensor placement locations</p> required <code>X_train</code> <code>ndarray</code> <p>(n, d); Locations in the environment used to approximate the monitoring regions</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> <code>optimizer</code> <code>str</code> <p>Name of an optimizer available in the apricot library</p> <code>'naive'</code> <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(m, d); Solution sensor placement locations</p> Source code in <code>sgptools/models/greedy_mi.py</code> <pre><code>def get_greedy_mi_sol(num_sensors, candidates, X_train, noise_variance, kernel, \n                      transform=None, optimizer='naive'):\n    \"\"\"Get sensor placement solutions using the GP-based mutual information approach (submodular objective function). \n    Uses a greedy algorithm to select sensor placements from a given discrete set of candidates locations.\n\n    Refer to the following papers for more details:\n        - Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies [Krause et al., 2008]\n        - Data-driven learning and planning for environmental sampling [Ma et al., 2018]\n\n    Args:\n        num_sensors (int): Number of sensor locations to optimize\n        candidates (ndarray): (n, d); Candidate sensor placement locations\n        X_train (ndarray): (n, d); Locations in the environment used to approximate the monitoring regions\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        transform (Transform): Transform object\n        optimizer (str): Name of an optimizer available in the apricot library\n\n    Returns:\n        Xu (ndarray): (m, d); Solution sensor placement locations\n    \"\"\"\n    mi_model = GreedyMI(candidates, X_train, noise_variance, kernel, transform)\n    model = CustomSelection(num_sensors,\n                            mi_model.mutual_info,\n                            optimizer=optimizer,\n                            verbose=False)\n    sol = model.fit_transform(np.arange(len(candidates)).reshape(-1, 1))\n    return candidates[sol.reshape(-1)]\n</code></pre>"},{"location":"api/models/Greedy-SGP.html","title":"Greedy SGP","text":""},{"location":"api/models/Greedy-SGP.html#sgptools.models.greedy_sgp","title":"<code>sgptools.models.greedy_sgp</code>","text":""},{"location":"api/models/Greedy-SGP.html#sgptools.models.greedy_sgp.GreedySGP","title":"<code>GreedySGP</code>","text":"<p>Helper class to compute SGP's ELBO/optimization bound for a given set of sensor locations. Used by <code>get_greedy_sgp_sol</code> function to compute the solution sensor placements using the Greedy-SGP method.</p> Refer to the following papers for more details <ul> <li>Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>num_inducing</code> <code>int</code> <p>Number of inducing points</p> required <code>S</code> <code>ndarray</code> <p>(n, d); Candidate sensor placement locations</p> required <code>V</code> <code>ndarray</code> <p>(n, d); Locations in the environment used to approximate the monitoring regions</p> required <code>noise_variance</code> <code>float</code> <p>Data noise variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> Source code in <code>sgptools/models/greedy_sgp.py</code> <pre><code>class GreedySGP:\n    \"\"\"Helper class to compute SGP's ELBO/optimization bound for a given set of sensor locations.\n    Used by `get_greedy_sgp_sol` function to compute the solution sensor placements using the Greedy-SGP method.\n\n    Refer to the following papers for more details:\n        - Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [[Jakkala and Akella, 2023](https://www.itskalvik.com/publication/sgp-sp/)]\n\n    Args:\n        num_inducing (int): Number of inducing points\n        S (ndarray): (n, d); Candidate sensor placement locations\n        V (ndarray): (n, d); Locations in the environment used to approximate the monitoring regions\n        noise_variance (float): Data noise variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        transform (Transform): Transform object\n    \"\"\"\n    def __init__(self, num_inducing, S, V, noise_variance, kernel, \n                 transform=None):\n        self.sgp = AugmentedSGPR((V, np.zeros((len(V), 1))),\n                                 noise_variance=noise_variance,\n                                 kernel=kernel, \n                                 inducing_variable=S[:num_inducing],\n                                 transform=transform)\n        self.locs = S\n        self.num_inducing = num_inducing\n        self.inducing_dim = S.shape[1]\n\n    def bound(self, x):\n        \"\"\"Computes the SGP's optimization bound using the inducing points `x` \n\n        Args:\n            x (ndarray): (n); Indices of the solution placement locations\n\n        Returns:\n            elbo (float): Evidence lower bound/SGP's optimization bound value\n        \"\"\"\n        x = np.array(x).reshape(-1).astype(int)\n        Xu = np.ones((self.num_inducing, self.inducing_dim), dtype=np.float32)\n\n        # Initialize all inducing points at the first solution placement location.\n        # Ensures that the number of inducing points is always fixed and no additional\n        # information is passed to the SGP\n        Xu *= self.locs[x][0]\n\n        # Copy all given solution placements to the inducing points set\n        Xu[-len(x):] = self.locs[x]\n\n        # Update the SGP inducing points\n        self.sgp.inducing_variable.Z.assign(Xu)\n        return self.sgp.elbo().numpy() # return the ELBO\n</code></pre>"},{"location":"api/models/Greedy-SGP.html#sgptools.models.greedy_sgp.GreedySGP.bound","title":"<code>bound(x)</code>","text":"<p>Computes the SGP's optimization bound using the inducing points <code>x</code> </p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>ndarray</code> <p>(n); Indices of the solution placement locations</p> required <p>Returns:</p> Name Type Description <code>elbo</code> <code>float</code> <p>Evidence lower bound/SGP's optimization bound value</p> Source code in <code>sgptools/models/greedy_sgp.py</code> <pre><code>def bound(self, x):\n    \"\"\"Computes the SGP's optimization bound using the inducing points `x` \n\n    Args:\n        x (ndarray): (n); Indices of the solution placement locations\n\n    Returns:\n        elbo (float): Evidence lower bound/SGP's optimization bound value\n    \"\"\"\n    x = np.array(x).reshape(-1).astype(int)\n    Xu = np.ones((self.num_inducing, self.inducing_dim), dtype=np.float32)\n\n    # Initialize all inducing points at the first solution placement location.\n    # Ensures that the number of inducing points is always fixed and no additional\n    # information is passed to the SGP\n    Xu *= self.locs[x][0]\n\n    # Copy all given solution placements to the inducing points set\n    Xu[-len(x):] = self.locs[x]\n\n    # Update the SGP inducing points\n    self.sgp.inducing_variable.Z.assign(Xu)\n    return self.sgp.elbo().numpy() # return the ELBO\n</code></pre>"},{"location":"api/models/Greedy-SGP.html#sgptools.models.greedy_sgp.get_greedy_sgp_sol","title":"<code>get_greedy_sgp_sol(num_sensors, candidates, X_train, noise_variance, kernel, transform=None)</code>","text":"<p>Get sensor placement solutions using the Greedy-SGP method. Uses a greedy algorithm to  select sensor placements from a given discrete set of candidates locations.</p> Refer to the following papers for more details <ul> <li>Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>num_sensors</code> <code>int</code> <p>Number of sensor locations to optimize</p> required <code>candidates</code> <code>ndarray</code> <p>(n, d); Candidate sensor placement locations</p> required <code>X_train</code> <code>ndarray</code> <p>(n, d); Locations in the environment used to approximate the monitoring regions</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(m, d); Solution sensor placement locations</p> Source code in <code>sgptools/models/greedy_sgp.py</code> <pre><code>def get_greedy_sgp_sol(num_sensors, candidates, X_train, noise_variance, kernel, \n                       transform=None):\n    \"\"\"Get sensor placement solutions using the Greedy-SGP method. Uses a greedy algorithm to \n    select sensor placements from a given discrete set of candidates locations.\n\n    Refer to the following papers for more details:\n        - Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [[Jakkala and Akella, 2023](https://www.itskalvik.com/publication/sgp-sp/)]\n\n    Args:\n        num_sensors (int): Number of sensor locations to optimize\n        candidates (ndarray): (n, d); Candidate sensor placement locations\n        X_train (ndarray): (n, d); Locations in the environment used to approximate the monitoring regions\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        transform (Transform): Transform object\n\n    Returns:\n        Xu (ndarray): (m, d); Solution sensor placement locations\n    \"\"\"\n    sgp_model = GreedySGP(num_sensors, candidates, X_train, \n                          noise_variance, kernel, transform=transform)\n    model = CustomSelection(num_sensors,\n                            sgp_model.bound,\n                            optimizer='naive',\n                            verbose=False)\n    sol = model.fit_transform(np.arange(len(candidates)).reshape(-1, 1))\n    return candidates[sol.reshape(-1)]\n</code></pre>"},{"location":"api/models/core/index.html","title":"","text":""},{"location":"api/models/core/index.html#sgptools.models.core","title":"<code>sgptools.models.core</code>","text":"<p>Core modules in this package:</p> <ul> <li><code>augmented_gpr</code>: Provides a Gaussian process model with expand and aggregate functions</li> <li><code>augmented_sgpr</code>: Provides a sparse Gaussian process model with update, expand, and aggregate functions</li> <li><code>osgpr</code>: Provides a streaming sparse Gaussian process model along with initialization function</li> <li><code>transformations</code>: Provides transforms to model complex sensor field of views and handle informative path planning</li> </ul>"},{"location":"api/models/core/AugmentedGPR.html","title":"AugmentedGPR","text":""},{"location":"api/models/core/AugmentedGPR.html#sgptools.models.core.augmented_gpr","title":"<code>sgptools.models.core.augmented_gpr</code>","text":"<p>Provides a Gaussian process model with expand and aggregate functions</p>"},{"location":"api/models/core/AugmentedGPR.html#sgptools.models.core.augmented_gpr.AugmentedGPR","title":"<code>AugmentedGPR</code>","text":"<p>               Bases: <code>GPR</code></p> <p>GPR model from the GPFlow library augmented to use a transform object's expand and aggregate functions on the data points where necessary.  </p> Refer to the following papers for more details <ul> <li>Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]</li> <li>Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>tuple</code> <p>(X, y) ndarrays with inputs (n, d) and labels (n, 1)</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> Source code in <code>sgptools/models/core/augmented_gpr.py</code> <pre><code>class AugmentedGPR(GPR):\n    \"\"\"GPR model from the GPFlow library augmented to use a transform object's\n    expand and aggregate functions on the data points where necessary.  \n\n    Refer to the following papers for more details:\n        - Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]\n        - Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]\n\n    Args:\n        data (tuple): (X, y) ndarrays with inputs (n, d) and labels (n, 1)\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        noise_variance (float): data variance\n        transform (Transform): Transform object\n    \"\"\"\n    def __init__(\n        self,\n        *args,\n        transform=None,\n        **kwargs\n    ):\n        super().__init__(\n            *args,\n            **kwargs\n        )\n        if transform is None:\n            self.transform = Transform()\n        else:\n            self.transform = transform\n\n    def predict_f(\n        self, Xnew: InputData, \n        full_cov: bool = True, \n        full_output_cov: bool = False,\n        aggregate_train: bool = False,\n    ) -&gt; MeanAndVariance:\n        assert_params_false(self.predict_f, full_output_cov=full_output_cov)\n        if self.transform is not None:\n            Xnew = self.transform.expand(Xnew)\n\n        X, Y = self.data\n        err = Y - self.mean_function(X)\n\n        kmm = self.kernel(X)\n        knn = self.kernel(Xnew, full_cov=full_cov)\n        kmn = self.kernel(X, Xnew)\n        kmm_plus_s = add_likelihood_noise_cov(kmm, self.likelihood, X)\n\n        if self.transform is not None:\n            kmn = self.transform.aggregate(tf.transpose(kmn))\n            kmn = tf.transpose(kmn)\n            knn = self.transform.aggregate(knn)\n\n        if aggregate_train:\n            kmm_plus_s = self.transform.aggregate(kmm_plus_s)\n            err = self.transform.aggregate(err)\n            # reduce kmn only if it was not reduced before\n            # which can when train and test data are the same size\n            if kmn.shape[0] != kmn.shape[1]:\n                kmn = self.transform.aggregate(kmn)\n\n        conditional = gpflow.conditionals.base_conditional\n        f_mean_zero, f_var = conditional(\n            kmn, kmm_plus_s, knn, err, full_cov=full_cov, white=False\n        )  # [N, P], [N, P] or [P, N, N]\n        f_mean = f_mean_zero + self.mean_function(Xnew)\n        return f_mean, f_var\n</code></pre>"},{"location":"api/models/core/AugmentedSGPR.html","title":"AugmentedSGPR","text":""},{"location":"api/models/core/AugmentedSGPR.html#sgptools.models.core.augmented_sgpr","title":"<code>sgptools.models.core.augmented_sgpr</code>","text":"<p>Provides a sparse Gaussian process model with update, expand, and aggregate functions</p>"},{"location":"api/models/core/AugmentedSGPR.html#sgptools.models.core.augmented_sgpr.AugmentedSGPR","title":"<code>AugmentedSGPR</code>","text":"<p>               Bases: <code>SGPR</code></p> <p>SGPR model from the GPFlow library augmented to use a transform object's expand and aggregate functions on the inducing points where necessary. The object has an additional update function to update the kernel and noise variance parameters  (currently, the online updates part works only with RBF kernels).  </p> Refer to the following papers for more details <ul> <li>Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]</li> <li>Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>tuple</code> <p>(X, y) ndarrays with inputs (n, d) and labels (n, 1)</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>inducing_variable</code> <code>ndarray</code> <p>(m, d); Initial inducing points</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> <code>inducing_variable_time</code> <code>ndarray</code> <p>(m, d); Temporal dimensions of the inducing points,                                  used when modeling spatio-temporal IPP</p> <code>None</code> Source code in <code>sgptools/models/core/augmented_sgpr.py</code> <pre><code>class AugmentedSGPR(SGPR):\n    \"\"\"SGPR model from the GPFlow library augmented to use a transform object's\n    expand and aggregate functions on the inducing points where necessary. The object\n    has an additional update function to update the kernel and noise variance parameters \n    (currently, the online updates part works only with RBF kernels).  \n\n\n    Refer to the following papers for more details:\n        - Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]\n        - Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]\n\n    Args:\n        data (tuple): (X, y) ndarrays with inputs (n, d) and labels (n, 1)\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        noise_variance (float): data variance\n        inducing_variable (ndarray): (m, d); Initial inducing points\n        transform (Transform): Transform object\n        inducing_variable_time (ndarray): (m, d); Temporal dimensions of the inducing points, \n                                            used when modeling spatio-temporal IPP\n    \"\"\"\n    def __init__(\n        self,\n        *args,\n        transform=None,\n        inducing_variable_time=None,\n        **kwargs\n    ):\n        super().__init__(\n            *args,\n            **kwargs\n        )\n        if transform is None:\n            self.transform = Transform()\n        else:\n            self.transform = transform\n\n        if inducing_variable_time is not None:\n            self.inducing_variable_time = inducingpoint_wrapper(inducing_variable_time)\n            self.transform.inducing_variable_time = self.inducing_variable_time\n        else:\n            self.inducing_variable_time = None\n\n    def update(self, noise_variance, kernel):\n        \"\"\"Update SGP noise variance and kernel function parameters\n\n        Args:\n            noise_variance (float): data variance\n            kernel (gpflow.kernels.Kernel): gpflow kernel function\n        \"\"\"\n        self.likelihood.variance.assign(noise_variance)\n        self.kernel.lengthscales.assign(kernel.lengthscales)\n        self.kernel.variance.assign(kernel.variance)\n\n    def _common_calculation(self) -&gt; \"SGPR.CommonTensors\":\n        \"\"\"\n        Matrices used in log-det calculation\n        :return: A , B, LB, AAT with :math:`LL\u1d40 = K\u1d64\u1d64 , A = L\u207b\u00b9K_{uf}/\u03c3, AAT = AA\u1d40,\n            B = AAT+I, LBLB\u1d40 = B`\n            A is M x N, B is M x M, LB is M x M, AAT is M x M\n        \"\"\"\n        x, _ = self.data\n\n        iv = self.inducing_variable.Z  # [M]\n        iv = self.transform.expand(iv)\n\n        kuf = self.kernel(iv, x)\n        kuf = self.transform.aggregate(kuf)\n\n        kuu = self.kernel(iv) + 1e-6 * tf.eye(tf.shape(iv)[0], dtype=iv.dtype)\n        kuu = self.transform.aggregate(kuu)\n\n        L = tf.linalg.cholesky(kuu)\n\n        sigma_sq = self.likelihood.variance\n        sigma = tf.sqrt(sigma_sq)\n\n        # Compute intermediate matrices\n        A = tf.linalg.triangular_solve(L, kuf, lower=True) / sigma\n        AAT = tf.linalg.matmul(A, A, transpose_b=True)\n        B = add_noise_cov(AAT, tf.cast(1.0, AAT.dtype))\n        LB = tf.linalg.cholesky(B)\n\n        return self.CommonTensors(sigma_sq, sigma, A, B, LB, AAT, L)\n\n    def elbo(self) -&gt; tf.Tensor:\n        \"\"\"\n        Construct a tensorflow function to compute the bound on the marginal\n        likelihood. For a derivation of the terms in here, see the associated\n        SGPR notebook.\n        \"\"\"\n        common = self._common_calculation()\n        output_shape = tf.shape(self.data[-1])\n        num_data = to_default_float(output_shape[0])\n        output_dim = to_default_float(output_shape[1])\n        const = -0.5 * num_data * output_dim * np.log(2 * np.pi)\n        logdet = self.logdet_term(common)\n        quad = self.quad_term(common)\n        constraints = self.transform.constraints(self.inducing_variable.Z)\n        return const + logdet + quad + constraints\n\n    def predict_f(\n        self, Xnew: InputData, full_cov: bool = False, full_output_cov: bool = False\n    ) -&gt; MeanAndVariance:\n\n        # could copy into posterior into a fused version\n        \"\"\"\n        Compute the mean and variance of the latent function at some new points\n        Xnew. For a derivation of the terms in here, see the associated SGPR\n        notebook.\n        \"\"\"\n        X_data, Y_data = self.data\n\n        iv = self.inducing_variable.Z\n        iv = self.transform.expand(iv)\n\n        num_inducing = tf.shape(iv)[0]\n\n        err = Y_data - self.mean_function(X_data)\n        kuf = self.kernel(iv, X_data)\n        kuu = self.kernel(iv) + 1e-6 * tf.eye(num_inducing, dtype=iv.dtype)\n        Kus = self.kernel(iv, Xnew)\n        sigma = tf.sqrt(self.likelihood.variance)\n        L = tf.linalg.cholesky(kuu)\n        A = tf.linalg.triangular_solve(L, kuf, lower=True) / sigma\n        B = tf.linalg.matmul(A, A, transpose_b=True) + tf.eye(\n            num_inducing, dtype=default_float()\n        )  # cache qinv\n        LB = tf.linalg.cholesky(B)\n        Aerr = tf.linalg.matmul(A, err)\n        c = tf.linalg.triangular_solve(LB, Aerr, lower=True) / sigma\n        tmp1 = tf.linalg.triangular_solve(L, Kus, lower=True)\n        tmp2 = tf.linalg.triangular_solve(LB, tmp1, lower=True)\n        mean = tf.linalg.matmul(tmp2, c, transpose_a=True)\n        if full_cov:\n            var = (\n                self.kernel(Xnew)\n                + tf.linalg.matmul(tmp2, tmp2, transpose_a=True)\n                - tf.linalg.matmul(tmp1, tmp1, transpose_a=True)\n            )\n            var = tf.tile(var[None, ...], [self.num_latent_gps, 1, 1])  # [P, N, N]\n        else:\n            var = (\n                self.kernel(Xnew, full_cov=False)\n                + tf.reduce_sum(tf.square(tmp2), 0)\n                - tf.reduce_sum(tf.square(tmp1), 0)\n            )\n            var = tf.tile(var[:, None], [1, self.num_latent_gps])\n\n        return mean + self.mean_function(Xnew), var\n</code></pre>"},{"location":"api/models/core/AugmentedSGPR.html#sgptools.models.core.augmented_sgpr.AugmentedSGPR.elbo","title":"<code>elbo()</code>","text":"<p>Construct a tensorflow function to compute the bound on the marginal likelihood. For a derivation of the terms in here, see the associated SGPR notebook.</p> Source code in <code>sgptools/models/core/augmented_sgpr.py</code> <pre><code>def elbo(self) -&gt; tf.Tensor:\n    \"\"\"\n    Construct a tensorflow function to compute the bound on the marginal\n    likelihood. For a derivation of the terms in here, see the associated\n    SGPR notebook.\n    \"\"\"\n    common = self._common_calculation()\n    output_shape = tf.shape(self.data[-1])\n    num_data = to_default_float(output_shape[0])\n    output_dim = to_default_float(output_shape[1])\n    const = -0.5 * num_data * output_dim * np.log(2 * np.pi)\n    logdet = self.logdet_term(common)\n    quad = self.quad_term(common)\n    constraints = self.transform.constraints(self.inducing_variable.Z)\n    return const + logdet + quad + constraints\n</code></pre>"},{"location":"api/models/core/AugmentedSGPR.html#sgptools.models.core.augmented_sgpr.AugmentedSGPR.predict_f","title":"<code>predict_f(Xnew, full_cov=False, full_output_cov=False)</code>","text":"<p>Compute the mean and variance of the latent function at some new points Xnew. For a derivation of the terms in here, see the associated SGPR notebook.</p> Source code in <code>sgptools/models/core/augmented_sgpr.py</code> <pre><code>def predict_f(\n    self, Xnew: InputData, full_cov: bool = False, full_output_cov: bool = False\n) -&gt; MeanAndVariance:\n\n    # could copy into posterior into a fused version\n    \"\"\"\n    Compute the mean and variance of the latent function at some new points\n    Xnew. For a derivation of the terms in here, see the associated SGPR\n    notebook.\n    \"\"\"\n    X_data, Y_data = self.data\n\n    iv = self.inducing_variable.Z\n    iv = self.transform.expand(iv)\n\n    num_inducing = tf.shape(iv)[0]\n\n    err = Y_data - self.mean_function(X_data)\n    kuf = self.kernel(iv, X_data)\n    kuu = self.kernel(iv) + 1e-6 * tf.eye(num_inducing, dtype=iv.dtype)\n    Kus = self.kernel(iv, Xnew)\n    sigma = tf.sqrt(self.likelihood.variance)\n    L = tf.linalg.cholesky(kuu)\n    A = tf.linalg.triangular_solve(L, kuf, lower=True) / sigma\n    B = tf.linalg.matmul(A, A, transpose_b=True) + tf.eye(\n        num_inducing, dtype=default_float()\n    )  # cache qinv\n    LB = tf.linalg.cholesky(B)\n    Aerr = tf.linalg.matmul(A, err)\n    c = tf.linalg.triangular_solve(LB, Aerr, lower=True) / sigma\n    tmp1 = tf.linalg.triangular_solve(L, Kus, lower=True)\n    tmp2 = tf.linalg.triangular_solve(LB, tmp1, lower=True)\n    mean = tf.linalg.matmul(tmp2, c, transpose_a=True)\n    if full_cov:\n        var = (\n            self.kernel(Xnew)\n            + tf.linalg.matmul(tmp2, tmp2, transpose_a=True)\n            - tf.linalg.matmul(tmp1, tmp1, transpose_a=True)\n        )\n        var = tf.tile(var[None, ...], [self.num_latent_gps, 1, 1])  # [P, N, N]\n    else:\n        var = (\n            self.kernel(Xnew, full_cov=False)\n            + tf.reduce_sum(tf.square(tmp2), 0)\n            - tf.reduce_sum(tf.square(tmp1), 0)\n        )\n        var = tf.tile(var[:, None], [1, self.num_latent_gps])\n\n    return mean + self.mean_function(Xnew), var\n</code></pre>"},{"location":"api/models/core/AugmentedSGPR.html#sgptools.models.core.augmented_sgpr.AugmentedSGPR.update","title":"<code>update(noise_variance, kernel)</code>","text":"<p>Update SGP noise variance and kernel function parameters</p> <p>Parameters:</p> Name Type Description Default <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required Source code in <code>sgptools/models/core/augmented_sgpr.py</code> <pre><code>def update(self, noise_variance, kernel):\n    \"\"\"Update SGP noise variance and kernel function parameters\n\n    Args:\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n    \"\"\"\n    self.likelihood.variance.assign(noise_variance)\n    self.kernel.lengthscales.assign(kernel.lengthscales)\n    self.kernel.variance.assign(kernel.variance)\n</code></pre>"},{"location":"api/models/core/OSGPR.html","title":"OSGPR","text":""},{"location":"api/models/core/OSGPR.html#sgptools.models.core.osgpr","title":"<code>sgptools.models.core.osgpr</code>","text":"<p>Provides a streaming sparse Gaussian process model along with initialization function</p>"},{"location":"api/models/core/OSGPR.html#sgptools.models.core.osgpr.OSGPR_VFE","title":"<code>OSGPR_VFE</code>","text":"<p>               Bases: <code>GPModel</code>, <code>InternalDataTrainingLossMixin</code></p> <p>Online Sparse Variational GP regression model from streaming_sparse_gp</p> Refer to the following paper for more details <ul> <li>Streaming Gaussian process approximations [Bui et al., 2017]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>tuple</code> <p>(X, y) ndarrays with inputs (n, d) and labels (n, 1)</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>mu_old</code> <code>ndarray</code> <p>mean of old <code>q(u)</code>; here <code>u</code> are the latents corresponding to the inducing points <code>Z_old</code></p> required <code>Su_old</code> <code>ndarray</code> <p>posterior covariance of old <code>q(u)</code></p> required <code>Kaa_old</code> <code>ndarray</code> <p>prior covariance of old <code>q(u)</code></p> required <code>Z_old</code> <code>ndarray</code> <p>(m_old, d): Old initial inducing points</p> required <code>Z</code> <code>ndarray</code> <p>(m_new, d): New initial inducing points</p> required <code>mean_function</code> <code>function</code> <p>GP mean function</p> <code>None</code> Source code in <code>sgptools/models/core/osgpr.py</code> <pre><code>class OSGPR_VFE(GPModel, InternalDataTrainingLossMixin):\n    \"\"\"Online Sparse Variational GP regression model from [streaming_sparse_gp](https://github.com/thangbui/streaming_sparse_gp/tree/master)\n\n    Refer to the following paper for more details:\n        - Streaming Gaussian process approximations [Bui et al., 2017]\n\n    Args:\n        data (tuple): (X, y) ndarrays with inputs (n, d) and labels (n, 1)\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        mu_old (ndarray): mean of old `q(u)`; here `u` are the latents corresponding to the inducing points `Z_old`\n        Su_old (ndarray): posterior covariance of old `q(u)`\n        Kaa_old (ndarray): prior covariance of old `q(u)`\n        Z_old (ndarray): (m_old, d): Old initial inducing points\n        Z (ndarray): (m_new, d): New initial inducing points\n        mean_function (function): GP mean function\n    \"\"\"\n    def __init__(self, data, kernel, mu_old, Su_old, Kaa_old, Z_old, Z, mean_function=None):\n        self.X, self.Y = self.data = gpflow.models.util.data_input_to_tensor(data)\n        likelihood = gpflow.likelihoods.Gaussian()\n        num_latent_gps = GPModel.calc_num_latent_gps_from_data(data, kernel, likelihood)\n        super().__init__(kernel, likelihood, mean_function, num_latent_gps)\n\n        self.inducing_variable = InducingPoints(Z)\n        self.num_data = self.X.shape[0]\n\n        self.mu_old = tf.Variable(mu_old, shape=tf.TensorShape(None), trainable=False)\n        self.M_old = Z_old.shape[0]\n        self.Su_old = tf.Variable(Su_old, shape=tf.TensorShape(None), trainable=False)\n        self.Kaa_old = tf.Variable(Kaa_old, shape=tf.TensorShape(None), trainable=False)\n        self.Z_old = tf.Variable(Z_old, shape=tf.TensorShape(None), trainable=False)\n\n    def update(self, data):\n        \"\"\"Configure the OSGPR to adapt to a new batch of data. \n        Note: The OSGPR needs to be trained using gradient-based approaches after update.\n\n        Args:\n            data (tuple): (X, y) ndarrays with new batch of inputs (n, d) and labels (n, 1)\n        \"\"\"\n        self.X, self.Y = self.data = gpflow.models.util.data_input_to_tensor(data)\n        self.num_data = self.X.shape[0]\n\n        self.Z_old = tf.Variable(self.inducing_variable.Z.numpy(), \n                                 shape=tf.TensorShape(None), \n                                 trainable=False)\n\n        # Get posterior mean and covariance for the old inducing points\n        mu_old, Su_old = self.predict_f(self.Z_old, full_cov=True)\n        self.mu_old = tf.Variable(mu_old, shape=tf.TensorShape(None), trainable=False)\n        self.Su_old = tf.Variable(Su_old, shape=tf.TensorShape(None), trainable=False)\n\n        # Get the prior covariance matrix for the old inducing points\n        Kaa_old = self.kernel(self.Z_old)\n        self.Kaa_old = tf.Variable(Kaa_old, shape=tf.TensorShape(None), trainable=False)\n\n    def _common_terms(self):\n        Mb = self.inducing_variable.num_inducing\n        Ma = self.M_old\n        # jitter = gpflow.default_jitter()\n        jitter = gpflow.utilities.to_default_float(1e-4)\n        sigma2 = self.likelihood.variance\n        sigma = tf.sqrt(sigma2)\n\n        Saa = self.Su_old\n        ma = self.mu_old\n\n        # a is old inducing points, b is new\n        # f is training points\n        # s is test points\n        Kbf = covariances.Kuf(self.inducing_variable, self.kernel, self.X)\n        Kbb = covariances.Kuu(self.inducing_variable, self.kernel, jitter=jitter)\n        Kba = covariances.Kuf(self.inducing_variable, self.kernel, self.Z_old)\n        Kaa_cur = gpflow.utilities.add_noise_cov(self.kernel(self.Z_old), jitter)\n        Kaa = gpflow.utilities.add_noise_cov(self.Kaa_old, jitter)\n\n        err = self.Y - self.mean_function(self.X)\n\n        Sainv_ma = tf.linalg.solve(Saa, ma)\n        Sinv_y = self.Y / sigma2\n        c1 = tf.matmul(Kbf, Sinv_y)\n        c2 = tf.matmul(Kba, Sainv_ma)\n        c = c1 + c2\n\n        Lb = tf.linalg.cholesky(Kbb)\n        Lbinv_c = tf.linalg.triangular_solve(Lb, c, lower=True)\n        Lbinv_Kba = tf.linalg.triangular_solve(Lb, Kba, lower=True)\n        Lbinv_Kbf = tf.linalg.triangular_solve(Lb, Kbf, lower=True) / sigma\n        d1 = tf.matmul(Lbinv_Kbf, Lbinv_Kbf, transpose_b=True)\n\n        LSa = tf.linalg.cholesky(Saa)\n        Kab_Lbinv = tf.linalg.matrix_transpose(Lbinv_Kba)\n        LSainv_Kab_Lbinv = tf.linalg.triangular_solve(\n            LSa, Kab_Lbinv, lower=True)\n        d2 = tf.matmul(LSainv_Kab_Lbinv, LSainv_Kab_Lbinv, transpose_a=True)\n\n        La = tf.linalg.cholesky(Kaa)\n        Lainv_Kab_Lbinv = tf.linalg.triangular_solve(\n            La, Kab_Lbinv, lower=True)\n        d3 = tf.matmul(Lainv_Kab_Lbinv, Lainv_Kab_Lbinv, transpose_a=True)\n\n        D = tf.eye(Mb, dtype=gpflow.default_float()) + d1 + d2 - d3\n        D = gpflow.utilities.add_noise_cov(D, jitter)\n        LD = tf.linalg.cholesky(D)\n\n        LDinv_Lbinv_c = tf.linalg.triangular_solve(LD, Lbinv_c, lower=True)\n\n        return (Kbf, Kba, Kaa, Kaa_cur, La, Kbb, Lb, D, LD,\n                Lbinv_Kba, LDinv_Lbinv_c, err, d1)\n\n    def maximum_log_likelihood_objective(self):\n        \"\"\"\n        Construct a tensorflow function to compute the bound on the marginal\n        likelihood. \n        \"\"\"\n\n        Mb = self.inducing_variable.num_inducing\n        Ma = self.M_old\n        jitter = gpflow.default_jitter()\n        # jitter = gpflow.utilities.to_default_float(1e-4)\n        sigma2 = self.likelihood.variance\n        sigma = tf.sqrt(sigma2)\n        N = self.num_data\n\n        Saa = self.Su_old\n        ma = self.mu_old\n\n        # a is old inducing points, b is new\n        # f is training points\n        Kfdiag = self.kernel(self.X, full_cov=False)\n        (Kbf, Kba, Kaa, Kaa_cur, La, Kbb, Lb, D, LD,\n            Lbinv_Kba, LDinv_Lbinv_c, err, Qff) = self._common_terms()\n\n        LSa = tf.linalg.cholesky(Saa)\n        Lainv_ma = tf.linalg.triangular_solve(LSa, ma, lower=True)\n\n        # constant term\n        bound = -0.5 * N * np.log(2 * np.pi)\n        # quadratic term\n        bound += -0.5 * tf.reduce_sum(tf.square(err)) / sigma2\n        # bound += -0.5 * tf.reduce_sum(ma * Sainv_ma)\n        bound += -0.5 * tf.reduce_sum(tf.square(Lainv_ma))\n        bound += 0.5 * tf.reduce_sum(tf.square(LDinv_Lbinv_c))\n        # log det term\n        bound += -0.5 * N * tf.reduce_sum(tf.math.log(sigma2))\n        bound += - tf.reduce_sum(tf.math.log(tf.linalg.diag_part(LD)))\n\n        # delta 1: trace term\n        bound += -0.5 * tf.reduce_sum(Kfdiag) / sigma2\n        bound += 0.5 * tf.reduce_sum(tf.linalg.diag_part(Qff))\n\n        # delta 2: a and b difference\n        bound += tf.reduce_sum(tf.math.log(tf.linalg.diag_part(La)))\n        bound += - tf.reduce_sum(tf.math.log(tf.linalg.diag_part(LSa)))\n\n        Kaadiff = Kaa_cur - tf.matmul(Lbinv_Kba, Lbinv_Kba, transpose_a=True)\n        Sainv_Kaadiff = tf.linalg.solve(Saa, Kaadiff)\n        Kainv_Kaadiff = tf.linalg.solve(Kaa, Kaadiff)\n\n        bound += -0.5 * tf.reduce_sum(\n            tf.linalg.diag_part(Sainv_Kaadiff) - tf.linalg.diag_part(Kainv_Kaadiff))\n\n        return bound\n\n    def predict_f(self, Xnew, full_cov=False):\n        \"\"\"\n        Compute the mean and variance of the latent function at some new points\n        Xnew. \n        \"\"\"\n\n        # jitter = gpflow.default_jitter()\n        jitter = gpflow.utilities.to_default_float(1e-4)\n\n        # a is old inducing points, b is new\n        # f is training points\n        # s is test points\n        Kbs = covariances.Kuf(self.inducing_variable, self.kernel, Xnew)\n        (Kbf, Kba, Kaa, Kaa_cur, La, Kbb, Lb, D, LD,\n            Lbinv_Kba, LDinv_Lbinv_c, err, Qff) = self._common_terms()\n\n        Lbinv_Kbs = tf.linalg.triangular_solve(Lb, Kbs, lower=True)\n        LDinv_Lbinv_Kbs = tf.linalg.triangular_solve(LD, Lbinv_Kbs, lower=True)\n        mean = tf.matmul(LDinv_Lbinv_Kbs, LDinv_Lbinv_c, transpose_a=True)\n\n        if full_cov:\n            Kss = self.kernel(Xnew) + jitter * tf.eye(tf.shape(Xnew)[0], dtype=gpflow.default_float())\n            var1 = Kss\n            var2 = - tf.matmul(Lbinv_Kbs, Lbinv_Kbs, transpose_a=True)\n            var3 = tf.matmul(LDinv_Lbinv_Kbs, LDinv_Lbinv_Kbs, transpose_a=True)\n            var = var1 + var2 + var3\n        else:\n            var1 = self.kernel(Xnew, full_cov=False)\n            var2 = -tf.reduce_sum(tf.square(Lbinv_Kbs), axis=0)\n            var3 = tf.reduce_sum(tf.square(LDinv_Lbinv_Kbs), axis=0)\n            var = var1 + var2 + var3\n\n        return mean + self.mean_function(Xnew), var\n</code></pre>"},{"location":"api/models/core/OSGPR.html#sgptools.models.core.osgpr.OSGPR_VFE.maximum_log_likelihood_objective","title":"<code>maximum_log_likelihood_objective()</code>","text":"<p>Construct a tensorflow function to compute the bound on the marginal likelihood.</p> Source code in <code>sgptools/models/core/osgpr.py</code> <pre><code>def maximum_log_likelihood_objective(self):\n    \"\"\"\n    Construct a tensorflow function to compute the bound on the marginal\n    likelihood. \n    \"\"\"\n\n    Mb = self.inducing_variable.num_inducing\n    Ma = self.M_old\n    jitter = gpflow.default_jitter()\n    # jitter = gpflow.utilities.to_default_float(1e-4)\n    sigma2 = self.likelihood.variance\n    sigma = tf.sqrt(sigma2)\n    N = self.num_data\n\n    Saa = self.Su_old\n    ma = self.mu_old\n\n    # a is old inducing points, b is new\n    # f is training points\n    Kfdiag = self.kernel(self.X, full_cov=False)\n    (Kbf, Kba, Kaa, Kaa_cur, La, Kbb, Lb, D, LD,\n        Lbinv_Kba, LDinv_Lbinv_c, err, Qff) = self._common_terms()\n\n    LSa = tf.linalg.cholesky(Saa)\n    Lainv_ma = tf.linalg.triangular_solve(LSa, ma, lower=True)\n\n    # constant term\n    bound = -0.5 * N * np.log(2 * np.pi)\n    # quadratic term\n    bound += -0.5 * tf.reduce_sum(tf.square(err)) / sigma2\n    # bound += -0.5 * tf.reduce_sum(ma * Sainv_ma)\n    bound += -0.5 * tf.reduce_sum(tf.square(Lainv_ma))\n    bound += 0.5 * tf.reduce_sum(tf.square(LDinv_Lbinv_c))\n    # log det term\n    bound += -0.5 * N * tf.reduce_sum(tf.math.log(sigma2))\n    bound += - tf.reduce_sum(tf.math.log(tf.linalg.diag_part(LD)))\n\n    # delta 1: trace term\n    bound += -0.5 * tf.reduce_sum(Kfdiag) / sigma2\n    bound += 0.5 * tf.reduce_sum(tf.linalg.diag_part(Qff))\n\n    # delta 2: a and b difference\n    bound += tf.reduce_sum(tf.math.log(tf.linalg.diag_part(La)))\n    bound += - tf.reduce_sum(tf.math.log(tf.linalg.diag_part(LSa)))\n\n    Kaadiff = Kaa_cur - tf.matmul(Lbinv_Kba, Lbinv_Kba, transpose_a=True)\n    Sainv_Kaadiff = tf.linalg.solve(Saa, Kaadiff)\n    Kainv_Kaadiff = tf.linalg.solve(Kaa, Kaadiff)\n\n    bound += -0.5 * tf.reduce_sum(\n        tf.linalg.diag_part(Sainv_Kaadiff) - tf.linalg.diag_part(Kainv_Kaadiff))\n\n    return bound\n</code></pre>"},{"location":"api/models/core/OSGPR.html#sgptools.models.core.osgpr.OSGPR_VFE.predict_f","title":"<code>predict_f(Xnew, full_cov=False)</code>","text":"<p>Compute the mean and variance of the latent function at some new points Xnew.</p> Source code in <code>sgptools/models/core/osgpr.py</code> <pre><code>def predict_f(self, Xnew, full_cov=False):\n    \"\"\"\n    Compute the mean and variance of the latent function at some new points\n    Xnew. \n    \"\"\"\n\n    # jitter = gpflow.default_jitter()\n    jitter = gpflow.utilities.to_default_float(1e-4)\n\n    # a is old inducing points, b is new\n    # f is training points\n    # s is test points\n    Kbs = covariances.Kuf(self.inducing_variable, self.kernel, Xnew)\n    (Kbf, Kba, Kaa, Kaa_cur, La, Kbb, Lb, D, LD,\n        Lbinv_Kba, LDinv_Lbinv_c, err, Qff) = self._common_terms()\n\n    Lbinv_Kbs = tf.linalg.triangular_solve(Lb, Kbs, lower=True)\n    LDinv_Lbinv_Kbs = tf.linalg.triangular_solve(LD, Lbinv_Kbs, lower=True)\n    mean = tf.matmul(LDinv_Lbinv_Kbs, LDinv_Lbinv_c, transpose_a=True)\n\n    if full_cov:\n        Kss = self.kernel(Xnew) + jitter * tf.eye(tf.shape(Xnew)[0], dtype=gpflow.default_float())\n        var1 = Kss\n        var2 = - tf.matmul(Lbinv_Kbs, Lbinv_Kbs, transpose_a=True)\n        var3 = tf.matmul(LDinv_Lbinv_Kbs, LDinv_Lbinv_Kbs, transpose_a=True)\n        var = var1 + var2 + var3\n    else:\n        var1 = self.kernel(Xnew, full_cov=False)\n        var2 = -tf.reduce_sum(tf.square(Lbinv_Kbs), axis=0)\n        var3 = tf.reduce_sum(tf.square(LDinv_Lbinv_Kbs), axis=0)\n        var = var1 + var2 + var3\n\n    return mean + self.mean_function(Xnew), var\n</code></pre>"},{"location":"api/models/core/OSGPR.html#sgptools.models.core.osgpr.OSGPR_VFE.update","title":"<code>update(data)</code>","text":"<p>Configure the OSGPR to adapt to a new batch of data.  Note: The OSGPR needs to be trained using gradient-based approaches after update.</p> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>tuple</code> <p>(X, y) ndarrays with new batch of inputs (n, d) and labels (n, 1)</p> required Source code in <code>sgptools/models/core/osgpr.py</code> <pre><code>def update(self, data):\n    \"\"\"Configure the OSGPR to adapt to a new batch of data. \n    Note: The OSGPR needs to be trained using gradient-based approaches after update.\n\n    Args:\n        data (tuple): (X, y) ndarrays with new batch of inputs (n, d) and labels (n, 1)\n    \"\"\"\n    self.X, self.Y = self.data = gpflow.models.util.data_input_to_tensor(data)\n    self.num_data = self.X.shape[0]\n\n    self.Z_old = tf.Variable(self.inducing_variable.Z.numpy(), \n                             shape=tf.TensorShape(None), \n                             trainable=False)\n\n    # Get posterior mean and covariance for the old inducing points\n    mu_old, Su_old = self.predict_f(self.Z_old, full_cov=True)\n    self.mu_old = tf.Variable(mu_old, shape=tf.TensorShape(None), trainable=False)\n    self.Su_old = tf.Variable(Su_old, shape=tf.TensorShape(None), trainable=False)\n\n    # Get the prior covariance matrix for the old inducing points\n    Kaa_old = self.kernel(self.Z_old)\n    self.Kaa_old = tf.Variable(Kaa_old, shape=tf.TensorShape(None), trainable=False)\n</code></pre>"},{"location":"api/models/core/OSGPR.html#sgptools.models.core.osgpr.init_osgpr","title":"<code>init_osgpr(X_train, num_inducing=10, lengthscales=1.0, variance=1.0, noise_variance=0.001, kernel=None)</code>","text":"<p>Initialize a VFE OSGPR model with an RBF kernel with  unit variance and lengthcales, and 0.001 noise variance. Used in the Online Continuous SGP approach. </p> <p>Parameters:</p> Name Type Description Default <code>X_train</code> <code>ndarray</code> <p>(n, d); Unlabeled random sampled training points.              They only effect the initial inducing point locations,              i.e., limits them to the bounds of the data</p> required <code>num_inducing</code> <code>int</code> <p>Number of inducing points</p> <code>10</code> <code>lengthscales</code> <code>float or list</code> <p>Kernel lengthscale(s), if passed as a list,                      each element corresponds to each data dimension</p> <code>1.0</code> <code>variance</code> <code>float</code> <p>Kernel variance</p> <code>1.0</code> <code>noise_variance</code> <code>float</code> <p>Data noise variance</p> <code>0.001</code> <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> <code>None</code> <p>Returns:</p> Name Type Description <code>online_param</code> <code>OSGPR_VFE</code> <p>Initialized online sparse Gaussian process model</p> Source code in <code>sgptools/models/core/osgpr.py</code> <pre><code>def init_osgpr(X_train, \n               num_inducing=10, \n               lengthscales=1.0, \n               variance=1.0,\n               noise_variance=0.001,\n               kernel=None):\n    \"\"\"Initialize a VFE OSGPR model with an RBF kernel with \n    unit variance and lengthcales, and 0.001 noise variance.\n    Used in the Online Continuous SGP approach. \n\n    Args:\n        X_train (ndarray): (n, d); Unlabeled random sampled training points. \n                        They only effect the initial inducing point locations, \n                        i.e., limits them to the bounds of the data\n        num_inducing (int): Number of inducing points\n        lengthscales (float or list): Kernel lengthscale(s), if passed as a list, \n                                each element corresponds to each data dimension\n        variance (float): Kernel variance\n        noise_variance (float): Data noise variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n\n    Returns:\n        online_param (OSGPR_VFE): Initialized online sparse Gaussian process model\n    \"\"\"\n\n    if kernel is None:\n        kernel = gpflow.kernels.SquaredExponential(lengthscales=lengthscales, \n                                                   variance=variance)\n\n    y_train = np.zeros((len(X_train), 1), dtype=X_train.dtype)\n    Z_init = get_inducing_pts(X_train, num_inducing)\n    init_param = gpflow.models.SGPR((X_train, y_train),\n                                    kernel, \n                                    inducing_variable=Z_init, \n                                    noise_variance=noise_variance)\n\n    # Initialize the OSGPR model using the parameters from the SGPR model\n    # The X_train and y_train here will be overwritten in the online phase \n    X_train = np.array([[0, 0], [0, 0]])\n    y_train = np.array([0, 0]).reshape(-1, 1)\n    Zopt = init_param.inducing_variable.Z.numpy()\n    mu, Su = init_param.predict_f(Zopt, full_cov=True)\n    Kaa = init_param.kernel(Zopt)\n    online_param = OSGPR_VFE((X_train[:2], y_train[:2]),\n                             init_param.kernel,\n                             mu, Su[0], Kaa,\n                             Zopt, Zopt)\n    online_param.likelihood.variance.assign(init_param.likelihood.variance)\n\n    return online_param\n</code></pre>"},{"location":"api/models/core/Transformations.html","title":"Transformations","text":""},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations","title":"<code>sgptools.models.core.transformations</code>","text":"<p>Provides transforms to model complex sensor field of views and handle informative path planning</p>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.IPPTransform","title":"<code>IPPTransform</code>","text":"<p>               Bases: <code>Transform</code></p> <p>Transform to model IPP problems</p> Usage details <ul> <li>For point sensing, set <code>sampling_rate = 2</code></li> <li>For continuous sensing, set <code>sampling_rate &gt; 2</code> (account for the information along the path)</li> <li>For continuous sensing with aggregation, set <code>sampling_rate &gt; 2</code> and <code>aggregate_fov = True</code> (faster but solution quality is a bit diminished)</li> <li>If using a non-point FoV model with continuous sampling, only the FoV inducing points are aggregated</li> <li>For multi-robot case, set <code>num_robots &gt; 1</code></li> <li>For onlineIPP use <code>update_fixed</code> to freeze the visited waypoints</li> </ul> <p>Parameters:</p> Name Type Description Default <code>sampling_rate</code> <code>int</code> <p>Number of points to sample between each pair of inducing points</p> <code>2</code> <code>distance_budget</code> <code>float</code> <p>Distance budget for the path</p> <code>None</code> <code>num_robots</code> <code>int</code> <p>Number of robots</p> <code>1</code> <code>Xu_fixed</code> <code>ndarray</code> <p>(num_robots, num_visited, num_dim); Visited waypoints that don't need to be optimized</p> <code>None</code> <code>num_dim</code> <code>int</code> <p>Number of dimensions of the inducing points</p> <code>2</code> <code>sensor_model</code> <code>Transform</code> <p>Transform object to expand each inducing point to <code>p</code> points                        approximating each sensor's FoV</p> <code>None</code> <code>aggregate_fov</code> <code>bool</code> <p>Used only when sampling_rate &gt; 2, i.e., when using a continuous sensing model.                    If <code>True</code>, covariances corresponding to interpolated inducing points along each edge                   of the path are aggregated to reduce the matrix inversion cost.</p> <code>False</code> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>class IPPTransform(Transform):\n    \"\"\"Transform to model IPP problems\n\n    Usage details: \n        * For point sensing, set `sampling_rate = 2`\n        * For continuous sensing, set `sampling_rate &gt; 2` (account for the information along the path)\n        * For continuous sensing with aggregation, set `sampling_rate &gt; 2` and `aggregate_fov = True` (faster but solution quality is a bit diminished)\n        * If using a non-point FoV model with continuous sampling, only the FoV inducing points are aggregated\n        * For multi-robot case, set `num_robots &gt; 1`\n        * For onlineIPP use `update_fixed` to freeze the visited waypoints\n\n    Args:\n        sampling_rate (int): Number of points to sample between each pair of inducing points\n        distance_budget (float): Distance budget for the path\n        num_robots (int): Number of robots\n        Xu_fixed (ndarray): (num_robots, num_visited, num_dim); Visited waypoints that don't need to be optimized\n        num_dim (int): Number of dimensions of the inducing points\n        sensor_model (Transform): Transform object to expand each inducing point to `p` points \n                                  approximating each sensor's FoV\n        aggregate_fov (bool): Used only when sampling_rate &gt; 2, i.e., when using a continuous sensing model. \n                              If `True`, covariances corresponding to interpolated inducing points along each edge\n                              of the path are aggregated to reduce the matrix inversion cost.\n    \"\"\"\n    def __init__(self, \n                 sampling_rate=2, \n                 distance_budget=None, \n                 num_robots=1,\n                 Xu_fixed=None,\n                 num_dim=2,\n                 sensor_model=None,\n                 aggregate_fov=False,\n                 **kwargs):\n        super().__init__(**kwargs)\n        if sampling_rate &lt; 2:\n            raise ValueError('Sampling rate must be greater than 2.')\n\n        self.sampling_rate = sampling_rate\n        self.distance_budget = distance_budget\n        self.num_robots = num_robots\n        self.num_dim = num_dim\n        self.sensor_model = sensor_model\n\n        # Set aggregation size to sampling rate if aggregate_fov is True\n        # and sampling rate is enabled (greater than 2)\n        if aggregate_fov:\n            if self.sensor_model is not None:\n                self.sensor_model.enable_aggregation()\n            elif sampling_rate &gt; 2:\n                self.aggregation_size = sampling_rate\n\n        # Initilize variable to store visited waypoints for onlineIPP\n        if Xu_fixed is not None:\n            self.Xu_fixed = tf.Variable(Xu_fixed, \n                                        shape=tf.TensorShape(None), \n                                        trainable=False)\n        else:\n            self.Xu_fixed = None\n\n    def update_Xu_fixed(self, Xu_fixed):\n        \"\"\"Function to update the visited waypoints\n\n        Args:\n            Xu_fixed (ndarray): numpy array (num_robots, num_visited_waypoints, num_dim)\n        \"\"\"\n        self.num_fixed = Xu_fixed.shape[1]\n        if self.Xu_fixed is not None:\n            self.Xu_fixed.assign(Xu_fixed)\n        else:\n            self.Xu_fixed = tf.Variable(Xu_fixed, \n                                        shape=tf.TensorShape(None), \n                                        trainable=False)\n\n    def expand(self, Xu, expand_sensor_model=True):\n        \"\"\"Sample points between each pair of inducing points to form the path\n\n        Args:\n            Xu (ndarray): (num_robots x num_inducing, num_dim); Inducing points in the num_dim dimensional space\n            expand_sensor_model (bool): Only add the fixed inducing points without other sensor/path transforms, \n                                        used for online IPP\n\n        Returns:\n            Xu (ndarray): Expansion transformed inducing points\n        \"\"\"\n        # If using single-robot offline IPP with point sensing, return inducing points as is.\n        if self.sampling_rate == 2 and self.Xu_fixed is None and self.sensor_model is None:\n            return Xu\n\n        Xu = tf.reshape(Xu, (self.num_robots, -1, self.num_dim))\n\n        # If using online IPP, add visited waypoints that won't be optimized anymore\n        if self.Xu_fixed is not None:\n            Xu = tf.concat([self.Xu_fixed, Xu[:, self.num_fixed:]], axis=1)\n\n        if not expand_sensor_model:\n            return tf.reshape(Xu, (-1, self.num_dim))\n\n        # Interpolate additional inducing points between waypoints to approximate \n        # the continuous data sensing model\n        if self.sampling_rate &gt; 2:\n            Xu = tf.linspace(Xu[:, :-1], Xu[:, 1:], self.sampling_rate)\n            Xu = tf.transpose(Xu, perm=[1, 2, 0, 3])\n            Xu = tf.reshape(Xu, (self.num_robots, -1, self.num_dim))\n\n        if self.sensor_model is not None:\n            Xu_ = []\n            for i in range(self.num_robots):\n                Xu_.append(self.sensor_model.expand(Xu[i]))\n            Xu = tf.concat(Xu_, axis=0)\n            return Xu\n\n        Xu = tf.reshape(Xu, (-1, self.num_dim))\n        return Xu\n\n    def aggregate(self, k):\n        \"\"\"Applies the aggregation transform to kernel matrices. Checks `sensor_model` \n           and uses the appropriate aggregation transform. \n\n        Args:\n            k (tensor): (mp, mp)/(mp, n); Kernel matrix. \n                        `m` is the number of inducing points,\n                        `p` is the number of points each inducing point is mapped,\n                        `n` is the number of training data points.\n\n        Returns:\n            k (tensor): (m, m)/(m, n); Aggregated kernel matrix\n        \"\"\"\n        if self.sensor_model is not None:\n            return self.sensor_model.aggregate(k)\n        else:\n            return super().aggregate(k)\n\n    def constraints(self, Xu):\n        \"\"\"Computes the distance constraint term that is added to the SGP's optimization function.\n        Each robot can be assigned a different distance budget.\n\n        Args:\n            Xu (ndarray): Inducing points from which to compute the distance constraints\n\n        Returns:\n            loss (float): distance constraint term\n        \"\"\"\n        if self.distance_budget is None:\n            return 0.\n        else:\n            # Only do fixed points expansion transform\n            Xu = self.expand(Xu, expand_sensor_model=False)\n            dist = self.distance(Xu)-self.distance_budget\n            dist = tf.reduce_sum(tf.nn.relu(dist))\n            loss = -dist*self.constraint_weight\n            return loss\n\n    def distance(self, Xu):\n        \"\"\"Computes the distance incured by sequentially visiting the inducing points\n        Args:\n            Xu (ndarray): (m, num_dim); Inducing points from which to compute the path lengths\n                          `m` is the number of inducing points\n                          `num_dim` dimension of the data collection environment\n        Returns:\n            dist (float or tensor of floats): path length(s)\n        \"\"\"\n        Xu = tf.reshape(Xu, (self.num_robots, -1, self.num_dim))\n        if self.sensor_model is not None:\n            dists = []\n            for i in range(self.num_robots):\n                dists.append(self.sensor_model.distance(Xu[i]))\n            dists = tf.concat(dists, axis=0)\n            return dists\n        else:\n            # Assumes 2D waypoints by default\n            dist = tf.norm(Xu[:, 1:, :2] - Xu[:, :-1, :2], axis=-1)\n            dist = tf.reduce_sum(dist, axis=1)\n            return dist\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.IPPTransform.aggregate","title":"<code>aggregate(k)</code>","text":"<p>Applies the aggregation transform to kernel matrices. Checks <code>sensor_model</code>     and uses the appropriate aggregation transform. </p> <p>Parameters:</p> Name Type Description Default <code>k</code> <code>tensor</code> <p>(mp, mp)/(mp, n); Kernel matrix.          <code>m</code> is the number of inducing points,         <code>p</code> is the number of points each inducing point is mapped,         <code>n</code> is the number of training data points.</p> required <p>Returns:</p> Name Type Description <code>k</code> <code>tensor</code> <p>(m, m)/(m, n); Aggregated kernel matrix</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def aggregate(self, k):\n    \"\"\"Applies the aggregation transform to kernel matrices. Checks `sensor_model` \n       and uses the appropriate aggregation transform. \n\n    Args:\n        k (tensor): (mp, mp)/(mp, n); Kernel matrix. \n                    `m` is the number of inducing points,\n                    `p` is the number of points each inducing point is mapped,\n                    `n` is the number of training data points.\n\n    Returns:\n        k (tensor): (m, m)/(m, n); Aggregated kernel matrix\n    \"\"\"\n    if self.sensor_model is not None:\n        return self.sensor_model.aggregate(k)\n    else:\n        return super().aggregate(k)\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.IPPTransform.constraints","title":"<code>constraints(Xu)</code>","text":"<p>Computes the distance constraint term that is added to the SGP's optimization function. Each robot can be assigned a different distance budget.</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>Inducing points from which to compute the distance constraints</p> required <p>Returns:</p> Name Type Description <code>loss</code> <code>float</code> <p>distance constraint term</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def constraints(self, Xu):\n    \"\"\"Computes the distance constraint term that is added to the SGP's optimization function.\n    Each robot can be assigned a different distance budget.\n\n    Args:\n        Xu (ndarray): Inducing points from which to compute the distance constraints\n\n    Returns:\n        loss (float): distance constraint term\n    \"\"\"\n    if self.distance_budget is None:\n        return 0.\n    else:\n        # Only do fixed points expansion transform\n        Xu = self.expand(Xu, expand_sensor_model=False)\n        dist = self.distance(Xu)-self.distance_budget\n        dist = tf.reduce_sum(tf.nn.relu(dist))\n        loss = -dist*self.constraint_weight\n        return loss\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.IPPTransform.distance","title":"<code>distance(Xu)</code>","text":"<p>Computes the distance incured by sequentially visiting the inducing points Args:     Xu (ndarray): (m, num_dim); Inducing points from which to compute the path lengths                   <code>m</code> is the number of inducing points                   <code>num_dim</code> dimension of the data collection environment Returns:     dist (float or tensor of floats): path length(s)</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def distance(self, Xu):\n    \"\"\"Computes the distance incured by sequentially visiting the inducing points\n    Args:\n        Xu (ndarray): (m, num_dim); Inducing points from which to compute the path lengths\n                      `m` is the number of inducing points\n                      `num_dim` dimension of the data collection environment\n    Returns:\n        dist (float or tensor of floats): path length(s)\n    \"\"\"\n    Xu = tf.reshape(Xu, (self.num_robots, -1, self.num_dim))\n    if self.sensor_model is not None:\n        dists = []\n        for i in range(self.num_robots):\n            dists.append(self.sensor_model.distance(Xu[i]))\n        dists = tf.concat(dists, axis=0)\n        return dists\n    else:\n        # Assumes 2D waypoints by default\n        dist = tf.norm(Xu[:, 1:, :2] - Xu[:, :-1, :2], axis=-1)\n        dist = tf.reduce_sum(dist, axis=1)\n        return dist\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.IPPTransform.expand","title":"<code>expand(Xu, expand_sensor_model=True)</code>","text":"<p>Sample points between each pair of inducing points to form the path</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(num_robots x num_inducing, num_dim); Inducing points in the num_dim dimensional space</p> required <code>expand_sensor_model</code> <code>bool</code> <p>Only add the fixed inducing points without other sensor/path transforms,                          used for online IPP</p> <code>True</code> <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>Expansion transformed inducing points</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def expand(self, Xu, expand_sensor_model=True):\n    \"\"\"Sample points between each pair of inducing points to form the path\n\n    Args:\n        Xu (ndarray): (num_robots x num_inducing, num_dim); Inducing points in the num_dim dimensional space\n        expand_sensor_model (bool): Only add the fixed inducing points without other sensor/path transforms, \n                                    used for online IPP\n\n    Returns:\n        Xu (ndarray): Expansion transformed inducing points\n    \"\"\"\n    # If using single-robot offline IPP with point sensing, return inducing points as is.\n    if self.sampling_rate == 2 and self.Xu_fixed is None and self.sensor_model is None:\n        return Xu\n\n    Xu = tf.reshape(Xu, (self.num_robots, -1, self.num_dim))\n\n    # If using online IPP, add visited waypoints that won't be optimized anymore\n    if self.Xu_fixed is not None:\n        Xu = tf.concat([self.Xu_fixed, Xu[:, self.num_fixed:]], axis=1)\n\n    if not expand_sensor_model:\n        return tf.reshape(Xu, (-1, self.num_dim))\n\n    # Interpolate additional inducing points between waypoints to approximate \n    # the continuous data sensing model\n    if self.sampling_rate &gt; 2:\n        Xu = tf.linspace(Xu[:, :-1], Xu[:, 1:], self.sampling_rate)\n        Xu = tf.transpose(Xu, perm=[1, 2, 0, 3])\n        Xu = tf.reshape(Xu, (self.num_robots, -1, self.num_dim))\n\n    if self.sensor_model is not None:\n        Xu_ = []\n        for i in range(self.num_robots):\n            Xu_.append(self.sensor_model.expand(Xu[i]))\n        Xu = tf.concat(Xu_, axis=0)\n        return Xu\n\n    Xu = tf.reshape(Xu, (-1, self.num_dim))\n    return Xu\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.IPPTransform.update_Xu_fixed","title":"<code>update_Xu_fixed(Xu_fixed)</code>","text":"<p>Function to update the visited waypoints</p> <p>Parameters:</p> Name Type Description Default <code>Xu_fixed</code> <code>ndarray</code> <p>numpy array (num_robots, num_visited_waypoints, num_dim)</p> required Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def update_Xu_fixed(self, Xu_fixed):\n    \"\"\"Function to update the visited waypoints\n\n    Args:\n        Xu_fixed (ndarray): numpy array (num_robots, num_visited_waypoints, num_dim)\n    \"\"\"\n    self.num_fixed = Xu_fixed.shape[1]\n    if self.Xu_fixed is not None:\n        self.Xu_fixed.assign(Xu_fixed)\n    else:\n        self.Xu_fixed = tf.Variable(Xu_fixed, \n                                    shape=tf.TensorShape(None), \n                                    trainable=False)\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareHeightTransform","title":"<code>SquareHeightTransform</code>","text":"<p>               Bases: <code>Transform</code></p> <p>Non-point Transform to model a height-dependent square FoV</p> <p>Parameters:</p> Name Type Description Default <code>num_side</code> <code>int</code> <p>Number of points along each side of the FoV</p> required <code>aggregate_fov</code> <code>bool</code> <p>If <code>True</code>, covariances corresponding to interpolated inducing points used to                    approximate the sensor FoV are aggregated to reduce the matrix inversion cost</p> <code>False</code> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>class SquareHeightTransform(Transform):\n    \"\"\"Non-point Transform to model a height-dependent square FoV\n\n    Args:\n        num_side (int): Number of points along each side of the FoV\n        aggregate_fov (bool): If `True`, covariances corresponding to interpolated inducing points used to \n                              approximate the sensor FoV are aggregated to reduce the matrix inversion cost\n    \"\"\"\n    def __init__(self, num_side, aggregate_fov=False, **kwargs):\n        super().__init__(**kwargs)\n        self.num_side = num_side\n\n        if aggregate_fov:\n            self.enable_aggregation()\n\n    def enable_aggregation(self, size=None):\n        \"\"\"Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the \n        covariance matrix size.\n\n        Args:\n            size (int): If None, all the interpolated inducing points within the FoV are aggregated. Alternatively, \n                        the number of inducing points to aggregate can be explicitly defined using this variable.\n        \"\"\"\n        if size is None:\n            self.aggregation_size = self.num_side**2\n        else:\n            self.aggregation_size = size\n\n    def expand(self, Xu):     \n        \"\"\"\n        Applies the expansion transform to the inducing points\n\n        Args:\n            Xu (ndarray): (m, 3); Inducing points in the 3D position space.\n                        `m` is the number of inducing points,\n                        `3` is the dimension of the space (x, y, z)\n\n        Returns:\n            Xu (ndarray): (mp, 2); Inducing points in input space.\n                        `p` is the number of points each inducing point is mapped \n                        to in order to form the FoV.\n        \"\"\"\n        x, y, h = tf.split(Xu, num_or_size_splits=3, axis=1)\n        x = tf.reshape(x, [-1,])\n        y = tf.reshape(y, [-1,])\n        h = tf.reshape(h, [-1,])\n\n        delta = h / (self.num_side - 1)\n\n        pts = []\n        for i in range(self.num_side):\n            pts.append(tf.linspace([x - h/2, y - (h/2) + (delta * i)], \n                                   [x + h/2, y - (h/2) + (delta * i)], \n                                   self.num_side, \n                                   axis=1))\n        xy = tf.concat(pts, axis=1)\n        xy = tf.transpose(xy, [2, 1, 0])\n        xy = tf.reshape(xy, [-1, 2])\n        xy = self._reshape(xy, tf.shape(Xu)[0])\n        return xy\n\n    def _reshape(self, X, num_inducing):\n        \"\"\"Reorder the inducing points to be in the correct order for aggregation with square height FoV\n\n        Args:\n            X (ndarray): (mp, 2); Inducing points in input space. `p` is the number of points each \n                        inducing point is mapped to in order to form the FoV.\n\n        Returns:\n            Xu (ndarray): (mp, 2); Reorder inducing points\n        \"\"\"\n        X = tf.reshape(X, (num_inducing, -1, self.num_side, self.num_side, 2))\n        X = tf.transpose(X, (0, 2, 1, 3, 4))\n        X = tf.reshape(X, (-1, 2))\n        return X\n\n    def distance(self, Xu):\n        \"\"\"Computes the distance incured by sequentially visiting the inducing points\n        Args:\n            Xu (ndarray): (m, 3); Inducing points from which to compute the path lengths.\n                          `m` is the number of inducing points.\n\n        Returns:\n            dist (float): path lengths\n        \"\"\"\n        Xu = tf.reshape(Xu, (-1, 3))\n        dist = tf.norm(Xu[1:] - Xu[:-1], axis=-1)\n        dist = tf.reduce_sum(dist, axis=0)\n        return dist\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareHeightTransform.distance","title":"<code>distance(Xu)</code>","text":"<p>Computes the distance incured by sequentially visiting the inducing points Args:     Xu (ndarray): (m, 3); Inducing points from which to compute the path lengths.                   <code>m</code> is the number of inducing points.</p> <p>Returns:</p> Name Type Description <code>dist</code> <code>float</code> <p>path lengths</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def distance(self, Xu):\n    \"\"\"Computes the distance incured by sequentially visiting the inducing points\n    Args:\n        Xu (ndarray): (m, 3); Inducing points from which to compute the path lengths.\n                      `m` is the number of inducing points.\n\n    Returns:\n        dist (float): path lengths\n    \"\"\"\n    Xu = tf.reshape(Xu, (-1, 3))\n    dist = tf.norm(Xu[1:] - Xu[:-1], axis=-1)\n    dist = tf.reduce_sum(dist, axis=0)\n    return dist\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareHeightTransform.enable_aggregation","title":"<code>enable_aggregation(size=None)</code>","text":"<p>Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the  covariance matrix size.</p> <p>Parameters:</p> Name Type Description Default <code>size</code> <code>int</code> <p>If None, all the interpolated inducing points within the FoV are aggregated. Alternatively,          the number of inducing points to aggregate can be explicitly defined using this variable.</p> <code>None</code> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def enable_aggregation(self, size=None):\n    \"\"\"Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the \n    covariance matrix size.\n\n    Args:\n        size (int): If None, all the interpolated inducing points within the FoV are aggregated. Alternatively, \n                    the number of inducing points to aggregate can be explicitly defined using this variable.\n    \"\"\"\n    if size is None:\n        self.aggregation_size = self.num_side**2\n    else:\n        self.aggregation_size = size\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareHeightTransform.expand","title":"<code>expand(Xu)</code>","text":"<p>Applies the expansion transform to the inducing points</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(m, 3); Inducing points in the 3D position space.         <code>m</code> is the number of inducing points,         <code>3</code> is the dimension of the space (x, y, z)</p> required <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(mp, 2); Inducing points in input space.         <code>p</code> is the number of points each inducing point is mapped          to in order to form the FoV.</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def expand(self, Xu):     \n    \"\"\"\n    Applies the expansion transform to the inducing points\n\n    Args:\n        Xu (ndarray): (m, 3); Inducing points in the 3D position space.\n                    `m` is the number of inducing points,\n                    `3` is the dimension of the space (x, y, z)\n\n    Returns:\n        Xu (ndarray): (mp, 2); Inducing points in input space.\n                    `p` is the number of points each inducing point is mapped \n                    to in order to form the FoV.\n    \"\"\"\n    x, y, h = tf.split(Xu, num_or_size_splits=3, axis=1)\n    x = tf.reshape(x, [-1,])\n    y = tf.reshape(y, [-1,])\n    h = tf.reshape(h, [-1,])\n\n    delta = h / (self.num_side - 1)\n\n    pts = []\n    for i in range(self.num_side):\n        pts.append(tf.linspace([x - h/2, y - (h/2) + (delta * i)], \n                               [x + h/2, y - (h/2) + (delta * i)], \n                               self.num_side, \n                               axis=1))\n    xy = tf.concat(pts, axis=1)\n    xy = tf.transpose(xy, [2, 1, 0])\n    xy = tf.reshape(xy, [-1, 2])\n    xy = self._reshape(xy, tf.shape(Xu)[0])\n    return xy\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareTransform","title":"<code>SquareTransform</code>","text":"<p>               Bases: <code>Transform</code></p> <p>Non-point Transform to model a square FoV. Only works for single robot cases. </p> <p>Parameters:</p> Name Type Description Default <code>length</code> <code>float</code> <p>Length of the square FoV</p> required <code>num_side</code> <code>int</code> <p>Number of points along each side of the FoV</p> required <code>aggregate_fov</code> <code>bool</code> <p>If <code>True</code>, covariances corresponding to interpolated inducing points used to                    approximate the sensor FoV are aggregated to reduce the matrix inversion cost</p> <code>False</code> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>class SquareTransform(Transform):\n    \"\"\"Non-point Transform to model a square FoV. Only works for single robot cases. \n\n    Args:\n        length (float): Length of the square FoV\n        num_side (int): Number of points along each side of the FoV\n        aggregate_fov (bool): If `True`, covariances corresponding to interpolated inducing points used to \n                              approximate the sensor FoV are aggregated to reduce the matrix inversion cost\n    \"\"\"\n    def __init__(self, length, num_side, aggregate_fov=False, **kwargs):\n        super().__init__(**kwargs)\n        self.length = length\n        self.num_side = num_side\n        self.length_factor=length/(self.num_side)\n        self.num_length = int(length/self.length_factor)\n\n        if aggregate_fov:\n            self.enable_aggregation()\n\n    def enable_aggregation(self, size=None):\n        \"\"\"Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the \n        covariance matrix size.\n\n        Args:\n            size (int): If None, all the interpolated inducing points within the FoV are aggregated. Alternatively, \n                        the number of inducing points to aggregate can be explicitly defined using this variable.\n        \"\"\"\n        if size is None:\n            self.aggregation_size = self.num_side**2\n        else:\n            self.aggregation_size = size\n\n    def expand(self, Xu):\n        \"\"\"Applies the expansion transformation to the inducing points\n\n        Args:\n            Xu (ndarray): (m, 3); Inducing points in the position and orientation space.\n                          `m` is the number of inducing points,\n                          `3` is the dimension of the space (x, y, angle in radians)\n\n        Returns:\n            Xu (ndarray): (mp, 2); Inducing points in input space.\n                        `p` is the number of points each inducing point is mapped \n                         to in order to form the FoV.\n        \"\"\"\n        x, y, theta = tf.split(Xu, num_or_size_splits=3, axis=1)\n        x     = tf.reshape(x, [-1,])\n        y     = tf.reshape(y, [-1,])\n        theta = tf.reshape(theta, [-1,])\n\n        points = []\n        for i in range(-int(np.floor((self.num_side)/2)), int(np.ceil((self.num_side)/2))):\n            points.append(tf.linspace([(x + (i * self.length_factor) * tf.cos(theta)) - self.length/2 * tf.cos(theta+np.pi/2), \n                                       (y + (i * self.length_factor) * tf.sin(theta)) - self.length/2 * tf.sin(theta+np.pi/2)], \n                                      [(x + (i * self.length_factor) * tf.cos(theta)) + self.length/2 * tf.cos(theta+np.pi/2), \n                                       (y + (i * self.length_factor) * tf.sin(theta)) + self.length/2 * tf.sin(theta+np.pi/2)], \n                                      self.num_side, axis=1))\n        xy = tf.concat(points, axis=1)\n        xy = tf.transpose(xy, [2, 1, 0])\n        xy = tf.reshape(xy, (-1, 2))\n        return xy\n\n    def distance(self, Xu):\n        \"\"\"Computes the distance incured by sequentially visiting the inducing points\n        Args:\n            Xu (ndarray): (m, 3); Inducing points from which to compute the path lengths.\n                          `m` is the number of inducing points.\n\n        Returns:\n            dist (float): path lengths\n        \"\"\"\n        Xu = tf.reshape(Xu, (-1, 3))[:, :2]\n        dist = tf.norm(Xu[1:] - Xu[:-1], axis=-1)\n        dist = tf.reduce_sum(dist, axis=0)\n        return dist\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareTransform.distance","title":"<code>distance(Xu)</code>","text":"<p>Computes the distance incured by sequentially visiting the inducing points Args:     Xu (ndarray): (m, 3); Inducing points from which to compute the path lengths.                   <code>m</code> is the number of inducing points.</p> <p>Returns:</p> Name Type Description <code>dist</code> <code>float</code> <p>path lengths</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def distance(self, Xu):\n    \"\"\"Computes the distance incured by sequentially visiting the inducing points\n    Args:\n        Xu (ndarray): (m, 3); Inducing points from which to compute the path lengths.\n                      `m` is the number of inducing points.\n\n    Returns:\n        dist (float): path lengths\n    \"\"\"\n    Xu = tf.reshape(Xu, (-1, 3))[:, :2]\n    dist = tf.norm(Xu[1:] - Xu[:-1], axis=-1)\n    dist = tf.reduce_sum(dist, axis=0)\n    return dist\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareTransform.enable_aggregation","title":"<code>enable_aggregation(size=None)</code>","text":"<p>Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the  covariance matrix size.</p> <p>Parameters:</p> Name Type Description Default <code>size</code> <code>int</code> <p>If None, all the interpolated inducing points within the FoV are aggregated. Alternatively,          the number of inducing points to aggregate can be explicitly defined using this variable.</p> <code>None</code> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def enable_aggregation(self, size=None):\n    \"\"\"Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the \n    covariance matrix size.\n\n    Args:\n        size (int): If None, all the interpolated inducing points within the FoV are aggregated. Alternatively, \n                    the number of inducing points to aggregate can be explicitly defined using this variable.\n    \"\"\"\n    if size is None:\n        self.aggregation_size = self.num_side**2\n    else:\n        self.aggregation_size = size\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareTransform.expand","title":"<code>expand(Xu)</code>","text":"<p>Applies the expansion transformation to the inducing points</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(m, 3); Inducing points in the position and orientation space.           <code>m</code> is the number of inducing points,           <code>3</code> is the dimension of the space (x, y, angle in radians)</p> required <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(mp, 2); Inducing points in input space.         <code>p</code> is the number of points each inducing point is mapped           to in order to form the FoV.</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def expand(self, Xu):\n    \"\"\"Applies the expansion transformation to the inducing points\n\n    Args:\n        Xu (ndarray): (m, 3); Inducing points in the position and orientation space.\n                      `m` is the number of inducing points,\n                      `3` is the dimension of the space (x, y, angle in radians)\n\n    Returns:\n        Xu (ndarray): (mp, 2); Inducing points in input space.\n                    `p` is the number of points each inducing point is mapped \n                     to in order to form the FoV.\n    \"\"\"\n    x, y, theta = tf.split(Xu, num_or_size_splits=3, axis=1)\n    x     = tf.reshape(x, [-1,])\n    y     = tf.reshape(y, [-1,])\n    theta = tf.reshape(theta, [-1,])\n\n    points = []\n    for i in range(-int(np.floor((self.num_side)/2)), int(np.ceil((self.num_side)/2))):\n        points.append(tf.linspace([(x + (i * self.length_factor) * tf.cos(theta)) - self.length/2 * tf.cos(theta+np.pi/2), \n                                   (y + (i * self.length_factor) * tf.sin(theta)) - self.length/2 * tf.sin(theta+np.pi/2)], \n                                  [(x + (i * self.length_factor) * tf.cos(theta)) + self.length/2 * tf.cos(theta+np.pi/2), \n                                   (y + (i * self.length_factor) * tf.sin(theta)) + self.length/2 * tf.sin(theta+np.pi/2)], \n                                  self.num_side, axis=1))\n    xy = tf.concat(points, axis=1)\n    xy = tf.transpose(xy, [2, 1, 0])\n    xy = tf.reshape(xy, (-1, 2))\n    return xy\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.Transform","title":"<code>Transform</code>","text":"<p>Base class for transformations of the inducing points, including expansion and aggregation transforms.</p> Refer to the following papers for more details <ul> <li>Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]</li> <li>Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>aggregation_size</code> <code>int</code> <p>Number of consecutive inducing points to aggregate</p> <code>None</code> <code>constraint_weight</code> <code>float</code> <p>Weight term that controls the importance of the                         constraint terms in the SGP's optimization objective</p> <code>1.0</code> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>class Transform:\n    \"\"\"Base class for transformations of the inducing points, including expansion and aggregation transforms.\n\n    Refer to the following papers for more details:\n        - Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]\n        - Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]\n\n    Args:\n        aggregation_size (int): Number of consecutive inducing points to aggregate\n        constraint_weight (float): Weight term that controls the importance of the \n                                   constraint terms in the SGP's optimization objective \n    \"\"\"\n    def __init__(self, \n                 aggregation_size=None, \n                 constraint_weight=1.0,\n                 **kwargs):\n        self.aggregation_size = aggregation_size\n        self.constraint_weight = constraint_weight\n\n    def expand(self, Xu):\n        \"\"\"Applies the expansion transform to the inducing points\n\n        Args:\n            Xu (ndarray): Expansion transformed inducing points\n        \"\"\"\n        return Xu\n\n    def aggregate(self, k):\n        \"\"\"Applies the aggregation transform to kernel matrices\n\n        Args:\n            k (tensor): (mp, mp)/(mp, n); Kernel matrix. \n                        `m` is the number of inducing points,\n                        `p` is the number of points each inducing point is mapped,\n                        `n` is the number of training data points.\n\n        Returns:\n            k (tensor): (m, m)/(m, n); Aggregated kernel matrix\n        \"\"\"\n        if self.aggregation_size is None:\n            return k\n\n        if k.shape[0] == k.shape[1]:\n            # Handle Kuu which is a square matrix\n            k = tf.expand_dims(tf.expand_dims(k, axis=0), axis=-1)\n            k = tf.nn.avg_pool(k,\n                               ksize=[1, self.aggregation_size, self.aggregation_size, 1],\n                               strides=[1, self.aggregation_size, self.aggregation_size, 1],\n                               padding='VALID')\n            k = tf.squeeze(k, axis=[0, -1])\n        else:\n            # Handle Kuf which is a rectangular matrix\n            k = tf.expand_dims(k, axis=0)\n            k = tf.nn.avg_pool(k,\n                               ksize=[1, self.aggregation_size, 1],\n                               strides=[1, self.aggregation_size, 1],\n                               padding='VALID')\n            k = tf.squeeze(k, axis=[0])\n        return k\n\n    def constraints(self, Xu):\n        \"\"\"Computes the constraint terms that are added to the SGP's optimization function\n\n        Args:\n            Xu (ndarray): Inducing points from which to compute the constraints\n\n        Returns:\n            c (float): constraint terms (eg., distance constraint)\n        \"\"\"\n        return 0.\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.Transform.aggregate","title":"<code>aggregate(k)</code>","text":"<p>Applies the aggregation transform to kernel matrices</p> <p>Parameters:</p> Name Type Description Default <code>k</code> <code>tensor</code> <p>(mp, mp)/(mp, n); Kernel matrix.          <code>m</code> is the number of inducing points,         <code>p</code> is the number of points each inducing point is mapped,         <code>n</code> is the number of training data points.</p> required <p>Returns:</p> Name Type Description <code>k</code> <code>tensor</code> <p>(m, m)/(m, n); Aggregated kernel matrix</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def aggregate(self, k):\n    \"\"\"Applies the aggregation transform to kernel matrices\n\n    Args:\n        k (tensor): (mp, mp)/(mp, n); Kernel matrix. \n                    `m` is the number of inducing points,\n                    `p` is the number of points each inducing point is mapped,\n                    `n` is the number of training data points.\n\n    Returns:\n        k (tensor): (m, m)/(m, n); Aggregated kernel matrix\n    \"\"\"\n    if self.aggregation_size is None:\n        return k\n\n    if k.shape[0] == k.shape[1]:\n        # Handle Kuu which is a square matrix\n        k = tf.expand_dims(tf.expand_dims(k, axis=0), axis=-1)\n        k = tf.nn.avg_pool(k,\n                           ksize=[1, self.aggregation_size, self.aggregation_size, 1],\n                           strides=[1, self.aggregation_size, self.aggregation_size, 1],\n                           padding='VALID')\n        k = tf.squeeze(k, axis=[0, -1])\n    else:\n        # Handle Kuf which is a rectangular matrix\n        k = tf.expand_dims(k, axis=0)\n        k = tf.nn.avg_pool(k,\n                           ksize=[1, self.aggregation_size, 1],\n                           strides=[1, self.aggregation_size, 1],\n                           padding='VALID')\n        k = tf.squeeze(k, axis=[0])\n    return k\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.Transform.constraints","title":"<code>constraints(Xu)</code>","text":"<p>Computes the constraint terms that are added to the SGP's optimization function</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>Inducing points from which to compute the constraints</p> required <p>Returns:</p> Name Type Description <code>c</code> <code>float</code> <p>constraint terms (eg., distance constraint)</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def constraints(self, Xu):\n    \"\"\"Computes the constraint terms that are added to the SGP's optimization function\n\n    Args:\n        Xu (ndarray): Inducing points from which to compute the constraints\n\n    Returns:\n        c (float): constraint terms (eg., distance constraint)\n    \"\"\"\n    return 0.\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.Transform.expand","title":"<code>expand(Xu)</code>","text":"<p>Applies the expansion transform to the inducing points</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>Expansion transformed inducing points</p> required Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def expand(self, Xu):\n    \"\"\"Applies the expansion transform to the inducing points\n\n    Args:\n        Xu (ndarray): Expansion transformed inducing points\n    \"\"\"\n    return Xu\n</code></pre>"},{"location":"api/utils/index.html","title":"","text":""},{"location":"api/utils/index.html#sgptools.utils","title":"<code>sgptools.utils</code>","text":"<p>General utilities to support the functionalities of this package:</p> <ul> <li><code>data</code>: Provides utilities to preprocess datasets</li> <li><code>gpflow</code>: Provides utilities to interface with GPflow</li> <li><code>metrics</code>: Provides utilities to quantify the solution quality</li> <li><code>misc</code>: Provides miscellaneous helper functions</li> <li><code>tsp</code>: Provides utilities to run TSP/VRP solver</li> </ul>"},{"location":"api/utils/Data.html","title":"Data","text":""},{"location":"api/utils/Data.html#sgptools.utils.data","title":"<code>sgptools.utils.data</code>","text":""},{"location":"api/utils/Data.html#sgptools.utils.data.get_dataset","title":"<code>get_dataset(dataset_path=None, num_train=1000, num_test=2500, num_candidates=150, **kwargs)</code>","text":"<p>Method to generate/load datasets and preprocess them for SP/IPP. The method uses kmeans to  generate train and test sets.</p> <p>Parameters:</p> Name Type Description Default <code>dataset_path</code> <code>str</code> <p>Path to a tif dataset file. If None, the method will generate synthetic data.</p> <code>None</code> <code>num_train</code> <code>int</code> <p>Number of training samples to generate.</p> <code>1000</code> <code>num_test</code> <code>int</code> <p>Number of testing samples to generate.</p> <code>2500</code> <code>num_candidates</code> <code>int</code> <p>Number of candidate locations to generate.</p> <code>150</code> <p>Returns:</p> Name Type Description <code>X_train</code> <code>ndarray</code> <p>(n, d); Training set inputs</p> <code>y_train</code> <code>ndarray</code> <p>(n, 1); Training set labels</p> <code>X_test</code> <code>ndarray</code> <p>(n, d); Testing set inputs</p> <code>y_test</code> <code>ndarray</code> <p>(n, 1); Testing set labels</p> <code>candidates</code> <code>ndarray</code> <p>(n, d); Candidate sensor placement locations</p> <code>X</code> <code>ndarray</code> <p>(n, d); Full dataset inputs</p> <code>y</code> <code>ndarray</code> <p>(n, 1); Full dataset labels</p> Source code in <code>sgptools/utils/data.py</code> <pre><code>def get_dataset(dataset_path=None,\n                num_train=1000,\n                num_test=2500, \n                num_candidates=150,\n                **kwargs):\n    \"\"\"Method to generate/load datasets and preprocess them for SP/IPP. The method uses kmeans to \n    generate train and test sets.\n\n    Args:\n        dataset_path (str): Path to a tif dataset file. If None, the method will generate synthetic data.\n        num_train (int): Number of training samples to generate.\n        num_test (int): Number of testing samples to generate.\n        num_candidates (int): Number of candidate locations to generate.\n\n    Returns:\n       X_train (ndarray): (n, d); Training set inputs\n       y_train (ndarray): (n, 1); Training set labels\n       X_test (ndarray): (n, d); Testing set inputs\n       y_test (ndarray): (n, 1); Testing set labels\n       candidates (ndarray): (n, d); Candidate sensor placement locations\n       X (ndarray): (n, d); Full dataset inputs\n       y (ndarray): (n, 1); Full dataset labels\n    \"\"\"\n    # Load the data\n    if dataset_path is not None:\n        X, y = prep_tif_dataset(dataset_path=dataset_path)\n    else:\n        X, y = prep_synthetic_dataset(**kwargs)\n\n    X_train = get_inducing_pts(X, num_train)\n    X_train, y_train = cont2disc(X_train, X, y)\n\n    X_test = get_inducing_pts(X, num_test)\n    X_test, y_test = cont2disc(X_test, X, y)\n\n    candidates = get_inducing_pts(X, num_candidates)\n    candidates = cont2disc(candidates, X)\n\n    # Standardize data\n    X_scaler = StandardScaler()\n    X_scaler.fit(X_train)\n    X_train = X_scaler.transform(X_train)*10.0\n    X_test = X_scaler.transform(X_test)*10.0\n    X = X_scaler.transform(X)*10.0\n    candidates = X_scaler.transform(candidates)*10.0\n\n    y_scaler = StandardScaler()\n    y_scaler.fit(y_train)\n    y_train = y_scaler.transform(y_train)\n    y_test = y_scaler.transform(y_test)\n    y = y_scaler.transform(y)\n\n    return X_train, y_train, X_test, y_test, candidates, X, y\n</code></pre>"},{"location":"api/utils/Data.html#sgptools.utils.data.point_pos","title":"<code>point_pos(point, d, theta)</code>","text":"<p>Generate a point at a distance d from a point at angle theta.</p> <p>Parameters:</p> Name Type Description Default <code>point</code> <code>ndarray</code> <p>(N, 2); array of points</p> required <code>d</code> <code>float</code> <p>distance</p> required <code>theta</code> <code>float</code> <p>angle in radians</p> required <p>Returns:</p> Name Type Description <code>X</code> <code>ndarray</code> <p>(N,); array of x-coordinate</p> <code>Y</code> <code>ndarray</code> <p>(N,); array of y-coordinate</p> Source code in <code>sgptools/utils/data.py</code> <pre><code>def point_pos(point, d, theta):\n    '''\n    Generate a point at a distance d from a point at angle theta.\n\n    Args:\n        point (ndarray): (N, 2); array of points\n        d (float): distance\n        theta (float): angle in radians\n\n    Returns:\n        X  (ndarray): (N,); array of x-coordinate\n        Y  (ndarray): (N,); array of y-coordinate\n    '''\n    return np.c_[point[:, 0] + d*np.cos(theta), point[:, 1] + d*np.sin(theta)]\n</code></pre>"},{"location":"api/utils/Data.html#sgptools.utils.data.prep_synthetic_dataset","title":"<code>prep_synthetic_dataset(shape=(50, 50), min_height=0.0, max_height=30.0, roughness=0.5, random_seed=None, **kwargs)</code>","text":"<p>Generates a 50x50 grid of synthetic elevation data using the diamond square algorithm.</p> Refer to the following repo for more details <ul> <li>https://github.com/buckinha/DiamondSquare</li> </ul> <p>Parameters:</p> Name Type Description Default <code>shape</code> <code>tuple</code> <p>(x, y); Grid size along the x and y axis</p> <code>(50, 50)</code> <code>min_height</code> <code>float</code> <p>Minimum allowed height in the sampled data</p> <code>0.0</code> <code>max_height</code> <code>float</code> <p>Maximum allowed height in the sampled data</p> <code>30.0</code> <code>roughness</code> <code>float</code> <p>Roughness of the sampled data</p> <code>0.5</code> <code>random_seed</code> <code>int</code> <p>Random seed for reproducibility</p> <code>None</code> <p>Returns:</p> Name Type Description <code>X</code> <code>ndarray</code> <p>(n, d); Dataset input features</p> <code>y</code> <code>ndarray</code> <p>(n, 1); Dataset labels</p> Source code in <code>sgptools/utils/data.py</code> <pre><code>def prep_synthetic_dataset(shape=(50, 50), \n                           min_height=0.0, \n                           max_height=30.0, \n                           roughness=0.5,\n                           random_seed=None,\n                           **kwargs):\n    '''Generates a 50x50 grid of synthetic elevation data using the diamond square algorithm.\n\n    Refer to the following repo for more details:\n        - [https://github.com/buckinha/DiamondSquare](https://github.com/buckinha/DiamondSquare)\n\n    Args:\n        shape (tuple): (x, y); Grid size along the x and y axis\n        min_height (float): Minimum allowed height in the sampled data\n        max_height (float): Maximum allowed height in the sampled data\n        roughness (float): Roughness of the sampled data\n        random_seed (int): Random seed for reproducibility\n\n    Returns:\n       X (ndarray): (n, d); Dataset input features\n       y (ndarray): (n, 1); Dataset labels\n    '''\n    data = diamond_square(shape=shape,\n                          min_height=min_height, \n                          max_height=max_height, \n                          roughness=roughness,\n                          random_seed=random_seed,\n                          **kwargs)\n\n    # create x and y coordinates from the extent\n    x_coords = np.arange(0, data.shape[0])/10\n    y_coords = np.arange(0, data.shape[1])/10\n    xx, yy = np.meshgrid(x_coords, y_coords)\n    X = np.c_[xx.ravel(), yy.ravel()]\n    y = data.ravel()\n    y = y.reshape(-1, 1)\n\n    return X.astype(float), y.astype(float)\n</code></pre>"},{"location":"api/utils/Data.html#sgptools.utils.data.prep_tif_dataset","title":"<code>prep_tif_dataset(dataset_path)</code>","text":"<p>Load and preprocess a dataset from a GeoTIFF file (.tif file). The input features  are set to the x and y pixel block coordinates and the labels are read from the file. The method also removes all invalid points.</p> <p>Large tif files  need to be downsampled using the following command:  <code>gdalwarp -tr 50 50 &lt;input&gt;.tif &lt;output&gt;.tif</code></p> <p>Args:     dataset_path (str): Path to the dataset file, used only when dataset_type is 'tif'.</p> <p>Returns:    X (ndarray): (n, d); Dataset input features    y (ndarray): (n, 1); Dataset labels</p> Source code in <code>sgptools/utils/data.py</code> <pre><code>def prep_tif_dataset(dataset_path):\n    '''Load and preprocess a dataset from a GeoTIFF file (.tif file). The input features \n    are set to the x and y pixel block coordinates and the labels are read from the file.\n    The method also removes all invalid points.\n\n    Large tif files \n    need to be downsampled using the following command: \n    ```gdalwarp -tr 50 50 &lt;input&gt;.tif &lt;output&gt;.tif```\n\n    Args:\n        dataset_path (str): Path to the dataset file, used only when dataset_type is 'tif'.\n\n    Returns:\n       X (ndarray): (n, d); Dataset input features\n       y (ndarray): (n, 1); Dataset labels\n    '''\n    data = PIL.Image.open(dataset_path)\n    data = np.array(data)\n\n    # create x and y coordinates from the extent\n    x_coords = np.arange(0, data.shape[1])/10\n    y_coords = np.arange(data.shape[0], 0, -1)/10\n    xx, yy = np.meshgrid(x_coords, y_coords)\n    X = np.c_[xx.ravel(), yy.ravel()]\n    y = data.ravel()\n\n    # Remove invalid labels\n    y[np.where(y==-999999.0)] = np.nan\n    X = X[~np.isnan(y)]\n    y = y[~np.isnan(y)]\n\n    X = X.reshape(-1, 2)\n    y = y.reshape(-1, 1)\n\n    return X.astype(float), y.astype(float)\n</code></pre>"},{"location":"api/utils/Data.html#sgptools.utils.data.remove_circle_patches","title":"<code>remove_circle_patches(X, Y, circle_patches)</code>","text":"<p>Remove points inside polycircle patchesgons.</p> <p>Parameters:</p> Name Type Description Default <code>X</code> <code> (ndarray</code> <p>(N,); array of x-coordinate</p> required <code>Y</code> <code> (ndarray</code> <p>(N,); array of y-coordinate</p> required <code>circle_patches</code> <code>list of matplotlib circle patches</code> <p>Circle patches to remove from the X, Y points</p> required <p>Returns:</p> Name Type Description <code>X</code> <code>ndarray</code> <p>(N,); array of x-coordinate</p> <code>Y</code> <code>ndarray</code> <p>(N,); array of y-coordinate</p> Source code in <code>sgptools/utils/data.py</code> <pre><code>def remove_circle_patches(X, Y, circle_patches):\n    '''\n    Remove points inside polycircle patchesgons.\n\n    Args:\n        X  (ndarray): (N,); array of x-coordinate\n        Y  (ndarray): (N,); array of y-coordinate\n        circle_patches (list of matplotlib circle patches): Circle patches to remove from the X, Y points\n\n    Returns:\n        X  (ndarray): (N,); array of x-coordinate\n        Y  (ndarray): (N,); array of y-coordinate\n    '''\n    points = np.array([X.flatten(), Y.flatten()]).T\n    for circle_patch in circle_patches:\n        points = points[~circle_patch.contains_points(points)]\n    return points[:, 0], points[:, 1]\n</code></pre>"},{"location":"api/utils/Data.html#sgptools.utils.data.remove_polygons","title":"<code>remove_polygons(X, Y, polygons)</code>","text":"<p>Remove points inside polygons.</p> <p>Parameters:</p> Name Type Description Default <code>X</code> <code> (ndarray</code> <p>(N,); array of x-coordinate</p> required <code>Y</code> <code> (ndarray</code> <p>(N,); array of y-coordinate</p> required <code>polygons</code> <code>list of matplotlib path polygon</code> <p>Polygons to remove from the X, Y points</p> required <p>Returns:</p> Name Type Description <code>X</code> <code>ndarray</code> <p>(N,); array of x-coordinate</p> <code>Y</code> <code>ndarray</code> <p>(N,); array of y-coordinate</p> Source code in <code>sgptools/utils/data.py</code> <pre><code>def remove_polygons(X, Y, polygons):\n    '''\n    Remove points inside polygons.\n\n    Args:\n        X  (ndarray): (N,); array of x-coordinate\n        Y  (ndarray): (N,); array of y-coordinate\n        polygons (list of matplotlib path polygon): Polygons to remove from the X, Y points\n\n    Returns:\n        X  (ndarray): (N,); array of x-coordinate\n        Y  (ndarray): (N,); array of y-coordinate\n    '''\n    points = np.array([X.flatten(), Y.flatten()]).T\n    for polygon in polygons:\n        p = path.Path(polygon)\n        points = points[~p.contains_points(points)]\n    return points[:, 0], points[:, 1]\n</code></pre>"},{"location":"api/utils/GPflow.html","title":"GPflow","text":""},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow","title":"<code>sgptools.utils.gpflow</code>","text":""},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow.TraceInducingPts","title":"<code>TraceInducingPts</code>","text":"<p>               Bases: <code>MonitorTask</code></p> <p>GPflow monitoring task, used to trace the inducing points states at every step during optimization. </p> <p>Parameters:</p> Name Type Description Default <code>model</code> <code>sgpr</code> <p>GPflow GP/SGP model</p> required Source code in <code>sgptools/utils/gpflow.py</code> <pre><code>class TraceInducingPts(gpflow.monitor.MonitorTask):\n    '''\n    GPflow monitoring task, used to trace the inducing points\n    states at every step during optimization. \n\n    Args:\n        model (gpflow.models.sgpr): GPflow GP/SGP model\n    '''\n    def __init__(self, model):\n        super().__init__()\n        self.trace = []\n        self.model = model\n\n    def run(self, **kwargs):\n        '''\n        Method used to extract the inducing points and \n        apply IPP fixed points transform if available\n        '''\n        Xu = self.model.inducing_variable.Z\n        Xu_exp = self.model.transform.expand(Xu, \n                            expand_sensor_model=False).numpy()\n        self.trace.append(Xu_exp)\n\n    def get_trace(self):\n        '''\n        Returns the inducing points collected at each optimization step\n\n        Returns:\n            trace (ndarray): (n, m, d); Array with the inducing points.\n                            `n` is the number of optimization steps;\n                            `m` is the number of inducing points;\n                            `d` is the dimension of the inducing points.\n        '''\n        return np.array(self.trace)\n</code></pre>"},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow.TraceInducingPts.get_trace","title":"<code>get_trace()</code>","text":"<p>Returns the inducing points collected at each optimization step</p> <p>Returns:</p> Name Type Description <code>trace</code> <code>ndarray</code> <p>(n, m, d); Array with the inducing points.             <code>n</code> is the number of optimization steps;             <code>m</code> is the number of inducing points;             <code>d</code> is the dimension of the inducing points.</p> Source code in <code>sgptools/utils/gpflow.py</code> <pre><code>def get_trace(self):\n    '''\n    Returns the inducing points collected at each optimization step\n\n    Returns:\n        trace (ndarray): (n, m, d); Array with the inducing points.\n                        `n` is the number of optimization steps;\n                        `m` is the number of inducing points;\n                        `d` is the dimension of the inducing points.\n    '''\n    return np.array(self.trace)\n</code></pre>"},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow.TraceInducingPts.run","title":"<code>run(**kwargs)</code>","text":"<p>Method used to extract the inducing points and  apply IPP fixed points transform if available</p> Source code in <code>sgptools/utils/gpflow.py</code> <pre><code>def run(self, **kwargs):\n    '''\n    Method used to extract the inducing points and \n    apply IPP fixed points transform if available\n    '''\n    Xu = self.model.inducing_variable.Z\n    Xu_exp = self.model.transform.expand(Xu, \n                        expand_sensor_model=False).numpy()\n    self.trace.append(Xu_exp)\n</code></pre>"},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow.get_model_params","title":"<code>get_model_params(X_train, y_train, max_steps=1500, lr=0.01, print_params=True, lengthscales=1.0, variance=1.0, noise_variance=0.1, kernel=None, return_gp=False, train_inducing_pts=False, num_inducing_pts=500, **kwargs)</code>","text":"<p>Train a GP on the given training set.  Trains a sparse GP if the training set is larger than 1000 samples.</p> <p>Parameters:</p> Name Type Description Default <code>X_train</code> <code>ndarray</code> <p>(n, d); Training set inputs</p> required <code>y_train</code> <code>ndarray</code> <p>(n, 1); Training set labels</p> required <code>max_steps</code> <code>int</code> <p>Maximum number of optimization steps</p> <code>1500</code> <code>lr</code> <code>float</code> <p>Optimization learning rate</p> <code>0.01</code> <code>print_params</code> <code>bool</code> <p>If True, prints the optimized GP parameters</p> <code>True</code> <code>lengthscales</code> <code>float or list</code> <p>Kernel lengthscale(s), if passed as a list,                      each element corresponds to each data dimension</p> <code>1.0</code> <code>variance</code> <code>float</code> <p>Kernel variance</p> <code>1.0</code> <code>noise_variance</code> <code>float</code> <p>Data noise variance</p> <code>0.1</code> <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> <code>None</code> <code>return_gp</code> <code>bool</code> <p>If True, returns the trained GP model</p> <code>False</code> <code>train_inducing_pts</code> <code>bool</code> <p>If True, trains the inducing points when                        using a sparse GP model</p> <code>False</code> <code>num_inducing_pts</code> <code>int</code> <p>Number of inducing points to use when training                     a sparse GP model</p> <code>500</code> <p>Returns:</p> Name Type Description <code>loss</code> <code>list</code> <p>Loss values obtained during training</p> <code>variance</code> <code>float</code> <p>Optimized data noise variance</p> <code>kernel</code> <code>Kernel</code> <p>Optimized gpflow kernel function</p> <code>gp</code> <code>GPR</code> <p>Optimized gpflow GP model.                      Returned only if <code>return_gp=True</code>.</p> Source code in <code>sgptools/utils/gpflow.py</code> <pre><code>def get_model_params(X_train, y_train, \n                     max_steps=1500, \n                     lr=1e-2, \n                     print_params=True, \n                     lengthscales=1.0, \n                     variance=1.0, \n                     noise_variance=0.1,\n                     kernel=None,\n                     return_gp=False,\n                     train_inducing_pts=False,\n                     num_inducing_pts=500,\n                     **kwargs):\n    \"\"\"Train a GP on the given training set. \n    Trains a sparse GP if the training set is larger than 1000 samples.\n\n    Args:\n        X_train (ndarray): (n, d); Training set inputs\n        y_train (ndarray): (n, 1); Training set labels\n        max_steps (int): Maximum number of optimization steps\n        lr (float): Optimization learning rate\n        print_params (bool): If True, prints the optimized GP parameters\n        lengthscales (float or list): Kernel lengthscale(s), if passed as a list, \n                                each element corresponds to each data dimension\n        variance (float): Kernel variance\n        noise_variance (float): Data noise variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        return_gp (bool): If True, returns the trained GP model\n        train_inducing_pts (bool): If True, trains the inducing points when\n                                   using a sparse GP model\n        num_inducing_pts (int): Number of inducing points to use when training\n                                a sparse GP model\n\n    Returns:\n        loss (list): Loss values obtained during training\n        variance (float): Optimized data noise variance\n        kernel (gpflow.kernels.Kernel): Optimized gpflow kernel function\n        gp (gpflow.models.GPR): Optimized gpflow GP model. \n                                Returned only if ```return_gp=True```.\n\n    \"\"\"\n    if kernel is None:\n        kernel = gpflow.kernels.SquaredExponential(lengthscales=lengthscales, \n                                                   variance=variance)\n\n    if len(X_train) &lt;= 1500:\n        gpr = gpflow.models.GPR(data=(X_train, y_train), \n                                kernel=kernel,\n                                noise_variance=noise_variance)\n        trainable_variables=gpr.trainable_variables\n    else:\n        inducing_pts = get_inducing_pts(X_train, num_inducing_pts)\n        gpr = gpflow.models.SGPR(data=(X_train, y_train), \n                                 kernel=kernel,\n                                 inducing_variable=inducing_pts,\n                                 noise_variance=noise_variance)\n        if train_inducing_pts:\n            trainable_variables=gpr.trainable_variables\n        else:\n            trainable_variables=gpr.trainable_variables[1:]\n\n    if max_steps &gt; 0:\n        loss = optimize_model(gpr, max_steps=max_steps, lr=lr, \n                              trainable_variables=trainable_variables,\n                              **kwargs)\n    else:\n        loss = 0\n\n    if print_params:\n        print_summary(gpr)\n\n    if return_gp:\n        return loss, gpr.likelihood.variance, kernel, gpr\n    else:\n        return loss, gpr.likelihood.variance, kernel\n</code></pre>"},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow.optimize_model","title":"<code>optimize_model(model, max_steps=2000, kernel_grad=True, lr=0.01, optimizer='scipy', method=None, verbose=False, trace_fn=None, convergence_criterion=True, trainable_variables=None, tol=None)</code>","text":"<p>Trains a GP/SGP model</p> <p>Parameters:</p> Name Type Description Default <code>model</code> <code>models</code> <p>GPflow GP/SGP model to train.</p> required <code>max_steps</code> <code>int</code> <p>Maximum number of training steps.</p> <code>2000</code> <code>kernel_grad</code> <code>bool</code> <p>If <code>False</code>, the kernel parameters will not be optimized.                  Ignored when <code>trainable_variables</code> are passed.</p> <code>True</code> <code>lr</code> <code>float</code> <p>Optimization learning rate.</p> <code>0.01</code> <code>optimizer</code> <code>str</code> <p>Optimizer to use for training (<code>scipy</code> or <code>tf</code>).</p> <code>'scipy'</code> <code>method</code> <code>str</code> <p>Optimization method refer to scipy minimize            and tf optimizers for full list</p> <code>None</code> <code>verbose</code> <code>bool</code> <p>If <code>True</code>, the training progress will be printed when using Scipy.</p> <code>False</code> <code>trace_fn</code> <code>str</code> <p>Function to trace metrics during training.              If <code>None</code>, the loss values are returned;             If <code>traceXu</code>, it the inducing points states at each optimization step are returned (increases computation time).</p> <code>None</code> <code>convergence_criterion</code> <code>bool</code> <p>If <code>True</code> and using a tensorflow optimizer, it                            enables early stopping when the loss plateaus.</p> <code>True</code> <code>trainable_variables</code> <code>list</code> <p>List of model variables to train.</p> <code>None</code> <code>tol</code> <code>float</code> <p>Convergence tolerance to decide when to stop optimization.</p> <code>None</code> Source code in <code>sgptools/utils/gpflow.py</code> <pre><code>def optimize_model(model, \n                   max_steps=2000, \n                   kernel_grad=True, \n                   lr=1e-2, \n                   optimizer='scipy', \n                   method=None,\n                   verbose=False, \n                   trace_fn=None,\n                   convergence_criterion=True,\n                   trainable_variables=None,\n                   tol=None):\n    \"\"\"\n    Trains a GP/SGP model\n\n    Args:\n        model (gpflow.models): GPflow GP/SGP model to train.\n        max_steps (int): Maximum number of training steps.\n        kernel_grad (bool): If `False`, the kernel parameters will not be optimized. \n                            Ignored when `trainable_variables` are passed.\n        lr (float): Optimization learning rate.\n        optimizer (str): Optimizer to use for training (`scipy` or `tf`).\n        method (str): Optimization method refer to [scipy minimize](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html#scipy.optimize.minimize) \n                      and [tf optimizers](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers) for full list\n        verbose (bool): If `True`, the training progress will be printed when using Scipy.\n        trace_fn (str): Function to trace metrics during training. \n                        If `None`, the loss values are returned;\n                        If `traceXu`, it the inducing points states at each optimization step are returned (increases computation time).\n        convergence_criterion (bool): If `True` and using a tensorflow optimizer, it \n                                      enables early stopping when the loss plateaus.\n        trainable_variables (list): List of model variables to train.\n        tol (float): Convergence tolerance to decide when to stop optimization.\n    \"\"\"\n    # Train all variables if trainable_variables are not provided\n    # If kernel_gradient is False, disable the kernel parameter gradient updates\n    if trainable_variables is None and kernel_grad:\n        trainable_variables=model.trainable_variables\n    elif trainable_variables is None and not kernel_grad:\n        trainable_variables=model.trainable_variables[:1]\n\n    if optimizer == 'scipy':\n        if method is None:\n            method = 'L-BFGS-B'\n\n        if trace_fn == 'traceXu':\n            execute_task = TraceInducingPts(model)\n            task_group = gpflow.monitor.MonitorTaskGroup(execute_task, \n                                                         period=1)\n            trace_fn = gpflow.monitor.Monitor(task_group)\n\n        opt = gpflow.optimizers.Scipy()\n        losses = opt.minimize(model.training_loss,\n                              trainable_variables,\n                              method=method,\n                              options=dict(disp=verbose, maxiter=max_steps),\n                              tol=tol,\n                              step_callback=trace_fn)\n        if trace_fn is None:\n            losses = losses.fun\n        else:\n            losses = trace_fn.task_groups[0].tasks[0].get_trace()\n    else:\n        if trace_fn is None:\n            trace_fn = lambda x: x.loss\n        elif trace_fn == 'traceXu':\n            def trace_fn(traceable_quantities):\n                return model.inducing_variable.Z.numpy()\n\n        if method is None:\n            method = 'adam'\n        opt = tf.keras.optimizers.get(method)\n        opt.learning_rate = lr\n        loss_fn = model.training_loss\n        if convergence_criterion:\n            convergence_criterion = tfp.optimizer.convergence_criteria.LossNotDecreasing(\n                                            atol=1e-5, \n                                            window_size=50,\n                                            min_num_steps=int(max_steps*0.1))\n        else:\n            convergence_criterion = None\n        losses = tfp.math.minimize(loss_fn,\n                                   trainable_variables=trainable_variables,\n                                   num_steps=max_steps,\n                                   optimizer=opt,\n                                   convergence_criterion=convergence_criterion,\n                                   trace_fn=trace_fn)\n        losses = losses.numpy()\n\n    return losses\n</code></pre>"},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow.plot_loss","title":"<code>plot_loss(losses, save_file=None)</code>","text":"<p>Helper function to plot the training loss</p> <p>Parameters:</p> Name Type Description Default <code>losses</code> <code>list</code> <p>list of loss values</p> required <code>save_file</code> <code>str</code> <p>If passed, the loss plot will be saved to the <code>save_file</code></p> <code>None</code> Source code in <code>sgptools/utils/gpflow.py</code> <pre><code>def plot_loss(losses, save_file=None):\n    \"\"\"Helper function to plot the training loss\n\n    Args:\n        losses (list): list of loss values\n        save_file (str): If passed, the loss plot will be saved to the `save_file`\n    \"\"\"\n    plt.plot(losses)\n    plt.title('Log Likelihood')\n    plt.xlabel('Iteration')\n    plt.ylabel('Log Likelihood')\n    ax = plt.gca()\n    ax.ticklabel_format(useOffset=False)\n\n    if save_file is not None:\n        plt.savefig(save_file, bbox_inches='tight')\n        plt.close()\n    else:\n        plt.show()\n</code></pre>"},{"location":"api/utils/Metrics.html","title":"Metrics","text":""},{"location":"api/utils/Metrics.html#sgptools.utils.metrics","title":"<code>sgptools.utils.metrics</code>","text":""},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.gaussian_entropy","title":"<code>gaussian_entropy(K)</code>","text":"<p>Computes GP-based entropy from a kernel matrix</p> <p>Parameters:</p> Name Type Description Default <code>K</code> <code>ndarray</code> <p>(n, n); kernel matrix</p> required <p>Returns:</p> Name Type Description <code>entropy</code> <code>float</code> <p>Entropy computed from the kernel matrix</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def gaussian_entropy(K):\n    \"\"\"Computes GP-based entropy from a kernel matrix\n\n    Args:\n        K (ndarray): (n, n); kernel matrix\n\n    Returns:\n        entropy (float): Entropy computed from the kernel matrix\n    \"\"\"\n    return multivariate_normal(mean=None, cov=K, allow_singular=True).entropy()\n</code></pre>"},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.get_distance","title":"<code>get_distance(X)</code>","text":"<p>Compute the length of a path (L2-norm)</p> <p>Parameters:</p> Name Type Description Default <code>X</code> <code>ndarray</code> <p>(m, d); Waypoints of a path</p> required <p>Returns:</p> Name Type Description <code>dist</code> <code>float</code> <p>Total path length</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def get_distance(X):\n    \"\"\"Compute the length of a path (L2-norm)\n\n    Args:\n        X (ndarray): (m, d); Waypoints of a path\n\n    Returns:\n        dist (float): Total path length\n    \"\"\"\n    dist = np.linalg.norm(X[1:] - X[:-1], axis=-1)\n    dist = np.sum(dist)\n    return dist\n</code></pre>"},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.get_elbo","title":"<code>get_elbo(Xu, X_env, noise_variance, kernel, baseline=False)</code>","text":"<p>Computes the ELBO of the SGP, corrected to be positive</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(m, d); Sensing locations</p> required <code>X_env</code> <code>ndarray</code> <p>(n, d); Data points used to approximate the bounds of the environment</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>baseline</code> <code>bool</code> <p>If True, the ELBO is adjusted to be positive</p> <code>False</code> <p>Returns:</p> Name Type Description <code>elbo</code> <code>float</code> <p>ELBO of the SGP</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def get_elbo(Xu, X_env, noise_variance, kernel, baseline=False):\n    \"\"\"Computes the ELBO of the SGP, corrected to be positive\n\n    Args:\n        Xu (ndarray): (m, d); Sensing locations\n        X_env (ndarray): (n, d); Data points used to approximate the bounds of the environment\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        baseline (bool): If True, the ELBO is adjusted to be positive\n\n    Returns:\n        elbo (float): ELBO of the SGP\n    \"\"\"\n    if baseline:\n        sgpr = gpflow.models.SGPR(X_env,\n                                  noise_variance=noise_variance,\n                                  kernel=kernel,\n                                  inducing_variable=[[0, 0]])\n        baseline = sgpr.elbo().numpy()\n    else:\n        baseline = 0.0\n\n    sgpr = gpflow.models.SGPR(X_env,\n                              noise_variance=noise_variance,\n                              kernel=kernel, \n                              inducing_variable=Xu)\n    return (sgpr.elbo() - baseline).numpy()\n</code></pre>"},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.get_kl","title":"<code>get_kl(Xu, X_env, noise_variance, kernel)</code>","text":"<p>Computes the KL divergence between the SGP and the GP</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(m, d); Sensing locations</p> required <code>X_env</code> <code>ndarray</code> <p>(n, d); Data points used to approximate the bounds of the environment</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <p>Returns:</p> Name Type Description <code>kl</code> <code>float</code> <p>KL divergence between the SGP and the GP</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def get_kl(Xu, X_env, noise_variance, kernel):\n    \"\"\"Computes the KL divergence between the SGP and the GP\n\n    Args:\n        Xu (ndarray): (m, d); Sensing locations\n        X_env (ndarray): (n, d); Data points used to approximate the bounds of the environment\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n\n    Returns:\n        kl (float): KL divergence between the SGP and the GP\n    \"\"\"\n    sgpr = gpflow.models.SGPR(X_env,\n                              noise_variance=noise_variance,\n                              kernel=kernel,\n                              inducing_variable=Xu)\n\n    common = sgpr._common_calculation()\n    sigma_sq = common.sigma_sq\n    AAT = common.AAT\n\n    x, _ = sgpr.data\n    kdiag = sgpr.kernel(x, full_cov=False)\n\n    # tr(K) / \u03c3\u00b2\n    trace_k = tf.reduce_sum(kdiag / sigma_sq)\n    # tr(Q) / \u03c3\u00b2\n    trace_q = tf.reduce_sum(tf.linalg.diag_part(AAT))\n    # tr(K - Q) / \u03c3\u00b2\n    trace = trace_k - trace_q\n    trace = 0.5 * trace\n\n    return float(trace.numpy())\n</code></pre>"},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.get_mi","title":"<code>get_mi(Xu, candidate_locs, noise_variance, kernel)</code>","text":"<p>Computes mutual information between the sensing locations and the candidate locations</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(m, d); Sensing locations</p> required <code>candidate_locs</code> <code>ndarray</code> <p>(n, d); Candidate sensing locations </p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <p>Returns:</p> Name Type Description <code>mi</code> <code>float</code> <p>Mutual information computed using a GP</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def get_mi(Xu, candidate_locs, noise_variance, kernel):\n    \"\"\"Computes mutual information between the sensing locations and the candidate locations\n\n    Args:\n        Xu (ndarray): (m, d); Sensing locations\n        candidate_locs (ndarray): (n, d); Candidate sensing locations \n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n\n    Returns:\n        mi (float): Mutual information computed using a GP\n    \"\"\"\n    Xu = np.array(Xu)\n    candidate_locs = np.array(candidate_locs)\n\n    gp = gpflow.models.GPR(data=(Xu, np.zeros((len(Xu), 1))),\n                           kernel=kernel,\n                           noise_variance=noise_variance)\n    _, sigma_a = gp.predict_f(candidate_locs, full_cov=True)\n    sigma_a = sigma_a.numpy()[0]\n    cond_entropy = gaussian_entropy(sigma_a)\n\n    K = kernel(candidate_locs, full_cov=True).numpy()\n    K += noise_variance * np.eye(len(candidate_locs))\n    entropy = gaussian_entropy(K)\n\n    return float(entropy - cond_entropy)\n</code></pre>"},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.get_reconstruction","title":"<code>get_reconstruction(sensor_data, X_test, noise_variance, kernel)</code>","text":"<p>Computes the GP-based data field estimates with the solution placements as the training set</p> <p>Parameters:</p> Name Type Description Default <code>sensor_data</code> <code>ndarray tuple</code> <p>((m, d), (m, 1)); Sensing locations' input                   and corresponding ground truth labels</p> required <code>X_test</code> <code>ndarray</code> <p>(n, d); Testing data input locations</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <p>Returns:</p> Name Type Description <code>y_pred</code> <code>ndarray</code> <p>(n, 1); Predicted data field estimates</p> <code>y_var</code> <code>ndarray</code> <p>(n, 1); Prediction variance at each location in the data field</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def get_reconstruction(sensor_data, X_test, noise_variance, kernel):\n    \"\"\"Computes the GP-based data field estimates with the solution placements as the training set\n\n    Args:\n        sensor_data (ndarray tuple): ((m, d), (m, 1)); Sensing locations' input \n                             and corresponding ground truth labels\n        X_test (ndarray): (n, d); Testing data input locations\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n\n    Returns:\n        y_pred (ndarray): (n, 1); Predicted data field estimates\n        y_var (ndarray): (n, 1); Prediction variance at each location in the data field\n    \"\"\"\n    Xu_X, Xu_y = sensor_data\n\n    # Get the GP predictions\n    gpr = gpflow.models.GPR((Xu_X, Xu_y),\n                            noise_variance=noise_variance,\n                            kernel=kernel)\n    y_pred, y_var = gpr.predict_f(X_test)\n    y_pred = y_pred.numpy().reshape(-1, 1)\n\n    return y_pred, y_var\n</code></pre>"},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.get_rmse","title":"<code>get_rmse(y_pred, y_test)</code>","text":"<p>Computes the root-mean-square error between <code>y_pred</code> and <code>y_test</code></p> <p>Parameters:</p> Name Type Description Default <code>y_pred</code> <code>ndarray</code> <p>(n, 1); Predicted data field estimate</p> required <code>y_test</code> <code>ndarray</code> <p>(n, 1); Ground truth data field </p> required <p>Returns:</p> Name Type Description <code>rmse</code> <code>float</code> <p>Computed RMSE</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def get_rmse(y_pred, y_test):\n    \"\"\"Computes the root-mean-square error between `y_pred` and `y_test`\n\n    Args:\n        y_pred (ndarray): (n, 1); Predicted data field estimate\n        y_test (ndarray): (n, 1); Ground truth data field \n\n    Returns:\n        rmse (float): Computed RMSE\n    \"\"\"\n    return np.sqrt(np.mean(np.square(y_pred - y_test)))\n</code></pre>"},{"location":"api/utils/Misc.html","title":"Misc","text":""},{"location":"api/utils/Misc.html#sgptools.utils.misc","title":"<code>sgptools.utils.misc</code>","text":""},{"location":"api/utils/Misc.html#sgptools.utils.misc.cont2disc","title":"<code>cont2disc(Xu, candidates, candidate_labels=None)</code>","text":"<p>Map continuous space locations to a discrete set of candidate location</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(m, 2); Continuous space points</p> required <code>candidates</code> <code>ndarray</code> <p>(n, 2); Discrete set of candidate locations</p> required <code>candidate_labels</code> <code>ndarray</code> <p>(n, 1); Labels corresponding to the discrete set of candidate locations</p> <code>None</code> <p>Returns:</p> Name Type Description <code>Xu_x</code> <code>ndarray</code> <p>Discrete space points' locations </p> <code>Xu_y</code> <code>ndarray</code> <p>Labels of the discrete space points. Returned only if <code>candidate_labels</code>             was passed to the function</p> Source code in <code>sgptools/utils/misc.py</code> <pre><code>def cont2disc(Xu, candidates, candidate_labels=None):\n    \"\"\"Map continuous space locations to a discrete set of candidate location\n\n    Args:\n        Xu (ndarray): (m, 2); Continuous space points\n        candidates (ndarray): (n, 2); Discrete set of candidate locations\n        candidate_labels (ndarray): (n, 1); Labels corresponding to the discrete set of candidate locations\n\n    Returns:\n        Xu_x (ndarray): Discrete space points' locations \n        Xu_y (ndarray): Labels of the discrete space points. Returned only if `candidate_labels`\n                        was passed to the function\n\n    \"\"\"\n    # Sanity check to ensure that there are sensing locations and candidates to match\n    if len(candidates)==0 or len(Xu)==0:\n        if candidate_labels is not None:\n            return [], []\n        else:\n            return []\n\n    dists = pairwise_distances(candidates, Y=Xu, metric='euclidean')\n    row_ind, _ = linear_sum_assignment(dists)\n    Xu_X = candidates[row_ind].copy()\n    if candidate_labels is not None:\n        Xu_y = candidate_labels[row_ind].copy()\n        return Xu_X, Xu_y\n    else:\n        return Xu_X\n</code></pre>"},{"location":"api/utils/Misc.html#sgptools.utils.misc.get_inducing_pts","title":"<code>get_inducing_pts(data, num_inducing, orientation=False, random=False)</code>","text":"<p>Selects a subset of the data points to be used as inducing points.  The default approach uses kmeans to select the subset. </p> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>ndarray</code> <p>(n, 2); Data points to select the inducing points from </p> required <code>num_inducing</code> <code>int</code> <p>Number of inducing points</p> required <code>orientation</code> <code>bool</code> <p>If True, add an additional dimension to model the sensor                  FoV rotation angle</p> <code>False</code> <code>random</code> <code>bool</code> <p>If True, the subset of inducing points are selected randomly             instead of using kmeans</p> <code>False</code> <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(m, d); Inducing points in the position and orientation space.             <code>m</code> is the number of inducing points,              <code>d</code> is the dimension of the space (x, y, optional - angle in radians)</p> Source code in <code>sgptools/utils/misc.py</code> <pre><code>def get_inducing_pts(data, num_inducing, orientation=False, random=False):\n    \"\"\"Selects a subset of the data points to be used as inducing points. \n    The default approach uses kmeans to select the subset. \n\n    Args:\n        data (ndarray): (n, 2); Data points to select the inducing points from \n        num_inducing (int): Number of inducing points\n        orientation (bool): If True, add an additional dimension to model the sensor \n                            FoV rotation angle\n        random (bool): If True, the subset of inducing points are selected randomly \n                       instead of using kmeans\n\n    Returns:\n        Xu (ndarray): (m, d); Inducing points in the position and orientation space.\n                        `m` is the number of inducing points, \n                        `d` is the dimension of the space (x, y, optional - angle in radians)\n    \"\"\"\n    if random:\n        idx = np.random.randint(len(data), size=num_inducing)\n        Xu = data[idx]\n    else:\n        Xu = kmeans2(data, num_inducing, minit=\"points\")[0]\n    if orientation:\n        thetas = np.random.uniform(0, 2 * np.pi, size=(Xu.shape[0], 1))\n        Xu = np.concatenate([Xu, thetas], axis=1)\n    return Xu\n</code></pre>"},{"location":"api/utils/Misc.html#sgptools.utils.misc.interpolate_path","title":"<code>interpolate_path(waypoints, sampling_rate=0.05)</code>","text":"<p>Interpolate additional points between the given waypoints to simulate continuous sensing robots</p> <p>Parameters:</p> Name Type Description Default <code>waypoints</code> <code>(n, d)</code> <p>Waypoints of the robot's path</p> required <code>sampling_rate</code> <code>float</code> <p>Distance between each pair of interpolated points</p> <code>0.05</code> <p>Returns:</p> Name Type Description <code>path</code> <code>ndarray</code> <p>(p, d) Interpolated path, <code>p</code> depends on the sampling_rate rate</p> Source code in <code>sgptools/utils/misc.py</code> <pre><code>def interpolate_path(waypoints, sampling_rate=0.05):\n    \"\"\"Interpolate additional points between the given waypoints to simulate continuous sensing robots\n\n    Args:\n        waypoints (n, d): Waypoints of the robot's path\n        sampling_rate (float): Distance between each pair of interpolated points\n\n    Returns:\n        path (ndarray): (p, d) Interpolated path, `p` depends on the sampling_rate rate\n    \"\"\"\n    interpolated_path = []\n    for i in range(2, len(waypoints)+1):\n        dist = get_distance(waypoints[i-2:i])\n        num_samples = int(dist / sampling_rate)\n        points = np.linspace(waypoints[i-1], waypoints[i-2], num_samples)\n        interpolated_path.extend(points)\n    return np.array(interpolated_path)\n</code></pre>"},{"location":"api/utils/Misc.html#sgptools.utils.misc.plot_paths","title":"<code>plot_paths(paths, candidates=None, title=None)</code>","text":"<p>Function to plot the IPP solution paths</p> <p>Parameters:</p> Name Type Description Default <code>paths</code> <code>ndarray</code> <p>(r, m, 2); <code>r</code> paths with <code>m</code> waypoints each</p> required <code>candidates</code> <code>ndarray</code> <p>(n, 2); Candidate unlabeled locations used in the SGP-based sensor placement approach</p> <code>None</code> <code>title</code> <code>str</code> <p>Title of the plot</p> <code>None</code> Source code in <code>sgptools/utils/misc.py</code> <pre><code>def plot_paths(paths, candidates=None, title=None):\n    \"\"\"Function to plot the IPP solution paths\n\n    Args:\n        paths (ndarray): (r, m, 2); `r` paths with `m` waypoints each\n        candidates (ndarray): (n, 2); Candidate unlabeled locations used in the SGP-based sensor placement approach\n        title (str): Title of the plot\n    \"\"\"\n    plt.figure()\n    for i, path in enumerate(paths):\n        plt.plot(path[:, 0], path[:, 1], \n                    c='r', label='Path', zorder=1, marker='o')\n        plt.scatter(path[0, 0], path[0, 1], \n                    c='g', label='Start', zorder=2, marker='o')\n        if candidates is not None:\n            plt.scatter(candidates[:, 0], candidates[:, 1], \n                        c='k', s=1, label='Unlabeled Train-Set Points', zorder=0)\n        if i==0:\n            plt.legend(bbox_to_anchor=(1.0, 1.02))\n    if title is not None:\n        plt.title(title)\n    plt.gca().set_aspect('equal')\n    plt.xlabel('X')\n    plt.ylabel('Y')\n</code></pre>"},{"location":"api/utils/Misc.html#sgptools.utils.misc.ploygon2candidats","title":"<code>ploygon2candidats(vertices, num_samples=5000, random_seed=2024)</code>","text":"<p>Sample unlabeled candidates within a polygon</p> <p>Parameters:</p> Name Type Description Default <code>vertices</code> <code>ndarray</code> <p>(v, 2) of vertices that define the polygon</p> required <code>num_samples</code> <code>int</code> <p>Number of samples to generate</p> <code>5000</code> <code>random_seed</code> <code>int</code> <p>Random seed for reproducibility</p> <code>2024</code> <p>Returns:</p> Name Type Description <code>candidates</code> <code>ndarray</code> <p>(n, 2); Candidate sensor placement locations</p> Source code in <code>sgptools/utils/misc.py</code> <pre><code>def ploygon2candidats(vertices, \n                      num_samples=5000, \n                      random_seed=2024):\n    \"\"\"Sample unlabeled candidates within a polygon\n\n    Args:\n        vertices (ndarray): (v, 2) of vertices that define the polygon\n        num_samples (int): Number of samples to generate\n        random_seed (int): Random seed for reproducibility\n\n    Returns:\n       candidates (ndarray): (n, 2); Candidate sensor placement locations\n    \"\"\"\n    poly = geometry.Polygon(vertices)\n    sampler = gpd.GeoSeries([poly])\n    candidates = sampler.sample_points(size=num_samples,\n                                       rng=random_seed)\n    candidates = candidates.get_coordinates().to_numpy()\n    return candidates\n</code></pre>"},{"location":"api/utils/Misc.html#sgptools.utils.misc.project_waypoints","title":"<code>project_waypoints(waypoints, candidates)</code>","text":"<p>Project the waypoints back to the candidate set while retaining the  waypoint visitation order.</p> <p>Parameters:</p> Name Type Description Default <code>waypoints</code> <code>(n, d)</code> <p>Waypoints of the robot's path</p> required <code>candidates</code> <code>ndarray</code> <p>(n, 2); Discrete set of candidate locations</p> required <p>Returns:</p> Name Type Description <code>waypoints</code> <code>(n, d)</code> <p>Projected waypoints of the robot's path</p> Source code in <code>sgptools/utils/misc.py</code> <pre><code>def project_waypoints(waypoints, candidates):\n    \"\"\"Project the waypoints back to the candidate set while retaining the \n    waypoint visitation order.\n\n    Args:\n        waypoints (n, d): Waypoints of the robot's path\n        candidates (ndarray): (n, 2); Discrete set of candidate locations\n\n    Returns:\n        waypoints (n, d): Projected waypoints of the robot's path\n    \"\"\"\n    waypoints_disc = cont2disc(waypoints, candidates)\n    waypoints_valid = _reoder_path(waypoints, waypoints_disc)\n    return waypoints_valid\n</code></pre>"},{"location":"api/utils/TSP.html","title":"TSP","text":""},{"location":"api/utils/TSP.html#sgptools.utils.tsp","title":"<code>sgptools.utils.tsp</code>","text":""},{"location":"api/utils/TSP.html#sgptools.utils.tsp.resample_path","title":"<code>resample_path(waypoints, num_inducing=10)</code>","text":"<p>Function to map path with arbitrary number of waypoints to  inducing points path with fixed number of waypoints</p> <p>Parameters:</p> Name Type Description Default <code>waypoints</code> <code>ndarray</code> <p>(num_waypoints, ndim); waypoints of path from vrp solver</p> required <code>num_inducing</code> <code>int</code> <p>Number of inducing points (waypoints) in the returned path</p> <code>10</code> <p>Returns:</p> Name Type Description <code>points</code> <code>ndarray</code> <p>(num_inducing, ndim); Resampled path</p> Source code in <code>sgptools/utils/tsp.py</code> <pre><code>def resample_path(waypoints, num_inducing=10):\n    \"\"\"Function to map path with arbitrary number of waypoints to \n    inducing points path with fixed number of waypoints\n\n    Args:\n        waypoints (ndarray): (num_waypoints, ndim); waypoints of path from vrp solver\n        num_inducing (int): Number of inducing points (waypoints) in the returned path\n\n    Returns:\n        points (ndarray): (num_inducing, ndim); Resampled path\n    \"\"\"\n    ndim = np.shape(waypoints)[-1]\n    if not (ndim==2 or ndim==3):\n        raise Exception(f\"ndim={ndim} is not supported for path resampling!\")\n    line = LineString(waypoints)\n    distances = np.linspace(0, line.length, num_inducing)\n    points = [line.interpolate(distance) for distance in distances]\n    if ndim==2:\n        points = np.array([[p.x, p.y] for p in points])\n    elif ndim==3:\n        points = np.array([[p.x, p.y, p.z] for p in points])\n    return points\n</code></pre>"},{"location":"api/utils/TSP.html#sgptools.utils.tsp.run_tsp","title":"<code>run_tsp(nodes, num_vehicles=1, max_dist=25, depth=1, resample=None, start_nodes=None, end_nodes=None, time_limit=10)</code>","text":"<p>Method to run TSP/VRP with arbitrary start and end nodes,  and without any distance constraint</p> <p>Parameters:</p> Name Type Description Default <code>nodes</code> <code>ndarray</code> <p>(# nodes, ndim); Nodes to visit </p> required <code>num_vehicles</code> <code>int</code> <p>Number of robots/vehicles</p> <code>1</code> <code>max_dist</code> <code>float</code> <p>Maximum distance allowed for each path when handling mutli-robot case</p> <code>25</code> <code>depth</code> <code>int</code> <p>Internal parameter used to track re-try recursion depth</p> <code>1</code> <code>resample</code> <code>int</code> <p>Each solution path will be resampled to have             <code>resample</code> number of points</p> <code>None</code> <code>start_nodes</code> <code>ndarray</code> <p>(# num_vehicles, ndim); Optionl array of start nodes from which                      to start each vehicle's solution path</p> <code>None</code> <code>end_nodes</code> <code>ndarray</code> <p>(# num_vehicles, ndim); Optionl array of end nodes at which                      to end each vehicle's solution path</p> <code>None</code> <code>time_limit</code> <code>int</code> <p>TSP runtime time limit in seconds</p> <code>10</code> <p>Returns:</p> Name Type Description <code>paths</code> <code>ndarray</code> <p>Solution paths</p> <code>distances</code> <code>list</code> <p>List of path lengths</p> Source code in <code>sgptools/utils/tsp.py</code> <pre><code>def run_tsp(nodes, \n            num_vehicles=1, \n            max_dist=25, \n            depth=1, \n            resample=None, \n            start_nodes=None,\n            end_nodes=None,\n            time_limit=10):\n    \"\"\"Method to run TSP/VRP with arbitrary start and end nodes, \n    and without any distance constraint\n\n    Args:\n        nodes (ndarray): (# nodes, ndim); Nodes to visit \n        num_vehicles (int): Number of robots/vehicles\n        max_dist (float): Maximum distance allowed for each path when handling mutli-robot case\n        depth (int): Internal parameter used to track re-try recursion depth\n        resample (int): Each solution path will be resampled to have\n                        `resample` number of points\n        start_nodes (ndarray): (# num_vehicles, ndim); Optionl array of start nodes from which \n                                to start each vehicle's solution path\n        end_nodes (ndarray): (# num_vehicles, ndim); Optionl array of end nodes at which \n                                to end each vehicle's solution path\n        time_limit (int): TSP runtime time limit in seconds\n\n    Returns:\n        paths (ndarray): Solution paths\n        distances (list): List of path lengths\n    \"\"\"\n    if depth &gt; 5:\n        print('Warning: Max depth reached')\n        return None, None\n\n    # Add the start and end nodes to the node list\n    if end_nodes is not None:\n        assert end_nodes.shape == (num_vehicles, nodes.shape[-1]), \\\n            \"Incorrect end_nodes shape, should be (num_vehicles, ndim)!\"\n        nodes = np.concatenate([end_nodes, nodes])\n    if start_nodes is not None:\n        assert start_nodes.shape == (num_vehicles, nodes.shape[-1]), \\\n            \"Incorrect start_nodes shape, should be (num_vehicles, ndim)!\"\n        nodes = np.concatenate([start_nodes, nodes])\n\n    # Add dummy 0 location to get arbitrary start and end node sols\n    if start_nodes is None or end_nodes is None:\n        distance_mat = np.zeros((len(nodes)+1, len(nodes)+1))\n        distance_mat[1:, 1:] = pairwise_distances(nodes, nodes)*1e4\n        trim_paths = True #shift to account for dummy node\n    else:\n        distance_mat = pairwise_distances(nodes, nodes)*1e4\n        trim_paths = False\n    distance_mat = distance_mat.astype(int)\n    max_dist = int(max_dist*1e4)\n\n    # Get start and end node indices for ortools\n    if start_nodes is None:\n        start_idx = np.zeros(num_vehicles, dtype=int)\n        num_start_nodes = 0\n    else:\n        start_idx = np.arange(num_vehicles)+int(trim_paths)\n        num_start_nodes = len(start_nodes)\n\n    if end_nodes is None:\n        end_idx = np.zeros(num_vehicles, dtype=int)\n    else:\n        end_idx = np.arange(num_vehicles)+num_start_nodes+int(trim_paths)\n\n    # used by ortools\n    def distance_callback(from_index, to_index):\n        from_node = manager.IndexToNode(from_index)\n        to_node = manager.IndexToNode(to_index)\n        return distance_mat[from_node][to_node]\n\n    # num_locations, num vehicles, start, end\n    manager = pywrapcp.RoutingIndexManager(len(distance_mat), \n                                           num_vehicles, \n                                           start_idx.tolist(),\n                                           end_idx.tolist())\n    routing = pywrapcp.RoutingModel(manager)\n    transit_callback_index = routing.RegisterTransitCallback(distance_callback)\n    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n\n    if num_vehicles &gt; 1:\n        # Dummy distaance constraint to ensure all paths have similar length\n        dimension_name = \"Distance\"\n        routing.AddDimension(\n            transit_callback_index,\n            0,  # no slack\n            max_dist,  # vehicle maximum travel distance\n            True,  # start cumul to zero\n            dimension_name,\n        )\n        distance_dimension = routing.GetDimensionOrDie(dimension_name)\n        distance_dimension.SetGlobalSpanCostCoefficient(100)\n\n    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n    search_parameters.first_solution_strategy = (\n        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n    )\n    search_parameters.local_search_metaheuristic = (\n        routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH\n    )\n    search_parameters.time_limit.seconds = time_limit\n    solution = routing.SolveWithParameters(search_parameters)\n\n    paths = None\n    if solution is not None:\n        paths, distances = get_routes(manager, routing, \n                                      solution, num_vehicles, \n                                      start_idx, end_idx, trim_paths)\n        for path in paths:\n            if len(path) &lt; 2:\n                print('TSP Warning: Empty path detected')\n                return run_tsp(nodes, num_vehicles, int(np.mean(distances)*(1.5/depth)), depth+1)\n    else:\n        print('TSP Warning: No solution found')\n        return run_tsp(nodes, num_vehicles, int(max_dist*1.5), depth+1)\n\n    # Map paths from node indices to node locations\n    paths = [nodes[path] for path in paths]\n\n    # Resample each solution path to have resample number of points\n    if resample is not None:\n        paths = np.array([resample_path(path, resample) for path in paths])\n\n    # Convert distances back to floats in the original scale of the nodes\n    distances = np.array(distances)/1e4\n    return paths, distances\n</code></pre>"},{"location":"examples/AIPP_non_point_FoV.html","title":"Adaptive-IPP with Non-Point FoV","text":"In\u00a0[\u00a0]: Copied! <pre>import os\nos.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"\n\nimport numpy as np\n\nfrom sgptools.utils.metrics import *\nfrom sgptools.utils.data import *\nfrom sgptools.utils.misc import *\nfrom sgptools.models.core.osgpr import *\nfrom sgptools.utils.tsp import run_tsp\nfrom sgptools.utils.gpflow import get_model_params\nfrom sgptools.models.continuous_sgp import *\nfrom sgptools.models.core.transformations import *\nfrom gpflow.utilities.traversal import print_summary\n\nfrom matplotlib import colors\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Polygon\nimport mpl_toolkits.mplot3d.art3d as art3d\nfrom scipy.optimize import linear_sum_assignment\nfrom sklearn.metrics import pairwise_distances\n\nfrom time import time\nfrom copy import deepcopy\n\nnp.random.seed(1234)\ntf.random.set_seed(1234)\n</pre> import os os.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"  import numpy as np  from sgptools.utils.metrics import * from sgptools.utils.data import * from sgptools.utils.misc import * from sgptools.models.core.osgpr import * from sgptools.utils.tsp import run_tsp from sgptools.utils.gpflow import get_model_params from sgptools.models.continuous_sgp import * from sgptools.models.core.transformations import * from gpflow.utilities.traversal import print_summary  from matplotlib import colors import matplotlib.pyplot as plt from matplotlib.patches import Polygon import mpl_toolkits.mplot3d.art3d as art3d from scipy.optimize import linear_sum_assignment from sklearn.metrics import pairwise_distances  from time import time from copy import deepcopy  np.random.seed(1234) tf.random.set_seed(1234) In\u00a0[2]: Copied! <pre>'''\nUtility to map the inducing points to FoV vertices\n'''\ndef get_vertices(Xu, X_fov):\n    X_fov = X_fov.reshape(len(Xu), -1, 2)\n    vertices = np.zeros((len(Xu), 5, 2))\n    vertices[:, 0] = X_fov.min(axis=1)\n    vertices[:, 1] = np.array([X_fov[:, :, 0].min(axis=1), \n                              X_fov[:, :, 1].max(axis=1)]).T\n    vertices[:, 2] = X_fov.max(axis=1)\n    vertices[:, 3] = np.array([X_fov[:, :, 0].max(axis=1), \n                              X_fov[:, :, 1].min(axis=1)]).T\n    vertices[:, 4] = X_fov.mean(axis=1)\n\n    dists = pairwise_distances(vertices[:, 4], Y=Xu[:, :2], \n                               metric='euclidean')\n    _, idx = linear_sum_assignment(dists)\n\n    vertices[:, 4] = Xu[idx][:, :2]\n    return vertices\n\n'''\nMethod to plot the solution with FoVs and environment reconstruction\n'''\ndef plot_results(X_inducing, X_fov, current_idx=0, fname=None):\n    # Setup 3D plot\n    fig = plt.figure(figsize=(5, 5))\n    ax = fig.add_subplot(111, projection='3d', computed_zorder=False)\n\n    current_idx += 1 # Increment by one for plotting\n\n    # Plot the reconstructed environment state\n    num_fov_pts = len(X_fov)//len(X_inducing)\n    sensor_data = cont2disc(X_fov[:num_fov_pts*(current_idx)], X, y)\n    y_pred, _ = get_reconstruction(sensor_data, X_test, \n                                   noise_variance_opt, kernel_opt)\n    norm = colors.Normalize(y_train.min(), y_train.max())\n    _ = ax.plot_surface(X_test[:, 0].reshape(test_dim),\n                        X_test[:, 1].reshape(test_dim),\n                        np.atleast_2d(-0.1),\n                        facecolors=plt.cm.jet(norm(y_pred.reshape(test_dim))),\n                        shade=False,\n                        alpha=0.8,\n                        zorder=0)\n\n    # Plot the solution path\n    ax.scatter(X_inducing[:current_idx, 0], \n               X_inducing[:current_idx, 1], \n               X_inducing[:current_idx, 2], c='C3')\n    ax.scatter(X_inducing[current_idx:, 0], \n               X_inducing[current_idx:, 1], \n               X_inducing[current_idx:, 2], c='C2')\n    ax.plot(X_inducing[:, 0], X_inducing[:, 1], X_inducing[:, 2], 'k-')\n\n    # Plot the FoV vertices\n    vertices = get_vertices(X_inducing, X_fov)\n    for i in range(vertices.shape[0]):\n        color = 'C3' if i &lt; current_idx else 'C2'\n        verts = []\n        verts.append([vertices[i, 0], vertices[i, 1], \n                      vertices[i, 2], vertices[i, 3]])\n        fov = Polygon(np.array(verts)[0, :, :2],\n                      linewidth=1.5, \n                      edgecolor=color,\n                      facecolor=color,\n                      fill=False,\n                      zorder=15)\n        ax.add_patch(fov)\n        art3d.pathpatch_2d_to_3d(fov)\n\n    # Configure other plot settings\n    ax.set_xlabel('X')\n    ax.set_ylabel('Y')\n    ax.set_zlabel('Z')\n\n    ax.set_xlim(np.min(X[:, 0])-0.5, np.max(X[:, 0])+0.5)\n    ax.set_ylim(np.min(X[:, 1])-0.5, np.max(X[:, 1])+0.5)\n    ax.set_zlim(0, 14)\n\n    ax.view_init(elev=30, azim=45+180)\n    ax.set_title(f'Waypoint: {current_idx}, RMSE: {get_rmse(y_pred, y_test):.2f}\\nData Field Reconstruction', y=0.99)\n\n    plt.tight_layout()\n    if fname is not None:\n        plt.savefig(fname, bbox_inches='tight')\n    plt.show()\n</pre> ''' Utility to map the inducing points to FoV vertices ''' def get_vertices(Xu, X_fov):     X_fov = X_fov.reshape(len(Xu), -1, 2)     vertices = np.zeros((len(Xu), 5, 2))     vertices[:, 0] = X_fov.min(axis=1)     vertices[:, 1] = np.array([X_fov[:, :, 0].min(axis=1),                                X_fov[:, :, 1].max(axis=1)]).T     vertices[:, 2] = X_fov.max(axis=1)     vertices[:, 3] = np.array([X_fov[:, :, 0].max(axis=1),                                X_fov[:, :, 1].min(axis=1)]).T     vertices[:, 4] = X_fov.mean(axis=1)      dists = pairwise_distances(vertices[:, 4], Y=Xu[:, :2],                                 metric='euclidean')     _, idx = linear_sum_assignment(dists)      vertices[:, 4] = Xu[idx][:, :2]     return vertices  ''' Method to plot the solution with FoVs and environment reconstruction ''' def plot_results(X_inducing, X_fov, current_idx=0, fname=None):     # Setup 3D plot     fig = plt.figure(figsize=(5, 5))     ax = fig.add_subplot(111, projection='3d', computed_zorder=False)      current_idx += 1 # Increment by one for plotting      # Plot the reconstructed environment state     num_fov_pts = len(X_fov)//len(X_inducing)     sensor_data = cont2disc(X_fov[:num_fov_pts*(current_idx)], X, y)     y_pred, _ = get_reconstruction(sensor_data, X_test,                                     noise_variance_opt, kernel_opt)     norm = colors.Normalize(y_train.min(), y_train.max())     _ = ax.plot_surface(X_test[:, 0].reshape(test_dim),                         X_test[:, 1].reshape(test_dim),                         np.atleast_2d(-0.1),                         facecolors=plt.cm.jet(norm(y_pred.reshape(test_dim))),                         shade=False,                         alpha=0.8,                         zorder=0)      # Plot the solution path     ax.scatter(X_inducing[:current_idx, 0],                 X_inducing[:current_idx, 1],                 X_inducing[:current_idx, 2], c='C3')     ax.scatter(X_inducing[current_idx:, 0],                 X_inducing[current_idx:, 1],                 X_inducing[current_idx:, 2], c='C2')     ax.plot(X_inducing[:, 0], X_inducing[:, 1], X_inducing[:, 2], 'k-')      # Plot the FoV vertices     vertices = get_vertices(X_inducing, X_fov)     for i in range(vertices.shape[0]):         color = 'C3' if i &lt; current_idx else 'C2'         verts = []         verts.append([vertices[i, 0], vertices[i, 1],                        vertices[i, 2], vertices[i, 3]])         fov = Polygon(np.array(verts)[0, :, :2],                       linewidth=1.5,                        edgecolor=color,                       facecolor=color,                       fill=False,                       zorder=15)         ax.add_patch(fov)         art3d.pathpatch_2d_to_3d(fov)      # Configure other plot settings     ax.set_xlabel('X')     ax.set_ylabel('Y')     ax.set_zlabel('Z')      ax.set_xlim(np.min(X[:, 0])-0.5, np.max(X[:, 0])+0.5)     ax.set_ylim(np.min(X[:, 1])-0.5, np.max(X[:, 1])+0.5)     ax.set_zlim(0, 14)      ax.view_init(elev=30, azim=45+180)     ax.set_title(f'Waypoint: {current_idx}, RMSE: {get_rmse(y_pred, y_test):.2f}\\nData Field Reconstruction', y=0.99)      plt.tight_layout()     if fname is not None:         plt.savefig(fname, bbox_inches='tight')     plt.show() In\u00a0[3]: Copied! <pre>'''\nMethod to run AIPP using the SGP-based approach\n'''\ndef run_aipp(X_train, ipp_model, Xu_init, path2data):\n    total_time_param = 0\n    total_time_ipp = 0\n    num_robots = Xu_init.shape[0]\n    num_waypoints = Xu_init.shape[1]\n    curr_sol = Xu_init\n    plot_results(ipp_model.transform.expand(curr_sol.reshape(-1, 3), \n                                            expand_sensor_model=False).numpy(),\n                 ipp_model.transform.expand(curr_sol.reshape(-1, 3)).numpy())\n\n    # Initialize the hyperparameters and SSGP\n    init_kernel = deepcopy(ipp_model.kernel)\n    init_noise_variance = ipp_model.likelihood.variance\n    param_model = init_osgpr(X_train, \n                             num_inducing=40, \n                             kernel=init_kernel,\n                             noise_variance=init_noise_variance)\n\n    sol_data_X = []\n    sol_data_y = []\n    for time_step in range(num_waypoints):\n        # Get a new batch of data from the last visited waypoint\n        last_visited = curr_sol[:, time_step].copy()\n        # Apply the sensor model expansion with fixed points IPP expansion\n        data_pts = ipp_model.transform.sensor_model.expand(last_visited).numpy()\n        data_X_batch, data_y_batch = path2data(data_pts)\n        sol_data_X.extend(data_X_batch)\n        sol_data_y.extend(data_y_batch)\n\n        # Skip param and path update for the last waypoint\n        if time_step == num_waypoints-1:\n            break\n\n        # Update the SSGP hyperparameter model\n        param_model.update((np.array(data_X_batch), \n                            np.array(data_y_batch)))\n        # Train only the kernel parameters and noise variance.\n        # Keeping the SSGP inducing points fixed results in better \n        # hyperparameter estimation\n        start_time = time()\n        optimize_model(param_model, \n                       trainable_variables=param_model.trainable_variables[1:], \n                       optimizer='scipy',\n                       method='CG')\n        end_time = time()\n        total_time_param += end_time - start_time\n        print_summary(param_model.kernel)\n\n        # Update the SGP-IPP model\n        Xu_visited = curr_sol.copy()[:, :time_step+1]\n        ipp_model.transform.update_Xu_fixed(Xu_visited)\n        ipp_model.update(param_model.likelihood.variance, \n                         param_model.kernel)\n        start_time = time()\n        _ = optimize_model(ipp_model,\n                           kernel_grad=False, \n                           optimizer='scipy',\n                           method='CG')\n        end_time = time()\n        total_time_ipp += end_time - start_time\n        curr_sol = ipp_model.inducing_variable.Z\n        curr_sol = ipp_model.transform.expand(curr_sol, \n                                              expand_sensor_model=False).numpy()\n        curr_sol = curr_sol.reshape(num_robots, num_waypoints, 3)\n\n        plot_results(ipp_model.transform.expand(curr_sol.reshape(-1, 3),\n                                                expand_sensor_model=False).numpy(),\n                     ipp_model.transform.expand(curr_sol.reshape(-1, 3)).numpy(),\n                     time_step+1)\n\n    return np.array(sol_data_X), np.array(sol_data_y), total_time_param, total_time_ipp\n</pre> ''' Method to run AIPP using the SGP-based approach ''' def run_aipp(X_train, ipp_model, Xu_init, path2data):     total_time_param = 0     total_time_ipp = 0     num_robots = Xu_init.shape[0]     num_waypoints = Xu_init.shape[1]     curr_sol = Xu_init     plot_results(ipp_model.transform.expand(curr_sol.reshape(-1, 3),                                              expand_sensor_model=False).numpy(),                  ipp_model.transform.expand(curr_sol.reshape(-1, 3)).numpy())      # Initialize the hyperparameters and SSGP     init_kernel = deepcopy(ipp_model.kernel)     init_noise_variance = ipp_model.likelihood.variance     param_model = init_osgpr(X_train,                               num_inducing=40,                               kernel=init_kernel,                              noise_variance=init_noise_variance)      sol_data_X = []     sol_data_y = []     for time_step in range(num_waypoints):         # Get a new batch of data from the last visited waypoint         last_visited = curr_sol[:, time_step].copy()         # Apply the sensor model expansion with fixed points IPP expansion         data_pts = ipp_model.transform.sensor_model.expand(last_visited).numpy()         data_X_batch, data_y_batch = path2data(data_pts)         sol_data_X.extend(data_X_batch)         sol_data_y.extend(data_y_batch)          # Skip param and path update for the last waypoint         if time_step == num_waypoints-1:             break          # Update the SSGP hyperparameter model         param_model.update((np.array(data_X_batch),                              np.array(data_y_batch)))         # Train only the kernel parameters and noise variance.         # Keeping the SSGP inducing points fixed results in better          # hyperparameter estimation         start_time = time()         optimize_model(param_model,                         trainable_variables=param_model.trainable_variables[1:],                         optimizer='scipy',                        method='CG')         end_time = time()         total_time_param += end_time - start_time         print_summary(param_model.kernel)          # Update the SGP-IPP model         Xu_visited = curr_sol.copy()[:, :time_step+1]         ipp_model.transform.update_Xu_fixed(Xu_visited)         ipp_model.update(param_model.likelihood.variance,                           param_model.kernel)         start_time = time()         _ = optimize_model(ipp_model,                            kernel_grad=False,                             optimizer='scipy',                            method='CG')         end_time = time()         total_time_ipp += end_time - start_time         curr_sol = ipp_model.inducing_variable.Z         curr_sol = ipp_model.transform.expand(curr_sol,                                                expand_sensor_model=False).numpy()         curr_sol = curr_sol.reshape(num_robots, num_waypoints, 3)          plot_results(ipp_model.transform.expand(curr_sol.reshape(-1, 3),                                                 expand_sensor_model=False).numpy(),                      ipp_model.transform.expand(curr_sol.reshape(-1, 3)).numpy(),                      time_step+1)      return np.array(sol_data_X), np.array(sol_data_y), total_time_param, total_time_ipp In\u00a0[4]: Copied! <pre># Get the synthetic elevation data\nX_train, y_train, X_test, y_test, candidates, X, y = get_dataset(random_seed=0)\ntrain_dim = np.sqrt(X_train.shape[0]).astype(int)\n\n# Train a GP and get the model parameters\nprint('Optimized Hyperparameters')\n_, noise_variance_opt, kernel_opt = get_model_params(X_train, y_train, \n                                                     lengthscales=[1.0, 1.0], \n                                                     optimizer='scipy')\n\n# Build a new test set on a grid for easy plotting in a 3D plot\ngpr = gpflow.models.GPR((X_test, y_test),\n                        noise_variance=noise_variance_opt,\n                        kernel=kernel_opt)\ntest_dim = (100, 100)\nX_test1 = np.linspace(np.min(X[:, 0]), np.max(X[:, 0]), test_dim[0])\nX_test2 = np.linspace(np.min(X[:, 1]), np.max(X[:, 1]), test_dim[1])\nX_test1, X_test2 = np.meshgrid(X_test1, X_test2)\nX_test = np.stack([X_test1.ravel(), X_test2.ravel()], axis=1)\ny_test, y_var = gpr.predict_f(X_test)\ny_test = y_test.numpy()\n\n# Plot the data field\nplt.imshow(y_test.reshape(test_dim), cmap=\"jet\", origin='lower')\nplt.xlabel('X')\nplt.ylabel('Y')\nplt.title('Elevation Data Field')\nplt.colorbar(label='Normalized Elevation')\nplt.show()\n</pre> # Get the synthetic elevation data X_train, y_train, X_test, y_test, candidates, X, y = get_dataset(random_seed=0) train_dim = np.sqrt(X_train.shape[0]).astype(int)  # Train a GP and get the model parameters print('Optimized Hyperparameters') _, noise_variance_opt, kernel_opt = get_model_params(X_train, y_train,                                                       lengthscales=[1.0, 1.0],                                                       optimizer='scipy')  # Build a new test set on a grid for easy plotting in a 3D plot gpr = gpflow.models.GPR((X_test, y_test),                         noise_variance=noise_variance_opt,                         kernel=kernel_opt) test_dim = (100, 100) X_test1 = np.linspace(np.min(X[:, 0]), np.max(X[:, 0]), test_dim[0]) X_test2 = np.linspace(np.min(X[:, 1]), np.max(X[:, 1]), test_dim[1]) X_test1, X_test2 = np.meshgrid(X_test1, X_test2) X_test = np.stack([X_test1.ravel(), X_test2.ravel()], axis=1) y_test, y_var = gpr.predict_f(X_test) y_test = y_test.numpy()  # Plot the data field plt.imshow(y_test.reshape(test_dim), cmap=\"jet\", origin='lower') plt.xlabel('X') plt.ylabel('Y') plt.title('Elevation Data Field') plt.colorbar(label='Normalized Elevation') plt.show() <pre>Optimized Hyperparameters\n\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.8371517535182331  \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [2.45802 2.50701]   \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.04342255274528023 \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> In\u00a0[5]: Copied! <pre>num_robots = 1\nnum_placements = 12\nnum_side = 5  # Use a 5x5 point FoV approximation\n\n# Setup the initial AIPP hyperparameters\nprint('Initial AIPP Hyperparameters')\n_, noise_variance, kernel = get_model_params(X_train, y_train, \n                                             lengthscales=[1.0, 1.0], \n                                             optimizer='scipy',\n                                             max_steps=0)\n\n# Get initial inducing points\nXu_init = get_inducing_pts(X_train, num_placements)\n# Initalize the height dimension at 2.0 for all points\nXu_init = np.concatenate((Xu_init, np.ones((Xu_init.shape[0], 1))*2.0), axis=1)\nXu_init[:, :2] = run_tsp(Xu_init[:, :2])[0][0]\n\n# Initialize the non-point FoV transform\nfov_transform = SquareHeightTransform(num_side=num_side)\ntransform = IPPTransform(num_dim=3,\n                         num_robots=1,\n                         sensor_model=fov_transform,\n                         aggregate_fov=True)\n\n# Initialize the IPP model\nsgpr, _ = continuous_sgp(num_placements,\n                         X_train, \n                         noise_variance,\n                         kernel,\n                         transform=transform,\n                         Xu_init=Xu_init,\n                         max_steps=0)\nsgp_sol_sp = sgpr.inducing_variable.Z.numpy()\n\n# Setup the data acquisition function to emulate the robot's sensor\npath2data = lambda x : cont2disc(x, X, y)\n\n# Generate the SGP-based AIPP solution\n_, _, param_time, ipp_time = run_aipp(X_train, \n                                      sgpr,\n                                      Xu_init.reshape(1, -1, 3), \n                                      path2data)\n\nprint(f'Total Hyperparameter Update Time:   {param_time:.2f}s')\nprint(f'Total IPP Update Time:              {ipp_time:.2f}s')\n\nprint(f'Average Hyperparameter Update Time: {param_time/num_placements:.2f}s')\nprint(f'Average IPP Update Time:            {ipp_time/num_placements:.2f}s')\n</pre> num_robots = 1 num_placements = 12 num_side = 5  # Use a 5x5 point FoV approximation  # Setup the initial AIPP hyperparameters print('Initial AIPP Hyperparameters') _, noise_variance, kernel = get_model_params(X_train, y_train,                                               lengthscales=[1.0, 1.0],                                               optimizer='scipy',                                              max_steps=0)  # Get initial inducing points Xu_init = get_inducing_pts(X_train, num_placements) # Initalize the height dimension at 2.0 for all points Xu_init = np.concatenate((Xu_init, np.ones((Xu_init.shape[0], 1))*2.0), axis=1) Xu_init[:, :2] = run_tsp(Xu_init[:, :2])[0][0]  # Initialize the non-point FoV transform fov_transform = SquareHeightTransform(num_side=num_side) transform = IPPTransform(num_dim=3,                          num_robots=1,                          sensor_model=fov_transform,                          aggregate_fov=True)  # Initialize the IPP model sgpr, _ = continuous_sgp(num_placements,                          X_train,                           noise_variance,                          kernel,                          transform=transform,                          Xu_init=Xu_init,                          max_steps=0) sgp_sol_sp = sgpr.inducing_variable.Z.numpy()  # Setup the data acquisition function to emulate the robot's sensor path2data = lambda x : cont2disc(x, X, y)  # Generate the SGP-based AIPP solution _, _, param_time, ipp_time = run_aipp(X_train,                                        sgpr,                                       Xu_init.reshape(1, -1, 3),                                        path2data)  print(f'Total Hyperparameter Update Time:   {param_time:.2f}s') print(f'Total IPP Update Time:              {ipp_time:.2f}s')  print(f'Average Hyperparameter Update Time: {param_time/num_placements:.2f}s') print(f'Average IPP Update Time:            {ipp_time/num_placements:.2f}s') <pre>Initial AIPP Hyperparameters\n\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 1.0                 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [1. 1.]             \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.09999999999999999 \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.02830909998623969 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [2.52935 4.99828]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.03608860733575918 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.66389 4.2298 ]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value                \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.047688497211455655 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.6262  4.20678]    \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value                \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.047733701955419004 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.09326 4.03863]    \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.05702257365194937 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.27988 4.50424]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.06604187384338019 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.3692  4.76543]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.09160363580094916 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.83409 5.53632]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.09052131666299722 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.84722 5.51582]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.08877117097798798 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.63615 4.87825]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value              \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.0936660767287354 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.72264 5.59398]  \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.10593340089380918 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.63767 6.41376]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>Total Hyperparameter Update Time:   22.51s\nTotal IPP Update Time:              31.34s\nAverage Hyperparameter Update Time: 1.88s\nAverage IPP Update Time:            2.61s\n</pre>"},{"location":"examples/AIPP_non_point_FoV.html#adaptive-ipp-with-non-point-fov","title":"Adaptive-IPP with Non-Point FoV\u00b6","text":""},{"location":"examples/AIPP_non_point_FoV.html#generate-synthetic-data-and-initial-sgp-hyperparameters","title":"Generate synthetic data and initial SGP hyperparameters\u00b6","text":""},{"location":"examples/AIPP_non_point_FoV.html#generate-the-aipp-solution","title":"Generate the AIPP solution\u00b6","text":""},{"location":"examples/Baselines.html","title":"Baselines","text":"In\u00a0[\u00a0]: Copied! <pre>import os\nos.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"\n\nimport numpy as np\nfrom time import time\nimport tensorflow as tf\n\nfrom sgptools.utils.data import *\nfrom sgptools.utils.misc import *\nfrom sgptools.utils.metrics import *\nfrom sgptools.utils.tsp import run_tsp\nfrom sgptools.utils.gpflow import get_model_params\n\nfrom sgptools.models.bo import *\nfrom sgptools.models.cma_es import *\nfrom sgptools.models.greedy_mi import *\nfrom sgptools.models.greedy_sgp import *\nfrom sgptools.models.continuous_sgp import *\nfrom sgptools.models.core.transformations import IPPTransform\n\nimport matplotlib.pyplot as plt\n\nnp.random.seed(1234)\ntf.random.set_seed(1234)\n</pre> import os os.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"  import numpy as np from time import time import tensorflow as tf  from sgptools.utils.data import * from sgptools.utils.misc import * from sgptools.utils.metrics import * from sgptools.utils.tsp import run_tsp from sgptools.utils.gpflow import get_model_params  from sgptools.models.bo import * from sgptools.models.cma_es import * from sgptools.models.greedy_mi import * from sgptools.models.greedy_sgp import * from sgptools.models.continuous_sgp import * from sgptools.models.core.transformations import IPPTransform  import matplotlib.pyplot as plt  np.random.seed(1234) tf.random.set_seed(1234) In\u00a0[2]: Copied! <pre>num_robots = 2\nnum_placements = 10\n\n# Configure IPPTransform\ntransform = IPPTransform(num_robots=num_robots)\n\n# Get the synthetic elevation data\nX_train, y_train, X_test, y_test, candidates, X, y = get_dataset()\n\n# Train the GP and get the model parameters\n_, noise_variance, kernel = get_model_params(X_train, y_train, \n                                             lengthscales=[3.0, 3.0], \n                                             optimizer='scipy',\n                                             max_steps=0)\nplt.scatter(X[:, 0], X[:, 1], c=y, cmap='jet')\nplt.title(\"Ground Truth\")\nplt.xlabel('X')\nplt.ylabel('Y')\nplt.gca().set_aspect('equal')\nplt.show()\n\n# Get the initial waypoints\nXu_init = get_inducing_pts(X_train, num_placements*num_robots)\nXu_init, _ = run_tsp(Xu_init, \n                     num_vehicles=num_robots,\n                     max_dist=100,\n                     resample=num_placements)\nXu_init = Xu_init.reshape(-1, 2)\n</pre> num_robots = 2 num_placements = 10  # Configure IPPTransform transform = IPPTransform(num_robots=num_robots)  # Get the synthetic elevation data X_train, y_train, X_test, y_test, candidates, X, y = get_dataset()  # Train the GP and get the model parameters _, noise_variance, kernel = get_model_params(X_train, y_train,                                               lengthscales=[3.0, 3.0],                                               optimizer='scipy',                                              max_steps=0) plt.scatter(X[:, 0], X[:, 1], c=y, cmap='jet') plt.title(\"Ground Truth\") plt.xlabel('X') plt.ylabel('Y') plt.gca().set_aspect('equal') plt.show()  # Get the initial waypoints Xu_init = get_inducing_pts(X_train, num_placements*num_robots) Xu_init, _ = run_tsp(Xu_init,                       num_vehicles=num_robots,                      max_dist=100,                      resample=num_placements) Xu_init = Xu_init.reshape(-1, 2) <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 1.0                 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3. 3.]             \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.09999999999999999 \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> In\u00a0[3]: Copied! <pre># Convert a given path to the data (X, y) alongst the path\ndef path2data(path, X, y):\n    data_X = []\n    data_y = []\n    num_robots, _, _ = path.shape\n    for r in range(num_robots):\n        X_new, y_new = cont2disc(path[r], X, y)\n        data_X.extend(X_new)\n        data_y.extend(y_new)\n    return np.array(data_X), np.array(data_y)\n\n# Plot the solution paths\ndef plot_paths(paths, title=None):\n    plt.figure()\n    for i, path in enumerate(paths):\n        plt.plot(path[:, 0], path[:, 1], \n                 c='r', label='Path', \n                 zorder=1, marker='o')\n    if title is not None:\n        plt.title(title)\n    plt.xlabel('X')\n    plt.ylabel('Y')\n</pre> # Convert a given path to the data (X, y) alongst the path def path2data(path, X, y):     data_X = []     data_y = []     num_robots, _, _ = path.shape     for r in range(num_robots):         X_new, y_new = cont2disc(path[r], X, y)         data_X.extend(X_new)         data_y.extend(y_new)     return np.array(data_X), np.array(data_y)  # Plot the solution paths def plot_paths(paths, title=None):     plt.figure()     for i, path in enumerate(paths):         plt.plot(path[:, 0], path[:, 1],                   c='r', label='Path',                   zorder=1, marker='o')     if title is not None:         plt.title(title)     plt.xlabel('X')     plt.ylabel('Y') In\u00a0[4]: Copied! <pre>start = time()\nmodel, _ = continuous_sgp(num_placements, \n                          X_train, \n                          noise_variance, \n                          kernel,\n                          transform,\n                          Xu_init=Xu_init.copy())\nend = time()\nrun_time = end - start\n\n# Get solution inducing points\nsol = model.inducing_variable.Z.numpy()\nsol_paths = sol.reshape(num_robots, num_placements, 2)\n\n# Convert solution path waypoints to sensing points along the whole path\nX_path, y_path = path2data(sol_paths, X, y)\n\n# Compute RMSE\ny_pred, _ = get_reconstruction((X_path, y_path), \n                                X, \n                                noise_variance, \n                                kernel)\nrmse = get_rmse(y_pred, y)\n\n# Plot the solution\nplot_paths(sol_paths, title=f\"Continuous-SGP | RMSE: {rmse:.2f} | Time: {run_time:.2f}s\")\nplt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet')\nplt.gca().set_aspect('equal')\n</pre> start = time() model, _ = continuous_sgp(num_placements,                            X_train,                            noise_variance,                            kernel,                           transform,                           Xu_init=Xu_init.copy()) end = time() run_time = end - start  # Get solution inducing points sol = model.inducing_variable.Z.numpy() sol_paths = sol.reshape(num_robots, num_placements, 2)  # Convert solution path waypoints to sensing points along the whole path X_path, y_path = path2data(sol_paths, X, y)  # Compute RMSE y_pred, _ = get_reconstruction((X_path, y_path),                                  X,                                  noise_variance,                                  kernel) rmse = get_rmse(y_pred, y)  # Plot the solution plot_paths(sol_paths, title=f\"Continuous-SGP | RMSE: {rmse:.2f} | Time: {run_time:.2f}s\") plt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet') plt.gca().set_aspect('equal') In\u00a0[5]: Copied! <pre>start = time()\nsol = get_greedy_sgp_sol(num_placements*num_robots, \n                         candidates, \n                         candidates, \n                         noise_variance, \n                         kernel,\n                         transform=transform)\nend = time()\nrun_time = end - start\nsol_paths = sol.reshape(num_robots, num_placements, 2)\n\n# Convert solution path waypoints to sensing points along the whole path\nX_path, y_path = path2data(sol_paths, X, y)\n\n# Compute RMSE\ny_pred, _ = get_reconstruction((X_path, y_path), \n                                X, \n                                noise_variance, \n                                kernel)\nrmse = get_rmse(y_pred, y)\n\n# Plot the solution\nplot_paths(sol_paths, title=f\"Discrete-SGP | RMSE: {rmse:.2f} | Time: {run_time:.2f}s\")\nplt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet')\nplt.gca().set_aspect('equal')\n</pre> start = time() sol = get_greedy_sgp_sol(num_placements*num_robots,                           candidates,                           candidates,                           noise_variance,                           kernel,                          transform=transform) end = time() run_time = end - start sol_paths = sol.reshape(num_robots, num_placements, 2)  # Convert solution path waypoints to sensing points along the whole path X_path, y_path = path2data(sol_paths, X, y)  # Compute RMSE y_pred, _ = get_reconstruction((X_path, y_path),                                  X,                                  noise_variance,                                  kernel) rmse = get_rmse(y_pred, y)  # Plot the solution plot_paths(sol_paths, title=f\"Discrete-SGP | RMSE: {rmse:.2f} | Time: {run_time:.2f}s\") plt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet') plt.gca().set_aspect('equal') In\u00a0[6]: Copied! <pre>start = time()\nsol = get_greedy_mi_sol(num_placements*num_robots, \n                        candidates, \n                        candidates, \n                        noise_variance, \n                        kernel,\n                        transform=transform)\nend = time()\nrun_time = end - start\nsol_paths = sol.reshape(num_robots, num_placements, 2)\n\n# Convert solution path waypoints to sensing points along the whole path\nX_path, y_path = path2data(sol_paths, X, y)\n\n# Compute RMSE\ny_pred, _ = get_reconstruction((X_path, y_path), \n                                X, \n                                noise_variance, \n                                kernel)\nrmes = get_rmse(y_pred, y)\n\n# Plot the solution\nplot_paths(sol_paths, title=f\"Greedy-MI | RMSE: {rmes:.2f} | Time: {run_time:.2f}s\")\nplt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet')\nplt.gca().set_aspect('equal')\n</pre> start = time() sol = get_greedy_mi_sol(num_placements*num_robots,                          candidates,                          candidates,                          noise_variance,                          kernel,                         transform=transform) end = time() run_time = end - start sol_paths = sol.reshape(num_robots, num_placements, 2)  # Convert solution path waypoints to sensing points along the whole path X_path, y_path = path2data(sol_paths, X, y)  # Compute RMSE y_pred, _ = get_reconstruction((X_path, y_path),                                  X,                                  noise_variance,                                  kernel) rmes = get_rmse(y_pred, y)  # Plot the solution plot_paths(sol_paths, title=f\"Greedy-MI | RMSE: {rmes:.2f} | Time: {run_time:.2f}s\") plt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet') plt.gca().set_aspect('equal') In\u00a0[7]: Copied! <pre>start = time()\nmodel = CMA_ES(candidates, \n               noise_variance, \n               kernel, \n               transform=transform)\nsol = model.optimize(num_sensors=num_placements*num_robots, \n                     X_init=Xu_init.copy())\nend = time()\nrun_time = end - start\nsol_paths = sol.reshape(num_robots, num_placements, 2)\n\n# Convert solution path waypoints to sensing points along the whole path\nX_path, y_path = path2data(sol_paths, X, y)\ny_pred, _ = get_reconstruction((X_path, y_path), \n                                X, \n                                noise_variance, \n                                kernel)\n\n# Compute RMSE\nrmes = get_rmse(y_pred, y)\n\n# Plot the solution\nplot_paths(sol_paths, title=f\"CMA-ES-MI | RMSE: {rmes:.2f} | Time: {run_time:.2f}s\")\nplt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet')\nplt.gca().set_aspect('equal')\n</pre> start = time() model = CMA_ES(candidates,                 noise_variance,                 kernel,                 transform=transform) sol = model.optimize(num_sensors=num_placements*num_robots,                       X_init=Xu_init.copy()) end = time() run_time = end - start sol_paths = sol.reshape(num_robots, num_placements, 2)  # Convert solution path waypoints to sensing points along the whole path X_path, y_path = path2data(sol_paths, X, y) y_pred, _ = get_reconstruction((X_path, y_path),                                  X,                                  noise_variance,                                  kernel)  # Compute RMSE rmes = get_rmse(y_pred, y)  # Plot the solution plot_paths(sol_paths, title=f\"CMA-ES-MI | RMSE: {rmes:.2f} | Time: {run_time:.2f}s\") plt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet') plt.gca().set_aspect('equal') In\u00a0[8]: Copied! <pre>start = time()\nmodel = BayesianOpt(candidates, \n                    noise_variance,\n                    kernel,\n                    transform=transform)\nsol = model.optimize(num_sensors=num_placements*num_robots, \n                     X_init=Xu_init.copy())\nend = time()\nrun_time = end - start\nsol_paths = sol.reshape(num_robots, num_placements, 2)\n\n# Convert solution path waypoints to sensing points along the whole path\nX_path, y_path = path2data(sol_paths, X, y)\ny_pred, _ = get_reconstruction((X_path, y_path), \n                                X, \n                                noise_variance, \n                                kernel)\n\n# Compute RMSE\nrmes = get_rmse(y_pred, y)\n\n# Plot the solution\nplot_paths(sol_paths, title=f\"BO-MI | RMSE: {rmes:.2f} | Time: {run_time:.2f}s\")\nplt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet')\nplt.gca().set_aspect('equal')\n</pre> start = time() model = BayesianOpt(candidates,                      noise_variance,                     kernel,                     transform=transform) sol = model.optimize(num_sensors=num_placements*num_robots,                       X_init=Xu_init.copy()) end = time() run_time = end - start sol_paths = sol.reshape(num_robots, num_placements, 2)  # Convert solution path waypoints to sensing points along the whole path X_path, y_path = path2data(sol_paths, X, y) y_pred, _ = get_reconstruction((X_path, y_path),                                  X,                                  noise_variance,                                  kernel)  # Compute RMSE rmes = get_rmse(y_pred, y)  # Plot the solution plot_paths(sol_paths, title=f\"BO-MI | RMSE: {rmes:.2f} | Time: {run_time:.2f}s\") plt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet') plt.gca().set_aspect('equal')"},{"location":"examples/Baselines.html#baselines","title":"Baselines\u00b6","text":""},{"location":"examples/Baselines.html#set-the-initial-parameters-and-generate-synthetic-data","title":"Set the initial parameters and generate synthetic data\u00b6","text":"<p>(no distance budget constraints)</p>"},{"location":"examples/Baselines.html#helper-functions","title":"Helper functions\u00b6","text":""},{"location":"examples/Baselines.html#continuous-sgp","title":"Continuous-SGP\u00b6","text":""},{"location":"examples/Baselines.html#discrete-sgp","title":"Discrete-SGP\u00b6","text":""},{"location":"examples/Baselines.html#greedy-mutual-information","title":"Greedy Mutual Information\u00b6","text":""},{"location":"examples/Baselines.html#cma-es-mutual-information-genetic-algorithm","title":"CMA-ES Mutual Information (Genetic Algorithm)\u00b6","text":""},{"location":"examples/Baselines.html#bayesian-optimization-mutual-information","title":"Bayesian Optimization Mutual Information\u00b6","text":""},{"location":"examples/IPP.html","title":"SGP-Based IPP","text":"In\u00a0[\u00a0]: Copied! <pre>import os\nos.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom sgptools.utils.metrics import *\nfrom sgptools.utils.data import *\nfrom sgptools.utils.misc import *\nfrom sgptools.utils.tsp import run_tsp\nfrom sgptools.utils.gpflow import get_model_params\nfrom sgptools.models.continuous_sgp import *\nfrom sgptools.models.core.transformations import *\n\nimport matplotlib.pyplot as plt\n\nnp.random.seed(1234)\ntf.random.set_seed(1234)\n</pre> import os os.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"  import numpy as np import tensorflow as tf  from sgptools.utils.metrics import * from sgptools.utils.data import * from sgptools.utils.misc import * from sgptools.utils.tsp import run_tsp from sgptools.utils.gpflow import get_model_params from sgptools.models.continuous_sgp import * from sgptools.models.core.transformations import *  import matplotlib.pyplot as plt  np.random.seed(1234) tf.random.set_seed(1234) In\u00a0[2]: Copied! <pre># Get the synthetic elevation data\nX_train, y_train, X_test, y_test, candidates, X, y = get_dataset()\n\n# Train the GP and get the model parameters\n_, noise_variance, kernel = get_model_params(X_train, y_train, \n                                             lengthscales=[1.0, 1.0], \n                                             optimizer='scipy')\nplt.scatter(X[:, 0], X[:, 1], c=y, cmap='jet')\nplt.title(\"Ground Truth\")\nplt.xlabel('X')\nplt.ylabel('Y')\nplt.gca().set_aspect('equal')\nplt.show()\n</pre> # Get the synthetic elevation data X_train, y_train, X_test, y_test, candidates, X, y = get_dataset()  # Train the GP and get the model parameters _, noise_variance, kernel = get_model_params(X_train, y_train,                                               lengthscales=[1.0, 1.0],                                               optimizer='scipy') plt.scatter(X[:, 0], X[:, 1], c=y, cmap='jet') plt.title(\"Ground Truth\") plt.xlabel('X') plt.ylabel('Y') plt.gca().set_aspect('equal') plt.show() <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.6576268242419765  \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [2.95895 2.25221]   \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.02576014673391919 \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> In\u00a0[3]: Copied! <pre>num_robots = 1\nnum_placements = 6\n\n# Initialize inducing points and get initial path\nXu_init = get_inducing_pts(X_train, \n                           num_placements*num_robots)\nXu_init, _ = run_tsp(Xu_init, \n                     num_vehicles=num_robots)\nXu_init = Xu_init[0]\n\n# Setup unconstrained IPP transform and optimize it\ntransform = IPPTransform(num_robots=num_robots, num_dim=2)\nsgpr, _ = continuous_sgp(num_placements, \n                         X_train, \n                         noise_variance, \n                         kernel,\n                         transform,\n                         Xu_init=Xu_init, \n                         max_steps=1000)\n\n# Get solution inducing points\nsgp_sol_sp = sgpr.inducing_variable.Z.numpy()\nsgp_paths = sgp_sol_sp.reshape(num_robots, num_placements, 2)\n\n# Plot the solution\nplot_paths(sgp_paths, candidates,\n           title=\"Offline Single-Robot IPP with Point Sensing\")\n</pre> num_robots = 1 num_placements = 6  # Initialize inducing points and get initial path Xu_init = get_inducing_pts(X_train,                             num_placements*num_robots) Xu_init, _ = run_tsp(Xu_init,                       num_vehicles=num_robots) Xu_init = Xu_init[0]  # Setup unconstrained IPP transform and optimize it transform = IPPTransform(num_robots=num_robots, num_dim=2) sgpr, _ = continuous_sgp(num_placements,                           X_train,                           noise_variance,                           kernel,                          transform,                          Xu_init=Xu_init,                           max_steps=1000)  # Get solution inducing points sgp_sol_sp = sgpr.inducing_variable.Z.numpy() sgp_paths = sgp_sol_sp.reshape(num_robots, num_placements, 2)  # Plot the solution plot_paths(sgp_paths, candidates,            title=\"Offline Single-Robot IPP with Point Sensing\") In\u00a0[4]: Copied! <pre>num_robots = 1\nnum_placements = 6\n\n# Initialize inducing points and get initial path\nXu_init = get_inducing_pts(X_train, num_placements*num_robots)\nXu_init, _ = run_tsp(Xu_init, \n                     num_vehicles=num_robots)\nXu_init = Xu_init[0]\n\n# Setup unconstrained IPP transform with sampling rate=5\n# and optimize it\ntransform = IPPTransform(num_robots=num_robots,\n                           num_dim=2,\n                           sampling_rate=5)\nsgpr, _ = continuous_sgp(num_placements, \n                         X_train, \n                         noise_variance, \n                         kernel,\n                         transform,\n                         Xu_init=Xu_init, \n                         max_steps=1000)\n\n# Get solution inducing points\nsgp_sol_sp = sgpr.inducing_variable.Z.numpy()\nsgp_paths = sgp_sol_sp.reshape(num_robots, num_placements, 2)\n\n# Plot the solution\nplot_paths(sgp_paths, candidates, \n           title=\"Offline Single-Robot IPP with Continuous Sensing\")\n</pre> num_robots = 1 num_placements = 6  # Initialize inducing points and get initial path Xu_init = get_inducing_pts(X_train, num_placements*num_robots) Xu_init, _ = run_tsp(Xu_init,                       num_vehicles=num_robots) Xu_init = Xu_init[0]  # Setup unconstrained IPP transform with sampling rate=5 # and optimize it transform = IPPTransform(num_robots=num_robots,                            num_dim=2,                            sampling_rate=5) sgpr, _ = continuous_sgp(num_placements,                           X_train,                           noise_variance,                           kernel,                          transform,                          Xu_init=Xu_init,                           max_steps=1000)  # Get solution inducing points sgp_sol_sp = sgpr.inducing_variable.Z.numpy() sgp_paths = sgp_sol_sp.reshape(num_robots, num_placements, 2)  # Plot the solution plot_paths(sgp_paths, candidates,             title=\"Offline Single-Robot IPP with Continuous Sensing\") In\u00a0[5]: Copied! <pre>num_robots = 1\nnum_placements = 4\n\n# Initialize inducing points and get initial path\nXu_init = get_inducing_pts(X_train, num_placements*num_robots)\nXu_init, _ = run_tsp(Xu_init, \n                     num_vehicles=num_robots)\nXu_init = Xu_init[0]\n\n# Add random angles to inducing points to initialize the rotation angle of the FoVs\nangles = np.random.uniform(-np.pi, np.pi, num_placements).reshape(-1, 1)\nXu_init = np.concatenate([Xu_init, angles], axis=1)\n\n# Setup unconstrained IPP transform with square FoV transform \n# with length 5 and 3x3 point approximation, \n# and continuous sensing model (sampling rate of 3), and optimize it\nsquare_transform = SquareTransform(length=5, num_side=3)\ntransform = IPPTransform(num_robots=num_robots,\n                         num_dim=3,\n                         sampling_rate=3,\n                         sensor_model=square_transform)\nsgpr, _ = continuous_sgp(num_placements, \n                         X_train, \n                         noise_variance, \n                         kernel,\n                         transform,\n                         Xu_init=Xu_init, \n                         max_steps=1000)\n\n# Get solution inducing points\nsgp_sol_sp = sgpr.inducing_variable.Z.numpy()[:, :2]\nsgp_paths = sgp_sol_sp.reshape(num_robots, -1 , 2)\n\n# Plot the solution path and the FoVs\nplot_paths(sgp_paths, candidates, \n           title=\"Offline Single-Robot IPP with Continuous Sensing\")\nsgp_sol_sp = sgpr.inducing_variable.Z\nsgp_sol_sp = sgpr.transform.expand(sgp_sol_sp,).numpy()\nplt.scatter(sgp_sol_sp[:, 0], sgp_sol_sp[:, 1], s=10, c='r')\n</pre> num_robots = 1 num_placements = 4  # Initialize inducing points and get initial path Xu_init = get_inducing_pts(X_train, num_placements*num_robots) Xu_init, _ = run_tsp(Xu_init,                       num_vehicles=num_robots) Xu_init = Xu_init[0]  # Add random angles to inducing points to initialize the rotation angle of the FoVs angles = np.random.uniform(-np.pi, np.pi, num_placements).reshape(-1, 1) Xu_init = np.concatenate([Xu_init, angles], axis=1)  # Setup unconstrained IPP transform with square FoV transform  # with length 5 and 3x3 point approximation,  # and continuous sensing model (sampling rate of 3), and optimize it square_transform = SquareTransform(length=5, num_side=3) transform = IPPTransform(num_robots=num_robots,                          num_dim=3,                          sampling_rate=3,                          sensor_model=square_transform) sgpr, _ = continuous_sgp(num_placements,                           X_train,                           noise_variance,                           kernel,                          transform,                          Xu_init=Xu_init,                           max_steps=1000)  # Get solution inducing points sgp_sol_sp = sgpr.inducing_variable.Z.numpy()[:, :2] sgp_paths = sgp_sol_sp.reshape(num_robots, -1 , 2)  # Plot the solution path and the FoVs plot_paths(sgp_paths, candidates,             title=\"Offline Single-Robot IPP with Continuous Sensing\") sgp_sol_sp = sgpr.inducing_variable.Z sgp_sol_sp = sgpr.transform.expand(sgp_sol_sp,).numpy() plt.scatter(sgp_sol_sp[:, 0], sgp_sol_sp[:, 1], s=10, c='r') Out[5]: <pre>&lt;matplotlib.collections.PathCollection at 0x7f5688999990&gt;</pre> In\u00a0[6]: Copied! <pre>num_robots = 3\nnum_placements = 6\n\n# Initialize inducing points and get initial path \n# resample to ensure same number of waypoints in each path\nXu_init = get_inducing_pts(X_train, num_placements*num_robots)\nXu_init, _ = run_tsp(Xu_init, \n                     num_vehicles=num_robots,\n                     max_dist=100,\n                     resample=num_placements)\nXu_init = Xu_init.reshape(-1, 2)\n\n# Setup distance constrained 3 robot IPP transform\n# with continuous sensing model (sampling rate of 5) and \n# a distance budget of 30.0 and optimize it\ntransform = IPPTransform(num_robots=num_robots,\n                         num_dim=2,\n                         sampling_rate=5,\n                         distance_budget=30.,\n                         constraint_weight=100.)\nsgpr, _ = continuous_sgp(num_placements, \n                         X_train, \n                         noise_variance, \n                         kernel,\n                         transform,\n                         Xu_init=Xu_init, \n                         max_steps=5000)\n\n# Get solution inducing points\nsol_sp = sgpr.inducing_variable.Z.numpy()\nsol_paths = sol_sp.reshape(num_robots, \n                           num_placements, 2)\n\n# Plot the solution path\nplot_paths(sol_paths, candidates, title=\"Optimized paths\")\nprint(\"Distances:\", transform.distance(sol_sp).numpy())\n</pre> num_robots = 3 num_placements = 6  # Initialize inducing points and get initial path  # resample to ensure same number of waypoints in each path Xu_init = get_inducing_pts(X_train, num_placements*num_robots) Xu_init, _ = run_tsp(Xu_init,                       num_vehicles=num_robots,                      max_dist=100,                      resample=num_placements) Xu_init = Xu_init.reshape(-1, 2)  # Setup distance constrained 3 robot IPP transform # with continuous sensing model (sampling rate of 5) and  # a distance budget of 30.0 and optimize it transform = IPPTransform(num_robots=num_robots,                          num_dim=2,                          sampling_rate=5,                          distance_budget=30.,                          constraint_weight=100.) sgpr, _ = continuous_sgp(num_placements,                           X_train,                           noise_variance,                           kernel,                          transform,                          Xu_init=Xu_init,                           max_steps=5000)  # Get solution inducing points sol_sp = sgpr.inducing_variable.Z.numpy() sol_paths = sol_sp.reshape(num_robots,                             num_placements, 2)  # Plot the solution path plot_paths(sol_paths, candidates, title=\"Optimized paths\") print(\"Distances:\", transform.distance(sol_sp).numpy()) <pre>Distances: [30.         29.9984054  29.98115566]\n</pre>"},{"location":"examples/IPP.html#sgp-based-ipp","title":"SGP-Based IPP\u00b6","text":""},{"location":"examples/IPP.html#get-the-data-and-initial-sgp-hyperparameters","title":"Get the data and initial SGP hyperparameters\u00b6","text":""},{"location":"examples/IPP.html#single-robot-ipp-with-point-sensing","title":"Single-Robot IPP with Point Sensing\u00b6","text":""},{"location":"examples/IPP.html#single-robot-ipp-with-continuous-sensing","title":"Single-Robot IPP with Continuous Sensing\u00b6","text":""},{"location":"examples/IPP.html#single-robot-ipp-with-continuous-non-point-fov-sensor","title":"Single-Robot IPP with Continuous Non-point FoV Sensor\u00b6","text":""},{"location":"examples/IPP.html#multi-robot-ipp-with-continuous-sensing-and-a-distance-budget","title":"Multi-Robot IPP with Continuous Sensing and a Distance Budget\u00b6","text":""},{"location":"examples/non_point_FoV.html","title":"IPP with Non-Point FoV","text":"In\u00a0[\u00a0]: Copied! <pre>import os\nos.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"\n\nimport numpy as np\n\nfrom sgptools.utils.metrics import *\nfrom sgptools.utils.data import *\nfrom sgptools.utils.misc import *\nfrom sgptools.models.core.osgpr import *\nfrom sgptools.utils.tsp import run_tsp\nfrom sgptools.utils.gpflow import get_model_params\nfrom sgptools.models.continuous_sgp import *\nfrom sgptools.models.core.transformations import *\nfrom gpflow.utilities.traversal import print_summary\n\nfrom matplotlib import colors\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Polygon\nimport mpl_toolkits.mplot3d.art3d as art3d\nfrom scipy.optimize import linear_sum_assignment\nfrom sklearn.metrics import pairwise_distances\n\nfrom time import time\nfrom copy import deepcopy\n\nnp.random.seed(1234)\ntf.random.set_seed(1234)\n</pre> import os os.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"  import numpy as np  from sgptools.utils.metrics import * from sgptools.utils.data import * from sgptools.utils.misc import * from sgptools.models.core.osgpr import * from sgptools.utils.tsp import run_tsp from sgptools.utils.gpflow import get_model_params from sgptools.models.continuous_sgp import * from sgptools.models.core.transformations import * from gpflow.utilities.traversal import print_summary  from matplotlib import colors import matplotlib.pyplot as plt from matplotlib.patches import Polygon import mpl_toolkits.mplot3d.art3d as art3d from scipy.optimize import linear_sum_assignment from sklearn.metrics import pairwise_distances  from time import time from copy import deepcopy  np.random.seed(1234) tf.random.set_seed(1234) In\u00a0[2]: Copied! <pre>'''\nUtility to map the inducing points to FoV vertices\n'''\ndef get_vertices(Xu, X_fov):\n    X_fov = X_fov.reshape(len(Xu), -1, 2)\n    vertices = np.zeros((len(Xu), 5, 2))\n    vertices[:, 0] = X_fov.min(axis=1)\n    vertices[:, 1] = np.array([X_fov[:, :, 0].min(axis=1), \n                              X_fov[:, :, 1].max(axis=1)]).T\n    vertices[:, 2] = X_fov.max(axis=1)\n    vertices[:, 3] = np.array([X_fov[:, :, 0].max(axis=1), \n                              X_fov[:, :, 1].min(axis=1)]).T\n    vertices[:, 4] = X_fov.mean(axis=1)\n\n    dists = pairwise_distances(vertices[:, 4], Y=Xu[:, :2], \n                               metric='euclidean')\n    _, idx = linear_sum_assignment(dists)\n\n    vertices[:, 4] = Xu[idx][:, :2]\n    return vertices\n\n'''\nMethod to plot the solution with FoVs and environment reconstruction\n'''\ndef plot_results(X_inducing, X_fov, fname=None):\n    # Setup 3D plot\n    fig = plt.figure(figsize=(5, 5))\n    ax = fig.add_subplot(111, projection='3d', computed_zorder=False)\n\n    # Plot the reconstructed environment state\n    sensor_data = cont2disc(X_fov, X, y)\n    y_pred, _ = get_reconstruction(sensor_data, X_test, \n                                   noise_variance_opt, kernel_opt)\n    norm = colors.Normalize(y_train.min(), y_train.max())\n    _ = ax.plot_surface(X_test[:, 0].reshape(test_dim),\n                        X_test[:, 1].reshape(test_dim),\n                        np.atleast_2d(-0.1),\n                        facecolors=plt.cm.jet(norm(y_pred.reshape(test_dim))),\n                        shade=False,\n                        alpha=0.8,\n                        zorder=0)\n\n    # Plot the solution path\n    ax.scatter(X_inducing[:, 0], \n               X_inducing[:, 1], \n               X_inducing[:, 2], c='C3')\n    ax.plot(X_inducing[:, 0], X_inducing[:, 1], X_inducing[:, 2], 'k-')\n\n    # Plot the FoV vertices\n    vertices = get_vertices(X_inducing, X_fov)\n    for i in range(vertices.shape[0]):\n        color = 'C3'\n        verts = []\n        verts.append([vertices[i, 0], vertices[i, 1], \n                      vertices[i, 2], vertices[i, 3]])\n        fov = Polygon(np.array(verts)[0, :, :2],\n                      linewidth=1.5, \n                      edgecolor=color,\n                      facecolor=color,\n                      fill=False,\n                      zorder=15)\n        ax.add_patch(fov)\n        art3d.pathpatch_2d_to_3d(fov)\n\n    # Configure other plot settings\n    ax.set_xlabel('X')\n    ax.set_ylabel('Y')\n    ax.set_zlabel('Z')\n\n    ax.set_xlim(np.min(X[:, 0])-0.5, np.max(X[:, 0])+0.5)\n    ax.set_ylim(np.min(X[:, 1])-0.5, np.max(X[:, 1])+0.5)\n    ax.set_zlim(0, 14)\n\n    ax.view_init(elev=30, azim=45+180)\n    ax.set_title(f'Num Waypoints: {num_placements}, RMSE: {get_rmse(y_pred, y_test):.2f}\\nData Field Reconstruction', y=0.99)\n\n    plt.tight_layout()\n    if fname is not None:\n        plt.savefig(fname, bbox_inches='tight')\n    plt.show()\n</pre> ''' Utility to map the inducing points to FoV vertices ''' def get_vertices(Xu, X_fov):     X_fov = X_fov.reshape(len(Xu), -1, 2)     vertices = np.zeros((len(Xu), 5, 2))     vertices[:, 0] = X_fov.min(axis=1)     vertices[:, 1] = np.array([X_fov[:, :, 0].min(axis=1),                                X_fov[:, :, 1].max(axis=1)]).T     vertices[:, 2] = X_fov.max(axis=1)     vertices[:, 3] = np.array([X_fov[:, :, 0].max(axis=1),                                X_fov[:, :, 1].min(axis=1)]).T     vertices[:, 4] = X_fov.mean(axis=1)      dists = pairwise_distances(vertices[:, 4], Y=Xu[:, :2],                                 metric='euclidean')     _, idx = linear_sum_assignment(dists)      vertices[:, 4] = Xu[idx][:, :2]     return vertices  ''' Method to plot the solution with FoVs and environment reconstruction ''' def plot_results(X_inducing, X_fov, fname=None):     # Setup 3D plot     fig = plt.figure(figsize=(5, 5))     ax = fig.add_subplot(111, projection='3d', computed_zorder=False)      # Plot the reconstructed environment state     sensor_data = cont2disc(X_fov, X, y)     y_pred, _ = get_reconstruction(sensor_data, X_test,                                     noise_variance_opt, kernel_opt)     norm = colors.Normalize(y_train.min(), y_train.max())     _ = ax.plot_surface(X_test[:, 0].reshape(test_dim),                         X_test[:, 1].reshape(test_dim),                         np.atleast_2d(-0.1),                         facecolors=plt.cm.jet(norm(y_pred.reshape(test_dim))),                         shade=False,                         alpha=0.8,                         zorder=0)      # Plot the solution path     ax.scatter(X_inducing[:, 0],                 X_inducing[:, 1],                 X_inducing[:, 2], c='C3')     ax.plot(X_inducing[:, 0], X_inducing[:, 1], X_inducing[:, 2], 'k-')      # Plot the FoV vertices     vertices = get_vertices(X_inducing, X_fov)     for i in range(vertices.shape[0]):         color = 'C3'         verts = []         verts.append([vertices[i, 0], vertices[i, 1],                        vertices[i, 2], vertices[i, 3]])         fov = Polygon(np.array(verts)[0, :, :2],                       linewidth=1.5,                        edgecolor=color,                       facecolor=color,                       fill=False,                       zorder=15)         ax.add_patch(fov)         art3d.pathpatch_2d_to_3d(fov)      # Configure other plot settings     ax.set_xlabel('X')     ax.set_ylabel('Y')     ax.set_zlabel('Z')      ax.set_xlim(np.min(X[:, 0])-0.5, np.max(X[:, 0])+0.5)     ax.set_ylim(np.min(X[:, 1])-0.5, np.max(X[:, 1])+0.5)     ax.set_zlim(0, 14)      ax.view_init(elev=30, azim=45+180)     ax.set_title(f'Num Waypoints: {num_placements}, RMSE: {get_rmse(y_pred, y_test):.2f}\\nData Field Reconstruction', y=0.99)      plt.tight_layout()     if fname is not None:         plt.savefig(fname, bbox_inches='tight')     plt.show() In\u00a0[3]: Copied! <pre># Get the synthetic elevation data\nX_train, y_train, X_test, y_test, candidates, X, y = get_dataset(random_seed=0)\ntrain_dim = np.sqrt(X_train.shape[0]).astype(int)\n\n# Train a GP and get the model parameters\nprint('Optimized Hyperparameters')\n_, noise_variance_opt, kernel_opt = get_model_params(X_train, y_train, \n                                                     lengthscales=[1.0, 1.0], \n                                                     optimizer='scipy')\n\n# Build a new test set on a grid for easy plotting in a 3D plot\ngpr = gpflow.models.GPR((X_test, y_test),\n                        noise_variance=noise_variance_opt,\n                        kernel=kernel_opt)\ntest_dim = (100, 100)\nX_test1 = np.linspace(np.min(X[:, 0]), np.max(X[:, 0]), test_dim[0])\nX_test2 = np.linspace(np.min(X[:, 1]), np.max(X[:, 1]), test_dim[1])\nX_test1, X_test2 = np.meshgrid(X_test1, X_test2)\nX_test = np.stack([X_test1.ravel(), X_test2.ravel()], axis=1)\ny_test, y_var = gpr.predict_f(X_test)\ny_test = y_test.numpy()\n\n# Plot the data field\nplt.imshow(y_test.reshape(test_dim), cmap=\"jet\", origin='lower')\nplt.xlabel('X')\nplt.ylabel('Y')\nplt.title('Elevation Data Field')\nplt.colorbar(label='Normalized Elevation')\nplt.show()\n</pre> # Get the synthetic elevation data X_train, y_train, X_test, y_test, candidates, X, y = get_dataset(random_seed=0) train_dim = np.sqrt(X_train.shape[0]).astype(int)  # Train a GP and get the model parameters print('Optimized Hyperparameters') _, noise_variance_opt, kernel_opt = get_model_params(X_train, y_train,                                                       lengthscales=[1.0, 1.0],                                                       optimizer='scipy')  # Build a new test set on a grid for easy plotting in a 3D plot gpr = gpflow.models.GPR((X_test, y_test),                         noise_variance=noise_variance_opt,                         kernel=kernel_opt) test_dim = (100, 100) X_test1 = np.linspace(np.min(X[:, 0]), np.max(X[:, 0]), test_dim[0]) X_test2 = np.linspace(np.min(X[:, 1]), np.max(X[:, 1]), test_dim[1]) X_test1, X_test2 = np.meshgrid(X_test1, X_test2) X_test = np.stack([X_test1.ravel(), X_test2.ravel()], axis=1) y_test, y_var = gpr.predict_f(X_test) y_test = y_test.numpy()  # Plot the data field plt.imshow(y_test.reshape(test_dim), cmap=\"jet\", origin='lower') plt.xlabel('X') plt.ylabel('Y') plt.title('Elevation Data Field') plt.colorbar(label='Normalized Elevation') plt.show() <pre>Optimized Hyperparameters\n\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.8371517535182331  \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [2.45802 2.50701]   \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.04342255274528023 \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> In\u00a0[4]: Copied! <pre>num_robots = 1\nnum_placements = 9\nnum_side = 5  # Use a 5x5 point FoV approximation\n\n# Setup the hyperparameters\n_, noise_variance, kernel = get_model_params(X_train, y_train, \n                                             lengthscales=[3.0, 3.0], \n                                             optimizer='scipy',\n                                             max_steps=0,\n                                             print_params=False)\n\n# Get initial inducing points\nXu_init = get_inducing_pts(X_train, num_placements)\n# Initalize the height dimension at 2.0 for all points\nXu_init = np.concatenate((Xu_init, np.ones((Xu_init.shape[0], 1))*2.0), axis=1)\nXu_init[:, :2] = run_tsp(Xu_init[:, :2])[0][0]\n\n# Initialize the non-point FoV IPP model and optimize it\ntransform = SquareHeightTransform(num_side=num_side)\nsgpr, _ = continuous_sgp(num_placements,\n                         X_train, \n                         noise_variance,\n                         kernel,\n                         transform=transform,\n                         Xu_init=Xu_init.copy())\nsgp_sol_sp = sgpr.inducing_variable.Z.numpy()\n</pre> num_robots = 1 num_placements = 9 num_side = 5  # Use a 5x5 point FoV approximation  # Setup the hyperparameters _, noise_variance, kernel = get_model_params(X_train, y_train,                                               lengthscales=[3.0, 3.0],                                               optimizer='scipy',                                              max_steps=0,                                              print_params=False)  # Get initial inducing points Xu_init = get_inducing_pts(X_train, num_placements) # Initalize the height dimension at 2.0 for all points Xu_init = np.concatenate((Xu_init, np.ones((Xu_init.shape[0], 1))*2.0), axis=1) Xu_init[:, :2] = run_tsp(Xu_init[:, :2])[0][0]  # Initialize the non-point FoV IPP model and optimize it transform = SquareHeightTransform(num_side=num_side) sgpr, _ = continuous_sgp(num_placements,                          X_train,                           noise_variance,                          kernel,                          transform=transform,                          Xu_init=Xu_init.copy()) sgp_sol_sp = sgpr.inducing_variable.Z.numpy() In\u00a0[5]: Copied! <pre>plot_results(Xu_init.reshape(-1, 3), \n             sgpr.transform.expand(Xu_init.reshape(-1, 3)).numpy())\n</pre> plot_results(Xu_init.reshape(-1, 3),               sgpr.transform.expand(Xu_init.reshape(-1, 3)).numpy()) In\u00a0[6]: Copied! <pre>plot_results(sgp_sol_sp.reshape(-1, 3), \n             sgpr.transform.expand(sgp_sol_sp.reshape(-1, 3)).numpy())\n</pre> plot_results(sgp_sol_sp.reshape(-1, 3),               sgpr.transform.expand(sgp_sol_sp.reshape(-1, 3)).numpy())"},{"location":"examples/non_point_FoV.html#ipp-with-non-point-fov","title":"IPP with Non-Point FoV\u00b6","text":""},{"location":"examples/non_point_FoV.html#generate-synthetic-data-and-initial-sgp-hyperparameters","title":"Generate synthetic data and initial SGP hyperparameters\u00b6","text":""},{"location":"examples/non_point_FoV.html#generate-the-ipp-solution","title":"Generate the IPP solution\u00b6","text":""},{"location":"examples/non_point_FoV.html#initial-solution","title":"Initial Solution\u00b6","text":""},{"location":"examples/non_point_FoV.html#optimized-solution","title":"Optimized Solution\u00b6","text":""},{"location":"examples/non_stationary_kernel.html","title":"Non-Stationary Kernel","text":"In\u00a0[\u00a0]: Copied! <pre>import tensorflow as tf\nimport numpy as np\nimport gpflow\ngpflow.config.set_default_float(np.float32)\n\nfrom sgptools.utils.metrics import *\nfrom sgptools.utils.misc import *\nfrom sgptools.models.continuous_sgp import *\nfrom sgptools.kernels.neural_kernel import init_neural_kernel\nfrom sgptools.utils.gpflow import get_model_params\n\nfrom sklearn.preprocessing import StandardScaler\nimport matplotlib.pyplot as plt\n\nnp.random.seed(10)\ntf.random.set_seed(10)\n</pre> import tensorflow as tf import numpy as np import gpflow gpflow.config.set_default_float(np.float32)  from sgptools.utils.metrics import * from sgptools.utils.misc import * from sgptools.models.continuous_sgp import * from sgptools.kernels.neural_kernel import init_neural_kernel from sgptools.utils.gpflow import get_model_params  from sklearn.preprocessing import StandardScaler import matplotlib.pyplot as plt  np.random.seed(10) tf.random.set_seed(10) In\u00a0[2]: Copied! <pre>def non_stationary_function(X, Y):\n    Z1 = np.sin(X/10) + np.cos(Y/10)\n    Z2 = np.cos(X/6) + np.cos(Y/6)\n    Z = np.concatenate([Z1[:, ::2], Z2[:, ::2]], \n                       axis=1)\n    return Z\n\n# Generate data\ntrain_data_dims = (40, 20)\nx = np.linspace(0, 200, train_data_dims[0])\ny = np.linspace(0, 100, train_data_dims[1])\nX, Y = np.meshgrid(x, y)\nZ = non_stationary_function(X, Y)\n\nX_train = np.stack([X.ravel(), Y.ravel()], \n                   axis=1).astype(np.float32)\nY_train = Z.ravel()[:, None].astype(np.float32)\nprint('Train Data:', X_train.shape, Y_train.shape)\n\ntest_data_dims = (100, 50)\nx = np.linspace(0, 200, test_data_dims[0])\ny = np.linspace(0, 100, test_data_dims[1])\nX, Y = np.meshgrid(x, y)\nZ = non_stationary_function(X, Y)\n\nX_test = np.stack([X.ravel(), Y.ravel()], \n                  axis=1).astype(np.float32)\nY_test = Z.ravel()[:, None].astype(np.float32)\nprint('Test Data:', X_test.shape, Y_test.shape)\n\n# Normalize the data\nXscalar = StandardScaler()\nX_train = Xscalar.fit_transform(X_train)\nX_test = Xscalar.transform(X_test)\n\nyScalar = StandardScaler()\nY_train = yScalar.fit_transform(Y_train)\nY_test = yScalar.transform(Y_test)\n\n# Plot the data\nplt.figure()\nplt.imshow(Y_train.reshape(train_data_dims[1], \n                           train_data_dims[0]), \n           cmap='jet')\nplt.gca().set_aspect('equal')\nplt.title('Train Data')\nplt.show()\n\nplt.figure()\nplt.imshow(Y_test.reshape(test_data_dims[1], \n                          test_data_dims[0]), \n           cmap='jet')\nplt.gca().set_aspect('equal')\nplt.title('Test Data')\nplt.show()\n</pre> def non_stationary_function(X, Y):     Z1 = np.sin(X/10) + np.cos(Y/10)     Z2 = np.cos(X/6) + np.cos(Y/6)     Z = np.concatenate([Z1[:, ::2], Z2[:, ::2]],                         axis=1)     return Z  # Generate data train_data_dims = (40, 20) x = np.linspace(0, 200, train_data_dims[0]) y = np.linspace(0, 100, train_data_dims[1]) X, Y = np.meshgrid(x, y) Z = non_stationary_function(X, Y)  X_train = np.stack([X.ravel(), Y.ravel()],                     axis=1).astype(np.float32) Y_train = Z.ravel()[:, None].astype(np.float32) print('Train Data:', X_train.shape, Y_train.shape)  test_data_dims = (100, 50) x = np.linspace(0, 200, test_data_dims[0]) y = np.linspace(0, 100, test_data_dims[1]) X, Y = np.meshgrid(x, y) Z = non_stationary_function(X, Y)  X_test = np.stack([X.ravel(), Y.ravel()],                    axis=1).astype(np.float32) Y_test = Z.ravel()[:, None].astype(np.float32) print('Test Data:', X_test.shape, Y_test.shape)  # Normalize the data Xscalar = StandardScaler() X_train = Xscalar.fit_transform(X_train) X_test = Xscalar.transform(X_test)  yScalar = StandardScaler() Y_train = yScalar.fit_transform(Y_train) Y_test = yScalar.transform(Y_test)  # Plot the data plt.figure() plt.imshow(Y_train.reshape(train_data_dims[1],                             train_data_dims[0]),             cmap='jet') plt.gca().set_aspect('equal') plt.title('Train Data') plt.show()  plt.figure() plt.imshow(Y_test.reshape(test_data_dims[1],                            test_data_dims[0]),             cmap='jet') plt.gca().set_aspect('equal') plt.title('Test Data') plt.show() <pre>Train Data: (800, 2) (800, 1)\nTest Data: (5000, 2) (5000, 1)\n</pre> In\u00a0[3]: Copied! <pre># Model / inference hyperparameters\nnum_inducing = 100\nlr = 1e-2\nmax_steps = 5000\nQ = 3\n\n# Initialize non-stationary kernel model\nprint('Initializing model...')\nZ = get_inducing_pts(X_train, num_inducing)\nmodel = init_neural_kernel(X_train, Y_train, \n                           n_inits=5,\n                           inducing_variable=Z,\n                           Q=Q, \n                           hidden_sizes=(4, 4))\n\n# Create optimizer\nlearning_rate = tf.keras.optimizers.schedules.ExponentialDecay(\n                      lr, \n                      decay_steps=50, \n                      decay_rate=0.98)\nloss = optimize_model(model, \n                      max_steps=max_steps,\n                      lr=learning_rate,\n                      optimizer='tf')\n\n# Plot loss\nplt.plot(loss)\nplt.xlabel('Iteration')\nplt.ylabel('Loss')\nplt.show()\n\nkernels = []\nnoice_vars = []\nlabels = ['Non Stationary Kernel', 'Stationary Kernel']\n\nkernels.append(model.kernel)\nnoice_vars.append(model.likelihood.variance.numpy())\n\n# Get stationary kernel\n_, noise_variance, kernel = get_model_params(X_train, Y_train, \n                                             lengthscales=[1.0, 0.5])\nkernels.append(kernel)\nnoice_vars.append(noise_variance)\n</pre> # Model / inference hyperparameters num_inducing = 100 lr = 1e-2 max_steps = 5000 Q = 3  # Initialize non-stationary kernel model print('Initializing model...') Z = get_inducing_pts(X_train, num_inducing) model = init_neural_kernel(X_train, Y_train,                             n_inits=5,                            inducing_variable=Z,                            Q=Q,                             hidden_sizes=(4, 4))  # Create optimizer learning_rate = tf.keras.optimizers.schedules.ExponentialDecay(                       lr,                        decay_steps=50,                        decay_rate=0.98) loss = optimize_model(model,                        max_steps=max_steps,                       lr=learning_rate,                       optimizer='tf')  # Plot loss plt.plot(loss) plt.xlabel('Iteration') plt.ylabel('Loss') plt.show()  kernels = [] noice_vars = [] labels = ['Non Stationary Kernel', 'Stationary Kernel']  kernels.append(model.kernel) noice_vars.append(model.likelihood.variance.numpy())  # Get stationary kernel _, noise_variance, kernel = get_model_params(X_train, Y_train,                                               lengthscales=[1.0, 0.5]) kernels.append(kernel) noice_vars.append(noise_variance) <pre>Initializing model...\nInitializing neural spectral kernel...\n</pre> <pre>2024-05-05 12:08:35.281864: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1960] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\n</pre> <pre>Best init: -1165.898315\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value             \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float32 \u2502 0.59679           \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 (2,)    \u2502 float32 \u2502 [1.16673 0.37082] \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float32 \u2502 0.52522           \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> In\u00a0[4]: Copied! <pre>for i, (kernel, noice_var) in enumerate(zip(kernels, noice_vars)):\n        print(labels[i])\n        for num_placements in [4, 9]:\n                # Get initial inducing points\n                Xu_init = get_inducing_pts(X_train, num_placements)*0.5\n                Xu_init.astype(np.float32)\n\n                # Setup SGP and optimize it\n                sgpr, _ = continuous_sgp(num_placements, \n                                         X_train,\n                                         noise_variance=noice_var,\n                                         kernel=kernel,\n                                         Xu_init=Xu_init)\n                sgp_sol_sp = sgpr.inducing_variable.Z.numpy()\n\n                # Get the GP predictions\n                Xu_X, Xu_y = cont2disc(sgp_sol_sp, candidates=X_test,\n                                       candidate_labels=Y_test)\n                gpr = gpflow.models.GPR((Xu_X, Xu_y),\n                                        noise_variance=noice_var,\n                                        kernel=kernel)\n                y_pred, y_var = gpr.predict_f(X_test)\n                y_pred = y_pred.numpy()\n\n                rmse = np.sqrt(np.mean((y_pred - Y_test)**2))\n\n                # Plot the results\n                plt.figure()\n                plt.imshow(y_pred.reshape(test_data_dims[1], test_data_dims[0]),\n                        cmap='jet', origin='lower',\n                        extent=[np.min(X_test[:, 0]), np.max(X_test[:, 0]),\n                                np.min(X_test[:, 1]), np.max(X_test[:, 1])])\n                plt.gca().set_aspect('equal')\n                plt.scatter(sgp_sol_sp[:, 0], sgp_sol_sp[:, 1], c='k', marker='p', s=100)\n                plt.title('Number of Sensors: {}    RMSE: {:.3f}'.format(num_placements, rmse))\n                plt.xlabel('X')\n                plt.ylabel('Y')\n                plt.show()\n</pre> for i, (kernel, noice_var) in enumerate(zip(kernels, noice_vars)):         print(labels[i])         for num_placements in [4, 9]:                 # Get initial inducing points                 Xu_init = get_inducing_pts(X_train, num_placements)*0.5                 Xu_init.astype(np.float32)                  # Setup SGP and optimize it                 sgpr, _ = continuous_sgp(num_placements,                                           X_train,                                          noise_variance=noice_var,                                          kernel=kernel,                                          Xu_init=Xu_init)                 sgp_sol_sp = sgpr.inducing_variable.Z.numpy()                  # Get the GP predictions                 Xu_X, Xu_y = cont2disc(sgp_sol_sp, candidates=X_test,                                        candidate_labels=Y_test)                 gpr = gpflow.models.GPR((Xu_X, Xu_y),                                         noise_variance=noice_var,                                         kernel=kernel)                 y_pred, y_var = gpr.predict_f(X_test)                 y_pred = y_pred.numpy()                  rmse = np.sqrt(np.mean((y_pred - Y_test)**2))                  # Plot the results                 plt.figure()                 plt.imshow(y_pred.reshape(test_data_dims[1], test_data_dims[0]),                         cmap='jet', origin='lower',                         extent=[np.min(X_test[:, 0]), np.max(X_test[:, 0]),                                 np.min(X_test[:, 1]), np.max(X_test[:, 1])])                 plt.gca().set_aspect('equal')                 plt.scatter(sgp_sol_sp[:, 0], sgp_sol_sp[:, 1], c='k', marker='p', s=100)                 plt.title('Number of Sensors: {}    RMSE: {:.3f}'.format(num_placements, rmse))                 plt.xlabel('X')                 plt.ylabel('Y')                 plt.show() <pre>Non Stationary Kernel\n</pre> <pre>Stationary Kernel\n</pre>"},{"location":"examples/non_stationary_kernel.html#non-stationary-kernel","title":"Non-Stationary Kernel\u00b6","text":""},{"location":"examples/non_stationary_kernel.html#generate-synthetic-non-stationary-data","title":"Generate synthetic non-stationary data\u00b6","text":""},{"location":"examples/non_stationary_kernel.html#learn-kernel-parameters-from-train-data","title":"Learn kernel parameters from train data\u00b6","text":""},{"location":"examples/non_stationary_kernel.html#get-continuous-sgp-solution-with-a-baseline-stationary-kernel-and-the-learned-neural-kernel-function","title":"Get Continuous-SGP solution with a baseline stationary kernel and the learned neural kernel function\u00b6","text":""},{"location":"examples/obstacles.html","title":"Environment with Obstacles","text":"In\u00a0[\u00a0]: Copied! <pre>import matplotlib.pyplot as plt\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom sgptools.utils.gpflow import get_model_params\nfrom sgptools.utils.data import remove_polygons, remove_circle_patches\nfrom sgptools.models.continuous_sgp import *\n\nnp.random.seed(0)\ntf.random.set_seed(0)\n</pre> import matplotlib.pyplot as plt  import numpy as np import tensorflow as tf  from sgptools.utils.gpflow import get_model_params from sgptools.utils.data import remove_polygons, remove_circle_patches from sgptools.models.continuous_sgp import *  np.random.seed(0) tf.random.set_seed(0) In\u00a0[2]: Copied! <pre># Generate synthetic training data\ndata_dims = (50, 25)\nx = np.linspace(0, 200, data_dims[0])\ny = np.linspace(0, 100, data_dims[1])\nX, Y = np.meshgrid(x, y)\n\nX, Y = remove_circle_patches(X, Y, [plt.Circle((30, 60), 15)])\nX, Y = remove_polygons(X, Y, [[[80, 20], [105, 20], [105, 60], [80, 60]],])\nX, Y = remove_polygons(X, Y, [[[130, 20], [180, 20], [160, 60]],])\n\nplt.figure(figsize=(10, 5))\nplt.scatter(X, Y, s=1)\n\nax = plt.gca()\nax.add_patch(plt.Polygon([[80, 20], [105, 20], [105, 60], [80, 60]], \n             fill=None, hatch='//', color='C1', label='Obstacle'))\nax.add_patch(plt.Polygon([[130, 20], [180, 20], [160, 60]], \n             fill=None, hatch='//', color='C1'))\nax.add_patch(plt.Circle((30, 60), 15, \n             fill=None, hatch='//', color='C1'))\nax.set_aspect('equal')\n\nplt.xlabel('x', fontsize=14)\nplt.ylabel('y', fontsize=14)\nplt.xlim(0, 200)\nplt.ylim(0, 100)\n\nplt.show()\n\nX_train = np.array([X, Y]).T\ny_train = np.zeros((X_train.shape[0], 1))\nX_train = X_train.astype(float)\ny_train = y_train.astype(float)\nprint(X_train.shape, y_train.shape)\n</pre> # Generate synthetic training data data_dims = (50, 25) x = np.linspace(0, 200, data_dims[0]) y = np.linspace(0, 100, data_dims[1]) X, Y = np.meshgrid(x, y)  X, Y = remove_circle_patches(X, Y, [plt.Circle((30, 60), 15)]) X, Y = remove_polygons(X, Y, [[[80, 20], [105, 20], [105, 60], [80, 60]],]) X, Y = remove_polygons(X, Y, [[[130, 20], [180, 20], [160, 60]],])  plt.figure(figsize=(10, 5)) plt.scatter(X, Y, s=1)  ax = plt.gca() ax.add_patch(plt.Polygon([[80, 20], [105, 20], [105, 60], [80, 60]],               fill=None, hatch='//', color='C1', label='Obstacle')) ax.add_patch(plt.Polygon([[130, 20], [180, 20], [160, 60]],               fill=None, hatch='//', color='C1')) ax.add_patch(plt.Circle((30, 60), 15,               fill=None, hatch='//', color='C1')) ax.set_aspect('equal')  plt.xlabel('x', fontsize=14) plt.ylabel('y', fontsize=14) plt.xlim(0, 200) plt.ylim(0, 100)  plt.show()  X_train = np.array([X, Y]).T y_train = np.zeros((X_train.shape[0], 1)) X_train = X_train.astype(float) y_train = y_train.astype(float) print(X_train.shape, y_train.shape) <pre>(1089, 2) (1089, 1)\n</pre> In\u00a0[3]: Copied! <pre>num_inducing = 200\n\n# Get initial GP parameters\n_, noise_variance, rbf = get_model_params(np.zeros((1, 2)), np.zeros((1, 1)), \n                             lengthscales=12.3254, \n                             variance=0.561265,\n                             noise_variance=2e-6,\n                             max_steps=0)\n\n# Optimize the inducing points/sensor locations\nsgpr, _ = continuous_sgp(num_inducing,\n                         X_train,\n                         kernel=rbf,\n                         noise_variance=noise_variance,\n                         lr=1e-2,\n                         max_steps=5000)\nsgp_sol_sp = sgpr.inducing_variable.Z.numpy()\n</pre> num_inducing = 200  # Get initial GP parameters _, noise_variance, rbf = get_model_params(np.zeros((1, 2)), np.zeros((1, 1)),                               lengthscales=12.3254,                               variance=0.561265,                              noise_variance=2e-6,                              max_steps=0)  # Optimize the inducing points/sensor locations sgpr, _ = continuous_sgp(num_inducing,                          X_train,                          kernel=rbf,                          noise_variance=noise_variance,                          lr=1e-2,                          max_steps=5000) sgp_sol_sp = sgpr.inducing_variable.Z.numpy() <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502     value \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502  0.561265 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 12.3254   \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502  2e-06    \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> In\u00a0[4]: Copied! <pre># Plot the sensor locations and the obstacles\nplt.figure(figsize=(10, 5))\nplt.scatter(sgp_sol_sp[:, 0], sgp_sol_sp[:, 1], \n            color='C0', \n            s=2, \n            label='Solution Placements')\n\nax = plt.gca()\nax.add_patch(plt.Polygon([[80, 20], [105, 20], [105, 60], [80, 60]], \n             fill=None, hatch='//', color='C1', label='Obstacle'))\nax.add_patch(plt.Polygon([[130, 20], [180, 20], [160, 60]], \n             fill=None, hatch='//', color='C1'))\nax.add_patch(plt.Circle((30, 60), 15, \n             fill=None, hatch='//', color='C1'))\nax.set_aspect('equal')\n\nplt.xlabel('x', fontsize=14)\nplt.ylabel('y', fontsize=14)\nplt.xlim(0, 200)\nplt.ylim(0, 100)\n\nplt.show()\n</pre> # Plot the sensor locations and the obstacles plt.figure(figsize=(10, 5)) plt.scatter(sgp_sol_sp[:, 0], sgp_sol_sp[:, 1],              color='C0',              s=2,              label='Solution Placements')  ax = plt.gca() ax.add_patch(plt.Polygon([[80, 20], [105, 20], [105, 60], [80, 60]],               fill=None, hatch='//', color='C1', label='Obstacle')) ax.add_patch(plt.Polygon([[130, 20], [180, 20], [160, 60]],               fill=None, hatch='//', color='C1')) ax.add_patch(plt.Circle((30, 60), 15,               fill=None, hatch='//', color='C1')) ax.set_aspect('equal')  plt.xlabel('x', fontsize=14) plt.ylabel('y', fontsize=14) plt.xlim(0, 200) plt.ylim(0, 100)  plt.show()"},{"location":"examples/obstacles.html#environment-with-obstacles","title":"Environment with Obstacles\u00b6","text":""},{"location":"examples/obstacles.html#note-that-all-the-solution-placements-avoid-the-obstacles","title":"Note that all the solution placements avoid the obstacles\u00b6","text":""}]}
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+var __index = {"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"index.html","title":"Home","text":"<p>SGP-Tools is a software suite for Sensor Placement and Informative Path Planning.</p> <p>The library includes python code for the following:</p> <ul> <li>Greedy algorithm-based approaches</li> <li>Bayesian optimization-based approaches</li> <li>Genetic algorithm-based approaches</li> <li>Sparse Gaussian process (SGP)-based approaches</li> </ul> <p> </p>"},{"location":"index.html#installation","title":"Installation","text":"<p>The library is available as a <code>pip</code> package. To install the package, run the following command:</p> <pre><code>python3 -m pip install sgptools\n</code></pre> <p>Installation from source:</p> <pre><code>git clone https://github.com/itskalvik/sgp-tools.git\ncd sgp-tools/\npython3 -m pip install -r requirements.txt\npython3 -m pip install -e .\n</code></pre> <p>Note: The requirements.txt file contains packages and their latest versions that were last verified to be working without any issues.</p>"},{"location":"index.html#quick-start","title":"Quick Start","text":"<p>Please refer to the example Jupyter notebooks demonstrating the methods included in the library \ud83d\ude04</p>"},{"location":"index.html#method-summary","title":"Method Summary","text":""},{"location":"index.html#datasets","title":"Datasets","text":"<ul> <li>High-resolution topography data can be downloaded from NOAA Digital Coast</li> <li>High-resolution bathymetry data can be downloaded from NOAA Digital Coast</li> </ul>"},{"location":"index.html#about","title":"About","text":"<p>Please consider citing the following papers if you use SGP-Tools in your academic work \ud83d\ude04</p> <pre><code>@misc{JakkalaA23SP,\nAUTHOR={Kalvik Jakkala and Srinivas Akella},\nTITLE={Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces},\nNOTE= {Preprint},\nYEAR={2023},\nURL={https://itskalvik.github.io/publication/sgp-sp},\n}\n\n@inproceedings{JakkalaA24IPP,\nAUTHOR={Kalvik Jakkala and Srinivas Akella},\nTITLE={Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes},\nbooktitle={IEEE International Conference on Robotics and Automation, {ICRA}},\nYEAR={2024},\nPUBLISHER = {{IEEE}},\nURL={https://itskalvik.github.io/publication/sgp-ipp}\n}\n</code></pre>"},{"location":"index.html#acknowledgements","title":"Acknowledgements","text":"<p>This work was funded in part by the UNC Charlotte Office of Research and Economic Development and by NSF under Award Number IIP-1919233.</p>"},{"location":"index.html#license","title":"License","text":"<p>The SGP-Tools software suite is licensed under the terms of the Apache License 2.0. See LICENSE for more information.</p>"},{"location":"blueos-sgp-tools.html","title":"BlueOS SGP-Tools","text":"<p>A BlueOS Extension for Autonomous Approximate Bathymetric Surveys</p>"},{"location":"blueos-sgp-tools.html#what-does-it-do","title":"What Does It Do?","text":"<p>Autonomous Surface Vehicles (ASVs), such as the BlueRobotics BlueBoat, are well-suited for bathymetric surveys. However, it is often the case that an exhaustive survey mapping the depth at every location in an area is infeasible or unnecessary. In such cases, we can leverage variations in the underwater terrain to determine a few critical locations for data collection, which would result in a good approximation of the area's bathymetry.</p> <p>The SGP-Tools python library provides path planners to address the above problem, known as the informative path planning problem. The BlueOS SGP-Tools extension uses this library to determine ideal locations for the ASV to collect data and controls the ASV to autonomously visit the selected locations.</p> <p>The following shows the path planner adaptively planning paths for four robots tasked with surveying a given area:</p> <p> </p>"},{"location":"blueos-sgp-tools.html#usage","title":"Usage","text":"<ol> <li> <p>First, we need to define the survey area and the robot launch location. The extension can read this data from QGC plan files. The survey area and launch position must be defined using a polygon-shaped geofence drawn in QGC and saved as <code>mission.plan</code>.</p> <p> </p> </li> <li> <p>Once you have the plan file, copy it to the robot using the <code>File Browser</code> feature in BlueOS's <code>Pirate Mode</code>. The <code>mission.plan</code> file should be uploaded to the following directory: <code>/extensions/sgptools/</code></p> <p>Once the <code>mission.plan</code> file is uploaded, restart the extension to ensure that the new file is used for the mission.</p> <p> </p> </li> <li> <p>Finally, use the terminal provided by the SGP-Tools extension to start the mission with the following command:     <pre><code>ros2 launch ros_sgp_tools single_robot.launch.py\n</code></pre></p> <p> </p> </li> <li> <p>The sensor data, along with the corresponding GPS coordinates, will be logged to an HDF5 file in the <code>DATA_FOLDER</code>, where the <code>mission.plan</code> was uploaded. </p> <p>We can estimate the bathymetry of the entire survey area using the collected data and visualize a normalized version with the following command (\u26a0\ufe0f Do not run this during the mission, as it will disrupt the path planner): <pre><code>ros2 launch ros_sgp_tools visualize_data.launch.py\n</code></pre></p> <p>The above command will publish a point cloud that can be viewed using foxglove. You can access it from a web browser at https://app.foxglove.dev/. Use the <code>open connection</code> feature and change the address from <code>localhost</code> to the IP address of the ASV.</p> <p> </p> <p>You can control the point cloud density using the <code>num_samples</code> parameter. You can set this from foxglove's <code>Parameters</code> panel.</p> <p>By default, the latest mission log will be visualized. You can visualize a specific mission log using the following command (replace <code>&lt;log folder name&gt;</code> with  the log folder name):</p> <pre><code>ros2 launch ros_sgp_tools visualize_data.launch.py mission_log:=&lt;log folder name&gt;\n</code></pre> </li> </ol>"},{"location":"blueos-sgp-tools.html#parameters","title":"Parameters","text":"<p>You can control the following extension parameters by running the following command in the terminal provided by the SGP-Tools extension:</p> <pre><code>export &lt;parameter_name&gt;=&lt;parameter_value&gt;\n</code></pre> <p>The parameters reset to their default values after rebooting. They can be made permanent by configuring the parameters using the app environment variables on the BlueOS extensions page in pirate mode.</p>"},{"location":"blueos-sgp-tools.html#available-parameters","title":"Available Parameters:","text":"<ul> <li> <p><code>PING2_PORT</code> (<code>default: /dev/ttyUSB0</code>): </p> <p>Specifies the device to which the Ping2 sonar is mounted. </p> </li> <li> <p><code>NUM_WAYPOINTS</code> (<code>default: 20</code>): </p> <p>The number of waypoints optimized by the path planner. Increasing the number of waypoints gives a more complex path which potentally covers a larger area but also requires more compute and could result in slower online path updates. </p> </li> <li> <p><code>SAMPLING_RATE</code> (<code>default: 2</code>): </p> <p>The number of points to sample along each edge of the path during path planning. Increasing the samping rate results in the planner being aware of the data being collected along the whole path instead of assuming that the data is collected only at the vertices. This method requires relatively less compute then increasing the number of waypints, but still adds to the overall compute cost.</p> </li> <li> <p><code>START_FOXGLOVE</code> (<code>default: False</code>): </p> <p>Enables foxglove, a web-based data visualization platform similar to RViz.</p> </li> <li> <p><code>ADAPTIVE_IPP</code> (<code>default: True</code>): </p> <p>Enables adaptive IPP, which uses the data streaming from the sonar to learn the correlations in the underwater bathymetry and further optimizes the future waypoints to maximize collecting new informative data.</p> </li> <li> <p><code>NAMESPACE</code> (<code>default: robot_0</code>): </p> <p>ROS2 namespace, useful when multiple ROS2 robots are operating on the same network.</p> </li> <li> <p><code>DATA_TYPE</code> (<code>default: Ping2</code>): </p> <p>Type of sensor to be used by the path planner. Currently, only the BlueRobotics Ping Sonar is supported.</p> </li> <li> <p><code>FAKE_DATA</code> (<code>default: False</code>):</p> <p>Enables a fake sensor data publisher used for testing. Requires <code>DATA_TYPE=SerialPing2</code>.</p> </li> </ul>"},{"location":"blueos-sgp-tools.html#hardware-configuration","title":"Hardware Configuration","text":"<ul> <li> <p>This extension works only on 64-bit operating systems. You can install the latest version of BlueOS on 64-bit Raspberry Pi OS Lite by running the following command on the Pi (ensure the username is set to <code>pi</code>):     <pre><code>sudo su -c 'curl -fsSL https://raw.githubusercontent.com/bluerobotics/blueos-docker/master/install/install.sh | bash'\n</code></pre></p> </li> <li> <p>The extension requires over 4GB of memory+swap. Please ensure that the swap size is large enough to accommodate the extension. The extension will copy the shell script <code>config_swap.sh</code> to <code>/usr/blueos/extensions/sgptools/</code> folder on the underlying device. You can use this script to increase the swap size before starting the path planner. </p> <p>You will have to use <code>Pirate Mode</code> to access BlueOS's built-in terminal and run the script on the underlying device via the <code>red-pill</code> utility. Use the following commands to enable <code>red-pill</code> and increase the swap size:  <pre><code>red-pill\nsudo bash /usr/blueos/extensions/sgptools/config_swap.sh\n</code></pre></p> <p> </p> </li> </ul>"},{"location":"blueos-sgp-tools.html#disclaimer","title":"Disclaimer \u26a0\ufe0f","text":"<p>This extension, when executed properly, will take control of the ASV and could potentially collide the vehicle with obstacles in the environment. Please use it with caution.</p>"},{"location":"docker-sgp-tools.html","title":"docker-sgp-tools","text":"<p>The docker-sgp-tools package provides the following docker-compose scripts:</p> <ul> <li><code>robot-compose.yml</code>: A minimal docker container used to run SGP-Tools ROS2 package on ArduPilot-based robots.</li> <li><code>sitl-compose.yml</code>: A GUI-based docker container with ROS2, Gazebo, ArduPilot SITL, and SGP-Tools used for simulating ArduPilot vehicles and testing SGP-Tools IPP code. </li> </ul>"},{"location":"docker-sgp-tools.html#prerequisites","title":"Prerequisites","text":"<ul> <li>docker</li> <li>docker-compose</li> <li>(Optional) WSLg</li> </ul>"},{"location":"docker-sgp-tools.html#getting-started","title":"Getting Started","text":""},{"location":"docker-sgp-tools.html#starting-the-containers","title":"Starting the Containers","text":"<p>Run the following commands to start the SITL docker container:</p> <pre><code>git clone --recurse-submodules https://github.com/itskalvik/docker-sgp-tools.git\ncd docker-sgp-tools\ndocker-compose -f sitl-compose.yml pull\ndocker-compose -f sitl-compose.yml up -d\ndocker-compose -f sitl-compose.yml exec sgptools bash\n</code></pre> <p>Use <code>robot-compose.yml</code> to run the minimal docker container. </p>"},{"location":"docker-sgp-tools.html#running-sgp-tools-onlineadaptive-ipp-with-gazeboardurover-simulator","title":"Running SGP-Tools Online/Adaptive IPP with Gazebo/ArduRover Simulator","text":"<p>Use <code>docker-compose -f sitl-compose.yml exec sgptools bash</code> to get a new terminal. Run the following commands in separate terminals in the docker container:</p> <ul> <li> <p>Launch Gazebo with the AION R1 UGV:     <pre><code>gz sim -v4 -r r1_rover_runway.sdf\n</code></pre>     To simulate a BlueBoat refer to this documentation. Note that the container already has Wave Sim installed in it.</p> </li> <li> <p>Launch ArduRover SITL:     <pre><code>sim_vehicle.py -v Rover -f rover-skid --model JSON --add-param-file=$HOME/SITL_Models/Gazebo/config/r1_rover.param --console --map -N -l 35.30371178789218,-80.73099267294185,0.,0.\n</code></pre>     Note: Restart sim_vechile.py if you get the following message: <code>paramftp: bad count 1294 should be 1284</code></p> </li> <li> <p>Launch the SGP-Tools Online/Adaptive IPP method:     <pre><code>ros2 launch ros_sgp_tools single_robot.launch.py\n</code></pre></p> </li> </ul>"},{"location":"docker-sgp-tools.html#building-the-docker-containers-from-scratch","title":"Building the Docker Containers from Scratch","text":"<p>First, setup buildx to build the containers for both arm64 and amd64 platforms:  <pre><code>docker buildx create --name multi-arch \\\n  --platform \"linux/arm64,linux/amd64\" \\\n  --driver \"docker-container\"\ndocker buildx use multi-arch\n</code></pre></p> <p>Next, clone the repo and build the container.  <pre><code>git clone --recurse-submodules https://github.com/itskalvik/docker-sgp-tools.git\ncd docker-sgp-tools\ndocker-compose -f sitl-compose.yml build \n</code></pre></p> <p>Use <code>robot-compose.yml</code> to build the minimal docker container.</p>"},{"location":"docker-sgp-tools.html#other-commands","title":"Other commands","text":"<ul> <li> <p>The docker-compose down command stops and removes containers, networks, volumes, and images, making it suitable for completely clearing all resources deployed by an application.</p> <pre><code>docker-compose -f sitl-compose.yml down\n</code></pre> </li> <li> <p>The docker-compose stop command just pauses running containers without removing them, which is ideal for temporary halts.</p> <pre><code>docker-compose -f sitl-compose.yml stop\n</code></pre> </li> </ul>"},{"location":"docker-sgp-tools.html#references","title":"References","text":"<p>Based on Dockerfiles from the following repos:</p> <ul> <li>https://github.com/Robotic-Decision-Making-Lab/blue</li> <li>https://github.com/ryomo/ros2-gazebo-docker</li> </ul>"},{"location":"ros_sgp_tools.html","title":"ros_sgp_tools","text":"<p>The ros_sgp_tools package provides a ROS2 companion package for the SGP-Tools python library that can be deployed on ArduPilot-based vehicles. </p> <ul> <li>The package can be used to run online/adaptive IPP on ArduPilot based UGVs and ASVs. </li> <li>The package can also be used with Gazebo/Ardupilot SITL.</li> <li>To use our Docker container with the preconfigured development environment, please refer to the documentation here. </li> </ul>"},{"location":"ros_sgp_tools.html#running-sgp-tools-onlineadaptive-ipp-with-gazeboardurover-simulator","title":"Running SGP-Tools Online/Adaptive IPP with Gazebo/ArduRover Simulator","text":"<p>Run the following commands in separate terminals:</p> <ul> <li> <p>Launch Gazebo with the AION R1 UGV:     <pre><code>gz sim -v4 -r r1_rover_runway.sdf\n</code></pre>     To simulate a BlueBoat refer to this documentation.</p> </li> <li> <p>Launch ArduRover SITL:     <pre><code>sim_vehicle.py -v Rover -f rover-skid --model JSON --add-param-file=$HOME/SITL_Models/Gazebo/config/r1_rover.param --console --map -N -l 35.30371178789218,-80.73099267294185,0.,0.\n</code></pre>     Note: </p> <ul> <li>Restart sim_vechile.py if you get the following message: <code>paramftp: bad count</code></li> <li>Ensure the MAV Console shows that the vehicle has a GPS lock before running the next command</li> <li>Ensure the MAV Map shows the vehicle before running the next command</li> </ul> </li> <li> <p>Launch the SGP-Tools Online/Adaptive IPP method:     <pre><code>ros2 launch ros_sgp_tools single_robot.launch.py\n</code></pre></p> </li> </ul>"},{"location":"ros_sgp_tools.html#environment-setup","title":"Environment setup","text":"<ul> <li>To use our Docker container with the preconfigured development environment, please refer to the documentation here. </li> </ul> <p>Alternatively, please follow the following instructions to configure the development envirnoment on your local machine. </p> <ul> <li>Install ROS 2 Humble <pre><code>sudo apt install ros-humble-desktop\necho \"source /opt/ros/humble/setup.bash\" &gt;&gt; ~/.bashrc\nsource ~/.bashrc\n</code></pre></li> <li>Create a ROS 2 workspace   <pre><code>mkdir -p ~/ros2_ws/src\ncd ~/ros2_ws/\ncolcon build --symlink-install\necho \"source $HOME/ros2_ws/install/setup.bash\" &gt;&gt; ~/.bashrc\nsource ~/.bashrc\n</code></pre></li> <li>Install Gazebo Garden</li> <li>Install ArduPilot SITL</li> <li>Install ardupilot_gazebo</li> <li>Get SITL_Models repo</li> <li>Setup environment variables</li> </ul> <pre><code>echo \"export GZ_VERSION=garden\" &gt;&gt; ~/.bashrc\necho \"export GZ_SIM_SYSTEM_PLUGIN_PATH=$HOME/ardupilot_gazebo/build:${GZ_SIM_SYSTEM_PLUGIN_PATH}\" &gt;&gt; ~/.bashrc\necho \"export GZ_SIM_RESOURCE_PATH=$HOME/ardupilot_gazebo/models:$HOME/ardupilot_gazebo/worlds:$HOME/SITL_Models/Gazebo/models:$HOME/SITL_Models/Gazebo/worlds:$GZ_SIM_RESOURCE_PATH\" &gt;&gt; ~/.bashrc\n</code></pre>"},{"location":"ros_sgp_tools.html#package-setup","title":"Package setup","text":"<ul> <li>Install apt dependencies    <pre><code>sudo apt install ros-humble-mavros* ros-$ROS_DISTRO-cv-bridge\n</code></pre></li> <li>Install pip dependencies    <pre><code>pip3 install -r requirements.txt\n</code></pre></li> <li>Clone repo and build package   <pre><code>cd ~/ros2_ws/src\ngit clone https://github.com/itskalvik/ros_sgp_tools.git\ncd ..\ncolcon build --symlink-install\n</code></pre></li> </ul>"},{"location":"api/kernels/index.html","title":"","text":""},{"location":"api/kernels/index.html#sgptools.kernels","title":"<code>sgptools.kernels</code>","text":"<p>Special kernel functions in this package:</p> <ul> <li><code>neural_kernel</code>: Provides a neural spectral kernel function that uses a mixture of multilayer perceptrons</li> </ul>"},{"location":"api/kernels/Neural-Kernel.html","title":"Neural Kernel","text":""},{"location":"api/kernels/Neural-Kernel.html#sgptools.kernels.neural_kernel","title":"<code>sgptools.kernels.neural_kernel</code>","text":"<p>Provides a neural spectral kernel function along with an initialization function</p>"},{"location":"api/kernels/Neural-Kernel.html#sgptools.kernels.neural_kernel.NeuralSpectralKernel","title":"<code>NeuralSpectralKernel</code>","text":"<p>               Bases: <code>Kernel</code></p> <p>Neural Spectral Kernel function (non-stationary kernel function).  Based on the implementation from the following repo</p> Refer to the following papers for more details <ul> <li>Neural Non-Stationary Spectral Kernel [Remes et al., 2018]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>input_dim</code> <code>int</code> <p>Number of data dimensions</p> required <code>active_dims</code> <code>int</code> <p>Number of data dimensions that are used for computing the covariances</p> <code>None</code> <code>Q</code> <code>int</code> <p>Number of MLP mixture components used in the kernel function</p> <code>1</code> <code>hidden_sizes</code> <code>list</code> <p>Number of hidden units in each MLP layer. Length of the list determines the number of layers.</p> <code>[32, 32]</code> Source code in <code>sgptools/kernels/neural_kernel.py</code> <pre><code>class NeuralSpectralKernel(gpflow.kernels.Kernel):\n    \"\"\"Neural Spectral Kernel function (non-stationary kernel function). \n    Based on the implementation from the following [repo](https://github.com/sremes/nssm-gp/tree/master?tab=readme-ov-file)\n\n    Refer to the following papers for more details:\n        - Neural Non-Stationary Spectral Kernel [Remes et al., 2018]\n\n    Args:\n        input_dim (int): Number of data dimensions\n        active_dims (int): Number of data dimensions that are used for computing the covariances\n        Q (int): Number of MLP mixture components used in the kernel function\n        hidden_sizes (list): Number of hidden units in each MLP layer. Length of the list determines the number of layers.\n    \"\"\"\n    def __init__(self, input_dim, active_dims=None, Q=1, hidden_sizes=[32, 32]):\n        super().__init__(active_dims=active_dims)\n\n        self.input_dim = input_dim\n        self.Q = Q\n        self.num_hidden = len(hidden_sizes)\n\n        self.freq = []\n        self.length = []\n        self.var = []\n        for q in range(self.Q):\n            freq = keras.Sequential([layers.Dense(hidden_sizes[i], activation='selu') for i in range(self.num_hidden)] + \n                                    [layers.Dense(input_dim, activation='softplus')])\n            length = keras.Sequential([layers.Dense(hidden_sizes[i], activation='selu') for i in range(self.num_hidden)] +\n                                   [layers.Dense(input_dim, activation='softplus')])\n            var = keras.Sequential([layers.Dense(hidden_sizes[i], activation='selu') for i in range(self.num_hidden)] +\n                                   [layers.Dense(1, activation='softplus')])\n            self.freq.append(freq)\n            self.length.append(length)\n            self.var.append(var)\n\n    def K(self, X, X2=None):\n        \"\"\"Computes the covariances between/amongst the input variables\n\n        Args:\n            X (ndarray): Variables to compute the covariance matrix\n            X2 (ndarray): If passed, the covariance between X and X2 is computed. Otherwise, \n                          the covariance between X and X is computed.\n\n        Returns:\n            cov (ndarray): covariance matrix\n        \"\"\"\n        if X2 is None:\n            X2 = X\n            equal = True\n        else:\n            equal = False\n\n        kern = 0.0\n        for q in range(self.Q):\n            # compute latent function values by the neural network\n            freq, freq2 = self.freq[q](X), self.freq[q](X2)\n            lens, lens2 = self.length[q](X), self.length[q](X2)\n            var, var2 = self.var[q](X), self.var[q](X2)\n\n            # compute length-scale term\n            Xr = tf.expand_dims(X, 1)  # N1 1 D\n            X2r = tf.expand_dims(X2, 0)  # 1 N2 D\n            l1 = tf.expand_dims(lens, 1)  # N1 1 D\n            l2 = tf.expand_dims(lens2, 0)  # 1 N2 D\n            L = tf.square(l1) + tf.square(l2)  # N1 N2 D\n            #D = tf.square((Xr - X2r) / L)  # N1 N2 D\n            D = tf.square(Xr - X2r) / L  # N1 N2 D\n            D = tf.reduce_sum(D, 2)  # N1 N2\n            det = tf.sqrt(2 * l1 * l2 / L)  # N1 N2 D\n            det = tf.reduce_prod(det, 2)  # N1 N2\n            E = det * tf.exp(-D)  # N1 N2\n\n            # compute cosine term\n            muX = (tf.reduce_sum(freq * X, 1, keepdims=True)\n                   - tf.transpose(tf.reduce_sum(freq2 * X2, 1, keepdims=True)))\n            COS = tf.cos(2 * np.pi * muX)\n\n            # compute kernel variance term\n            WW = tf.matmul(var, var2, transpose_b=True)  # w*w'^T\n\n            # compute the q'th kernel component\n            kern += WW * E * COS\n        if equal:\n            return robust_kernel(kern, tf.shape(X)[0])\n        else:\n            return kern\n\n    def K_diag(self, X):\n        kd = default_jitter()\n        for q in range(self.Q):\n            kd += tf.square(self.var[q](X))\n        return tf.squeeze(kd)\n</code></pre>"},{"location":"api/kernels/Neural-Kernel.html#sgptools.kernels.neural_kernel.NeuralSpectralKernel.K","title":"<code>K(X, X2=None)</code>","text":"<p>Computes the covariances between/amongst the input variables</p> <p>Parameters:</p> Name Type Description Default <code>X</code> <code>ndarray</code> <p>Variables to compute the covariance matrix</p> required <code>X2</code> <code>ndarray</code> <p>If passed, the covariance between X and X2 is computed. Otherwise,            the covariance between X and X is computed.</p> <code>None</code> <p>Returns:</p> Name Type Description <code>cov</code> <code>ndarray</code> <p>covariance matrix</p> Source code in <code>sgptools/kernels/neural_kernel.py</code> <pre><code>def K(self, X, X2=None):\n    \"\"\"Computes the covariances between/amongst the input variables\n\n    Args:\n        X (ndarray): Variables to compute the covariance matrix\n        X2 (ndarray): If passed, the covariance between X and X2 is computed. Otherwise, \n                      the covariance between X and X is computed.\n\n    Returns:\n        cov (ndarray): covariance matrix\n    \"\"\"\n    if X2 is None:\n        X2 = X\n        equal = True\n    else:\n        equal = False\n\n    kern = 0.0\n    for q in range(self.Q):\n        # compute latent function values by the neural network\n        freq, freq2 = self.freq[q](X), self.freq[q](X2)\n        lens, lens2 = self.length[q](X), self.length[q](X2)\n        var, var2 = self.var[q](X), self.var[q](X2)\n\n        # compute length-scale term\n        Xr = tf.expand_dims(X, 1)  # N1 1 D\n        X2r = tf.expand_dims(X2, 0)  # 1 N2 D\n        l1 = tf.expand_dims(lens, 1)  # N1 1 D\n        l2 = tf.expand_dims(lens2, 0)  # 1 N2 D\n        L = tf.square(l1) + tf.square(l2)  # N1 N2 D\n        #D = tf.square((Xr - X2r) / L)  # N1 N2 D\n        D = tf.square(Xr - X2r) / L  # N1 N2 D\n        D = tf.reduce_sum(D, 2)  # N1 N2\n        det = tf.sqrt(2 * l1 * l2 / L)  # N1 N2 D\n        det = tf.reduce_prod(det, 2)  # N1 N2\n        E = det * tf.exp(-D)  # N1 N2\n\n        # compute cosine term\n        muX = (tf.reduce_sum(freq * X, 1, keepdims=True)\n               - tf.transpose(tf.reduce_sum(freq2 * X2, 1, keepdims=True)))\n        COS = tf.cos(2 * np.pi * muX)\n\n        # compute kernel variance term\n        WW = tf.matmul(var, var2, transpose_b=True)  # w*w'^T\n\n        # compute the q'th kernel component\n        kern += WW * E * COS\n    if equal:\n        return robust_kernel(kern, tf.shape(X)[0])\n    else:\n        return kern\n</code></pre>"},{"location":"api/kernels/Neural-Kernel.html#sgptools.kernels.neural_kernel.init_neural_kernel","title":"<code>init_neural_kernel(x, y, inducing_variable, Q, n_inits=1, hidden_sizes=None)</code>","text":"<p>Helper function to initialize a Neural Spectral Kernel function (non-stationary kernel function).  Based on the implementation from the following repo</p> Refer to the following papers for more details <ul> <li>Neural Non-Stationary Spectral Kernel [Remes et al., 2018]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>ndarray</code> <p>(n, d); Input training set points</p> required <code>y</code> <code>ndarray</code> <p>(n, 1); Training set labels</p> required <code>inducing_variable</code> <code>ndarray</code> <p>(m, d); Initial inducing points</p> required <code>Q</code> <code>int</code> <p>Number of MLP mixture components used in the kernel function</p> required <code>n_inits</code> <code>int</code> <p>Number of times to initalize the kernel function (returns the best model)</p> <code>1</code> <code>hidden_sizes</code> <code>list</code> <p>Number of hidden units in each MLP layer. Length of the list determines the number of layers.</p> <code>None</code> Source code in <code>sgptools/kernels/neural_kernel.py</code> <pre><code>def init_neural_kernel(x, y, inducing_variable, Q, n_inits=1, hidden_sizes=None):\n    \"\"\"Helper function to initialize a Neural Spectral Kernel function (non-stationary kernel function). \n    Based on the implementation from the following [repo](https://github.com/sremes/nssm-gp/tree/master?tab=readme-ov-file)\n\n    Refer to the following papers for more details:\n        - Neural Non-Stationary Spectral Kernel [Remes et al., 2018]\n\n    Args:\n        x (ndarray): (n, d); Input training set points\n        y (ndarray): (n, 1); Training set labels\n        inducing_variable (ndarray): (m, d); Initial inducing points\n        Q (int): Number of MLP mixture components used in the kernel function\n        n_inits (int): Number of times to initalize the kernel function (returns the best model)\n        hidden_sizes (list): Number of hidden units in each MLP layer. Length of the list determines the number of layers.\n    \"\"\"\n    x, y = data_input_to_tensor((x, y))\n\n    print('Initializing neural spectral kernel...')\n    best_loglik = -np.inf\n    best_m = None\n    N, input_dim = x.shape\n\n    for k in range(n_inits):\n        # gpflow.reset_default_graph_and_session()\n        k = NeuralSpectralKernel(input_dim=input_dim, Q=Q, \n                                    hidden_sizes=hidden_sizes)\n        model = SGPR((x, y), inducing_variable=inducing_variable, \n                        kernel=k)\n        loglik = model.elbo()\n        if loglik &gt; best_loglik:\n            best_loglik = loglik\n            best_m = model\n        del model\n        gc.collect()\n    print('Best init: %f' % best_loglik)\n\n    return best_m\n</code></pre>"},{"location":"api/models/index.html","title":"","text":""},{"location":"api/models/index.html#sgptools.models","title":"<code>sgptools.models</code>","text":"<p>Sensor placement and informative path planning methods in this package:</p> <ul> <li><code>bo</code>: Provides a Bayesian optimization based approach that maximizes mutual-information to get sensor placements</li> <li><code>cma_es</code>: Provides a genetic algorithm (CMA-ES) based approach that maximizes mutual-information to get sensor placements</li> <li><code>continuous_sgp</code>: Provides an SGP-based sensor placement approach that is optimized using gradient descent</li> <li><code>greedy_mi</code>: Provides a greedy algorithm based approach that maximizes mutual-information to get sensor placements</li> <li><code>greedy_sgp</code>: Provides an SGP-based sensor placement approach that is optimized using a greedy algorithm</li> </ul>"},{"location":"api/models/Bayesian-Optimization.html","title":"Bayesian Optimization","text":""},{"location":"api/models/Bayesian-Optimization.html#sgptools.models.bo","title":"<code>sgptools.models.bo</code>","text":""},{"location":"api/models/Bayesian-Optimization.html#sgptools.models.bo.BayesianOpt","title":"<code>BayesianOpt</code>","text":"<p>Class for optimizing sensor placements using Bayesian Optimization</p> Refer to the following papers for more details <ul> <li>UAV route planning for active disease classification [Vivaldini et al., 2019]</li> <li>Occupancy map building through Bayesian exploration [Francis et al., 2019]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>X_train</code> <code>ndarray</code> <p>(n, d); Locations in the environment used to approximate the monitoring regions</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> Source code in <code>sgptools/models/bo.py</code> <pre><code>class BayesianOpt:\n    \"\"\"Class for optimizing sensor placements using Bayesian Optimization\n\n    Refer to the following papers for more details:\n        - UAV route planning for active disease classification [Vivaldini et al., 2019]\n        - Occupancy map building through Bayesian exploration [Francis et al., 2019]\n\n    Args:\n        X_train (ndarray): (n, d); Locations in the environment used to approximate the monitoring regions\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        transform (Transform): Transform object\n    \"\"\"\n    def __init__(self, X_train, noise_variance, kernel,\n                 transform=None):\n        self.X_train = X_train\n        self.noise_variance = noise_variance\n        self.kernel = kernel\n        self.num_dim = X_train.shape[-1]\n        self.transform = transform\n\n        # use the boundaries of the region as the search space\n        self.pbounds_dim = []\n        for i in range(self.num_dim):\n            self.pbounds_dim.append((np.min(X_train[:, i]), np.max(X_train[:, i])))\n\n    def objective(self, **kwargs):\n        \"\"\"Objective function (GP-based Mutual Information)\n\n        Args:\n            x&lt;i&gt; (ndarray): (1, d); Current solution sensor placement location i\n        \"\"\"\n        # MI does not depend on waypoint order (reshape to -1, num_dim)\n        X = []\n        for i in range(len(kwargs)):\n            X.append(kwargs['x{}'.format(i)])\n        X = np.array(X).reshape(-1, self.num_dim)\n        if self.transform is not None:\n            X = self.transform.expand(X)\n            constraints_loss = self.transform.constraints(X)\n\n        try:\n            mi = get_mi(X, self.X_train, self.noise_variance, self.kernel)\n            mi += constraints_loss\n            mi = mi.numpy()\n        except:\n            mi = -1e4 # if the cholskey decomposition fails\n        return mi\n\n    def optimize(self, \n                 num_sensors=10, \n                 max_steps=100,  \n                 X_init=None,\n                 init_points=10,\n                 verbose=0, \n                 seed=1234):\n        \"\"\"Optimizes the sensor placements using Bayesian Optimization without any constraints\n\n        Args:\n            num_sensors (int): Number of sensor locations to optimize.\n            max_steps (int): Maximum number of optimization steps. \n            X_init (ndarray): (m, d); Initial inducing points.\n            init_points (int): Number of random solutions used for initial exploration. \n                               Random exploration can help by diversifying the exploration space. \n            verbose (int): The level of verbosity.\n            seed (int): The algorithm will use it to seed the randomnumber generator, ensuring replicability.\n\n        Returns:\n            Xu (ndarray): (m, d); Solution sensor placement locations\n        \"\"\"\n        if X_init is None:\n            X_init = get_inducing_pts(self.X_train, num_sensors, random=True)\n        else:\n            num_sensors = len(X_init.reshape(-1, self.num_dim))\n        X_init = X_init.reshape(-1)\n\n        pbounds = {}\n        for i in range(self.num_dim*num_sensors):\n            pbounds['x{}'.format(i)] = self.pbounds_dim[i%self.num_dim]\n\n        optimizer = BayesianOptimization(f=self.objective,\n                                         pbounds=pbounds,\n                                         verbose=verbose,\n                                         random_state=seed,\n                                         allow_duplicate_points=True)\n        optimizer.maximize(init_points=init_points,\n                           n_iter=max_steps)\n\n        sol = []\n        for i in range(self.num_dim*num_sensors):\n            sol.append(optimizer.max['params']['x{}'.format(i)])\n        sol = np.array(sol).reshape(-1, self.num_dim)\n        if self.transform is not None:\n            sol = self.transform.expand(sol,\n                                        expand_sensor_model=False)\n            if not isinstance(sol, np.ndarray):\n                sol = sol.numpy()\n        return sol.reshape(-1, self.num_dim)\n</code></pre>"},{"location":"api/models/Bayesian-Optimization.html#sgptools.models.bo.BayesianOpt.objective","title":"<code>objective(**kwargs)</code>","text":"<p>Objective function (GP-based Mutual Information)</p> <p>Parameters:</p> Name Type Description Default <code>x&lt;i&gt;</code> <code>ndarray</code> <p>(1, d); Current solution sensor placement location i</p> required Source code in <code>sgptools/models/bo.py</code> <pre><code>def objective(self, **kwargs):\n    \"\"\"Objective function (GP-based Mutual Information)\n\n    Args:\n        x&lt;i&gt; (ndarray): (1, d); Current solution sensor placement location i\n    \"\"\"\n    # MI does not depend on waypoint order (reshape to -1, num_dim)\n    X = []\n    for i in range(len(kwargs)):\n        X.append(kwargs['x{}'.format(i)])\n    X = np.array(X).reshape(-1, self.num_dim)\n    if self.transform is not None:\n        X = self.transform.expand(X)\n        constraints_loss = self.transform.constraints(X)\n\n    try:\n        mi = get_mi(X, self.X_train, self.noise_variance, self.kernel)\n        mi += constraints_loss\n        mi = mi.numpy()\n    except:\n        mi = -1e4 # if the cholskey decomposition fails\n    return mi\n</code></pre>"},{"location":"api/models/Bayesian-Optimization.html#sgptools.models.bo.BayesianOpt.optimize","title":"<code>optimize(num_sensors=10, max_steps=100, X_init=None, init_points=10, verbose=0, seed=1234)</code>","text":"<p>Optimizes the sensor placements using Bayesian Optimization without any constraints</p> <p>Parameters:</p> Name Type Description Default <code>num_sensors</code> <code>int</code> <p>Number of sensor locations to optimize.</p> <code>10</code> <code>max_steps</code> <code>int</code> <p>Maximum number of optimization steps. </p> <code>100</code> <code>X_init</code> <code>ndarray</code> <p>(m, d); Initial inducing points.</p> <code>None</code> <code>init_points</code> <code>int</code> <p>Number of random solutions used for initial exploration.                 Random exploration can help by diversifying the exploration space. </p> <code>10</code> <code>verbose</code> <code>int</code> <p>The level of verbosity.</p> <code>0</code> <code>seed</code> <code>int</code> <p>The algorithm will use it to seed the randomnumber generator, ensuring replicability.</p> <code>1234</code> <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(m, d); Solution sensor placement locations</p> Source code in <code>sgptools/models/bo.py</code> <pre><code>def optimize(self, \n             num_sensors=10, \n             max_steps=100,  \n             X_init=None,\n             init_points=10,\n             verbose=0, \n             seed=1234):\n    \"\"\"Optimizes the sensor placements using Bayesian Optimization without any constraints\n\n    Args:\n        num_sensors (int): Number of sensor locations to optimize.\n        max_steps (int): Maximum number of optimization steps. \n        X_init (ndarray): (m, d); Initial inducing points.\n        init_points (int): Number of random solutions used for initial exploration. \n                           Random exploration can help by diversifying the exploration space. \n        verbose (int): The level of verbosity.\n        seed (int): The algorithm will use it to seed the randomnumber generator, ensuring replicability.\n\n    Returns:\n        Xu (ndarray): (m, d); Solution sensor placement locations\n    \"\"\"\n    if X_init is None:\n        X_init = get_inducing_pts(self.X_train, num_sensors, random=True)\n    else:\n        num_sensors = len(X_init.reshape(-1, self.num_dim))\n    X_init = X_init.reshape(-1)\n\n    pbounds = {}\n    for i in range(self.num_dim*num_sensors):\n        pbounds['x{}'.format(i)] = self.pbounds_dim[i%self.num_dim]\n\n    optimizer = BayesianOptimization(f=self.objective,\n                                     pbounds=pbounds,\n                                     verbose=verbose,\n                                     random_state=seed,\n                                     allow_duplicate_points=True)\n    optimizer.maximize(init_points=init_points,\n                       n_iter=max_steps)\n\n    sol = []\n    for i in range(self.num_dim*num_sensors):\n        sol.append(optimizer.max['params']['x{}'.format(i)])\n    sol = np.array(sol).reshape(-1, self.num_dim)\n    if self.transform is not None:\n        sol = self.transform.expand(sol,\n                                    expand_sensor_model=False)\n        if not isinstance(sol, np.ndarray):\n            sol = sol.numpy()\n    return sol.reshape(-1, self.num_dim)\n</code></pre>"},{"location":"api/models/Continuous-SGP.html","title":"Continuous SGP","text":""},{"location":"api/models/Continuous-SGP.html#sgptools.models.continuous_sgp","title":"<code>sgptools.models.continuous_sgp</code>","text":""},{"location":"api/models/Continuous-SGP.html#sgptools.models.continuous_sgp.continuous_sgp","title":"<code>continuous_sgp(num_inducing, X_train, noise_variance, kernel, transform=None, Xu_init=None, Xu_time=None, orientation=False, **kwargs)</code>","text":"<p>Get sensor placement solutions using the Continuous-SGP method</p> Refer to the following papers for more details <ul> <li>Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]</li> <li>Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>num_inducing</code> <code>int</code> <p>Number of inducing points</p> required <code>X_train</code> <code>ndarray</code> <p>(n, d); Unlabeled random sampled training points</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> <code>Xu_init</code> <code>ndarray</code> <p>(m, d); Initial inducing points</p> <code>None</code> <code>Xu_time</code> <code>ndarray</code> <p>(t, d); Temporal inducing points used in spatio-temporal models</p> <code>None</code> <code>orientation</code> <code>bool</code> <p>If True, a additionl dimension is added to the                  inducing points to represent the FoV orientation</p> <code>False</code> <p>Returns:</p> Name Type Description <code>sgpr</code> <code>AugmentedSGPR</code> <p>Optimized sparse Gaussian process model</p> <code>loss</code> <code>ndarray</code> <p>Loss values computed during training</p> Source code in <code>sgptools/models/continuous_sgp.py</code> <pre><code>def continuous_sgp(num_inducing, X_train, noise_variance, kernel, \n                   transform=None,\n                   Xu_init=None, \n                   Xu_time=None, \n                   orientation=False,\n                   **kwargs):\n    \"\"\"Get sensor placement solutions using the Continuous-SGP method\n\n    Refer to the following papers for more details:\n        - Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [[Jakkala and Akella, 2023](https://www.itskalvik.com/publication/sgp-sp/)]\n        - Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [[Jakkala and Akella, 2024](https://www.itskalvik.com/publication/sgp-ipp/)]\n\n    Args:\n        num_inducing (int): Number of inducing points\n        X_train (ndarray): (n, d); Unlabeled random sampled training points\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        transform (Transform): Transform object\n        Xu_init (ndarray): (m, d); Initial inducing points\n        Xu_time (ndarray): (t, d); Temporal inducing points used in spatio-temporal models\n        orientation (bool): If True, a additionl dimension is added to the \n                            inducing points to represent the FoV orientation\n\n    Returns:\n        sgpr (AugmentedSGPR): Optimized sparse Gaussian process model\n        loss (ndarray): Loss values computed during training\n    \"\"\"\n    # Generate init inducing points\n    if Xu_init is None:\n        Xu_init = get_inducing_pts(X_train, num_inducing, \n                                   orientation=orientation)\n\n    # Fit spare GP\n    sgpr = AugmentedSGPR((X_train, np.zeros((len(X_train), 1)).astype(X_train.dtype)),\n                         noise_variance=noise_variance,\n                         kernel=kernel, \n                         inducing_variable=Xu_init,\n                         inducing_variable_time=Xu_time,\n                         transform=transform)\n\n    # Train the mode\n    loss = optimize_model(sgpr,\n                          kernel_grad=False, \n                          **kwargs)\n\n    return sgpr, loss\n</code></pre>"},{"location":"api/models/Genetic-Algorithm.html","title":"Genetic Algorithm","text":""},{"location":"api/models/Genetic-Algorithm.html#sgptools.models.cma_es","title":"<code>sgptools.models.cma_es</code>","text":""},{"location":"api/models/Genetic-Algorithm.html#sgptools.models.cma_es.CMA_ES","title":"<code>CMA_ES</code>","text":"<p>Class for optimizing sensor placements using CMA-ES (a genetic algorithm)</p> Refer to the following paper for more details <ul> <li>Adaptive Continuous-Space Informative Path Planning for Online Environmental Monitoring [Hitz et al., 2017]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>X_train</code> <code>ndarray</code> <p>(n, d); Locations in the environment used to approximate the monitoring regions</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>distance_budget</code> <code>float</code> <p>Distance budget for when treating the inducing points                       as waypoints of a path</p> <code>None</code> <code>num_robots</code> <code>int</code> <p>Number of robots, used when modeling                multi-robot IPP with a distance budget</p> <code>1</code> <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> Source code in <code>sgptools/models/cma_es.py</code> <pre><code>class CMA_ES:\n    \"\"\"Class for optimizing sensor placements using CMA-ES (a genetic algorithm)\n\n    Refer to the following paper for more details:\n        - Adaptive Continuous-Space Informative Path Planning for Online Environmental Monitoring [Hitz et al., 2017]\n\n    Args:\n        X_train (ndarray): (n, d); Locations in the environment used to approximate the monitoring regions\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        distance_budget (float): Distance budget for when treating the inducing points \n                                 as waypoints of a path\n        num_robots (int): Number of robots, used when modeling \n                          multi-robot IPP with a distance budget\n        transform (Transform): Transform object\n    \"\"\"\n    def __init__(self, X_train, noise_variance, kernel,\n                 distance_budget=None,\n                 num_robots=1,\n                 transform=None):\n        self.boundaries = geometry.MultiPoint([[p[0], p[1]] for p in X_train]).convex_hull\n        self.X_train = X_train\n        self.noise_variance = noise_variance\n        self.kernel = kernel\n        self.num_dim = X_train.shape[-1]\n        self.distance_budget = distance_budget\n        self.num_robots = num_robots\n        self.transform = transform\n\n    def update(self, noise_variance, kernel):\n        \"\"\"Update GP noise variance and kernel function parameters\n\n        Args:\n            noise_variance (float): data variance\n            kernel (gpflow.kernels.Kernel): gpflow kernel function\n        \"\"\"\n        self.noise_variance = noise_variance\n        self.kernel = kernel\n\n    def objective(self, X):\n        \"\"\"Objective function (GP-based Mutual Information)\n\n        Args:\n            X (ndarray): (n, d); Current solution sensor placement locations\n        \"\"\"\n        # MI does not depend on waypoint order (reshape to -1, num_dim)\n        X = np.array(X).reshape(-1, self.num_dim)\n        constraints_loss = 0.0\n        if self.transform is not None:\n            X = self.transform.expand(X)\n            constraints_loss = self.transform.constraints(X)\n\n        try:\n            mi = -get_mi(X, self.X_train, self.noise_variance, self.kernel)\n            mi -= constraints_loss\n            mi = mi.numpy()\n        except:\n            mi = 0.0 # if the cholskey decomposition fails\n        return mi\n\n    def optimize(self, \n                 num_sensors=10, \n                 max_steps=5000, \n                 tol=1e-6, \n                 X_init=None,\n                 verbose=0,\n                 seed=1234):\n        \"\"\"Optimizes the sensor placements using CMA-ES without any constraints\n\n        Args:\n            num_sensors (int): Number of sensor locations to optimize\n            max_steps (int): Maximum number of optimization steps\n            tol (float): Convergence tolerance to decide when to stop optimization\n            X_init (ndarray): (m, d); Initial inducing points\n            verbose (int): The level of verbosity.\n            seed (int): The algorithm will use it to seed the randomnumber generator, ensuring replicability.\n\n        Returns:\n            Xu (ndarray): (m, d); Solution sensor placement locations\n        \"\"\"\n        sigma0 = 1.0\n\n        if X_init is None:\n            X_init = get_inducing_pts(self.X_train, num_sensors, random=True)\n        X_init = X_init.reshape(-1)\n\n        xopt, _ = cma.fmin2(self.objective, X_init, sigma0, \n                            options={'maxfevals': max_steps,\n                                     'verb_disp': verbose,\n                                     'tolfun': tol,\n                                     'seed': seed},\n                            restarts=5)\n\n        xopt = np.array(xopt).reshape(-1, self.num_dim)\n        if self.transform is not None:\n            xopt = self.transform.expand(xopt, \n                                         expand_sensor_model=False)\n            if not isinstance(xopt, np.ndarray):\n                xopt = xopt.numpy()\n        return xopt.reshape(-1, self.num_dim)\n</code></pre>"},{"location":"api/models/Genetic-Algorithm.html#sgptools.models.cma_es.CMA_ES.objective","title":"<code>objective(X)</code>","text":"<p>Objective function (GP-based Mutual Information)</p> <p>Parameters:</p> Name Type Description Default <code>X</code> <code>ndarray</code> <p>(n, d); Current solution sensor placement locations</p> required Source code in <code>sgptools/models/cma_es.py</code> <pre><code>def objective(self, X):\n    \"\"\"Objective function (GP-based Mutual Information)\n\n    Args:\n        X (ndarray): (n, d); Current solution sensor placement locations\n    \"\"\"\n    # MI does not depend on waypoint order (reshape to -1, num_dim)\n    X = np.array(X).reshape(-1, self.num_dim)\n    constraints_loss = 0.0\n    if self.transform is not None:\n        X = self.transform.expand(X)\n        constraints_loss = self.transform.constraints(X)\n\n    try:\n        mi = -get_mi(X, self.X_train, self.noise_variance, self.kernel)\n        mi -= constraints_loss\n        mi = mi.numpy()\n    except:\n        mi = 0.0 # if the cholskey decomposition fails\n    return mi\n</code></pre>"},{"location":"api/models/Genetic-Algorithm.html#sgptools.models.cma_es.CMA_ES.optimize","title":"<code>optimize(num_sensors=10, max_steps=5000, tol=1e-06, X_init=None, verbose=0, seed=1234)</code>","text":"<p>Optimizes the sensor placements using CMA-ES without any constraints</p> <p>Parameters:</p> Name Type Description Default <code>num_sensors</code> <code>int</code> <p>Number of sensor locations to optimize</p> <code>10</code> <code>max_steps</code> <code>int</code> <p>Maximum number of optimization steps</p> <code>5000</code> <code>tol</code> <code>float</code> <p>Convergence tolerance to decide when to stop optimization</p> <code>1e-06</code> <code>X_init</code> <code>ndarray</code> <p>(m, d); Initial inducing points</p> <code>None</code> <code>verbose</code> <code>int</code> <p>The level of verbosity.</p> <code>0</code> <code>seed</code> <code>int</code> <p>The algorithm will use it to seed the randomnumber generator, ensuring replicability.</p> <code>1234</code> <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(m, d); Solution sensor placement locations</p> Source code in <code>sgptools/models/cma_es.py</code> <pre><code>def optimize(self, \n             num_sensors=10, \n             max_steps=5000, \n             tol=1e-6, \n             X_init=None,\n             verbose=0,\n             seed=1234):\n    \"\"\"Optimizes the sensor placements using CMA-ES without any constraints\n\n    Args:\n        num_sensors (int): Number of sensor locations to optimize\n        max_steps (int): Maximum number of optimization steps\n        tol (float): Convergence tolerance to decide when to stop optimization\n        X_init (ndarray): (m, d); Initial inducing points\n        verbose (int): The level of verbosity.\n        seed (int): The algorithm will use it to seed the randomnumber generator, ensuring replicability.\n\n    Returns:\n        Xu (ndarray): (m, d); Solution sensor placement locations\n    \"\"\"\n    sigma0 = 1.0\n\n    if X_init is None:\n        X_init = get_inducing_pts(self.X_train, num_sensors, random=True)\n    X_init = X_init.reshape(-1)\n\n    xopt, _ = cma.fmin2(self.objective, X_init, sigma0, \n                        options={'maxfevals': max_steps,\n                                 'verb_disp': verbose,\n                                 'tolfun': tol,\n                                 'seed': seed},\n                        restarts=5)\n\n    xopt = np.array(xopt).reshape(-1, self.num_dim)\n    if self.transform is not None:\n        xopt = self.transform.expand(xopt, \n                                     expand_sensor_model=False)\n        if not isinstance(xopt, np.ndarray):\n            xopt = xopt.numpy()\n    return xopt.reshape(-1, self.num_dim)\n</code></pre>"},{"location":"api/models/Genetic-Algorithm.html#sgptools.models.cma_es.CMA_ES.update","title":"<code>update(noise_variance, kernel)</code>","text":"<p>Update GP noise variance and kernel function parameters</p> <p>Parameters:</p> Name Type Description Default <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required Source code in <code>sgptools/models/cma_es.py</code> <pre><code>def update(self, noise_variance, kernel):\n    \"\"\"Update GP noise variance and kernel function parameters\n\n    Args:\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n    \"\"\"\n    self.noise_variance = noise_variance\n    self.kernel = kernel\n</code></pre>"},{"location":"api/models/Greedy-MI.html","title":"Greedy MI","text":""},{"location":"api/models/Greedy-MI.html#sgptools.models.greedy_mi","title":"<code>sgptools.models.greedy_mi</code>","text":""},{"location":"api/models/Greedy-MI.html#sgptools.models.greedy_mi.GreedyMI","title":"<code>GreedyMI</code>","text":"<p>Helper class to compute mutual information using a Gaussian process for a given set of sensor locations. Used by <code>get_greedy_mi_sol</code> function to compute the solution sensor placements using the Greedy-MI method.</p> Refer to the following papers for more details <ul> <li>Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies [Krause et al., 2008]</li> <li>Data-driven learning and planning for environmental sampling [Ma et al., 2018]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>S</code> <code>ndarray</code> <p>(n, d); Candidate sensor placement locations</p> required <code>V</code> <code>ndarray</code> <p>(n, d); Locations in the environment used to approximate the monitoring regions</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> Source code in <code>sgptools/models/greedy_mi.py</code> <pre><code>class GreedyMI:\n    \"\"\"Helper class to compute mutual information using a Gaussian process for a given set of sensor locations.\n    Used by `get_greedy_mi_sol` function to compute the solution sensor placements using the Greedy-MI method.\n\n    Refer to the following papers for more details:\n        - Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies [Krause et al., 2008]\n        - Data-driven learning and planning for environmental sampling [Ma et al., 2018]\n\n    Args:\n        S (ndarray): (n, d); Candidate sensor placement locations\n        V (ndarray): (n, d); Locations in the environment used to approximate the monitoring regions\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        transform (Transform): Transform object\n    \"\"\"\n    def __init__(self, S, V, noise_variance, kernel, \n                 transform=None):\n        self.S = S\n        self.V = V\n        self.kernel = kernel\n        self.input_dim = S.shape[1]\n        self.noise_variance = noise_variance\n        self.transform = transform\n\n    def mutual_info(self, x):\n        \"\"\"Computes mutual information using the points `x` \n\n        Args:\n            x (ndarray): (n); Indices of the solution placement locations\n\n        Returns:\n            MI (float): Mutual information between the placement x and candidate locations\n        \"\"\"\n        x = np.array(x).reshape(-1).astype(int)\n        A = self.S[x[:-1]].reshape(-1, self.input_dim)\n        y = self.S[x[-1]].reshape(-1, self.input_dim)\n\n        if len(A) == 0:\n            sigma_a = 1.0\n        else:\n            if self.transform is not None:\n                A = self.transform.expand(A)\n            a_gp = GPR(data=(A, np.zeros((len(A), 1))),\n                       kernel=self.kernel,\n                       noise_variance=self.noise_variance)\n            _, sigma_a = a_gp.predict_f(y)\n\n        # Remove locations in A\u222ay from V to build A bar (Refer to Krause et al., 2008)\n        V_ = self.V.copy()\n        V_rows = V_.view([('', V_.dtype)] * V_.shape[1])\n\n        if self.transform is not None:\n            solution = self.S[x].reshape(-1, self.input_dim)\n            A_ = self.transform.expand(solution)\n        else:\n            A_ = self.S[x]\n        A_rows = A_.view([('', V_.dtype)] * A_.shape[1])\n\n        V_ = np.setdiff1d(V_rows, A_rows).view(V_.dtype).reshape(-1, V_.shape[1])\n\n        self.v_gp = GPR(data=(V_, np.zeros((len(V_), 1))), \n                        kernel=self.kernel,\n                        noise_variance=self.noise_variance)\n        _, sigma_v = self.v_gp.predict_f(y)\n\n        return (sigma_a/sigma_v).numpy().squeeze()\n</code></pre>"},{"location":"api/models/Greedy-MI.html#sgptools.models.greedy_mi.GreedyMI.mutual_info","title":"<code>mutual_info(x)</code>","text":"<p>Computes mutual information using the points <code>x</code> </p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>ndarray</code> <p>(n); Indices of the solution placement locations</p> required <p>Returns:</p> Name Type Description <code>MI</code> <code>float</code> <p>Mutual information between the placement x and candidate locations</p> Source code in <code>sgptools/models/greedy_mi.py</code> <pre><code>def mutual_info(self, x):\n    \"\"\"Computes mutual information using the points `x` \n\n    Args:\n        x (ndarray): (n); Indices of the solution placement locations\n\n    Returns:\n        MI (float): Mutual information between the placement x and candidate locations\n    \"\"\"\n    x = np.array(x).reshape(-1).astype(int)\n    A = self.S[x[:-1]].reshape(-1, self.input_dim)\n    y = self.S[x[-1]].reshape(-1, self.input_dim)\n\n    if len(A) == 0:\n        sigma_a = 1.0\n    else:\n        if self.transform is not None:\n            A = self.transform.expand(A)\n        a_gp = GPR(data=(A, np.zeros((len(A), 1))),\n                   kernel=self.kernel,\n                   noise_variance=self.noise_variance)\n        _, sigma_a = a_gp.predict_f(y)\n\n    # Remove locations in A\u222ay from V to build A bar (Refer to Krause et al., 2008)\n    V_ = self.V.copy()\n    V_rows = V_.view([('', V_.dtype)] * V_.shape[1])\n\n    if self.transform is not None:\n        solution = self.S[x].reshape(-1, self.input_dim)\n        A_ = self.transform.expand(solution)\n    else:\n        A_ = self.S[x]\n    A_rows = A_.view([('', V_.dtype)] * A_.shape[1])\n\n    V_ = np.setdiff1d(V_rows, A_rows).view(V_.dtype).reshape(-1, V_.shape[1])\n\n    self.v_gp = GPR(data=(V_, np.zeros((len(V_), 1))), \n                    kernel=self.kernel,\n                    noise_variance=self.noise_variance)\n    _, sigma_v = self.v_gp.predict_f(y)\n\n    return (sigma_a/sigma_v).numpy().squeeze()\n</code></pre>"},{"location":"api/models/Greedy-MI.html#sgptools.models.greedy_mi.get_greedy_mi_sol","title":"<code>get_greedy_mi_sol(num_sensors, candidates, X_train, noise_variance, kernel, transform=None, optimizer='naive')</code>","text":"<p>Get sensor placement solutions using the GP-based mutual information approach (submodular objective function).  Uses a greedy algorithm to select sensor placements from a given discrete set of candidates locations.</p> Refer to the following papers for more details <ul> <li>Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies [Krause et al., 2008]</li> <li>Data-driven learning and planning for environmental sampling [Ma et al., 2018]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>num_sensors</code> <code>int</code> <p>Number of sensor locations to optimize</p> required <code>candidates</code> <code>ndarray</code> <p>(n, d); Candidate sensor placement locations</p> required <code>X_train</code> <code>ndarray</code> <p>(n, d); Locations in the environment used to approximate the monitoring regions</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> <code>optimizer</code> <code>str</code> <p>Name of an optimizer available in the apricot library</p> <code>'naive'</code> <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(m, d); Solution sensor placement locations</p> Source code in <code>sgptools/models/greedy_mi.py</code> <pre><code>def get_greedy_mi_sol(num_sensors, candidates, X_train, noise_variance, kernel, \n                      transform=None, optimizer='naive'):\n    \"\"\"Get sensor placement solutions using the GP-based mutual information approach (submodular objective function). \n    Uses a greedy algorithm to select sensor placements from a given discrete set of candidates locations.\n\n    Refer to the following papers for more details:\n        - Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies [Krause et al., 2008]\n        - Data-driven learning and planning for environmental sampling [Ma et al., 2018]\n\n    Args:\n        num_sensors (int): Number of sensor locations to optimize\n        candidates (ndarray): (n, d); Candidate sensor placement locations\n        X_train (ndarray): (n, d); Locations in the environment used to approximate the monitoring regions\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        transform (Transform): Transform object\n        optimizer (str): Name of an optimizer available in the apricot library\n\n    Returns:\n        Xu (ndarray): (m, d); Solution sensor placement locations\n    \"\"\"\n    mi_model = GreedyMI(candidates, X_train, noise_variance, kernel, transform)\n    model = CustomSelection(num_sensors,\n                            mi_model.mutual_info,\n                            optimizer=optimizer,\n                            verbose=False)\n    sol = model.fit_transform(np.arange(len(candidates)).reshape(-1, 1))\n    return candidates[sol.reshape(-1)]\n</code></pre>"},{"location":"api/models/Greedy-SGP.html","title":"Greedy SGP","text":""},{"location":"api/models/Greedy-SGP.html#sgptools.models.greedy_sgp","title":"<code>sgptools.models.greedy_sgp</code>","text":""},{"location":"api/models/Greedy-SGP.html#sgptools.models.greedy_sgp.GreedySGP","title":"<code>GreedySGP</code>","text":"<p>Helper class to compute SGP's ELBO/optimization bound for a given set of sensor locations. Used by <code>get_greedy_sgp_sol</code> function to compute the solution sensor placements using the Greedy-SGP method.</p> Refer to the following papers for more details <ul> <li>Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>num_inducing</code> <code>int</code> <p>Number of inducing points</p> required <code>S</code> <code>ndarray</code> <p>(n, d); Candidate sensor placement locations</p> required <code>V</code> <code>ndarray</code> <p>(n, d); Locations in the environment used to approximate the monitoring regions</p> required <code>noise_variance</code> <code>float</code> <p>Data noise variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> Source code in <code>sgptools/models/greedy_sgp.py</code> <pre><code>class GreedySGP:\n    \"\"\"Helper class to compute SGP's ELBO/optimization bound for a given set of sensor locations.\n    Used by `get_greedy_sgp_sol` function to compute the solution sensor placements using the Greedy-SGP method.\n\n    Refer to the following papers for more details:\n        - Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [[Jakkala and Akella, 2023](https://www.itskalvik.com/publication/sgp-sp/)]\n\n    Args:\n        num_inducing (int): Number of inducing points\n        S (ndarray): (n, d); Candidate sensor placement locations\n        V (ndarray): (n, d); Locations in the environment used to approximate the monitoring regions\n        noise_variance (float): Data noise variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        transform (Transform): Transform object\n    \"\"\"\n    def __init__(self, num_inducing, S, V, noise_variance, kernel, \n                 transform=None):\n        self.sgp = AugmentedSGPR((V, np.zeros((len(V), 1))),\n                                 noise_variance=noise_variance,\n                                 kernel=kernel, \n                                 inducing_variable=S[:num_inducing],\n                                 transform=transform)\n        self.locs = S\n        self.num_inducing = num_inducing\n        self.inducing_dim = S.shape[1]\n\n    def bound(self, x):\n        \"\"\"Computes the SGP's optimization bound using the inducing points `x` \n\n        Args:\n            x (ndarray): (n); Indices of the solution placement locations\n\n        Returns:\n            elbo (float): Evidence lower bound/SGP's optimization bound value\n        \"\"\"\n        x = np.array(x).reshape(-1).astype(int)\n        Xu = np.ones((self.num_inducing, self.inducing_dim), dtype=np.float32)\n\n        # Initialize all inducing points at the first solution placement location.\n        # Ensures that the number of inducing points is always fixed and no additional\n        # information is passed to the SGP\n        Xu *= self.locs[x][0]\n\n        # Copy all given solution placements to the inducing points set\n        Xu[-len(x):] = self.locs[x]\n\n        # Update the SGP inducing points\n        self.sgp.inducing_variable.Z.assign(Xu)\n        return self.sgp.elbo().numpy() # return the ELBO\n</code></pre>"},{"location":"api/models/Greedy-SGP.html#sgptools.models.greedy_sgp.GreedySGP.bound","title":"<code>bound(x)</code>","text":"<p>Computes the SGP's optimization bound using the inducing points <code>x</code> </p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>ndarray</code> <p>(n); Indices of the solution placement locations</p> required <p>Returns:</p> Name Type Description <code>elbo</code> <code>float</code> <p>Evidence lower bound/SGP's optimization bound value</p> Source code in <code>sgptools/models/greedy_sgp.py</code> <pre><code>def bound(self, x):\n    \"\"\"Computes the SGP's optimization bound using the inducing points `x` \n\n    Args:\n        x (ndarray): (n); Indices of the solution placement locations\n\n    Returns:\n        elbo (float): Evidence lower bound/SGP's optimization bound value\n    \"\"\"\n    x = np.array(x).reshape(-1).astype(int)\n    Xu = np.ones((self.num_inducing, self.inducing_dim), dtype=np.float32)\n\n    # Initialize all inducing points at the first solution placement location.\n    # Ensures that the number of inducing points is always fixed and no additional\n    # information is passed to the SGP\n    Xu *= self.locs[x][0]\n\n    # Copy all given solution placements to the inducing points set\n    Xu[-len(x):] = self.locs[x]\n\n    # Update the SGP inducing points\n    self.sgp.inducing_variable.Z.assign(Xu)\n    return self.sgp.elbo().numpy() # return the ELBO\n</code></pre>"},{"location":"api/models/Greedy-SGP.html#sgptools.models.greedy_sgp.get_greedy_sgp_sol","title":"<code>get_greedy_sgp_sol(num_sensors, candidates, X_train, noise_variance, kernel, transform=None)</code>","text":"<p>Get sensor placement solutions using the Greedy-SGP method. Uses a greedy algorithm to  select sensor placements from a given discrete set of candidates locations.</p> Refer to the following papers for more details <ul> <li>Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>num_sensors</code> <code>int</code> <p>Number of sensor locations to optimize</p> required <code>candidates</code> <code>ndarray</code> <p>(n, d); Candidate sensor placement locations</p> required <code>X_train</code> <code>ndarray</code> <p>(n, d); Locations in the environment used to approximate the monitoring regions</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(m, d); Solution sensor placement locations</p> Source code in <code>sgptools/models/greedy_sgp.py</code> <pre><code>def get_greedy_sgp_sol(num_sensors, candidates, X_train, noise_variance, kernel, \n                       transform=None):\n    \"\"\"Get sensor placement solutions using the Greedy-SGP method. Uses a greedy algorithm to \n    select sensor placements from a given discrete set of candidates locations.\n\n    Refer to the following papers for more details:\n        - Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [[Jakkala and Akella, 2023](https://www.itskalvik.com/publication/sgp-sp/)]\n\n    Args:\n        num_sensors (int): Number of sensor locations to optimize\n        candidates (ndarray): (n, d); Candidate sensor placement locations\n        X_train (ndarray): (n, d); Locations in the environment used to approximate the monitoring regions\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        transform (Transform): Transform object\n\n    Returns:\n        Xu (ndarray): (m, d); Solution sensor placement locations\n    \"\"\"\n    sgp_model = GreedySGP(num_sensors, candidates, X_train, \n                          noise_variance, kernel, transform=transform)\n    model = CustomSelection(num_sensors,\n                            sgp_model.bound,\n                            optimizer='naive',\n                            verbose=False)\n    sol = model.fit_transform(np.arange(len(candidates)).reshape(-1, 1))\n    return candidates[sol.reshape(-1)]\n</code></pre>"},{"location":"api/models/core/index.html","title":"","text":""},{"location":"api/models/core/index.html#sgptools.models.core","title":"<code>sgptools.models.core</code>","text":"<p>Core modules in this package:</p> <ul> <li><code>augmented_gpr</code>: Provides a Gaussian process model with expand and aggregate functions</li> <li><code>augmented_sgpr</code>: Provides a sparse Gaussian process model with update, expand, and aggregate functions</li> <li><code>osgpr</code>: Provides a streaming sparse Gaussian process model along with initialization function</li> <li><code>transformations</code>: Provides transforms to model complex sensor field of views and handle informative path planning</li> </ul>"},{"location":"api/models/core/AugmentedGPR.html","title":"AugmentedGPR","text":""},{"location":"api/models/core/AugmentedGPR.html#sgptools.models.core.augmented_gpr","title":"<code>sgptools.models.core.augmented_gpr</code>","text":"<p>Provides a Gaussian process model with expand and aggregate functions</p>"},{"location":"api/models/core/AugmentedGPR.html#sgptools.models.core.augmented_gpr.AugmentedGPR","title":"<code>AugmentedGPR</code>","text":"<p>               Bases: <code>GPR</code></p> <p>GPR model from the GPFlow library augmented to use a transform object's expand and aggregate functions on the data points where necessary.  </p> Refer to the following papers for more details <ul> <li>Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]</li> <li>Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>tuple</code> <p>(X, y) ndarrays with inputs (n, d) and labels (n, 1)</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> Source code in <code>sgptools/models/core/augmented_gpr.py</code> <pre><code>class AugmentedGPR(GPR):\n    \"\"\"GPR model from the GPFlow library augmented to use a transform object's\n    expand and aggregate functions on the data points where necessary.  \n\n    Refer to the following papers for more details:\n        - Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]\n        - Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]\n\n    Args:\n        data (tuple): (X, y) ndarrays with inputs (n, d) and labels (n, 1)\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        noise_variance (float): data variance\n        transform (Transform): Transform object\n    \"\"\"\n    def __init__(\n        self,\n        *args,\n        transform=None,\n        **kwargs\n    ):\n        super().__init__(\n            *args,\n            **kwargs\n        )\n        if transform is None:\n            self.transform = Transform()\n        else:\n            self.transform = transform\n\n    def predict_f(\n        self, Xnew: InputData, \n        full_cov: bool = True, \n        full_output_cov: bool = False,\n        aggregate_train: bool = False,\n    ) -&gt; MeanAndVariance:\n        assert_params_false(self.predict_f, full_output_cov=full_output_cov)\n        if self.transform is not None:\n            Xnew = self.transform.expand(Xnew)\n\n        X, Y = self.data\n        err = Y - self.mean_function(X)\n\n        kmm = self.kernel(X)\n        knn = self.kernel(Xnew, full_cov=full_cov)\n        kmn = self.kernel(X, Xnew)\n        kmm_plus_s = add_likelihood_noise_cov(kmm, self.likelihood, X)\n\n        if self.transform is not None:\n            kmn = self.transform.aggregate(tf.transpose(kmn))\n            kmn = tf.transpose(kmn)\n            knn = self.transform.aggregate(knn)\n\n        if aggregate_train:\n            kmm_plus_s = self.transform.aggregate(kmm_plus_s)\n            err = self.transform.aggregate(err)\n            # reduce kmn only if it was not reduced before\n            # which can when train and test data are the same size\n            if kmn.shape[0] != kmn.shape[1]:\n                kmn = self.transform.aggregate(kmn)\n\n        conditional = gpflow.conditionals.base_conditional\n        f_mean_zero, f_var = conditional(\n            kmn, kmm_plus_s, knn, err, full_cov=full_cov, white=False\n        )  # [N, P], [N, P] or [P, N, N]\n        f_mean = f_mean_zero + self.mean_function(Xnew)\n        return f_mean, f_var\n</code></pre>"},{"location":"api/models/core/AugmentedSGPR.html","title":"AugmentedSGPR","text":""},{"location":"api/models/core/AugmentedSGPR.html#sgptools.models.core.augmented_sgpr","title":"<code>sgptools.models.core.augmented_sgpr</code>","text":"<p>Provides a sparse Gaussian process model with update, expand, and aggregate functions</p>"},{"location":"api/models/core/AugmentedSGPR.html#sgptools.models.core.augmented_sgpr.AugmentedSGPR","title":"<code>AugmentedSGPR</code>","text":"<p>               Bases: <code>SGPR</code></p> <p>SGPR model from the GPFlow library augmented to use a transform object's expand and aggregate functions on the inducing points where necessary. The object has an additional update function to update the kernel and noise variance parameters  (currently, the online updates part works only with RBF kernels).  </p> Refer to the following papers for more details <ul> <li>Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]</li> <li>Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>tuple</code> <p>(X, y) ndarrays with inputs (n, d) and labels (n, 1)</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>inducing_variable</code> <code>ndarray</code> <p>(m, d); Initial inducing points</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> <code>inducing_variable_time</code> <code>ndarray</code> <p>(m, d); Temporal dimensions of the inducing points,                                  used when modeling spatio-temporal IPP</p> <code>None</code> Source code in <code>sgptools/models/core/augmented_sgpr.py</code> <pre><code>class AugmentedSGPR(SGPR):\n    \"\"\"SGPR model from the GPFlow library augmented to use a transform object's\n    expand and aggregate functions on the inducing points where necessary. The object\n    has an additional update function to update the kernel and noise variance parameters \n    (currently, the online updates part works only with RBF kernels).  \n\n\n    Refer to the following papers for more details:\n        - Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]\n        - Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]\n\n    Args:\n        data (tuple): (X, y) ndarrays with inputs (n, d) and labels (n, 1)\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        noise_variance (float): data variance\n        inducing_variable (ndarray): (m, d); Initial inducing points\n        transform (Transform): Transform object\n        inducing_variable_time (ndarray): (m, d); Temporal dimensions of the inducing points, \n                                            used when modeling spatio-temporal IPP\n    \"\"\"\n    def __init__(\n        self,\n        *args,\n        transform=None,\n        inducing_variable_time=None,\n        **kwargs\n    ):\n        super().__init__(\n            *args,\n            **kwargs\n        )\n        if transform is None:\n            self.transform = Transform()\n        else:\n            self.transform = transform\n\n        if inducing_variable_time is not None:\n            self.inducing_variable_time = inducingpoint_wrapper(inducing_variable_time)\n            self.transform.inducing_variable_time = self.inducing_variable_time\n        else:\n            self.inducing_variable_time = None\n\n    def update(self, noise_variance, kernel):\n        \"\"\"Update SGP noise variance and kernel function parameters\n\n        Args:\n            noise_variance (float): data variance\n            kernel (gpflow.kernels.Kernel): gpflow kernel function\n        \"\"\"\n        self.likelihood.variance.assign(noise_variance)\n        self.kernel.lengthscales.assign(kernel.lengthscales)\n        self.kernel.variance.assign(kernel.variance)\n\n    def _common_calculation(self) -&gt; \"SGPR.CommonTensors\":\n        \"\"\"\n        Matrices used in log-det calculation\n        :return: A , B, LB, AAT with :math:`LL\u1d40 = K\u1d64\u1d64 , A = L\u207b\u00b9K_{uf}/\u03c3, AAT = AA\u1d40,\n            B = AAT+I, LBLB\u1d40 = B`\n            A is M x N, B is M x M, LB is M x M, AAT is M x M\n        \"\"\"\n        x, _ = self.data\n\n        iv = self.inducing_variable.Z  # [M]\n        iv = self.transform.expand(iv)\n\n        kuf = self.kernel(iv, x)\n        kuf = self.transform.aggregate(kuf)\n\n        kuu = self.kernel(iv) + 1e-6 * tf.eye(tf.shape(iv)[0], dtype=iv.dtype)\n        kuu = self.transform.aggregate(kuu)\n\n        L = tf.linalg.cholesky(kuu)\n\n        sigma_sq = self.likelihood.variance\n        sigma = tf.sqrt(sigma_sq)\n\n        # Compute intermediate matrices\n        A = tf.linalg.triangular_solve(L, kuf, lower=True) / sigma\n        AAT = tf.linalg.matmul(A, A, transpose_b=True)\n        B = add_noise_cov(AAT, tf.cast(1.0, AAT.dtype))\n        LB = tf.linalg.cholesky(B)\n\n        return self.CommonTensors(sigma_sq, sigma, A, B, LB, AAT, L)\n\n    def elbo(self) -&gt; tf.Tensor:\n        \"\"\"\n        Construct a tensorflow function to compute the bound on the marginal\n        likelihood. For a derivation of the terms in here, see the associated\n        SGPR notebook.\n        \"\"\"\n        common = self._common_calculation()\n        output_shape = tf.shape(self.data[-1])\n        num_data = to_default_float(output_shape[0])\n        output_dim = to_default_float(output_shape[1])\n        const = -0.5 * num_data * output_dim * np.log(2 * np.pi)\n        logdet = self.logdet_term(common)\n        quad = self.quad_term(common)\n        constraints = self.transform.constraints(self.inducing_variable.Z)\n        return const + logdet + quad + constraints\n\n    def predict_f(\n        self, Xnew: InputData, full_cov: bool = False, full_output_cov: bool = False\n    ) -&gt; MeanAndVariance:\n\n        # could copy into posterior into a fused version\n        \"\"\"\n        Compute the mean and variance of the latent function at some new points\n        Xnew. For a derivation of the terms in here, see the associated SGPR\n        notebook.\n        \"\"\"\n        X_data, Y_data = self.data\n\n        iv = self.inducing_variable.Z\n        iv = self.transform.expand(iv)\n\n        num_inducing = tf.shape(iv)[0]\n\n        err = Y_data - self.mean_function(X_data)\n        kuf = self.kernel(iv, X_data)\n        kuu = self.kernel(iv) + 1e-6 * tf.eye(num_inducing, dtype=iv.dtype)\n        Kus = self.kernel(iv, Xnew)\n        sigma = tf.sqrt(self.likelihood.variance)\n        L = tf.linalg.cholesky(kuu)\n        A = tf.linalg.triangular_solve(L, kuf, lower=True) / sigma\n        B = tf.linalg.matmul(A, A, transpose_b=True) + tf.eye(\n            num_inducing, dtype=default_float()\n        )  # cache qinv\n        LB = tf.linalg.cholesky(B)\n        Aerr = tf.linalg.matmul(A, err)\n        c = tf.linalg.triangular_solve(LB, Aerr, lower=True) / sigma\n        tmp1 = tf.linalg.triangular_solve(L, Kus, lower=True)\n        tmp2 = tf.linalg.triangular_solve(LB, tmp1, lower=True)\n        mean = tf.linalg.matmul(tmp2, c, transpose_a=True)\n        if full_cov:\n            var = (\n                self.kernel(Xnew)\n                + tf.linalg.matmul(tmp2, tmp2, transpose_a=True)\n                - tf.linalg.matmul(tmp1, tmp1, transpose_a=True)\n            )\n            var = tf.tile(var[None, ...], [self.num_latent_gps, 1, 1])  # [P, N, N]\n        else:\n            var = (\n                self.kernel(Xnew, full_cov=False)\n                + tf.reduce_sum(tf.square(tmp2), 0)\n                - tf.reduce_sum(tf.square(tmp1), 0)\n            )\n            var = tf.tile(var[:, None], [1, self.num_latent_gps])\n\n        return mean + self.mean_function(Xnew), var\n</code></pre>"},{"location":"api/models/core/AugmentedSGPR.html#sgptools.models.core.augmented_sgpr.AugmentedSGPR.elbo","title":"<code>elbo()</code>","text":"<p>Construct a tensorflow function to compute the bound on the marginal likelihood. For a derivation of the terms in here, see the associated SGPR notebook.</p> Source code in <code>sgptools/models/core/augmented_sgpr.py</code> <pre><code>def elbo(self) -&gt; tf.Tensor:\n    \"\"\"\n    Construct a tensorflow function to compute the bound on the marginal\n    likelihood. For a derivation of the terms in here, see the associated\n    SGPR notebook.\n    \"\"\"\n    common = self._common_calculation()\n    output_shape = tf.shape(self.data[-1])\n    num_data = to_default_float(output_shape[0])\n    output_dim = to_default_float(output_shape[1])\n    const = -0.5 * num_data * output_dim * np.log(2 * np.pi)\n    logdet = self.logdet_term(common)\n    quad = self.quad_term(common)\n    constraints = self.transform.constraints(self.inducing_variable.Z)\n    return const + logdet + quad + constraints\n</code></pre>"},{"location":"api/models/core/AugmentedSGPR.html#sgptools.models.core.augmented_sgpr.AugmentedSGPR.predict_f","title":"<code>predict_f(Xnew, full_cov=False, full_output_cov=False)</code>","text":"<p>Compute the mean and variance of the latent function at some new points Xnew. For a derivation of the terms in here, see the associated SGPR notebook.</p> Source code in <code>sgptools/models/core/augmented_sgpr.py</code> <pre><code>def predict_f(\n    self, Xnew: InputData, full_cov: bool = False, full_output_cov: bool = False\n) -&gt; MeanAndVariance:\n\n    # could copy into posterior into a fused version\n    \"\"\"\n    Compute the mean and variance of the latent function at some new points\n    Xnew. For a derivation of the terms in here, see the associated SGPR\n    notebook.\n    \"\"\"\n    X_data, Y_data = self.data\n\n    iv = self.inducing_variable.Z\n    iv = self.transform.expand(iv)\n\n    num_inducing = tf.shape(iv)[0]\n\n    err = Y_data - self.mean_function(X_data)\n    kuf = self.kernel(iv, X_data)\n    kuu = self.kernel(iv) + 1e-6 * tf.eye(num_inducing, dtype=iv.dtype)\n    Kus = self.kernel(iv, Xnew)\n    sigma = tf.sqrt(self.likelihood.variance)\n    L = tf.linalg.cholesky(kuu)\n    A = tf.linalg.triangular_solve(L, kuf, lower=True) / sigma\n    B = tf.linalg.matmul(A, A, transpose_b=True) + tf.eye(\n        num_inducing, dtype=default_float()\n    )  # cache qinv\n    LB = tf.linalg.cholesky(B)\n    Aerr = tf.linalg.matmul(A, err)\n    c = tf.linalg.triangular_solve(LB, Aerr, lower=True) / sigma\n    tmp1 = tf.linalg.triangular_solve(L, Kus, lower=True)\n    tmp2 = tf.linalg.triangular_solve(LB, tmp1, lower=True)\n    mean = tf.linalg.matmul(tmp2, c, transpose_a=True)\n    if full_cov:\n        var = (\n            self.kernel(Xnew)\n            + tf.linalg.matmul(tmp2, tmp2, transpose_a=True)\n            - tf.linalg.matmul(tmp1, tmp1, transpose_a=True)\n        )\n        var = tf.tile(var[None, ...], [self.num_latent_gps, 1, 1])  # [P, N, N]\n    else:\n        var = (\n            self.kernel(Xnew, full_cov=False)\n            + tf.reduce_sum(tf.square(tmp2), 0)\n            - tf.reduce_sum(tf.square(tmp1), 0)\n        )\n        var = tf.tile(var[:, None], [1, self.num_latent_gps])\n\n    return mean + self.mean_function(Xnew), var\n</code></pre>"},{"location":"api/models/core/AugmentedSGPR.html#sgptools.models.core.augmented_sgpr.AugmentedSGPR.update","title":"<code>update(noise_variance, kernel)</code>","text":"<p>Update SGP noise variance and kernel function parameters</p> <p>Parameters:</p> Name Type Description Default <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required Source code in <code>sgptools/models/core/augmented_sgpr.py</code> <pre><code>def update(self, noise_variance, kernel):\n    \"\"\"Update SGP noise variance and kernel function parameters\n\n    Args:\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n    \"\"\"\n    self.likelihood.variance.assign(noise_variance)\n    self.kernel.lengthscales.assign(kernel.lengthscales)\n    self.kernel.variance.assign(kernel.variance)\n</code></pre>"},{"location":"api/models/core/OSGPR.html","title":"OSGPR","text":""},{"location":"api/models/core/OSGPR.html#sgptools.models.core.osgpr","title":"<code>sgptools.models.core.osgpr</code>","text":"<p>Provides a streaming sparse Gaussian process model along with initialization function</p>"},{"location":"api/models/core/OSGPR.html#sgptools.models.core.osgpr.OSGPR_VFE","title":"<code>OSGPR_VFE</code>","text":"<p>               Bases: <code>GPModel</code>, <code>InternalDataTrainingLossMixin</code></p> <p>Online Sparse Variational GP regression model from streaming_sparse_gp</p> Refer to the following paper for more details <ul> <li>Streaming Gaussian process approximations [Bui et al., 2017]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>tuple</code> <p>(X, y) ndarrays with inputs (n, d) and labels (n, 1)</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>mu_old</code> <code>ndarray</code> <p>mean of old <code>q(u)</code>; here <code>u</code> are the latents corresponding to the inducing points <code>Z_old</code></p> required <code>Su_old</code> <code>ndarray</code> <p>posterior covariance of old <code>q(u)</code></p> required <code>Kaa_old</code> <code>ndarray</code> <p>prior covariance of old <code>q(u)</code></p> required <code>Z_old</code> <code>ndarray</code> <p>(m_old, d): Old initial inducing points</p> required <code>Z</code> <code>ndarray</code> <p>(m_new, d): New initial inducing points</p> required <code>mean_function</code> <code>function</code> <p>GP mean function</p> <code>None</code> Source code in <code>sgptools/models/core/osgpr.py</code> <pre><code>class OSGPR_VFE(GPModel, InternalDataTrainingLossMixin):\n    \"\"\"Online Sparse Variational GP regression model from [streaming_sparse_gp](https://github.com/thangbui/streaming_sparse_gp/tree/master)\n\n    Refer to the following paper for more details:\n        - Streaming Gaussian process approximations [Bui et al., 2017]\n\n    Args:\n        data (tuple): (X, y) ndarrays with inputs (n, d) and labels (n, 1)\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        mu_old (ndarray): mean of old `q(u)`; here `u` are the latents corresponding to the inducing points `Z_old`\n        Su_old (ndarray): posterior covariance of old `q(u)`\n        Kaa_old (ndarray): prior covariance of old `q(u)`\n        Z_old (ndarray): (m_old, d): Old initial inducing points\n        Z (ndarray): (m_new, d): New initial inducing points\n        mean_function (function): GP mean function\n    \"\"\"\n    def __init__(self, data, kernel, mu_old, Su_old, Kaa_old, Z_old, Z, mean_function=None):\n        self.X, self.Y = self.data = gpflow.models.util.data_input_to_tensor(data)\n        likelihood = gpflow.likelihoods.Gaussian()\n        num_latent_gps = GPModel.calc_num_latent_gps_from_data(data, kernel, likelihood)\n        super().__init__(kernel, likelihood, mean_function, num_latent_gps)\n\n        self.inducing_variable = InducingPoints(Z)\n        self.num_data = self.X.shape[0]\n\n        self.mu_old = tf.Variable(mu_old, shape=tf.TensorShape(None), trainable=False)\n        self.M_old = Z_old.shape[0]\n        self.Su_old = tf.Variable(Su_old, shape=tf.TensorShape(None), trainable=False)\n        self.Kaa_old = tf.Variable(Kaa_old, shape=tf.TensorShape(None), trainable=False)\n        self.Z_old = tf.Variable(Z_old, shape=tf.TensorShape(None), trainable=False)\n\n    def update(self, data):\n        \"\"\"Configure the OSGPR to adapt to a new batch of data. \n        Note: The OSGPR needs to be trained using gradient-based approaches after update.\n\n        Args:\n            data (tuple): (X, y) ndarrays with new batch of inputs (n, d) and labels (n, 1)\n        \"\"\"\n        self.X, self.Y = self.data = gpflow.models.util.data_input_to_tensor(data)\n        self.num_data = self.X.shape[0]\n\n        self.Z_old = tf.Variable(self.inducing_variable.Z.numpy(), \n                                 shape=tf.TensorShape(None), \n                                 trainable=False)\n\n        # Get posterior mean and covariance for the old inducing points\n        mu_old, Su_old = self.predict_f(self.Z_old, full_cov=True)\n        self.mu_old = tf.Variable(mu_old, shape=tf.TensorShape(None), trainable=False)\n        self.Su_old = tf.Variable(Su_old, shape=tf.TensorShape(None), trainable=False)\n\n        # Get the prior covariance matrix for the old inducing points\n        Kaa_old = self.kernel(self.Z_old)\n        self.Kaa_old = tf.Variable(Kaa_old, shape=tf.TensorShape(None), trainable=False)\n\n    def _common_terms(self):\n        Mb = self.inducing_variable.num_inducing\n        Ma = self.M_old\n        # jitter = gpflow.default_jitter()\n        jitter = gpflow.utilities.to_default_float(1e-4)\n        sigma2 = self.likelihood.variance\n        sigma = tf.sqrt(sigma2)\n\n        Saa = self.Su_old\n        ma = self.mu_old\n\n        # a is old inducing points, b is new\n        # f is training points\n        # s is test points\n        Kbf = covariances.Kuf(self.inducing_variable, self.kernel, self.X)\n        Kbb = covariances.Kuu(self.inducing_variable, self.kernel, jitter=jitter)\n        Kba = covariances.Kuf(self.inducing_variable, self.kernel, self.Z_old)\n        Kaa_cur = gpflow.utilities.add_noise_cov(self.kernel(self.Z_old), jitter)\n        Kaa = gpflow.utilities.add_noise_cov(self.Kaa_old, jitter)\n\n        err = self.Y - self.mean_function(self.X)\n\n        Sainv_ma = tf.linalg.solve(Saa, ma)\n        Sinv_y = self.Y / sigma2\n        c1 = tf.matmul(Kbf, Sinv_y)\n        c2 = tf.matmul(Kba, Sainv_ma)\n        c = c1 + c2\n\n        Lb = tf.linalg.cholesky(Kbb)\n        Lbinv_c = tf.linalg.triangular_solve(Lb, c, lower=True)\n        Lbinv_Kba = tf.linalg.triangular_solve(Lb, Kba, lower=True)\n        Lbinv_Kbf = tf.linalg.triangular_solve(Lb, Kbf, lower=True) / sigma\n        d1 = tf.matmul(Lbinv_Kbf, Lbinv_Kbf, transpose_b=True)\n\n        LSa = tf.linalg.cholesky(Saa)\n        Kab_Lbinv = tf.linalg.matrix_transpose(Lbinv_Kba)\n        LSainv_Kab_Lbinv = tf.linalg.triangular_solve(\n            LSa, Kab_Lbinv, lower=True)\n        d2 = tf.matmul(LSainv_Kab_Lbinv, LSainv_Kab_Lbinv, transpose_a=True)\n\n        La = tf.linalg.cholesky(Kaa)\n        Lainv_Kab_Lbinv = tf.linalg.triangular_solve(\n            La, Kab_Lbinv, lower=True)\n        d3 = tf.matmul(Lainv_Kab_Lbinv, Lainv_Kab_Lbinv, transpose_a=True)\n\n        D = tf.eye(Mb, dtype=gpflow.default_float()) + d1 + d2 - d3\n        D = gpflow.utilities.add_noise_cov(D, jitter)\n        LD = tf.linalg.cholesky(D)\n\n        LDinv_Lbinv_c = tf.linalg.triangular_solve(LD, Lbinv_c, lower=True)\n\n        return (Kbf, Kba, Kaa, Kaa_cur, La, Kbb, Lb, D, LD,\n                Lbinv_Kba, LDinv_Lbinv_c, err, d1)\n\n    def maximum_log_likelihood_objective(self):\n        \"\"\"\n        Construct a tensorflow function to compute the bound on the marginal\n        likelihood. \n        \"\"\"\n\n        Mb = self.inducing_variable.num_inducing\n        Ma = self.M_old\n        jitter = gpflow.default_jitter()\n        # jitter = gpflow.utilities.to_default_float(1e-4)\n        sigma2 = self.likelihood.variance\n        sigma = tf.sqrt(sigma2)\n        N = self.num_data\n\n        Saa = self.Su_old\n        ma = self.mu_old\n\n        # a is old inducing points, b is new\n        # f is training points\n        Kfdiag = self.kernel(self.X, full_cov=False)\n        (Kbf, Kba, Kaa, Kaa_cur, La, Kbb, Lb, D, LD,\n            Lbinv_Kba, LDinv_Lbinv_c, err, Qff) = self._common_terms()\n\n        LSa = tf.linalg.cholesky(Saa)\n        Lainv_ma = tf.linalg.triangular_solve(LSa, ma, lower=True)\n\n        # constant term\n        bound = -0.5 * N * np.log(2 * np.pi)\n        # quadratic term\n        bound += -0.5 * tf.reduce_sum(tf.square(err)) / sigma2\n        # bound += -0.5 * tf.reduce_sum(ma * Sainv_ma)\n        bound += -0.5 * tf.reduce_sum(tf.square(Lainv_ma))\n        bound += 0.5 * tf.reduce_sum(tf.square(LDinv_Lbinv_c))\n        # log det term\n        bound += -0.5 * N * tf.reduce_sum(tf.math.log(sigma2))\n        bound += - tf.reduce_sum(tf.math.log(tf.linalg.diag_part(LD)))\n\n        # delta 1: trace term\n        bound += -0.5 * tf.reduce_sum(Kfdiag) / sigma2\n        bound += 0.5 * tf.reduce_sum(tf.linalg.diag_part(Qff))\n\n        # delta 2: a and b difference\n        bound += tf.reduce_sum(tf.math.log(tf.linalg.diag_part(La)))\n        bound += - tf.reduce_sum(tf.math.log(tf.linalg.diag_part(LSa)))\n\n        Kaadiff = Kaa_cur - tf.matmul(Lbinv_Kba, Lbinv_Kba, transpose_a=True)\n        Sainv_Kaadiff = tf.linalg.solve(Saa, Kaadiff)\n        Kainv_Kaadiff = tf.linalg.solve(Kaa, Kaadiff)\n\n        bound += -0.5 * tf.reduce_sum(\n            tf.linalg.diag_part(Sainv_Kaadiff) - tf.linalg.diag_part(Kainv_Kaadiff))\n\n        return bound\n\n    def predict_f(self, Xnew, full_cov=False):\n        \"\"\"\n        Compute the mean and variance of the latent function at some new points\n        Xnew. \n        \"\"\"\n\n        # jitter = gpflow.default_jitter()\n        jitter = gpflow.utilities.to_default_float(1e-4)\n\n        # a is old inducing points, b is new\n        # f is training points\n        # s is test points\n        Kbs = covariances.Kuf(self.inducing_variable, self.kernel, Xnew)\n        (Kbf, Kba, Kaa, Kaa_cur, La, Kbb, Lb, D, LD,\n            Lbinv_Kba, LDinv_Lbinv_c, err, Qff) = self._common_terms()\n\n        Lbinv_Kbs = tf.linalg.triangular_solve(Lb, Kbs, lower=True)\n        LDinv_Lbinv_Kbs = tf.linalg.triangular_solve(LD, Lbinv_Kbs, lower=True)\n        mean = tf.matmul(LDinv_Lbinv_Kbs, LDinv_Lbinv_c, transpose_a=True)\n\n        if full_cov:\n            Kss = self.kernel(Xnew) + jitter * tf.eye(tf.shape(Xnew)[0], dtype=gpflow.default_float())\n            var1 = Kss\n            var2 = - tf.matmul(Lbinv_Kbs, Lbinv_Kbs, transpose_a=True)\n            var3 = tf.matmul(LDinv_Lbinv_Kbs, LDinv_Lbinv_Kbs, transpose_a=True)\n            var = var1 + var2 + var3\n        else:\n            var1 = self.kernel(Xnew, full_cov=False)\n            var2 = -tf.reduce_sum(tf.square(Lbinv_Kbs), axis=0)\n            var3 = tf.reduce_sum(tf.square(LDinv_Lbinv_Kbs), axis=0)\n            var = var1 + var2 + var3\n\n        return mean + self.mean_function(Xnew), var\n</code></pre>"},{"location":"api/models/core/OSGPR.html#sgptools.models.core.osgpr.OSGPR_VFE.maximum_log_likelihood_objective","title":"<code>maximum_log_likelihood_objective()</code>","text":"<p>Construct a tensorflow function to compute the bound on the marginal likelihood.</p> Source code in <code>sgptools/models/core/osgpr.py</code> <pre><code>def maximum_log_likelihood_objective(self):\n    \"\"\"\n    Construct a tensorflow function to compute the bound on the marginal\n    likelihood. \n    \"\"\"\n\n    Mb = self.inducing_variable.num_inducing\n    Ma = self.M_old\n    jitter = gpflow.default_jitter()\n    # jitter = gpflow.utilities.to_default_float(1e-4)\n    sigma2 = self.likelihood.variance\n    sigma = tf.sqrt(sigma2)\n    N = self.num_data\n\n    Saa = self.Su_old\n    ma = self.mu_old\n\n    # a is old inducing points, b is new\n    # f is training points\n    Kfdiag = self.kernel(self.X, full_cov=False)\n    (Kbf, Kba, Kaa, Kaa_cur, La, Kbb, Lb, D, LD,\n        Lbinv_Kba, LDinv_Lbinv_c, err, Qff) = self._common_terms()\n\n    LSa = tf.linalg.cholesky(Saa)\n    Lainv_ma = tf.linalg.triangular_solve(LSa, ma, lower=True)\n\n    # constant term\n    bound = -0.5 * N * np.log(2 * np.pi)\n    # quadratic term\n    bound += -0.5 * tf.reduce_sum(tf.square(err)) / sigma2\n    # bound += -0.5 * tf.reduce_sum(ma * Sainv_ma)\n    bound += -0.5 * tf.reduce_sum(tf.square(Lainv_ma))\n    bound += 0.5 * tf.reduce_sum(tf.square(LDinv_Lbinv_c))\n    # log det term\n    bound += -0.5 * N * tf.reduce_sum(tf.math.log(sigma2))\n    bound += - tf.reduce_sum(tf.math.log(tf.linalg.diag_part(LD)))\n\n    # delta 1: trace term\n    bound += -0.5 * tf.reduce_sum(Kfdiag) / sigma2\n    bound += 0.5 * tf.reduce_sum(tf.linalg.diag_part(Qff))\n\n    # delta 2: a and b difference\n    bound += tf.reduce_sum(tf.math.log(tf.linalg.diag_part(La)))\n    bound += - tf.reduce_sum(tf.math.log(tf.linalg.diag_part(LSa)))\n\n    Kaadiff = Kaa_cur - tf.matmul(Lbinv_Kba, Lbinv_Kba, transpose_a=True)\n    Sainv_Kaadiff = tf.linalg.solve(Saa, Kaadiff)\n    Kainv_Kaadiff = tf.linalg.solve(Kaa, Kaadiff)\n\n    bound += -0.5 * tf.reduce_sum(\n        tf.linalg.diag_part(Sainv_Kaadiff) - tf.linalg.diag_part(Kainv_Kaadiff))\n\n    return bound\n</code></pre>"},{"location":"api/models/core/OSGPR.html#sgptools.models.core.osgpr.OSGPR_VFE.predict_f","title":"<code>predict_f(Xnew, full_cov=False)</code>","text":"<p>Compute the mean and variance of the latent function at some new points Xnew.</p> Source code in <code>sgptools/models/core/osgpr.py</code> <pre><code>def predict_f(self, Xnew, full_cov=False):\n    \"\"\"\n    Compute the mean and variance of the latent function at some new points\n    Xnew. \n    \"\"\"\n\n    # jitter = gpflow.default_jitter()\n    jitter = gpflow.utilities.to_default_float(1e-4)\n\n    # a is old inducing points, b is new\n    # f is training points\n    # s is test points\n    Kbs = covariances.Kuf(self.inducing_variable, self.kernel, Xnew)\n    (Kbf, Kba, Kaa, Kaa_cur, La, Kbb, Lb, D, LD,\n        Lbinv_Kba, LDinv_Lbinv_c, err, Qff) = self._common_terms()\n\n    Lbinv_Kbs = tf.linalg.triangular_solve(Lb, Kbs, lower=True)\n    LDinv_Lbinv_Kbs = tf.linalg.triangular_solve(LD, Lbinv_Kbs, lower=True)\n    mean = tf.matmul(LDinv_Lbinv_Kbs, LDinv_Lbinv_c, transpose_a=True)\n\n    if full_cov:\n        Kss = self.kernel(Xnew) + jitter * tf.eye(tf.shape(Xnew)[0], dtype=gpflow.default_float())\n        var1 = Kss\n        var2 = - tf.matmul(Lbinv_Kbs, Lbinv_Kbs, transpose_a=True)\n        var3 = tf.matmul(LDinv_Lbinv_Kbs, LDinv_Lbinv_Kbs, transpose_a=True)\n        var = var1 + var2 + var3\n    else:\n        var1 = self.kernel(Xnew, full_cov=False)\n        var2 = -tf.reduce_sum(tf.square(Lbinv_Kbs), axis=0)\n        var3 = tf.reduce_sum(tf.square(LDinv_Lbinv_Kbs), axis=0)\n        var = var1 + var2 + var3\n\n    return mean + self.mean_function(Xnew), var\n</code></pre>"},{"location":"api/models/core/OSGPR.html#sgptools.models.core.osgpr.OSGPR_VFE.update","title":"<code>update(data)</code>","text":"<p>Configure the OSGPR to adapt to a new batch of data.  Note: The OSGPR needs to be trained using gradient-based approaches after update.</p> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>tuple</code> <p>(X, y) ndarrays with new batch of inputs (n, d) and labels (n, 1)</p> required Source code in <code>sgptools/models/core/osgpr.py</code> <pre><code>def update(self, data):\n    \"\"\"Configure the OSGPR to adapt to a new batch of data. \n    Note: The OSGPR needs to be trained using gradient-based approaches after update.\n\n    Args:\n        data (tuple): (X, y) ndarrays with new batch of inputs (n, d) and labels (n, 1)\n    \"\"\"\n    self.X, self.Y = self.data = gpflow.models.util.data_input_to_tensor(data)\n    self.num_data = self.X.shape[0]\n\n    self.Z_old = tf.Variable(self.inducing_variable.Z.numpy(), \n                             shape=tf.TensorShape(None), \n                             trainable=False)\n\n    # Get posterior mean and covariance for the old inducing points\n    mu_old, Su_old = self.predict_f(self.Z_old, full_cov=True)\n    self.mu_old = tf.Variable(mu_old, shape=tf.TensorShape(None), trainable=False)\n    self.Su_old = tf.Variable(Su_old, shape=tf.TensorShape(None), trainable=False)\n\n    # Get the prior covariance matrix for the old inducing points\n    Kaa_old = self.kernel(self.Z_old)\n    self.Kaa_old = tf.Variable(Kaa_old, shape=tf.TensorShape(None), trainable=False)\n</code></pre>"},{"location":"api/models/core/OSGPR.html#sgptools.models.core.osgpr.init_osgpr","title":"<code>init_osgpr(X_train, num_inducing=10, lengthscales=1.0, variance=1.0, noise_variance=0.001, kernel=None)</code>","text":"<p>Initialize a VFE OSGPR model with an RBF kernel with  unit variance and lengthcales, and 0.001 noise variance. Used in the Online Continuous SGP approach. </p> <p>Parameters:</p> Name Type Description Default <code>X_train</code> <code>ndarray</code> <p>(n, d); Unlabeled random sampled training points.              They only effect the initial inducing point locations,              i.e., limits them to the bounds of the data</p> required <code>num_inducing</code> <code>int</code> <p>Number of inducing points</p> <code>10</code> <code>lengthscales</code> <code>float or list</code> <p>Kernel lengthscale(s), if passed as a list,                      each element corresponds to each data dimension</p> <code>1.0</code> <code>variance</code> <code>float</code> <p>Kernel variance</p> <code>1.0</code> <code>noise_variance</code> <code>float</code> <p>Data noise variance</p> <code>0.001</code> <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> <code>None</code> <p>Returns:</p> Name Type Description <code>online_param</code> <code>OSGPR_VFE</code> <p>Initialized online sparse Gaussian process model</p> Source code in <code>sgptools/models/core/osgpr.py</code> <pre><code>def init_osgpr(X_train, \n               num_inducing=10, \n               lengthscales=1.0, \n               variance=1.0,\n               noise_variance=0.001,\n               kernel=None):\n    \"\"\"Initialize a VFE OSGPR model with an RBF kernel with \n    unit variance and lengthcales, and 0.001 noise variance.\n    Used in the Online Continuous SGP approach. \n\n    Args:\n        X_train (ndarray): (n, d); Unlabeled random sampled training points. \n                        They only effect the initial inducing point locations, \n                        i.e., limits them to the bounds of the data\n        num_inducing (int): Number of inducing points\n        lengthscales (float or list): Kernel lengthscale(s), if passed as a list, \n                                each element corresponds to each data dimension\n        variance (float): Kernel variance\n        noise_variance (float): Data noise variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n\n    Returns:\n        online_param (OSGPR_VFE): Initialized online sparse Gaussian process model\n    \"\"\"\n\n    if kernel is None:\n        kernel = gpflow.kernels.SquaredExponential(lengthscales=lengthscales, \n                                                   variance=variance)\n\n    y_train = np.zeros((len(X_train), 1), dtype=X_train.dtype)\n    Z_init = get_inducing_pts(X_train, num_inducing)\n    init_param = gpflow.models.SGPR((X_train, y_train),\n                                    kernel, \n                                    inducing_variable=Z_init, \n                                    noise_variance=noise_variance)\n\n    # Initialize the OSGPR model using the parameters from the SGPR model\n    # The X_train and y_train here will be overwritten in the online phase \n    X_train = np.array([[0, 0], [0, 0]])\n    y_train = np.array([0, 0]).reshape(-1, 1)\n    Zopt = init_param.inducing_variable.Z.numpy()\n    mu, Su = init_param.predict_f(Zopt, full_cov=True)\n    Kaa = init_param.kernel(Zopt)\n    online_param = OSGPR_VFE((X_train[:2], y_train[:2]),\n                             init_param.kernel,\n                             mu, Su[0], Kaa,\n                             Zopt, Zopt)\n    online_param.likelihood.variance.assign(init_param.likelihood.variance)\n\n    return online_param\n</code></pre>"},{"location":"api/models/core/Transformations.html","title":"Transformations","text":""},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations","title":"<code>sgptools.models.core.transformations</code>","text":"<p>Provides transforms to model complex sensor field of views and handle informative path planning</p>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.IPPTransform","title":"<code>IPPTransform</code>","text":"<p>               Bases: <code>Transform</code></p> <p>Transform to model IPP problems</p> Usage details <ul> <li>For point sensing, set <code>sampling_rate = 2</code></li> <li>For continuous sensing, set <code>sampling_rate &gt; 2</code> (account for the information along the path)</li> <li>For continuous sensing with aggregation, set <code>sampling_rate &gt; 2</code> and <code>aggregate_fov = True</code> (faster but solution quality is a bit diminished)</li> <li>If using a non-point FoV model with continuous sampling, only the FoV inducing points are aggregated</li> <li>For multi-robot case, set <code>num_robots &gt; 1</code></li> <li>For onlineIPP use <code>update_fixed</code> to freeze the visited waypoints</li> </ul> <p>Parameters:</p> Name Type Description Default <code>sampling_rate</code> <code>int</code> <p>Number of points to sample between each pair of inducing points</p> <code>2</code> <code>distance_budget</code> <code>float</code> <p>Distance budget for the path</p> <code>None</code> <code>num_robots</code> <code>int</code> <p>Number of robots</p> <code>1</code> <code>Xu_fixed</code> <code>ndarray</code> <p>(num_robots, num_visited, num_dim); Visited waypoints that don't need to be optimized</p> <code>None</code> <code>num_dim</code> <code>int</code> <p>Number of dimensions of the inducing points</p> <code>2</code> <code>sensor_model</code> <code>Transform</code> <p>Transform object to expand each inducing point to <code>p</code> points                        approximating each sensor's FoV</p> <code>None</code> <code>aggregate_fov</code> <code>bool</code> <p>Used only when sampling_rate &gt; 2, i.e., when using a continuous sensing model.                    If <code>True</code>, covariances corresponding to interpolated inducing points along each edge                   of the path are aggregated to reduce the matrix inversion cost.</p> <code>False</code> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>class IPPTransform(Transform):\n    \"\"\"Transform to model IPP problems\n\n    Usage details: \n        * For point sensing, set `sampling_rate = 2`\n        * For continuous sensing, set `sampling_rate &gt; 2` (account for the information along the path)\n        * For continuous sensing with aggregation, set `sampling_rate &gt; 2` and `aggregate_fov = True` (faster but solution quality is a bit diminished)\n        * If using a non-point FoV model with continuous sampling, only the FoV inducing points are aggregated\n        * For multi-robot case, set `num_robots &gt; 1`\n        * For onlineIPP use `update_fixed` to freeze the visited waypoints\n\n    Args:\n        sampling_rate (int): Number of points to sample between each pair of inducing points\n        distance_budget (float): Distance budget for the path\n        num_robots (int): Number of robots\n        Xu_fixed (ndarray): (num_robots, num_visited, num_dim); Visited waypoints that don't need to be optimized\n        num_dim (int): Number of dimensions of the inducing points\n        sensor_model (Transform): Transform object to expand each inducing point to `p` points \n                                  approximating each sensor's FoV\n        aggregate_fov (bool): Used only when sampling_rate &gt; 2, i.e., when using a continuous sensing model. \n                              If `True`, covariances corresponding to interpolated inducing points along each edge\n                              of the path are aggregated to reduce the matrix inversion cost.\n    \"\"\"\n    def __init__(self, \n                 sampling_rate=2, \n                 distance_budget=None, \n                 num_robots=1,\n                 Xu_fixed=None,\n                 num_dim=2,\n                 sensor_model=None,\n                 aggregate_fov=False,\n                 **kwargs):\n        super().__init__(**kwargs)\n        if sampling_rate &lt; 2:\n            raise ValueError('Sampling rate must be greater than 2.')\n\n        self.sampling_rate = sampling_rate\n        self.distance_budget = distance_budget\n        self.num_robots = num_robots\n        self.num_dim = num_dim\n        self.sensor_model = sensor_model\n\n        # Set aggregation size to sampling rate if aggregate_fov is True\n        # and sampling rate is enabled (greater than 2)\n        if aggregate_fov:\n            if self.sensor_model is not None:\n                self.sensor_model.enable_aggregation()\n            elif sampling_rate &gt; 2:\n                self.aggregation_size = sampling_rate\n\n        # Initilize variable to store visited waypoints for onlineIPP\n        if Xu_fixed is not None:\n            self.Xu_fixed = tf.Variable(Xu_fixed, \n                                        shape=tf.TensorShape(None), \n                                        trainable=False)\n        else:\n            self.Xu_fixed = None\n\n    def update_Xu_fixed(self, Xu_fixed):\n        \"\"\"Function to update the visited waypoints\n\n        Args:\n            Xu_fixed (ndarray): numpy array (num_robots, num_visited_waypoints, num_dim)\n        \"\"\"\n        self.num_fixed = Xu_fixed.shape[1]\n        if self.Xu_fixed is not None:\n            self.Xu_fixed.assign(Xu_fixed)\n        else:\n            self.Xu_fixed = tf.Variable(Xu_fixed, \n                                        shape=tf.TensorShape(None), \n                                        trainable=False)\n\n    def expand(self, Xu, expand_sensor_model=True):\n        \"\"\"Sample points between each pair of inducing points to form the path\n\n        Args:\n            Xu (ndarray): (num_robots x num_inducing, num_dim); Inducing points in the num_dim dimensional space\n            expand_sensor_model (bool): Only add the fixed inducing points without other sensor/path transforms, \n                                        used for online IPP\n\n        Returns:\n            Xu (ndarray): Expansion transformed inducing points\n        \"\"\"\n        # If using single-robot offline IPP with point sensing, return inducing points as is.\n        if self.sampling_rate == 2 and self.Xu_fixed is None and self.sensor_model is None:\n            return Xu\n\n        Xu = tf.reshape(Xu, (self.num_robots, -1, self.num_dim))\n\n        # If using online IPP, add visited waypoints that won't be optimized anymore\n        if self.Xu_fixed is not None:\n            Xu = tf.concat([self.Xu_fixed, Xu[:, self.num_fixed:]], axis=1)\n\n        if not expand_sensor_model:\n            return tf.reshape(Xu, (-1, self.num_dim))\n\n        # Interpolate additional inducing points between waypoints to approximate \n        # the continuous data sensing model\n        if self.sampling_rate &gt; 2:\n            Xu = tf.linspace(Xu[:, :-1], Xu[:, 1:], self.sampling_rate)\n            Xu = tf.transpose(Xu, perm=[1, 2, 0, 3])\n            Xu = tf.reshape(Xu, (self.num_robots, -1, self.num_dim))\n\n        if self.sensor_model is not None:\n            Xu_ = []\n            for i in range(self.num_robots):\n                Xu_.append(self.sensor_model.expand(Xu[i]))\n            Xu = tf.concat(Xu_, axis=0)\n            return Xu\n\n        Xu = tf.reshape(Xu, (-1, self.num_dim))\n        return Xu\n\n    def aggregate(self, k):\n        \"\"\"Applies the aggregation transform to kernel matrices. Checks `sensor_model` \n           and uses the appropriate aggregation transform. \n\n        Args:\n            k (tensor): (mp, mp)/(mp, n); Kernel matrix. \n                        `m` is the number of inducing points,\n                        `p` is the number of points each inducing point is mapped,\n                        `n` is the number of training data points.\n\n        Returns:\n            k (tensor): (m, m)/(m, n); Aggregated kernel matrix\n        \"\"\"\n        if self.sensor_model is not None:\n            return self.sensor_model.aggregate(k)\n        else:\n            return super().aggregate(k)\n\n    def constraints(self, Xu):\n        \"\"\"Computes the distance constraint term that is added to the SGP's optimization function.\n        Each robot can be assigned a different distance budget.\n\n        Args:\n            Xu (ndarray): Inducing points from which to compute the distance constraints\n\n        Returns:\n            loss (float): distance constraint term\n        \"\"\"\n        if self.distance_budget is None:\n            return 0.\n        else:\n            # Only do fixed points expansion transform\n            Xu = self.expand(Xu, expand_sensor_model=False)\n            dist = self.distance(Xu)-self.distance_budget\n            dist = tf.reduce_sum(tf.nn.relu(dist))\n            loss = -dist*self.constraint_weight\n            return loss\n\n    def distance(self, Xu):\n        \"\"\"Computes the distance incured by sequentially visiting the inducing points\n        Args:\n            Xu (ndarray): (m, num_dim); Inducing points from which to compute the path lengths\n                          `m` is the number of inducing points\n                          `num_dim` dimension of the data collection environment\n        Returns:\n            dist (float or tensor of floats): path length(s)\n        \"\"\"\n        Xu = tf.reshape(Xu, (self.num_robots, -1, self.num_dim))\n        if self.sensor_model is not None:\n            dists = []\n            for i in range(self.num_robots):\n                dists.append(self.sensor_model.distance(Xu[i]))\n            dists = tf.concat(dists, axis=0)\n            return dists\n        else:\n            # Assumes 2D waypoints by default\n            dist = tf.norm(Xu[:, 1:, :2] - Xu[:, :-1, :2], axis=-1)\n            dist = tf.reduce_sum(dist, axis=1)\n            return dist\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.IPPTransform.aggregate","title":"<code>aggregate(k)</code>","text":"<p>Applies the aggregation transform to kernel matrices. Checks <code>sensor_model</code>     and uses the appropriate aggregation transform. </p> <p>Parameters:</p> Name Type Description Default <code>k</code> <code>tensor</code> <p>(mp, mp)/(mp, n); Kernel matrix.          <code>m</code> is the number of inducing points,         <code>p</code> is the number of points each inducing point is mapped,         <code>n</code> is the number of training data points.</p> required <p>Returns:</p> Name Type Description <code>k</code> <code>tensor</code> <p>(m, m)/(m, n); Aggregated kernel matrix</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def aggregate(self, k):\n    \"\"\"Applies the aggregation transform to kernel matrices. Checks `sensor_model` \n       and uses the appropriate aggregation transform. \n\n    Args:\n        k (tensor): (mp, mp)/(mp, n); Kernel matrix. \n                    `m` is the number of inducing points,\n                    `p` is the number of points each inducing point is mapped,\n                    `n` is the number of training data points.\n\n    Returns:\n        k (tensor): (m, m)/(m, n); Aggregated kernel matrix\n    \"\"\"\n    if self.sensor_model is not None:\n        return self.sensor_model.aggregate(k)\n    else:\n        return super().aggregate(k)\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.IPPTransform.constraints","title":"<code>constraints(Xu)</code>","text":"<p>Computes the distance constraint term that is added to the SGP's optimization function. Each robot can be assigned a different distance budget.</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>Inducing points from which to compute the distance constraints</p> required <p>Returns:</p> Name Type Description <code>loss</code> <code>float</code> <p>distance constraint term</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def constraints(self, Xu):\n    \"\"\"Computes the distance constraint term that is added to the SGP's optimization function.\n    Each robot can be assigned a different distance budget.\n\n    Args:\n        Xu (ndarray): Inducing points from which to compute the distance constraints\n\n    Returns:\n        loss (float): distance constraint term\n    \"\"\"\n    if self.distance_budget is None:\n        return 0.\n    else:\n        # Only do fixed points expansion transform\n        Xu = self.expand(Xu, expand_sensor_model=False)\n        dist = self.distance(Xu)-self.distance_budget\n        dist = tf.reduce_sum(tf.nn.relu(dist))\n        loss = -dist*self.constraint_weight\n        return loss\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.IPPTransform.distance","title":"<code>distance(Xu)</code>","text":"<p>Computes the distance incured by sequentially visiting the inducing points Args:     Xu (ndarray): (m, num_dim); Inducing points from which to compute the path lengths                   <code>m</code> is the number of inducing points                   <code>num_dim</code> dimension of the data collection environment Returns:     dist (float or tensor of floats): path length(s)</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def distance(self, Xu):\n    \"\"\"Computes the distance incured by sequentially visiting the inducing points\n    Args:\n        Xu (ndarray): (m, num_dim); Inducing points from which to compute the path lengths\n                      `m` is the number of inducing points\n                      `num_dim` dimension of the data collection environment\n    Returns:\n        dist (float or tensor of floats): path length(s)\n    \"\"\"\n    Xu = tf.reshape(Xu, (self.num_robots, -1, self.num_dim))\n    if self.sensor_model is not None:\n        dists = []\n        for i in range(self.num_robots):\n            dists.append(self.sensor_model.distance(Xu[i]))\n        dists = tf.concat(dists, axis=0)\n        return dists\n    else:\n        # Assumes 2D waypoints by default\n        dist = tf.norm(Xu[:, 1:, :2] - Xu[:, :-1, :2], axis=-1)\n        dist = tf.reduce_sum(dist, axis=1)\n        return dist\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.IPPTransform.expand","title":"<code>expand(Xu, expand_sensor_model=True)</code>","text":"<p>Sample points between each pair of inducing points to form the path</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(num_robots x num_inducing, num_dim); Inducing points in the num_dim dimensional space</p> required <code>expand_sensor_model</code> <code>bool</code> <p>Only add the fixed inducing points without other sensor/path transforms,                          used for online IPP</p> <code>True</code> <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>Expansion transformed inducing points</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def expand(self, Xu, expand_sensor_model=True):\n    \"\"\"Sample points between each pair of inducing points to form the path\n\n    Args:\n        Xu (ndarray): (num_robots x num_inducing, num_dim); Inducing points in the num_dim dimensional space\n        expand_sensor_model (bool): Only add the fixed inducing points without other sensor/path transforms, \n                                    used for online IPP\n\n    Returns:\n        Xu (ndarray): Expansion transformed inducing points\n    \"\"\"\n    # If using single-robot offline IPP with point sensing, return inducing points as is.\n    if self.sampling_rate == 2 and self.Xu_fixed is None and self.sensor_model is None:\n        return Xu\n\n    Xu = tf.reshape(Xu, (self.num_robots, -1, self.num_dim))\n\n    # If using online IPP, add visited waypoints that won't be optimized anymore\n    if self.Xu_fixed is not None:\n        Xu = tf.concat([self.Xu_fixed, Xu[:, self.num_fixed:]], axis=1)\n\n    if not expand_sensor_model:\n        return tf.reshape(Xu, (-1, self.num_dim))\n\n    # Interpolate additional inducing points between waypoints to approximate \n    # the continuous data sensing model\n    if self.sampling_rate &gt; 2:\n        Xu = tf.linspace(Xu[:, :-1], Xu[:, 1:], self.sampling_rate)\n        Xu = tf.transpose(Xu, perm=[1, 2, 0, 3])\n        Xu = tf.reshape(Xu, (self.num_robots, -1, self.num_dim))\n\n    if self.sensor_model is not None:\n        Xu_ = []\n        for i in range(self.num_robots):\n            Xu_.append(self.sensor_model.expand(Xu[i]))\n        Xu = tf.concat(Xu_, axis=0)\n        return Xu\n\n    Xu = tf.reshape(Xu, (-1, self.num_dim))\n    return Xu\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.IPPTransform.update_Xu_fixed","title":"<code>update_Xu_fixed(Xu_fixed)</code>","text":"<p>Function to update the visited waypoints</p> <p>Parameters:</p> Name Type Description Default <code>Xu_fixed</code> <code>ndarray</code> <p>numpy array (num_robots, num_visited_waypoints, num_dim)</p> required Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def update_Xu_fixed(self, Xu_fixed):\n    \"\"\"Function to update the visited waypoints\n\n    Args:\n        Xu_fixed (ndarray): numpy array (num_robots, num_visited_waypoints, num_dim)\n    \"\"\"\n    self.num_fixed = Xu_fixed.shape[1]\n    if self.Xu_fixed is not None:\n        self.Xu_fixed.assign(Xu_fixed)\n    else:\n        self.Xu_fixed = tf.Variable(Xu_fixed, \n                                    shape=tf.TensorShape(None), \n                                    trainable=False)\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareHeightTransform","title":"<code>SquareHeightTransform</code>","text":"<p>               Bases: <code>Transform</code></p> <p>Non-point Transform to model a height-dependent square FoV</p> <p>Parameters:</p> Name Type Description Default <code>num_side</code> <code>int</code> <p>Number of points along each side of the FoV</p> required <code>aggregate_fov</code> <code>bool</code> <p>If <code>True</code>, covariances corresponding to interpolated inducing points used to                    approximate the sensor FoV are aggregated to reduce the matrix inversion cost</p> <code>False</code> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>class SquareHeightTransform(Transform):\n    \"\"\"Non-point Transform to model a height-dependent square FoV\n\n    Args:\n        num_side (int): Number of points along each side of the FoV\n        aggregate_fov (bool): If `True`, covariances corresponding to interpolated inducing points used to \n                              approximate the sensor FoV are aggregated to reduce the matrix inversion cost\n    \"\"\"\n    def __init__(self, num_side, aggregate_fov=False, **kwargs):\n        super().__init__(**kwargs)\n        self.num_side = num_side\n\n        if aggregate_fov:\n            self.enable_aggregation()\n\n    def enable_aggregation(self, size=None):\n        \"\"\"Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the \n        covariance matrix size.\n\n        Args:\n            size (int): If None, all the interpolated inducing points within the FoV are aggregated. Alternatively, \n                        the number of inducing points to aggregate can be explicitly defined using this variable.\n        \"\"\"\n        if size is None:\n            self.aggregation_size = self.num_side**2\n        else:\n            self.aggregation_size = size\n\n    def expand(self, Xu):     \n        \"\"\"\n        Applies the expansion transform to the inducing points\n\n        Args:\n            Xu (ndarray): (m, 3); Inducing points in the 3D position space.\n                        `m` is the number of inducing points,\n                        `3` is the dimension of the space (x, y, z)\n\n        Returns:\n            Xu (ndarray): (mp, 2); Inducing points in input space.\n                        `p` is the number of points each inducing point is mapped \n                        to in order to form the FoV.\n        \"\"\"\n        x, y, h = tf.split(Xu, num_or_size_splits=3, axis=1)\n        x = tf.reshape(x, [-1,])\n        y = tf.reshape(y, [-1,])\n        h = tf.reshape(h, [-1,])\n\n        delta = h / (self.num_side - 1)\n\n        pts = []\n        for i in range(self.num_side):\n            pts.append(tf.linspace([x - h/2, y - (h/2) + (delta * i)], \n                                   [x + h/2, y - (h/2) + (delta * i)], \n                                   self.num_side, \n                                   axis=1))\n        xy = tf.concat(pts, axis=1)\n        xy = tf.transpose(xy, [2, 1, 0])\n        xy = tf.reshape(xy, [-1, 2])\n        xy = self._reshape(xy, tf.shape(Xu)[0])\n        return xy\n\n    def _reshape(self, X, num_inducing):\n        \"\"\"Reorder the inducing points to be in the correct order for aggregation with square height FoV\n\n        Args:\n            X (ndarray): (mp, 2); Inducing points in input space. `p` is the number of points each \n                        inducing point is mapped to in order to form the FoV.\n\n        Returns:\n            Xu (ndarray): (mp, 2); Reorder inducing points\n        \"\"\"\n        X = tf.reshape(X, (num_inducing, -1, self.num_side, self.num_side, 2))\n        X = tf.transpose(X, (0, 2, 1, 3, 4))\n        X = tf.reshape(X, (-1, 2))\n        return X\n\n    def distance(self, Xu):\n        \"\"\"Computes the distance incured by sequentially visiting the inducing points\n        Args:\n            Xu (ndarray): (m, 3); Inducing points from which to compute the path lengths.\n                          `m` is the number of inducing points.\n\n        Returns:\n            dist (float): path lengths\n        \"\"\"\n        Xu = tf.reshape(Xu, (-1, 3))\n        dist = tf.norm(Xu[1:] - Xu[:-1], axis=-1)\n        dist = tf.reduce_sum(dist, axis=0)\n        return dist\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareHeightTransform.distance","title":"<code>distance(Xu)</code>","text":"<p>Computes the distance incured by sequentially visiting the inducing points Args:     Xu (ndarray): (m, 3); Inducing points from which to compute the path lengths.                   <code>m</code> is the number of inducing points.</p> <p>Returns:</p> Name Type Description <code>dist</code> <code>float</code> <p>path lengths</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def distance(self, Xu):\n    \"\"\"Computes the distance incured by sequentially visiting the inducing points\n    Args:\n        Xu (ndarray): (m, 3); Inducing points from which to compute the path lengths.\n                      `m` is the number of inducing points.\n\n    Returns:\n        dist (float): path lengths\n    \"\"\"\n    Xu = tf.reshape(Xu, (-1, 3))\n    dist = tf.norm(Xu[1:] - Xu[:-1], axis=-1)\n    dist = tf.reduce_sum(dist, axis=0)\n    return dist\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareHeightTransform.enable_aggregation","title":"<code>enable_aggregation(size=None)</code>","text":"<p>Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the  covariance matrix size.</p> <p>Parameters:</p> Name Type Description Default <code>size</code> <code>int</code> <p>If None, all the interpolated inducing points within the FoV are aggregated. Alternatively,          the number of inducing points to aggregate can be explicitly defined using this variable.</p> <code>None</code> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def enable_aggregation(self, size=None):\n    \"\"\"Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the \n    covariance matrix size.\n\n    Args:\n        size (int): If None, all the interpolated inducing points within the FoV are aggregated. Alternatively, \n                    the number of inducing points to aggregate can be explicitly defined using this variable.\n    \"\"\"\n    if size is None:\n        self.aggregation_size = self.num_side**2\n    else:\n        self.aggregation_size = size\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareHeightTransform.expand","title":"<code>expand(Xu)</code>","text":"<p>Applies the expansion transform to the inducing points</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(m, 3); Inducing points in the 3D position space.         <code>m</code> is the number of inducing points,         <code>3</code> is the dimension of the space (x, y, z)</p> required <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(mp, 2); Inducing points in input space.         <code>p</code> is the number of points each inducing point is mapped          to in order to form the FoV.</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def expand(self, Xu):     \n    \"\"\"\n    Applies the expansion transform to the inducing points\n\n    Args:\n        Xu (ndarray): (m, 3); Inducing points in the 3D position space.\n                    `m` is the number of inducing points,\n                    `3` is the dimension of the space (x, y, z)\n\n    Returns:\n        Xu (ndarray): (mp, 2); Inducing points in input space.\n                    `p` is the number of points each inducing point is mapped \n                    to in order to form the FoV.\n    \"\"\"\n    x, y, h = tf.split(Xu, num_or_size_splits=3, axis=1)\n    x = tf.reshape(x, [-1,])\n    y = tf.reshape(y, [-1,])\n    h = tf.reshape(h, [-1,])\n\n    delta = h / (self.num_side - 1)\n\n    pts = []\n    for i in range(self.num_side):\n        pts.append(tf.linspace([x - h/2, y - (h/2) + (delta * i)], \n                               [x + h/2, y - (h/2) + (delta * i)], \n                               self.num_side, \n                               axis=1))\n    xy = tf.concat(pts, axis=1)\n    xy = tf.transpose(xy, [2, 1, 0])\n    xy = tf.reshape(xy, [-1, 2])\n    xy = self._reshape(xy, tf.shape(Xu)[0])\n    return xy\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareTransform","title":"<code>SquareTransform</code>","text":"<p>               Bases: <code>Transform</code></p> <p>Non-point Transform to model a square FoV. Only works for single robot cases. </p> <p>Parameters:</p> Name Type Description Default <code>length</code> <code>float</code> <p>Length of the square FoV</p> required <code>num_side</code> <code>int</code> <p>Number of points along each side of the FoV</p> required <code>aggregate_fov</code> <code>bool</code> <p>If <code>True</code>, covariances corresponding to interpolated inducing points used to                    approximate the sensor FoV are aggregated to reduce the matrix inversion cost</p> <code>False</code> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>class SquareTransform(Transform):\n    \"\"\"Non-point Transform to model a square FoV. Only works for single robot cases. \n\n    Args:\n        length (float): Length of the square FoV\n        num_side (int): Number of points along each side of the FoV\n        aggregate_fov (bool): If `True`, covariances corresponding to interpolated inducing points used to \n                              approximate the sensor FoV are aggregated to reduce the matrix inversion cost\n    \"\"\"\n    def __init__(self, length, num_side, aggregate_fov=False, **kwargs):\n        super().__init__(**kwargs)\n        self.length = length\n        self.num_side = num_side\n        self.length_factor=length/(self.num_side)\n        self.num_length = int(length/self.length_factor)\n\n        if aggregate_fov:\n            self.enable_aggregation()\n\n    def enable_aggregation(self, size=None):\n        \"\"\"Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the \n        covariance matrix size.\n\n        Args:\n            size (int): If None, all the interpolated inducing points within the FoV are aggregated. Alternatively, \n                        the number of inducing points to aggregate can be explicitly defined using this variable.\n        \"\"\"\n        if size is None:\n            self.aggregation_size = self.num_side**2\n        else:\n            self.aggregation_size = size\n\n    def expand(self, Xu):\n        \"\"\"Applies the expansion transformation to the inducing points\n\n        Args:\n            Xu (ndarray): (m, 3); Inducing points in the position and orientation space.\n                          `m` is the number of inducing points,\n                          `3` is the dimension of the space (x, y, angle in radians)\n\n        Returns:\n            Xu (ndarray): (mp, 2); Inducing points in input space.\n                        `p` is the number of points each inducing point is mapped \n                         to in order to form the FoV.\n        \"\"\"\n        x, y, theta = tf.split(Xu, num_or_size_splits=3, axis=1)\n        x     = tf.reshape(x, [-1,])\n        y     = tf.reshape(y, [-1,])\n        theta = tf.reshape(theta, [-1,])\n\n        points = []\n        for i in range(-int(np.floor((self.num_side)/2)), int(np.ceil((self.num_side)/2))):\n            points.append(tf.linspace([(x + (i * self.length_factor) * tf.cos(theta)) - self.length/2 * tf.cos(theta+np.pi/2), \n                                       (y + (i * self.length_factor) * tf.sin(theta)) - self.length/2 * tf.sin(theta+np.pi/2)], \n                                      [(x + (i * self.length_factor) * tf.cos(theta)) + self.length/2 * tf.cos(theta+np.pi/2), \n                                       (y + (i * self.length_factor) * tf.sin(theta)) + self.length/2 * tf.sin(theta+np.pi/2)], \n                                      self.num_side, axis=1))\n        xy = tf.concat(points, axis=1)\n        xy = tf.transpose(xy, [2, 1, 0])\n        xy = tf.reshape(xy, (-1, 2))\n        return xy\n\n    def distance(self, Xu):\n        \"\"\"Computes the distance incured by sequentially visiting the inducing points\n        Args:\n            Xu (ndarray): (m, 3); Inducing points from which to compute the path lengths.\n                          `m` is the number of inducing points.\n\n        Returns:\n            dist (float): path lengths\n        \"\"\"\n        Xu = tf.reshape(Xu, (-1, 3))[:, :2]\n        dist = tf.norm(Xu[1:] - Xu[:-1], axis=-1)\n        dist = tf.reduce_sum(dist, axis=0)\n        return dist\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareTransform.distance","title":"<code>distance(Xu)</code>","text":"<p>Computes the distance incured by sequentially visiting the inducing points Args:     Xu (ndarray): (m, 3); Inducing points from which to compute the path lengths.                   <code>m</code> is the number of inducing points.</p> <p>Returns:</p> Name Type Description <code>dist</code> <code>float</code> <p>path lengths</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def distance(self, Xu):\n    \"\"\"Computes the distance incured by sequentially visiting the inducing points\n    Args:\n        Xu (ndarray): (m, 3); Inducing points from which to compute the path lengths.\n                      `m` is the number of inducing points.\n\n    Returns:\n        dist (float): path lengths\n    \"\"\"\n    Xu = tf.reshape(Xu, (-1, 3))[:, :2]\n    dist = tf.norm(Xu[1:] - Xu[:-1], axis=-1)\n    dist = tf.reduce_sum(dist, axis=0)\n    return dist\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareTransform.enable_aggregation","title":"<code>enable_aggregation(size=None)</code>","text":"<p>Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the  covariance matrix size.</p> <p>Parameters:</p> Name Type Description Default <code>size</code> <code>int</code> <p>If None, all the interpolated inducing points within the FoV are aggregated. Alternatively,          the number of inducing points to aggregate can be explicitly defined using this variable.</p> <code>None</code> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def enable_aggregation(self, size=None):\n    \"\"\"Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the \n    covariance matrix size.\n\n    Args:\n        size (int): If None, all the interpolated inducing points within the FoV are aggregated. Alternatively, \n                    the number of inducing points to aggregate can be explicitly defined using this variable.\n    \"\"\"\n    if size is None:\n        self.aggregation_size = self.num_side**2\n    else:\n        self.aggregation_size = size\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareTransform.expand","title":"<code>expand(Xu)</code>","text":"<p>Applies the expansion transformation to the inducing points</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(m, 3); Inducing points in the position and orientation space.           <code>m</code> is the number of inducing points,           <code>3</code> is the dimension of the space (x, y, angle in radians)</p> required <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(mp, 2); Inducing points in input space.         <code>p</code> is the number of points each inducing point is mapped           to in order to form the FoV.</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def expand(self, Xu):\n    \"\"\"Applies the expansion transformation to the inducing points\n\n    Args:\n        Xu (ndarray): (m, 3); Inducing points in the position and orientation space.\n                      `m` is the number of inducing points,\n                      `3` is the dimension of the space (x, y, angle in radians)\n\n    Returns:\n        Xu (ndarray): (mp, 2); Inducing points in input space.\n                    `p` is the number of points each inducing point is mapped \n                     to in order to form the FoV.\n    \"\"\"\n    x, y, theta = tf.split(Xu, num_or_size_splits=3, axis=1)\n    x     = tf.reshape(x, [-1,])\n    y     = tf.reshape(y, [-1,])\n    theta = tf.reshape(theta, [-1,])\n\n    points = []\n    for i in range(-int(np.floor((self.num_side)/2)), int(np.ceil((self.num_side)/2))):\n        points.append(tf.linspace([(x + (i * self.length_factor) * tf.cos(theta)) - self.length/2 * tf.cos(theta+np.pi/2), \n                                   (y + (i * self.length_factor) * tf.sin(theta)) - self.length/2 * tf.sin(theta+np.pi/2)], \n                                  [(x + (i * self.length_factor) * tf.cos(theta)) + self.length/2 * tf.cos(theta+np.pi/2), \n                                   (y + (i * self.length_factor) * tf.sin(theta)) + self.length/2 * tf.sin(theta+np.pi/2)], \n                                  self.num_side, axis=1))\n    xy = tf.concat(points, axis=1)\n    xy = tf.transpose(xy, [2, 1, 0])\n    xy = tf.reshape(xy, (-1, 2))\n    return xy\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.Transform","title":"<code>Transform</code>","text":"<p>Base class for transformations of the inducing points, including expansion and aggregation transforms.</p> Refer to the following papers for more details <ul> <li>Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]</li> <li>Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>aggregation_size</code> <code>int</code> <p>Number of consecutive inducing points to aggregate</p> <code>None</code> <code>constraint_weight</code> <code>float</code> <p>Weight term that controls the importance of the                         constraint terms in the SGP's optimization objective</p> <code>1.0</code> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>class Transform:\n    \"\"\"Base class for transformations of the inducing points, including expansion and aggregation transforms.\n\n    Refer to the following papers for more details:\n        - Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]\n        - Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]\n\n    Args:\n        aggregation_size (int): Number of consecutive inducing points to aggregate\n        constraint_weight (float): Weight term that controls the importance of the \n                                   constraint terms in the SGP's optimization objective \n    \"\"\"\n    def __init__(self, \n                 aggregation_size=None, \n                 constraint_weight=1.0,\n                 **kwargs):\n        self.aggregation_size = aggregation_size\n        self.constraint_weight = constraint_weight\n\n    def expand(self, Xu):\n        \"\"\"Applies the expansion transform to the inducing points\n\n        Args:\n            Xu (ndarray): Expansion transformed inducing points\n        \"\"\"\n        return Xu\n\n    def aggregate(self, k):\n        \"\"\"Applies the aggregation transform to kernel matrices\n\n        Args:\n            k (tensor): (mp, mp)/(mp, n); Kernel matrix. \n                        `m` is the number of inducing points,\n                        `p` is the number of points each inducing point is mapped,\n                        `n` is the number of training data points.\n\n        Returns:\n            k (tensor): (m, m)/(m, n); Aggregated kernel matrix\n        \"\"\"\n        if self.aggregation_size is None:\n            return k\n\n        if k.shape[0] == k.shape[1]:\n            # Handle Kuu which is a square matrix\n            k = tf.expand_dims(tf.expand_dims(k, axis=0), axis=-1)\n            k = tf.nn.avg_pool(k,\n                               ksize=[1, self.aggregation_size, self.aggregation_size, 1],\n                               strides=[1, self.aggregation_size, self.aggregation_size, 1],\n                               padding='VALID')\n            k = tf.squeeze(k, axis=[0, -1])\n        else:\n            # Handle Kuf which is a rectangular matrix\n            k = tf.expand_dims(k, axis=0)\n            k = tf.nn.avg_pool(k,\n                               ksize=[1, self.aggregation_size, 1],\n                               strides=[1, self.aggregation_size, 1],\n                               padding='VALID')\n            k = tf.squeeze(k, axis=[0])\n        return k\n\n    def constraints(self, Xu):\n        \"\"\"Computes the constraint terms that are added to the SGP's optimization function\n\n        Args:\n            Xu (ndarray): Inducing points from which to compute the constraints\n\n        Returns:\n            c (float): constraint terms (eg., distance constraint)\n        \"\"\"\n        return 0.\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.Transform.aggregate","title":"<code>aggregate(k)</code>","text":"<p>Applies the aggregation transform to kernel matrices</p> <p>Parameters:</p> Name Type Description Default <code>k</code> <code>tensor</code> <p>(mp, mp)/(mp, n); Kernel matrix.          <code>m</code> is the number of inducing points,         <code>p</code> is the number of points each inducing point is mapped,         <code>n</code> is the number of training data points.</p> required <p>Returns:</p> Name Type Description <code>k</code> <code>tensor</code> <p>(m, m)/(m, n); Aggregated kernel matrix</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def aggregate(self, k):\n    \"\"\"Applies the aggregation transform to kernel matrices\n\n    Args:\n        k (tensor): (mp, mp)/(mp, n); Kernel matrix. \n                    `m` is the number of inducing points,\n                    `p` is the number of points each inducing point is mapped,\n                    `n` is the number of training data points.\n\n    Returns:\n        k (tensor): (m, m)/(m, n); Aggregated kernel matrix\n    \"\"\"\n    if self.aggregation_size is None:\n        return k\n\n    if k.shape[0] == k.shape[1]:\n        # Handle Kuu which is a square matrix\n        k = tf.expand_dims(tf.expand_dims(k, axis=0), axis=-1)\n        k = tf.nn.avg_pool(k,\n                           ksize=[1, self.aggregation_size, self.aggregation_size, 1],\n                           strides=[1, self.aggregation_size, self.aggregation_size, 1],\n                           padding='VALID')\n        k = tf.squeeze(k, axis=[0, -1])\n    else:\n        # Handle Kuf which is a rectangular matrix\n        k = tf.expand_dims(k, axis=0)\n        k = tf.nn.avg_pool(k,\n                           ksize=[1, self.aggregation_size, 1],\n                           strides=[1, self.aggregation_size, 1],\n                           padding='VALID')\n        k = tf.squeeze(k, axis=[0])\n    return k\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.Transform.constraints","title":"<code>constraints(Xu)</code>","text":"<p>Computes the constraint terms that are added to the SGP's optimization function</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>Inducing points from which to compute the constraints</p> required <p>Returns:</p> Name Type Description <code>c</code> <code>float</code> <p>constraint terms (eg., distance constraint)</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def constraints(self, Xu):\n    \"\"\"Computes the constraint terms that are added to the SGP's optimization function\n\n    Args:\n        Xu (ndarray): Inducing points from which to compute the constraints\n\n    Returns:\n        c (float): constraint terms (eg., distance constraint)\n    \"\"\"\n    return 0.\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.Transform.expand","title":"<code>expand(Xu)</code>","text":"<p>Applies the expansion transform to the inducing points</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>Expansion transformed inducing points</p> required Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def expand(self, Xu):\n    \"\"\"Applies the expansion transform to the inducing points\n\n    Args:\n        Xu (ndarray): Expansion transformed inducing points\n    \"\"\"\n    return Xu\n</code></pre>"},{"location":"api/utils/index.html","title":"","text":""},{"location":"api/utils/index.html#sgptools.utils","title":"<code>sgptools.utils</code>","text":"<p>General utilities to support the functionalities of this package:</p> <ul> <li><code>data</code>: Provides utilities to preprocess datasets</li> <li><code>gpflow</code>: Provides utilities to interface with GPflow</li> <li><code>metrics</code>: Provides utilities to quantify the solution quality</li> <li><code>misc</code>: Provides miscellaneous helper functions</li> <li><code>tsp</code>: Provides utilities to run TSP/VRP solver</li> </ul>"},{"location":"api/utils/Data.html","title":"Data","text":""},{"location":"api/utils/Data.html#sgptools.utils.data","title":"<code>sgptools.utils.data</code>","text":""},{"location":"api/utils/Data.html#sgptools.utils.data.get_dataset","title":"<code>get_dataset(dataset_path=None, num_train=1000, num_test=2500, num_candidates=150, **kwargs)</code>","text":"<p>Method to generate/load datasets and preprocess them for SP/IPP. The method uses kmeans to  generate train and test sets.</p> <p>Parameters:</p> Name Type Description Default <code>dataset_path</code> <code>str</code> <p>Path to a tif dataset file. If None, the method will generate synthetic data.</p> <code>None</code> <code>num_train</code> <code>int</code> <p>Number of training samples to generate.</p> <code>1000</code> <code>num_test</code> <code>int</code> <p>Number of testing samples to generate.</p> <code>2500</code> <code>num_candidates</code> <code>int</code> <p>Number of candidate locations to generate.</p> <code>150</code> <p>Returns:</p> Name Type Description <code>X_train</code> <code>ndarray</code> <p>(n, d); Training set inputs</p> <code>y_train</code> <code>ndarray</code> <p>(n, 1); Training set labels</p> <code>X_test</code> <code>ndarray</code> <p>(n, d); Testing set inputs</p> <code>y_test</code> <code>ndarray</code> <p>(n, 1); Testing set labels</p> <code>candidates</code> <code>ndarray</code> <p>(n, d); Candidate sensor placement locations</p> <code>X</code> <code>ndarray</code> <p>(n, d); Full dataset inputs</p> <code>y</code> <code>ndarray</code> <p>(n, 1); Full dataset labels</p> Source code in <code>sgptools/utils/data.py</code> <pre><code>def get_dataset(dataset_path=None,\n                num_train=1000,\n                num_test=2500, \n                num_candidates=150,\n                **kwargs):\n    \"\"\"Method to generate/load datasets and preprocess them for SP/IPP. The method uses kmeans to \n    generate train and test sets.\n\n    Args:\n        dataset_path (str): Path to a tif dataset file. If None, the method will generate synthetic data.\n        num_train (int): Number of training samples to generate.\n        num_test (int): Number of testing samples to generate.\n        num_candidates (int): Number of candidate locations to generate.\n\n    Returns:\n       X_train (ndarray): (n, d); Training set inputs\n       y_train (ndarray): (n, 1); Training set labels\n       X_test (ndarray): (n, d); Testing set inputs\n       y_test (ndarray): (n, 1); Testing set labels\n       candidates (ndarray): (n, d); Candidate sensor placement locations\n       X (ndarray): (n, d); Full dataset inputs\n       y (ndarray): (n, 1); Full dataset labels\n    \"\"\"\n    # Load the data\n    if dataset_path is not None:\n        X, y = prep_tif_dataset(dataset_path=dataset_path)\n    else:\n        X, y = prep_synthetic_dataset(**kwargs)\n\n    X_train = get_inducing_pts(X, num_train)\n    X_train, y_train = cont2disc(X_train, X, y)\n\n    X_test = get_inducing_pts(X, num_test)\n    X_test, y_test = cont2disc(X_test, X, y)\n\n    candidates = get_inducing_pts(X, num_candidates)\n    candidates = cont2disc(candidates, X)\n\n    # Standardize data\n    X_scaler = StandardScaler()\n    X_scaler.fit(X_train)\n    X_train = X_scaler.transform(X_train)*10.0\n    X_test = X_scaler.transform(X_test)*10.0\n    X = X_scaler.transform(X)*10.0\n    candidates = X_scaler.transform(candidates)*10.0\n\n    y_scaler = StandardScaler()\n    y_scaler.fit(y_train)\n    y_train = y_scaler.transform(y_train)\n    y_test = y_scaler.transform(y_test)\n    y = y_scaler.transform(y)\n\n    return X_train, y_train, X_test, y_test, candidates, X, y\n</code></pre>"},{"location":"api/utils/Data.html#sgptools.utils.data.point_pos","title":"<code>point_pos(point, d, theta)</code>","text":"<p>Generate a point at a distance d from a point at angle theta.</p> <p>Parameters:</p> Name Type Description Default <code>point</code> <code>ndarray</code> <p>(N, 2); array of points</p> required <code>d</code> <code>float</code> <p>distance</p> required <code>theta</code> <code>float</code> <p>angle in radians</p> required <p>Returns:</p> Name Type Description <code>X</code> <code>ndarray</code> <p>(N,); array of x-coordinate</p> <code>Y</code> <code>ndarray</code> <p>(N,); array of y-coordinate</p> Source code in <code>sgptools/utils/data.py</code> <pre><code>def point_pos(point, d, theta):\n    '''\n    Generate a point at a distance d from a point at angle theta.\n\n    Args:\n        point (ndarray): (N, 2); array of points\n        d (float): distance\n        theta (float): angle in radians\n\n    Returns:\n        X  (ndarray): (N,); array of x-coordinate\n        Y  (ndarray): (N,); array of y-coordinate\n    '''\n    return np.c_[point[:, 0] + d*np.cos(theta), point[:, 1] + d*np.sin(theta)]\n</code></pre>"},{"location":"api/utils/Data.html#sgptools.utils.data.prep_synthetic_dataset","title":"<code>prep_synthetic_dataset(shape=(50, 50), min_height=0.0, max_height=30.0, roughness=0.5, random_seed=None, **kwargs)</code>","text":"<p>Generates a 50x50 grid of synthetic elevation data using the diamond square algorithm.</p> Refer to the following repo for more details <ul> <li>https://github.com/buckinha/DiamondSquare</li> </ul> <p>Parameters:</p> Name Type Description Default <code>shape</code> <code>tuple</code> <p>(x, y); Grid size along the x and y axis</p> <code>(50, 50)</code> <code>min_height</code> <code>float</code> <p>Minimum allowed height in the sampled data</p> <code>0.0</code> <code>max_height</code> <code>float</code> <p>Maximum allowed height in the sampled data</p> <code>30.0</code> <code>roughness</code> <code>float</code> <p>Roughness of the sampled data</p> <code>0.5</code> <code>random_seed</code> <code>int</code> <p>Random seed for reproducibility</p> <code>None</code> <p>Returns:</p> Name Type Description <code>X</code> <code>ndarray</code> <p>(n, d); Dataset input features</p> <code>y</code> <code>ndarray</code> <p>(n, 1); Dataset labels</p> Source code in <code>sgptools/utils/data.py</code> <pre><code>def prep_synthetic_dataset(shape=(50, 50), \n                           min_height=0.0, \n                           max_height=30.0, \n                           roughness=0.5,\n                           random_seed=None,\n                           **kwargs):\n    '''Generates a 50x50 grid of synthetic elevation data using the diamond square algorithm.\n\n    Refer to the following repo for more details:\n        - [https://github.com/buckinha/DiamondSquare](https://github.com/buckinha/DiamondSquare)\n\n    Args:\n        shape (tuple): (x, y); Grid size along the x and y axis\n        min_height (float): Minimum allowed height in the sampled data\n        max_height (float): Maximum allowed height in the sampled data\n        roughness (float): Roughness of the sampled data\n        random_seed (int): Random seed for reproducibility\n\n    Returns:\n       X (ndarray): (n, d); Dataset input features\n       y (ndarray): (n, 1); Dataset labels\n    '''\n    data = diamond_square(shape=shape,\n                          min_height=min_height, \n                          max_height=max_height, \n                          roughness=roughness,\n                          random_seed=random_seed,\n                          **kwargs)\n\n    # create x and y coordinates from the extent\n    x_coords = np.arange(0, data.shape[0])/10\n    y_coords = np.arange(0, data.shape[1])/10\n    xx, yy = np.meshgrid(x_coords, y_coords)\n    X = np.c_[xx.ravel(), yy.ravel()]\n    y = data.ravel()\n    y = y.reshape(-1, 1)\n\n    return X.astype(float), y.astype(float)\n</code></pre>"},{"location":"api/utils/Data.html#sgptools.utils.data.prep_tif_dataset","title":"<code>prep_tif_dataset(dataset_path)</code>","text":"<p>Load and preprocess a dataset from a GeoTIFF file (.tif file). The input features  are set to the x and y pixel block coordinates and the labels are read from the file. The method also removes all invalid points.</p> <p>Large tif files  need to be downsampled using the following command:  <code>gdalwarp -tr 50 50 &lt;input&gt;.tif &lt;output&gt;.tif</code></p> <p>Args:     dataset_path (str): Path to the dataset file, used only when dataset_type is 'tif'.</p> <p>Returns:    X (ndarray): (n, d); Dataset input features    y (ndarray): (n, 1); Dataset labels</p> Source code in <code>sgptools/utils/data.py</code> <pre><code>def prep_tif_dataset(dataset_path):\n    '''Load and preprocess a dataset from a GeoTIFF file (.tif file). The input features \n    are set to the x and y pixel block coordinates and the labels are read from the file.\n    The method also removes all invalid points.\n\n    Large tif files \n    need to be downsampled using the following command: \n    ```gdalwarp -tr 50 50 &lt;input&gt;.tif &lt;output&gt;.tif```\n\n    Args:\n        dataset_path (str): Path to the dataset file, used only when dataset_type is 'tif'.\n\n    Returns:\n       X (ndarray): (n, d); Dataset input features\n       y (ndarray): (n, 1); Dataset labels\n    '''\n    data = PIL.Image.open(dataset_path)\n    data = np.array(data)\n\n    # create x and y coordinates from the extent\n    x_coords = np.arange(0, data.shape[1])/10\n    y_coords = np.arange(data.shape[0], 0, -1)/10\n    xx, yy = np.meshgrid(x_coords, y_coords)\n    X = np.c_[xx.ravel(), yy.ravel()]\n    y = data.ravel()\n\n    # Remove invalid labels\n    y[np.where(y==-999999.0)] = np.nan\n    X = X[~np.isnan(y)]\n    y = y[~np.isnan(y)]\n\n    X = X.reshape(-1, 2)\n    y = y.reshape(-1, 1)\n\n    return X.astype(float), y.astype(float)\n</code></pre>"},{"location":"api/utils/Data.html#sgptools.utils.data.remove_circle_patches","title":"<code>remove_circle_patches(X, Y, circle_patches)</code>","text":"<p>Remove points inside polycircle patchesgons.</p> <p>Parameters:</p> Name Type Description Default <code>X</code> <code> (ndarray</code> <p>(N,); array of x-coordinate</p> required <code>Y</code> <code> (ndarray</code> <p>(N,); array of y-coordinate</p> required <code>circle_patches</code> <code>list of matplotlib circle patches</code> <p>Circle patches to remove from the X, Y points</p> required <p>Returns:</p> Name Type Description <code>X</code> <code>ndarray</code> <p>(N,); array of x-coordinate</p> <code>Y</code> <code>ndarray</code> <p>(N,); array of y-coordinate</p> Source code in <code>sgptools/utils/data.py</code> <pre><code>def remove_circle_patches(X, Y, circle_patches):\n    '''\n    Remove points inside polycircle patchesgons.\n\n    Args:\n        X  (ndarray): (N,); array of x-coordinate\n        Y  (ndarray): (N,); array of y-coordinate\n        circle_patches (list of matplotlib circle patches): Circle patches to remove from the X, Y points\n\n    Returns:\n        X  (ndarray): (N,); array of x-coordinate\n        Y  (ndarray): (N,); array of y-coordinate\n    '''\n    points = np.array([X.flatten(), Y.flatten()]).T\n    for circle_patch in circle_patches:\n        points = points[~circle_patch.contains_points(points)]\n    return points[:, 0], points[:, 1]\n</code></pre>"},{"location":"api/utils/Data.html#sgptools.utils.data.remove_polygons","title":"<code>remove_polygons(X, Y, polygons)</code>","text":"<p>Remove points inside polygons.</p> <p>Parameters:</p> Name Type Description Default <code>X</code> <code> (ndarray</code> <p>(N,); array of x-coordinate</p> required <code>Y</code> <code> (ndarray</code> <p>(N,); array of y-coordinate</p> required <code>polygons</code> <code>list of matplotlib path polygon</code> <p>Polygons to remove from the X, Y points</p> required <p>Returns:</p> Name Type Description <code>X</code> <code>ndarray</code> <p>(N,); array of x-coordinate</p> <code>Y</code> <code>ndarray</code> <p>(N,); array of y-coordinate</p> Source code in <code>sgptools/utils/data.py</code> <pre><code>def remove_polygons(X, Y, polygons):\n    '''\n    Remove points inside polygons.\n\n    Args:\n        X  (ndarray): (N,); array of x-coordinate\n        Y  (ndarray): (N,); array of y-coordinate\n        polygons (list of matplotlib path polygon): Polygons to remove from the X, Y points\n\n    Returns:\n        X  (ndarray): (N,); array of x-coordinate\n        Y  (ndarray): (N,); array of y-coordinate\n    '''\n    points = np.array([X.flatten(), Y.flatten()]).T\n    for polygon in polygons:\n        p = path.Path(polygon)\n        points = points[~p.contains_points(points)]\n    return points[:, 0], points[:, 1]\n</code></pre>"},{"location":"api/utils/GPflow.html","title":"GPflow","text":""},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow","title":"<code>sgptools.utils.gpflow</code>","text":""},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow.TraceInducingPts","title":"<code>TraceInducingPts</code>","text":"<p>               Bases: <code>MonitorTask</code></p> <p>GPflow monitoring task, used to trace the inducing points states at every step during optimization. </p> <p>Parameters:</p> Name Type Description Default <code>model</code> <code>sgpr</code> <p>GPflow GP/SGP model</p> required Source code in <code>sgptools/utils/gpflow.py</code> <pre><code>class TraceInducingPts(gpflow.monitor.MonitorTask):\n    '''\n    GPflow monitoring task, used to trace the inducing points\n    states at every step during optimization. \n\n    Args:\n        model (gpflow.models.sgpr): GPflow GP/SGP model\n    '''\n    def __init__(self, model):\n        super().__init__()\n        self.trace = []\n        self.model = model\n\n    def run(self, **kwargs):\n        '''\n        Method used to extract the inducing points and \n        apply IPP fixed points transform if available\n        '''\n        Xu = self.model.inducing_variable.Z\n        Xu_exp = self.model.transform.expand(Xu, \n                            expand_sensor_model=False).numpy()\n        self.trace.append(Xu_exp)\n\n    def get_trace(self):\n        '''\n        Returns the inducing points collected at each optimization step\n\n        Returns:\n            trace (ndarray): (n, m, d); Array with the inducing points.\n                            `n` is the number of optimization steps;\n                            `m` is the number of inducing points;\n                            `d` is the dimension of the inducing points.\n        '''\n        return np.array(self.trace)\n</code></pre>"},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow.TraceInducingPts.get_trace","title":"<code>get_trace()</code>","text":"<p>Returns the inducing points collected at each optimization step</p> <p>Returns:</p> Name Type Description <code>trace</code> <code>ndarray</code> <p>(n, m, d); Array with the inducing points.             <code>n</code> is the number of optimization steps;             <code>m</code> is the number of inducing points;             <code>d</code> is the dimension of the inducing points.</p> Source code in <code>sgptools/utils/gpflow.py</code> <pre><code>def get_trace(self):\n    '''\n    Returns the inducing points collected at each optimization step\n\n    Returns:\n        trace (ndarray): (n, m, d); Array with the inducing points.\n                        `n` is the number of optimization steps;\n                        `m` is the number of inducing points;\n                        `d` is the dimension of the inducing points.\n    '''\n    return np.array(self.trace)\n</code></pre>"},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow.TraceInducingPts.run","title":"<code>run(**kwargs)</code>","text":"<p>Method used to extract the inducing points and  apply IPP fixed points transform if available</p> Source code in <code>sgptools/utils/gpflow.py</code> <pre><code>def run(self, **kwargs):\n    '''\n    Method used to extract the inducing points and \n    apply IPP fixed points transform if available\n    '''\n    Xu = self.model.inducing_variable.Z\n    Xu_exp = self.model.transform.expand(Xu, \n                        expand_sensor_model=False).numpy()\n    self.trace.append(Xu_exp)\n</code></pre>"},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow.get_model_params","title":"<code>get_model_params(X_train, y_train, max_steps=1500, lr=0.01, print_params=True, lengthscales=1.0, variance=1.0, noise_variance=0.1, kernel=None, return_gp=False, train_inducing_pts=False, num_inducing_pts=500, **kwargs)</code>","text":"<p>Train a GP on the given training set.  Trains a sparse GP if the training set is larger than 1000 samples.</p> <p>Parameters:</p> Name Type Description Default <code>X_train</code> <code>ndarray</code> <p>(n, d); Training set inputs</p> required <code>y_train</code> <code>ndarray</code> <p>(n, 1); Training set labels</p> required <code>max_steps</code> <code>int</code> <p>Maximum number of optimization steps</p> <code>1500</code> <code>lr</code> <code>float</code> <p>Optimization learning rate</p> <code>0.01</code> <code>print_params</code> <code>bool</code> <p>If True, prints the optimized GP parameters</p> <code>True</code> <code>lengthscales</code> <code>float or list</code> <p>Kernel lengthscale(s), if passed as a list,                      each element corresponds to each data dimension</p> <code>1.0</code> <code>variance</code> <code>float</code> <p>Kernel variance</p> <code>1.0</code> <code>noise_variance</code> <code>float</code> <p>Data noise variance</p> <code>0.1</code> <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> <code>None</code> <code>return_gp</code> <code>bool</code> <p>If True, returns the trained GP model</p> <code>False</code> <code>train_inducing_pts</code> <code>bool</code> <p>If True, trains the inducing points when                        using a sparse GP model</p> <code>False</code> <code>num_inducing_pts</code> <code>int</code> <p>Number of inducing points to use when training                     a sparse GP model</p> <code>500</code> <p>Returns:</p> Name Type Description <code>loss</code> <code>list</code> <p>Loss values obtained during training</p> <code>variance</code> <code>float</code> <p>Optimized data noise variance</p> <code>kernel</code> <code>Kernel</code> <p>Optimized gpflow kernel function</p> <code>gp</code> <code>GPR</code> <p>Optimized gpflow GP model.                      Returned only if <code>return_gp=True</code>.</p> Source code in <code>sgptools/utils/gpflow.py</code> <pre><code>def get_model_params(X_train, y_train, \n                     max_steps=1500, \n                     lr=1e-2, \n                     print_params=True, \n                     lengthscales=1.0, \n                     variance=1.0, \n                     noise_variance=0.1,\n                     kernel=None,\n                     return_gp=False,\n                     train_inducing_pts=False,\n                     num_inducing_pts=500,\n                     **kwargs):\n    \"\"\"Train a GP on the given training set. \n    Trains a sparse GP if the training set is larger than 1000 samples.\n\n    Args:\n        X_train (ndarray): (n, d); Training set inputs\n        y_train (ndarray): (n, 1); Training set labels\n        max_steps (int): Maximum number of optimization steps\n        lr (float): Optimization learning rate\n        print_params (bool): If True, prints the optimized GP parameters\n        lengthscales (float or list): Kernel lengthscale(s), if passed as a list, \n                                each element corresponds to each data dimension\n        variance (float): Kernel variance\n        noise_variance (float): Data noise variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        return_gp (bool): If True, returns the trained GP model\n        train_inducing_pts (bool): If True, trains the inducing points when\n                                   using a sparse GP model\n        num_inducing_pts (int): Number of inducing points to use when training\n                                a sparse GP model\n\n    Returns:\n        loss (list): Loss values obtained during training\n        variance (float): Optimized data noise variance\n        kernel (gpflow.kernels.Kernel): Optimized gpflow kernel function\n        gp (gpflow.models.GPR): Optimized gpflow GP model. \n                                Returned only if ```return_gp=True```.\n\n    \"\"\"\n    if kernel is None:\n        kernel = gpflow.kernels.SquaredExponential(lengthscales=lengthscales, \n                                                   variance=variance)\n\n    if len(X_train) &lt;= 1500:\n        gpr = gpflow.models.GPR(data=(X_train, y_train), \n                                kernel=kernel,\n                                noise_variance=noise_variance)\n        trainable_variables=gpr.trainable_variables\n    else:\n        inducing_pts = get_inducing_pts(X_train, num_inducing_pts)\n        gpr = gpflow.models.SGPR(data=(X_train, y_train), \n                                 kernel=kernel,\n                                 inducing_variable=inducing_pts,\n                                 noise_variance=noise_variance)\n        if train_inducing_pts:\n            trainable_variables=gpr.trainable_variables\n        else:\n            trainable_variables=gpr.trainable_variables[1:]\n\n    if max_steps &gt; 0:\n        loss = optimize_model(gpr, max_steps=max_steps, lr=lr, \n                              trainable_variables=trainable_variables,\n                              **kwargs)\n    else:\n        loss = 0\n\n    if print_params:\n        print_summary(gpr)\n\n    if return_gp:\n        return loss, gpr.likelihood.variance, kernel, gpr\n    else:\n        return loss, gpr.likelihood.variance, kernel\n</code></pre>"},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow.optimize_model","title":"<code>optimize_model(model, max_steps=2000, kernel_grad=True, lr=0.01, optimizer='scipy', method=None, verbose=False, trace_fn=None, convergence_criterion=True, trainable_variables=None, tol=None)</code>","text":"<p>Trains a GP/SGP model</p> <p>Parameters:</p> Name Type Description Default <code>model</code> <code>models</code> <p>GPflow GP/SGP model to train.</p> required <code>max_steps</code> <code>int</code> <p>Maximum number of training steps.</p> <code>2000</code> <code>kernel_grad</code> <code>bool</code> <p>If <code>False</code>, the kernel parameters will not be optimized.                  Ignored when <code>trainable_variables</code> are passed.</p> <code>True</code> <code>lr</code> <code>float</code> <p>Optimization learning rate.</p> <code>0.01</code> <code>optimizer</code> <code>str</code> <p>Optimizer to use for training (<code>scipy</code> or <code>tf</code>).</p> <code>'scipy'</code> <code>method</code> <code>str</code> <p>Optimization method refer to scipy minimize            and tf optimizers for full list</p> <code>None</code> <code>verbose</code> <code>bool</code> <p>If <code>True</code>, the training progress will be printed when using Scipy.</p> <code>False</code> <code>trace_fn</code> <code>str</code> <p>Function to trace metrics during training.              If <code>None</code>, the loss values are returned;             If <code>traceXu</code>, it the inducing points states at each optimization step are returned (increases computation time).</p> <code>None</code> <code>convergence_criterion</code> <code>bool</code> <p>If <code>True</code> and using a tensorflow optimizer, it                            enables early stopping when the loss plateaus.</p> <code>True</code> <code>trainable_variables</code> <code>list</code> <p>List of model variables to train.</p> <code>None</code> <code>tol</code> <code>float</code> <p>Convergence tolerance to decide when to stop optimization.</p> <code>None</code> Source code in <code>sgptools/utils/gpflow.py</code> <pre><code>def optimize_model(model, \n                   max_steps=2000, \n                   kernel_grad=True, \n                   lr=1e-2, \n                   optimizer='scipy', \n                   method=None,\n                   verbose=False, \n                   trace_fn=None,\n                   convergence_criterion=True,\n                   trainable_variables=None,\n                   tol=None):\n    \"\"\"\n    Trains a GP/SGP model\n\n    Args:\n        model (gpflow.models): GPflow GP/SGP model to train.\n        max_steps (int): Maximum number of training steps.\n        kernel_grad (bool): If `False`, the kernel parameters will not be optimized. \n                            Ignored when `trainable_variables` are passed.\n        lr (float): Optimization learning rate.\n        optimizer (str): Optimizer to use for training (`scipy` or `tf`).\n        method (str): Optimization method refer to [scipy minimize](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html#scipy.optimize.minimize) \n                      and [tf optimizers](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers) for full list\n        verbose (bool): If `True`, the training progress will be printed when using Scipy.\n        trace_fn (str): Function to trace metrics during training. \n                        If `None`, the loss values are returned;\n                        If `traceXu`, it the inducing points states at each optimization step are returned (increases computation time).\n        convergence_criterion (bool): If `True` and using a tensorflow optimizer, it \n                                      enables early stopping when the loss plateaus.\n        trainable_variables (list): List of model variables to train.\n        tol (float): Convergence tolerance to decide when to stop optimization.\n    \"\"\"\n    # Train all variables if trainable_variables are not provided\n    # If kernel_gradient is False, disable the kernel parameter gradient updates\n    if trainable_variables is None and kernel_grad:\n        trainable_variables=model.trainable_variables\n    elif trainable_variables is None and not kernel_grad:\n        trainable_variables=model.trainable_variables[:1]\n\n    if optimizer == 'scipy':\n        if method is None:\n            method = 'L-BFGS-B'\n\n        if trace_fn == 'traceXu':\n            execute_task = TraceInducingPts(model)\n            task_group = gpflow.monitor.MonitorTaskGroup(execute_task, \n                                                         period=1)\n            trace_fn = gpflow.monitor.Monitor(task_group)\n\n        opt = gpflow.optimizers.Scipy()\n        losses = opt.minimize(model.training_loss,\n                              trainable_variables,\n                              method=method,\n                              options=dict(disp=verbose, maxiter=max_steps),\n                              tol=tol,\n                              step_callback=trace_fn)\n        if trace_fn is None:\n            losses = losses.fun\n        else:\n            losses = trace_fn.task_groups[0].tasks[0].get_trace()\n    else:\n        if trace_fn is None:\n            trace_fn = lambda x: x.loss\n        elif trace_fn == 'traceXu':\n            def trace_fn(traceable_quantities):\n                return model.inducing_variable.Z.numpy()\n\n        if method is None:\n            method = 'adam'\n        opt = tf.keras.optimizers.get(method)\n        opt.learning_rate = lr\n        loss_fn = model.training_loss\n        if convergence_criterion:\n            convergence_criterion = tfp.optimizer.convergence_criteria.LossNotDecreasing(\n                                            atol=1e-5, \n                                            window_size=50,\n                                            min_num_steps=int(max_steps*0.1))\n        else:\n            convergence_criterion = None\n        losses = tfp.math.minimize(loss_fn,\n                                   trainable_variables=trainable_variables,\n                                   num_steps=max_steps,\n                                   optimizer=opt,\n                                   convergence_criterion=convergence_criterion,\n                                   trace_fn=trace_fn)\n        losses = losses.numpy()\n\n    return losses\n</code></pre>"},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow.plot_loss","title":"<code>plot_loss(losses, save_file=None)</code>","text":"<p>Helper function to plot the training loss</p> <p>Parameters:</p> Name Type Description Default <code>losses</code> <code>list</code> <p>list of loss values</p> required <code>save_file</code> <code>str</code> <p>If passed, the loss plot will be saved to the <code>save_file</code></p> <code>None</code> Source code in <code>sgptools/utils/gpflow.py</code> <pre><code>def plot_loss(losses, save_file=None):\n    \"\"\"Helper function to plot the training loss\n\n    Args:\n        losses (list): list of loss values\n        save_file (str): If passed, the loss plot will be saved to the `save_file`\n    \"\"\"\n    plt.plot(losses)\n    plt.title('Log Likelihood')\n    plt.xlabel('Iteration')\n    plt.ylabel('Log Likelihood')\n    ax = plt.gca()\n    ax.ticklabel_format(useOffset=False)\n\n    if save_file is not None:\n        plt.savefig(save_file, bbox_inches='tight')\n        plt.close()\n    else:\n        plt.show()\n</code></pre>"},{"location":"api/utils/Metrics.html","title":"Metrics","text":""},{"location":"api/utils/Metrics.html#sgptools.utils.metrics","title":"<code>sgptools.utils.metrics</code>","text":""},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.gaussian_entropy","title":"<code>gaussian_entropy(K)</code>","text":"<p>Computes GP-based entropy from a kernel matrix</p> <p>Parameters:</p> Name Type Description Default <code>K</code> <code>ndarray</code> <p>(n, n); kernel matrix</p> required <p>Returns:</p> Name Type Description <code>entropy</code> <code>float</code> <p>Entropy computed from the kernel matrix</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def gaussian_entropy(K):\n    \"\"\"Computes GP-based entropy from a kernel matrix\n\n    Args:\n        K (ndarray): (n, n); kernel matrix\n\n    Returns:\n        entropy (float): Entropy computed from the kernel matrix\n    \"\"\"\n    return multivariate_normal(mean=None, cov=K, allow_singular=True).entropy()\n</code></pre>"},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.get_distance","title":"<code>get_distance(X)</code>","text":"<p>Compute the length of a path (L2-norm)</p> <p>Parameters:</p> Name Type Description Default <code>X</code> <code>ndarray</code> <p>(m, d); Waypoints of a path</p> required <p>Returns:</p> Name Type Description <code>dist</code> <code>float</code> <p>Total path length</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def get_distance(X):\n    \"\"\"Compute the length of a path (L2-norm)\n\n    Args:\n        X (ndarray): (m, d); Waypoints of a path\n\n    Returns:\n        dist (float): Total path length\n    \"\"\"\n    dist = np.linalg.norm(X[1:] - X[:-1], axis=-1)\n    dist = np.sum(dist)\n    return dist\n</code></pre>"},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.get_elbo","title":"<code>get_elbo(Xu, X_env, noise_variance, kernel, baseline=False)</code>","text":"<p>Computes the ELBO of the SGP, corrected to be positive</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(m, d); Sensing locations</p> required <code>X_env</code> <code>ndarray</code> <p>(n, d); Data points used to approximate the bounds of the environment</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>baseline</code> <code>bool</code> <p>If True, the ELBO is adjusted to be positive</p> <code>False</code> <p>Returns:</p> Name Type Description <code>elbo</code> <code>float</code> <p>ELBO of the SGP</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def get_elbo(Xu, X_env, noise_variance, kernel, baseline=False):\n    \"\"\"Computes the ELBO of the SGP, corrected to be positive\n\n    Args:\n        Xu (ndarray): (m, d); Sensing locations\n        X_env (ndarray): (n, d); Data points used to approximate the bounds of the environment\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        baseline (bool): If True, the ELBO is adjusted to be positive\n\n    Returns:\n        elbo (float): ELBO of the SGP\n    \"\"\"\n    if baseline:\n        sgpr = gpflow.models.SGPR(X_env,\n                                  noise_variance=noise_variance,\n                                  kernel=kernel,\n                                  inducing_variable=[[0, 0]])\n        baseline = sgpr.elbo().numpy()\n    else:\n        baseline = 0.0\n\n    sgpr = gpflow.models.SGPR(X_env,\n                              noise_variance=noise_variance,\n                              kernel=kernel, \n                              inducing_variable=Xu)\n    return (sgpr.elbo() - baseline).numpy()\n</code></pre>"},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.get_kl","title":"<code>get_kl(Xu, X_env, noise_variance, kernel)</code>","text":"<p>Computes the KL divergence between the SGP and the GP</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(m, d); Sensing locations</p> required <code>X_env</code> <code>ndarray</code> <p>(n, d); Data points used to approximate the bounds of the environment</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <p>Returns:</p> Name Type Description <code>kl</code> <code>float</code> <p>KL divergence between the SGP and the GP</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def get_kl(Xu, X_env, noise_variance, kernel):\n    \"\"\"Computes the KL divergence between the SGP and the GP\n\n    Args:\n        Xu (ndarray): (m, d); Sensing locations\n        X_env (ndarray): (n, d); Data points used to approximate the bounds of the environment\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n\n    Returns:\n        kl (float): KL divergence between the SGP and the GP\n    \"\"\"\n    sgpr = gpflow.models.SGPR(X_env,\n                              noise_variance=noise_variance,\n                              kernel=kernel,\n                              inducing_variable=Xu)\n\n    common = sgpr._common_calculation()\n    sigma_sq = common.sigma_sq\n    AAT = common.AAT\n\n    x, _ = sgpr.data\n    kdiag = sgpr.kernel(x, full_cov=False)\n\n    # tr(K) / \u03c3\u00b2\n    trace_k = tf.reduce_sum(kdiag / sigma_sq)\n    # tr(Q) / \u03c3\u00b2\n    trace_q = tf.reduce_sum(tf.linalg.diag_part(AAT))\n    # tr(K - Q) / \u03c3\u00b2\n    trace = trace_k - trace_q\n    trace = 0.5 * trace\n\n    return float(trace.numpy())\n</code></pre>"},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.get_mi","title":"<code>get_mi(Xu, candidate_locs, noise_variance, kernel)</code>","text":"<p>Computes mutual information between the sensing locations and the candidate locations</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(m, d); Sensing locations</p> required <code>candidate_locs</code> <code>ndarray</code> <p>(n, d); Candidate sensing locations </p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <p>Returns:</p> Name Type Description <code>mi</code> <code>float</code> <p>Mutual information computed using a GP</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def get_mi(Xu, candidate_locs, noise_variance, kernel):\n    \"\"\"Computes mutual information between the sensing locations and the candidate locations\n\n    Args:\n        Xu (ndarray): (m, d); Sensing locations\n        candidate_locs (ndarray): (n, d); Candidate sensing locations \n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n\n    Returns:\n        mi (float): Mutual information computed using a GP\n    \"\"\"\n    Xu = np.array(Xu)\n    candidate_locs = np.array(candidate_locs)\n\n    gp = gpflow.models.GPR(data=(Xu, np.zeros((len(Xu), 1))),\n                           kernel=kernel,\n                           noise_variance=noise_variance)\n    _, sigma_a = gp.predict_f(candidate_locs, full_cov=True)\n    sigma_a = sigma_a.numpy()[0]\n    cond_entropy = gaussian_entropy(sigma_a)\n\n    K = kernel(candidate_locs, full_cov=True).numpy()\n    K += noise_variance * np.eye(len(candidate_locs))\n    entropy = gaussian_entropy(K)\n\n    return float(entropy - cond_entropy)\n</code></pre>"},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.get_reconstruction","title":"<code>get_reconstruction(sensor_data, X_test, noise_variance, kernel)</code>","text":"<p>Computes the GP-based data field estimates with the solution placements as the training set</p> <p>Parameters:</p> Name Type Description Default <code>sensor_data</code> <code>ndarray tuple</code> <p>((m, d), (m, 1)); Sensing locations' input                   and corresponding ground truth labels</p> required <code>X_test</code> <code>ndarray</code> <p>(n, d); Testing data input locations</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <p>Returns:</p> Name Type Description <code>y_pred</code> <code>ndarray</code> <p>(n, 1); Predicted data field estimates</p> <code>y_var</code> <code>ndarray</code> <p>(n, 1); Prediction variance at each location in the data field</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def get_reconstruction(sensor_data, X_test, noise_variance, kernel):\n    \"\"\"Computes the GP-based data field estimates with the solution placements as the training set\n\n    Args:\n        sensor_data (ndarray tuple): ((m, d), (m, 1)); Sensing locations' input \n                             and corresponding ground truth labels\n        X_test (ndarray): (n, d); Testing data input locations\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n\n    Returns:\n        y_pred (ndarray): (n, 1); Predicted data field estimates\n        y_var (ndarray): (n, 1); Prediction variance at each location in the data field\n    \"\"\"\n    Xu_X, Xu_y = sensor_data\n\n    # Get the GP predictions\n    gpr = gpflow.models.GPR((Xu_X, Xu_y),\n                            noise_variance=noise_variance,\n                            kernel=kernel)\n    y_pred, y_var = gpr.predict_f(X_test)\n    y_pred = y_pred.numpy().reshape(-1, 1)\n\n    return y_pred, y_var\n</code></pre>"},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.get_rmse","title":"<code>get_rmse(y_pred, y_test)</code>","text":"<p>Computes the root-mean-square error between <code>y_pred</code> and <code>y_test</code></p> <p>Parameters:</p> Name Type Description Default <code>y_pred</code> <code>ndarray</code> <p>(n, 1); Predicted data field estimate</p> required <code>y_test</code> <code>ndarray</code> <p>(n, 1); Ground truth data field </p> required <p>Returns:</p> Name Type Description <code>rmse</code> <code>float</code> <p>Computed RMSE</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def get_rmse(y_pred, y_test):\n    \"\"\"Computes the root-mean-square error between `y_pred` and `y_test`\n\n    Args:\n        y_pred (ndarray): (n, 1); Predicted data field estimate\n        y_test (ndarray): (n, 1); Ground truth data field \n\n    Returns:\n        rmse (float): Computed RMSE\n    \"\"\"\n    return np.sqrt(np.mean(np.square(y_pred - y_test)))\n</code></pre>"},{"location":"api/utils/Misc.html","title":"Misc","text":""},{"location":"api/utils/Misc.html#sgptools.utils.misc","title":"<code>sgptools.utils.misc</code>","text":""},{"location":"api/utils/Misc.html#sgptools.utils.misc.cont2disc","title":"<code>cont2disc(Xu, candidates, candidate_labels=None)</code>","text":"<p>Map continuous space locations to a discrete set of candidate location</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(m, 2); Continuous space points</p> required <code>candidates</code> <code>ndarray</code> <p>(n, 2); Discrete set of candidate locations</p> required <code>candidate_labels</code> <code>ndarray</code> <p>(n, 1); Labels corresponding to the discrete set of candidate locations</p> <code>None</code> <p>Returns:</p> Name Type Description <code>Xu_x</code> <code>ndarray</code> <p>Discrete space points' locations </p> <code>Xu_y</code> <code>ndarray</code> <p>Labels of the discrete space points. Returned only if <code>candidate_labels</code>             was passed to the function</p> Source code in <code>sgptools/utils/misc.py</code> <pre><code>def cont2disc(Xu, candidates, candidate_labels=None):\n    \"\"\"Map continuous space locations to a discrete set of candidate location\n\n    Args:\n        Xu (ndarray): (m, 2); Continuous space points\n        candidates (ndarray): (n, 2); Discrete set of candidate locations\n        candidate_labels (ndarray): (n, 1); Labels corresponding to the discrete set of candidate locations\n\n    Returns:\n        Xu_x (ndarray): Discrete space points' locations \n        Xu_y (ndarray): Labels of the discrete space points. Returned only if `candidate_labels`\n                        was passed to the function\n\n    \"\"\"\n    # Sanity check to ensure that there are sensing locations and candidates to match\n    if len(candidates)==0 or len(Xu)==0:\n        if candidate_labels is not None:\n            return [], []\n        else:\n            return []\n\n    dists = pairwise_distances(candidates, Y=Xu, metric='euclidean')\n    row_ind, _ = linear_sum_assignment(dists)\n    Xu_X = candidates[row_ind].copy()\n    if candidate_labels is not None:\n        Xu_y = candidate_labels[row_ind].copy()\n        return Xu_X, Xu_y\n    else:\n        return Xu_X\n</code></pre>"},{"location":"api/utils/Misc.html#sgptools.utils.misc.get_inducing_pts","title":"<code>get_inducing_pts(data, num_inducing, orientation=False, random=False)</code>","text":"<p>Selects a subset of the data points to be used as inducing points.  The default approach uses kmeans to select the subset. </p> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>ndarray</code> <p>(n, 2); Data points to select the inducing points from </p> required <code>num_inducing</code> <code>int</code> <p>Number of inducing points</p> required <code>orientation</code> <code>bool</code> <p>If True, add an additional dimension to model the sensor                  FoV rotation angle</p> <code>False</code> <code>random</code> <code>bool</code> <p>If True, the subset of inducing points are selected randomly             instead of using kmeans</p> <code>False</code> <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(m, d); Inducing points in the position and orientation space.             <code>m</code> is the number of inducing points,              <code>d</code> is the dimension of the space (x, y, optional - angle in radians)</p> Source code in <code>sgptools/utils/misc.py</code> <pre><code>def get_inducing_pts(data, num_inducing, orientation=False, random=False):\n    \"\"\"Selects a subset of the data points to be used as inducing points. \n    The default approach uses kmeans to select the subset. \n\n    Args:\n        data (ndarray): (n, 2); Data points to select the inducing points from \n        num_inducing (int): Number of inducing points\n        orientation (bool): If True, add an additional dimension to model the sensor \n                            FoV rotation angle\n        random (bool): If True, the subset of inducing points are selected randomly \n                       instead of using kmeans\n\n    Returns:\n        Xu (ndarray): (m, d); Inducing points in the position and orientation space.\n                        `m` is the number of inducing points, \n                        `d` is the dimension of the space (x, y, optional - angle in radians)\n    \"\"\"\n    if random:\n        idx = np.random.randint(len(data), size=num_inducing)\n        Xu = data[idx]\n    else:\n        Xu = kmeans2(data, num_inducing, minit=\"points\")[0]\n    if orientation:\n        thetas = np.random.uniform(0, 2 * np.pi, size=(Xu.shape[0], 1))\n        Xu = np.concatenate([Xu, thetas], axis=1)\n    return Xu\n</code></pre>"},{"location":"api/utils/Misc.html#sgptools.utils.misc.interpolate_path","title":"<code>interpolate_path(waypoints, sampling_rate=0.05)</code>","text":"<p>Interpolate additional points between the given waypoints to simulate continuous sensing robots</p> <p>Parameters:</p> Name Type Description Default <code>waypoints</code> <code>(n, d)</code> <p>Waypoints of the robot's path</p> required <code>sampling_rate</code> <code>float</code> <p>Distance between each pair of interpolated points</p> <code>0.05</code> <p>Returns:</p> Name Type Description <code>path</code> <code>ndarray</code> <p>(p, d) Interpolated path, <code>p</code> depends on the sampling_rate rate</p> Source code in <code>sgptools/utils/misc.py</code> <pre><code>def interpolate_path(waypoints, sampling_rate=0.05):\n    \"\"\"Interpolate additional points between the given waypoints to simulate continuous sensing robots\n\n    Args:\n        waypoints (n, d): Waypoints of the robot's path\n        sampling_rate (float): Distance between each pair of interpolated points\n\n    Returns:\n        path (ndarray): (p, d) Interpolated path, `p` depends on the sampling_rate rate\n    \"\"\"\n    interpolated_path = []\n    for i in range(2, len(waypoints)+1):\n        dist = get_distance(waypoints[i-2:i])\n        num_samples = int(dist / sampling_rate)\n        points = np.linspace(waypoints[i-1], waypoints[i-2], num_samples)\n        interpolated_path.extend(points)\n    return np.array(interpolated_path)\n</code></pre>"},{"location":"api/utils/Misc.html#sgptools.utils.misc.plot_paths","title":"<code>plot_paths(paths, candidates=None, title=None)</code>","text":"<p>Function to plot the IPP solution paths</p> <p>Parameters:</p> Name Type Description Default <code>paths</code> <code>ndarray</code> <p>(r, m, 2); <code>r</code> paths with <code>m</code> waypoints each</p> required <code>candidates</code> <code>ndarray</code> <p>(n, 2); Candidate unlabeled locations used in the SGP-based sensor placement approach</p> <code>None</code> <code>title</code> <code>str</code> <p>Title of the plot</p> <code>None</code> Source code in <code>sgptools/utils/misc.py</code> <pre><code>def plot_paths(paths, candidates=None, title=None):\n    \"\"\"Function to plot the IPP solution paths\n\n    Args:\n        paths (ndarray): (r, m, 2); `r` paths with `m` waypoints each\n        candidates (ndarray): (n, 2); Candidate unlabeled locations used in the SGP-based sensor placement approach\n        title (str): Title of the plot\n    \"\"\"\n    plt.figure()\n    for i, path in enumerate(paths):\n        plt.plot(path[:, 0], path[:, 1], \n                    c='r', label='Path', zorder=1, marker='o')\n        plt.scatter(path[0, 0], path[0, 1], \n                    c='g', label='Start', zorder=2, marker='o')\n        if candidates is not None:\n            plt.scatter(candidates[:, 0], candidates[:, 1], \n                        c='k', s=1, label='Unlabeled Train-Set Points', zorder=0)\n        if i==0:\n            plt.legend(bbox_to_anchor=(1.0, 1.02))\n    if title is not None:\n        plt.title(title)\n    plt.gca().set_aspect('equal')\n    plt.xlabel('X')\n    plt.ylabel('Y')\n</code></pre>"},{"location":"api/utils/Misc.html#sgptools.utils.misc.ploygon2candidats","title":"<code>ploygon2candidats(vertices, num_samples=5000, random_seed=2024)</code>","text":"<p>Sample unlabeled candidates within a polygon</p> <p>Parameters:</p> Name Type Description Default <code>vertices</code> <code>ndarray</code> <p>(v, 2) of vertices that define the polygon</p> required <code>num_samples</code> <code>int</code> <p>Number of samples to generate</p> <code>5000</code> <code>random_seed</code> <code>int</code> <p>Random seed for reproducibility</p> <code>2024</code> <p>Returns:</p> Name Type Description <code>candidates</code> <code>ndarray</code> <p>(n, 2); Candidate sensor placement locations</p> Source code in <code>sgptools/utils/misc.py</code> <pre><code>def ploygon2candidats(vertices, \n                      num_samples=5000, \n                      random_seed=2024):\n    \"\"\"Sample unlabeled candidates within a polygon\n\n    Args:\n        vertices (ndarray): (v, 2) of vertices that define the polygon\n        num_samples (int): Number of samples to generate\n        random_seed (int): Random seed for reproducibility\n\n    Returns:\n       candidates (ndarray): (n, 2); Candidate sensor placement locations\n    \"\"\"\n    poly = geometry.Polygon(vertices)\n    sampler = gpd.GeoSeries([poly])\n    candidates = sampler.sample_points(size=num_samples,\n                                       rng=random_seed)\n    candidates = candidates.get_coordinates().to_numpy()\n    return candidates\n</code></pre>"},{"location":"api/utils/Misc.html#sgptools.utils.misc.project_waypoints","title":"<code>project_waypoints(waypoints, candidates)</code>","text":"<p>Project the waypoints back to the candidate set while retaining the  waypoint visitation order.</p> <p>Parameters:</p> Name Type Description Default <code>waypoints</code> <code>(n, d)</code> <p>Waypoints of the robot's path</p> required <code>candidates</code> <code>ndarray</code> <p>(n, 2); Discrete set of candidate locations</p> required <p>Returns:</p> Name Type Description <code>waypoints</code> <code>(n, d)</code> <p>Projected waypoints of the robot's path</p> Source code in <code>sgptools/utils/misc.py</code> <pre><code>def project_waypoints(waypoints, candidates):\n    \"\"\"Project the waypoints back to the candidate set while retaining the \n    waypoint visitation order.\n\n    Args:\n        waypoints (n, d): Waypoints of the robot's path\n        candidates (ndarray): (n, 2); Discrete set of candidate locations\n\n    Returns:\n        waypoints (n, d): Projected waypoints of the robot's path\n    \"\"\"\n    waypoints_disc = cont2disc(waypoints, candidates)\n    waypoints_valid = _reoder_path(waypoints, waypoints_disc)\n    return waypoints_valid\n</code></pre>"},{"location":"api/utils/TSP.html","title":"TSP","text":""},{"location":"api/utils/TSP.html#sgptools.utils.tsp","title":"<code>sgptools.utils.tsp</code>","text":""},{"location":"api/utils/TSP.html#sgptools.utils.tsp.resample_path","title":"<code>resample_path(waypoints, num_inducing=10)</code>","text":"<p>Function to map path with arbitrary number of waypoints to  inducing points path with fixed number of waypoints</p> <p>Parameters:</p> Name Type Description Default <code>waypoints</code> <code>ndarray</code> <p>(num_waypoints, ndim); waypoints of path from vrp solver</p> required <code>num_inducing</code> <code>int</code> <p>Number of inducing points (waypoints) in the returned path</p> <code>10</code> <p>Returns:</p> Name Type Description <code>points</code> <code>ndarray</code> <p>(num_inducing, ndim); Resampled path</p> Source code in <code>sgptools/utils/tsp.py</code> <pre><code>def resample_path(waypoints, num_inducing=10):\n    \"\"\"Function to map path with arbitrary number of waypoints to \n    inducing points path with fixed number of waypoints\n\n    Args:\n        waypoints (ndarray): (num_waypoints, ndim); waypoints of path from vrp solver\n        num_inducing (int): Number of inducing points (waypoints) in the returned path\n\n    Returns:\n        points (ndarray): (num_inducing, ndim); Resampled path\n    \"\"\"\n    ndim = np.shape(waypoints)[-1]\n    if not (ndim==2 or ndim==3):\n        raise Exception(f\"ndim={ndim} is not supported for path resampling!\")\n    line = LineString(waypoints)\n    distances = np.linspace(0, line.length, num_inducing)\n    points = [line.interpolate(distance) for distance in distances]\n    if ndim==2:\n        points = np.array([[p.x, p.y] for p in points])\n    elif ndim==3:\n        points = np.array([[p.x, p.y, p.z] for p in points])\n    return points\n</code></pre>"},{"location":"api/utils/TSP.html#sgptools.utils.tsp.run_tsp","title":"<code>run_tsp(nodes, num_vehicles=1, max_dist=25, depth=1, resample=None, start_nodes=None, end_nodes=None, time_limit=10)</code>","text":"<p>Method to run TSP/VRP with arbitrary start and end nodes,  and without any distance constraint</p> <p>Parameters:</p> Name Type Description Default <code>nodes</code> <code>ndarray</code> <p>(# nodes, ndim); Nodes to visit </p> required <code>num_vehicles</code> <code>int</code> <p>Number of robots/vehicles</p> <code>1</code> <code>max_dist</code> <code>float</code> <p>Maximum distance allowed for each path when handling mutli-robot case</p> <code>25</code> <code>depth</code> <code>int</code> <p>Internal parameter used to track re-try recursion depth</p> <code>1</code> <code>resample</code> <code>int</code> <p>Each solution path will be resampled to have             <code>resample</code> number of points</p> <code>None</code> <code>start_nodes</code> <code>ndarray</code> <p>(# num_vehicles, ndim); Optionl array of start nodes from which                      to start each vehicle's solution path</p> <code>None</code> <code>end_nodes</code> <code>ndarray</code> <p>(# num_vehicles, ndim); Optionl array of end nodes at which                      to end each vehicle's solution path</p> <code>None</code> <code>time_limit</code> <code>int</code> <p>TSP runtime time limit in seconds</p> <code>10</code> <p>Returns:</p> Name Type Description <code>paths</code> <code>ndarray</code> <p>Solution paths</p> <code>distances</code> <code>list</code> <p>List of path lengths</p> Source code in <code>sgptools/utils/tsp.py</code> <pre><code>def run_tsp(nodes, \n            num_vehicles=1, \n            max_dist=25, \n            depth=1, \n            resample=None, \n            start_nodes=None,\n            end_nodes=None,\n            time_limit=10):\n    \"\"\"Method to run TSP/VRP with arbitrary start and end nodes, \n    and without any distance constraint\n\n    Args:\n        nodes (ndarray): (# nodes, ndim); Nodes to visit \n        num_vehicles (int): Number of robots/vehicles\n        max_dist (float): Maximum distance allowed for each path when handling mutli-robot case\n        depth (int): Internal parameter used to track re-try recursion depth\n        resample (int): Each solution path will be resampled to have\n                        `resample` number of points\n        start_nodes (ndarray): (# num_vehicles, ndim); Optionl array of start nodes from which \n                                to start each vehicle's solution path\n        end_nodes (ndarray): (# num_vehicles, ndim); Optionl array of end nodes at which \n                                to end each vehicle's solution path\n        time_limit (int): TSP runtime time limit in seconds\n\n    Returns:\n        paths (ndarray): Solution paths\n        distances (list): List of path lengths\n    \"\"\"\n    if depth &gt; 5:\n        print('Warning: Max depth reached')\n        return None, None\n\n    # Add the start and end nodes to the node list\n    if end_nodes is not None:\n        assert end_nodes.shape == (num_vehicles, nodes.shape[-1]), \\\n            \"Incorrect end_nodes shape, should be (num_vehicles, ndim)!\"\n        nodes = np.concatenate([end_nodes, nodes])\n    if start_nodes is not None:\n        assert start_nodes.shape == (num_vehicles, nodes.shape[-1]), \\\n            \"Incorrect start_nodes shape, should be (num_vehicles, ndim)!\"\n        nodes = np.concatenate([start_nodes, nodes])\n\n    # Add dummy 0 location to get arbitrary start and end node sols\n    if start_nodes is None or end_nodes is None:\n        distance_mat = np.zeros((len(nodes)+1, len(nodes)+1))\n        distance_mat[1:, 1:] = pairwise_distances(nodes, nodes)*1e4\n        trim_paths = True #shift to account for dummy node\n    else:\n        distance_mat = pairwise_distances(nodes, nodes)*1e4\n        trim_paths = False\n    distance_mat = distance_mat.astype(int)\n    max_dist = int(max_dist*1e4)\n\n    # Get start and end node indices for ortools\n    if start_nodes is None:\n        start_idx = np.zeros(num_vehicles, dtype=int)\n        num_start_nodes = 0\n    else:\n        start_idx = np.arange(num_vehicles)+int(trim_paths)\n        num_start_nodes = len(start_nodes)\n\n    if end_nodes is None:\n        end_idx = np.zeros(num_vehicles, dtype=int)\n    else:\n        end_idx = np.arange(num_vehicles)+num_start_nodes+int(trim_paths)\n\n    # used by ortools\n    def distance_callback(from_index, to_index):\n        from_node = manager.IndexToNode(from_index)\n        to_node = manager.IndexToNode(to_index)\n        return distance_mat[from_node][to_node]\n\n    # num_locations, num vehicles, start, end\n    manager = pywrapcp.RoutingIndexManager(len(distance_mat), \n                                           num_vehicles, \n                                           start_idx.tolist(),\n                                           end_idx.tolist())\n    routing = pywrapcp.RoutingModel(manager)\n    transit_callback_index = routing.RegisterTransitCallback(distance_callback)\n    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n\n    if num_vehicles &gt; 1:\n        # Dummy distaance constraint to ensure all paths have similar length\n        dimension_name = \"Distance\"\n        routing.AddDimension(\n            transit_callback_index,\n            0,  # no slack\n            max_dist,  # vehicle maximum travel distance\n            True,  # start cumul to zero\n            dimension_name,\n        )\n        distance_dimension = routing.GetDimensionOrDie(dimension_name)\n        distance_dimension.SetGlobalSpanCostCoefficient(100)\n\n    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n    search_parameters.first_solution_strategy = (\n        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n    )\n    search_parameters.local_search_metaheuristic = (\n        routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH\n    )\n    search_parameters.time_limit.seconds = time_limit\n    solution = routing.SolveWithParameters(search_parameters)\n\n    paths = None\n    if solution is not None:\n        paths, distances = get_routes(manager, routing, \n                                      solution, num_vehicles, \n                                      start_idx, end_idx, trim_paths)\n        for path in paths:\n            if len(path) &lt; 2:\n                print('TSP Warning: Empty path detected')\n                return run_tsp(nodes, num_vehicles, int(np.mean(distances)*(1.5/depth)), depth+1)\n    else:\n        print('TSP Warning: No solution found')\n        return run_tsp(nodes, num_vehicles, int(max_dist*1.5), depth+1)\n\n    # Map paths from node indices to node locations\n    paths = [nodes[path] for path in paths]\n\n    # Resample each solution path to have resample number of points\n    if resample is not None:\n        paths = np.array([resample_path(path, resample) for path in paths])\n\n    # Convert distances back to floats in the original scale of the nodes\n    distances = np.array(distances)/1e4\n    return paths, distances\n</code></pre>"},{"location":"examples/AIPP_non_point_FoV.html","title":"Adaptive-IPP with Non-Point FoV","text":"In\u00a0[\u00a0]: Copied! <pre>import os\nos.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"\n\nimport numpy as np\n\nfrom sgptools.utils.metrics import *\nfrom sgptools.utils.data import *\nfrom sgptools.utils.misc import *\nfrom sgptools.models.core.osgpr import *\nfrom sgptools.utils.tsp import run_tsp\nfrom sgptools.utils.gpflow import get_model_params\nfrom sgptools.models.continuous_sgp import *\nfrom sgptools.models.core.transformations import *\nfrom gpflow.utilities.traversal import print_summary\n\nfrom matplotlib import colors\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Polygon\nimport mpl_toolkits.mplot3d.art3d as art3d\nfrom scipy.optimize import linear_sum_assignment\nfrom sklearn.metrics import pairwise_distances\n\nfrom time import time\nfrom copy import deepcopy\n\nnp.random.seed(1234)\ntf.random.set_seed(1234)\n</pre> import os os.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"  import numpy as np  from sgptools.utils.metrics import * from sgptools.utils.data import * from sgptools.utils.misc import * from sgptools.models.core.osgpr import * from sgptools.utils.tsp import run_tsp from sgptools.utils.gpflow import get_model_params from sgptools.models.continuous_sgp import * from sgptools.models.core.transformations import * from gpflow.utilities.traversal import print_summary  from matplotlib import colors import matplotlib.pyplot as plt from matplotlib.patches import Polygon import mpl_toolkits.mplot3d.art3d as art3d from scipy.optimize import linear_sum_assignment from sklearn.metrics import pairwise_distances  from time import time from copy import deepcopy  np.random.seed(1234) tf.random.set_seed(1234) In\u00a0[2]: Copied! <pre>'''\nUtility to map the inducing points to FoV vertices\n'''\ndef get_vertices(Xu, X_fov):\n    X_fov = X_fov.reshape(len(Xu), -1, 2)\n    vertices = np.zeros((len(Xu), 5, 2))\n    vertices[:, 0] = X_fov.min(axis=1)\n    vertices[:, 1] = np.array([X_fov[:, :, 0].min(axis=1), \n                              X_fov[:, :, 1].max(axis=1)]).T\n    vertices[:, 2] = X_fov.max(axis=1)\n    vertices[:, 3] = np.array([X_fov[:, :, 0].max(axis=1), \n                              X_fov[:, :, 1].min(axis=1)]).T\n    vertices[:, 4] = X_fov.mean(axis=1)\n\n    dists = pairwise_distances(vertices[:, 4], Y=Xu[:, :2], \n                               metric='euclidean')\n    _, idx = linear_sum_assignment(dists)\n\n    vertices[:, 4] = Xu[idx][:, :2]\n    return vertices\n\n'''\nMethod to plot the solution with FoVs and environment reconstruction\n'''\ndef plot_results(X_inducing, X_fov, current_idx=0, fname=None):\n    # Setup 3D plot\n    fig = plt.figure(figsize=(5, 5))\n    ax = fig.add_subplot(111, projection='3d', computed_zorder=False)\n\n    current_idx += 1 # Increment by one for plotting\n\n    # Plot the reconstructed environment state\n    num_fov_pts = len(X_fov)//len(X_inducing)\n    sensor_data = cont2disc(X_fov[:num_fov_pts*(current_idx)], X, y)\n    y_pred, _ = get_reconstruction(sensor_data, X_test, \n                                   noise_variance_opt, kernel_opt)\n    norm = colors.Normalize(y_train.min(), y_train.max())\n    _ = ax.plot_surface(X_test[:, 0].reshape(test_dim),\n                        X_test[:, 1].reshape(test_dim),\n                        np.atleast_2d(-0.1),\n                        facecolors=plt.cm.jet(norm(y_pred.reshape(test_dim))),\n                        shade=False,\n                        alpha=0.8,\n                        zorder=0)\n\n    # Plot the solution path\n    ax.scatter(X_inducing[:current_idx, 0], \n               X_inducing[:current_idx, 1], \n               X_inducing[:current_idx, 2], c='C3')\n    ax.scatter(X_inducing[current_idx:, 0], \n               X_inducing[current_idx:, 1], \n               X_inducing[current_idx:, 2], c='C2')\n    ax.plot(X_inducing[:, 0], X_inducing[:, 1], X_inducing[:, 2], 'k-')\n\n    # Plot the FoV vertices\n    vertices = get_vertices(X_inducing, X_fov)\n    for i in range(vertices.shape[0]):\n        color = 'C3' if i &lt; current_idx else 'C2'\n        verts = []\n        verts.append([vertices[i, 0], vertices[i, 1], \n                      vertices[i, 2], vertices[i, 3]])\n        fov = Polygon(np.array(verts)[0, :, :2],\n                      linewidth=1.5, \n                      edgecolor=color,\n                      facecolor=color,\n                      fill=False,\n                      zorder=15)\n        ax.add_patch(fov)\n        art3d.pathpatch_2d_to_3d(fov)\n\n    # Configure other plot settings\n    ax.set_xlabel('X')\n    ax.set_ylabel('Y')\n    ax.set_zlabel('Z')\n\n    ax.set_xlim(np.min(X[:, 0])-0.5, np.max(X[:, 0])+0.5)\n    ax.set_ylim(np.min(X[:, 1])-0.5, np.max(X[:, 1])+0.5)\n    ax.set_zlim(0, 14)\n\n    ax.view_init(elev=30, azim=45+180)\n    ax.set_title(f'Waypoint: {current_idx}, RMSE: {get_rmse(y_pred, y_test):.2f}\\nData Field Reconstruction', y=0.99)\n\n    plt.tight_layout()\n    if fname is not None:\n        plt.savefig(fname, bbox_inches='tight')\n    plt.show()\n</pre> ''' Utility to map the inducing points to FoV vertices ''' def get_vertices(Xu, X_fov):     X_fov = X_fov.reshape(len(Xu), -1, 2)     vertices = np.zeros((len(Xu), 5, 2))     vertices[:, 0] = X_fov.min(axis=1)     vertices[:, 1] = np.array([X_fov[:, :, 0].min(axis=1),                                X_fov[:, :, 1].max(axis=1)]).T     vertices[:, 2] = X_fov.max(axis=1)     vertices[:, 3] = np.array([X_fov[:, :, 0].max(axis=1),                                X_fov[:, :, 1].min(axis=1)]).T     vertices[:, 4] = X_fov.mean(axis=1)      dists = pairwise_distances(vertices[:, 4], Y=Xu[:, :2],                                 metric='euclidean')     _, idx = linear_sum_assignment(dists)      vertices[:, 4] = Xu[idx][:, :2]     return vertices  ''' Method to plot the solution with FoVs and environment reconstruction ''' def plot_results(X_inducing, X_fov, current_idx=0, fname=None):     # Setup 3D plot     fig = plt.figure(figsize=(5, 5))     ax = fig.add_subplot(111, projection='3d', computed_zorder=False)      current_idx += 1 # Increment by one for plotting      # Plot the reconstructed environment state     num_fov_pts = len(X_fov)//len(X_inducing)     sensor_data = cont2disc(X_fov[:num_fov_pts*(current_idx)], X, y)     y_pred, _ = get_reconstruction(sensor_data, X_test,                                     noise_variance_opt, kernel_opt)     norm = colors.Normalize(y_train.min(), y_train.max())     _ = ax.plot_surface(X_test[:, 0].reshape(test_dim),                         X_test[:, 1].reshape(test_dim),                         np.atleast_2d(-0.1),                         facecolors=plt.cm.jet(norm(y_pred.reshape(test_dim))),                         shade=False,                         alpha=0.8,                         zorder=0)      # Plot the solution path     ax.scatter(X_inducing[:current_idx, 0],                 X_inducing[:current_idx, 1],                 X_inducing[:current_idx, 2], c='C3')     ax.scatter(X_inducing[current_idx:, 0],                 X_inducing[current_idx:, 1],                 X_inducing[current_idx:, 2], c='C2')     ax.plot(X_inducing[:, 0], X_inducing[:, 1], X_inducing[:, 2], 'k-')      # Plot the FoV vertices     vertices = get_vertices(X_inducing, X_fov)     for i in range(vertices.shape[0]):         color = 'C3' if i &lt; current_idx else 'C2'         verts = []         verts.append([vertices[i, 0], vertices[i, 1],                        vertices[i, 2], vertices[i, 3]])         fov = Polygon(np.array(verts)[0, :, :2],                       linewidth=1.5,                        edgecolor=color,                       facecolor=color,                       fill=False,                       zorder=15)         ax.add_patch(fov)         art3d.pathpatch_2d_to_3d(fov)      # Configure other plot settings     ax.set_xlabel('X')     ax.set_ylabel('Y')     ax.set_zlabel('Z')      ax.set_xlim(np.min(X[:, 0])-0.5, np.max(X[:, 0])+0.5)     ax.set_ylim(np.min(X[:, 1])-0.5, np.max(X[:, 1])+0.5)     ax.set_zlim(0, 14)      ax.view_init(elev=30, azim=45+180)     ax.set_title(f'Waypoint: {current_idx}, RMSE: {get_rmse(y_pred, y_test):.2f}\\nData Field Reconstruction', y=0.99)      plt.tight_layout()     if fname is not None:         plt.savefig(fname, bbox_inches='tight')     plt.show() In\u00a0[3]: Copied! <pre>'''\nMethod to run AIPP using the SGP-based approach\n'''\ndef run_aipp(X_train, ipp_model, Xu_init, path2data):\n    total_time_param = 0\n    total_time_ipp = 0\n    num_robots = Xu_init.shape[0]\n    num_waypoints = Xu_init.shape[1]\n    curr_sol = Xu_init\n    plot_results(ipp_model.transform.expand(curr_sol.reshape(-1, 3), \n                                            expand_sensor_model=False).numpy(),\n                 ipp_model.transform.expand(curr_sol.reshape(-1, 3)).numpy())\n\n    # Initialize the hyperparameters and SSGP\n    init_kernel = deepcopy(ipp_model.kernel)\n    init_noise_variance = ipp_model.likelihood.variance\n    param_model = init_osgpr(X_train, \n                             num_inducing=40, \n                             kernel=init_kernel,\n                             noise_variance=init_noise_variance)\n\n    sol_data_X = []\n    sol_data_y = []\n    for time_step in range(num_waypoints):\n        # Get a new batch of data from the last visited waypoint\n        last_visited = curr_sol[:, time_step].copy()\n        # Apply the sensor model expansion with fixed points IPP expansion\n        data_pts = ipp_model.transform.sensor_model.expand(last_visited).numpy()\n        data_X_batch, data_y_batch = path2data(data_pts)\n        sol_data_X.extend(data_X_batch)\n        sol_data_y.extend(data_y_batch)\n\n        # Skip param and path update for the last waypoint\n        if time_step == num_waypoints-1:\n            break\n\n        # Update the SSGP hyperparameter model\n        param_model.update((np.array(data_X_batch), \n                            np.array(data_y_batch)))\n        # Train only the kernel parameters and noise variance.\n        # Keeping the SSGP inducing points fixed results in better \n        # hyperparameter estimation\n        start_time = time()\n        optimize_model(param_model, \n                       trainable_variables=param_model.trainable_variables[1:], \n                       optimizer='scipy',\n                       method='CG')\n        end_time = time()\n        total_time_param += end_time - start_time\n        print_summary(param_model.kernel)\n\n        # Update the SGP-IPP model\n        Xu_visited = curr_sol.copy()[:, :time_step+1]\n        ipp_model.transform.update_Xu_fixed(Xu_visited)\n        ipp_model.update(param_model.likelihood.variance, \n                         param_model.kernel)\n        start_time = time()\n        _ = optimize_model(ipp_model,\n                           kernel_grad=False, \n                           optimizer='scipy',\n                           method='CG')\n        end_time = time()\n        total_time_ipp += end_time - start_time\n        curr_sol = ipp_model.inducing_variable.Z\n        curr_sol = ipp_model.transform.expand(curr_sol, \n                                              expand_sensor_model=False).numpy()\n        curr_sol = curr_sol.reshape(num_robots, num_waypoints, 3)\n\n        plot_results(ipp_model.transform.expand(curr_sol.reshape(-1, 3),\n                                                expand_sensor_model=False).numpy(),\n                     ipp_model.transform.expand(curr_sol.reshape(-1, 3)).numpy(),\n                     time_step+1)\n\n    return np.array(sol_data_X), np.array(sol_data_y), total_time_param, total_time_ipp\n</pre> ''' Method to run AIPP using the SGP-based approach ''' def run_aipp(X_train, ipp_model, Xu_init, path2data):     total_time_param = 0     total_time_ipp = 0     num_robots = Xu_init.shape[0]     num_waypoints = Xu_init.shape[1]     curr_sol = Xu_init     plot_results(ipp_model.transform.expand(curr_sol.reshape(-1, 3),                                              expand_sensor_model=False).numpy(),                  ipp_model.transform.expand(curr_sol.reshape(-1, 3)).numpy())      # Initialize the hyperparameters and SSGP     init_kernel = deepcopy(ipp_model.kernel)     init_noise_variance = ipp_model.likelihood.variance     param_model = init_osgpr(X_train,                               num_inducing=40,                               kernel=init_kernel,                              noise_variance=init_noise_variance)      sol_data_X = []     sol_data_y = []     for time_step in range(num_waypoints):         # Get a new batch of data from the last visited waypoint         last_visited = curr_sol[:, time_step].copy()         # Apply the sensor model expansion with fixed points IPP expansion         data_pts = ipp_model.transform.sensor_model.expand(last_visited).numpy()         data_X_batch, data_y_batch = path2data(data_pts)         sol_data_X.extend(data_X_batch)         sol_data_y.extend(data_y_batch)          # Skip param and path update for the last waypoint         if time_step == num_waypoints-1:             break          # Update the SSGP hyperparameter model         param_model.update((np.array(data_X_batch),                              np.array(data_y_batch)))         # Train only the kernel parameters and noise variance.         # Keeping the SSGP inducing points fixed results in better          # hyperparameter estimation         start_time = time()         optimize_model(param_model,                         trainable_variables=param_model.trainable_variables[1:],                         optimizer='scipy',                        method='CG')         end_time = time()         total_time_param += end_time - start_time         print_summary(param_model.kernel)          # Update the SGP-IPP model         Xu_visited = curr_sol.copy()[:, :time_step+1]         ipp_model.transform.update_Xu_fixed(Xu_visited)         ipp_model.update(param_model.likelihood.variance,                           param_model.kernel)         start_time = time()         _ = optimize_model(ipp_model,                            kernel_grad=False,                             optimizer='scipy',                            method='CG')         end_time = time()         total_time_ipp += end_time - start_time         curr_sol = ipp_model.inducing_variable.Z         curr_sol = ipp_model.transform.expand(curr_sol,                                                expand_sensor_model=False).numpy()         curr_sol = curr_sol.reshape(num_robots, num_waypoints, 3)          plot_results(ipp_model.transform.expand(curr_sol.reshape(-1, 3),                                                 expand_sensor_model=False).numpy(),                      ipp_model.transform.expand(curr_sol.reshape(-1, 3)).numpy(),                      time_step+1)      return np.array(sol_data_X), np.array(sol_data_y), total_time_param, total_time_ipp In\u00a0[4]: Copied! <pre># Get the synthetic elevation data\nX_train, y_train, X_test, y_test, candidates, X, y = get_dataset(random_seed=0)\ntrain_dim = np.sqrt(X_train.shape[0]).astype(int)\n\n# Train a GP and get the model parameters\nprint('Optimized Hyperparameters')\n_, noise_variance_opt, kernel_opt = get_model_params(X_train, y_train, \n                                                     lengthscales=[1.0, 1.0], \n                                                     optimizer='scipy')\n\n# Build a new test set on a grid for easy plotting in a 3D plot\ngpr = gpflow.models.GPR((X_test, y_test),\n                        noise_variance=noise_variance_opt,\n                        kernel=kernel_opt)\ntest_dim = (100, 100)\nX_test1 = np.linspace(np.min(X[:, 0]), np.max(X[:, 0]), test_dim[0])\nX_test2 = np.linspace(np.min(X[:, 1]), np.max(X[:, 1]), test_dim[1])\nX_test1, X_test2 = np.meshgrid(X_test1, X_test2)\nX_test = np.stack([X_test1.ravel(), X_test2.ravel()], axis=1)\ny_test, y_var = gpr.predict_f(X_test)\ny_test = y_test.numpy()\n\n# Plot the data field\nplt.imshow(y_test.reshape(test_dim), cmap=\"jet\", origin='lower')\nplt.xlabel('X')\nplt.ylabel('Y')\nplt.title('Elevation Data Field')\nplt.colorbar(label='Normalized Elevation')\nplt.show()\n</pre> # Get the synthetic elevation data X_train, y_train, X_test, y_test, candidates, X, y = get_dataset(random_seed=0) train_dim = np.sqrt(X_train.shape[0]).astype(int)  # Train a GP and get the model parameters print('Optimized Hyperparameters') _, noise_variance_opt, kernel_opt = get_model_params(X_train, y_train,                                                       lengthscales=[1.0, 1.0],                                                       optimizer='scipy')  # Build a new test set on a grid for easy plotting in a 3D plot gpr = gpflow.models.GPR((X_test, y_test),                         noise_variance=noise_variance_opt,                         kernel=kernel_opt) test_dim = (100, 100) X_test1 = np.linspace(np.min(X[:, 0]), np.max(X[:, 0]), test_dim[0]) X_test2 = np.linspace(np.min(X[:, 1]), np.max(X[:, 1]), test_dim[1]) X_test1, X_test2 = np.meshgrid(X_test1, X_test2) X_test = np.stack([X_test1.ravel(), X_test2.ravel()], axis=1) y_test, y_var = gpr.predict_f(X_test) y_test = y_test.numpy()  # Plot the data field plt.imshow(y_test.reshape(test_dim), cmap=\"jet\", origin='lower') plt.xlabel('X') plt.ylabel('Y') plt.title('Elevation Data Field') plt.colorbar(label='Normalized Elevation') plt.show() <pre>Optimized Hyperparameters\n\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.8371517535182331  \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [2.45802 2.50701]   \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.04342255274528023 \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> In\u00a0[5]: Copied! <pre>num_robots = 1\nnum_placements = 12\nnum_side = 5  # Use a 5x5 point FoV approximation\n\n# Setup the initial AIPP hyperparameters\nprint('Initial AIPP Hyperparameters')\n_, noise_variance, kernel = get_model_params(X_train, y_train, \n                                             lengthscales=[1.0, 1.0], \n                                             optimizer='scipy',\n                                             max_steps=0)\n\n# Get initial inducing points\nXu_init = get_inducing_pts(X_train, num_placements)\n# Initalize the height dimension at 2.0 for all points\nXu_init = np.concatenate((Xu_init, np.ones((Xu_init.shape[0], 1))*2.0), axis=1)\nXu_init[:, :2] = run_tsp(Xu_init[:, :2])[0][0]\n\n# Initialize the non-point FoV transform\nfov_transform = SquareHeightTransform(num_side=num_side)\ntransform = IPPTransform(num_dim=3,\n                         num_robots=1,\n                         sensor_model=fov_transform,\n                         aggregate_fov=True)\n\n# Initialize the IPP model\nsgpr, _ = continuous_sgp(num_placements,\n                         X_train, \n                         noise_variance,\n                         kernel,\n                         transform=transform,\n                         Xu_init=Xu_init,\n                         max_steps=0)\nsgp_sol_sp = sgpr.inducing_variable.Z.numpy()\n\n# Setup the data acquisition function to emulate the robot's sensor\npath2data = lambda x : cont2disc(x, X, y)\n\n# Generate the SGP-based AIPP solution\n_, _, param_time, ipp_time = run_aipp(X_train, \n                                      sgpr,\n                                      Xu_init.reshape(1, -1, 3), \n                                      path2data)\n\nprint(f'Total Hyperparameter Update Time:   {param_time:.2f}s')\nprint(f'Total IPP Update Time:              {ipp_time:.2f}s')\n\nprint(f'Average Hyperparameter Update Time: {param_time/num_placements:.2f}s')\nprint(f'Average IPP Update Time:            {ipp_time/num_placements:.2f}s')\n</pre> num_robots = 1 num_placements = 12 num_side = 5  # Use a 5x5 point FoV approximation  # Setup the initial AIPP hyperparameters print('Initial AIPP Hyperparameters') _, noise_variance, kernel = get_model_params(X_train, y_train,                                               lengthscales=[1.0, 1.0],                                               optimizer='scipy',                                              max_steps=0)  # Get initial inducing points Xu_init = get_inducing_pts(X_train, num_placements) # Initalize the height dimension at 2.0 for all points Xu_init = np.concatenate((Xu_init, np.ones((Xu_init.shape[0], 1))*2.0), axis=1) Xu_init[:, :2] = run_tsp(Xu_init[:, :2])[0][0]  # Initialize the non-point FoV transform fov_transform = SquareHeightTransform(num_side=num_side) transform = IPPTransform(num_dim=3,                          num_robots=1,                          sensor_model=fov_transform,                          aggregate_fov=True)  # Initialize the IPP model sgpr, _ = continuous_sgp(num_placements,                          X_train,                           noise_variance,                          kernel,                          transform=transform,                          Xu_init=Xu_init,                          max_steps=0) sgp_sol_sp = sgpr.inducing_variable.Z.numpy()  # Setup the data acquisition function to emulate the robot's sensor path2data = lambda x : cont2disc(x, X, y)  # Generate the SGP-based AIPP solution _, _, param_time, ipp_time = run_aipp(X_train,                                        sgpr,                                       Xu_init.reshape(1, -1, 3),                                        path2data)  print(f'Total Hyperparameter Update Time:   {param_time:.2f}s') print(f'Total IPP Update Time:              {ipp_time:.2f}s')  print(f'Average Hyperparameter Update Time: {param_time/num_placements:.2f}s') print(f'Average IPP Update Time:            {ipp_time/num_placements:.2f}s') <pre>Initial AIPP Hyperparameters\n\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 1.0                 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [1. 1.]             \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.09999999999999999 \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.02830909998623969 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [2.52935 4.99828]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.03608860733575918 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.66389 4.2298 ]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value                \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.047688497211455655 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.6262  4.20678]    \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value                \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.047733701955419004 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.09326 4.03863]    \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.05702257365194937 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.27988 4.50424]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.06604187384338019 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.3692  4.76543]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.09160363580094916 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.83409 5.53632]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.09052131666299722 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.84722 5.51582]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.08877117097798798 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.63615 4.87825]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value              \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.0936660767287354 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.72264 5.59398]  \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.10593340089380918 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.63767 6.41376]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>Total Hyperparameter Update Time:   22.51s\nTotal IPP Update Time:              31.34s\nAverage Hyperparameter Update Time: 1.88s\nAverage IPP Update Time:            2.61s\n</pre>"},{"location":"examples/AIPP_non_point_FoV.html#adaptive-ipp-with-non-point-fov","title":"Adaptive-IPP with Non-Point FoV\u00b6","text":""},{"location":"examples/AIPP_non_point_FoV.html#generate-synthetic-data-and-initial-sgp-hyperparameters","title":"Generate synthetic data and initial SGP hyperparameters\u00b6","text":""},{"location":"examples/AIPP_non_point_FoV.html#generate-the-aipp-solution","title":"Generate the AIPP solution\u00b6","text":""},{"location":"examples/Baselines.html","title":"Baselines","text":"In\u00a0[\u00a0]: Copied! <pre>import os\nos.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"\n\nimport numpy as np\nfrom time import time\nimport tensorflow as tf\n\nfrom sgptools.utils.data import *\nfrom sgptools.utils.misc import *\nfrom sgptools.utils.metrics import *\nfrom sgptools.utils.tsp import run_tsp\nfrom sgptools.utils.gpflow import get_model_params\n\nfrom sgptools.models.bo import *\nfrom sgptools.models.cma_es import *\nfrom sgptools.models.greedy_mi import *\nfrom sgptools.models.greedy_sgp import *\nfrom sgptools.models.continuous_sgp import *\nfrom sgptools.models.core.transformations import IPPTransform\n\nimport matplotlib.pyplot as plt\n\nnp.random.seed(1234)\ntf.random.set_seed(1234)\n</pre> import os os.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"  import numpy as np from time import time import tensorflow as tf  from sgptools.utils.data import * from sgptools.utils.misc import * from sgptools.utils.metrics import * from sgptools.utils.tsp import run_tsp from sgptools.utils.gpflow import get_model_params  from sgptools.models.bo import * from sgptools.models.cma_es import * from sgptools.models.greedy_mi import * from sgptools.models.greedy_sgp import * from sgptools.models.continuous_sgp import * from sgptools.models.core.transformations import IPPTransform  import matplotlib.pyplot as plt  np.random.seed(1234) tf.random.set_seed(1234) In\u00a0[2]: Copied! <pre>num_robots = 2\nnum_placements = 10\n\n# Configure IPPTransform\ntransform = IPPTransform(num_robots=num_robots)\n\n# Get the synthetic elevation data\nX_train, y_train, X_test, y_test, candidates, X, y = get_dataset()\n\n# Train the GP and get the model parameters\n_, noise_variance, kernel = get_model_params(X_train, y_train, \n                                             lengthscales=[3.0, 3.0], \n                                             optimizer='scipy',\n                                             max_steps=0)\nplt.scatter(X[:, 0], X[:, 1], c=y, cmap='jet')\nplt.title(\"Ground Truth\")\nplt.xlabel('X')\nplt.ylabel('Y')\nplt.gca().set_aspect('equal')\nplt.show()\n\n# Get the initial waypoints\nXu_init = get_inducing_pts(X_train, num_placements*num_robots)\nXu_init, _ = run_tsp(Xu_init, \n                     num_vehicles=num_robots,\n                     max_dist=100,\n                     resample=num_placements)\nXu_init = Xu_init.reshape(-1, 2)\n</pre> num_robots = 2 num_placements = 10  # Configure IPPTransform transform = IPPTransform(num_robots=num_robots)  # Get the synthetic elevation data X_train, y_train, X_test, y_test, candidates, X, y = get_dataset()  # Train the GP and get the model parameters _, noise_variance, kernel = get_model_params(X_train, y_train,                                               lengthscales=[3.0, 3.0],                                               optimizer='scipy',                                              max_steps=0) plt.scatter(X[:, 0], X[:, 1], c=y, cmap='jet') plt.title(\"Ground Truth\") plt.xlabel('X') plt.ylabel('Y') plt.gca().set_aspect('equal') plt.show()  # Get the initial waypoints Xu_init = get_inducing_pts(X_train, num_placements*num_robots) Xu_init, _ = run_tsp(Xu_init,                       num_vehicles=num_robots,                      max_dist=100,                      resample=num_placements) Xu_init = Xu_init.reshape(-1, 2) <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 1.0                 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3. 3.]             \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.09999999999999999 \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> In\u00a0[3]: Copied! <pre># Convert a given path to the data (X, y) alongst the path\ndef path2data(path, X, y):\n    data_X = []\n    data_y = []\n    num_robots, _, _ = path.shape\n    for r in range(num_robots):\n        X_new, y_new = cont2disc(path[r], X, y)\n        data_X.extend(X_new)\n        data_y.extend(y_new)\n    return np.array(data_X), np.array(data_y)\n\n# Plot the solution paths\ndef plot_paths(paths, title=None):\n    plt.figure()\n    for i, path in enumerate(paths):\n        plt.plot(path[:, 0], path[:, 1], \n                 c='r', label='Path', \n                 zorder=1, marker='o')\n    if title is not None:\n        plt.title(title)\n    plt.xlabel('X')\n    plt.ylabel('Y')\n</pre> # Convert a given path to the data (X, y) alongst the path def path2data(path, X, y):     data_X = []     data_y = []     num_robots, _, _ = path.shape     for r in range(num_robots):         X_new, y_new = cont2disc(path[r], X, y)         data_X.extend(X_new)         data_y.extend(y_new)     return np.array(data_X), np.array(data_y)  # Plot the solution paths def plot_paths(paths, title=None):     plt.figure()     for i, path in enumerate(paths):         plt.plot(path[:, 0], path[:, 1],                   c='r', label='Path',                   zorder=1, marker='o')     if title is not None:         plt.title(title)     plt.xlabel('X')     plt.ylabel('Y') In\u00a0[4]: Copied! <pre>start = time()\nmodel, _ = continuous_sgp(num_placements, \n                          X_train, \n                          noise_variance, \n                          kernel,\n                          transform,\n                          Xu_init=Xu_init.copy())\nend = time()\nrun_time = end - start\n\n# Get solution inducing points\nsol = model.inducing_variable.Z.numpy()\nsol_paths = sol.reshape(num_robots, num_placements, 2)\n\n# Convert solution path waypoints to sensing points along the whole path\nX_path, y_path = path2data(sol_paths, X, y)\n\n# Compute RMSE\ny_pred, _ = get_reconstruction((X_path, y_path), \n                                X, \n                                noise_variance, \n                                kernel)\nrmse = get_rmse(y_pred, y)\n\n# Plot the solution\nplot_paths(sol_paths, title=f\"Continuous-SGP | RMSE: {rmse:.2f} | Time: {run_time:.2f}s\")\nplt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet')\nplt.gca().set_aspect('equal')\n</pre> start = time() model, _ = continuous_sgp(num_placements,                            X_train,                            noise_variance,                            kernel,                           transform,                           Xu_init=Xu_init.copy()) end = time() run_time = end - start  # Get solution inducing points sol = model.inducing_variable.Z.numpy() sol_paths = sol.reshape(num_robots, num_placements, 2)  # Convert solution path waypoints to sensing points along the whole path X_path, y_path = path2data(sol_paths, X, y)  # Compute RMSE y_pred, _ = get_reconstruction((X_path, y_path),                                  X,                                  noise_variance,                                  kernel) rmse = get_rmse(y_pred, y)  # Plot the solution plot_paths(sol_paths, title=f\"Continuous-SGP | RMSE: {rmse:.2f} | Time: {run_time:.2f}s\") plt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet') plt.gca().set_aspect('equal') In\u00a0[5]: Copied! <pre>start = time()\nsol = get_greedy_sgp_sol(num_placements*num_robots, \n                         candidates, \n                         candidates, \n                         noise_variance, \n                         kernel,\n                         transform=transform)\nend = time()\nrun_time = end - start\nsol_paths = sol.reshape(num_robots, num_placements, 2)\n\n# Convert solution path waypoints to sensing points along the whole path\nX_path, y_path = path2data(sol_paths, X, y)\n\n# Compute RMSE\ny_pred, _ = get_reconstruction((X_path, y_path), \n                                X, \n                                noise_variance, \n                                kernel)\nrmse = get_rmse(y_pred, y)\n\n# Plot the solution\nplot_paths(sol_paths, title=f\"Discrete-SGP | RMSE: {rmse:.2f} | Time: {run_time:.2f}s\")\nplt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet')\nplt.gca().set_aspect('equal')\n</pre> start = time() sol = get_greedy_sgp_sol(num_placements*num_robots,                           candidates,                           candidates,                           noise_variance,                           kernel,                          transform=transform) end = time() run_time = end - start sol_paths = sol.reshape(num_robots, num_placements, 2)  # Convert solution path waypoints to sensing points along the whole path X_path, y_path = path2data(sol_paths, X, y)  # Compute RMSE y_pred, _ = get_reconstruction((X_path, y_path),                                  X,                                  noise_variance,                                  kernel) rmse = get_rmse(y_pred, y)  # Plot the solution plot_paths(sol_paths, title=f\"Discrete-SGP | RMSE: {rmse:.2f} | Time: {run_time:.2f}s\") plt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet') plt.gca().set_aspect('equal') In\u00a0[6]: Copied! <pre>start = time()\nsol = get_greedy_mi_sol(num_placements*num_robots, \n                        candidates, \n                        candidates, \n                        noise_variance, \n                        kernel,\n                        transform=transform)\nend = time()\nrun_time = end - start\nsol_paths = sol.reshape(num_robots, num_placements, 2)\n\n# Convert solution path waypoints to sensing points along the whole path\nX_path, y_path = path2data(sol_paths, X, y)\n\n# Compute RMSE\ny_pred, _ = get_reconstruction((X_path, y_path), \n                                X, \n                                noise_variance, \n                                kernel)\nrmes = get_rmse(y_pred, y)\n\n# Plot the solution\nplot_paths(sol_paths, title=f\"Greedy-MI | RMSE: {rmes:.2f} | Time: {run_time:.2f}s\")\nplt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet')\nplt.gca().set_aspect('equal')\n</pre> start = time() sol = get_greedy_mi_sol(num_placements*num_robots,                          candidates,                          candidates,                          noise_variance,                          kernel,                         transform=transform) end = time() run_time = end - start sol_paths = sol.reshape(num_robots, num_placements, 2)  # Convert solution path waypoints to sensing points along the whole path X_path, y_path = path2data(sol_paths, X, y)  # Compute RMSE y_pred, _ = get_reconstruction((X_path, y_path),                                  X,                                  noise_variance,                                  kernel) rmes = get_rmse(y_pred, y)  # Plot the solution plot_paths(sol_paths, title=f\"Greedy-MI | RMSE: {rmes:.2f} | Time: {run_time:.2f}s\") plt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet') plt.gca().set_aspect('equal') In\u00a0[7]: Copied! <pre>start = time()\nmodel = CMA_ES(candidates, \n               noise_variance, \n               kernel, \n               transform=transform)\nsol = model.optimize(num_sensors=num_placements*num_robots, \n                     X_init=Xu_init.copy())\nend = time()\nrun_time = end - start\nsol_paths = sol.reshape(num_robots, num_placements, 2)\n\n# Convert solution path waypoints to sensing points along the whole path\nX_path, y_path = path2data(sol_paths, X, y)\ny_pred, _ = get_reconstruction((X_path, y_path), \n                                X, \n                                noise_variance, \n                                kernel)\n\n# Compute RMSE\nrmes = get_rmse(y_pred, y)\n\n# Plot the solution\nplot_paths(sol_paths, title=f\"CMA-ES-MI | RMSE: {rmes:.2f} | Time: {run_time:.2f}s\")\nplt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet')\nplt.gca().set_aspect('equal')\n</pre> start = time() model = CMA_ES(candidates,                 noise_variance,                 kernel,                 transform=transform) sol = model.optimize(num_sensors=num_placements*num_robots,                       X_init=Xu_init.copy()) end = time() run_time = end - start sol_paths = sol.reshape(num_robots, num_placements, 2)  # Convert solution path waypoints to sensing points along the whole path X_path, y_path = path2data(sol_paths, X, y) y_pred, _ = get_reconstruction((X_path, y_path),                                  X,                                  noise_variance,                                  kernel)  # Compute RMSE rmes = get_rmse(y_pred, y)  # Plot the solution plot_paths(sol_paths, title=f\"CMA-ES-MI | RMSE: {rmes:.2f} | Time: {run_time:.2f}s\") plt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet') plt.gca().set_aspect('equal') In\u00a0[8]: Copied! <pre>start = time()\nmodel = BayesianOpt(candidates, \n                    noise_variance,\n                    kernel,\n                    transform=transform)\nsol = model.optimize(num_sensors=num_placements*num_robots, \n                     X_init=Xu_init.copy())\nend = time()\nrun_time = end - start\nsol_paths = sol.reshape(num_robots, num_placements, 2)\n\n# Convert solution path waypoints to sensing points along the whole path\nX_path, y_path = path2data(sol_paths, X, y)\ny_pred, _ = get_reconstruction((X_path, y_path), \n                                X, \n                                noise_variance, \n                                kernel)\n\n# Compute RMSE\nrmes = get_rmse(y_pred, y)\n\n# Plot the solution\nplot_paths(sol_paths, title=f\"BO-MI | RMSE: {rmes:.2f} | Time: {run_time:.2f}s\")\nplt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet')\nplt.gca().set_aspect('equal')\n</pre> start = time() model = BayesianOpt(candidates,                      noise_variance,                     kernel,                     transform=transform) sol = model.optimize(num_sensors=num_placements*num_robots,                       X_init=Xu_init.copy()) end = time() run_time = end - start sol_paths = sol.reshape(num_robots, num_placements, 2)  # Convert solution path waypoints to sensing points along the whole path X_path, y_path = path2data(sol_paths, X, y) y_pred, _ = get_reconstruction((X_path, y_path),                                  X,                                  noise_variance,                                  kernel)  # Compute RMSE rmes = get_rmse(y_pred, y)  # Plot the solution plot_paths(sol_paths, title=f\"BO-MI | RMSE: {rmes:.2f} | Time: {run_time:.2f}s\") plt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet') plt.gca().set_aspect('equal')"},{"location":"examples/Baselines.html#baselines","title":"Baselines\u00b6","text":""},{"location":"examples/Baselines.html#set-the-initial-parameters-and-generate-synthetic-data","title":"Set the initial parameters and generate synthetic data\u00b6","text":"<p>(no distance budget constraints)</p>"},{"location":"examples/Baselines.html#helper-functions","title":"Helper functions\u00b6","text":""},{"location":"examples/Baselines.html#continuous-sgp","title":"Continuous-SGP\u00b6","text":""},{"location":"examples/Baselines.html#discrete-sgp","title":"Discrete-SGP\u00b6","text":""},{"location":"examples/Baselines.html#greedy-mutual-information","title":"Greedy Mutual Information\u00b6","text":""},{"location":"examples/Baselines.html#cma-es-mutual-information-genetic-algorithm","title":"CMA-ES Mutual Information (Genetic Algorithm)\u00b6","text":""},{"location":"examples/Baselines.html#bayesian-optimization-mutual-information","title":"Bayesian Optimization Mutual Information\u00b6","text":""},{"location":"examples/IPP.html","title":"SGP-Based IPP","text":"In\u00a0[\u00a0]: Copied! <pre>import os\nos.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom sgptools.utils.metrics import *\nfrom sgptools.utils.data import *\nfrom sgptools.utils.misc import *\nfrom sgptools.utils.tsp import run_tsp\nfrom sgptools.utils.gpflow import get_model_params\nfrom sgptools.models.continuous_sgp import *\nfrom sgptools.models.core.transformations import *\n\nimport matplotlib.pyplot as plt\n\nnp.random.seed(1234)\ntf.random.set_seed(1234)\n</pre> import os os.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"  import numpy as np import tensorflow as tf  from sgptools.utils.metrics import * from sgptools.utils.data import * from sgptools.utils.misc import * from sgptools.utils.tsp import run_tsp from sgptools.utils.gpflow import get_model_params from sgptools.models.continuous_sgp import * from sgptools.models.core.transformations import *  import matplotlib.pyplot as plt  np.random.seed(1234) tf.random.set_seed(1234) In\u00a0[2]: Copied! <pre># Get the synthetic elevation data\nX_train, y_train, X_test, y_test, candidates, X, y = get_dataset()\n\n# Train the GP and get the model parameters\n_, noise_variance, kernel = get_model_params(X_train, y_train, \n                                             lengthscales=[1.0, 1.0], \n                                             optimizer='scipy')\nplt.scatter(X[:, 0], X[:, 1], c=y, cmap='jet')\nplt.title(\"Ground Truth\")\nplt.xlabel('X')\nplt.ylabel('Y')\nplt.gca().set_aspect('equal')\nplt.show()\n</pre> # Get the synthetic elevation data X_train, y_train, X_test, y_test, candidates, X, y = get_dataset()  # Train the GP and get the model parameters _, noise_variance, kernel = get_model_params(X_train, y_train,                                               lengthscales=[1.0, 1.0],                                               optimizer='scipy') plt.scatter(X[:, 0], X[:, 1], c=y, cmap='jet') plt.title(\"Ground Truth\") plt.xlabel('X') plt.ylabel('Y') plt.gca().set_aspect('equal') plt.show() <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.6576268242419765  \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [2.95895 2.25221]   \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.02576014673391919 \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> In\u00a0[3]: Copied! <pre>num_robots = 1\nnum_placements = 6\n\n# Initialize inducing points and get initial path\nXu_init = get_inducing_pts(X_train, \n                           num_placements*num_robots)\nXu_init, _ = run_tsp(Xu_init, \n                     num_vehicles=num_robots)\nXu_init = Xu_init[0]\n\n# Setup unconstrained IPP transform and optimize it\ntransform = IPPTransform(num_robots=num_robots, num_dim=2)\nsgpr, _ = continuous_sgp(num_placements, \n                         X_train, \n                         noise_variance, \n                         kernel,\n                         transform,\n                         Xu_init=Xu_init, \n                         max_steps=1000)\n\n# Get solution inducing points\nsgp_sol_sp = sgpr.inducing_variable.Z.numpy()\nsgp_paths = sgp_sol_sp.reshape(num_robots, num_placements, 2)\n\n# Plot the solution\nplot_paths(sgp_paths, candidates,\n           title=\"Offline Single-Robot IPP with Point Sensing\")\n</pre> num_robots = 1 num_placements = 6  # Initialize inducing points and get initial path Xu_init = get_inducing_pts(X_train,                             num_placements*num_robots) Xu_init, _ = run_tsp(Xu_init,                       num_vehicles=num_robots) Xu_init = Xu_init[0]  # Setup unconstrained IPP transform and optimize it transform = IPPTransform(num_robots=num_robots, num_dim=2) sgpr, _ = continuous_sgp(num_placements,                           X_train,                           noise_variance,                           kernel,                          transform,                          Xu_init=Xu_init,                           max_steps=1000)  # Get solution inducing points sgp_sol_sp = sgpr.inducing_variable.Z.numpy() sgp_paths = sgp_sol_sp.reshape(num_robots, num_placements, 2)  # Plot the solution plot_paths(sgp_paths, candidates,            title=\"Offline Single-Robot IPP with Point Sensing\") In\u00a0[4]: Copied! <pre>num_robots = 1\nnum_placements = 6\n\n# Initialize inducing points and get initial path\nXu_init = get_inducing_pts(X_train, num_placements*num_robots)\nXu_init, _ = run_tsp(Xu_init, \n                     num_vehicles=num_robots)\nXu_init = Xu_init[0]\n\n# Setup unconstrained IPP transform with sampling rate=5\n# and optimize it\ntransform = IPPTransform(num_robots=num_robots,\n                           num_dim=2,\n                           sampling_rate=5)\nsgpr, _ = continuous_sgp(num_placements, \n                         X_train, \n                         noise_variance, \n                         kernel,\n                         transform,\n                         Xu_init=Xu_init, \n                         max_steps=1000)\n\n# Get solution inducing points\nsgp_sol_sp = sgpr.inducing_variable.Z.numpy()\nsgp_paths = sgp_sol_sp.reshape(num_robots, num_placements, 2)\n\n# Plot the solution\nplot_paths(sgp_paths, candidates, \n           title=\"Offline Single-Robot IPP with Continuous Sensing\")\n</pre> num_robots = 1 num_placements = 6  # Initialize inducing points and get initial path Xu_init = get_inducing_pts(X_train, num_placements*num_robots) Xu_init, _ = run_tsp(Xu_init,                       num_vehicles=num_robots) Xu_init = Xu_init[0]  # Setup unconstrained IPP transform with sampling rate=5 # and optimize it transform = IPPTransform(num_robots=num_robots,                            num_dim=2,                            sampling_rate=5) sgpr, _ = continuous_sgp(num_placements,                           X_train,                           noise_variance,                           kernel,                          transform,                          Xu_init=Xu_init,                           max_steps=1000)  # Get solution inducing points sgp_sol_sp = sgpr.inducing_variable.Z.numpy() sgp_paths = sgp_sol_sp.reshape(num_robots, num_placements, 2)  # Plot the solution plot_paths(sgp_paths, candidates,             title=\"Offline Single-Robot IPP with Continuous Sensing\") In\u00a0[5]: Copied! <pre>num_robots = 1\nnum_placements = 4\n\n# Initialize inducing points and get initial path\nXu_init = get_inducing_pts(X_train, num_placements*num_robots)\nXu_init, _ = run_tsp(Xu_init, \n                     num_vehicles=num_robots)\nXu_init = Xu_init[0]\n\n# Add random angles to inducing points to initialize the rotation angle of the FoVs\nangles = np.random.uniform(-np.pi, np.pi, num_placements).reshape(-1, 1)\nXu_init = np.concatenate([Xu_init, angles], axis=1)\n\n# Setup unconstrained IPP transform with square FoV transform \n# with length 5 and 3x3 point approximation, \n# and continuous sensing model (sampling rate of 3), and optimize it\nsquare_transform = SquareTransform(length=5, num_side=3)\ntransform = IPPTransform(num_robots=num_robots,\n                         num_dim=3,\n                         sampling_rate=3,\n                         sensor_model=square_transform)\nsgpr, _ = continuous_sgp(num_placements, \n                         X_train, \n                         noise_variance, \n                         kernel,\n                         transform,\n                         Xu_init=Xu_init, \n                         max_steps=1000)\n\n# Get solution inducing points\nsgp_sol_sp = sgpr.inducing_variable.Z.numpy()[:, :2]\nsgp_paths = sgp_sol_sp.reshape(num_robots, -1 , 2)\n\n# Plot the solution path and the FoVs\nplot_paths(sgp_paths, candidates, \n           title=\"Offline Single-Robot IPP with Continuous Sensing\")\nsgp_sol_sp = sgpr.inducing_variable.Z\nsgp_sol_sp = sgpr.transform.expand(sgp_sol_sp,).numpy()\nplt.scatter(sgp_sol_sp[:, 0], sgp_sol_sp[:, 1], s=10, c='r')\n</pre> num_robots = 1 num_placements = 4  # Initialize inducing points and get initial path Xu_init = get_inducing_pts(X_train, num_placements*num_robots) Xu_init, _ = run_tsp(Xu_init,                       num_vehicles=num_robots) Xu_init = Xu_init[0]  # Add random angles to inducing points to initialize the rotation angle of the FoVs angles = np.random.uniform(-np.pi, np.pi, num_placements).reshape(-1, 1) Xu_init = np.concatenate([Xu_init, angles], axis=1)  # Setup unconstrained IPP transform with square FoV transform  # with length 5 and 3x3 point approximation,  # and continuous sensing model (sampling rate of 3), and optimize it square_transform = SquareTransform(length=5, num_side=3) transform = IPPTransform(num_robots=num_robots,                          num_dim=3,                          sampling_rate=3,                          sensor_model=square_transform) sgpr, _ = continuous_sgp(num_placements,                           X_train,                           noise_variance,                           kernel,                          transform,                          Xu_init=Xu_init,                           max_steps=1000)  # Get solution inducing points sgp_sol_sp = sgpr.inducing_variable.Z.numpy()[:, :2] sgp_paths = sgp_sol_sp.reshape(num_robots, -1 , 2)  # Plot the solution path and the FoVs plot_paths(sgp_paths, candidates,             title=\"Offline Single-Robot IPP with Continuous Sensing\") sgp_sol_sp = sgpr.inducing_variable.Z sgp_sol_sp = sgpr.transform.expand(sgp_sol_sp,).numpy() plt.scatter(sgp_sol_sp[:, 0], sgp_sol_sp[:, 1], s=10, c='r') Out[5]: <pre>&lt;matplotlib.collections.PathCollection at 0x7f5688999990&gt;</pre> In\u00a0[6]: Copied! <pre>num_robots = 3\nnum_placements = 6\n\n# Initialize inducing points and get initial path \n# resample to ensure same number of waypoints in each path\nXu_init = get_inducing_pts(X_train, num_placements*num_robots)\nXu_init, _ = run_tsp(Xu_init, \n                     num_vehicles=num_robots,\n                     max_dist=100,\n                     resample=num_placements)\nXu_init = Xu_init.reshape(-1, 2)\n\n# Setup distance constrained 3 robot IPP transform\n# with continuous sensing model (sampling rate of 5) and \n# a distance budget of 30.0 and optimize it\ntransform = IPPTransform(num_robots=num_robots,\n                         num_dim=2,\n                         sampling_rate=5,\n                         distance_budget=30.,\n                         constraint_weight=100.)\nsgpr, _ = continuous_sgp(num_placements, \n                         X_train, \n                         noise_variance, \n                         kernel,\n                         transform,\n                         Xu_init=Xu_init, \n                         max_steps=5000)\n\n# Get solution inducing points\nsol_sp = sgpr.inducing_variable.Z.numpy()\nsol_paths = sol_sp.reshape(num_robots, \n                           num_placements, 2)\n\n# Plot the solution path\nplot_paths(sol_paths, candidates, title=\"Optimized paths\")\nprint(\"Distances:\", transform.distance(sol_sp).numpy())\n</pre> num_robots = 3 num_placements = 6  # Initialize inducing points and get initial path  # resample to ensure same number of waypoints in each path Xu_init = get_inducing_pts(X_train, num_placements*num_robots) Xu_init, _ = run_tsp(Xu_init,                       num_vehicles=num_robots,                      max_dist=100,                      resample=num_placements) Xu_init = Xu_init.reshape(-1, 2)  # Setup distance constrained 3 robot IPP transform # with continuous sensing model (sampling rate of 5) and  # a distance budget of 30.0 and optimize it transform = IPPTransform(num_robots=num_robots,                          num_dim=2,                          sampling_rate=5,                          distance_budget=30.,                          constraint_weight=100.) sgpr, _ = continuous_sgp(num_placements,                           X_train,                           noise_variance,                           kernel,                          transform,                          Xu_init=Xu_init,                           max_steps=5000)  # Get solution inducing points sol_sp = sgpr.inducing_variable.Z.numpy() sol_paths = sol_sp.reshape(num_robots,                             num_placements, 2)  # Plot the solution path plot_paths(sol_paths, candidates, title=\"Optimized paths\") print(\"Distances:\", transform.distance(sol_sp).numpy()) <pre>Distances: [30.         29.9984054  29.98115566]\n</pre>"},{"location":"examples/IPP.html#sgp-based-ipp","title":"SGP-Based IPP\u00b6","text":""},{"location":"examples/IPP.html#get-the-data-and-initial-sgp-hyperparameters","title":"Get the data and initial SGP hyperparameters\u00b6","text":""},{"location":"examples/IPP.html#single-robot-ipp-with-point-sensing","title":"Single-Robot IPP with Point Sensing\u00b6","text":""},{"location":"examples/IPP.html#single-robot-ipp-with-continuous-sensing","title":"Single-Robot IPP with Continuous Sensing\u00b6","text":""},{"location":"examples/IPP.html#single-robot-ipp-with-continuous-non-point-fov-sensor","title":"Single-Robot IPP with Continuous Non-point FoV Sensor\u00b6","text":""},{"location":"examples/IPP.html#multi-robot-ipp-with-continuous-sensing-and-a-distance-budget","title":"Multi-Robot IPP with Continuous Sensing and a Distance Budget\u00b6","text":""},{"location":"examples/non_point_FoV.html","title":"IPP with Non-Point FoV","text":"In\u00a0[\u00a0]: Copied! <pre>import os\nos.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"\n\nimport numpy as np\n\nfrom sgptools.utils.metrics import *\nfrom sgptools.utils.data import *\nfrom sgptools.utils.misc import *\nfrom sgptools.models.core.osgpr import *\nfrom sgptools.utils.tsp import run_tsp\nfrom sgptools.utils.gpflow import get_model_params\nfrom sgptools.models.continuous_sgp import *\nfrom sgptools.models.core.transformations import *\nfrom gpflow.utilities.traversal import print_summary\n\nfrom matplotlib import colors\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Polygon\nimport mpl_toolkits.mplot3d.art3d as art3d\nfrom scipy.optimize import linear_sum_assignment\nfrom sklearn.metrics import pairwise_distances\n\nfrom time import time\nfrom copy import deepcopy\n\nnp.random.seed(1234)\ntf.random.set_seed(1234)\n</pre> import os os.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"  import numpy as np  from sgptools.utils.metrics import * from sgptools.utils.data import * from sgptools.utils.misc import * from sgptools.models.core.osgpr import * from sgptools.utils.tsp import run_tsp from sgptools.utils.gpflow import get_model_params from sgptools.models.continuous_sgp import * from sgptools.models.core.transformations import * from gpflow.utilities.traversal import print_summary  from matplotlib import colors import matplotlib.pyplot as plt from matplotlib.patches import Polygon import mpl_toolkits.mplot3d.art3d as art3d from scipy.optimize import linear_sum_assignment from sklearn.metrics import pairwise_distances  from time import time from copy import deepcopy  np.random.seed(1234) tf.random.set_seed(1234) In\u00a0[2]: Copied! <pre>'''\nUtility to map the inducing points to FoV vertices\n'''\ndef get_vertices(Xu, X_fov):\n    X_fov = X_fov.reshape(len(Xu), -1, 2)\n    vertices = np.zeros((len(Xu), 5, 2))\n    vertices[:, 0] = X_fov.min(axis=1)\n    vertices[:, 1] = np.array([X_fov[:, :, 0].min(axis=1), \n                              X_fov[:, :, 1].max(axis=1)]).T\n    vertices[:, 2] = X_fov.max(axis=1)\n    vertices[:, 3] = np.array([X_fov[:, :, 0].max(axis=1), \n                              X_fov[:, :, 1].min(axis=1)]).T\n    vertices[:, 4] = X_fov.mean(axis=1)\n\n    dists = pairwise_distances(vertices[:, 4], Y=Xu[:, :2], \n                               metric='euclidean')\n    _, idx = linear_sum_assignment(dists)\n\n    vertices[:, 4] = Xu[idx][:, :2]\n    return vertices\n\n'''\nMethod to plot the solution with FoVs and environment reconstruction\n'''\ndef plot_results(X_inducing, X_fov, fname=None):\n    # Setup 3D plot\n    fig = plt.figure(figsize=(5, 5))\n    ax = fig.add_subplot(111, projection='3d', computed_zorder=False)\n\n    # Plot the reconstructed environment state\n    sensor_data = cont2disc(X_fov, X, y)\n    y_pred, _ = get_reconstruction(sensor_data, X_test, \n                                   noise_variance_opt, kernel_opt)\n    norm = colors.Normalize(y_train.min(), y_train.max())\n    _ = ax.plot_surface(X_test[:, 0].reshape(test_dim),\n                        X_test[:, 1].reshape(test_dim),\n                        np.atleast_2d(-0.1),\n                        facecolors=plt.cm.jet(norm(y_pred.reshape(test_dim))),\n                        shade=False,\n                        alpha=0.8,\n                        zorder=0)\n\n    # Plot the solution path\n    ax.scatter(X_inducing[:, 0], \n               X_inducing[:, 1], \n               X_inducing[:, 2], c='C3')\n    ax.plot(X_inducing[:, 0], X_inducing[:, 1], X_inducing[:, 2], 'k-')\n\n    # Plot the FoV vertices\n    vertices = get_vertices(X_inducing, X_fov)\n    for i in range(vertices.shape[0]):\n        color = 'C3'\n        verts = []\n        verts.append([vertices[i, 0], vertices[i, 1], \n                      vertices[i, 2], vertices[i, 3]])\n        fov = Polygon(np.array(verts)[0, :, :2],\n                      linewidth=1.5, \n                      edgecolor=color,\n                      facecolor=color,\n                      fill=False,\n                      zorder=15)\n        ax.add_patch(fov)\n        art3d.pathpatch_2d_to_3d(fov)\n\n    # Configure other plot settings\n    ax.set_xlabel('X')\n    ax.set_ylabel('Y')\n    ax.set_zlabel('Z')\n\n    ax.set_xlim(np.min(X[:, 0])-0.5, np.max(X[:, 0])+0.5)\n    ax.set_ylim(np.min(X[:, 1])-0.5, np.max(X[:, 1])+0.5)\n    ax.set_zlim(0, 14)\n\n    ax.view_init(elev=30, azim=45+180)\n    ax.set_title(f'Num Waypoints: {num_placements}, RMSE: {get_rmse(y_pred, y_test):.2f}\\nData Field Reconstruction', y=0.99)\n\n    plt.tight_layout()\n    if fname is not None:\n        plt.savefig(fname, bbox_inches='tight')\n    plt.show()\n</pre> ''' Utility to map the inducing points to FoV vertices ''' def get_vertices(Xu, X_fov):     X_fov = X_fov.reshape(len(Xu), -1, 2)     vertices = np.zeros((len(Xu), 5, 2))     vertices[:, 0] = X_fov.min(axis=1)     vertices[:, 1] = np.array([X_fov[:, :, 0].min(axis=1),                                X_fov[:, :, 1].max(axis=1)]).T     vertices[:, 2] = X_fov.max(axis=1)     vertices[:, 3] = np.array([X_fov[:, :, 0].max(axis=1),                                X_fov[:, :, 1].min(axis=1)]).T     vertices[:, 4] = X_fov.mean(axis=1)      dists = pairwise_distances(vertices[:, 4], Y=Xu[:, :2],                                 metric='euclidean')     _, idx = linear_sum_assignment(dists)      vertices[:, 4] = Xu[idx][:, :2]     return vertices  ''' Method to plot the solution with FoVs and environment reconstruction ''' def plot_results(X_inducing, X_fov, fname=None):     # Setup 3D plot     fig = plt.figure(figsize=(5, 5))     ax = fig.add_subplot(111, projection='3d', computed_zorder=False)      # Plot the reconstructed environment state     sensor_data = cont2disc(X_fov, X, y)     y_pred, _ = get_reconstruction(sensor_data, X_test,                                     noise_variance_opt, kernel_opt)     norm = colors.Normalize(y_train.min(), y_train.max())     _ = ax.plot_surface(X_test[:, 0].reshape(test_dim),                         X_test[:, 1].reshape(test_dim),                         np.atleast_2d(-0.1),                         facecolors=plt.cm.jet(norm(y_pred.reshape(test_dim))),                         shade=False,                         alpha=0.8,                         zorder=0)      # Plot the solution path     ax.scatter(X_inducing[:, 0],                 X_inducing[:, 1],                 X_inducing[:, 2], c='C3')     ax.plot(X_inducing[:, 0], X_inducing[:, 1], X_inducing[:, 2], 'k-')      # Plot the FoV vertices     vertices = get_vertices(X_inducing, X_fov)     for i in range(vertices.shape[0]):         color = 'C3'         verts = []         verts.append([vertices[i, 0], vertices[i, 1],                        vertices[i, 2], vertices[i, 3]])         fov = Polygon(np.array(verts)[0, :, :2],                       linewidth=1.5,                        edgecolor=color,                       facecolor=color,                       fill=False,                       zorder=15)         ax.add_patch(fov)         art3d.pathpatch_2d_to_3d(fov)      # Configure other plot settings     ax.set_xlabel('X')     ax.set_ylabel('Y')     ax.set_zlabel('Z')      ax.set_xlim(np.min(X[:, 0])-0.5, np.max(X[:, 0])+0.5)     ax.set_ylim(np.min(X[:, 1])-0.5, np.max(X[:, 1])+0.5)     ax.set_zlim(0, 14)      ax.view_init(elev=30, azim=45+180)     ax.set_title(f'Num Waypoints: {num_placements}, RMSE: {get_rmse(y_pred, y_test):.2f}\\nData Field Reconstruction', y=0.99)      plt.tight_layout()     if fname is not None:         plt.savefig(fname, bbox_inches='tight')     plt.show() In\u00a0[3]: Copied! <pre># Get the synthetic elevation data\nX_train, y_train, X_test, y_test, candidates, X, y = get_dataset(random_seed=0)\ntrain_dim = np.sqrt(X_train.shape[0]).astype(int)\n\n# Train a GP and get the model parameters\nprint('Optimized Hyperparameters')\n_, noise_variance_opt, kernel_opt = get_model_params(X_train, y_train, \n                                                     lengthscales=[1.0, 1.0], \n                                                     optimizer='scipy')\n\n# Build a new test set on a grid for easy plotting in a 3D plot\ngpr = gpflow.models.GPR((X_test, y_test),\n                        noise_variance=noise_variance_opt,\n                        kernel=kernel_opt)\ntest_dim = (100, 100)\nX_test1 = np.linspace(np.min(X[:, 0]), np.max(X[:, 0]), test_dim[0])\nX_test2 = np.linspace(np.min(X[:, 1]), np.max(X[:, 1]), test_dim[1])\nX_test1, X_test2 = np.meshgrid(X_test1, X_test2)\nX_test = np.stack([X_test1.ravel(), X_test2.ravel()], axis=1)\ny_test, y_var = gpr.predict_f(X_test)\ny_test = y_test.numpy()\n\n# Plot the data field\nplt.imshow(y_test.reshape(test_dim), cmap=\"jet\", origin='lower')\nplt.xlabel('X')\nplt.ylabel('Y')\nplt.title('Elevation Data Field')\nplt.colorbar(label='Normalized Elevation')\nplt.show()\n</pre> # Get the synthetic elevation data X_train, y_train, X_test, y_test, candidates, X, y = get_dataset(random_seed=0) train_dim = np.sqrt(X_train.shape[0]).astype(int)  # Train a GP and get the model parameters print('Optimized Hyperparameters') _, noise_variance_opt, kernel_opt = get_model_params(X_train, y_train,                                                       lengthscales=[1.0, 1.0],                                                       optimizer='scipy')  # Build a new test set on a grid for easy plotting in a 3D plot gpr = gpflow.models.GPR((X_test, y_test),                         noise_variance=noise_variance_opt,                         kernel=kernel_opt) test_dim = (100, 100) X_test1 = np.linspace(np.min(X[:, 0]), np.max(X[:, 0]), test_dim[0]) X_test2 = np.linspace(np.min(X[:, 1]), np.max(X[:, 1]), test_dim[1]) X_test1, X_test2 = np.meshgrid(X_test1, X_test2) X_test = np.stack([X_test1.ravel(), X_test2.ravel()], axis=1) y_test, y_var = gpr.predict_f(X_test) y_test = y_test.numpy()  # Plot the data field plt.imshow(y_test.reshape(test_dim), cmap=\"jet\", origin='lower') plt.xlabel('X') plt.ylabel('Y') plt.title('Elevation Data Field') plt.colorbar(label='Normalized Elevation') plt.show() <pre>Optimized Hyperparameters\n\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.8371517535182331  \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [2.45802 2.50701]   \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.04342255274528023 \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> In\u00a0[4]: Copied! <pre>num_robots = 1\nnum_placements = 9\nnum_side = 5  # Use a 5x5 point FoV approximation\n\n# Setup the hyperparameters\n_, noise_variance, kernel = get_model_params(X_train, y_train, \n                                             lengthscales=[3.0, 3.0], \n                                             optimizer='scipy',\n                                             max_steps=0,\n                                             print_params=False)\n\n# Get initial inducing points\nXu_init = get_inducing_pts(X_train, num_placements)\n# Initalize the height dimension at 2.0 for all points\nXu_init = np.concatenate((Xu_init, np.ones((Xu_init.shape[0], 1))*2.0), axis=1)\nXu_init[:, :2] = run_tsp(Xu_init[:, :2])[0][0]\n\n# Initialize the non-point FoV IPP model and optimize it\ntransform = SquareHeightTransform(num_side=num_side)\nsgpr, _ = continuous_sgp(num_placements,\n                         X_train, \n                         noise_variance,\n                         kernel,\n                         transform=transform,\n                         Xu_init=Xu_init.copy())\nsgp_sol_sp = sgpr.inducing_variable.Z.numpy()\n</pre> num_robots = 1 num_placements = 9 num_side = 5  # Use a 5x5 point FoV approximation  # Setup the hyperparameters _, noise_variance, kernel = get_model_params(X_train, y_train,                                               lengthscales=[3.0, 3.0],                                               optimizer='scipy',                                              max_steps=0,                                              print_params=False)  # Get initial inducing points Xu_init = get_inducing_pts(X_train, num_placements) # Initalize the height dimension at 2.0 for all points Xu_init = np.concatenate((Xu_init, np.ones((Xu_init.shape[0], 1))*2.0), axis=1) Xu_init[:, :2] = run_tsp(Xu_init[:, :2])[0][0]  # Initialize the non-point FoV IPP model and optimize it transform = SquareHeightTransform(num_side=num_side) sgpr, _ = continuous_sgp(num_placements,                          X_train,                           noise_variance,                          kernel,                          transform=transform,                          Xu_init=Xu_init.copy()) sgp_sol_sp = sgpr.inducing_variable.Z.numpy() In\u00a0[5]: Copied! <pre>plot_results(Xu_init.reshape(-1, 3), \n             sgpr.transform.expand(Xu_init.reshape(-1, 3)).numpy())\n</pre> plot_results(Xu_init.reshape(-1, 3),               sgpr.transform.expand(Xu_init.reshape(-1, 3)).numpy()) In\u00a0[6]: Copied! <pre>plot_results(sgp_sol_sp.reshape(-1, 3), \n             sgpr.transform.expand(sgp_sol_sp.reshape(-1, 3)).numpy())\n</pre> plot_results(sgp_sol_sp.reshape(-1, 3),               sgpr.transform.expand(sgp_sol_sp.reshape(-1, 3)).numpy())"},{"location":"examples/non_point_FoV.html#ipp-with-non-point-fov","title":"IPP with Non-Point FoV\u00b6","text":""},{"location":"examples/non_point_FoV.html#generate-synthetic-data-and-initial-sgp-hyperparameters","title":"Generate synthetic data and initial SGP hyperparameters\u00b6","text":""},{"location":"examples/non_point_FoV.html#generate-the-ipp-solution","title":"Generate the IPP solution\u00b6","text":""},{"location":"examples/non_point_FoV.html#initial-solution","title":"Initial Solution\u00b6","text":""},{"location":"examples/non_point_FoV.html#optimized-solution","title":"Optimized Solution\u00b6","text":""},{"location":"examples/non_stationary_kernel.html","title":"Non-Stationary Kernel","text":"In\u00a0[\u00a0]: Copied! <pre>import tensorflow as tf\nimport numpy as np\nimport gpflow\ngpflow.config.set_default_float(np.float32)\n\nfrom sgptools.utils.metrics import *\nfrom sgptools.utils.misc import *\nfrom sgptools.models.continuous_sgp import *\nfrom sgptools.kernels.neural_kernel import init_neural_kernel\nfrom sgptools.utils.gpflow import get_model_params\n\nfrom sklearn.preprocessing import StandardScaler\nimport matplotlib.pyplot as plt\n\nnp.random.seed(10)\ntf.random.set_seed(10)\n</pre> import tensorflow as tf import numpy as np import gpflow gpflow.config.set_default_float(np.float32)  from sgptools.utils.metrics import * from sgptools.utils.misc import * from sgptools.models.continuous_sgp import * from sgptools.kernels.neural_kernel import init_neural_kernel from sgptools.utils.gpflow import get_model_params  from sklearn.preprocessing import StandardScaler import matplotlib.pyplot as plt  np.random.seed(10) tf.random.set_seed(10) In\u00a0[2]: Copied! <pre>def non_stationary_function(X, Y):\n    Z1 = np.sin(X/10) + np.cos(Y/10)\n    Z2 = np.cos(X/6) + np.cos(Y/6)\n    Z = np.concatenate([Z1[:, ::2], Z2[:, ::2]], \n                       axis=1)\n    return Z\n\n# Generate data\ntrain_data_dims = (40, 20)\nx = np.linspace(0, 200, train_data_dims[0])\ny = np.linspace(0, 100, train_data_dims[1])\nX, Y = np.meshgrid(x, y)\nZ = non_stationary_function(X, Y)\n\nX_train = np.stack([X.ravel(), Y.ravel()], \n                   axis=1).astype(np.float32)\nY_train = Z.ravel()[:, None].astype(np.float32)\nprint('Train Data:', X_train.shape, Y_train.shape)\n\ntest_data_dims = (100, 50)\nx = np.linspace(0, 200, test_data_dims[0])\ny = np.linspace(0, 100, test_data_dims[1])\nX, Y = np.meshgrid(x, y)\nZ = non_stationary_function(X, Y)\n\nX_test = np.stack([X.ravel(), Y.ravel()], \n                  axis=1).astype(np.float32)\nY_test = Z.ravel()[:, None].astype(np.float32)\nprint('Test Data:', X_test.shape, Y_test.shape)\n\n# Normalize the data\nXscalar = StandardScaler()\nX_train = Xscalar.fit_transform(X_train)\nX_test = Xscalar.transform(X_test)\n\nyScalar = StandardScaler()\nY_train = yScalar.fit_transform(Y_train)\nY_test = yScalar.transform(Y_test)\n\n# Plot the data\nplt.figure()\nplt.imshow(Y_train.reshape(train_data_dims[1], \n                           train_data_dims[0]), \n           cmap='jet')\nplt.gca().set_aspect('equal')\nplt.title('Train Data')\nplt.show()\n\nplt.figure()\nplt.imshow(Y_test.reshape(test_data_dims[1], \n                          test_data_dims[0]), \n           cmap='jet')\nplt.gca().set_aspect('equal')\nplt.title('Test Data')\nplt.show()\n</pre> def non_stationary_function(X, Y):     Z1 = np.sin(X/10) + np.cos(Y/10)     Z2 = np.cos(X/6) + np.cos(Y/6)     Z = np.concatenate([Z1[:, ::2], Z2[:, ::2]],                         axis=1)     return Z  # Generate data train_data_dims = (40, 20) x = np.linspace(0, 200, train_data_dims[0]) y = np.linspace(0, 100, train_data_dims[1]) X, Y = np.meshgrid(x, y) Z = non_stationary_function(X, Y)  X_train = np.stack([X.ravel(), Y.ravel()],                     axis=1).astype(np.float32) Y_train = Z.ravel()[:, None].astype(np.float32) print('Train Data:', X_train.shape, Y_train.shape)  test_data_dims = (100, 50) x = np.linspace(0, 200, test_data_dims[0]) y = np.linspace(0, 100, test_data_dims[1]) X, Y = np.meshgrid(x, y) Z = non_stationary_function(X, Y)  X_test = np.stack([X.ravel(), Y.ravel()],                    axis=1).astype(np.float32) Y_test = Z.ravel()[:, None].astype(np.float32) print('Test Data:', X_test.shape, Y_test.shape)  # Normalize the data Xscalar = StandardScaler() X_train = Xscalar.fit_transform(X_train) X_test = Xscalar.transform(X_test)  yScalar = StandardScaler() Y_train = yScalar.fit_transform(Y_train) Y_test = yScalar.transform(Y_test)  # Plot the data plt.figure() plt.imshow(Y_train.reshape(train_data_dims[1],                             train_data_dims[0]),             cmap='jet') plt.gca().set_aspect('equal') plt.title('Train Data') plt.show()  plt.figure() plt.imshow(Y_test.reshape(test_data_dims[1],                            test_data_dims[0]),             cmap='jet') plt.gca().set_aspect('equal') plt.title('Test Data') plt.show() <pre>Train Data: (800, 2) (800, 1)\nTest Data: (5000, 2) (5000, 1)\n</pre> In\u00a0[3]: Copied! <pre># Model / inference hyperparameters\nnum_inducing = 100\nlr = 1e-2\nmax_steps = 5000\nQ = 3\n\n# Initialize non-stationary kernel model\nprint('Initializing model...')\nZ = get_inducing_pts(X_train, num_inducing)\nmodel = init_neural_kernel(X_train, Y_train, \n                           n_inits=5,\n                           inducing_variable=Z,\n                           Q=Q, \n                           hidden_sizes=(4, 4))\n\n# Create optimizer\nlearning_rate = tf.keras.optimizers.schedules.ExponentialDecay(\n                      lr, \n                      decay_steps=50, \n                      decay_rate=0.98)\nloss = optimize_model(model, \n                      max_steps=max_steps,\n                      lr=learning_rate,\n                      optimizer='tf')\n\n# Plot loss\nplt.plot(loss)\nplt.xlabel('Iteration')\nplt.ylabel('Loss')\nplt.show()\n\nkernels = []\nnoice_vars = []\nlabels = ['Non Stationary Kernel', 'Stationary Kernel']\n\nkernels.append(model.kernel)\nnoice_vars.append(model.likelihood.variance.numpy())\n\n# Get stationary kernel\n_, noise_variance, kernel = get_model_params(X_train, Y_train, \n                                             lengthscales=[1.0, 0.5])\nkernels.append(kernel)\nnoice_vars.append(noise_variance)\n</pre> # Model / inference hyperparameters num_inducing = 100 lr = 1e-2 max_steps = 5000 Q = 3  # Initialize non-stationary kernel model print('Initializing model...') Z = get_inducing_pts(X_train, num_inducing) model = init_neural_kernel(X_train, Y_train,                             n_inits=5,                            inducing_variable=Z,                            Q=Q,                             hidden_sizes=(4, 4))  # Create optimizer learning_rate = tf.keras.optimizers.schedules.ExponentialDecay(                       lr,                        decay_steps=50,                        decay_rate=0.98) loss = optimize_model(model,                        max_steps=max_steps,                       lr=learning_rate,                       optimizer='tf')  # Plot loss plt.plot(loss) plt.xlabel('Iteration') plt.ylabel('Loss') plt.show()  kernels = [] noice_vars = [] labels = ['Non Stationary Kernel', 'Stationary Kernel']  kernels.append(model.kernel) noice_vars.append(model.likelihood.variance.numpy())  # Get stationary kernel _, noise_variance, kernel = get_model_params(X_train, Y_train,                                               lengthscales=[1.0, 0.5]) kernels.append(kernel) noice_vars.append(noise_variance) <pre>Initializing model...\nInitializing neural spectral kernel...\n</pre> <pre>2024-05-05 12:08:35.281864: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1960] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\n</pre> <pre>Best init: -1165.898315\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value             \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float32 \u2502 0.59679           \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 (2,)    \u2502 float32 \u2502 [1.16673 0.37082] \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float32 \u2502 0.52522           \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> In\u00a0[4]: Copied! <pre>for i, (kernel, noice_var) in enumerate(zip(kernels, noice_vars)):\n        print(labels[i])\n        for num_placements in [4, 9]:\n                # Get initial inducing points\n                Xu_init = get_inducing_pts(X_train, num_placements)*0.5\n                Xu_init.astype(np.float32)\n\n                # Setup SGP and optimize it\n                sgpr, _ = continuous_sgp(num_placements, \n                                         X_train,\n                                         noise_variance=noice_var,\n                                         kernel=kernel,\n                                         Xu_init=Xu_init)\n                sgp_sol_sp = sgpr.inducing_variable.Z.numpy()\n\n                # Get the GP predictions\n                Xu_X, Xu_y = cont2disc(sgp_sol_sp, candidates=X_test,\n                                       candidate_labels=Y_test)\n                gpr = gpflow.models.GPR((Xu_X, Xu_y),\n                                        noise_variance=noice_var,\n                                        kernel=kernel)\n                y_pred, y_var = gpr.predict_f(X_test)\n                y_pred = y_pred.numpy()\n\n                rmse = np.sqrt(np.mean((y_pred - Y_test)**2))\n\n                # Plot the results\n                plt.figure()\n                plt.imshow(y_pred.reshape(test_data_dims[1], test_data_dims[0]),\n                        cmap='jet', origin='lower',\n                        extent=[np.min(X_test[:, 0]), np.max(X_test[:, 0]),\n                                np.min(X_test[:, 1]), np.max(X_test[:, 1])])\n                plt.gca().set_aspect('equal')\n                plt.scatter(sgp_sol_sp[:, 0], sgp_sol_sp[:, 1], c='k', marker='p', s=100)\n                plt.title('Number of Sensors: {}    RMSE: {:.3f}'.format(num_placements, rmse))\n                plt.xlabel('X')\n                plt.ylabel('Y')\n                plt.show()\n</pre> for i, (kernel, noice_var) in enumerate(zip(kernels, noice_vars)):         print(labels[i])         for num_placements in [4, 9]:                 # Get initial inducing points                 Xu_init = get_inducing_pts(X_train, num_placements)*0.5                 Xu_init.astype(np.float32)                  # Setup SGP and optimize it                 sgpr, _ = continuous_sgp(num_placements,                                           X_train,                                          noise_variance=noice_var,                                          kernel=kernel,                                          Xu_init=Xu_init)                 sgp_sol_sp = sgpr.inducing_variable.Z.numpy()                  # Get the GP predictions                 Xu_X, Xu_y = cont2disc(sgp_sol_sp, candidates=X_test,                                        candidate_labels=Y_test)                 gpr = gpflow.models.GPR((Xu_X, Xu_y),                                         noise_variance=noice_var,                                         kernel=kernel)                 y_pred, y_var = gpr.predict_f(X_test)                 y_pred = y_pred.numpy()                  rmse = np.sqrt(np.mean((y_pred - Y_test)**2))                  # Plot the results                 plt.figure()                 plt.imshow(y_pred.reshape(test_data_dims[1], test_data_dims[0]),                         cmap='jet', origin='lower',                         extent=[np.min(X_test[:, 0]), np.max(X_test[:, 0]),                                 np.min(X_test[:, 1]), np.max(X_test[:, 1])])                 plt.gca().set_aspect('equal')                 plt.scatter(sgp_sol_sp[:, 0], sgp_sol_sp[:, 1], c='k', marker='p', s=100)                 plt.title('Number of Sensors: {}    RMSE: {:.3f}'.format(num_placements, rmse))                 plt.xlabel('X')                 plt.ylabel('Y')                 plt.show() <pre>Non Stationary Kernel\n</pre> <pre>Stationary Kernel\n</pre>"},{"location":"examples/non_stationary_kernel.html#non-stationary-kernel","title":"Non-Stationary Kernel\u00b6","text":""},{"location":"examples/non_stationary_kernel.html#generate-synthetic-non-stationary-data","title":"Generate synthetic non-stationary data\u00b6","text":""},{"location":"examples/non_stationary_kernel.html#learn-kernel-parameters-from-train-data","title":"Learn kernel parameters from train data\u00b6","text":""},{"location":"examples/non_stationary_kernel.html#get-continuous-sgp-solution-with-a-baseline-stationary-kernel-and-the-learned-neural-kernel-function","title":"Get Continuous-SGP solution with a baseline stationary kernel and the learned neural kernel function\u00b6","text":""},{"location":"examples/obstacles.html","title":"Environment with Obstacles","text":"In\u00a0[\u00a0]: Copied! <pre>import matplotlib.pyplot as plt\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom sgptools.utils.gpflow import get_model_params\nfrom sgptools.utils.data import remove_polygons, remove_circle_patches\nfrom sgptools.models.continuous_sgp import *\n\nnp.random.seed(0)\ntf.random.set_seed(0)\n</pre> import matplotlib.pyplot as plt  import numpy as np import tensorflow as tf  from sgptools.utils.gpflow import get_model_params from sgptools.utils.data import remove_polygons, remove_circle_patches from sgptools.models.continuous_sgp import *  np.random.seed(0) tf.random.set_seed(0) In\u00a0[2]: Copied! <pre># Generate synthetic training data\ndata_dims = (50, 25)\nx = np.linspace(0, 200, data_dims[0])\ny = np.linspace(0, 100, data_dims[1])\nX, Y = np.meshgrid(x, y)\n\nX, Y = remove_circle_patches(X, Y, [plt.Circle((30, 60), 15)])\nX, Y = remove_polygons(X, Y, [[[80, 20], [105, 20], [105, 60], [80, 60]],])\nX, Y = remove_polygons(X, Y, [[[130, 20], [180, 20], [160, 60]],])\n\nplt.figure(figsize=(10, 5))\nplt.scatter(X, Y, s=1)\n\nax = plt.gca()\nax.add_patch(plt.Polygon([[80, 20], [105, 20], [105, 60], [80, 60]], \n             fill=None, hatch='//', color='C1', label='Obstacle'))\nax.add_patch(plt.Polygon([[130, 20], [180, 20], [160, 60]], \n             fill=None, hatch='//', color='C1'))\nax.add_patch(plt.Circle((30, 60), 15, \n             fill=None, hatch='//', color='C1'))\nax.set_aspect('equal')\n\nplt.xlabel('x', fontsize=14)\nplt.ylabel('y', fontsize=14)\nplt.xlim(0, 200)\nplt.ylim(0, 100)\n\nplt.show()\n\nX_train = np.array([X, Y]).T\ny_train = np.zeros((X_train.shape[0], 1))\nX_train = X_train.astype(float)\ny_train = y_train.astype(float)\nprint(X_train.shape, y_train.shape)\n</pre> # Generate synthetic training data data_dims = (50, 25) x = np.linspace(0, 200, data_dims[0]) y = np.linspace(0, 100, data_dims[1]) X, Y = np.meshgrid(x, y)  X, Y = remove_circle_patches(X, Y, [plt.Circle((30, 60), 15)]) X, Y = remove_polygons(X, Y, [[[80, 20], [105, 20], [105, 60], [80, 60]],]) X, Y = remove_polygons(X, Y, [[[130, 20], [180, 20], [160, 60]],])  plt.figure(figsize=(10, 5)) plt.scatter(X, Y, s=1)  ax = plt.gca() ax.add_patch(plt.Polygon([[80, 20], [105, 20], [105, 60], [80, 60]],               fill=None, hatch='//', color='C1', label='Obstacle')) ax.add_patch(plt.Polygon([[130, 20], [180, 20], [160, 60]],               fill=None, hatch='//', color='C1')) ax.add_patch(plt.Circle((30, 60), 15,               fill=None, hatch='//', color='C1')) ax.set_aspect('equal')  plt.xlabel('x', fontsize=14) plt.ylabel('y', fontsize=14) plt.xlim(0, 200) plt.ylim(0, 100)  plt.show()  X_train = np.array([X, Y]).T y_train = np.zeros((X_train.shape[0], 1)) X_train = X_train.astype(float) y_train = y_train.astype(float) print(X_train.shape, y_train.shape) <pre>(1089, 2) (1089, 1)\n</pre> In\u00a0[3]: Copied! <pre>num_inducing = 200\n\n# Get initial GP parameters\n_, noise_variance, rbf = get_model_params(np.zeros((1, 2)), np.zeros((1, 1)), \n                             lengthscales=12.3254, \n                             variance=0.561265,\n                             noise_variance=2e-6,\n                             max_steps=0)\n\n# Optimize the inducing points/sensor locations\nsgpr, _ = continuous_sgp(num_inducing,\n                         X_train,\n                         kernel=rbf,\n                         noise_variance=noise_variance,\n                         lr=1e-2,\n                         max_steps=5000)\nsgp_sol_sp = sgpr.inducing_variable.Z.numpy()\n</pre> num_inducing = 200  # Get initial GP parameters _, noise_variance, rbf = get_model_params(np.zeros((1, 2)), np.zeros((1, 1)),                               lengthscales=12.3254,                               variance=0.561265,                              noise_variance=2e-6,                              max_steps=0)  # Optimize the inducing points/sensor locations sgpr, _ = continuous_sgp(num_inducing,                          X_train,                          kernel=rbf,                          noise_variance=noise_variance,                          lr=1e-2,                          max_steps=5000) sgp_sol_sp = sgpr.inducing_variable.Z.numpy() <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502     value \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502  0.561265 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 12.3254   \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502  2e-06    \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> In\u00a0[4]: Copied! <pre># Plot the sensor locations and the obstacles\nplt.figure(figsize=(10, 5))\nplt.scatter(sgp_sol_sp[:, 0], sgp_sol_sp[:, 1], \n            color='C0', \n            s=2, \n            label='Solution Placements')\n\nax = plt.gca()\nax.add_patch(plt.Polygon([[80, 20], [105, 20], [105, 60], [80, 60]], \n             fill=None, hatch='//', color='C1', label='Obstacle'))\nax.add_patch(plt.Polygon([[130, 20], [180, 20], [160, 60]], \n             fill=None, hatch='//', color='C1'))\nax.add_patch(plt.Circle((30, 60), 15, \n             fill=None, hatch='//', color='C1'))\nax.set_aspect('equal')\n\nplt.xlabel('x', fontsize=14)\nplt.ylabel('y', fontsize=14)\nplt.xlim(0, 200)\nplt.ylim(0, 100)\n\nplt.show()\n</pre> # Plot the sensor locations and the obstacles plt.figure(figsize=(10, 5)) plt.scatter(sgp_sol_sp[:, 0], sgp_sol_sp[:, 1],              color='C0',              s=2,              label='Solution Placements')  ax = plt.gca() ax.add_patch(plt.Polygon([[80, 20], [105, 20], [105, 60], [80, 60]],               fill=None, hatch='//', color='C1', label='Obstacle')) ax.add_patch(plt.Polygon([[130, 20], [180, 20], [160, 60]],               fill=None, hatch='//', color='C1')) ax.add_patch(plt.Circle((30, 60), 15,               fill=None, hatch='//', color='C1')) ax.set_aspect('equal')  plt.xlabel('x', fontsize=14) plt.ylabel('y', fontsize=14) plt.xlim(0, 200) plt.ylim(0, 100)  plt.show()"},{"location":"examples/obstacles.html#environment-with-obstacles","title":"Environment with Obstacles\u00b6","text":""},{"location":"examples/obstacles.html#note-that-all-the-solution-placements-avoid-the-obstacles","title":"Note that all the solution placements avoid the obstacles\u00b6","text":""}]}
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+++ b/search/search_index.json
@@ -1 +1 @@
-{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"index.html","title":"Home","text":"<p>SGP-Tools is a software suite for Sensor Placement and Informative Path Planning.</p> <p>The library includes python code for the following:</p> <ul> <li>Greedy algorithm-based approaches</li> <li>Bayesian optimization-based approaches</li> <li>Genetic algorithm-based approaches</li> <li>Sparse Gaussian process (SGP)-based approaches</li> </ul> <p> </p>"},{"location":"index.html#installation","title":"Installation","text":"<p>The library is available as a <code>pip</code> package. To install the package, run the following command:</p> <pre><code>python3 -m pip install sgptools\n</code></pre> <p>Installation from source:</p> <pre><code>git clone https://github.com/itskalvik/sgp-tools.git\ncd sgp-tools/\npython3 -m pip install -r requirements.txt\npython3 -m pip install -e .\n</code></pre> <p>Note: The requirements.txt file contains packages and their latest versions that were last verified to be working without any issues.</p>"},{"location":"index.html#quick-start","title":"Quick Start","text":"<p>Please refer to the example Jupyter notebooks demonstrating the methods included in the library \ud83d\ude04</p>"},{"location":"index.html#method-summary","title":"Method Summary","text":""},{"location":"index.html#datasets","title":"Datasets","text":"<ul> <li>High-resolution topography data can be downloaded from NOAA Digital Coast</li> <li>High-resolution bathymetry data can be downloaded from NOAA Digital Coast</li> </ul>"},{"location":"index.html#about","title":"About","text":"<p>Please consider citing the following papers if you use SGP-Tools in your academic work \ud83d\ude04</p> <pre><code>@misc{JakkalaA23SP,\nAUTHOR={Kalvik Jakkala and Srinivas Akella},\nTITLE={Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces},\nNOTE= {Preprint},\nYEAR={2023},\nURL={https://itskalvik.github.io/publication/sgp-sp},\n}\n\n@inproceedings{JakkalaA24IPP,\nAUTHOR={Kalvik Jakkala and Srinivas Akella},\nTITLE={Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes},\nbooktitle={IEEE International Conference on Robotics and Automation, {ICRA}},\nYEAR={2024},\nPUBLISHER = {{IEEE}},\nURL={https://itskalvik.github.io/publication/sgp-ipp}\n}\n</code></pre>"},{"location":"index.html#acknowledgements","title":"Acknowledgements","text":"<p>This work was funded in part by the UNC Charlotte Office of Research and Economic Development and by NSF under Award Number IIP-1919233.</p>"},{"location":"index.html#license","title":"License","text":"<p>The SGP-Tools software suite is licensed under the terms of the Apache License 2.0. See LICENSE for more information.</p>"},{"location":"docker-sgp-tools.html","title":"docker-sgp-tools","text":""},{"location":"docker-sgp-tools.html#docker-sgp-tools","title":"docker-sgp-tools","text":"<p>The docker-sgp-tools package provides the following docker-compose scripts:</p> <ul> <li><code>robot-compose.yml</code>: A minimal docker container used to run SGP-Tools ROS2 package on ArduPilot-based robots.</li> <li><code>sitl-compose.yml</code>: A GUI-based docker container with ROS2, Gazebo, ArduPilot SITL, and SGP-Tools used for simulating ArduPilot vehicles and testing SGP-Tools IPP code. </li> </ul> <p></p>"},{"location":"docker-sgp-tools.html#prerequisites","title":"Prerequisites","text":"<ul> <li>docker</li> <li>docker-compose</li> <li>(Optional) WSLg</li> </ul>"},{"location":"docker-sgp-tools.html#getting-started","title":"Getting Started","text":""},{"location":"docker-sgp-tools.html#starting-the-containers","title":"Starting the Containers","text":"<p>Run the following commands to start the SITL docker container:</p> <pre><code>git clone --recurse-submodules https://github.com/itskalvik/docker-sgp-tools.git\ncd docker-sgp-tools\ndocker-compose -f sitl-compose.yml pull\ndocker-compose -f sitl-compose.yml up -d\ndocker-compose -f sitl-compose.yml exec sgptools bash\n</code></pre> <p>Use <code>robot-compose.yml</code> to run the minimal docker container. </p>"},{"location":"docker-sgp-tools.html#running-sgp-tools-onlineadaptive-ipp-with-gazeboardurover-simulator","title":"Running SGP-Tools Online/Adaptive IPP with Gazebo/ArduRover Simulator","text":"<p>Use <code>docker-compose -f sitl-compose.yml exec sgptools bash</code> to get a new terminal. Run the following commands in separate terminals in the docker container:</p> <ul> <li> <p>Launch Gazebo with the AION R1 UGV:     <pre><code>gz sim -v4 -r r1_rover_runway.sdf\n</code></pre>     To simulate a BlueBoat refer to this documentation. Note that the container already has Wave Sim installed in it.</p> </li> <li> <p>Launch ArduRover SITL:     <pre><code>sim_vehicle.py -v Rover -f rover-skid --model JSON --add-param-file=$HOME/SITL_Models/Gazebo/config/r1_rover.param --console --map -N -l 35.30371178789218,-80.73099267294185,0.,0.\n</code></pre>     Note: Restart sim_vechile.py if you get the following message: <code>paramftp: bad count 1294 should be 1284</code></p> </li> <li> <p>Launch the SGP-Tools Online/Adaptive IPP method:     <pre><code>ros2 launch ros_sgp_tools single_robot.launch.py\n</code></pre></p> </li> </ul>"},{"location":"docker-sgp-tools.html#building-the-docker-containers-from-scratch","title":"Building the Docker Containers from Scratch","text":"<p>First, setup buildx to build the containers for both arm64 and amd64 platforms:  <pre><code>docker buildx create --name multi-arch \\\n  --platform \"linux/arm64,linux/amd64\" \\\n  --driver \"docker-container\"\ndocker buildx use multi-arch\n</code></pre></p> <p>Next, clone the repo and build the container.  <pre><code>git clone --recurse-submodules https://github.com/itskalvik/docker-sgp-tools.git\ncd docker-sgp-tools\ndocker-compose -f sitl-compose.yml build \n</code></pre></p> <p>Use <code>robot-compose.yml</code> to build the minimal docker container.</p>"},{"location":"docker-sgp-tools.html#other-commands","title":"Other commands","text":"<ul> <li> <p>The docker-compose down command stops and removes containers, networks, volumes, and images, making it suitable for completely clearing all resources deployed by an application.</p> <pre><code>docker-compose -f sitl-compose.yml down\n</code></pre> </li> <li> <p>The docker-compose stop command just pauses running containers without removing them, which is ideal for temporary halts.</p> <pre><code>docker-compose -f sitl-compose.yml stop\n</code></pre> </li> </ul>"},{"location":"docker-sgp-tools.html#references","title":"References","text":"<p>Based on Dockerfiles from the following repos:</p> <ul> <li>https://github.com/Robotic-Decision-Making-Lab/blue</li> <li>https://github.com/ryomo/ros2-gazebo-docker</li> </ul>"},{"location":"ros_sgp_tools.html","title":"ros_sgp_tools","text":""},{"location":"ros_sgp_tools.html#ros_sgp_tools","title":"ros_sgp_tools","text":"<p>The ros_sgp_tools package provides a ROS2 companion package for the SGP-Tools python library that can be deployed on ArduPilot-based vehicles. </p> <ul> <li>The package can be used to run online/adaptive IPP on ArduPilot based UGVs and ASVs. </li> <li>The package can also be used with Gazebo/Ardupilot SITL.</li> <li>To use our Docker container with the preconfigured development environment, please refer to the documentation here. </li> </ul> <p></p>"},{"location":"ros_sgp_tools.html#running-sgp-tools-onlineadaptive-ipp-with-gazeboardurover-simulator","title":"Running SGP-Tools Online/Adaptive IPP with Gazebo/ArduRover Simulator","text":"<p>Run the following commands in separate terminals:</p> <ul> <li> <p>Launch Gazebo with the AION R1 UGV:     <pre><code>gz sim -v4 -r r1_rover_runway.sdf\n</code></pre>     To simulate a BlueBoat refer to this documentation.</p> </li> <li> <p>Launch ArduRover SITL:     <pre><code>sim_vehicle.py -v Rover -f rover-skid --model JSON --add-param-file=$HOME/SITL_Models/Gazebo/config/r1_rover.param --console --map -N -l 35.30371178789218,-80.73099267294185,0.,0.\n</code></pre>     Note: </p> <ul> <li>Restart sim_vechile.py if you get the following message: <code>paramftp: bad count</code></li> <li>Ensure the MAV Console shows that the vehicle has a GPS lock before running the next command</li> <li>Ensure the MAV Map shows the vehicle before running the next command</li> </ul> </li> <li> <p>Launch the SGP-Tools Online/Adaptive IPP method:     <pre><code>ros2 launch ros_sgp_tools single_robot.launch.py\n</code></pre></p> </li> </ul>"},{"location":"ros_sgp_tools.html#environment-setup","title":"Environment setup","text":"<ul> <li>To use our Docker container with the preconfigured development environment, please refer to the documentation here. </li> </ul> <p>Alternatively, please follow the following instructions to configure the development envirnoment on your local machine. </p> <ul> <li>Install ROS 2 Humble <pre><code>sudo apt install ros-humble-desktop\necho \"source /opt/ros/humble/setup.bash\" &gt;&gt; ~/.bashrc\nsource ~/.bashrc\n</code></pre></li> <li>Create a ROS 2 workspace   <pre><code>mkdir -p ~/ros2_ws/src\ncd ~/ros2_ws/\ncolcon build --symlink-install\necho \"source $HOME/ros2_ws/install/setup.bash\" &gt;&gt; ~/.bashrc\nsource ~/.bashrc\n</code></pre></li> <li>Install Gazebo Garden</li> <li>Install ArduPilot SITL</li> <li>Install ardupilot_gazebo</li> <li>Get SITL_Models repo</li> <li>Setup environment variables</li> </ul> <pre><code>echo \"export GZ_VERSION=garden\" &gt;&gt; ~/.bashrc\necho \"export GZ_SIM_SYSTEM_PLUGIN_PATH=$HOME/ardupilot_gazebo/build:${GZ_SIM_SYSTEM_PLUGIN_PATH}\" &gt;&gt; ~/.bashrc\necho \"export GZ_SIM_RESOURCE_PATH=$HOME/ardupilot_gazebo/models:$HOME/ardupilot_gazebo/worlds:$HOME/SITL_Models/Gazebo/models:$HOME/SITL_Models/Gazebo/worlds:$GZ_SIM_RESOURCE_PATH\" &gt;&gt; ~/.bashrc\n</code></pre>"},{"location":"ros_sgp_tools.html#package-setup","title":"Package setup","text":"<ul> <li>Install apt dependencies    <pre><code>sudo apt install ros-humble-mavros* ros-$ROS_DISTRO-cv-bridge\n</code></pre></li> <li>Install pip dependencies    <pre><code>pip3 install -r requirements.txt\n</code></pre></li> <li>Clone repo and build package   <pre><code>cd ~/ros2_ws/src\ngit clone https://github.com/itskalvik/ros_sgp_tools.git\ncd ..\ncolcon build --symlink-install\n</code></pre></li> </ul>"},{"location":"api/kernels/index.html","title":"","text":""},{"location":"api/kernels/index.html#sgptools.kernels","title":"<code>sgptools.kernels</code>","text":"<p>Special kernel functions in this package:</p> <ul> <li><code>neural_kernel</code>: Provides a neural spectral kernel function that uses a mixture of multilayer perceptrons</li> </ul>"},{"location":"api/kernels/Neural-Kernel.html","title":"Neural Kernel","text":""},{"location":"api/kernels/Neural-Kernel.html#sgptools.kernels.neural_kernel","title":"<code>sgptools.kernels.neural_kernel</code>","text":"<p>Provides a neural spectral kernel function along with an initialization function</p>"},{"location":"api/kernels/Neural-Kernel.html#sgptools.kernels.neural_kernel.NeuralSpectralKernel","title":"<code>NeuralSpectralKernel</code>","text":"<p>               Bases: <code>Kernel</code></p> <p>Neural Spectral Kernel function (non-stationary kernel function).  Based on the implementation from the following repo</p> Refer to the following papers for more details <ul> <li>Neural Non-Stationary Spectral Kernel [Remes et al., 2018]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>input_dim</code> <code>int</code> <p>Number of data dimensions</p> required <code>active_dims</code> <code>int</code> <p>Number of data dimensions that are used for computing the covariances</p> <code>None</code> <code>Q</code> <code>int</code> <p>Number of MLP mixture components used in the kernel function</p> <code>1</code> <code>hidden_sizes</code> <code>list</code> <p>Number of hidden units in each MLP layer. Length of the list determines the number of layers.</p> <code>[32, 32]</code> Source code in <code>sgptools/kernels/neural_kernel.py</code> <pre><code>class NeuralSpectralKernel(gpflow.kernels.Kernel):\n    \"\"\"Neural Spectral Kernel function (non-stationary kernel function). \n    Based on the implementation from the following [repo](https://github.com/sremes/nssm-gp/tree/master?tab=readme-ov-file)\n\n    Refer to the following papers for more details:\n        - Neural Non-Stationary Spectral Kernel [Remes et al., 2018]\n\n    Args:\n        input_dim (int): Number of data dimensions\n        active_dims (int): Number of data dimensions that are used for computing the covariances\n        Q (int): Number of MLP mixture components used in the kernel function\n        hidden_sizes (list): Number of hidden units in each MLP layer. Length of the list determines the number of layers.\n    \"\"\"\n    def __init__(self, input_dim, active_dims=None, Q=1, hidden_sizes=[32, 32]):\n        super().__init__(active_dims=active_dims)\n\n        self.input_dim = input_dim\n        self.Q = Q\n        self.num_hidden = len(hidden_sizes)\n\n        self.freq = []\n        self.length = []\n        self.var = []\n        for q in range(self.Q):\n            freq = keras.Sequential([layers.Dense(hidden_sizes[i], activation='selu') for i in range(self.num_hidden)] + \n                                    [layers.Dense(input_dim, activation='softplus')])\n            length = keras.Sequential([layers.Dense(hidden_sizes[i], activation='selu') for i in range(self.num_hidden)] +\n                                   [layers.Dense(input_dim, activation='softplus')])\n            var = keras.Sequential([layers.Dense(hidden_sizes[i], activation='selu') for i in range(self.num_hidden)] +\n                                   [layers.Dense(1, activation='softplus')])\n            self.freq.append(freq)\n            self.length.append(length)\n            self.var.append(var)\n\n    def K(self, X, X2=None):\n        \"\"\"Computes the covariances between/amongst the input variables\n\n        Args:\n            X (ndarray): Variables to compute the covariance matrix\n            X2 (ndarray): If passed, the covariance between X and X2 is computed. Otherwise, \n                          the covariance between X and X is computed.\n\n        Returns:\n            cov (ndarray): covariance matrix\n        \"\"\"\n        if X2 is None:\n            X2 = X\n            equal = True\n        else:\n            equal = False\n\n        kern = 0.0\n        for q in range(self.Q):\n            # compute latent function values by the neural network\n            freq, freq2 = self.freq[q](X), self.freq[q](X2)\n            lens, lens2 = self.length[q](X), self.length[q](X2)\n            var, var2 = self.var[q](X), self.var[q](X2)\n\n            # compute length-scale term\n            Xr = tf.expand_dims(X, 1)  # N1 1 D\n            X2r = tf.expand_dims(X2, 0)  # 1 N2 D\n            l1 = tf.expand_dims(lens, 1)  # N1 1 D\n            l2 = tf.expand_dims(lens2, 0)  # 1 N2 D\n            L = tf.square(l1) + tf.square(l2)  # N1 N2 D\n            #D = tf.square((Xr - X2r) / L)  # N1 N2 D\n            D = tf.square(Xr - X2r) / L  # N1 N2 D\n            D = tf.reduce_sum(D, 2)  # N1 N2\n            det = tf.sqrt(2 * l1 * l2 / L)  # N1 N2 D\n            det = tf.reduce_prod(det, 2)  # N1 N2\n            E = det * tf.exp(-D)  # N1 N2\n\n            # compute cosine term\n            muX = (tf.reduce_sum(freq * X, 1, keepdims=True)\n                   - tf.transpose(tf.reduce_sum(freq2 * X2, 1, keepdims=True)))\n            COS = tf.cos(2 * np.pi * muX)\n\n            # compute kernel variance term\n            WW = tf.matmul(var, var2, transpose_b=True)  # w*w'^T\n\n            # compute the q'th kernel component\n            kern += WW * E * COS\n        if equal:\n            return robust_kernel(kern, tf.shape(X)[0])\n        else:\n            return kern\n\n    def K_diag(self, X):\n        kd = default_jitter()\n        for q in range(self.Q):\n            kd += tf.square(self.var[q](X))\n        return tf.squeeze(kd)\n</code></pre>"},{"location":"api/kernels/Neural-Kernel.html#sgptools.kernels.neural_kernel.NeuralSpectralKernel.K","title":"<code>K(X, X2=None)</code>","text":"<p>Computes the covariances between/amongst the input variables</p> <p>Parameters:</p> Name Type Description Default <code>X</code> <code>ndarray</code> <p>Variables to compute the covariance matrix</p> required <code>X2</code> <code>ndarray</code> <p>If passed, the covariance between X and X2 is computed. Otherwise,            the covariance between X and X is computed.</p> <code>None</code> <p>Returns:</p> Name Type Description <code>cov</code> <code>ndarray</code> <p>covariance matrix</p> Source code in <code>sgptools/kernels/neural_kernel.py</code> <pre><code>def K(self, X, X2=None):\n    \"\"\"Computes the covariances between/amongst the input variables\n\n    Args:\n        X (ndarray): Variables to compute the covariance matrix\n        X2 (ndarray): If passed, the covariance between X and X2 is computed. Otherwise, \n                      the covariance between X and X is computed.\n\n    Returns:\n        cov (ndarray): covariance matrix\n    \"\"\"\n    if X2 is None:\n        X2 = X\n        equal = True\n    else:\n        equal = False\n\n    kern = 0.0\n    for q in range(self.Q):\n        # compute latent function values by the neural network\n        freq, freq2 = self.freq[q](X), self.freq[q](X2)\n        lens, lens2 = self.length[q](X), self.length[q](X2)\n        var, var2 = self.var[q](X), self.var[q](X2)\n\n        # compute length-scale term\n        Xr = tf.expand_dims(X, 1)  # N1 1 D\n        X2r = tf.expand_dims(X2, 0)  # 1 N2 D\n        l1 = tf.expand_dims(lens, 1)  # N1 1 D\n        l2 = tf.expand_dims(lens2, 0)  # 1 N2 D\n        L = tf.square(l1) + tf.square(l2)  # N1 N2 D\n        #D = tf.square((Xr - X2r) / L)  # N1 N2 D\n        D = tf.square(Xr - X2r) / L  # N1 N2 D\n        D = tf.reduce_sum(D, 2)  # N1 N2\n        det = tf.sqrt(2 * l1 * l2 / L)  # N1 N2 D\n        det = tf.reduce_prod(det, 2)  # N1 N2\n        E = det * tf.exp(-D)  # N1 N2\n\n        # compute cosine term\n        muX = (tf.reduce_sum(freq * X, 1, keepdims=True)\n               - tf.transpose(tf.reduce_sum(freq2 * X2, 1, keepdims=True)))\n        COS = tf.cos(2 * np.pi * muX)\n\n        # compute kernel variance term\n        WW = tf.matmul(var, var2, transpose_b=True)  # w*w'^T\n\n        # compute the q'th kernel component\n        kern += WW * E * COS\n    if equal:\n        return robust_kernel(kern, tf.shape(X)[0])\n    else:\n        return kern\n</code></pre>"},{"location":"api/kernels/Neural-Kernel.html#sgptools.kernels.neural_kernel.init_neural_kernel","title":"<code>init_neural_kernel(x, y, inducing_variable, Q, n_inits=1, hidden_sizes=None)</code>","text":"<p>Helper function to initialize a Neural Spectral Kernel function (non-stationary kernel function).  Based on the implementation from the following repo</p> Refer to the following papers for more details <ul> <li>Neural Non-Stationary Spectral Kernel [Remes et al., 2018]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>ndarray</code> <p>(n, d); Input training set points</p> required <code>y</code> <code>ndarray</code> <p>(n, 1); Training set labels</p> required <code>inducing_variable</code> <code>ndarray</code> <p>(m, d); Initial inducing points</p> required <code>Q</code> <code>int</code> <p>Number of MLP mixture components used in the kernel function</p> required <code>n_inits</code> <code>int</code> <p>Number of times to initalize the kernel function (returns the best model)</p> <code>1</code> <code>hidden_sizes</code> <code>list</code> <p>Number of hidden units in each MLP layer. Length of the list determines the number of layers.</p> <code>None</code> Source code in <code>sgptools/kernels/neural_kernel.py</code> <pre><code>def init_neural_kernel(x, y, inducing_variable, Q, n_inits=1, hidden_sizes=None):\n    \"\"\"Helper function to initialize a Neural Spectral Kernel function (non-stationary kernel function). \n    Based on the implementation from the following [repo](https://github.com/sremes/nssm-gp/tree/master?tab=readme-ov-file)\n\n    Refer to the following papers for more details:\n        - Neural Non-Stationary Spectral Kernel [Remes et al., 2018]\n\n    Args:\n        x (ndarray): (n, d); Input training set points\n        y (ndarray): (n, 1); Training set labels\n        inducing_variable (ndarray): (m, d); Initial inducing points\n        Q (int): Number of MLP mixture components used in the kernel function\n        n_inits (int): Number of times to initalize the kernel function (returns the best model)\n        hidden_sizes (list): Number of hidden units in each MLP layer. Length of the list determines the number of layers.\n    \"\"\"\n    x, y = data_input_to_tensor((x, y))\n\n    print('Initializing neural spectral kernel...')\n    best_loglik = -np.inf\n    best_m = None\n    N, input_dim = x.shape\n\n    for k in range(n_inits):\n        # gpflow.reset_default_graph_and_session()\n        k = NeuralSpectralKernel(input_dim=input_dim, Q=Q, \n                                    hidden_sizes=hidden_sizes)\n        model = SGPR((x, y), inducing_variable=inducing_variable, \n                        kernel=k)\n        loglik = model.elbo()\n        if loglik &gt; best_loglik:\n            best_loglik = loglik\n            best_m = model\n        del model\n        gc.collect()\n    print('Best init: %f' % best_loglik)\n\n    return best_m\n</code></pre>"},{"location":"api/models/index.html","title":"","text":""},{"location":"api/models/index.html#sgptools.models","title":"<code>sgptools.models</code>","text":"<p>Sensor placement and informative path planning methods in this package:</p> <ul> <li><code>bo</code>: Provides a Bayesian optimization based approach that maximizes mutual-information to get sensor placements</li> <li><code>cma_es</code>: Provides a genetic algorithm (CMA-ES) based approach that maximizes mutual-information to get sensor placements</li> <li><code>continuous_sgp</code>: Provides an SGP-based sensor placement approach that is optimized using gradient descent</li> <li><code>greedy_mi</code>: Provides a greedy algorithm based approach that maximizes mutual-information to get sensor placements</li> <li><code>greedy_sgp</code>: Provides an SGP-based sensor placement approach that is optimized using a greedy algorithm</li> </ul>"},{"location":"api/models/Bayesian-Optimization.html","title":"Bayesian Optimization","text":""},{"location":"api/models/Bayesian-Optimization.html#sgptools.models.bo","title":"<code>sgptools.models.bo</code>","text":""},{"location":"api/models/Bayesian-Optimization.html#sgptools.models.bo.BayesianOpt","title":"<code>BayesianOpt</code>","text":"<p>Class for optimizing sensor placements using Bayesian Optimization</p> Refer to the following papers for more details <ul> <li>UAV route planning for active disease classification [Vivaldini et al., 2019]</li> <li>Occupancy map building through Bayesian exploration [Francis et al., 2019]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>X_train</code> <code>ndarray</code> <p>(n, d); Locations in the environment used to approximate the monitoring regions</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> Source code in <code>sgptools/models/bo.py</code> <pre><code>class BayesianOpt:\n    \"\"\"Class for optimizing sensor placements using Bayesian Optimization\n\n    Refer to the following papers for more details:\n        - UAV route planning for active disease classification [Vivaldini et al., 2019]\n        - Occupancy map building through Bayesian exploration [Francis et al., 2019]\n\n    Args:\n        X_train (ndarray): (n, d); Locations in the environment used to approximate the monitoring regions\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        transform (Transform): Transform object\n    \"\"\"\n    def __init__(self, X_train, noise_variance, kernel,\n                 transform=None):\n        self.X_train = X_train\n        self.noise_variance = noise_variance\n        self.kernel = kernel\n        self.num_dim = X_train.shape[-1]\n        self.transform = transform\n\n        # use the boundaries of the region as the search space\n        self.pbounds_dim = []\n        for i in range(self.num_dim):\n            self.pbounds_dim.append((np.min(X_train[:, i]), np.max(X_train[:, i])))\n\n    def objective(self, **kwargs):\n        \"\"\"Objective function (GP-based Mutual Information)\n\n        Args:\n            x&lt;i&gt; (ndarray): (1, d); Current solution sensor placement location i\n        \"\"\"\n        # MI does not depend on waypoint order (reshape to -1, num_dim)\n        X = []\n        for i in range(len(kwargs)):\n            X.append(kwargs['x{}'.format(i)])\n        X = np.array(X).reshape(-1, self.num_dim)\n        if self.transform is not None:\n            X = self.transform.expand(X)\n            constraints_loss = self.transform.constraints(X)\n\n        try:\n            mi = get_mi(X, self.X_train, self.noise_variance, self.kernel)\n            mi += constraints_loss\n            mi = mi.numpy()\n        except:\n            mi = -1e4 # if the cholskey decomposition fails\n        return mi\n\n    def optimize(self, \n                 num_sensors=10, \n                 max_steps=100,  \n                 X_init=None,\n                 init_points=10,\n                 verbose=0, \n                 seed=1234):\n        \"\"\"Optimizes the sensor placements using Bayesian Optimization without any constraints\n\n        Args:\n            num_sensors (int): Number of sensor locations to optimize.\n            max_steps (int): Maximum number of optimization steps. \n            X_init (ndarray): (m, d); Initial inducing points.\n            init_points (int): Number of random solutions used for initial exploration. \n                               Random exploration can help by diversifying the exploration space. \n            verbose (int): The level of verbosity.\n            seed (int): The algorithm will use it to seed the randomnumber generator, ensuring replicability.\n\n        Returns:\n            Xu (ndarray): (m, d); Solution sensor placement locations\n        \"\"\"\n        if X_init is None:\n            X_init = get_inducing_pts(self.X_train, num_sensors, random=True)\n        else:\n            num_sensors = len(X_init.reshape(-1, self.num_dim))\n        X_init = X_init.reshape(-1)\n\n        pbounds = {}\n        for i in range(self.num_dim*num_sensors):\n            pbounds['x{}'.format(i)] = self.pbounds_dim[i%self.num_dim]\n\n        optimizer = BayesianOptimization(f=self.objective,\n                                         pbounds=pbounds,\n                                         verbose=verbose,\n                                         random_state=seed,\n                                         allow_duplicate_points=True)\n        optimizer.maximize(init_points=init_points,\n                           n_iter=max_steps)\n\n        sol = []\n        for i in range(self.num_dim*num_sensors):\n            sol.append(optimizer.max['params']['x{}'.format(i)])\n        sol = np.array(sol).reshape(-1, self.num_dim)\n        if self.transform is not None:\n            sol = self.transform.expand(sol,\n                                        expand_sensor_model=False)\n            if not isinstance(sol, np.ndarray):\n                sol = sol.numpy()\n        return sol.reshape(-1, self.num_dim)\n</code></pre>"},{"location":"api/models/Bayesian-Optimization.html#sgptools.models.bo.BayesianOpt.objective","title":"<code>objective(**kwargs)</code>","text":"<p>Objective function (GP-based Mutual Information)</p> <p>Parameters:</p> Name Type Description Default <code>x&lt;i&gt;</code> <code>ndarray</code> <p>(1, d); Current solution sensor placement location i</p> required Source code in <code>sgptools/models/bo.py</code> <pre><code>def objective(self, **kwargs):\n    \"\"\"Objective function (GP-based Mutual Information)\n\n    Args:\n        x&lt;i&gt; (ndarray): (1, d); Current solution sensor placement location i\n    \"\"\"\n    # MI does not depend on waypoint order (reshape to -1, num_dim)\n    X = []\n    for i in range(len(kwargs)):\n        X.append(kwargs['x{}'.format(i)])\n    X = np.array(X).reshape(-1, self.num_dim)\n    if self.transform is not None:\n        X = self.transform.expand(X)\n        constraints_loss = self.transform.constraints(X)\n\n    try:\n        mi = get_mi(X, self.X_train, self.noise_variance, self.kernel)\n        mi += constraints_loss\n        mi = mi.numpy()\n    except:\n        mi = -1e4 # if the cholskey decomposition fails\n    return mi\n</code></pre>"},{"location":"api/models/Bayesian-Optimization.html#sgptools.models.bo.BayesianOpt.optimize","title":"<code>optimize(num_sensors=10, max_steps=100, X_init=None, init_points=10, verbose=0, seed=1234)</code>","text":"<p>Optimizes the sensor placements using Bayesian Optimization without any constraints</p> <p>Parameters:</p> Name Type Description Default <code>num_sensors</code> <code>int</code> <p>Number of sensor locations to optimize.</p> <code>10</code> <code>max_steps</code> <code>int</code> <p>Maximum number of optimization steps. </p> <code>100</code> <code>X_init</code> <code>ndarray</code> <p>(m, d); Initial inducing points.</p> <code>None</code> <code>init_points</code> <code>int</code> <p>Number of random solutions used for initial exploration.                 Random exploration can help by diversifying the exploration space. </p> <code>10</code> <code>verbose</code> <code>int</code> <p>The level of verbosity.</p> <code>0</code> <code>seed</code> <code>int</code> <p>The algorithm will use it to seed the randomnumber generator, ensuring replicability.</p> <code>1234</code> <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(m, d); Solution sensor placement locations</p> Source code in <code>sgptools/models/bo.py</code> <pre><code>def optimize(self, \n             num_sensors=10, \n             max_steps=100,  \n             X_init=None,\n             init_points=10,\n             verbose=0, \n             seed=1234):\n    \"\"\"Optimizes the sensor placements using Bayesian Optimization without any constraints\n\n    Args:\n        num_sensors (int): Number of sensor locations to optimize.\n        max_steps (int): Maximum number of optimization steps. \n        X_init (ndarray): (m, d); Initial inducing points.\n        init_points (int): Number of random solutions used for initial exploration. \n                           Random exploration can help by diversifying the exploration space. \n        verbose (int): The level of verbosity.\n        seed (int): The algorithm will use it to seed the randomnumber generator, ensuring replicability.\n\n    Returns:\n        Xu (ndarray): (m, d); Solution sensor placement locations\n    \"\"\"\n    if X_init is None:\n        X_init = get_inducing_pts(self.X_train, num_sensors, random=True)\n    else:\n        num_sensors = len(X_init.reshape(-1, self.num_dim))\n    X_init = X_init.reshape(-1)\n\n    pbounds = {}\n    for i in range(self.num_dim*num_sensors):\n        pbounds['x{}'.format(i)] = self.pbounds_dim[i%self.num_dim]\n\n    optimizer = BayesianOptimization(f=self.objective,\n                                     pbounds=pbounds,\n                                     verbose=verbose,\n                                     random_state=seed,\n                                     allow_duplicate_points=True)\n    optimizer.maximize(init_points=init_points,\n                       n_iter=max_steps)\n\n    sol = []\n    for i in range(self.num_dim*num_sensors):\n        sol.append(optimizer.max['params']['x{}'.format(i)])\n    sol = np.array(sol).reshape(-1, self.num_dim)\n    if self.transform is not None:\n        sol = self.transform.expand(sol,\n                                    expand_sensor_model=False)\n        if not isinstance(sol, np.ndarray):\n            sol = sol.numpy()\n    return sol.reshape(-1, self.num_dim)\n</code></pre>"},{"location":"api/models/Continuous-SGP.html","title":"Continuous SGP","text":""},{"location":"api/models/Continuous-SGP.html#sgptools.models.continuous_sgp","title":"<code>sgptools.models.continuous_sgp</code>","text":""},{"location":"api/models/Continuous-SGP.html#sgptools.models.continuous_sgp.continuous_sgp","title":"<code>continuous_sgp(num_inducing, X_train, noise_variance, kernel, transform=None, Xu_init=None, Xu_time=None, orientation=False, **kwargs)</code>","text":"<p>Get sensor placement solutions using the Continuous-SGP method</p> Refer to the following papers for more details <ul> <li>Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]</li> <li>Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>num_inducing</code> <code>int</code> <p>Number of inducing points</p> required <code>X_train</code> <code>ndarray</code> <p>(n, d); Unlabeled random sampled training points</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> <code>Xu_init</code> <code>ndarray</code> <p>(m, d); Initial inducing points</p> <code>None</code> <code>Xu_time</code> <code>ndarray</code> <p>(t, d); Temporal inducing points used in spatio-temporal models</p> <code>None</code> <code>orientation</code> <code>bool</code> <p>If True, a additionl dimension is added to the                  inducing points to represent the FoV orientation</p> <code>False</code> <p>Returns:</p> Name Type Description <code>sgpr</code> <code>AugmentedSGPR</code> <p>Optimized sparse Gaussian process model</p> <code>loss</code> <code>ndarray</code> <p>Loss values computed during training</p> Source code in <code>sgptools/models/continuous_sgp.py</code> <pre><code>def continuous_sgp(num_inducing, X_train, noise_variance, kernel, \n                   transform=None,\n                   Xu_init=None, \n                   Xu_time=None, \n                   orientation=False,\n                   **kwargs):\n    \"\"\"Get sensor placement solutions using the Continuous-SGP method\n\n    Refer to the following papers for more details:\n        - Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [[Jakkala and Akella, 2023](https://www.itskalvik.com/publication/sgp-sp/)]\n        - Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [[Jakkala and Akella, 2024](https://www.itskalvik.com/publication/sgp-ipp/)]\n\n    Args:\n        num_inducing (int): Number of inducing points\n        X_train (ndarray): (n, d); Unlabeled random sampled training points\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        transform (Transform): Transform object\n        Xu_init (ndarray): (m, d); Initial inducing points\n        Xu_time (ndarray): (t, d); Temporal inducing points used in spatio-temporal models\n        orientation (bool): If True, a additionl dimension is added to the \n                            inducing points to represent the FoV orientation\n\n    Returns:\n        sgpr (AugmentedSGPR): Optimized sparse Gaussian process model\n        loss (ndarray): Loss values computed during training\n    \"\"\"\n    # Generate init inducing points\n    if Xu_init is None:\n        Xu_init = get_inducing_pts(X_train, num_inducing, \n                                   orientation=orientation)\n\n    # Fit spare GP\n    sgpr = AugmentedSGPR((X_train, np.zeros((len(X_train), 1)).astype(X_train.dtype)),\n                         noise_variance=noise_variance,\n                         kernel=kernel, \n                         inducing_variable=Xu_init,\n                         inducing_variable_time=Xu_time,\n                         transform=transform)\n\n    # Train the mode\n    loss = optimize_model(sgpr,\n                          kernel_grad=False, \n                          **kwargs)\n\n    return sgpr, loss\n</code></pre>"},{"location":"api/models/Genetic-Algorithm.html","title":"Genetic Algorithm","text":""},{"location":"api/models/Genetic-Algorithm.html#sgptools.models.cma_es","title":"<code>sgptools.models.cma_es</code>","text":""},{"location":"api/models/Genetic-Algorithm.html#sgptools.models.cma_es.CMA_ES","title":"<code>CMA_ES</code>","text":"<p>Class for optimizing sensor placements using CMA-ES (a genetic algorithm)</p> Refer to the following paper for more details <ul> <li>Adaptive Continuous-Space Informative Path Planning for Online Environmental Monitoring [Hitz et al., 2017]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>X_train</code> <code>ndarray</code> <p>(n, d); Locations in the environment used to approximate the monitoring regions</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>distance_budget</code> <code>float</code> <p>Distance budget for when treating the inducing points                       as waypoints of a path</p> <code>None</code> <code>num_robots</code> <code>int</code> <p>Number of robots, used when modeling                multi-robot IPP with a distance budget</p> <code>1</code> <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> Source code in <code>sgptools/models/cma_es.py</code> <pre><code>class CMA_ES:\n    \"\"\"Class for optimizing sensor placements using CMA-ES (a genetic algorithm)\n\n    Refer to the following paper for more details:\n        - Adaptive Continuous-Space Informative Path Planning for Online Environmental Monitoring [Hitz et al., 2017]\n\n    Args:\n        X_train (ndarray): (n, d); Locations in the environment used to approximate the monitoring regions\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        distance_budget (float): Distance budget for when treating the inducing points \n                                 as waypoints of a path\n        num_robots (int): Number of robots, used when modeling \n                          multi-robot IPP with a distance budget\n        transform (Transform): Transform object\n    \"\"\"\n    def __init__(self, X_train, noise_variance, kernel,\n                 distance_budget=None,\n                 num_robots=1,\n                 transform=None):\n        self.boundaries = geometry.MultiPoint([[p[0], p[1]] for p in X_train]).convex_hull\n        self.X_train = X_train\n        self.noise_variance = noise_variance\n        self.kernel = kernel\n        self.num_dim = X_train.shape[-1]\n        self.distance_budget = distance_budget\n        self.num_robots = num_robots\n        self.transform = transform\n\n    def update(self, noise_variance, kernel):\n        \"\"\"Update GP noise variance and kernel function parameters\n\n        Args:\n            noise_variance (float): data variance\n            kernel (gpflow.kernels.Kernel): gpflow kernel function\n        \"\"\"\n        self.noise_variance = noise_variance\n        self.kernel = kernel\n\n    def objective(self, X):\n        \"\"\"Objective function (GP-based Mutual Information)\n\n        Args:\n            X (ndarray): (n, d); Current solution sensor placement locations\n        \"\"\"\n        # MI does not depend on waypoint order (reshape to -1, num_dim)\n        X = np.array(X).reshape(-1, self.num_dim)\n        constraints_loss = 0.0\n        if self.transform is not None:\n            X = self.transform.expand(X)\n            constraints_loss = self.transform.constraints(X)\n\n        try:\n            mi = -get_mi(X, self.X_train, self.noise_variance, self.kernel)\n            mi -= constraints_loss\n            mi = mi.numpy()\n        except:\n            mi = 0.0 # if the cholskey decomposition fails\n        return mi\n\n    def optimize(self, \n                 num_sensors=10, \n                 max_steps=5000, \n                 tol=1e-6, \n                 X_init=None,\n                 verbose=0,\n                 seed=1234):\n        \"\"\"Optimizes the sensor placements using CMA-ES without any constraints\n\n        Args:\n            num_sensors (int): Number of sensor locations to optimize\n            max_steps (int): Maximum number of optimization steps\n            tol (float): Convergence tolerance to decide when to stop optimization\n            X_init (ndarray): (m, d); Initial inducing points\n            verbose (int): The level of verbosity.\n            seed (int): The algorithm will use it to seed the randomnumber generator, ensuring replicability.\n\n        Returns:\n            Xu (ndarray): (m, d); Solution sensor placement locations\n        \"\"\"\n        sigma0 = 1.0\n\n        if X_init is None:\n            X_init = get_inducing_pts(self.X_train, num_sensors, random=True)\n        X_init = X_init.reshape(-1)\n\n        xopt, _ = cma.fmin2(self.objective, X_init, sigma0, \n                            options={'maxfevals': max_steps,\n                                     'verb_disp': verbose,\n                                     'tolfun': tol,\n                                     'seed': seed},\n                            restarts=5)\n\n        xopt = np.array(xopt).reshape(-1, self.num_dim)\n        if self.transform is not None:\n            xopt = self.transform.expand(xopt, \n                                         expand_sensor_model=False)\n            if not isinstance(xopt, np.ndarray):\n                xopt = xopt.numpy()\n        return xopt.reshape(-1, self.num_dim)\n</code></pre>"},{"location":"api/models/Genetic-Algorithm.html#sgptools.models.cma_es.CMA_ES.objective","title":"<code>objective(X)</code>","text":"<p>Objective function (GP-based Mutual Information)</p> <p>Parameters:</p> Name Type Description Default <code>X</code> <code>ndarray</code> <p>(n, d); Current solution sensor placement locations</p> required Source code in <code>sgptools/models/cma_es.py</code> <pre><code>def objective(self, X):\n    \"\"\"Objective function (GP-based Mutual Information)\n\n    Args:\n        X (ndarray): (n, d); Current solution sensor placement locations\n    \"\"\"\n    # MI does not depend on waypoint order (reshape to -1, num_dim)\n    X = np.array(X).reshape(-1, self.num_dim)\n    constraints_loss = 0.0\n    if self.transform is not None:\n        X = self.transform.expand(X)\n        constraints_loss = self.transform.constraints(X)\n\n    try:\n        mi = -get_mi(X, self.X_train, self.noise_variance, self.kernel)\n        mi -= constraints_loss\n        mi = mi.numpy()\n    except:\n        mi = 0.0 # if the cholskey decomposition fails\n    return mi\n</code></pre>"},{"location":"api/models/Genetic-Algorithm.html#sgptools.models.cma_es.CMA_ES.optimize","title":"<code>optimize(num_sensors=10, max_steps=5000, tol=1e-06, X_init=None, verbose=0, seed=1234)</code>","text":"<p>Optimizes the sensor placements using CMA-ES without any constraints</p> <p>Parameters:</p> Name Type Description Default <code>num_sensors</code> <code>int</code> <p>Number of sensor locations to optimize</p> <code>10</code> <code>max_steps</code> <code>int</code> <p>Maximum number of optimization steps</p> <code>5000</code> <code>tol</code> <code>float</code> <p>Convergence tolerance to decide when to stop optimization</p> <code>1e-06</code> <code>X_init</code> <code>ndarray</code> <p>(m, d); Initial inducing points</p> <code>None</code> <code>verbose</code> <code>int</code> <p>The level of verbosity.</p> <code>0</code> <code>seed</code> <code>int</code> <p>The algorithm will use it to seed the randomnumber generator, ensuring replicability.</p> <code>1234</code> <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(m, d); Solution sensor placement locations</p> Source code in <code>sgptools/models/cma_es.py</code> <pre><code>def optimize(self, \n             num_sensors=10, \n             max_steps=5000, \n             tol=1e-6, \n             X_init=None,\n             verbose=0,\n             seed=1234):\n    \"\"\"Optimizes the sensor placements using CMA-ES without any constraints\n\n    Args:\n        num_sensors (int): Number of sensor locations to optimize\n        max_steps (int): Maximum number of optimization steps\n        tol (float): Convergence tolerance to decide when to stop optimization\n        X_init (ndarray): (m, d); Initial inducing points\n        verbose (int): The level of verbosity.\n        seed (int): The algorithm will use it to seed the randomnumber generator, ensuring replicability.\n\n    Returns:\n        Xu (ndarray): (m, d); Solution sensor placement locations\n    \"\"\"\n    sigma0 = 1.0\n\n    if X_init is None:\n        X_init = get_inducing_pts(self.X_train, num_sensors, random=True)\n    X_init = X_init.reshape(-1)\n\n    xopt, _ = cma.fmin2(self.objective, X_init, sigma0, \n                        options={'maxfevals': max_steps,\n                                 'verb_disp': verbose,\n                                 'tolfun': tol,\n                                 'seed': seed},\n                        restarts=5)\n\n    xopt = np.array(xopt).reshape(-1, self.num_dim)\n    if self.transform is not None:\n        xopt = self.transform.expand(xopt, \n                                     expand_sensor_model=False)\n        if not isinstance(xopt, np.ndarray):\n            xopt = xopt.numpy()\n    return xopt.reshape(-1, self.num_dim)\n</code></pre>"},{"location":"api/models/Genetic-Algorithm.html#sgptools.models.cma_es.CMA_ES.update","title":"<code>update(noise_variance, kernel)</code>","text":"<p>Update GP noise variance and kernel function parameters</p> <p>Parameters:</p> Name Type Description Default <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required Source code in <code>sgptools/models/cma_es.py</code> <pre><code>def update(self, noise_variance, kernel):\n    \"\"\"Update GP noise variance and kernel function parameters\n\n    Args:\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n    \"\"\"\n    self.noise_variance = noise_variance\n    self.kernel = kernel\n</code></pre>"},{"location":"api/models/Greedy-MI.html","title":"Greedy MI","text":""},{"location":"api/models/Greedy-MI.html#sgptools.models.greedy_mi","title":"<code>sgptools.models.greedy_mi</code>","text":""},{"location":"api/models/Greedy-MI.html#sgptools.models.greedy_mi.GreedyMI","title":"<code>GreedyMI</code>","text":"<p>Helper class to compute mutual information using a Gaussian process for a given set of sensor locations. Used by <code>get_greedy_mi_sol</code> function to compute the solution sensor placements using the Greedy-MI method.</p> Refer to the following papers for more details <ul> <li>Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies [Krause et al., 2008]</li> <li>Data-driven learning and planning for environmental sampling [Ma et al., 2018]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>S</code> <code>ndarray</code> <p>(n, d); Candidate sensor placement locations</p> required <code>V</code> <code>ndarray</code> <p>(n, d); Locations in the environment used to approximate the monitoring regions</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> Source code in <code>sgptools/models/greedy_mi.py</code> <pre><code>class GreedyMI:\n    \"\"\"Helper class to compute mutual information using a Gaussian process for a given set of sensor locations.\n    Used by `get_greedy_mi_sol` function to compute the solution sensor placements using the Greedy-MI method.\n\n    Refer to the following papers for more details:\n        - Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies [Krause et al., 2008]\n        - Data-driven learning and planning for environmental sampling [Ma et al., 2018]\n\n    Args:\n        S (ndarray): (n, d); Candidate sensor placement locations\n        V (ndarray): (n, d); Locations in the environment used to approximate the monitoring regions\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        transform (Transform): Transform object\n    \"\"\"\n    def __init__(self, S, V, noise_variance, kernel, \n                 transform=None):\n        self.S = S\n        self.V = V\n        self.kernel = kernel\n        self.input_dim = S.shape[1]\n        self.noise_variance = noise_variance\n        self.transform = transform\n\n    def mutual_info(self, x):\n        \"\"\"Computes mutual information using the points `x` \n\n        Args:\n            x (ndarray): (n); Indices of the solution placement locations\n\n        Returns:\n            MI (float): Mutual information between the placement x and candidate locations\n        \"\"\"\n        x = np.array(x).reshape(-1).astype(int)\n        A = self.S[x[:-1]].reshape(-1, self.input_dim)\n        y = self.S[x[-1]].reshape(-1, self.input_dim)\n\n        if len(A) == 0:\n            sigma_a = 1.0\n        else:\n            if self.transform is not None:\n                A = self.transform.expand(A)\n            a_gp = GPR(data=(A, np.zeros((len(A), 1))),\n                       kernel=self.kernel,\n                       noise_variance=self.noise_variance)\n            _, sigma_a = a_gp.predict_f(y)\n\n        # Remove locations in A\u222ay from V to build A bar (Refer to Krause et al., 2008)\n        V_ = self.V.copy()\n        V_rows = V_.view([('', V_.dtype)] * V_.shape[1])\n\n        if self.transform is not None:\n            solution = self.S[x].reshape(-1, self.input_dim)\n            A_ = self.transform.expand(solution)\n        else:\n            A_ = self.S[x]\n        A_rows = A_.view([('', V_.dtype)] * A_.shape[1])\n\n        V_ = np.setdiff1d(V_rows, A_rows).view(V_.dtype).reshape(-1, V_.shape[1])\n\n        self.v_gp = GPR(data=(V_, np.zeros((len(V_), 1))), \n                        kernel=self.kernel,\n                        noise_variance=self.noise_variance)\n        _, sigma_v = self.v_gp.predict_f(y)\n\n        return (sigma_a/sigma_v).numpy().squeeze()\n</code></pre>"},{"location":"api/models/Greedy-MI.html#sgptools.models.greedy_mi.GreedyMI.mutual_info","title":"<code>mutual_info(x)</code>","text":"<p>Computes mutual information using the points <code>x</code> </p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>ndarray</code> <p>(n); Indices of the solution placement locations</p> required <p>Returns:</p> Name Type Description <code>MI</code> <code>float</code> <p>Mutual information between the placement x and candidate locations</p> Source code in <code>sgptools/models/greedy_mi.py</code> <pre><code>def mutual_info(self, x):\n    \"\"\"Computes mutual information using the points `x` \n\n    Args:\n        x (ndarray): (n); Indices of the solution placement locations\n\n    Returns:\n        MI (float): Mutual information between the placement x and candidate locations\n    \"\"\"\n    x = np.array(x).reshape(-1).astype(int)\n    A = self.S[x[:-1]].reshape(-1, self.input_dim)\n    y = self.S[x[-1]].reshape(-1, self.input_dim)\n\n    if len(A) == 0:\n        sigma_a = 1.0\n    else:\n        if self.transform is not None:\n            A = self.transform.expand(A)\n        a_gp = GPR(data=(A, np.zeros((len(A), 1))),\n                   kernel=self.kernel,\n                   noise_variance=self.noise_variance)\n        _, sigma_a = a_gp.predict_f(y)\n\n    # Remove locations in A\u222ay from V to build A bar (Refer to Krause et al., 2008)\n    V_ = self.V.copy()\n    V_rows = V_.view([('', V_.dtype)] * V_.shape[1])\n\n    if self.transform is not None:\n        solution = self.S[x].reshape(-1, self.input_dim)\n        A_ = self.transform.expand(solution)\n    else:\n        A_ = self.S[x]\n    A_rows = A_.view([('', V_.dtype)] * A_.shape[1])\n\n    V_ = np.setdiff1d(V_rows, A_rows).view(V_.dtype).reshape(-1, V_.shape[1])\n\n    self.v_gp = GPR(data=(V_, np.zeros((len(V_), 1))), \n                    kernel=self.kernel,\n                    noise_variance=self.noise_variance)\n    _, sigma_v = self.v_gp.predict_f(y)\n\n    return (sigma_a/sigma_v).numpy().squeeze()\n</code></pre>"},{"location":"api/models/Greedy-MI.html#sgptools.models.greedy_mi.get_greedy_mi_sol","title":"<code>get_greedy_mi_sol(num_sensors, candidates, X_train, noise_variance, kernel, transform=None, optimizer='naive')</code>","text":"<p>Get sensor placement solutions using the GP-based mutual information approach (submodular objective function).  Uses a greedy algorithm to select sensor placements from a given discrete set of candidates locations.</p> Refer to the following papers for more details <ul> <li>Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies [Krause et al., 2008]</li> <li>Data-driven learning and planning for environmental sampling [Ma et al., 2018]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>num_sensors</code> <code>int</code> <p>Number of sensor locations to optimize</p> required <code>candidates</code> <code>ndarray</code> <p>(n, d); Candidate sensor placement locations</p> required <code>X_train</code> <code>ndarray</code> <p>(n, d); Locations in the environment used to approximate the monitoring regions</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> <code>optimizer</code> <code>str</code> <p>Name of an optimizer available in the apricot library</p> <code>'naive'</code> <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(m, d); Solution sensor placement locations</p> Source code in <code>sgptools/models/greedy_mi.py</code> <pre><code>def get_greedy_mi_sol(num_sensors, candidates, X_train, noise_variance, kernel, \n                      transform=None, optimizer='naive'):\n    \"\"\"Get sensor placement solutions using the GP-based mutual information approach (submodular objective function). \n    Uses a greedy algorithm to select sensor placements from a given discrete set of candidates locations.\n\n    Refer to the following papers for more details:\n        - Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies [Krause et al., 2008]\n        - Data-driven learning and planning for environmental sampling [Ma et al., 2018]\n\n    Args:\n        num_sensors (int): Number of sensor locations to optimize\n        candidates (ndarray): (n, d); Candidate sensor placement locations\n        X_train (ndarray): (n, d); Locations in the environment used to approximate the monitoring regions\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        transform (Transform): Transform object\n        optimizer (str): Name of an optimizer available in the apricot library\n\n    Returns:\n        Xu (ndarray): (m, d); Solution sensor placement locations\n    \"\"\"\n    mi_model = GreedyMI(candidates, X_train, noise_variance, kernel, transform)\n    model = CustomSelection(num_sensors,\n                            mi_model.mutual_info,\n                            optimizer=optimizer,\n                            verbose=False)\n    sol = model.fit_transform(np.arange(len(candidates)).reshape(-1, 1))\n    return candidates[sol.reshape(-1)]\n</code></pre>"},{"location":"api/models/Greedy-SGP.html","title":"Greedy SGP","text":""},{"location":"api/models/Greedy-SGP.html#sgptools.models.greedy_sgp","title":"<code>sgptools.models.greedy_sgp</code>","text":""},{"location":"api/models/Greedy-SGP.html#sgptools.models.greedy_sgp.GreedySGP","title":"<code>GreedySGP</code>","text":"<p>Helper class to compute SGP's ELBO/optimization bound for a given set of sensor locations. Used by <code>get_greedy_sgp_sol</code> function to compute the solution sensor placements using the Greedy-SGP method.</p> Refer to the following papers for more details <ul> <li>Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>num_inducing</code> <code>int</code> <p>Number of inducing points</p> required <code>S</code> <code>ndarray</code> <p>(n, d); Candidate sensor placement locations</p> required <code>V</code> <code>ndarray</code> <p>(n, d); Locations in the environment used to approximate the monitoring regions</p> required <code>noise_variance</code> <code>float</code> <p>Data noise variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> Source code in <code>sgptools/models/greedy_sgp.py</code> <pre><code>class GreedySGP:\n    \"\"\"Helper class to compute SGP's ELBO/optimization bound for a given set of sensor locations.\n    Used by `get_greedy_sgp_sol` function to compute the solution sensor placements using the Greedy-SGP method.\n\n    Refer to the following papers for more details:\n        - Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [[Jakkala and Akella, 2023](https://www.itskalvik.com/publication/sgp-sp/)]\n\n    Args:\n        num_inducing (int): Number of inducing points\n        S (ndarray): (n, d); Candidate sensor placement locations\n        V (ndarray): (n, d); Locations in the environment used to approximate the monitoring regions\n        noise_variance (float): Data noise variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        transform (Transform): Transform object\n    \"\"\"\n    def __init__(self, num_inducing, S, V, noise_variance, kernel, \n                 transform=None):\n        self.sgp = AugmentedSGPR((V, np.zeros((len(V), 1))),\n                                 noise_variance=noise_variance,\n                                 kernel=kernel, \n                                 inducing_variable=S[:num_inducing],\n                                 transform=transform)\n        self.locs = S\n        self.num_inducing = num_inducing\n        self.inducing_dim = S.shape[1]\n\n    def bound(self, x):\n        \"\"\"Computes the SGP's optimization bound using the inducing points `x` \n\n        Args:\n            x (ndarray): (n); Indices of the solution placement locations\n\n        Returns:\n            elbo (float): Evidence lower bound/SGP's optimization bound value\n        \"\"\"\n        x = np.array(x).reshape(-1).astype(int)\n        Xu = np.ones((self.num_inducing, self.inducing_dim), dtype=np.float32)\n\n        # Initialize all inducing points at the first solution placement location.\n        # Ensures that the number of inducing points is always fixed and no additional\n        # information is passed to the SGP\n        Xu *= self.locs[x][0]\n\n        # Copy all given solution placements to the inducing points set\n        Xu[-len(x):] = self.locs[x]\n\n        # Update the SGP inducing points\n        self.sgp.inducing_variable.Z.assign(Xu)\n        return self.sgp.elbo().numpy() # return the ELBO\n</code></pre>"},{"location":"api/models/Greedy-SGP.html#sgptools.models.greedy_sgp.GreedySGP.bound","title":"<code>bound(x)</code>","text":"<p>Computes the SGP's optimization bound using the inducing points <code>x</code> </p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>ndarray</code> <p>(n); Indices of the solution placement locations</p> required <p>Returns:</p> Name Type Description <code>elbo</code> <code>float</code> <p>Evidence lower bound/SGP's optimization bound value</p> Source code in <code>sgptools/models/greedy_sgp.py</code> <pre><code>def bound(self, x):\n    \"\"\"Computes the SGP's optimization bound using the inducing points `x` \n\n    Args:\n        x (ndarray): (n); Indices of the solution placement locations\n\n    Returns:\n        elbo (float): Evidence lower bound/SGP's optimization bound value\n    \"\"\"\n    x = np.array(x).reshape(-1).astype(int)\n    Xu = np.ones((self.num_inducing, self.inducing_dim), dtype=np.float32)\n\n    # Initialize all inducing points at the first solution placement location.\n    # Ensures that the number of inducing points is always fixed and no additional\n    # information is passed to the SGP\n    Xu *= self.locs[x][0]\n\n    # Copy all given solution placements to the inducing points set\n    Xu[-len(x):] = self.locs[x]\n\n    # Update the SGP inducing points\n    self.sgp.inducing_variable.Z.assign(Xu)\n    return self.sgp.elbo().numpy() # return the ELBO\n</code></pre>"},{"location":"api/models/Greedy-SGP.html#sgptools.models.greedy_sgp.get_greedy_sgp_sol","title":"<code>get_greedy_sgp_sol(num_sensors, candidates, X_train, noise_variance, kernel, transform=None)</code>","text":"<p>Get sensor placement solutions using the Greedy-SGP method. Uses a greedy algorithm to  select sensor placements from a given discrete set of candidates locations.</p> Refer to the following papers for more details <ul> <li>Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>num_sensors</code> <code>int</code> <p>Number of sensor locations to optimize</p> required <code>candidates</code> <code>ndarray</code> <p>(n, d); Candidate sensor placement locations</p> required <code>X_train</code> <code>ndarray</code> <p>(n, d); Locations in the environment used to approximate the monitoring regions</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(m, d); Solution sensor placement locations</p> Source code in <code>sgptools/models/greedy_sgp.py</code> <pre><code>def get_greedy_sgp_sol(num_sensors, candidates, X_train, noise_variance, kernel, \n                       transform=None):\n    \"\"\"Get sensor placement solutions using the Greedy-SGP method. Uses a greedy algorithm to \n    select sensor placements from a given discrete set of candidates locations.\n\n    Refer to the following papers for more details:\n        - Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [[Jakkala and Akella, 2023](https://www.itskalvik.com/publication/sgp-sp/)]\n\n    Args:\n        num_sensors (int): Number of sensor locations to optimize\n        candidates (ndarray): (n, d); Candidate sensor placement locations\n        X_train (ndarray): (n, d); Locations in the environment used to approximate the monitoring regions\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        transform (Transform): Transform object\n\n    Returns:\n        Xu (ndarray): (m, d); Solution sensor placement locations\n    \"\"\"\n    sgp_model = GreedySGP(num_sensors, candidates, X_train, \n                          noise_variance, kernel, transform=transform)\n    model = CustomSelection(num_sensors,\n                            sgp_model.bound,\n                            optimizer='naive',\n                            verbose=False)\n    sol = model.fit_transform(np.arange(len(candidates)).reshape(-1, 1))\n    return candidates[sol.reshape(-1)]\n</code></pre>"},{"location":"api/models/core/index.html","title":"","text":""},{"location":"api/models/core/index.html#sgptools.models.core","title":"<code>sgptools.models.core</code>","text":"<p>Core modules in this package:</p> <ul> <li><code>augmented_gpr</code>: Provides a Gaussian process model with expand and aggregate functions</li> <li><code>augmented_sgpr</code>: Provides a sparse Gaussian process model with update, expand, and aggregate functions</li> <li><code>osgpr</code>: Provides a streaming sparse Gaussian process model along with initialization function</li> <li><code>transformations</code>: Provides transforms to model complex sensor field of views and handle informative path planning</li> </ul>"},{"location":"api/models/core/AugmentedGPR.html","title":"AugmentedGPR","text":""},{"location":"api/models/core/AugmentedGPR.html#sgptools.models.core.augmented_gpr","title":"<code>sgptools.models.core.augmented_gpr</code>","text":"<p>Provides a Gaussian process model with expand and aggregate functions</p>"},{"location":"api/models/core/AugmentedGPR.html#sgptools.models.core.augmented_gpr.AugmentedGPR","title":"<code>AugmentedGPR</code>","text":"<p>               Bases: <code>GPR</code></p> <p>GPR model from the GPFlow library augmented to use a transform object's expand and aggregate functions on the data points where necessary.  </p> Refer to the following papers for more details <ul> <li>Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]</li> <li>Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>tuple</code> <p>(X, y) ndarrays with inputs (n, d) and labels (n, 1)</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> Source code in <code>sgptools/models/core/augmented_gpr.py</code> <pre><code>class AugmentedGPR(GPR):\n    \"\"\"GPR model from the GPFlow library augmented to use a transform object's\n    expand and aggregate functions on the data points where necessary.  \n\n    Refer to the following papers for more details:\n        - Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]\n        - Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]\n\n    Args:\n        data (tuple): (X, y) ndarrays with inputs (n, d) and labels (n, 1)\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        noise_variance (float): data variance\n        transform (Transform): Transform object\n    \"\"\"\n    def __init__(\n        self,\n        *args,\n        transform=None,\n        **kwargs\n    ):\n        super().__init__(\n            *args,\n            **kwargs\n        )\n        if transform is None:\n            self.transform = Transform()\n        else:\n            self.transform = transform\n\n    def predict_f(\n        self, Xnew: InputData, \n        full_cov: bool = True, \n        full_output_cov: bool = False,\n        aggregate_train: bool = False,\n    ) -&gt; MeanAndVariance:\n        assert_params_false(self.predict_f, full_output_cov=full_output_cov)\n        if self.transform is not None:\n            Xnew = self.transform.expand(Xnew)\n\n        X, Y = self.data\n        err = Y - self.mean_function(X)\n\n        kmm = self.kernel(X)\n        knn = self.kernel(Xnew, full_cov=full_cov)\n        kmn = self.kernel(X, Xnew)\n        kmm_plus_s = add_likelihood_noise_cov(kmm, self.likelihood, X)\n\n        if self.transform is not None:\n            kmn = self.transform.aggregate(tf.transpose(kmn))\n            kmn = tf.transpose(kmn)\n            knn = self.transform.aggregate(knn)\n\n        if aggregate_train:\n            kmm_plus_s = self.transform.aggregate(kmm_plus_s)\n            err = self.transform.aggregate(err)\n            # reduce kmn only if it was not reduced before\n            # which can when train and test data are the same size\n            if kmn.shape[0] != kmn.shape[1]:\n                kmn = self.transform.aggregate(kmn)\n\n        conditional = gpflow.conditionals.base_conditional\n        f_mean_zero, f_var = conditional(\n            kmn, kmm_plus_s, knn, err, full_cov=full_cov, white=False\n        )  # [N, P], [N, P] or [P, N, N]\n        f_mean = f_mean_zero + self.mean_function(Xnew)\n        return f_mean, f_var\n</code></pre>"},{"location":"api/models/core/AugmentedSGPR.html","title":"AugmentedSGPR","text":""},{"location":"api/models/core/AugmentedSGPR.html#sgptools.models.core.augmented_sgpr","title":"<code>sgptools.models.core.augmented_sgpr</code>","text":"<p>Provides a sparse Gaussian process model with update, expand, and aggregate functions</p>"},{"location":"api/models/core/AugmentedSGPR.html#sgptools.models.core.augmented_sgpr.AugmentedSGPR","title":"<code>AugmentedSGPR</code>","text":"<p>               Bases: <code>SGPR</code></p> <p>SGPR model from the GPFlow library augmented to use a transform object's expand and aggregate functions on the inducing points where necessary. The object has an additional update function to update the kernel and noise variance parameters  (currently, the online updates part works only with RBF kernels).  </p> Refer to the following papers for more details <ul> <li>Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]</li> <li>Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>tuple</code> <p>(X, y) ndarrays with inputs (n, d) and labels (n, 1)</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>inducing_variable</code> <code>ndarray</code> <p>(m, d); Initial inducing points</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> <code>inducing_variable_time</code> <code>ndarray</code> <p>(m, d); Temporal dimensions of the inducing points,                                  used when modeling spatio-temporal IPP</p> <code>None</code> Source code in <code>sgptools/models/core/augmented_sgpr.py</code> <pre><code>class AugmentedSGPR(SGPR):\n    \"\"\"SGPR model from the GPFlow library augmented to use a transform object's\n    expand and aggregate functions on the inducing points where necessary. The object\n    has an additional update function to update the kernel and noise variance parameters \n    (currently, the online updates part works only with RBF kernels).  \n\n\n    Refer to the following papers for more details:\n        - Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]\n        - Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]\n\n    Args:\n        data (tuple): (X, y) ndarrays with inputs (n, d) and labels (n, 1)\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        noise_variance (float): data variance\n        inducing_variable (ndarray): (m, d); Initial inducing points\n        transform (Transform): Transform object\n        inducing_variable_time (ndarray): (m, d); Temporal dimensions of the inducing points, \n                                            used when modeling spatio-temporal IPP\n    \"\"\"\n    def __init__(\n        self,\n        *args,\n        transform=None,\n        inducing_variable_time=None,\n        **kwargs\n    ):\n        super().__init__(\n            *args,\n            **kwargs\n        )\n        if transform is None:\n            self.transform = Transform()\n        else:\n            self.transform = transform\n\n        if inducing_variable_time is not None:\n            self.inducing_variable_time = inducingpoint_wrapper(inducing_variable_time)\n            self.transform.inducing_variable_time = self.inducing_variable_time\n        else:\n            self.inducing_variable_time = None\n\n    def update(self, noise_variance, kernel):\n        \"\"\"Update SGP noise variance and kernel function parameters\n\n        Args:\n            noise_variance (float): data variance\n            kernel (gpflow.kernels.Kernel): gpflow kernel function\n        \"\"\"\n        self.likelihood.variance.assign(noise_variance)\n        self.kernel.lengthscales.assign(kernel.lengthscales)\n        self.kernel.variance.assign(kernel.variance)\n\n    def _common_calculation(self) -&gt; \"SGPR.CommonTensors\":\n        \"\"\"\n        Matrices used in log-det calculation\n        :return: A , B, LB, AAT with :math:`LL\u1d40 = K\u1d64\u1d64 , A = L\u207b\u00b9K_{uf}/\u03c3, AAT = AA\u1d40,\n            B = AAT+I, LBLB\u1d40 = B`\n            A is M x N, B is M x M, LB is M x M, AAT is M x M\n        \"\"\"\n        x, _ = self.data\n\n        iv = self.inducing_variable.Z  # [M]\n        iv = self.transform.expand(iv)\n\n        kuf = self.kernel(iv, x)\n        kuf = self.transform.aggregate(kuf)\n\n        kuu = self.kernel(iv) + 1e-6 * tf.eye(tf.shape(iv)[0], dtype=iv.dtype)\n        kuu = self.transform.aggregate(kuu)\n\n        L = tf.linalg.cholesky(kuu)\n\n        sigma_sq = self.likelihood.variance\n        sigma = tf.sqrt(sigma_sq)\n\n        # Compute intermediate matrices\n        A = tf.linalg.triangular_solve(L, kuf, lower=True) / sigma\n        AAT = tf.linalg.matmul(A, A, transpose_b=True)\n        B = add_noise_cov(AAT, tf.cast(1.0, AAT.dtype))\n        LB = tf.linalg.cholesky(B)\n\n        return self.CommonTensors(sigma_sq, sigma, A, B, LB, AAT, L)\n\n    def elbo(self) -&gt; tf.Tensor:\n        \"\"\"\n        Construct a tensorflow function to compute the bound on the marginal\n        likelihood. For a derivation of the terms in here, see the associated\n        SGPR notebook.\n        \"\"\"\n        common = self._common_calculation()\n        output_shape = tf.shape(self.data[-1])\n        num_data = to_default_float(output_shape[0])\n        output_dim = to_default_float(output_shape[1])\n        const = -0.5 * num_data * output_dim * np.log(2 * np.pi)\n        logdet = self.logdet_term(common)\n        quad = self.quad_term(common)\n        constraints = self.transform.constraints(self.inducing_variable.Z)\n        return const + logdet + quad + constraints\n\n    def predict_f(\n        self, Xnew: InputData, full_cov: bool = False, full_output_cov: bool = False\n    ) -&gt; MeanAndVariance:\n\n        # could copy into posterior into a fused version\n        \"\"\"\n        Compute the mean and variance of the latent function at some new points\n        Xnew. For a derivation of the terms in here, see the associated SGPR\n        notebook.\n        \"\"\"\n        X_data, Y_data = self.data\n\n        iv = self.inducing_variable.Z\n        iv = self.transform.expand(iv)\n\n        num_inducing = tf.shape(iv)[0]\n\n        err = Y_data - self.mean_function(X_data)\n        kuf = self.kernel(iv, X_data)\n        kuu = self.kernel(iv) + 1e-6 * tf.eye(num_inducing, dtype=iv.dtype)\n        Kus = self.kernel(iv, Xnew)\n        sigma = tf.sqrt(self.likelihood.variance)\n        L = tf.linalg.cholesky(kuu)\n        A = tf.linalg.triangular_solve(L, kuf, lower=True) / sigma\n        B = tf.linalg.matmul(A, A, transpose_b=True) + tf.eye(\n            num_inducing, dtype=default_float()\n        )  # cache qinv\n        LB = tf.linalg.cholesky(B)\n        Aerr = tf.linalg.matmul(A, err)\n        c = tf.linalg.triangular_solve(LB, Aerr, lower=True) / sigma\n        tmp1 = tf.linalg.triangular_solve(L, Kus, lower=True)\n        tmp2 = tf.linalg.triangular_solve(LB, tmp1, lower=True)\n        mean = tf.linalg.matmul(tmp2, c, transpose_a=True)\n        if full_cov:\n            var = (\n                self.kernel(Xnew)\n                + tf.linalg.matmul(tmp2, tmp2, transpose_a=True)\n                - tf.linalg.matmul(tmp1, tmp1, transpose_a=True)\n            )\n            var = tf.tile(var[None, ...], [self.num_latent_gps, 1, 1])  # [P, N, N]\n        else:\n            var = (\n                self.kernel(Xnew, full_cov=False)\n                + tf.reduce_sum(tf.square(tmp2), 0)\n                - tf.reduce_sum(tf.square(tmp1), 0)\n            )\n            var = tf.tile(var[:, None], [1, self.num_latent_gps])\n\n        return mean + self.mean_function(Xnew), var\n</code></pre>"},{"location":"api/models/core/AugmentedSGPR.html#sgptools.models.core.augmented_sgpr.AugmentedSGPR.elbo","title":"<code>elbo()</code>","text":"<p>Construct a tensorflow function to compute the bound on the marginal likelihood. For a derivation of the terms in here, see the associated SGPR notebook.</p> Source code in <code>sgptools/models/core/augmented_sgpr.py</code> <pre><code>def elbo(self) -&gt; tf.Tensor:\n    \"\"\"\n    Construct a tensorflow function to compute the bound on the marginal\n    likelihood. For a derivation of the terms in here, see the associated\n    SGPR notebook.\n    \"\"\"\n    common = self._common_calculation()\n    output_shape = tf.shape(self.data[-1])\n    num_data = to_default_float(output_shape[0])\n    output_dim = to_default_float(output_shape[1])\n    const = -0.5 * num_data * output_dim * np.log(2 * np.pi)\n    logdet = self.logdet_term(common)\n    quad = self.quad_term(common)\n    constraints = self.transform.constraints(self.inducing_variable.Z)\n    return const + logdet + quad + constraints\n</code></pre>"},{"location":"api/models/core/AugmentedSGPR.html#sgptools.models.core.augmented_sgpr.AugmentedSGPR.predict_f","title":"<code>predict_f(Xnew, full_cov=False, full_output_cov=False)</code>","text":"<p>Compute the mean and variance of the latent function at some new points Xnew. For a derivation of the terms in here, see the associated SGPR notebook.</p> Source code in <code>sgptools/models/core/augmented_sgpr.py</code> <pre><code>def predict_f(\n    self, Xnew: InputData, full_cov: bool = False, full_output_cov: bool = False\n) -&gt; MeanAndVariance:\n\n    # could copy into posterior into a fused version\n    \"\"\"\n    Compute the mean and variance of the latent function at some new points\n    Xnew. For a derivation of the terms in here, see the associated SGPR\n    notebook.\n    \"\"\"\n    X_data, Y_data = self.data\n\n    iv = self.inducing_variable.Z\n    iv = self.transform.expand(iv)\n\n    num_inducing = tf.shape(iv)[0]\n\n    err = Y_data - self.mean_function(X_data)\n    kuf = self.kernel(iv, X_data)\n    kuu = self.kernel(iv) + 1e-6 * tf.eye(num_inducing, dtype=iv.dtype)\n    Kus = self.kernel(iv, Xnew)\n    sigma = tf.sqrt(self.likelihood.variance)\n    L = tf.linalg.cholesky(kuu)\n    A = tf.linalg.triangular_solve(L, kuf, lower=True) / sigma\n    B = tf.linalg.matmul(A, A, transpose_b=True) + tf.eye(\n        num_inducing, dtype=default_float()\n    )  # cache qinv\n    LB = tf.linalg.cholesky(B)\n    Aerr = tf.linalg.matmul(A, err)\n    c = tf.linalg.triangular_solve(LB, Aerr, lower=True) / sigma\n    tmp1 = tf.linalg.triangular_solve(L, Kus, lower=True)\n    tmp2 = tf.linalg.triangular_solve(LB, tmp1, lower=True)\n    mean = tf.linalg.matmul(tmp2, c, transpose_a=True)\n    if full_cov:\n        var = (\n            self.kernel(Xnew)\n            + tf.linalg.matmul(tmp2, tmp2, transpose_a=True)\n            - tf.linalg.matmul(tmp1, tmp1, transpose_a=True)\n        )\n        var = tf.tile(var[None, ...], [self.num_latent_gps, 1, 1])  # [P, N, N]\n    else:\n        var = (\n            self.kernel(Xnew, full_cov=False)\n            + tf.reduce_sum(tf.square(tmp2), 0)\n            - tf.reduce_sum(tf.square(tmp1), 0)\n        )\n        var = tf.tile(var[:, None], [1, self.num_latent_gps])\n\n    return mean + self.mean_function(Xnew), var\n</code></pre>"},{"location":"api/models/core/AugmentedSGPR.html#sgptools.models.core.augmented_sgpr.AugmentedSGPR.update","title":"<code>update(noise_variance, kernel)</code>","text":"<p>Update SGP noise variance and kernel function parameters</p> <p>Parameters:</p> Name Type Description Default <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required Source code in <code>sgptools/models/core/augmented_sgpr.py</code> <pre><code>def update(self, noise_variance, kernel):\n    \"\"\"Update SGP noise variance and kernel function parameters\n\n    Args:\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n    \"\"\"\n    self.likelihood.variance.assign(noise_variance)\n    self.kernel.lengthscales.assign(kernel.lengthscales)\n    self.kernel.variance.assign(kernel.variance)\n</code></pre>"},{"location":"api/models/core/OSGPR.html","title":"OSGPR","text":""},{"location":"api/models/core/OSGPR.html#sgptools.models.core.osgpr","title":"<code>sgptools.models.core.osgpr</code>","text":"<p>Provides a streaming sparse Gaussian process model along with initialization function</p>"},{"location":"api/models/core/OSGPR.html#sgptools.models.core.osgpr.OSGPR_VFE","title":"<code>OSGPR_VFE</code>","text":"<p>               Bases: <code>GPModel</code>, <code>InternalDataTrainingLossMixin</code></p> <p>Online Sparse Variational GP regression model from streaming_sparse_gp</p> Refer to the following paper for more details <ul> <li>Streaming Gaussian process approximations [Bui et al., 2017]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>tuple</code> <p>(X, y) ndarrays with inputs (n, d) and labels (n, 1)</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>mu_old</code> <code>ndarray</code> <p>mean of old <code>q(u)</code>; here <code>u</code> are the latents corresponding to the inducing points <code>Z_old</code></p> required <code>Su_old</code> <code>ndarray</code> <p>posterior covariance of old <code>q(u)</code></p> required <code>Kaa_old</code> <code>ndarray</code> <p>prior covariance of old <code>q(u)</code></p> required <code>Z_old</code> <code>ndarray</code> <p>(m_old, d): Old initial inducing points</p> required <code>Z</code> <code>ndarray</code> <p>(m_new, d): New initial inducing points</p> required <code>mean_function</code> <code>function</code> <p>GP mean function</p> <code>None</code> Source code in <code>sgptools/models/core/osgpr.py</code> <pre><code>class OSGPR_VFE(GPModel, InternalDataTrainingLossMixin):\n    \"\"\"Online Sparse Variational GP regression model from [streaming_sparse_gp](https://github.com/thangbui/streaming_sparse_gp/tree/master)\n\n    Refer to the following paper for more details:\n        - Streaming Gaussian process approximations [Bui et al., 2017]\n\n    Args:\n        data (tuple): (X, y) ndarrays with inputs (n, d) and labels (n, 1)\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        mu_old (ndarray): mean of old `q(u)`; here `u` are the latents corresponding to the inducing points `Z_old`\n        Su_old (ndarray): posterior covariance of old `q(u)`\n        Kaa_old (ndarray): prior covariance of old `q(u)`\n        Z_old (ndarray): (m_old, d): Old initial inducing points\n        Z (ndarray): (m_new, d): New initial inducing points\n        mean_function (function): GP mean function\n    \"\"\"\n    def __init__(self, data, kernel, mu_old, Su_old, Kaa_old, Z_old, Z, mean_function=None):\n        self.X, self.Y = self.data = gpflow.models.util.data_input_to_tensor(data)\n        likelihood = gpflow.likelihoods.Gaussian()\n        num_latent_gps = GPModel.calc_num_latent_gps_from_data(data, kernel, likelihood)\n        super().__init__(kernel, likelihood, mean_function, num_latent_gps)\n\n        self.inducing_variable = InducingPoints(Z)\n        self.num_data = self.X.shape[0]\n\n        self.mu_old = tf.Variable(mu_old, shape=tf.TensorShape(None), trainable=False)\n        self.M_old = Z_old.shape[0]\n        self.Su_old = tf.Variable(Su_old, shape=tf.TensorShape(None), trainable=False)\n        self.Kaa_old = tf.Variable(Kaa_old, shape=tf.TensorShape(None), trainable=False)\n        self.Z_old = tf.Variable(Z_old, shape=tf.TensorShape(None), trainable=False)\n\n    def update(self, data):\n        \"\"\"Configure the OSGPR to adapt to a new batch of data. \n        Note: The OSGPR needs to be trained using gradient-based approaches after update.\n\n        Args:\n            data (tuple): (X, y) ndarrays with new batch of inputs (n, d) and labels (n, 1)\n        \"\"\"\n        self.X, self.Y = self.data = gpflow.models.util.data_input_to_tensor(data)\n        self.num_data = self.X.shape[0]\n\n        self.Z_old = tf.Variable(self.inducing_variable.Z.numpy(), \n                                 shape=tf.TensorShape(None), \n                                 trainable=False)\n\n        # Get posterior mean and covariance for the old inducing points\n        mu_old, Su_old = self.predict_f(self.Z_old, full_cov=True)\n        self.mu_old = tf.Variable(mu_old, shape=tf.TensorShape(None), trainable=False)\n        self.Su_old = tf.Variable(Su_old, shape=tf.TensorShape(None), trainable=False)\n\n        # Get the prior covariance matrix for the old inducing points\n        Kaa_old = self.kernel(self.Z_old)\n        self.Kaa_old = tf.Variable(Kaa_old, shape=tf.TensorShape(None), trainable=False)\n\n    def _common_terms(self):\n        Mb = self.inducing_variable.num_inducing\n        Ma = self.M_old\n        # jitter = gpflow.default_jitter()\n        jitter = gpflow.utilities.to_default_float(1e-4)\n        sigma2 = self.likelihood.variance\n        sigma = tf.sqrt(sigma2)\n\n        Saa = self.Su_old\n        ma = self.mu_old\n\n        # a is old inducing points, b is new\n        # f is training points\n        # s is test points\n        Kbf = covariances.Kuf(self.inducing_variable, self.kernel, self.X)\n        Kbb = covariances.Kuu(self.inducing_variable, self.kernel, jitter=jitter)\n        Kba = covariances.Kuf(self.inducing_variable, self.kernel, self.Z_old)\n        Kaa_cur = gpflow.utilities.add_noise_cov(self.kernel(self.Z_old), jitter)\n        Kaa = gpflow.utilities.add_noise_cov(self.Kaa_old, jitter)\n\n        err = self.Y - self.mean_function(self.X)\n\n        Sainv_ma = tf.linalg.solve(Saa, ma)\n        Sinv_y = self.Y / sigma2\n        c1 = tf.matmul(Kbf, Sinv_y)\n        c2 = tf.matmul(Kba, Sainv_ma)\n        c = c1 + c2\n\n        Lb = tf.linalg.cholesky(Kbb)\n        Lbinv_c = tf.linalg.triangular_solve(Lb, c, lower=True)\n        Lbinv_Kba = tf.linalg.triangular_solve(Lb, Kba, lower=True)\n        Lbinv_Kbf = tf.linalg.triangular_solve(Lb, Kbf, lower=True) / sigma\n        d1 = tf.matmul(Lbinv_Kbf, Lbinv_Kbf, transpose_b=True)\n\n        LSa = tf.linalg.cholesky(Saa)\n        Kab_Lbinv = tf.linalg.matrix_transpose(Lbinv_Kba)\n        LSainv_Kab_Lbinv = tf.linalg.triangular_solve(\n            LSa, Kab_Lbinv, lower=True)\n        d2 = tf.matmul(LSainv_Kab_Lbinv, LSainv_Kab_Lbinv, transpose_a=True)\n\n        La = tf.linalg.cholesky(Kaa)\n        Lainv_Kab_Lbinv = tf.linalg.triangular_solve(\n            La, Kab_Lbinv, lower=True)\n        d3 = tf.matmul(Lainv_Kab_Lbinv, Lainv_Kab_Lbinv, transpose_a=True)\n\n        D = tf.eye(Mb, dtype=gpflow.default_float()) + d1 + d2 - d3\n        D = gpflow.utilities.add_noise_cov(D, jitter)\n        LD = tf.linalg.cholesky(D)\n\n        LDinv_Lbinv_c = tf.linalg.triangular_solve(LD, Lbinv_c, lower=True)\n\n        return (Kbf, Kba, Kaa, Kaa_cur, La, Kbb, Lb, D, LD,\n                Lbinv_Kba, LDinv_Lbinv_c, err, d1)\n\n    def maximum_log_likelihood_objective(self):\n        \"\"\"\n        Construct a tensorflow function to compute the bound on the marginal\n        likelihood. \n        \"\"\"\n\n        Mb = self.inducing_variable.num_inducing\n        Ma = self.M_old\n        jitter = gpflow.default_jitter()\n        # jitter = gpflow.utilities.to_default_float(1e-4)\n        sigma2 = self.likelihood.variance\n        sigma = tf.sqrt(sigma2)\n        N = self.num_data\n\n        Saa = self.Su_old\n        ma = self.mu_old\n\n        # a is old inducing points, b is new\n        # f is training points\n        Kfdiag = self.kernel(self.X, full_cov=False)\n        (Kbf, Kba, Kaa, Kaa_cur, La, Kbb, Lb, D, LD,\n            Lbinv_Kba, LDinv_Lbinv_c, err, Qff) = self._common_terms()\n\n        LSa = tf.linalg.cholesky(Saa)\n        Lainv_ma = tf.linalg.triangular_solve(LSa, ma, lower=True)\n\n        # constant term\n        bound = -0.5 * N * np.log(2 * np.pi)\n        # quadratic term\n        bound += -0.5 * tf.reduce_sum(tf.square(err)) / sigma2\n        # bound += -0.5 * tf.reduce_sum(ma * Sainv_ma)\n        bound += -0.5 * tf.reduce_sum(tf.square(Lainv_ma))\n        bound += 0.5 * tf.reduce_sum(tf.square(LDinv_Lbinv_c))\n        # log det term\n        bound += -0.5 * N * tf.reduce_sum(tf.math.log(sigma2))\n        bound += - tf.reduce_sum(tf.math.log(tf.linalg.diag_part(LD)))\n\n        # delta 1: trace term\n        bound += -0.5 * tf.reduce_sum(Kfdiag) / sigma2\n        bound += 0.5 * tf.reduce_sum(tf.linalg.diag_part(Qff))\n\n        # delta 2: a and b difference\n        bound += tf.reduce_sum(tf.math.log(tf.linalg.diag_part(La)))\n        bound += - tf.reduce_sum(tf.math.log(tf.linalg.diag_part(LSa)))\n\n        Kaadiff = Kaa_cur - tf.matmul(Lbinv_Kba, Lbinv_Kba, transpose_a=True)\n        Sainv_Kaadiff = tf.linalg.solve(Saa, Kaadiff)\n        Kainv_Kaadiff = tf.linalg.solve(Kaa, Kaadiff)\n\n        bound += -0.5 * tf.reduce_sum(\n            tf.linalg.diag_part(Sainv_Kaadiff) - tf.linalg.diag_part(Kainv_Kaadiff))\n\n        return bound\n\n    def predict_f(self, Xnew, full_cov=False):\n        \"\"\"\n        Compute the mean and variance of the latent function at some new points\n        Xnew. \n        \"\"\"\n\n        # jitter = gpflow.default_jitter()\n        jitter = gpflow.utilities.to_default_float(1e-4)\n\n        # a is old inducing points, b is new\n        # f is training points\n        # s is test points\n        Kbs = covariances.Kuf(self.inducing_variable, self.kernel, Xnew)\n        (Kbf, Kba, Kaa, Kaa_cur, La, Kbb, Lb, D, LD,\n            Lbinv_Kba, LDinv_Lbinv_c, err, Qff) = self._common_terms()\n\n        Lbinv_Kbs = tf.linalg.triangular_solve(Lb, Kbs, lower=True)\n        LDinv_Lbinv_Kbs = tf.linalg.triangular_solve(LD, Lbinv_Kbs, lower=True)\n        mean = tf.matmul(LDinv_Lbinv_Kbs, LDinv_Lbinv_c, transpose_a=True)\n\n        if full_cov:\n            Kss = self.kernel(Xnew) + jitter * tf.eye(tf.shape(Xnew)[0], dtype=gpflow.default_float())\n            var1 = Kss\n            var2 = - tf.matmul(Lbinv_Kbs, Lbinv_Kbs, transpose_a=True)\n            var3 = tf.matmul(LDinv_Lbinv_Kbs, LDinv_Lbinv_Kbs, transpose_a=True)\n            var = var1 + var2 + var3\n        else:\n            var1 = self.kernel(Xnew, full_cov=False)\n            var2 = -tf.reduce_sum(tf.square(Lbinv_Kbs), axis=0)\n            var3 = tf.reduce_sum(tf.square(LDinv_Lbinv_Kbs), axis=0)\n            var = var1 + var2 + var3\n\n        return mean + self.mean_function(Xnew), var\n</code></pre>"},{"location":"api/models/core/OSGPR.html#sgptools.models.core.osgpr.OSGPR_VFE.maximum_log_likelihood_objective","title":"<code>maximum_log_likelihood_objective()</code>","text":"<p>Construct a tensorflow function to compute the bound on the marginal likelihood.</p> Source code in <code>sgptools/models/core/osgpr.py</code> <pre><code>def maximum_log_likelihood_objective(self):\n    \"\"\"\n    Construct a tensorflow function to compute the bound on the marginal\n    likelihood. \n    \"\"\"\n\n    Mb = self.inducing_variable.num_inducing\n    Ma = self.M_old\n    jitter = gpflow.default_jitter()\n    # jitter = gpflow.utilities.to_default_float(1e-4)\n    sigma2 = self.likelihood.variance\n    sigma = tf.sqrt(sigma2)\n    N = self.num_data\n\n    Saa = self.Su_old\n    ma = self.mu_old\n\n    # a is old inducing points, b is new\n    # f is training points\n    Kfdiag = self.kernel(self.X, full_cov=False)\n    (Kbf, Kba, Kaa, Kaa_cur, La, Kbb, Lb, D, LD,\n        Lbinv_Kba, LDinv_Lbinv_c, err, Qff) = self._common_terms()\n\n    LSa = tf.linalg.cholesky(Saa)\n    Lainv_ma = tf.linalg.triangular_solve(LSa, ma, lower=True)\n\n    # constant term\n    bound = -0.5 * N * np.log(2 * np.pi)\n    # quadratic term\n    bound += -0.5 * tf.reduce_sum(tf.square(err)) / sigma2\n    # bound += -0.5 * tf.reduce_sum(ma * Sainv_ma)\n    bound += -0.5 * tf.reduce_sum(tf.square(Lainv_ma))\n    bound += 0.5 * tf.reduce_sum(tf.square(LDinv_Lbinv_c))\n    # log det term\n    bound += -0.5 * N * tf.reduce_sum(tf.math.log(sigma2))\n    bound += - tf.reduce_sum(tf.math.log(tf.linalg.diag_part(LD)))\n\n    # delta 1: trace term\n    bound += -0.5 * tf.reduce_sum(Kfdiag) / sigma2\n    bound += 0.5 * tf.reduce_sum(tf.linalg.diag_part(Qff))\n\n    # delta 2: a and b difference\n    bound += tf.reduce_sum(tf.math.log(tf.linalg.diag_part(La)))\n    bound += - tf.reduce_sum(tf.math.log(tf.linalg.diag_part(LSa)))\n\n    Kaadiff = Kaa_cur - tf.matmul(Lbinv_Kba, Lbinv_Kba, transpose_a=True)\n    Sainv_Kaadiff = tf.linalg.solve(Saa, Kaadiff)\n    Kainv_Kaadiff = tf.linalg.solve(Kaa, Kaadiff)\n\n    bound += -0.5 * tf.reduce_sum(\n        tf.linalg.diag_part(Sainv_Kaadiff) - tf.linalg.diag_part(Kainv_Kaadiff))\n\n    return bound\n</code></pre>"},{"location":"api/models/core/OSGPR.html#sgptools.models.core.osgpr.OSGPR_VFE.predict_f","title":"<code>predict_f(Xnew, full_cov=False)</code>","text":"<p>Compute the mean and variance of the latent function at some new points Xnew.</p> Source code in <code>sgptools/models/core/osgpr.py</code> <pre><code>def predict_f(self, Xnew, full_cov=False):\n    \"\"\"\n    Compute the mean and variance of the latent function at some new points\n    Xnew. \n    \"\"\"\n\n    # jitter = gpflow.default_jitter()\n    jitter = gpflow.utilities.to_default_float(1e-4)\n\n    # a is old inducing points, b is new\n    # f is training points\n    # s is test points\n    Kbs = covariances.Kuf(self.inducing_variable, self.kernel, Xnew)\n    (Kbf, Kba, Kaa, Kaa_cur, La, Kbb, Lb, D, LD,\n        Lbinv_Kba, LDinv_Lbinv_c, err, Qff) = self._common_terms()\n\n    Lbinv_Kbs = tf.linalg.triangular_solve(Lb, Kbs, lower=True)\n    LDinv_Lbinv_Kbs = tf.linalg.triangular_solve(LD, Lbinv_Kbs, lower=True)\n    mean = tf.matmul(LDinv_Lbinv_Kbs, LDinv_Lbinv_c, transpose_a=True)\n\n    if full_cov:\n        Kss = self.kernel(Xnew) + jitter * tf.eye(tf.shape(Xnew)[0], dtype=gpflow.default_float())\n        var1 = Kss\n        var2 = - tf.matmul(Lbinv_Kbs, Lbinv_Kbs, transpose_a=True)\n        var3 = tf.matmul(LDinv_Lbinv_Kbs, LDinv_Lbinv_Kbs, transpose_a=True)\n        var = var1 + var2 + var3\n    else:\n        var1 = self.kernel(Xnew, full_cov=False)\n        var2 = -tf.reduce_sum(tf.square(Lbinv_Kbs), axis=0)\n        var3 = tf.reduce_sum(tf.square(LDinv_Lbinv_Kbs), axis=0)\n        var = var1 + var2 + var3\n\n    return mean + self.mean_function(Xnew), var\n</code></pre>"},{"location":"api/models/core/OSGPR.html#sgptools.models.core.osgpr.OSGPR_VFE.update","title":"<code>update(data)</code>","text":"<p>Configure the OSGPR to adapt to a new batch of data.  Note: The OSGPR needs to be trained using gradient-based approaches after update.</p> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>tuple</code> <p>(X, y) ndarrays with new batch of inputs (n, d) and labels (n, 1)</p> required Source code in <code>sgptools/models/core/osgpr.py</code> <pre><code>def update(self, data):\n    \"\"\"Configure the OSGPR to adapt to a new batch of data. \n    Note: The OSGPR needs to be trained using gradient-based approaches after update.\n\n    Args:\n        data (tuple): (X, y) ndarrays with new batch of inputs (n, d) and labels (n, 1)\n    \"\"\"\n    self.X, self.Y = self.data = gpflow.models.util.data_input_to_tensor(data)\n    self.num_data = self.X.shape[0]\n\n    self.Z_old = tf.Variable(self.inducing_variable.Z.numpy(), \n                             shape=tf.TensorShape(None), \n                             trainable=False)\n\n    # Get posterior mean and covariance for the old inducing points\n    mu_old, Su_old = self.predict_f(self.Z_old, full_cov=True)\n    self.mu_old = tf.Variable(mu_old, shape=tf.TensorShape(None), trainable=False)\n    self.Su_old = tf.Variable(Su_old, shape=tf.TensorShape(None), trainable=False)\n\n    # Get the prior covariance matrix for the old inducing points\n    Kaa_old = self.kernel(self.Z_old)\n    self.Kaa_old = tf.Variable(Kaa_old, shape=tf.TensorShape(None), trainable=False)\n</code></pre>"},{"location":"api/models/core/OSGPR.html#sgptools.models.core.osgpr.init_osgpr","title":"<code>init_osgpr(X_train, num_inducing=10, lengthscales=1.0, variance=1.0, noise_variance=0.001, kernel=None)</code>","text":"<p>Initialize a VFE OSGPR model with an RBF kernel with  unit variance and lengthcales, and 0.001 noise variance. Used in the Online Continuous SGP approach. </p> <p>Parameters:</p> Name Type Description Default <code>X_train</code> <code>ndarray</code> <p>(n, d); Unlabeled random sampled training points.              They only effect the initial inducing point locations,              i.e., limits them to the bounds of the data</p> required <code>num_inducing</code> <code>int</code> <p>Number of inducing points</p> <code>10</code> <code>lengthscales</code> <code>float or list</code> <p>Kernel lengthscale(s), if passed as a list,                      each element corresponds to each data dimension</p> <code>1.0</code> <code>variance</code> <code>float</code> <p>Kernel variance</p> <code>1.0</code> <code>noise_variance</code> <code>float</code> <p>Data noise variance</p> <code>0.001</code> <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> <code>None</code> <p>Returns:</p> Name Type Description <code>online_param</code> <code>OSGPR_VFE</code> <p>Initialized online sparse Gaussian process model</p> Source code in <code>sgptools/models/core/osgpr.py</code> <pre><code>def init_osgpr(X_train, \n               num_inducing=10, \n               lengthscales=1.0, \n               variance=1.0,\n               noise_variance=0.001,\n               kernel=None):\n    \"\"\"Initialize a VFE OSGPR model with an RBF kernel with \n    unit variance and lengthcales, and 0.001 noise variance.\n    Used in the Online Continuous SGP approach. \n\n    Args:\n        X_train (ndarray): (n, d); Unlabeled random sampled training points. \n                        They only effect the initial inducing point locations, \n                        i.e., limits them to the bounds of the data\n        num_inducing (int): Number of inducing points\n        lengthscales (float or list): Kernel lengthscale(s), if passed as a list, \n                                each element corresponds to each data dimension\n        variance (float): Kernel variance\n        noise_variance (float): Data noise variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n\n    Returns:\n        online_param (OSGPR_VFE): Initialized online sparse Gaussian process model\n    \"\"\"\n\n    if kernel is None:\n        kernel = gpflow.kernels.SquaredExponential(lengthscales=lengthscales, \n                                                   variance=variance)\n\n    y_train = np.zeros((len(X_train), 1), dtype=X_train.dtype)\n    Z_init = get_inducing_pts(X_train, num_inducing)\n    init_param = gpflow.models.SGPR((X_train, y_train),\n                                    kernel, \n                                    inducing_variable=Z_init, \n                                    noise_variance=noise_variance)\n\n    # Initialize the OSGPR model using the parameters from the SGPR model\n    # The X_train and y_train here will be overwritten in the online phase \n    X_train = np.array([[0, 0], [0, 0]])\n    y_train = np.array([0, 0]).reshape(-1, 1)\n    Zopt = init_param.inducing_variable.Z.numpy()\n    mu, Su = init_param.predict_f(Zopt, full_cov=True)\n    Kaa = init_param.kernel(Zopt)\n    online_param = OSGPR_VFE((X_train[:2], y_train[:2]),\n                             init_param.kernel,\n                             mu, Su[0], Kaa,\n                             Zopt, Zopt)\n    online_param.likelihood.variance.assign(init_param.likelihood.variance)\n\n    return online_param\n</code></pre>"},{"location":"api/models/core/Transformations.html","title":"Transformations","text":""},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations","title":"<code>sgptools.models.core.transformations</code>","text":"<p>Provides transforms to model complex sensor field of views and handle informative path planning</p>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.IPPTransform","title":"<code>IPPTransform</code>","text":"<p>               Bases: <code>Transform</code></p> <p>Transform to model IPP problems</p> Usage details <ul> <li>For point sensing, set <code>sampling_rate = 2</code></li> <li>For continuous sensing, set <code>sampling_rate &gt; 2</code> (account for the information along the path)</li> <li>For continuous sensing with aggregation, set <code>sampling_rate &gt; 2</code> and <code>aggregate_fov = True</code> (faster but solution quality is a bit diminished)</li> <li>If using a non-point FoV model with continuous sampling, only the FoV inducing points are aggregated</li> <li>For multi-robot case, set <code>num_robots &gt; 1</code></li> <li>For onlineIPP use <code>update_fixed</code> to freeze the visited waypoints</li> </ul> <p>Parameters:</p> Name Type Description Default <code>sampling_rate</code> <code>int</code> <p>Number of points to sample between each pair of inducing points</p> <code>2</code> <code>distance_budget</code> <code>float</code> <p>Distance budget for the path</p> <code>None</code> <code>num_robots</code> <code>int</code> <p>Number of robots</p> <code>1</code> <code>Xu_fixed</code> <code>ndarray</code> <p>(num_robots, num_visited, num_dim); Visited waypoints that don't need to be optimized</p> <code>None</code> <code>num_dim</code> <code>int</code> <p>Number of dimensions of the inducing points</p> <code>2</code> <code>sensor_model</code> <code>Transform</code> <p>Transform object to expand each inducing point to <code>p</code> points                        approximating each sensor's FoV</p> <code>None</code> <code>aggregate_fov</code> <code>bool</code> <p>Used only when sampling_rate &gt; 2, i.e., when using a continuous sensing model.                    If <code>True</code>, covariances corresponding to interpolated inducing points along each edge                   of the path are aggregated to reduce the matrix inversion cost.</p> <code>False</code> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>class IPPTransform(Transform):\n    \"\"\"Transform to model IPP problems\n\n    Usage details: \n        * For point sensing, set `sampling_rate = 2`\n        * For continuous sensing, set `sampling_rate &gt; 2` (account for the information along the path)\n        * For continuous sensing with aggregation, set `sampling_rate &gt; 2` and `aggregate_fov = True` (faster but solution quality is a bit diminished)\n        * If using a non-point FoV model with continuous sampling, only the FoV inducing points are aggregated\n        * For multi-robot case, set `num_robots &gt; 1`\n        * For onlineIPP use `update_fixed` to freeze the visited waypoints\n\n    Args:\n        sampling_rate (int): Number of points to sample between each pair of inducing points\n        distance_budget (float): Distance budget for the path\n        num_robots (int): Number of robots\n        Xu_fixed (ndarray): (num_robots, num_visited, num_dim); Visited waypoints that don't need to be optimized\n        num_dim (int): Number of dimensions of the inducing points\n        sensor_model (Transform): Transform object to expand each inducing point to `p` points \n                                  approximating each sensor's FoV\n        aggregate_fov (bool): Used only when sampling_rate &gt; 2, i.e., when using a continuous sensing model. \n                              If `True`, covariances corresponding to interpolated inducing points along each edge\n                              of the path are aggregated to reduce the matrix inversion cost.\n    \"\"\"\n    def __init__(self, \n                 sampling_rate=2, \n                 distance_budget=None, \n                 num_robots=1,\n                 Xu_fixed=None,\n                 num_dim=2,\n                 sensor_model=None,\n                 aggregate_fov=False,\n                 **kwargs):\n        super().__init__(**kwargs)\n        if sampling_rate &lt; 2:\n            raise ValueError('Sampling rate must be greater than 2.')\n\n        self.sampling_rate = sampling_rate\n        self.distance_budget = distance_budget\n        self.num_robots = num_robots\n        self.num_dim = num_dim\n        self.sensor_model = sensor_model\n\n        # Set aggregation size to sampling rate if aggregate_fov is True\n        # and sampling rate is enabled (greater than 2)\n        if aggregate_fov:\n            if self.sensor_model is not None:\n                self.sensor_model.enable_aggregation()\n            elif sampling_rate &gt; 2:\n                self.aggregation_size = sampling_rate\n\n        # Initilize variable to store visited waypoints for onlineIPP\n        if Xu_fixed is not None:\n            self.Xu_fixed = tf.Variable(Xu_fixed, \n                                        shape=tf.TensorShape(None), \n                                        trainable=False)\n        else:\n            self.Xu_fixed = None\n\n    def update_Xu_fixed(self, Xu_fixed):\n        \"\"\"Function to update the visited waypoints\n\n        Args:\n            Xu_fixed (ndarray): numpy array (num_robots, num_visited_waypoints, num_dim)\n        \"\"\"\n        self.num_fixed = Xu_fixed.shape[1]\n        if self.Xu_fixed is not None:\n            self.Xu_fixed.assign(Xu_fixed)\n        else:\n            self.Xu_fixed = tf.Variable(Xu_fixed, \n                                        shape=tf.TensorShape(None), \n                                        trainable=False)\n\n    def expand(self, Xu, expand_sensor_model=True):\n        \"\"\"Sample points between each pair of inducing points to form the path\n\n        Args:\n            Xu (ndarray): (num_robots x num_inducing, num_dim); Inducing points in the num_dim dimensional space\n            expand_sensor_model (bool): Only add the fixed inducing points without other sensor/path transforms, \n                                        used for online IPP\n\n        Returns:\n            Xu (ndarray): Expansion transformed inducing points\n        \"\"\"\n        # If using single-robot offline IPP with point sensing, return inducing points as is.\n        if self.sampling_rate == 2 and self.Xu_fixed is None and self.sensor_model is None:\n            return Xu\n\n        Xu = tf.reshape(Xu, (self.num_robots, -1, self.num_dim))\n\n        # If using online IPP, add visited waypoints that won't be optimized anymore\n        if self.Xu_fixed is not None:\n            Xu = tf.concat([self.Xu_fixed, Xu[:, self.num_fixed:]], axis=1)\n\n        if not expand_sensor_model:\n            return tf.reshape(Xu, (-1, self.num_dim))\n\n        # Interpolate additional inducing points between waypoints to approximate \n        # the continuous data sensing model\n        if self.sampling_rate &gt; 2:\n            Xu = tf.linspace(Xu[:, :-1], Xu[:, 1:], self.sampling_rate)\n            Xu = tf.transpose(Xu, perm=[1, 2, 0, 3])\n            Xu = tf.reshape(Xu, (self.num_robots, -1, self.num_dim))\n\n        if self.sensor_model is not None:\n            Xu_ = []\n            for i in range(self.num_robots):\n                Xu_.append(self.sensor_model.expand(Xu[i]))\n            Xu = tf.concat(Xu_, axis=0)\n            return Xu\n\n        Xu = tf.reshape(Xu, (-1, self.num_dim))\n        return Xu\n\n    def aggregate(self, k):\n        \"\"\"Applies the aggregation transform to kernel matrices. Checks `sensor_model` \n           and uses the appropriate aggregation transform. \n\n        Args:\n            k (tensor): (mp, mp)/(mp, n); Kernel matrix. \n                        `m` is the number of inducing points,\n                        `p` is the number of points each inducing point is mapped,\n                        `n` is the number of training data points.\n\n        Returns:\n            k (tensor): (m, m)/(m, n); Aggregated kernel matrix\n        \"\"\"\n        if self.sensor_model is not None:\n            return self.sensor_model.aggregate(k)\n        else:\n            return super().aggregate(k)\n\n    def constraints(self, Xu):\n        \"\"\"Computes the distance constraint term that is added to the SGP's optimization function.\n        Each robot can be assigned a different distance budget.\n\n        Args:\n            Xu (ndarray): Inducing points from which to compute the distance constraints\n\n        Returns:\n            loss (float): distance constraint term\n        \"\"\"\n        if self.distance_budget is None:\n            return 0.\n        else:\n            # Only do fixed points expansion transform\n            Xu = self.expand(Xu, expand_sensor_model=False)\n            dist = self.distance(Xu)-self.distance_budget\n            dist = tf.reduce_sum(tf.nn.relu(dist))\n            loss = -dist*self.constraint_weight\n            return loss\n\n    def distance(self, Xu):\n        \"\"\"Computes the distance incured by sequentially visiting the inducing points\n        Args:\n            Xu (ndarray): (m, num_dim); Inducing points from which to compute the path lengths\n                          `m` is the number of inducing points\n                          `num_dim` dimension of the data collection environment\n        Returns:\n            dist (float or tensor of floats): path length(s)\n        \"\"\"\n        Xu = tf.reshape(Xu, (self.num_robots, -1, self.num_dim))\n        if self.sensor_model is not None:\n            dists = []\n            for i in range(self.num_robots):\n                dists.append(self.sensor_model.distance(Xu[i]))\n            dists = tf.concat(dists, axis=0)\n            return dists\n        else:\n            # Assumes 2D waypoints by default\n            dist = tf.norm(Xu[:, 1:, :2] - Xu[:, :-1, :2], axis=-1)\n            dist = tf.reduce_sum(dist, axis=1)\n            return dist\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.IPPTransform.aggregate","title":"<code>aggregate(k)</code>","text":"<p>Applies the aggregation transform to kernel matrices. Checks <code>sensor_model</code>     and uses the appropriate aggregation transform. </p> <p>Parameters:</p> Name Type Description Default <code>k</code> <code>tensor</code> <p>(mp, mp)/(mp, n); Kernel matrix.          <code>m</code> is the number of inducing points,         <code>p</code> is the number of points each inducing point is mapped,         <code>n</code> is the number of training data points.</p> required <p>Returns:</p> Name Type Description <code>k</code> <code>tensor</code> <p>(m, m)/(m, n); Aggregated kernel matrix</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def aggregate(self, k):\n    \"\"\"Applies the aggregation transform to kernel matrices. Checks `sensor_model` \n       and uses the appropriate aggregation transform. \n\n    Args:\n        k (tensor): (mp, mp)/(mp, n); Kernel matrix. \n                    `m` is the number of inducing points,\n                    `p` is the number of points each inducing point is mapped,\n                    `n` is the number of training data points.\n\n    Returns:\n        k (tensor): (m, m)/(m, n); Aggregated kernel matrix\n    \"\"\"\n    if self.sensor_model is not None:\n        return self.sensor_model.aggregate(k)\n    else:\n        return super().aggregate(k)\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.IPPTransform.constraints","title":"<code>constraints(Xu)</code>","text":"<p>Computes the distance constraint term that is added to the SGP's optimization function. Each robot can be assigned a different distance budget.</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>Inducing points from which to compute the distance constraints</p> required <p>Returns:</p> Name Type Description <code>loss</code> <code>float</code> <p>distance constraint term</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def constraints(self, Xu):\n    \"\"\"Computes the distance constraint term that is added to the SGP's optimization function.\n    Each robot can be assigned a different distance budget.\n\n    Args:\n        Xu (ndarray): Inducing points from which to compute the distance constraints\n\n    Returns:\n        loss (float): distance constraint term\n    \"\"\"\n    if self.distance_budget is None:\n        return 0.\n    else:\n        # Only do fixed points expansion transform\n        Xu = self.expand(Xu, expand_sensor_model=False)\n        dist = self.distance(Xu)-self.distance_budget\n        dist = tf.reduce_sum(tf.nn.relu(dist))\n        loss = -dist*self.constraint_weight\n        return loss\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.IPPTransform.distance","title":"<code>distance(Xu)</code>","text":"<p>Computes the distance incured by sequentially visiting the inducing points Args:     Xu (ndarray): (m, num_dim); Inducing points from which to compute the path lengths                   <code>m</code> is the number of inducing points                   <code>num_dim</code> dimension of the data collection environment Returns:     dist (float or tensor of floats): path length(s)</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def distance(self, Xu):\n    \"\"\"Computes the distance incured by sequentially visiting the inducing points\n    Args:\n        Xu (ndarray): (m, num_dim); Inducing points from which to compute the path lengths\n                      `m` is the number of inducing points\n                      `num_dim` dimension of the data collection environment\n    Returns:\n        dist (float or tensor of floats): path length(s)\n    \"\"\"\n    Xu = tf.reshape(Xu, (self.num_robots, -1, self.num_dim))\n    if self.sensor_model is not None:\n        dists = []\n        for i in range(self.num_robots):\n            dists.append(self.sensor_model.distance(Xu[i]))\n        dists = tf.concat(dists, axis=0)\n        return dists\n    else:\n        # Assumes 2D waypoints by default\n        dist = tf.norm(Xu[:, 1:, :2] - Xu[:, :-1, :2], axis=-1)\n        dist = tf.reduce_sum(dist, axis=1)\n        return dist\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.IPPTransform.expand","title":"<code>expand(Xu, expand_sensor_model=True)</code>","text":"<p>Sample points between each pair of inducing points to form the path</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(num_robots x num_inducing, num_dim); Inducing points in the num_dim dimensional space</p> required <code>expand_sensor_model</code> <code>bool</code> <p>Only add the fixed inducing points without other sensor/path transforms,                          used for online IPP</p> <code>True</code> <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>Expansion transformed inducing points</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def expand(self, Xu, expand_sensor_model=True):\n    \"\"\"Sample points between each pair of inducing points to form the path\n\n    Args:\n        Xu (ndarray): (num_robots x num_inducing, num_dim); Inducing points in the num_dim dimensional space\n        expand_sensor_model (bool): Only add the fixed inducing points without other sensor/path transforms, \n                                    used for online IPP\n\n    Returns:\n        Xu (ndarray): Expansion transformed inducing points\n    \"\"\"\n    # If using single-robot offline IPP with point sensing, return inducing points as is.\n    if self.sampling_rate == 2 and self.Xu_fixed is None and self.sensor_model is None:\n        return Xu\n\n    Xu = tf.reshape(Xu, (self.num_robots, -1, self.num_dim))\n\n    # If using online IPP, add visited waypoints that won't be optimized anymore\n    if self.Xu_fixed is not None:\n        Xu = tf.concat([self.Xu_fixed, Xu[:, self.num_fixed:]], axis=1)\n\n    if not expand_sensor_model:\n        return tf.reshape(Xu, (-1, self.num_dim))\n\n    # Interpolate additional inducing points between waypoints to approximate \n    # the continuous data sensing model\n    if self.sampling_rate &gt; 2:\n        Xu = tf.linspace(Xu[:, :-1], Xu[:, 1:], self.sampling_rate)\n        Xu = tf.transpose(Xu, perm=[1, 2, 0, 3])\n        Xu = tf.reshape(Xu, (self.num_robots, -1, self.num_dim))\n\n    if self.sensor_model is not None:\n        Xu_ = []\n        for i in range(self.num_robots):\n            Xu_.append(self.sensor_model.expand(Xu[i]))\n        Xu = tf.concat(Xu_, axis=0)\n        return Xu\n\n    Xu = tf.reshape(Xu, (-1, self.num_dim))\n    return Xu\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.IPPTransform.update_Xu_fixed","title":"<code>update_Xu_fixed(Xu_fixed)</code>","text":"<p>Function to update the visited waypoints</p> <p>Parameters:</p> Name Type Description Default <code>Xu_fixed</code> <code>ndarray</code> <p>numpy array (num_robots, num_visited_waypoints, num_dim)</p> required Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def update_Xu_fixed(self, Xu_fixed):\n    \"\"\"Function to update the visited waypoints\n\n    Args:\n        Xu_fixed (ndarray): numpy array (num_robots, num_visited_waypoints, num_dim)\n    \"\"\"\n    self.num_fixed = Xu_fixed.shape[1]\n    if self.Xu_fixed is not None:\n        self.Xu_fixed.assign(Xu_fixed)\n    else:\n        self.Xu_fixed = tf.Variable(Xu_fixed, \n                                    shape=tf.TensorShape(None), \n                                    trainable=False)\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareHeightTransform","title":"<code>SquareHeightTransform</code>","text":"<p>               Bases: <code>Transform</code></p> <p>Non-point Transform to model a height-dependent square FoV</p> <p>Parameters:</p> Name Type Description Default <code>num_side</code> <code>int</code> <p>Number of points along each side of the FoV</p> required <code>aggregate_fov</code> <code>bool</code> <p>If <code>True</code>, covariances corresponding to interpolated inducing points used to                    approximate the sensor FoV are aggregated to reduce the matrix inversion cost</p> <code>False</code> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>class SquareHeightTransform(Transform):\n    \"\"\"Non-point Transform to model a height-dependent square FoV\n\n    Args:\n        num_side (int): Number of points along each side of the FoV\n        aggregate_fov (bool): If `True`, covariances corresponding to interpolated inducing points used to \n                              approximate the sensor FoV are aggregated to reduce the matrix inversion cost\n    \"\"\"\n    def __init__(self, num_side, aggregate_fov=False, **kwargs):\n        super().__init__(**kwargs)\n        self.num_side = num_side\n\n        if aggregate_fov:\n            self.enable_aggregation()\n\n    def enable_aggregation(self, size=None):\n        \"\"\"Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the \n        covariance matrix size.\n\n        Args:\n            size (int): If None, all the interpolated inducing points within the FoV are aggregated. Alternatively, \n                        the number of inducing points to aggregate can be explicitly defined using this variable.\n        \"\"\"\n        if size is None:\n            self.aggregation_size = self.num_side**2\n        else:\n            self.aggregation_size = size\n\n    def expand(self, Xu):     \n        \"\"\"\n        Applies the expansion transform to the inducing points\n\n        Args:\n            Xu (ndarray): (m, 3); Inducing points in the 3D position space.\n                        `m` is the number of inducing points,\n                        `3` is the dimension of the space (x, y, z)\n\n        Returns:\n            Xu (ndarray): (mp, 2); Inducing points in input space.\n                        `p` is the number of points each inducing point is mapped \n                        to in order to form the FoV.\n        \"\"\"\n        x, y, h = tf.split(Xu, num_or_size_splits=3, axis=1)\n        x = tf.reshape(x, [-1,])\n        y = tf.reshape(y, [-1,])\n        h = tf.reshape(h, [-1,])\n\n        delta = h / (self.num_side - 1)\n\n        pts = []\n        for i in range(self.num_side):\n            pts.append(tf.linspace([x - h/2, y - (h/2) + (delta * i)], \n                                   [x + h/2, y - (h/2) + (delta * i)], \n                                   self.num_side, \n                                   axis=1))\n        xy = tf.concat(pts, axis=1)\n        xy = tf.transpose(xy, [2, 1, 0])\n        xy = tf.reshape(xy, [-1, 2])\n        xy = self._reshape(xy, tf.shape(Xu)[0])\n        return xy\n\n    def _reshape(self, X, num_inducing):\n        \"\"\"Reorder the inducing points to be in the correct order for aggregation with square height FoV\n\n        Args:\n            X (ndarray): (mp, 2); Inducing points in input space. `p` is the number of points each \n                        inducing point is mapped to in order to form the FoV.\n\n        Returns:\n            Xu (ndarray): (mp, 2); Reorder inducing points\n        \"\"\"\n        X = tf.reshape(X, (num_inducing, -1, self.num_side, self.num_side, 2))\n        X = tf.transpose(X, (0, 2, 1, 3, 4))\n        X = tf.reshape(X, (-1, 2))\n        return X\n\n    def distance(self, Xu):\n        \"\"\"Computes the distance incured by sequentially visiting the inducing points\n        Args:\n            Xu (ndarray): (m, 3); Inducing points from which to compute the path lengths.\n                          `m` is the number of inducing points.\n\n        Returns:\n            dist (float): path lengths\n        \"\"\"\n        Xu = tf.reshape(Xu, (-1, 3))\n        dist = tf.norm(Xu[1:] - Xu[:-1], axis=-1)\n        dist = tf.reduce_sum(dist, axis=0)\n        return dist\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareHeightTransform.distance","title":"<code>distance(Xu)</code>","text":"<p>Computes the distance incured by sequentially visiting the inducing points Args:     Xu (ndarray): (m, 3); Inducing points from which to compute the path lengths.                   <code>m</code> is the number of inducing points.</p> <p>Returns:</p> Name Type Description <code>dist</code> <code>float</code> <p>path lengths</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def distance(self, Xu):\n    \"\"\"Computes the distance incured by sequentially visiting the inducing points\n    Args:\n        Xu (ndarray): (m, 3); Inducing points from which to compute the path lengths.\n                      `m` is the number of inducing points.\n\n    Returns:\n        dist (float): path lengths\n    \"\"\"\n    Xu = tf.reshape(Xu, (-1, 3))\n    dist = tf.norm(Xu[1:] - Xu[:-1], axis=-1)\n    dist = tf.reduce_sum(dist, axis=0)\n    return dist\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareHeightTransform.enable_aggregation","title":"<code>enable_aggregation(size=None)</code>","text":"<p>Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the  covariance matrix size.</p> <p>Parameters:</p> Name Type Description Default <code>size</code> <code>int</code> <p>If None, all the interpolated inducing points within the FoV are aggregated. Alternatively,          the number of inducing points to aggregate can be explicitly defined using this variable.</p> <code>None</code> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def enable_aggregation(self, size=None):\n    \"\"\"Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the \n    covariance matrix size.\n\n    Args:\n        size (int): If None, all the interpolated inducing points within the FoV are aggregated. Alternatively, \n                    the number of inducing points to aggregate can be explicitly defined using this variable.\n    \"\"\"\n    if size is None:\n        self.aggregation_size = self.num_side**2\n    else:\n        self.aggregation_size = size\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareHeightTransform.expand","title":"<code>expand(Xu)</code>","text":"<p>Applies the expansion transform to the inducing points</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(m, 3); Inducing points in the 3D position space.         <code>m</code> is the number of inducing points,         <code>3</code> is the dimension of the space (x, y, z)</p> required <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(mp, 2); Inducing points in input space.         <code>p</code> is the number of points each inducing point is mapped          to in order to form the FoV.</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def expand(self, Xu):     \n    \"\"\"\n    Applies the expansion transform to the inducing points\n\n    Args:\n        Xu (ndarray): (m, 3); Inducing points in the 3D position space.\n                    `m` is the number of inducing points,\n                    `3` is the dimension of the space (x, y, z)\n\n    Returns:\n        Xu (ndarray): (mp, 2); Inducing points in input space.\n                    `p` is the number of points each inducing point is mapped \n                    to in order to form the FoV.\n    \"\"\"\n    x, y, h = tf.split(Xu, num_or_size_splits=3, axis=1)\n    x = tf.reshape(x, [-1,])\n    y = tf.reshape(y, [-1,])\n    h = tf.reshape(h, [-1,])\n\n    delta = h / (self.num_side - 1)\n\n    pts = []\n    for i in range(self.num_side):\n        pts.append(tf.linspace([x - h/2, y - (h/2) + (delta * i)], \n                               [x + h/2, y - (h/2) + (delta * i)], \n                               self.num_side, \n                               axis=1))\n    xy = tf.concat(pts, axis=1)\n    xy = tf.transpose(xy, [2, 1, 0])\n    xy = tf.reshape(xy, [-1, 2])\n    xy = self._reshape(xy, tf.shape(Xu)[0])\n    return xy\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareTransform","title":"<code>SquareTransform</code>","text":"<p>               Bases: <code>Transform</code></p> <p>Non-point Transform to model a square FoV. Only works for single robot cases. </p> <p>Parameters:</p> Name Type Description Default <code>length</code> <code>float</code> <p>Length of the square FoV</p> required <code>num_side</code> <code>int</code> <p>Number of points along each side of the FoV</p> required <code>aggregate_fov</code> <code>bool</code> <p>If <code>True</code>, covariances corresponding to interpolated inducing points used to                    approximate the sensor FoV are aggregated to reduce the matrix inversion cost</p> <code>False</code> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>class SquareTransform(Transform):\n    \"\"\"Non-point Transform to model a square FoV. Only works for single robot cases. \n\n    Args:\n        length (float): Length of the square FoV\n        num_side (int): Number of points along each side of the FoV\n        aggregate_fov (bool): If `True`, covariances corresponding to interpolated inducing points used to \n                              approximate the sensor FoV are aggregated to reduce the matrix inversion cost\n    \"\"\"\n    def __init__(self, length, num_side, aggregate_fov=False, **kwargs):\n        super().__init__(**kwargs)\n        self.length = length\n        self.num_side = num_side\n        self.length_factor=length/(self.num_side)\n        self.num_length = int(length/self.length_factor)\n\n        if aggregate_fov:\n            self.enable_aggregation()\n\n    def enable_aggregation(self, size=None):\n        \"\"\"Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the \n        covariance matrix size.\n\n        Args:\n            size (int): If None, all the interpolated inducing points within the FoV are aggregated. Alternatively, \n                        the number of inducing points to aggregate can be explicitly defined using this variable.\n        \"\"\"\n        if size is None:\n            self.aggregation_size = self.num_side**2\n        else:\n            self.aggregation_size = size\n\n    def expand(self, Xu):\n        \"\"\"Applies the expansion transformation to the inducing points\n\n        Args:\n            Xu (ndarray): (m, 3); Inducing points in the position and orientation space.\n                          `m` is the number of inducing points,\n                          `3` is the dimension of the space (x, y, angle in radians)\n\n        Returns:\n            Xu (ndarray): (mp, 2); Inducing points in input space.\n                        `p` is the number of points each inducing point is mapped \n                         to in order to form the FoV.\n        \"\"\"\n        x, y, theta = tf.split(Xu, num_or_size_splits=3, axis=1)\n        x     = tf.reshape(x, [-1,])\n        y     = tf.reshape(y, [-1,])\n        theta = tf.reshape(theta, [-1,])\n\n        points = []\n        for i in range(-int(np.floor((self.num_side)/2)), int(np.ceil((self.num_side)/2))):\n            points.append(tf.linspace([(x + (i * self.length_factor) * tf.cos(theta)) - self.length/2 * tf.cos(theta+np.pi/2), \n                                       (y + (i * self.length_factor) * tf.sin(theta)) - self.length/2 * tf.sin(theta+np.pi/2)], \n                                      [(x + (i * self.length_factor) * tf.cos(theta)) + self.length/2 * tf.cos(theta+np.pi/2), \n                                       (y + (i * self.length_factor) * tf.sin(theta)) + self.length/2 * tf.sin(theta+np.pi/2)], \n                                      self.num_side, axis=1))\n        xy = tf.concat(points, axis=1)\n        xy = tf.transpose(xy, [2, 1, 0])\n        xy = tf.reshape(xy, (-1, 2))\n        return xy\n\n    def distance(self, Xu):\n        \"\"\"Computes the distance incured by sequentially visiting the inducing points\n        Args:\n            Xu (ndarray): (m, 3); Inducing points from which to compute the path lengths.\n                          `m` is the number of inducing points.\n\n        Returns:\n            dist (float): path lengths\n        \"\"\"\n        Xu = tf.reshape(Xu, (-1, 3))[:, :2]\n        dist = tf.norm(Xu[1:] - Xu[:-1], axis=-1)\n        dist = tf.reduce_sum(dist, axis=0)\n        return dist\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareTransform.distance","title":"<code>distance(Xu)</code>","text":"<p>Computes the distance incured by sequentially visiting the inducing points Args:     Xu (ndarray): (m, 3); Inducing points from which to compute the path lengths.                   <code>m</code> is the number of inducing points.</p> <p>Returns:</p> Name Type Description <code>dist</code> <code>float</code> <p>path lengths</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def distance(self, Xu):\n    \"\"\"Computes the distance incured by sequentially visiting the inducing points\n    Args:\n        Xu (ndarray): (m, 3); Inducing points from which to compute the path lengths.\n                      `m` is the number of inducing points.\n\n    Returns:\n        dist (float): path lengths\n    \"\"\"\n    Xu = tf.reshape(Xu, (-1, 3))[:, :2]\n    dist = tf.norm(Xu[1:] - Xu[:-1], axis=-1)\n    dist = tf.reduce_sum(dist, axis=0)\n    return dist\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareTransform.enable_aggregation","title":"<code>enable_aggregation(size=None)</code>","text":"<p>Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the  covariance matrix size.</p> <p>Parameters:</p> Name Type Description Default <code>size</code> <code>int</code> <p>If None, all the interpolated inducing points within the FoV are aggregated. Alternatively,          the number of inducing points to aggregate can be explicitly defined using this variable.</p> <code>None</code> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def enable_aggregation(self, size=None):\n    \"\"\"Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the \n    covariance matrix size.\n\n    Args:\n        size (int): If None, all the interpolated inducing points within the FoV are aggregated. Alternatively, \n                    the number of inducing points to aggregate can be explicitly defined using this variable.\n    \"\"\"\n    if size is None:\n        self.aggregation_size = self.num_side**2\n    else:\n        self.aggregation_size = size\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareTransform.expand","title":"<code>expand(Xu)</code>","text":"<p>Applies the expansion transformation to the inducing points</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(m, 3); Inducing points in the position and orientation space.           <code>m</code> is the number of inducing points,           <code>3</code> is the dimension of the space (x, y, angle in radians)</p> required <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(mp, 2); Inducing points in input space.         <code>p</code> is the number of points each inducing point is mapped           to in order to form the FoV.</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def expand(self, Xu):\n    \"\"\"Applies the expansion transformation to the inducing points\n\n    Args:\n        Xu (ndarray): (m, 3); Inducing points in the position and orientation space.\n                      `m` is the number of inducing points,\n                      `3` is the dimension of the space (x, y, angle in radians)\n\n    Returns:\n        Xu (ndarray): (mp, 2); Inducing points in input space.\n                    `p` is the number of points each inducing point is mapped \n                     to in order to form the FoV.\n    \"\"\"\n    x, y, theta = tf.split(Xu, num_or_size_splits=3, axis=1)\n    x     = tf.reshape(x, [-1,])\n    y     = tf.reshape(y, [-1,])\n    theta = tf.reshape(theta, [-1,])\n\n    points = []\n    for i in range(-int(np.floor((self.num_side)/2)), int(np.ceil((self.num_side)/2))):\n        points.append(tf.linspace([(x + (i * self.length_factor) * tf.cos(theta)) - self.length/2 * tf.cos(theta+np.pi/2), \n                                   (y + (i * self.length_factor) * tf.sin(theta)) - self.length/2 * tf.sin(theta+np.pi/2)], \n                                  [(x + (i * self.length_factor) * tf.cos(theta)) + self.length/2 * tf.cos(theta+np.pi/2), \n                                   (y + (i * self.length_factor) * tf.sin(theta)) + self.length/2 * tf.sin(theta+np.pi/2)], \n                                  self.num_side, axis=1))\n    xy = tf.concat(points, axis=1)\n    xy = tf.transpose(xy, [2, 1, 0])\n    xy = tf.reshape(xy, (-1, 2))\n    return xy\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.Transform","title":"<code>Transform</code>","text":"<p>Base class for transformations of the inducing points, including expansion and aggregation transforms.</p> Refer to the following papers for more details <ul> <li>Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]</li> <li>Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>aggregation_size</code> <code>int</code> <p>Number of consecutive inducing points to aggregate</p> <code>None</code> <code>constraint_weight</code> <code>float</code> <p>Weight term that controls the importance of the                         constraint terms in the SGP's optimization objective</p> <code>1.0</code> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>class Transform:\n    \"\"\"Base class for transformations of the inducing points, including expansion and aggregation transforms.\n\n    Refer to the following papers for more details:\n        - Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]\n        - Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]\n\n    Args:\n        aggregation_size (int): Number of consecutive inducing points to aggregate\n        constraint_weight (float): Weight term that controls the importance of the \n                                   constraint terms in the SGP's optimization objective \n    \"\"\"\n    def __init__(self, \n                 aggregation_size=None, \n                 constraint_weight=1.0,\n                 **kwargs):\n        self.aggregation_size = aggregation_size\n        self.constraint_weight = constraint_weight\n\n    def expand(self, Xu):\n        \"\"\"Applies the expansion transform to the inducing points\n\n        Args:\n            Xu (ndarray): Expansion transformed inducing points\n        \"\"\"\n        return Xu\n\n    def aggregate(self, k):\n        \"\"\"Applies the aggregation transform to kernel matrices\n\n        Args:\n            k (tensor): (mp, mp)/(mp, n); Kernel matrix. \n                        `m` is the number of inducing points,\n                        `p` is the number of points each inducing point is mapped,\n                        `n` is the number of training data points.\n\n        Returns:\n            k (tensor): (m, m)/(m, n); Aggregated kernel matrix\n        \"\"\"\n        if self.aggregation_size is None:\n            return k\n\n        if k.shape[0] == k.shape[1]:\n            # Handle Kuu which is a square matrix\n            k = tf.expand_dims(tf.expand_dims(k, axis=0), axis=-1)\n            k = tf.nn.avg_pool(k,\n                               ksize=[1, self.aggregation_size, self.aggregation_size, 1],\n                               strides=[1, self.aggregation_size, self.aggregation_size, 1],\n                               padding='VALID')\n            k = tf.squeeze(k, axis=[0, -1])\n        else:\n            # Handle Kuf which is a rectangular matrix\n            k = tf.expand_dims(k, axis=0)\n            k = tf.nn.avg_pool(k,\n                               ksize=[1, self.aggregation_size, 1],\n                               strides=[1, self.aggregation_size, 1],\n                               padding='VALID')\n            k = tf.squeeze(k, axis=[0])\n        return k\n\n    def constraints(self, Xu):\n        \"\"\"Computes the constraint terms that are added to the SGP's optimization function\n\n        Args:\n            Xu (ndarray): Inducing points from which to compute the constraints\n\n        Returns:\n            c (float): constraint terms (eg., distance constraint)\n        \"\"\"\n        return 0.\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.Transform.aggregate","title":"<code>aggregate(k)</code>","text":"<p>Applies the aggregation transform to kernel matrices</p> <p>Parameters:</p> Name Type Description Default <code>k</code> <code>tensor</code> <p>(mp, mp)/(mp, n); Kernel matrix.          <code>m</code> is the number of inducing points,         <code>p</code> is the number of points each inducing point is mapped,         <code>n</code> is the number of training data points.</p> required <p>Returns:</p> Name Type Description <code>k</code> <code>tensor</code> <p>(m, m)/(m, n); Aggregated kernel matrix</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def aggregate(self, k):\n    \"\"\"Applies the aggregation transform to kernel matrices\n\n    Args:\n        k (tensor): (mp, mp)/(mp, n); Kernel matrix. \n                    `m` is the number of inducing points,\n                    `p` is the number of points each inducing point is mapped,\n                    `n` is the number of training data points.\n\n    Returns:\n        k (tensor): (m, m)/(m, n); Aggregated kernel matrix\n    \"\"\"\n    if self.aggregation_size is None:\n        return k\n\n    if k.shape[0] == k.shape[1]:\n        # Handle Kuu which is a square matrix\n        k = tf.expand_dims(tf.expand_dims(k, axis=0), axis=-1)\n        k = tf.nn.avg_pool(k,\n                           ksize=[1, self.aggregation_size, self.aggregation_size, 1],\n                           strides=[1, self.aggregation_size, self.aggregation_size, 1],\n                           padding='VALID')\n        k = tf.squeeze(k, axis=[0, -1])\n    else:\n        # Handle Kuf which is a rectangular matrix\n        k = tf.expand_dims(k, axis=0)\n        k = tf.nn.avg_pool(k,\n                           ksize=[1, self.aggregation_size, 1],\n                           strides=[1, self.aggregation_size, 1],\n                           padding='VALID')\n        k = tf.squeeze(k, axis=[0])\n    return k\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.Transform.constraints","title":"<code>constraints(Xu)</code>","text":"<p>Computes the constraint terms that are added to the SGP's optimization function</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>Inducing points from which to compute the constraints</p> required <p>Returns:</p> Name Type Description <code>c</code> <code>float</code> <p>constraint terms (eg., distance constraint)</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def constraints(self, Xu):\n    \"\"\"Computes the constraint terms that are added to the SGP's optimization function\n\n    Args:\n        Xu (ndarray): Inducing points from which to compute the constraints\n\n    Returns:\n        c (float): constraint terms (eg., distance constraint)\n    \"\"\"\n    return 0.\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.Transform.expand","title":"<code>expand(Xu)</code>","text":"<p>Applies the expansion transform to the inducing points</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>Expansion transformed inducing points</p> required Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def expand(self, Xu):\n    \"\"\"Applies the expansion transform to the inducing points\n\n    Args:\n        Xu (ndarray): Expansion transformed inducing points\n    \"\"\"\n    return Xu\n</code></pre>"},{"location":"api/utils/index.html","title":"","text":""},{"location":"api/utils/index.html#sgptools.utils","title":"<code>sgptools.utils</code>","text":"<p>General utilities to support the functionalities of this package:</p> <ul> <li><code>data</code>: Provides utilities to preprocess datasets</li> <li><code>gpflow</code>: Provides utilities to interface with GPflow</li> <li><code>metrics</code>: Provides utilities to quantify the solution quality</li> <li><code>misc</code>: Provides miscellaneous helper functions</li> <li><code>tsp</code>: Provides utilities to run TSP/VRP solver</li> </ul>"},{"location":"api/utils/Data.html","title":"Data","text":""},{"location":"api/utils/Data.html#sgptools.utils.data","title":"<code>sgptools.utils.data</code>","text":""},{"location":"api/utils/Data.html#sgptools.utils.data.get_dataset","title":"<code>get_dataset(dataset_path=None, num_train=1000, num_test=2500, num_candidates=150, **kwargs)</code>","text":"<p>Method to generate/load datasets and preprocess them for SP/IPP. The method uses kmeans to  generate train and test sets.</p> <p>Parameters:</p> Name Type Description Default <code>dataset_path</code> <code>str</code> <p>Path to a tif dataset file. If None, the method will generate synthetic data.</p> <code>None</code> <code>num_train</code> <code>int</code> <p>Number of training samples to generate.</p> <code>1000</code> <code>num_test</code> <code>int</code> <p>Number of testing samples to generate.</p> <code>2500</code> <code>num_candidates</code> <code>int</code> <p>Number of candidate locations to generate.</p> <code>150</code> <p>Returns:</p> Name Type Description <code>X_train</code> <code>ndarray</code> <p>(n, d); Training set inputs</p> <code>y_train</code> <code>ndarray</code> <p>(n, 1); Training set labels</p> <code>X_test</code> <code>ndarray</code> <p>(n, d); Testing set inputs</p> <code>y_test</code> <code>ndarray</code> <p>(n, 1); Testing set labels</p> <code>candidates</code> <code>ndarray</code> <p>(n, d); Candidate sensor placement locations</p> <code>X</code> <code>ndarray</code> <p>(n, d); Full dataset inputs</p> <code>y</code> <code>ndarray</code> <p>(n, 1); Full dataset labels</p> Source code in <code>sgptools/utils/data.py</code> <pre><code>def get_dataset(dataset_path=None,\n                num_train=1000,\n                num_test=2500, \n                num_candidates=150,\n                **kwargs):\n    \"\"\"Method to generate/load datasets and preprocess them for SP/IPP. The method uses kmeans to \n    generate train and test sets.\n\n    Args:\n        dataset_path (str): Path to a tif dataset file. If None, the method will generate synthetic data.\n        num_train (int): Number of training samples to generate.\n        num_test (int): Number of testing samples to generate.\n        num_candidates (int): Number of candidate locations to generate.\n\n    Returns:\n       X_train (ndarray): (n, d); Training set inputs\n       y_train (ndarray): (n, 1); Training set labels\n       X_test (ndarray): (n, d); Testing set inputs\n       y_test (ndarray): (n, 1); Testing set labels\n       candidates (ndarray): (n, d); Candidate sensor placement locations\n       X (ndarray): (n, d); Full dataset inputs\n       y (ndarray): (n, 1); Full dataset labels\n    \"\"\"\n    # Load the data\n    if dataset_path is not None:\n        X, y = prep_tif_dataset(dataset_path=dataset_path)\n    else:\n        X, y = prep_synthetic_dataset(**kwargs)\n\n    X_train = get_inducing_pts(X, num_train)\n    X_train, y_train = cont2disc(X_train, X, y)\n\n    X_test = get_inducing_pts(X, num_test)\n    X_test, y_test = cont2disc(X_test, X, y)\n\n    candidates = get_inducing_pts(X, num_candidates)\n    candidates = cont2disc(candidates, X)\n\n    # Standardize data\n    X_scaler = StandardScaler()\n    X_scaler.fit(X_train)\n    X_train = X_scaler.transform(X_train)*10.0\n    X_test = X_scaler.transform(X_test)*10.0\n    X = X_scaler.transform(X)*10.0\n    candidates = X_scaler.transform(candidates)*10.0\n\n    y_scaler = StandardScaler()\n    y_scaler.fit(y_train)\n    y_train = y_scaler.transform(y_train)\n    y_test = y_scaler.transform(y_test)\n    y = y_scaler.transform(y)\n\n    return X_train, y_train, X_test, y_test, candidates, X, y\n</code></pre>"},{"location":"api/utils/Data.html#sgptools.utils.data.point_pos","title":"<code>point_pos(point, d, theta)</code>","text":"<p>Generate a point at a distance d from a point at angle theta.</p> <p>Parameters:</p> Name Type Description Default <code>point</code> <code>ndarray</code> <p>(N, 2); array of points</p> required <code>d</code> <code>float</code> <p>distance</p> required <code>theta</code> <code>float</code> <p>angle in radians</p> required <p>Returns:</p> Name Type Description <code>X</code> <code>ndarray</code> <p>(N,); array of x-coordinate</p> <code>Y</code> <code>ndarray</code> <p>(N,); array of y-coordinate</p> Source code in <code>sgptools/utils/data.py</code> <pre><code>def point_pos(point, d, theta):\n    '''\n    Generate a point at a distance d from a point at angle theta.\n\n    Args:\n        point (ndarray): (N, 2); array of points\n        d (float): distance\n        theta (float): angle in radians\n\n    Returns:\n        X  (ndarray): (N,); array of x-coordinate\n        Y  (ndarray): (N,); array of y-coordinate\n    '''\n    return np.c_[point[:, 0] + d*np.cos(theta), point[:, 1] + d*np.sin(theta)]\n</code></pre>"},{"location":"api/utils/Data.html#sgptools.utils.data.prep_synthetic_dataset","title":"<code>prep_synthetic_dataset(shape=(50, 50), min_height=0.0, max_height=30.0, roughness=0.5, random_seed=None, **kwargs)</code>","text":"<p>Generates a 50x50 grid of synthetic elevation data using the diamond square algorithm.</p> Refer to the following repo for more details <ul> <li>https://github.com/buckinha/DiamondSquare</li> </ul> <p>Parameters:</p> Name Type Description Default <code>shape</code> <code>tuple</code> <p>(x, y); Grid size along the x and y axis</p> <code>(50, 50)</code> <code>min_height</code> <code>float</code> <p>Minimum allowed height in the sampled data</p> <code>0.0</code> <code>max_height</code> <code>float</code> <p>Maximum allowed height in the sampled data</p> <code>30.0</code> <code>roughness</code> <code>float</code> <p>Roughness of the sampled data</p> <code>0.5</code> <code>random_seed</code> <code>int</code> <p>Random seed for reproducibility</p> <code>None</code> <p>Returns:</p> Name Type Description <code>X</code> <code>ndarray</code> <p>(n, d); Dataset input features</p> <code>y</code> <code>ndarray</code> <p>(n, 1); Dataset labels</p> Source code in <code>sgptools/utils/data.py</code> <pre><code>def prep_synthetic_dataset(shape=(50, 50), \n                           min_height=0.0, \n                           max_height=30.0, \n                           roughness=0.5,\n                           random_seed=None,\n                           **kwargs):\n    '''Generates a 50x50 grid of synthetic elevation data using the diamond square algorithm.\n\n    Refer to the following repo for more details:\n        - [https://github.com/buckinha/DiamondSquare](https://github.com/buckinha/DiamondSquare)\n\n    Args:\n        shape (tuple): (x, y); Grid size along the x and y axis\n        min_height (float): Minimum allowed height in the sampled data\n        max_height (float): Maximum allowed height in the sampled data\n        roughness (float): Roughness of the sampled data\n        random_seed (int): Random seed for reproducibility\n\n    Returns:\n       X (ndarray): (n, d); Dataset input features\n       y (ndarray): (n, 1); Dataset labels\n    '''\n    data = diamond_square(shape=shape,\n                          min_height=min_height, \n                          max_height=max_height, \n                          roughness=roughness,\n                          random_seed=random_seed,\n                          **kwargs)\n\n    # create x and y coordinates from the extent\n    x_coords = np.arange(0, data.shape[0])/10\n    y_coords = np.arange(0, data.shape[1])/10\n    xx, yy = np.meshgrid(x_coords, y_coords)\n    X = np.c_[xx.ravel(), yy.ravel()]\n    y = data.ravel()\n    y = y.reshape(-1, 1)\n\n    return X.astype(float), y.astype(float)\n</code></pre>"},{"location":"api/utils/Data.html#sgptools.utils.data.prep_tif_dataset","title":"<code>prep_tif_dataset(dataset_path)</code>","text":"<p>Load and preprocess a dataset from a GeoTIFF file (.tif file). The input features  are set to the x and y pixel block coordinates and the labels are read from the file. The method also removes all invalid points.</p> <p>Large tif files  need to be downsampled using the following command:  <code>gdalwarp -tr 50 50 &lt;input&gt;.tif &lt;output&gt;.tif</code></p> <p>Args:     dataset_path (str): Path to the dataset file, used only when dataset_type is 'tif'.</p> <p>Returns:    X (ndarray): (n, d); Dataset input features    y (ndarray): (n, 1); Dataset labels</p> Source code in <code>sgptools/utils/data.py</code> <pre><code>def prep_tif_dataset(dataset_path):\n    '''Load and preprocess a dataset from a GeoTIFF file (.tif file). The input features \n    are set to the x and y pixel block coordinates and the labels are read from the file.\n    The method also removes all invalid points.\n\n    Large tif files \n    need to be downsampled using the following command: \n    ```gdalwarp -tr 50 50 &lt;input&gt;.tif &lt;output&gt;.tif```\n\n    Args:\n        dataset_path (str): Path to the dataset file, used only when dataset_type is 'tif'.\n\n    Returns:\n       X (ndarray): (n, d); Dataset input features\n       y (ndarray): (n, 1); Dataset labels\n    '''\n    data = PIL.Image.open(dataset_path)\n    data = np.array(data)\n\n    # create x and y coordinates from the extent\n    x_coords = np.arange(0, data.shape[1])/10\n    y_coords = np.arange(data.shape[0], 0, -1)/10\n    xx, yy = np.meshgrid(x_coords, y_coords)\n    X = np.c_[xx.ravel(), yy.ravel()]\n    y = data.ravel()\n\n    # Remove invalid labels\n    y[np.where(y==-999999.0)] = np.nan\n    X = X[~np.isnan(y)]\n    y = y[~np.isnan(y)]\n\n    X = X.reshape(-1, 2)\n    y = y.reshape(-1, 1)\n\n    return X.astype(float), y.astype(float)\n</code></pre>"},{"location":"api/utils/Data.html#sgptools.utils.data.remove_circle_patches","title":"<code>remove_circle_patches(X, Y, circle_patches)</code>","text":"<p>Remove points inside polycircle patchesgons.</p> <p>Parameters:</p> Name Type Description Default <code>X</code> <code> (ndarray</code> <p>(N,); array of x-coordinate</p> required <code>Y</code> <code> (ndarray</code> <p>(N,); array of y-coordinate</p> required <code>circle_patches</code> <code>list of matplotlib circle patches</code> <p>Circle patches to remove from the X, Y points</p> required <p>Returns:</p> Name Type Description <code>X</code> <code>ndarray</code> <p>(N,); array of x-coordinate</p> <code>Y</code> <code>ndarray</code> <p>(N,); array of y-coordinate</p> Source code in <code>sgptools/utils/data.py</code> <pre><code>def remove_circle_patches(X, Y, circle_patches):\n    '''\n    Remove points inside polycircle patchesgons.\n\n    Args:\n        X  (ndarray): (N,); array of x-coordinate\n        Y  (ndarray): (N,); array of y-coordinate\n        circle_patches (list of matplotlib circle patches): Circle patches to remove from the X, Y points\n\n    Returns:\n        X  (ndarray): (N,); array of x-coordinate\n        Y  (ndarray): (N,); array of y-coordinate\n    '''\n    points = np.array([X.flatten(), Y.flatten()]).T\n    for circle_patch in circle_patches:\n        points = points[~circle_patch.contains_points(points)]\n    return points[:, 0], points[:, 1]\n</code></pre>"},{"location":"api/utils/Data.html#sgptools.utils.data.remove_polygons","title":"<code>remove_polygons(X, Y, polygons)</code>","text":"<p>Remove points inside polygons.</p> <p>Parameters:</p> Name Type Description Default <code>X</code> <code> (ndarray</code> <p>(N,); array of x-coordinate</p> required <code>Y</code> <code> (ndarray</code> <p>(N,); array of y-coordinate</p> required <code>polygons</code> <code>list of matplotlib path polygon</code> <p>Polygons to remove from the X, Y points</p> required <p>Returns:</p> Name Type Description <code>X</code> <code>ndarray</code> <p>(N,); array of x-coordinate</p> <code>Y</code> <code>ndarray</code> <p>(N,); array of y-coordinate</p> Source code in <code>sgptools/utils/data.py</code> <pre><code>def remove_polygons(X, Y, polygons):\n    '''\n    Remove points inside polygons.\n\n    Args:\n        X  (ndarray): (N,); array of x-coordinate\n        Y  (ndarray): (N,); array of y-coordinate\n        polygons (list of matplotlib path polygon): Polygons to remove from the X, Y points\n\n    Returns:\n        X  (ndarray): (N,); array of x-coordinate\n        Y  (ndarray): (N,); array of y-coordinate\n    '''\n    points = np.array([X.flatten(), Y.flatten()]).T\n    for polygon in polygons:\n        p = path.Path(polygon)\n        points = points[~p.contains_points(points)]\n    return points[:, 0], points[:, 1]\n</code></pre>"},{"location":"api/utils/GPflow.html","title":"GPflow","text":""},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow","title":"<code>sgptools.utils.gpflow</code>","text":""},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow.TraceInducingPts","title":"<code>TraceInducingPts</code>","text":"<p>               Bases: <code>MonitorTask</code></p> <p>GPflow monitoring task, used to trace the inducing points states at every step during optimization. </p> <p>Parameters:</p> Name Type Description Default <code>model</code> <code>sgpr</code> <p>GPflow GP/SGP model</p> required Source code in <code>sgptools/utils/gpflow.py</code> <pre><code>class TraceInducingPts(gpflow.monitor.MonitorTask):\n    '''\n    GPflow monitoring task, used to trace the inducing points\n    states at every step during optimization. \n\n    Args:\n        model (gpflow.models.sgpr): GPflow GP/SGP model\n    '''\n    def __init__(self, model):\n        super().__init__()\n        self.trace = []\n        self.model = model\n\n    def run(self, **kwargs):\n        '''\n        Method used to extract the inducing points and \n        apply IPP fixed points transform if available\n        '''\n        Xu = self.model.inducing_variable.Z\n        Xu_exp = self.model.transform.expand(Xu, \n                            expand_sensor_model=False).numpy()\n        self.trace.append(Xu_exp)\n\n    def get_trace(self):\n        '''\n        Returns the inducing points collected at each optimization step\n\n        Returns:\n            trace (ndarray): (n, m, d); Array with the inducing points.\n                            `n` is the number of optimization steps;\n                            `m` is the number of inducing points;\n                            `d` is the dimension of the inducing points.\n        '''\n        return np.array(self.trace)\n</code></pre>"},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow.TraceInducingPts.get_trace","title":"<code>get_trace()</code>","text":"<p>Returns the inducing points collected at each optimization step</p> <p>Returns:</p> Name Type Description <code>trace</code> <code>ndarray</code> <p>(n, m, d); Array with the inducing points.             <code>n</code> is the number of optimization steps;             <code>m</code> is the number of inducing points;             <code>d</code> is the dimension of the inducing points.</p> Source code in <code>sgptools/utils/gpflow.py</code> <pre><code>def get_trace(self):\n    '''\n    Returns the inducing points collected at each optimization step\n\n    Returns:\n        trace (ndarray): (n, m, d); Array with the inducing points.\n                        `n` is the number of optimization steps;\n                        `m` is the number of inducing points;\n                        `d` is the dimension of the inducing points.\n    '''\n    return np.array(self.trace)\n</code></pre>"},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow.TraceInducingPts.run","title":"<code>run(**kwargs)</code>","text":"<p>Method used to extract the inducing points and  apply IPP fixed points transform if available</p> Source code in <code>sgptools/utils/gpflow.py</code> <pre><code>def run(self, **kwargs):\n    '''\n    Method used to extract the inducing points and \n    apply IPP fixed points transform if available\n    '''\n    Xu = self.model.inducing_variable.Z\n    Xu_exp = self.model.transform.expand(Xu, \n                        expand_sensor_model=False).numpy()\n    self.trace.append(Xu_exp)\n</code></pre>"},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow.get_model_params","title":"<code>get_model_params(X_train, y_train, max_steps=1500, lr=0.01, print_params=True, lengthscales=1.0, variance=1.0, noise_variance=0.1, kernel=None, return_gp=False, train_inducing_pts=False, num_inducing_pts=500, **kwargs)</code>","text":"<p>Train a GP on the given training set.  Trains a sparse GP if the training set is larger than 1000 samples.</p> <p>Parameters:</p> Name Type Description Default <code>X_train</code> <code>ndarray</code> <p>(n, d); Training set inputs</p> required <code>y_train</code> <code>ndarray</code> <p>(n, 1); Training set labels</p> required <code>max_steps</code> <code>int</code> <p>Maximum number of optimization steps</p> <code>1500</code> <code>lr</code> <code>float</code> <p>Optimization learning rate</p> <code>0.01</code> <code>print_params</code> <code>bool</code> <p>If True, prints the optimized GP parameters</p> <code>True</code> <code>lengthscales</code> <code>float or list</code> <p>Kernel lengthscale(s), if passed as a list,                      each element corresponds to each data dimension</p> <code>1.0</code> <code>variance</code> <code>float</code> <p>Kernel variance</p> <code>1.0</code> <code>noise_variance</code> <code>float</code> <p>Data noise variance</p> <code>0.1</code> <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> <code>None</code> <code>return_gp</code> <code>bool</code> <p>If True, returns the trained GP model</p> <code>False</code> <code>train_inducing_pts</code> <code>bool</code> <p>If True, trains the inducing points when                        using a sparse GP model</p> <code>False</code> <code>num_inducing_pts</code> <code>int</code> <p>Number of inducing points to use when training                     a sparse GP model</p> <code>500</code> <p>Returns:</p> Name Type Description <code>loss</code> <code>list</code> <p>Loss values obtained during training</p> <code>variance</code> <code>float</code> <p>Optimized data noise variance</p> <code>kernel</code> <code>Kernel</code> <p>Optimized gpflow kernel function</p> <code>gp</code> <code>GPR</code> <p>Optimized gpflow GP model.                      Returned only if <code>return_gp=True</code>.</p> Source code in <code>sgptools/utils/gpflow.py</code> <pre><code>def get_model_params(X_train, y_train, \n                     max_steps=1500, \n                     lr=1e-2, \n                     print_params=True, \n                     lengthscales=1.0, \n                     variance=1.0, \n                     noise_variance=0.1,\n                     kernel=None,\n                     return_gp=False,\n                     train_inducing_pts=False,\n                     num_inducing_pts=500,\n                     **kwargs):\n    \"\"\"Train a GP on the given training set. \n    Trains a sparse GP if the training set is larger than 1000 samples.\n\n    Args:\n        X_train (ndarray): (n, d); Training set inputs\n        y_train (ndarray): (n, 1); Training set labels\n        max_steps (int): Maximum number of optimization steps\n        lr (float): Optimization learning rate\n        print_params (bool): If True, prints the optimized GP parameters\n        lengthscales (float or list): Kernel lengthscale(s), if passed as a list, \n                                each element corresponds to each data dimension\n        variance (float): Kernel variance\n        noise_variance (float): Data noise variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        return_gp (bool): If True, returns the trained GP model\n        train_inducing_pts (bool): If True, trains the inducing points when\n                                   using a sparse GP model\n        num_inducing_pts (int): Number of inducing points to use when training\n                                a sparse GP model\n\n    Returns:\n        loss (list): Loss values obtained during training\n        variance (float): Optimized data noise variance\n        kernel (gpflow.kernels.Kernel): Optimized gpflow kernel function\n        gp (gpflow.models.GPR): Optimized gpflow GP model. \n                                Returned only if ```return_gp=True```.\n\n    \"\"\"\n    if kernel is None:\n        kernel = gpflow.kernels.SquaredExponential(lengthscales=lengthscales, \n                                                   variance=variance)\n\n    if len(X_train) &lt;= 1500:\n        gpr = gpflow.models.GPR(data=(X_train, y_train), \n                                kernel=kernel,\n                                noise_variance=noise_variance)\n        trainable_variables=gpr.trainable_variables\n    else:\n        inducing_pts = get_inducing_pts(X_train, num_inducing_pts)\n        gpr = gpflow.models.SGPR(data=(X_train, y_train), \n                                 kernel=kernel,\n                                 inducing_variable=inducing_pts,\n                                 noise_variance=noise_variance)\n        if train_inducing_pts:\n            trainable_variables=gpr.trainable_variables\n        else:\n            trainable_variables=gpr.trainable_variables[1:]\n\n    if max_steps &gt; 0:\n        loss = optimize_model(gpr, max_steps=max_steps, lr=lr, \n                              trainable_variables=trainable_variables,\n                              **kwargs)\n    else:\n        loss = 0\n\n    if print_params:\n        print_summary(gpr)\n\n    if return_gp:\n        return loss, gpr.likelihood.variance, kernel, gpr\n    else:\n        return loss, gpr.likelihood.variance, kernel\n</code></pre>"},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow.optimize_model","title":"<code>optimize_model(model, max_steps=2000, kernel_grad=True, lr=0.01, optimizer='scipy', method=None, verbose=False, trace_fn=None, convergence_criterion=True, trainable_variables=None, tol=None)</code>","text":"<p>Trains a GP/SGP model</p> <p>Parameters:</p> Name Type Description Default <code>model</code> <code>models</code> <p>GPflow GP/SGP model to train.</p> required <code>max_steps</code> <code>int</code> <p>Maximum number of training steps.</p> <code>2000</code> <code>kernel_grad</code> <code>bool</code> <p>If <code>False</code>, the kernel parameters will not be optimized.                  Ignored when <code>trainable_variables</code> are passed.</p> <code>True</code> <code>lr</code> <code>float</code> <p>Optimization learning rate.</p> <code>0.01</code> <code>optimizer</code> <code>str</code> <p>Optimizer to use for training (<code>scipy</code> or <code>tf</code>).</p> <code>'scipy'</code> <code>method</code> <code>str</code> <p>Optimization method refer to scipy minimize            and tf optimizers for full list</p> <code>None</code> <code>verbose</code> <code>bool</code> <p>If <code>True</code>, the training progress will be printed when using Scipy.</p> <code>False</code> <code>trace_fn</code> <code>str</code> <p>Function to trace metrics during training.              If <code>None</code>, the loss values are returned;             If <code>traceXu</code>, it the inducing points states at each optimization step are returned (increases computation time).</p> <code>None</code> <code>convergence_criterion</code> <code>bool</code> <p>If <code>True</code> and using a tensorflow optimizer, it                            enables early stopping when the loss plateaus.</p> <code>True</code> <code>trainable_variables</code> <code>list</code> <p>List of model variables to train.</p> <code>None</code> <code>tol</code> <code>float</code> <p>Convergence tolerance to decide when to stop optimization.</p> <code>None</code> Source code in <code>sgptools/utils/gpflow.py</code> <pre><code>def optimize_model(model, \n                   max_steps=2000, \n                   kernel_grad=True, \n                   lr=1e-2, \n                   optimizer='scipy', \n                   method=None,\n                   verbose=False, \n                   trace_fn=None,\n                   convergence_criterion=True,\n                   trainable_variables=None,\n                   tol=None):\n    \"\"\"\n    Trains a GP/SGP model\n\n    Args:\n        model (gpflow.models): GPflow GP/SGP model to train.\n        max_steps (int): Maximum number of training steps.\n        kernel_grad (bool): If `False`, the kernel parameters will not be optimized. \n                            Ignored when `trainable_variables` are passed.\n        lr (float): Optimization learning rate.\n        optimizer (str): Optimizer to use for training (`scipy` or `tf`).\n        method (str): Optimization method refer to [scipy minimize](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html#scipy.optimize.minimize) \n                      and [tf optimizers](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers) for full list\n        verbose (bool): If `True`, the training progress will be printed when using Scipy.\n        trace_fn (str): Function to trace metrics during training. \n                        If `None`, the loss values are returned;\n                        If `traceXu`, it the inducing points states at each optimization step are returned (increases computation time).\n        convergence_criterion (bool): If `True` and using a tensorflow optimizer, it \n                                      enables early stopping when the loss plateaus.\n        trainable_variables (list): List of model variables to train.\n        tol (float): Convergence tolerance to decide when to stop optimization.\n    \"\"\"\n    # Train all variables if trainable_variables are not provided\n    # If kernel_gradient is False, disable the kernel parameter gradient updates\n    if trainable_variables is None and kernel_grad:\n        trainable_variables=model.trainable_variables\n    elif trainable_variables is None and not kernel_grad:\n        trainable_variables=model.trainable_variables[:1]\n\n    if optimizer == 'scipy':\n        if method is None:\n            method = 'L-BFGS-B'\n\n        if trace_fn == 'traceXu':\n            execute_task = TraceInducingPts(model)\n            task_group = gpflow.monitor.MonitorTaskGroup(execute_task, \n                                                         period=1)\n            trace_fn = gpflow.monitor.Monitor(task_group)\n\n        opt = gpflow.optimizers.Scipy()\n        losses = opt.minimize(model.training_loss,\n                              trainable_variables,\n                              method=method,\n                              options=dict(disp=verbose, maxiter=max_steps),\n                              tol=tol,\n                              step_callback=trace_fn)\n        if trace_fn is None:\n            losses = losses.fun\n        else:\n            losses = trace_fn.task_groups[0].tasks[0].get_trace()\n    else:\n        if trace_fn is None:\n            trace_fn = lambda x: x.loss\n        elif trace_fn == 'traceXu':\n            def trace_fn(traceable_quantities):\n                return model.inducing_variable.Z.numpy()\n\n        if method is None:\n            method = 'adam'\n        opt = tf.keras.optimizers.get(method)\n        opt.learning_rate = lr\n        loss_fn = model.training_loss\n        if convergence_criterion:\n            convergence_criterion = tfp.optimizer.convergence_criteria.LossNotDecreasing(\n                                            atol=1e-5, \n                                            window_size=50,\n                                            min_num_steps=int(max_steps*0.1))\n        else:\n            convergence_criterion = None\n        losses = tfp.math.minimize(loss_fn,\n                                   trainable_variables=trainable_variables,\n                                   num_steps=max_steps,\n                                   optimizer=opt,\n                                   convergence_criterion=convergence_criterion,\n                                   trace_fn=trace_fn)\n        losses = losses.numpy()\n\n    return losses\n</code></pre>"},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow.plot_loss","title":"<code>plot_loss(losses, save_file=None)</code>","text":"<p>Helper function to plot the training loss</p> <p>Parameters:</p> Name Type Description Default <code>losses</code> <code>list</code> <p>list of loss values</p> required <code>save_file</code> <code>str</code> <p>If passed, the loss plot will be saved to the <code>save_file</code></p> <code>None</code> Source code in <code>sgptools/utils/gpflow.py</code> <pre><code>def plot_loss(losses, save_file=None):\n    \"\"\"Helper function to plot the training loss\n\n    Args:\n        losses (list): list of loss values\n        save_file (str): If passed, the loss plot will be saved to the `save_file`\n    \"\"\"\n    plt.plot(losses)\n    plt.title('Log Likelihood')\n    plt.xlabel('Iteration')\n    plt.ylabel('Log Likelihood')\n    ax = plt.gca()\n    ax.ticklabel_format(useOffset=False)\n\n    if save_file is not None:\n        plt.savefig(save_file, bbox_inches='tight')\n        plt.close()\n    else:\n        plt.show()\n</code></pre>"},{"location":"api/utils/Metrics.html","title":"Metrics","text":""},{"location":"api/utils/Metrics.html#sgptools.utils.metrics","title":"<code>sgptools.utils.metrics</code>","text":""},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.gaussian_entropy","title":"<code>gaussian_entropy(K)</code>","text":"<p>Computes GP-based entropy from a kernel matrix</p> <p>Parameters:</p> Name Type Description Default <code>K</code> <code>ndarray</code> <p>(n, n); kernel matrix</p> required <p>Returns:</p> Name Type Description <code>entropy</code> <code>float</code> <p>Entropy computed from the kernel matrix</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def gaussian_entropy(K):\n    \"\"\"Computes GP-based entropy from a kernel matrix\n\n    Args:\n        K (ndarray): (n, n); kernel matrix\n\n    Returns:\n        entropy (float): Entropy computed from the kernel matrix\n    \"\"\"\n    return multivariate_normal(mean=None, cov=K, allow_singular=True).entropy()\n</code></pre>"},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.get_distance","title":"<code>get_distance(X)</code>","text":"<p>Compute the length of a path (L2-norm)</p> <p>Parameters:</p> Name Type Description Default <code>X</code> <code>ndarray</code> <p>(m, d); Waypoints of a path</p> required <p>Returns:</p> Name Type Description <code>dist</code> <code>float</code> <p>Total path length</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def get_distance(X):\n    \"\"\"Compute the length of a path (L2-norm)\n\n    Args:\n        X (ndarray): (m, d); Waypoints of a path\n\n    Returns:\n        dist (float): Total path length\n    \"\"\"\n    dist = np.linalg.norm(X[1:] - X[:-1], axis=-1)\n    dist = np.sum(dist)\n    return dist\n</code></pre>"},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.get_elbo","title":"<code>get_elbo(Xu, X_env, noise_variance, kernel, baseline=False)</code>","text":"<p>Computes the ELBO of the SGP, corrected to be positive</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(m, d); Sensing locations</p> required <code>X_env</code> <code>ndarray</code> <p>(n, d); Data points used to approximate the bounds of the environment</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>baseline</code> <code>bool</code> <p>If True, the ELBO is adjusted to be positive</p> <code>False</code> <p>Returns:</p> Name Type Description <code>elbo</code> <code>float</code> <p>ELBO of the SGP</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def get_elbo(Xu, X_env, noise_variance, kernel, baseline=False):\n    \"\"\"Computes the ELBO of the SGP, corrected to be positive\n\n    Args:\n        Xu (ndarray): (m, d); Sensing locations\n        X_env (ndarray): (n, d); Data points used to approximate the bounds of the environment\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        baseline (bool): If True, the ELBO is adjusted to be positive\n\n    Returns:\n        elbo (float): ELBO of the SGP\n    \"\"\"\n    if baseline:\n        sgpr = gpflow.models.SGPR(X_env,\n                                  noise_variance=noise_variance,\n                                  kernel=kernel,\n                                  inducing_variable=[[0, 0]])\n        baseline = sgpr.elbo().numpy()\n    else:\n        baseline = 0.0\n\n    sgpr = gpflow.models.SGPR(X_env,\n                              noise_variance=noise_variance,\n                              kernel=kernel, \n                              inducing_variable=Xu)\n    return (sgpr.elbo() - baseline).numpy()\n</code></pre>"},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.get_kl","title":"<code>get_kl(Xu, X_env, noise_variance, kernel)</code>","text":"<p>Computes the KL divergence between the SGP and the GP</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(m, d); Sensing locations</p> required <code>X_env</code> <code>ndarray</code> <p>(n, d); Data points used to approximate the bounds of the environment</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <p>Returns:</p> Name Type Description <code>kl</code> <code>float</code> <p>KL divergence between the SGP and the GP</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def get_kl(Xu, X_env, noise_variance, kernel):\n    \"\"\"Computes the KL divergence between the SGP and the GP\n\n    Args:\n        Xu (ndarray): (m, d); Sensing locations\n        X_env (ndarray): (n, d); Data points used to approximate the bounds of the environment\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n\n    Returns:\n        kl (float): KL divergence between the SGP and the GP\n    \"\"\"\n    sgpr = gpflow.models.SGPR(X_env,\n                              noise_variance=noise_variance,\n                              kernel=kernel,\n                              inducing_variable=Xu)\n\n    common = sgpr._common_calculation()\n    sigma_sq = common.sigma_sq\n    AAT = common.AAT\n\n    x, _ = sgpr.data\n    kdiag = sgpr.kernel(x, full_cov=False)\n\n    # tr(K) / \u03c3\u00b2\n    trace_k = tf.reduce_sum(kdiag / sigma_sq)\n    # tr(Q) / \u03c3\u00b2\n    trace_q = tf.reduce_sum(tf.linalg.diag_part(AAT))\n    # tr(K - Q) / \u03c3\u00b2\n    trace = trace_k - trace_q\n    trace = 0.5 * trace\n\n    return float(trace.numpy())\n</code></pre>"},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.get_mi","title":"<code>get_mi(Xu, candidate_locs, noise_variance, kernel)</code>","text":"<p>Computes mutual information between the sensing locations and the candidate locations</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(m, d); Sensing locations</p> required <code>candidate_locs</code> <code>ndarray</code> <p>(n, d); Candidate sensing locations </p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <p>Returns:</p> Name Type Description <code>mi</code> <code>float</code> <p>Mutual information computed using a GP</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def get_mi(Xu, candidate_locs, noise_variance, kernel):\n    \"\"\"Computes mutual information between the sensing locations and the candidate locations\n\n    Args:\n        Xu (ndarray): (m, d); Sensing locations\n        candidate_locs (ndarray): (n, d); Candidate sensing locations \n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n\n    Returns:\n        mi (float): Mutual information computed using a GP\n    \"\"\"\n    Xu = np.array(Xu)\n    candidate_locs = np.array(candidate_locs)\n\n    gp = gpflow.models.GPR(data=(Xu, np.zeros((len(Xu), 1))),\n                           kernel=kernel,\n                           noise_variance=noise_variance)\n    _, sigma_a = gp.predict_f(candidate_locs, full_cov=True)\n    sigma_a = sigma_a.numpy()[0]\n    cond_entropy = gaussian_entropy(sigma_a)\n\n    K = kernel(candidate_locs, full_cov=True).numpy()\n    K += noise_variance * np.eye(len(candidate_locs))\n    entropy = gaussian_entropy(K)\n\n    return float(entropy - cond_entropy)\n</code></pre>"},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.get_reconstruction","title":"<code>get_reconstruction(sensor_data, X_test, noise_variance, kernel)</code>","text":"<p>Computes the GP-based data field estimates with the solution placements as the training set</p> <p>Parameters:</p> Name Type Description Default <code>sensor_data</code> <code>ndarray tuple</code> <p>((m, d), (m, 1)); Sensing locations' input                   and corresponding ground truth labels</p> required <code>X_test</code> <code>ndarray</code> <p>(n, d); Testing data input locations</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <p>Returns:</p> Name Type Description <code>y_pred</code> <code>ndarray</code> <p>(n, 1); Predicted data field estimates</p> <code>y_var</code> <code>ndarray</code> <p>(n, 1); Prediction variance at each location in the data field</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def get_reconstruction(sensor_data, X_test, noise_variance, kernel):\n    \"\"\"Computes the GP-based data field estimates with the solution placements as the training set\n\n    Args:\n        sensor_data (ndarray tuple): ((m, d), (m, 1)); Sensing locations' input \n                             and corresponding ground truth labels\n        X_test (ndarray): (n, d); Testing data input locations\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n\n    Returns:\n        y_pred (ndarray): (n, 1); Predicted data field estimates\n        y_var (ndarray): (n, 1); Prediction variance at each location in the data field\n    \"\"\"\n    Xu_X, Xu_y = sensor_data\n\n    # Get the GP predictions\n    gpr = gpflow.models.GPR((Xu_X, Xu_y),\n                            noise_variance=noise_variance,\n                            kernel=kernel)\n    y_pred, y_var = gpr.predict_f(X_test)\n    y_pred = y_pred.numpy().reshape(-1, 1)\n\n    return y_pred, y_var\n</code></pre>"},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.get_rmse","title":"<code>get_rmse(y_pred, y_test)</code>","text":"<p>Computes the root-mean-square error between <code>y_pred</code> and <code>y_test</code></p> <p>Parameters:</p> Name Type Description Default <code>y_pred</code> <code>ndarray</code> <p>(n, 1); Predicted data field estimate</p> required <code>y_test</code> <code>ndarray</code> <p>(n, 1); Ground truth data field </p> required <p>Returns:</p> Name Type Description <code>rmse</code> <code>float</code> <p>Computed RMSE</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def get_rmse(y_pred, y_test):\n    \"\"\"Computes the root-mean-square error between `y_pred` and `y_test`\n\n    Args:\n        y_pred (ndarray): (n, 1); Predicted data field estimate\n        y_test (ndarray): (n, 1); Ground truth data field \n\n    Returns:\n        rmse (float): Computed RMSE\n    \"\"\"\n    return np.sqrt(np.mean(np.square(y_pred - y_test)))\n</code></pre>"},{"location":"api/utils/Misc.html","title":"Misc","text":""},{"location":"api/utils/Misc.html#sgptools.utils.misc","title":"<code>sgptools.utils.misc</code>","text":""},{"location":"api/utils/Misc.html#sgptools.utils.misc.cont2disc","title":"<code>cont2disc(Xu, candidates, candidate_labels=None)</code>","text":"<p>Map continuous space locations to a discrete set of candidate location</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(m, 2); Continuous space points</p> required <code>candidates</code> <code>ndarray</code> <p>(n, 2); Discrete set of candidate locations</p> required <code>candidate_labels</code> <code>ndarray</code> <p>(n, 1); Labels corresponding to the discrete set of candidate locations</p> <code>None</code> <p>Returns:</p> Name Type Description <code>Xu_x</code> <code>ndarray</code> <p>Discrete space points' locations </p> <code>Xu_y</code> <code>ndarray</code> <p>Labels of the discrete space points. Returned only if <code>candidate_labels</code>             was passed to the function</p> Source code in <code>sgptools/utils/misc.py</code> <pre><code>def cont2disc(Xu, candidates, candidate_labels=None):\n    \"\"\"Map continuous space locations to a discrete set of candidate location\n\n    Args:\n        Xu (ndarray): (m, 2); Continuous space points\n        candidates (ndarray): (n, 2); Discrete set of candidate locations\n        candidate_labels (ndarray): (n, 1); Labels corresponding to the discrete set of candidate locations\n\n    Returns:\n        Xu_x (ndarray): Discrete space points' locations \n        Xu_y (ndarray): Labels of the discrete space points. Returned only if `candidate_labels`\n                        was passed to the function\n\n    \"\"\"\n    # Sanity check to ensure that there are sensing locations and candidates to match\n    if len(candidates)==0 or len(Xu)==0:\n        if candidate_labels is not None:\n            return [], []\n        else:\n            return []\n\n    dists = pairwise_distances(candidates, Y=Xu, metric='euclidean')\n    row_ind, _ = linear_sum_assignment(dists)\n    Xu_X = candidates[row_ind].copy()\n    if candidate_labels is not None:\n        Xu_y = candidate_labels[row_ind].copy()\n        return Xu_X, Xu_y\n    else:\n        return Xu_X\n</code></pre>"},{"location":"api/utils/Misc.html#sgptools.utils.misc.get_inducing_pts","title":"<code>get_inducing_pts(data, num_inducing, orientation=False, random=False)</code>","text":"<p>Selects a subset of the data points to be used as inducing points.  The default approach uses kmeans to select the subset. </p> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>ndarray</code> <p>(n, 2); Data points to select the inducing points from </p> required <code>num_inducing</code> <code>int</code> <p>Number of inducing points</p> required <code>orientation</code> <code>bool</code> <p>If True, add an additional dimension to model the sensor                  FoV rotation angle</p> <code>False</code> <code>random</code> <code>bool</code> <p>If True, the subset of inducing points are selected randomly             instead of using kmeans</p> <code>False</code> <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(m, d); Inducing points in the position and orientation space.             <code>m</code> is the number of inducing points,              <code>d</code> is the dimension of the space (x, y, optional - angle in radians)</p> Source code in <code>sgptools/utils/misc.py</code> <pre><code>def get_inducing_pts(data, num_inducing, orientation=False, random=False):\n    \"\"\"Selects a subset of the data points to be used as inducing points. \n    The default approach uses kmeans to select the subset. \n\n    Args:\n        data (ndarray): (n, 2); Data points to select the inducing points from \n        num_inducing (int): Number of inducing points\n        orientation (bool): If True, add an additional dimension to model the sensor \n                            FoV rotation angle\n        random (bool): If True, the subset of inducing points are selected randomly \n                       instead of using kmeans\n\n    Returns:\n        Xu (ndarray): (m, d); Inducing points in the position and orientation space.\n                        `m` is the number of inducing points, \n                        `d` is the dimension of the space (x, y, optional - angle in radians)\n    \"\"\"\n    if random:\n        idx = np.random.randint(len(data), size=num_inducing)\n        Xu = data[idx]\n    else:\n        Xu = kmeans2(data, num_inducing, minit=\"points\")[0]\n    if orientation:\n        thetas = np.random.uniform(0, 2 * np.pi, size=(Xu.shape[0], 1))\n        Xu = np.concatenate([Xu, thetas], axis=1)\n    return Xu\n</code></pre>"},{"location":"api/utils/Misc.html#sgptools.utils.misc.interpolate_path","title":"<code>interpolate_path(waypoints, sampling_rate=0.05)</code>","text":"<p>Interpolate additional points between the given waypoints to simulate continuous sensing robots</p> <p>Parameters:</p> Name Type Description Default <code>waypoints</code> <code>(n, d)</code> <p>Waypoints of the robot's path</p> required <code>sampling_rate</code> <code>float</code> <p>Distance between each pair of interpolated points</p> <code>0.05</code> <p>Returns:</p> Name Type Description <code>path</code> <code>ndarray</code> <p>(p, d) Interpolated path, <code>p</code> depends on the sampling_rate rate</p> Source code in <code>sgptools/utils/misc.py</code> <pre><code>def interpolate_path(waypoints, sampling_rate=0.05):\n    \"\"\"Interpolate additional points between the given waypoints to simulate continuous sensing robots\n\n    Args:\n        waypoints (n, d): Waypoints of the robot's path\n        sampling_rate (float): Distance between each pair of interpolated points\n\n    Returns:\n        path (ndarray): (p, d) Interpolated path, `p` depends on the sampling_rate rate\n    \"\"\"\n    interpolated_path = []\n    for i in range(2, len(waypoints)+1):\n        dist = get_distance(waypoints[i-2:i])\n        num_samples = int(dist / sampling_rate)\n        points = np.linspace(waypoints[i-1], waypoints[i-2], num_samples)\n        interpolated_path.extend(points)\n    return np.array(interpolated_path)\n</code></pre>"},{"location":"api/utils/Misc.html#sgptools.utils.misc.plot_paths","title":"<code>plot_paths(paths, candidates=None, title=None)</code>","text":"<p>Function to plot the IPP solution paths</p> <p>Parameters:</p> Name Type Description Default <code>paths</code> <code>ndarray</code> <p>(r, m, 2); <code>r</code> paths with <code>m</code> waypoints each</p> required <code>candidates</code> <code>ndarray</code> <p>(n, 2); Candidate unlabeled locations used in the SGP-based sensor placement approach</p> <code>None</code> <code>title</code> <code>str</code> <p>Title of the plot</p> <code>None</code> Source code in <code>sgptools/utils/misc.py</code> <pre><code>def plot_paths(paths, candidates=None, title=None):\n    \"\"\"Function to plot the IPP solution paths\n\n    Args:\n        paths (ndarray): (r, m, 2); `r` paths with `m` waypoints each\n        candidates (ndarray): (n, 2); Candidate unlabeled locations used in the SGP-based sensor placement approach\n        title (str): Title of the plot\n    \"\"\"\n    plt.figure()\n    for i, path in enumerate(paths):\n        plt.plot(path[:, 0], path[:, 1], \n                    c='r', label='Path', zorder=1, marker='o')\n        plt.scatter(path[0, 0], path[0, 1], \n                    c='g', label='Start', zorder=2, marker='o')\n        if candidates is not None:\n            plt.scatter(candidates[:, 0], candidates[:, 1], \n                        c='k', s=1, label='Unlabeled Train-Set Points', zorder=0)\n        if i==0:\n            plt.legend(bbox_to_anchor=(1.0, 1.02))\n    if title is not None:\n        plt.title(title)\n    plt.gca().set_aspect('equal')\n    plt.xlabel('X')\n    plt.ylabel('Y')\n</code></pre>"},{"location":"api/utils/Misc.html#sgptools.utils.misc.ploygon2candidats","title":"<code>ploygon2candidats(vertices, num_samples=5000, random_seed=2024)</code>","text":"<p>Sample unlabeled candidates within a polygon</p> <p>Parameters:</p> Name Type Description Default <code>vertices</code> <code>ndarray</code> <p>(v, 2) of vertices that define the polygon</p> required <code>num_samples</code> <code>int</code> <p>Number of samples to generate</p> <code>5000</code> <code>random_seed</code> <code>int</code> <p>Random seed for reproducibility</p> <code>2024</code> <p>Returns:</p> Name Type Description <code>candidates</code> <code>ndarray</code> <p>(n, 2); Candidate sensor placement locations</p> Source code in <code>sgptools/utils/misc.py</code> <pre><code>def ploygon2candidats(vertices, \n                      num_samples=5000, \n                      random_seed=2024):\n    \"\"\"Sample unlabeled candidates within a polygon\n\n    Args:\n        vertices (ndarray): (v, 2) of vertices that define the polygon\n        num_samples (int): Number of samples to generate\n        random_seed (int): Random seed for reproducibility\n\n    Returns:\n       candidates (ndarray): (n, 2); Candidate sensor placement locations\n    \"\"\"\n    poly = geometry.Polygon(vertices)\n    sampler = gpd.GeoSeries([poly])\n    candidates = sampler.sample_points(size=num_samples,\n                                       rng=random_seed)\n    candidates = candidates.get_coordinates().to_numpy()\n    return candidates\n</code></pre>"},{"location":"api/utils/Misc.html#sgptools.utils.misc.project_waypoints","title":"<code>project_waypoints(waypoints, candidates)</code>","text":"<p>Project the waypoints back to the candidate set while retaining the  waypoint visitation order.</p> <p>Parameters:</p> Name Type Description Default <code>waypoints</code> <code>(n, d)</code> <p>Waypoints of the robot's path</p> required <code>candidates</code> <code>ndarray</code> <p>(n, 2); Discrete set of candidate locations</p> required <p>Returns:</p> Name Type Description <code>waypoints</code> <code>(n, d)</code> <p>Projected waypoints of the robot's path</p> Source code in <code>sgptools/utils/misc.py</code> <pre><code>def project_waypoints(waypoints, candidates):\n    \"\"\"Project the waypoints back to the candidate set while retaining the \n    waypoint visitation order.\n\n    Args:\n        waypoints (n, d): Waypoints of the robot's path\n        candidates (ndarray): (n, 2); Discrete set of candidate locations\n\n    Returns:\n        waypoints (n, d): Projected waypoints of the robot's path\n    \"\"\"\n    waypoints_disc = cont2disc(waypoints, candidates)\n    waypoints_valid = _reoder_path(waypoints, waypoints_disc)\n    return waypoints_valid\n</code></pre>"},{"location":"api/utils/TSP.html","title":"TSP","text":""},{"location":"api/utils/TSP.html#sgptools.utils.tsp","title":"<code>sgptools.utils.tsp</code>","text":""},{"location":"api/utils/TSP.html#sgptools.utils.tsp.resample_path","title":"<code>resample_path(waypoints, num_inducing=10)</code>","text":"<p>Function to map path with arbitrary number of waypoints to  inducing points path with fixed number of waypoints</p> <p>Parameters:</p> Name Type Description Default <code>waypoints</code> <code>ndarray</code> <p>(num_waypoints, ndim); waypoints of path from vrp solver</p> required <code>num_inducing</code> <code>int</code> <p>Number of inducing points (waypoints) in the returned path</p> <code>10</code> <p>Returns:</p> Name Type Description <code>points</code> <code>ndarray</code> <p>(num_inducing, ndim); Resampled path</p> Source code in <code>sgptools/utils/tsp.py</code> <pre><code>def resample_path(waypoints, num_inducing=10):\n    \"\"\"Function to map path with arbitrary number of waypoints to \n    inducing points path with fixed number of waypoints\n\n    Args:\n        waypoints (ndarray): (num_waypoints, ndim); waypoints of path from vrp solver\n        num_inducing (int): Number of inducing points (waypoints) in the returned path\n\n    Returns:\n        points (ndarray): (num_inducing, ndim); Resampled path\n    \"\"\"\n    ndim = np.shape(waypoints)[-1]\n    if not (ndim==2 or ndim==3):\n        raise Exception(f\"ndim={ndim} is not supported for path resampling!\")\n    line = LineString(waypoints)\n    distances = np.linspace(0, line.length, num_inducing)\n    points = [line.interpolate(distance) for distance in distances]\n    if ndim==2:\n        points = np.array([[p.x, p.y] for p in points])\n    elif ndim==3:\n        points = np.array([[p.x, p.y, p.z] for p in points])\n    return points\n</code></pre>"},{"location":"api/utils/TSP.html#sgptools.utils.tsp.run_tsp","title":"<code>run_tsp(nodes, num_vehicles=1, max_dist=25, depth=1, resample=None, start_nodes=None, end_nodes=None, time_limit=10)</code>","text":"<p>Method to run TSP/VRP with arbitrary start and end nodes,  and without any distance constraint</p> <p>Parameters:</p> Name Type Description Default <code>nodes</code> <code>ndarray</code> <p>(# nodes, ndim); Nodes to visit </p> required <code>num_vehicles</code> <code>int</code> <p>Number of robots/vehicles</p> <code>1</code> <code>max_dist</code> <code>float</code> <p>Maximum distance allowed for each path when handling mutli-robot case</p> <code>25</code> <code>depth</code> <code>int</code> <p>Internal parameter used to track re-try recursion depth</p> <code>1</code> <code>resample</code> <code>int</code> <p>Each solution path will be resampled to have             <code>resample</code> number of points</p> <code>None</code> <code>start_nodes</code> <code>ndarray</code> <p>(# num_vehicles, ndim); Optionl array of start nodes from which                      to start each vehicle's solution path</p> <code>None</code> <code>end_nodes</code> <code>ndarray</code> <p>(# num_vehicles, ndim); Optionl array of end nodes at which                      to end each vehicle's solution path</p> <code>None</code> <code>time_limit</code> <code>int</code> <p>TSP runtime time limit in seconds</p> <code>10</code> <p>Returns:</p> Name Type Description <code>paths</code> <code>ndarray</code> <p>Solution paths</p> <code>distances</code> <code>list</code> <p>List of path lengths</p> Source code in <code>sgptools/utils/tsp.py</code> <pre><code>def run_tsp(nodes, \n            num_vehicles=1, \n            max_dist=25, \n            depth=1, \n            resample=None, \n            start_nodes=None,\n            end_nodes=None,\n            time_limit=10):\n    \"\"\"Method to run TSP/VRP with arbitrary start and end nodes, \n    and without any distance constraint\n\n    Args:\n        nodes (ndarray): (# nodes, ndim); Nodes to visit \n        num_vehicles (int): Number of robots/vehicles\n        max_dist (float): Maximum distance allowed for each path when handling mutli-robot case\n        depth (int): Internal parameter used to track re-try recursion depth\n        resample (int): Each solution path will be resampled to have\n                        `resample` number of points\n        start_nodes (ndarray): (# num_vehicles, ndim); Optionl array of start nodes from which \n                                to start each vehicle's solution path\n        end_nodes (ndarray): (# num_vehicles, ndim); Optionl array of end nodes at which \n                                to end each vehicle's solution path\n        time_limit (int): TSP runtime time limit in seconds\n\n    Returns:\n        paths (ndarray): Solution paths\n        distances (list): List of path lengths\n    \"\"\"\n    if depth &gt; 5:\n        print('Warning: Max depth reached')\n        return None, None\n\n    # Add the start and end nodes to the node list\n    if end_nodes is not None:\n        assert end_nodes.shape == (num_vehicles, nodes.shape[-1]), \\\n            \"Incorrect end_nodes shape, should be (num_vehicles, ndim)!\"\n        nodes = np.concatenate([end_nodes, nodes])\n    if start_nodes is not None:\n        assert start_nodes.shape == (num_vehicles, nodes.shape[-1]), \\\n            \"Incorrect start_nodes shape, should be (num_vehicles, ndim)!\"\n        nodes = np.concatenate([start_nodes, nodes])\n\n    # Add dummy 0 location to get arbitrary start and end node sols\n    if start_nodes is None or end_nodes is None:\n        distance_mat = np.zeros((len(nodes)+1, len(nodes)+1))\n        distance_mat[1:, 1:] = pairwise_distances(nodes, nodes)*1e4\n        trim_paths = True #shift to account for dummy node\n    else:\n        distance_mat = pairwise_distances(nodes, nodes)*1e4\n        trim_paths = False\n    distance_mat = distance_mat.astype(int)\n    max_dist = int(max_dist*1e4)\n\n    # Get start and end node indices for ortools\n    if start_nodes is None:\n        start_idx = np.zeros(num_vehicles, dtype=int)\n        num_start_nodes = 0\n    else:\n        start_idx = np.arange(num_vehicles)+int(trim_paths)\n        num_start_nodes = len(start_nodes)\n\n    if end_nodes is None:\n        end_idx = np.zeros(num_vehicles, dtype=int)\n    else:\n        end_idx = np.arange(num_vehicles)+num_start_nodes+int(trim_paths)\n\n    # used by ortools\n    def distance_callback(from_index, to_index):\n        from_node = manager.IndexToNode(from_index)\n        to_node = manager.IndexToNode(to_index)\n        return distance_mat[from_node][to_node]\n\n    # num_locations, num vehicles, start, end\n    manager = pywrapcp.RoutingIndexManager(len(distance_mat), \n                                           num_vehicles, \n                                           start_idx.tolist(),\n                                           end_idx.tolist())\n    routing = pywrapcp.RoutingModel(manager)\n    transit_callback_index = routing.RegisterTransitCallback(distance_callback)\n    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n\n    if num_vehicles &gt; 1:\n        # Dummy distaance constraint to ensure all paths have similar length\n        dimension_name = \"Distance\"\n        routing.AddDimension(\n            transit_callback_index,\n            0,  # no slack\n            max_dist,  # vehicle maximum travel distance\n            True,  # start cumul to zero\n            dimension_name,\n        )\n        distance_dimension = routing.GetDimensionOrDie(dimension_name)\n        distance_dimension.SetGlobalSpanCostCoefficient(100)\n\n    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n    search_parameters.first_solution_strategy = (\n        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n    )\n    search_parameters.local_search_metaheuristic = (\n        routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH\n    )\n    search_parameters.time_limit.seconds = time_limit\n    solution = routing.SolveWithParameters(search_parameters)\n\n    paths = None\n    if solution is not None:\n        paths, distances = get_routes(manager, routing, \n                                      solution, num_vehicles, \n                                      start_idx, end_idx, trim_paths)\n        for path in paths:\n            if len(path) &lt; 2:\n                print('TSP Warning: Empty path detected')\n                return run_tsp(nodes, num_vehicles, int(np.mean(distances)*(1.5/depth)), depth+1)\n    else:\n        print('TSP Warning: No solution found')\n        return run_tsp(nodes, num_vehicles, int(max_dist*1.5), depth+1)\n\n    # Map paths from node indices to node locations\n    paths = [nodes[path] for path in paths]\n\n    # Resample each solution path to have resample number of points\n    if resample is not None:\n        paths = np.array([resample_path(path, resample) for path in paths])\n\n    # Convert distances back to floats in the original scale of the nodes\n    distances = np.array(distances)/1e4\n    return paths, distances\n</code></pre>"},{"location":"examples/AIPP_non_point_FoV.html","title":"Adaptive-IPP with Non-Point FoV","text":"In\u00a0[\u00a0]: Copied! <pre>import os\nos.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"\n\nimport numpy as np\n\nfrom sgptools.utils.metrics import *\nfrom sgptools.utils.data import *\nfrom sgptools.utils.misc import *\nfrom sgptools.models.core.osgpr import *\nfrom sgptools.utils.tsp import run_tsp\nfrom sgptools.utils.gpflow import get_model_params\nfrom sgptools.models.continuous_sgp import *\nfrom sgptools.models.core.transformations import *\nfrom gpflow.utilities.traversal import print_summary\n\nfrom matplotlib import colors\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Polygon\nimport mpl_toolkits.mplot3d.art3d as art3d\nfrom scipy.optimize import linear_sum_assignment\nfrom sklearn.metrics import pairwise_distances\n\nfrom time import time\nfrom copy import deepcopy\n\nnp.random.seed(1234)\ntf.random.set_seed(1234)\n</pre> import os os.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"  import numpy as np  from sgptools.utils.metrics import * from sgptools.utils.data import * from sgptools.utils.misc import * from sgptools.models.core.osgpr import * from sgptools.utils.tsp import run_tsp from sgptools.utils.gpflow import get_model_params from sgptools.models.continuous_sgp import * from sgptools.models.core.transformations import * from gpflow.utilities.traversal import print_summary  from matplotlib import colors import matplotlib.pyplot as plt from matplotlib.patches import Polygon import mpl_toolkits.mplot3d.art3d as art3d from scipy.optimize import linear_sum_assignment from sklearn.metrics import pairwise_distances  from time import time from copy import deepcopy  np.random.seed(1234) tf.random.set_seed(1234) In\u00a0[2]: Copied! <pre>'''\nUtility to map the inducing points to FoV vertices\n'''\ndef get_vertices(Xu, X_fov):\n    X_fov = X_fov.reshape(len(Xu), -1, 2)\n    vertices = np.zeros((len(Xu), 5, 2))\n    vertices[:, 0] = X_fov.min(axis=1)\n    vertices[:, 1] = np.array([X_fov[:, :, 0].min(axis=1), \n                              X_fov[:, :, 1].max(axis=1)]).T\n    vertices[:, 2] = X_fov.max(axis=1)\n    vertices[:, 3] = np.array([X_fov[:, :, 0].max(axis=1), \n                              X_fov[:, :, 1].min(axis=1)]).T\n    vertices[:, 4] = X_fov.mean(axis=1)\n\n    dists = pairwise_distances(vertices[:, 4], Y=Xu[:, :2], \n                               metric='euclidean')\n    _, idx = linear_sum_assignment(dists)\n\n    vertices[:, 4] = Xu[idx][:, :2]\n    return vertices\n\n'''\nMethod to plot the solution with FoVs and environment reconstruction\n'''\ndef plot_results(X_inducing, X_fov, current_idx=0, fname=None):\n    # Setup 3D plot\n    fig = plt.figure(figsize=(5, 5))\n    ax = fig.add_subplot(111, projection='3d', computed_zorder=False)\n\n    current_idx += 1 # Increment by one for plotting\n\n    # Plot the reconstructed environment state\n    num_fov_pts = len(X_fov)//len(X_inducing)\n    sensor_data = cont2disc(X_fov[:num_fov_pts*(current_idx)], X, y)\n    y_pred, _ = get_reconstruction(sensor_data, X_test, \n                                   noise_variance_opt, kernel_opt)\n    norm = colors.Normalize(y_train.min(), y_train.max())\n    _ = ax.plot_surface(X_test[:, 0].reshape(test_dim),\n                        X_test[:, 1].reshape(test_dim),\n                        np.atleast_2d(-0.1),\n                        facecolors=plt.cm.jet(norm(y_pred.reshape(test_dim))),\n                        shade=False,\n                        alpha=0.8,\n                        zorder=0)\n\n    # Plot the solution path\n    ax.scatter(X_inducing[:current_idx, 0], \n               X_inducing[:current_idx, 1], \n               X_inducing[:current_idx, 2], c='C3')\n    ax.scatter(X_inducing[current_idx:, 0], \n               X_inducing[current_idx:, 1], \n               X_inducing[current_idx:, 2], c='C2')\n    ax.plot(X_inducing[:, 0], X_inducing[:, 1], X_inducing[:, 2], 'k-')\n\n    # Plot the FoV vertices\n    vertices = get_vertices(X_inducing, X_fov)\n    for i in range(vertices.shape[0]):\n        color = 'C3' if i &lt; current_idx else 'C2'\n        verts = []\n        verts.append([vertices[i, 0], vertices[i, 1], \n                      vertices[i, 2], vertices[i, 3]])\n        fov = Polygon(np.array(verts)[0, :, :2],\n                      linewidth=1.5, \n                      edgecolor=color,\n                      facecolor=color,\n                      fill=False,\n                      zorder=15)\n        ax.add_patch(fov)\n        art3d.pathpatch_2d_to_3d(fov)\n\n    # Configure other plot settings\n    ax.set_xlabel('X')\n    ax.set_ylabel('Y')\n    ax.set_zlabel('Z')\n\n    ax.set_xlim(np.min(X[:, 0])-0.5, np.max(X[:, 0])+0.5)\n    ax.set_ylim(np.min(X[:, 1])-0.5, np.max(X[:, 1])+0.5)\n    ax.set_zlim(0, 14)\n\n    ax.view_init(elev=30, azim=45+180)\n    ax.set_title(f'Waypoint: {current_idx}, RMSE: {get_rmse(y_pred, y_test):.2f}\\nData Field Reconstruction', y=0.99)\n\n    plt.tight_layout()\n    if fname is not None:\n        plt.savefig(fname, bbox_inches='tight')\n    plt.show()\n</pre> ''' Utility to map the inducing points to FoV vertices ''' def get_vertices(Xu, X_fov):     X_fov = X_fov.reshape(len(Xu), -1, 2)     vertices = np.zeros((len(Xu), 5, 2))     vertices[:, 0] = X_fov.min(axis=1)     vertices[:, 1] = np.array([X_fov[:, :, 0].min(axis=1),                                X_fov[:, :, 1].max(axis=1)]).T     vertices[:, 2] = X_fov.max(axis=1)     vertices[:, 3] = np.array([X_fov[:, :, 0].max(axis=1),                                X_fov[:, :, 1].min(axis=1)]).T     vertices[:, 4] = X_fov.mean(axis=1)      dists = pairwise_distances(vertices[:, 4], Y=Xu[:, :2],                                 metric='euclidean')     _, idx = linear_sum_assignment(dists)      vertices[:, 4] = Xu[idx][:, :2]     return vertices  ''' Method to plot the solution with FoVs and environment reconstruction ''' def plot_results(X_inducing, X_fov, current_idx=0, fname=None):     # Setup 3D plot     fig = plt.figure(figsize=(5, 5))     ax = fig.add_subplot(111, projection='3d', computed_zorder=False)      current_idx += 1 # Increment by one for plotting      # Plot the reconstructed environment state     num_fov_pts = len(X_fov)//len(X_inducing)     sensor_data = cont2disc(X_fov[:num_fov_pts*(current_idx)], X, y)     y_pred, _ = get_reconstruction(sensor_data, X_test,                                     noise_variance_opt, kernel_opt)     norm = colors.Normalize(y_train.min(), y_train.max())     _ = ax.plot_surface(X_test[:, 0].reshape(test_dim),                         X_test[:, 1].reshape(test_dim),                         np.atleast_2d(-0.1),                         facecolors=plt.cm.jet(norm(y_pred.reshape(test_dim))),                         shade=False,                         alpha=0.8,                         zorder=0)      # Plot the solution path     ax.scatter(X_inducing[:current_idx, 0],                 X_inducing[:current_idx, 1],                 X_inducing[:current_idx, 2], c='C3')     ax.scatter(X_inducing[current_idx:, 0],                 X_inducing[current_idx:, 1],                 X_inducing[current_idx:, 2], c='C2')     ax.plot(X_inducing[:, 0], X_inducing[:, 1], X_inducing[:, 2], 'k-')      # Plot the FoV vertices     vertices = get_vertices(X_inducing, X_fov)     for i in range(vertices.shape[0]):         color = 'C3' if i &lt; current_idx else 'C2'         verts = []         verts.append([vertices[i, 0], vertices[i, 1],                        vertices[i, 2], vertices[i, 3]])         fov = Polygon(np.array(verts)[0, :, :2],                       linewidth=1.5,                        edgecolor=color,                       facecolor=color,                       fill=False,                       zorder=15)         ax.add_patch(fov)         art3d.pathpatch_2d_to_3d(fov)      # Configure other plot settings     ax.set_xlabel('X')     ax.set_ylabel('Y')     ax.set_zlabel('Z')      ax.set_xlim(np.min(X[:, 0])-0.5, np.max(X[:, 0])+0.5)     ax.set_ylim(np.min(X[:, 1])-0.5, np.max(X[:, 1])+0.5)     ax.set_zlim(0, 14)      ax.view_init(elev=30, azim=45+180)     ax.set_title(f'Waypoint: {current_idx}, RMSE: {get_rmse(y_pred, y_test):.2f}\\nData Field Reconstruction', y=0.99)      plt.tight_layout()     if fname is not None:         plt.savefig(fname, bbox_inches='tight')     plt.show() In\u00a0[3]: Copied! <pre>'''\nMethod to run AIPP using the SGP-based approach\n'''\ndef run_aipp(X_train, ipp_model, Xu_init, path2data):\n    total_time_param = 0\n    total_time_ipp = 0\n    num_robots = Xu_init.shape[0]\n    num_waypoints = Xu_init.shape[1]\n    curr_sol = Xu_init\n    plot_results(ipp_model.transform.expand(curr_sol.reshape(-1, 3), \n                                            expand_sensor_model=False).numpy(),\n                 ipp_model.transform.expand(curr_sol.reshape(-1, 3)).numpy())\n\n    # Initialize the hyperparameters and SSGP\n    init_kernel = deepcopy(ipp_model.kernel)\n    init_noise_variance = ipp_model.likelihood.variance\n    param_model = init_osgpr(X_train, \n                             num_inducing=40, \n                             kernel=init_kernel,\n                             noise_variance=init_noise_variance)\n\n    sol_data_X = []\n    sol_data_y = []\n    for time_step in range(num_waypoints):\n        # Get a new batch of data from the last visited waypoint\n        last_visited = curr_sol[:, time_step].copy()\n        # Apply the sensor model expansion with fixed points IPP expansion\n        data_pts = ipp_model.transform.sensor_model.expand(last_visited).numpy()\n        data_X_batch, data_y_batch = path2data(data_pts)\n        sol_data_X.extend(data_X_batch)\n        sol_data_y.extend(data_y_batch)\n\n        # Skip param and path update for the last waypoint\n        if time_step == num_waypoints-1:\n            break\n\n        # Update the SSGP hyperparameter model\n        param_model.update((np.array(data_X_batch), \n                            np.array(data_y_batch)))\n        # Train only the kernel parameters and noise variance.\n        # Keeping the SSGP inducing points fixed results in better \n        # hyperparameter estimation\n        start_time = time()\n        optimize_model(param_model, \n                       trainable_variables=param_model.trainable_variables[1:], \n                       optimizer='scipy',\n                       method='CG')\n        end_time = time()\n        total_time_param += end_time - start_time\n        print_summary(param_model.kernel)\n\n        # Update the SGP-IPP model\n        Xu_visited = curr_sol.copy()[:, :time_step+1]\n        ipp_model.transform.update_Xu_fixed(Xu_visited)\n        ipp_model.update(param_model.likelihood.variance, \n                         param_model.kernel)\n        start_time = time()\n        _ = optimize_model(ipp_model,\n                           kernel_grad=False, \n                           optimizer='scipy',\n                           method='CG')\n        end_time = time()\n        total_time_ipp += end_time - start_time\n        curr_sol = ipp_model.inducing_variable.Z\n        curr_sol = ipp_model.transform.expand(curr_sol, \n                                              expand_sensor_model=False).numpy()\n        curr_sol = curr_sol.reshape(num_robots, num_waypoints, 3)\n\n        plot_results(ipp_model.transform.expand(curr_sol.reshape(-1, 3),\n                                                expand_sensor_model=False).numpy(),\n                     ipp_model.transform.expand(curr_sol.reshape(-1, 3)).numpy(),\n                     time_step+1)\n\n    return np.array(sol_data_X), np.array(sol_data_y), total_time_param, total_time_ipp\n</pre> ''' Method to run AIPP using the SGP-based approach ''' def run_aipp(X_train, ipp_model, Xu_init, path2data):     total_time_param = 0     total_time_ipp = 0     num_robots = Xu_init.shape[0]     num_waypoints = Xu_init.shape[1]     curr_sol = Xu_init     plot_results(ipp_model.transform.expand(curr_sol.reshape(-1, 3),                                              expand_sensor_model=False).numpy(),                  ipp_model.transform.expand(curr_sol.reshape(-1, 3)).numpy())      # Initialize the hyperparameters and SSGP     init_kernel = deepcopy(ipp_model.kernel)     init_noise_variance = ipp_model.likelihood.variance     param_model = init_osgpr(X_train,                               num_inducing=40,                               kernel=init_kernel,                              noise_variance=init_noise_variance)      sol_data_X = []     sol_data_y = []     for time_step in range(num_waypoints):         # Get a new batch of data from the last visited waypoint         last_visited = curr_sol[:, time_step].copy()         # Apply the sensor model expansion with fixed points IPP expansion         data_pts = ipp_model.transform.sensor_model.expand(last_visited).numpy()         data_X_batch, data_y_batch = path2data(data_pts)         sol_data_X.extend(data_X_batch)         sol_data_y.extend(data_y_batch)          # Skip param and path update for the last waypoint         if time_step == num_waypoints-1:             break          # Update the SSGP hyperparameter model         param_model.update((np.array(data_X_batch),                              np.array(data_y_batch)))         # Train only the kernel parameters and noise variance.         # Keeping the SSGP inducing points fixed results in better          # hyperparameter estimation         start_time = time()         optimize_model(param_model,                         trainable_variables=param_model.trainable_variables[1:],                         optimizer='scipy',                        method='CG')         end_time = time()         total_time_param += end_time - start_time         print_summary(param_model.kernel)          # Update the SGP-IPP model         Xu_visited = curr_sol.copy()[:, :time_step+1]         ipp_model.transform.update_Xu_fixed(Xu_visited)         ipp_model.update(param_model.likelihood.variance,                           param_model.kernel)         start_time = time()         _ = optimize_model(ipp_model,                            kernel_grad=False,                             optimizer='scipy',                            method='CG')         end_time = time()         total_time_ipp += end_time - start_time         curr_sol = ipp_model.inducing_variable.Z         curr_sol = ipp_model.transform.expand(curr_sol,                                                expand_sensor_model=False).numpy()         curr_sol = curr_sol.reshape(num_robots, num_waypoints, 3)          plot_results(ipp_model.transform.expand(curr_sol.reshape(-1, 3),                                                 expand_sensor_model=False).numpy(),                      ipp_model.transform.expand(curr_sol.reshape(-1, 3)).numpy(),                      time_step+1)      return np.array(sol_data_X), np.array(sol_data_y), total_time_param, total_time_ipp In\u00a0[4]: Copied! <pre># Get the synthetic elevation data\nX_train, y_train, X_test, y_test, candidates, X, y = get_dataset(random_seed=0)\ntrain_dim = np.sqrt(X_train.shape[0]).astype(int)\n\n# Train a GP and get the model parameters\nprint('Optimized Hyperparameters')\n_, noise_variance_opt, kernel_opt = get_model_params(X_train, y_train, \n                                                     lengthscales=[1.0, 1.0], \n                                                     optimizer='scipy')\n\n# Build a new test set on a grid for easy plotting in a 3D plot\ngpr = gpflow.models.GPR((X_test, y_test),\n                        noise_variance=noise_variance_opt,\n                        kernel=kernel_opt)\ntest_dim = (100, 100)\nX_test1 = np.linspace(np.min(X[:, 0]), np.max(X[:, 0]), test_dim[0])\nX_test2 = np.linspace(np.min(X[:, 1]), np.max(X[:, 1]), test_dim[1])\nX_test1, X_test2 = np.meshgrid(X_test1, X_test2)\nX_test = np.stack([X_test1.ravel(), X_test2.ravel()], axis=1)\ny_test, y_var = gpr.predict_f(X_test)\ny_test = y_test.numpy()\n\n# Plot the data field\nplt.imshow(y_test.reshape(test_dim), cmap=\"jet\", origin='lower')\nplt.xlabel('X')\nplt.ylabel('Y')\nplt.title('Elevation Data Field')\nplt.colorbar(label='Normalized Elevation')\nplt.show()\n</pre> # Get the synthetic elevation data X_train, y_train, X_test, y_test, candidates, X, y = get_dataset(random_seed=0) train_dim = np.sqrt(X_train.shape[0]).astype(int)  # Train a GP and get the model parameters print('Optimized Hyperparameters') _, noise_variance_opt, kernel_opt = get_model_params(X_train, y_train,                                                       lengthscales=[1.0, 1.0],                                                       optimizer='scipy')  # Build a new test set on a grid for easy plotting in a 3D plot gpr = gpflow.models.GPR((X_test, y_test),                         noise_variance=noise_variance_opt,                         kernel=kernel_opt) test_dim = (100, 100) X_test1 = np.linspace(np.min(X[:, 0]), np.max(X[:, 0]), test_dim[0]) X_test2 = np.linspace(np.min(X[:, 1]), np.max(X[:, 1]), test_dim[1]) X_test1, X_test2 = np.meshgrid(X_test1, X_test2) X_test = np.stack([X_test1.ravel(), X_test2.ravel()], axis=1) y_test, y_var = gpr.predict_f(X_test) y_test = y_test.numpy()  # Plot the data field plt.imshow(y_test.reshape(test_dim), cmap=\"jet\", origin='lower') plt.xlabel('X') plt.ylabel('Y') plt.title('Elevation Data Field') plt.colorbar(label='Normalized Elevation') plt.show() <pre>Optimized Hyperparameters\n\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.8371517535182331  \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [2.45802 2.50701]   \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.04342255274528023 \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> In\u00a0[5]: Copied! <pre>num_robots = 1\nnum_placements = 12\nnum_side = 5  # Use a 5x5 point FoV approximation\n\n# Setup the initial AIPP hyperparameters\nprint('Initial AIPP Hyperparameters')\n_, noise_variance, kernel = get_model_params(X_train, y_train, \n                                             lengthscales=[1.0, 1.0], \n                                             optimizer='scipy',\n                                             max_steps=0)\n\n# Get initial inducing points\nXu_init = get_inducing_pts(X_train, num_placements)\n# Initalize the height dimension at 2.0 for all points\nXu_init = np.concatenate((Xu_init, np.ones((Xu_init.shape[0], 1))*2.0), axis=1)\nXu_init[:, :2] = run_tsp(Xu_init[:, :2])[0][0]\n\n# Initialize the non-point FoV transform\nfov_transform = SquareHeightTransform(num_side=num_side)\ntransform = IPPTransform(num_dim=3,\n                         num_robots=1,\n                         sensor_model=fov_transform,\n                         aggregate_fov=True)\n\n# Initialize the IPP model\nsgpr, _ = continuous_sgp(num_placements,\n                         X_train, \n                         noise_variance,\n                         kernel,\n                         transform=transform,\n                         Xu_init=Xu_init,\n                         max_steps=0)\nsgp_sol_sp = sgpr.inducing_variable.Z.numpy()\n\n# Setup the data acquisition function to emulate the robot's sensor\npath2data = lambda x : cont2disc(x, X, y)\n\n# Generate the SGP-based AIPP solution\n_, _, param_time, ipp_time = run_aipp(X_train, \n                                      sgpr,\n                                      Xu_init.reshape(1, -1, 3), \n                                      path2data)\n\nprint(f'Total Hyperparameter Update Time:   {param_time:.2f}s')\nprint(f'Total IPP Update Time:              {ipp_time:.2f}s')\n\nprint(f'Average Hyperparameter Update Time: {param_time/num_placements:.2f}s')\nprint(f'Average IPP Update Time:            {ipp_time/num_placements:.2f}s')\n</pre> num_robots = 1 num_placements = 12 num_side = 5  # Use a 5x5 point FoV approximation  # Setup the initial AIPP hyperparameters print('Initial AIPP Hyperparameters') _, noise_variance, kernel = get_model_params(X_train, y_train,                                               lengthscales=[1.0, 1.0],                                               optimizer='scipy',                                              max_steps=0)  # Get initial inducing points Xu_init = get_inducing_pts(X_train, num_placements) # Initalize the height dimension at 2.0 for all points Xu_init = np.concatenate((Xu_init, np.ones((Xu_init.shape[0], 1))*2.0), axis=1) Xu_init[:, :2] = run_tsp(Xu_init[:, :2])[0][0]  # Initialize the non-point FoV transform fov_transform = SquareHeightTransform(num_side=num_side) transform = IPPTransform(num_dim=3,                          num_robots=1,                          sensor_model=fov_transform,                          aggregate_fov=True)  # Initialize the IPP model sgpr, _ = continuous_sgp(num_placements,                          X_train,                           noise_variance,                          kernel,                          transform=transform,                          Xu_init=Xu_init,                          max_steps=0) sgp_sol_sp = sgpr.inducing_variable.Z.numpy()  # Setup the data acquisition function to emulate the robot's sensor path2data = lambda x : cont2disc(x, X, y)  # Generate the SGP-based AIPP solution _, _, param_time, ipp_time = run_aipp(X_train,                                        sgpr,                                       Xu_init.reshape(1, -1, 3),                                        path2data)  print(f'Total Hyperparameter Update Time:   {param_time:.2f}s') print(f'Total IPP Update Time:              {ipp_time:.2f}s')  print(f'Average Hyperparameter Update Time: {param_time/num_placements:.2f}s') print(f'Average IPP Update Time:            {ipp_time/num_placements:.2f}s') <pre>Initial AIPP Hyperparameters\n\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 1.0                 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [1. 1.]             \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.09999999999999999 \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.02830909998623969 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [2.52935 4.99828]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.03608860733575918 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.66389 4.2298 ]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value                \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.047688497211455655 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.6262  4.20678]    \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value                \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.047733701955419004 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.09326 4.03863]    \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.05702257365194937 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.27988 4.50424]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.06604187384338019 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.3692  4.76543]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.09160363580094916 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.83409 5.53632]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.09052131666299722 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.84722 5.51582]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.08877117097798798 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.63615 4.87825]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value              \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.0936660767287354 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.72264 5.59398]  \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.10593340089380918 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.63767 6.41376]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>Total Hyperparameter Update Time:   22.51s\nTotal IPP Update Time:              31.34s\nAverage Hyperparameter Update Time: 1.88s\nAverage IPP Update Time:            2.61s\n</pre>"},{"location":"examples/AIPP_non_point_FoV.html#adaptive-ipp-with-non-point-fov","title":"Adaptive-IPP with Non-Point FoV\u00b6","text":""},{"location":"examples/AIPP_non_point_FoV.html#generate-synthetic-data-and-initial-sgp-hyperparameters","title":"Generate synthetic data and initial SGP hyperparameters\u00b6","text":""},{"location":"examples/AIPP_non_point_FoV.html#generate-the-aipp-solution","title":"Generate the AIPP solution\u00b6","text":""},{"location":"examples/Baselines.html","title":"Baselines","text":"In\u00a0[\u00a0]: Copied! <pre>import os\nos.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"\n\nimport numpy as np\nfrom time import time\nimport tensorflow as tf\n\nfrom sgptools.utils.data import *\nfrom sgptools.utils.misc import *\nfrom sgptools.utils.metrics import *\nfrom sgptools.utils.tsp import run_tsp\nfrom sgptools.utils.gpflow import get_model_params\n\nfrom sgptools.models.bo import *\nfrom sgptools.models.cma_es import *\nfrom sgptools.models.greedy_mi import *\nfrom sgptools.models.greedy_sgp import *\nfrom sgptools.models.continuous_sgp import *\nfrom sgptools.models.core.transformations import IPPTransform\n\nimport matplotlib.pyplot as plt\n\nnp.random.seed(1234)\ntf.random.set_seed(1234)\n</pre> import os os.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"  import numpy as np from time import time import tensorflow as tf  from sgptools.utils.data import * from sgptools.utils.misc import * from sgptools.utils.metrics import * from sgptools.utils.tsp import run_tsp from sgptools.utils.gpflow import get_model_params  from sgptools.models.bo import * from sgptools.models.cma_es import * from sgptools.models.greedy_mi import * from sgptools.models.greedy_sgp import * from sgptools.models.continuous_sgp import * from sgptools.models.core.transformations import IPPTransform  import matplotlib.pyplot as plt  np.random.seed(1234) tf.random.set_seed(1234) In\u00a0[2]: Copied! <pre>num_robots = 2\nnum_placements = 10\n\n# Configure IPPTransform\ntransform = IPPTransform(num_robots=num_robots)\n\n# Get the synthetic elevation data\nX_train, y_train, X_test, y_test, candidates, X, y = get_dataset()\n\n# Train the GP and get the model parameters\n_, noise_variance, kernel = get_model_params(X_train, y_train, \n                                             lengthscales=[3.0, 3.0], \n                                             optimizer='scipy',\n                                             max_steps=0)\nplt.scatter(X[:, 0], X[:, 1], c=y, cmap='jet')\nplt.title(\"Ground Truth\")\nplt.xlabel('X')\nplt.ylabel('Y')\nplt.gca().set_aspect('equal')\nplt.show()\n\n# Get the initial waypoints\nXu_init = get_inducing_pts(X_train, num_placements*num_robots)\nXu_init, _ = run_tsp(Xu_init, \n                     num_vehicles=num_robots,\n                     max_dist=100,\n                     resample=num_placements)\nXu_init = Xu_init.reshape(-1, 2)\n</pre> num_robots = 2 num_placements = 10  # Configure IPPTransform transform = IPPTransform(num_robots=num_robots)  # Get the synthetic elevation data X_train, y_train, X_test, y_test, candidates, X, y = get_dataset()  # Train the GP and get the model parameters _, noise_variance, kernel = get_model_params(X_train, y_train,                                               lengthscales=[3.0, 3.0],                                               optimizer='scipy',                                              max_steps=0) plt.scatter(X[:, 0], X[:, 1], c=y, cmap='jet') plt.title(\"Ground Truth\") plt.xlabel('X') plt.ylabel('Y') plt.gca().set_aspect('equal') plt.show()  # Get the initial waypoints Xu_init = get_inducing_pts(X_train, num_placements*num_robots) Xu_init, _ = run_tsp(Xu_init,                       num_vehicles=num_robots,                      max_dist=100,                      resample=num_placements) Xu_init = Xu_init.reshape(-1, 2) <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 1.0                 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3. 3.]             \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.09999999999999999 \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> In\u00a0[3]: Copied! <pre># Convert a given path to the data (X, y) alongst the path\ndef path2data(path, X, y):\n    data_X = []\n    data_y = []\n    num_robots, _, _ = path.shape\n    for r in range(num_robots):\n        X_new, y_new = cont2disc(path[r], X, y)\n        data_X.extend(X_new)\n        data_y.extend(y_new)\n    return np.array(data_X), np.array(data_y)\n\n# Plot the solution paths\ndef plot_paths(paths, title=None):\n    plt.figure()\n    for i, path in enumerate(paths):\n        plt.plot(path[:, 0], path[:, 1], \n                 c='r', label='Path', \n                 zorder=1, marker='o')\n    if title is not None:\n        plt.title(title)\n    plt.xlabel('X')\n    plt.ylabel('Y')\n</pre> # Convert a given path to the data (X, y) alongst the path def path2data(path, X, y):     data_X = []     data_y = []     num_robots, _, _ = path.shape     for r in range(num_robots):         X_new, y_new = cont2disc(path[r], X, y)         data_X.extend(X_new)         data_y.extend(y_new)     return np.array(data_X), np.array(data_y)  # Plot the solution paths def plot_paths(paths, title=None):     plt.figure()     for i, path in enumerate(paths):         plt.plot(path[:, 0], path[:, 1],                   c='r', label='Path',                   zorder=1, marker='o')     if title is not None:         plt.title(title)     plt.xlabel('X')     plt.ylabel('Y') In\u00a0[4]: Copied! <pre>start = time()\nmodel, _ = continuous_sgp(num_placements, \n                          X_train, \n                          noise_variance, \n                          kernel,\n                          transform,\n                          Xu_init=Xu_init.copy())\nend = time()\nrun_time = end - start\n\n# Get solution inducing points\nsol = model.inducing_variable.Z.numpy()\nsol_paths = sol.reshape(num_robots, num_placements, 2)\n\n# Convert solution path waypoints to sensing points along the whole path\nX_path, y_path = path2data(sol_paths, X, y)\n\n# Compute RMSE\ny_pred, _ = get_reconstruction((X_path, y_path), \n                                X, \n                                noise_variance, \n                                kernel)\nrmse = get_rmse(y_pred, y)\n\n# Plot the solution\nplot_paths(sol_paths, title=f\"Continuous-SGP | RMSE: {rmse:.2f} | Time: {run_time:.2f}s\")\nplt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet')\nplt.gca().set_aspect('equal')\n</pre> start = time() model, _ = continuous_sgp(num_placements,                            X_train,                            noise_variance,                            kernel,                           transform,                           Xu_init=Xu_init.copy()) end = time() run_time = end - start  # Get solution inducing points sol = model.inducing_variable.Z.numpy() sol_paths = sol.reshape(num_robots, num_placements, 2)  # Convert solution path waypoints to sensing points along the whole path X_path, y_path = path2data(sol_paths, X, y)  # Compute RMSE y_pred, _ = get_reconstruction((X_path, y_path),                                  X,                                  noise_variance,                                  kernel) rmse = get_rmse(y_pred, y)  # Plot the solution plot_paths(sol_paths, title=f\"Continuous-SGP | RMSE: {rmse:.2f} | Time: {run_time:.2f}s\") plt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet') plt.gca().set_aspect('equal') In\u00a0[5]: Copied! <pre>start = time()\nsol = get_greedy_sgp_sol(num_placements*num_robots, \n                         candidates, \n                         candidates, \n                         noise_variance, \n                         kernel,\n                         transform=transform)\nend = time()\nrun_time = end - start\nsol_paths = sol.reshape(num_robots, num_placements, 2)\n\n# Convert solution path waypoints to sensing points along the whole path\nX_path, y_path = path2data(sol_paths, X, y)\n\n# Compute RMSE\ny_pred, _ = get_reconstruction((X_path, y_path), \n                                X, \n                                noise_variance, \n                                kernel)\nrmse = get_rmse(y_pred, y)\n\n# Plot the solution\nplot_paths(sol_paths, title=f\"Discrete-SGP | RMSE: {rmse:.2f} | Time: {run_time:.2f}s\")\nplt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet')\nplt.gca().set_aspect('equal')\n</pre> start = time() sol = get_greedy_sgp_sol(num_placements*num_robots,                           candidates,                           candidates,                           noise_variance,                           kernel,                          transform=transform) end = time() run_time = end - start sol_paths = sol.reshape(num_robots, num_placements, 2)  # Convert solution path waypoints to sensing points along the whole path X_path, y_path = path2data(sol_paths, X, y)  # Compute RMSE y_pred, _ = get_reconstruction((X_path, y_path),                                  X,                                  noise_variance,                                  kernel) rmse = get_rmse(y_pred, y)  # Plot the solution plot_paths(sol_paths, title=f\"Discrete-SGP | RMSE: {rmse:.2f} | Time: {run_time:.2f}s\") plt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet') plt.gca().set_aspect('equal') In\u00a0[6]: Copied! <pre>start = time()\nsol = get_greedy_mi_sol(num_placements*num_robots, \n                        candidates, \n                        candidates, \n                        noise_variance, \n                        kernel,\n                        transform=transform)\nend = time()\nrun_time = end - start\nsol_paths = sol.reshape(num_robots, num_placements, 2)\n\n# Convert solution path waypoints to sensing points along the whole path\nX_path, y_path = path2data(sol_paths, X, y)\n\n# Compute RMSE\ny_pred, _ = get_reconstruction((X_path, y_path), \n                                X, \n                                noise_variance, \n                                kernel)\nrmes = get_rmse(y_pred, y)\n\n# Plot the solution\nplot_paths(sol_paths, title=f\"Greedy-MI | RMSE: {rmes:.2f} | Time: {run_time:.2f}s\")\nplt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet')\nplt.gca().set_aspect('equal')\n</pre> start = time() sol = get_greedy_mi_sol(num_placements*num_robots,                          candidates,                          candidates,                          noise_variance,                          kernel,                         transform=transform) end = time() run_time = end - start sol_paths = sol.reshape(num_robots, num_placements, 2)  # Convert solution path waypoints to sensing points along the whole path X_path, y_path = path2data(sol_paths, X, y)  # Compute RMSE y_pred, _ = get_reconstruction((X_path, y_path),                                  X,                                  noise_variance,                                  kernel) rmes = get_rmse(y_pred, y)  # Plot the solution plot_paths(sol_paths, title=f\"Greedy-MI | RMSE: {rmes:.2f} | Time: {run_time:.2f}s\") plt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet') plt.gca().set_aspect('equal') In\u00a0[7]: Copied! <pre>start = time()\nmodel = CMA_ES(candidates, \n               noise_variance, \n               kernel, \n               transform=transform)\nsol = model.optimize(num_sensors=num_placements*num_robots, \n                     X_init=Xu_init.copy())\nend = time()\nrun_time = end - start\nsol_paths = sol.reshape(num_robots, num_placements, 2)\n\n# Convert solution path waypoints to sensing points along the whole path\nX_path, y_path = path2data(sol_paths, X, y)\ny_pred, _ = get_reconstruction((X_path, y_path), \n                                X, \n                                noise_variance, \n                                kernel)\n\n# Compute RMSE\nrmes = get_rmse(y_pred, y)\n\n# Plot the solution\nplot_paths(sol_paths, title=f\"CMA-ES-MI | RMSE: {rmes:.2f} | Time: {run_time:.2f}s\")\nplt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet')\nplt.gca().set_aspect('equal')\n</pre> start = time() model = CMA_ES(candidates,                 noise_variance,                 kernel,                 transform=transform) sol = model.optimize(num_sensors=num_placements*num_robots,                       X_init=Xu_init.copy()) end = time() run_time = end - start sol_paths = sol.reshape(num_robots, num_placements, 2)  # Convert solution path waypoints to sensing points along the whole path X_path, y_path = path2data(sol_paths, X, y) y_pred, _ = get_reconstruction((X_path, y_path),                                  X,                                  noise_variance,                                  kernel)  # Compute RMSE rmes = get_rmse(y_pred, y)  # Plot the solution plot_paths(sol_paths, title=f\"CMA-ES-MI | RMSE: {rmes:.2f} | Time: {run_time:.2f}s\") plt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet') plt.gca().set_aspect('equal') In\u00a0[8]: Copied! <pre>start = time()\nmodel = BayesianOpt(candidates, \n                    noise_variance,\n                    kernel,\n                    transform=transform)\nsol = model.optimize(num_sensors=num_placements*num_robots, \n                     X_init=Xu_init.copy())\nend = time()\nrun_time = end - start\nsol_paths = sol.reshape(num_robots, num_placements, 2)\n\n# Convert solution path waypoints to sensing points along the whole path\nX_path, y_path = path2data(sol_paths, X, y)\ny_pred, _ = get_reconstruction((X_path, y_path), \n                                X, \n                                noise_variance, \n                                kernel)\n\n# Compute RMSE\nrmes = get_rmse(y_pred, y)\n\n# Plot the solution\nplot_paths(sol_paths, title=f\"BO-MI | RMSE: {rmes:.2f} | Time: {run_time:.2f}s\")\nplt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet')\nplt.gca().set_aspect('equal')\n</pre> start = time() model = BayesianOpt(candidates,                      noise_variance,                     kernel,                     transform=transform) sol = model.optimize(num_sensors=num_placements*num_robots,                       X_init=Xu_init.copy()) end = time() run_time = end - start sol_paths = sol.reshape(num_robots, num_placements, 2)  # Convert solution path waypoints to sensing points along the whole path X_path, y_path = path2data(sol_paths, X, y) y_pred, _ = get_reconstruction((X_path, y_path),                                  X,                                  noise_variance,                                  kernel)  # Compute RMSE rmes = get_rmse(y_pred, y)  # Plot the solution plot_paths(sol_paths, title=f\"BO-MI | RMSE: {rmes:.2f} | Time: {run_time:.2f}s\") plt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet') plt.gca().set_aspect('equal')"},{"location":"examples/Baselines.html#baselines","title":"Baselines\u00b6","text":""},{"location":"examples/Baselines.html#set-the-initial-parameters-and-generate-synthetic-data","title":"Set the initial parameters and generate synthetic data\u00b6","text":"<p>(no distance budget constraints)</p>"},{"location":"examples/Baselines.html#helper-functions","title":"Helper functions\u00b6","text":""},{"location":"examples/Baselines.html#continuous-sgp","title":"Continuous-SGP\u00b6","text":""},{"location":"examples/Baselines.html#discrete-sgp","title":"Discrete-SGP\u00b6","text":""},{"location":"examples/Baselines.html#greedy-mutual-information","title":"Greedy Mutual Information\u00b6","text":""},{"location":"examples/Baselines.html#cma-es-mutual-information-genetic-algorithm","title":"CMA-ES Mutual Information (Genetic Algorithm)\u00b6","text":""},{"location":"examples/Baselines.html#bayesian-optimization-mutual-information","title":"Bayesian Optimization Mutual Information\u00b6","text":""},{"location":"examples/IPP.html","title":"SGP-Based IPP","text":"In\u00a0[\u00a0]: Copied! <pre>import os\nos.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom sgptools.utils.metrics import *\nfrom sgptools.utils.data import *\nfrom sgptools.utils.misc import *\nfrom sgptools.utils.tsp import run_tsp\nfrom sgptools.utils.gpflow import get_model_params\nfrom sgptools.models.continuous_sgp import *\nfrom sgptools.models.core.transformations import *\n\nimport matplotlib.pyplot as plt\n\nnp.random.seed(1234)\ntf.random.set_seed(1234)\n</pre> import os os.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"  import numpy as np import tensorflow as tf  from sgptools.utils.metrics import * from sgptools.utils.data import * from sgptools.utils.misc import * from sgptools.utils.tsp import run_tsp from sgptools.utils.gpflow import get_model_params from sgptools.models.continuous_sgp import * from sgptools.models.core.transformations import *  import matplotlib.pyplot as plt  np.random.seed(1234) tf.random.set_seed(1234) In\u00a0[2]: Copied! <pre># Get the synthetic elevation data\nX_train, y_train, X_test, y_test, candidates, X, y = get_dataset()\n\n# Train the GP and get the model parameters\n_, noise_variance, kernel = get_model_params(X_train, y_train, \n                                             lengthscales=[1.0, 1.0], \n                                             optimizer='scipy')\nplt.scatter(X[:, 0], X[:, 1], c=y, cmap='jet')\nplt.title(\"Ground Truth\")\nplt.xlabel('X')\nplt.ylabel('Y')\nplt.gca().set_aspect('equal')\nplt.show()\n</pre> # Get the synthetic elevation data X_train, y_train, X_test, y_test, candidates, X, y = get_dataset()  # Train the GP and get the model parameters _, noise_variance, kernel = get_model_params(X_train, y_train,                                               lengthscales=[1.0, 1.0],                                               optimizer='scipy') plt.scatter(X[:, 0], X[:, 1], c=y, cmap='jet') plt.title(\"Ground Truth\") plt.xlabel('X') plt.ylabel('Y') plt.gca().set_aspect('equal') plt.show() <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.6576268242419765  \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [2.95895 2.25221]   \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.02576014673391919 \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> In\u00a0[3]: Copied! <pre>num_robots = 1\nnum_placements = 6\n\n# Initialize inducing points and get initial path\nXu_init = get_inducing_pts(X_train, \n                           num_placements*num_robots)\nXu_init, _ = run_tsp(Xu_init, \n                     num_vehicles=num_robots)\nXu_init = Xu_init[0]\n\n# Setup unconstrained IPP transform and optimize it\ntransform = IPPTransform(num_robots=num_robots, num_dim=2)\nsgpr, _ = continuous_sgp(num_placements, \n                         X_train, \n                         noise_variance, \n                         kernel,\n                         transform,\n                         Xu_init=Xu_init, \n                         max_steps=1000)\n\n# Get solution inducing points\nsgp_sol_sp = sgpr.inducing_variable.Z.numpy()\nsgp_paths = sgp_sol_sp.reshape(num_robots, num_placements, 2)\n\n# Plot the solution\nplot_paths(sgp_paths, candidates,\n           title=\"Offline Single-Robot IPP with Point Sensing\")\n</pre> num_robots = 1 num_placements = 6  # Initialize inducing points and get initial path Xu_init = get_inducing_pts(X_train,                             num_placements*num_robots) Xu_init, _ = run_tsp(Xu_init,                       num_vehicles=num_robots) Xu_init = Xu_init[0]  # Setup unconstrained IPP transform and optimize it transform = IPPTransform(num_robots=num_robots, num_dim=2) sgpr, _ = continuous_sgp(num_placements,                           X_train,                           noise_variance,                           kernel,                          transform,                          Xu_init=Xu_init,                           max_steps=1000)  # Get solution inducing points sgp_sol_sp = sgpr.inducing_variable.Z.numpy() sgp_paths = sgp_sol_sp.reshape(num_robots, num_placements, 2)  # Plot the solution plot_paths(sgp_paths, candidates,            title=\"Offline Single-Robot IPP with Point Sensing\") In\u00a0[4]: Copied! <pre>num_robots = 1\nnum_placements = 6\n\n# Initialize inducing points and get initial path\nXu_init = get_inducing_pts(X_train, num_placements*num_robots)\nXu_init, _ = run_tsp(Xu_init, \n                     num_vehicles=num_robots)\nXu_init = Xu_init[0]\n\n# Setup unconstrained IPP transform with sampling rate=5\n# and optimize it\ntransform = IPPTransform(num_robots=num_robots,\n                           num_dim=2,\n                           sampling_rate=5)\nsgpr, _ = continuous_sgp(num_placements, \n                         X_train, \n                         noise_variance, \n                         kernel,\n                         transform,\n                         Xu_init=Xu_init, \n                         max_steps=1000)\n\n# Get solution inducing points\nsgp_sol_sp = sgpr.inducing_variable.Z.numpy()\nsgp_paths = sgp_sol_sp.reshape(num_robots, num_placements, 2)\n\n# Plot the solution\nplot_paths(sgp_paths, candidates, \n           title=\"Offline Single-Robot IPP with Continuous Sensing\")\n</pre> num_robots = 1 num_placements = 6  # Initialize inducing points and get initial path Xu_init = get_inducing_pts(X_train, num_placements*num_robots) Xu_init, _ = run_tsp(Xu_init,                       num_vehicles=num_robots) Xu_init = Xu_init[0]  # Setup unconstrained IPP transform with sampling rate=5 # and optimize it transform = IPPTransform(num_robots=num_robots,                            num_dim=2,                            sampling_rate=5) sgpr, _ = continuous_sgp(num_placements,                           X_train,                           noise_variance,                           kernel,                          transform,                          Xu_init=Xu_init,                           max_steps=1000)  # Get solution inducing points sgp_sol_sp = sgpr.inducing_variable.Z.numpy() sgp_paths = sgp_sol_sp.reshape(num_robots, num_placements, 2)  # Plot the solution plot_paths(sgp_paths, candidates,             title=\"Offline Single-Robot IPP with Continuous Sensing\") In\u00a0[5]: Copied! <pre>num_robots = 1\nnum_placements = 4\n\n# Initialize inducing points and get initial path\nXu_init = get_inducing_pts(X_train, num_placements*num_robots)\nXu_init, _ = run_tsp(Xu_init, \n                     num_vehicles=num_robots)\nXu_init = Xu_init[0]\n\n# Add random angles to inducing points to initialize the rotation angle of the FoVs\nangles = np.random.uniform(-np.pi, np.pi, num_placements).reshape(-1, 1)\nXu_init = np.concatenate([Xu_init, angles], axis=1)\n\n# Setup unconstrained IPP transform with square FoV transform \n# with length 5 and 3x3 point approximation, \n# and continuous sensing model (sampling rate of 3), and optimize it\nsquare_transform = SquareTransform(length=5, num_side=3)\ntransform = IPPTransform(num_robots=num_robots,\n                         num_dim=3,\n                         sampling_rate=3,\n                         sensor_model=square_transform)\nsgpr, _ = continuous_sgp(num_placements, \n                         X_train, \n                         noise_variance, \n                         kernel,\n                         transform,\n                         Xu_init=Xu_init, \n                         max_steps=1000)\n\n# Get solution inducing points\nsgp_sol_sp = sgpr.inducing_variable.Z.numpy()[:, :2]\nsgp_paths = sgp_sol_sp.reshape(num_robots, -1 , 2)\n\n# Plot the solution path and the FoVs\nplot_paths(sgp_paths, candidates, \n           title=\"Offline Single-Robot IPP with Continuous Sensing\")\nsgp_sol_sp = sgpr.inducing_variable.Z\nsgp_sol_sp = sgpr.transform.expand(sgp_sol_sp,).numpy()\nplt.scatter(sgp_sol_sp[:, 0], sgp_sol_sp[:, 1], s=10, c='r')\n</pre> num_robots = 1 num_placements = 4  # Initialize inducing points and get initial path Xu_init = get_inducing_pts(X_train, num_placements*num_robots) Xu_init, _ = run_tsp(Xu_init,                       num_vehicles=num_robots) Xu_init = Xu_init[0]  # Add random angles to inducing points to initialize the rotation angle of the FoVs angles = np.random.uniform(-np.pi, np.pi, num_placements).reshape(-1, 1) Xu_init = np.concatenate([Xu_init, angles], axis=1)  # Setup unconstrained IPP transform with square FoV transform  # with length 5 and 3x3 point approximation,  # and continuous sensing model (sampling rate of 3), and optimize it square_transform = SquareTransform(length=5, num_side=3) transform = IPPTransform(num_robots=num_robots,                          num_dim=3,                          sampling_rate=3,                          sensor_model=square_transform) sgpr, _ = continuous_sgp(num_placements,                           X_train,                           noise_variance,                           kernel,                          transform,                          Xu_init=Xu_init,                           max_steps=1000)  # Get solution inducing points sgp_sol_sp = sgpr.inducing_variable.Z.numpy()[:, :2] sgp_paths = sgp_sol_sp.reshape(num_robots, -1 , 2)  # Plot the solution path and the FoVs plot_paths(sgp_paths, candidates,             title=\"Offline Single-Robot IPP with Continuous Sensing\") sgp_sol_sp = sgpr.inducing_variable.Z sgp_sol_sp = sgpr.transform.expand(sgp_sol_sp,).numpy() plt.scatter(sgp_sol_sp[:, 0], sgp_sol_sp[:, 1], s=10, c='r') Out[5]: <pre>&lt;matplotlib.collections.PathCollection at 0x7f5688999990&gt;</pre> In\u00a0[6]: Copied! <pre>num_robots = 3\nnum_placements = 6\n\n# Initialize inducing points and get initial path \n# resample to ensure same number of waypoints in each path\nXu_init = get_inducing_pts(X_train, num_placements*num_robots)\nXu_init, _ = run_tsp(Xu_init, \n                     num_vehicles=num_robots,\n                     max_dist=100,\n                     resample=num_placements)\nXu_init = Xu_init.reshape(-1, 2)\n\n# Setup distance constrained 3 robot IPP transform\n# with continuous sensing model (sampling rate of 5) and \n# a distance budget of 30.0 and optimize it\ntransform = IPPTransform(num_robots=num_robots,\n                         num_dim=2,\n                         sampling_rate=5,\n                         distance_budget=30.,\n                         constraint_weight=100.)\nsgpr, _ = continuous_sgp(num_placements, \n                         X_train, \n                         noise_variance, \n                         kernel,\n                         transform,\n                         Xu_init=Xu_init, \n                         max_steps=5000)\n\n# Get solution inducing points\nsol_sp = sgpr.inducing_variable.Z.numpy()\nsol_paths = sol_sp.reshape(num_robots, \n                           num_placements, 2)\n\n# Plot the solution path\nplot_paths(sol_paths, candidates, title=\"Optimized paths\")\nprint(\"Distances:\", transform.distance(sol_sp).numpy())\n</pre> num_robots = 3 num_placements = 6  # Initialize inducing points and get initial path  # resample to ensure same number of waypoints in each path Xu_init = get_inducing_pts(X_train, num_placements*num_robots) Xu_init, _ = run_tsp(Xu_init,                       num_vehicles=num_robots,                      max_dist=100,                      resample=num_placements) Xu_init = Xu_init.reshape(-1, 2)  # Setup distance constrained 3 robot IPP transform # with continuous sensing model (sampling rate of 5) and  # a distance budget of 30.0 and optimize it transform = IPPTransform(num_robots=num_robots,                          num_dim=2,                          sampling_rate=5,                          distance_budget=30.,                          constraint_weight=100.) sgpr, _ = continuous_sgp(num_placements,                           X_train,                           noise_variance,                           kernel,                          transform,                          Xu_init=Xu_init,                           max_steps=5000)  # Get solution inducing points sol_sp = sgpr.inducing_variable.Z.numpy() sol_paths = sol_sp.reshape(num_robots,                             num_placements, 2)  # Plot the solution path plot_paths(sol_paths, candidates, title=\"Optimized paths\") print(\"Distances:\", transform.distance(sol_sp).numpy()) <pre>Distances: [30.         29.9984054  29.98115566]\n</pre>"},{"location":"examples/IPP.html#sgp-based-ipp","title":"SGP-Based IPP\u00b6","text":""},{"location":"examples/IPP.html#get-the-data-and-initial-sgp-hyperparameters","title":"Get the data and initial SGP hyperparameters\u00b6","text":""},{"location":"examples/IPP.html#single-robot-ipp-with-point-sensing","title":"Single-Robot IPP with Point Sensing\u00b6","text":""},{"location":"examples/IPP.html#single-robot-ipp-with-continuous-sensing","title":"Single-Robot IPP with Continuous Sensing\u00b6","text":""},{"location":"examples/IPP.html#single-robot-ipp-with-continuous-non-point-fov-sensor","title":"Single-Robot IPP with Continuous Non-point FoV Sensor\u00b6","text":""},{"location":"examples/IPP.html#multi-robot-ipp-with-continuous-sensing-and-a-distance-budget","title":"Multi-Robot IPP with Continuous Sensing and a Distance Budget\u00b6","text":""},{"location":"examples/non_point_FoV.html","title":"IPP with Non-Point FoV","text":"In\u00a0[\u00a0]: Copied! <pre>import os\nos.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"\n\nimport numpy as np\n\nfrom sgptools.utils.metrics import *\nfrom sgptools.utils.data import *\nfrom sgptools.utils.misc import *\nfrom sgptools.models.core.osgpr import *\nfrom sgptools.utils.tsp import run_tsp\nfrom sgptools.utils.gpflow import get_model_params\nfrom sgptools.models.continuous_sgp import *\nfrom sgptools.models.core.transformations import *\nfrom gpflow.utilities.traversal import print_summary\n\nfrom matplotlib import colors\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Polygon\nimport mpl_toolkits.mplot3d.art3d as art3d\nfrom scipy.optimize import linear_sum_assignment\nfrom sklearn.metrics import pairwise_distances\n\nfrom time import time\nfrom copy import deepcopy\n\nnp.random.seed(1234)\ntf.random.set_seed(1234)\n</pre> import os os.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"  import numpy as np  from sgptools.utils.metrics import * from sgptools.utils.data import * from sgptools.utils.misc import * from sgptools.models.core.osgpr import * from sgptools.utils.tsp import run_tsp from sgptools.utils.gpflow import get_model_params from sgptools.models.continuous_sgp import * from sgptools.models.core.transformations import * from gpflow.utilities.traversal import print_summary  from matplotlib import colors import matplotlib.pyplot as plt from matplotlib.patches import Polygon import mpl_toolkits.mplot3d.art3d as art3d from scipy.optimize import linear_sum_assignment from sklearn.metrics import pairwise_distances  from time import time from copy import deepcopy  np.random.seed(1234) tf.random.set_seed(1234) In\u00a0[2]: Copied! <pre>'''\nUtility to map the inducing points to FoV vertices\n'''\ndef get_vertices(Xu, X_fov):\n    X_fov = X_fov.reshape(len(Xu), -1, 2)\n    vertices = np.zeros((len(Xu), 5, 2))\n    vertices[:, 0] = X_fov.min(axis=1)\n    vertices[:, 1] = np.array([X_fov[:, :, 0].min(axis=1), \n                              X_fov[:, :, 1].max(axis=1)]).T\n    vertices[:, 2] = X_fov.max(axis=1)\n    vertices[:, 3] = np.array([X_fov[:, :, 0].max(axis=1), \n                              X_fov[:, :, 1].min(axis=1)]).T\n    vertices[:, 4] = X_fov.mean(axis=1)\n\n    dists = pairwise_distances(vertices[:, 4], Y=Xu[:, :2], \n                               metric='euclidean')\n    _, idx = linear_sum_assignment(dists)\n\n    vertices[:, 4] = Xu[idx][:, :2]\n    return vertices\n\n'''\nMethod to plot the solution with FoVs and environment reconstruction\n'''\ndef plot_results(X_inducing, X_fov, fname=None):\n    # Setup 3D plot\n    fig = plt.figure(figsize=(5, 5))\n    ax = fig.add_subplot(111, projection='3d', computed_zorder=False)\n\n    # Plot the reconstructed environment state\n    sensor_data = cont2disc(X_fov, X, y)\n    y_pred, _ = get_reconstruction(sensor_data, X_test, \n                                   noise_variance_opt, kernel_opt)\n    norm = colors.Normalize(y_train.min(), y_train.max())\n    _ = ax.plot_surface(X_test[:, 0].reshape(test_dim),\n                        X_test[:, 1].reshape(test_dim),\n                        np.atleast_2d(-0.1),\n                        facecolors=plt.cm.jet(norm(y_pred.reshape(test_dim))),\n                        shade=False,\n                        alpha=0.8,\n                        zorder=0)\n\n    # Plot the solution path\n    ax.scatter(X_inducing[:, 0], \n               X_inducing[:, 1], \n               X_inducing[:, 2], c='C3')\n    ax.plot(X_inducing[:, 0], X_inducing[:, 1], X_inducing[:, 2], 'k-')\n\n    # Plot the FoV vertices\n    vertices = get_vertices(X_inducing, X_fov)\n    for i in range(vertices.shape[0]):\n        color = 'C3'\n        verts = []\n        verts.append([vertices[i, 0], vertices[i, 1], \n                      vertices[i, 2], vertices[i, 3]])\n        fov = Polygon(np.array(verts)[0, :, :2],\n                      linewidth=1.5, \n                      edgecolor=color,\n                      facecolor=color,\n                      fill=False,\n                      zorder=15)\n        ax.add_patch(fov)\n        art3d.pathpatch_2d_to_3d(fov)\n\n    # Configure other plot settings\n    ax.set_xlabel('X')\n    ax.set_ylabel('Y')\n    ax.set_zlabel('Z')\n\n    ax.set_xlim(np.min(X[:, 0])-0.5, np.max(X[:, 0])+0.5)\n    ax.set_ylim(np.min(X[:, 1])-0.5, np.max(X[:, 1])+0.5)\n    ax.set_zlim(0, 14)\n\n    ax.view_init(elev=30, azim=45+180)\n    ax.set_title(f'Num Waypoints: {num_placements}, RMSE: {get_rmse(y_pred, y_test):.2f}\\nData Field Reconstruction', y=0.99)\n\n    plt.tight_layout()\n    if fname is not None:\n        plt.savefig(fname, bbox_inches='tight')\n    plt.show()\n</pre> ''' Utility to map the inducing points to FoV vertices ''' def get_vertices(Xu, X_fov):     X_fov = X_fov.reshape(len(Xu), -1, 2)     vertices = np.zeros((len(Xu), 5, 2))     vertices[:, 0] = X_fov.min(axis=1)     vertices[:, 1] = np.array([X_fov[:, :, 0].min(axis=1),                                X_fov[:, :, 1].max(axis=1)]).T     vertices[:, 2] = X_fov.max(axis=1)     vertices[:, 3] = np.array([X_fov[:, :, 0].max(axis=1),                                X_fov[:, :, 1].min(axis=1)]).T     vertices[:, 4] = X_fov.mean(axis=1)      dists = pairwise_distances(vertices[:, 4], Y=Xu[:, :2],                                 metric='euclidean')     _, idx = linear_sum_assignment(dists)      vertices[:, 4] = Xu[idx][:, :2]     return vertices  ''' Method to plot the solution with FoVs and environment reconstruction ''' def plot_results(X_inducing, X_fov, fname=None):     # Setup 3D plot     fig = plt.figure(figsize=(5, 5))     ax = fig.add_subplot(111, projection='3d', computed_zorder=False)      # Plot the reconstructed environment state     sensor_data = cont2disc(X_fov, X, y)     y_pred, _ = get_reconstruction(sensor_data, X_test,                                     noise_variance_opt, kernel_opt)     norm = colors.Normalize(y_train.min(), y_train.max())     _ = ax.plot_surface(X_test[:, 0].reshape(test_dim),                         X_test[:, 1].reshape(test_dim),                         np.atleast_2d(-0.1),                         facecolors=plt.cm.jet(norm(y_pred.reshape(test_dim))),                         shade=False,                         alpha=0.8,                         zorder=0)      # Plot the solution path     ax.scatter(X_inducing[:, 0],                 X_inducing[:, 1],                 X_inducing[:, 2], c='C3')     ax.plot(X_inducing[:, 0], X_inducing[:, 1], X_inducing[:, 2], 'k-')      # Plot the FoV vertices     vertices = get_vertices(X_inducing, X_fov)     for i in range(vertices.shape[0]):         color = 'C3'         verts = []         verts.append([vertices[i, 0], vertices[i, 1],                        vertices[i, 2], vertices[i, 3]])         fov = Polygon(np.array(verts)[0, :, :2],                       linewidth=1.5,                        edgecolor=color,                       facecolor=color,                       fill=False,                       zorder=15)         ax.add_patch(fov)         art3d.pathpatch_2d_to_3d(fov)      # Configure other plot settings     ax.set_xlabel('X')     ax.set_ylabel('Y')     ax.set_zlabel('Z')      ax.set_xlim(np.min(X[:, 0])-0.5, np.max(X[:, 0])+0.5)     ax.set_ylim(np.min(X[:, 1])-0.5, np.max(X[:, 1])+0.5)     ax.set_zlim(0, 14)      ax.view_init(elev=30, azim=45+180)     ax.set_title(f'Num Waypoints: {num_placements}, RMSE: {get_rmse(y_pred, y_test):.2f}\\nData Field Reconstruction', y=0.99)      plt.tight_layout()     if fname is not None:         plt.savefig(fname, bbox_inches='tight')     plt.show() In\u00a0[3]: Copied! <pre># Get the synthetic elevation data\nX_train, y_train, X_test, y_test, candidates, X, y = get_dataset(random_seed=0)\ntrain_dim = np.sqrt(X_train.shape[0]).astype(int)\n\n# Train a GP and get the model parameters\nprint('Optimized Hyperparameters')\n_, noise_variance_opt, kernel_opt = get_model_params(X_train, y_train, \n                                                     lengthscales=[1.0, 1.0], \n                                                     optimizer='scipy')\n\n# Build a new test set on a grid for easy plotting in a 3D plot\ngpr = gpflow.models.GPR((X_test, y_test),\n                        noise_variance=noise_variance_opt,\n                        kernel=kernel_opt)\ntest_dim = (100, 100)\nX_test1 = np.linspace(np.min(X[:, 0]), np.max(X[:, 0]), test_dim[0])\nX_test2 = np.linspace(np.min(X[:, 1]), np.max(X[:, 1]), test_dim[1])\nX_test1, X_test2 = np.meshgrid(X_test1, X_test2)\nX_test = np.stack([X_test1.ravel(), X_test2.ravel()], axis=1)\ny_test, y_var = gpr.predict_f(X_test)\ny_test = y_test.numpy()\n\n# Plot the data field\nplt.imshow(y_test.reshape(test_dim), cmap=\"jet\", origin='lower')\nplt.xlabel('X')\nplt.ylabel('Y')\nplt.title('Elevation Data Field')\nplt.colorbar(label='Normalized Elevation')\nplt.show()\n</pre> # Get the synthetic elevation data X_train, y_train, X_test, y_test, candidates, X, y = get_dataset(random_seed=0) train_dim = np.sqrt(X_train.shape[0]).astype(int)  # Train a GP and get the model parameters print('Optimized Hyperparameters') _, noise_variance_opt, kernel_opt = get_model_params(X_train, y_train,                                                       lengthscales=[1.0, 1.0],                                                       optimizer='scipy')  # Build a new test set on a grid for easy plotting in a 3D plot gpr = gpflow.models.GPR((X_test, y_test),                         noise_variance=noise_variance_opt,                         kernel=kernel_opt) test_dim = (100, 100) X_test1 = np.linspace(np.min(X[:, 0]), np.max(X[:, 0]), test_dim[0]) X_test2 = np.linspace(np.min(X[:, 1]), np.max(X[:, 1]), test_dim[1]) X_test1, X_test2 = np.meshgrid(X_test1, X_test2) X_test = np.stack([X_test1.ravel(), X_test2.ravel()], axis=1) y_test, y_var = gpr.predict_f(X_test) y_test = y_test.numpy()  # Plot the data field plt.imshow(y_test.reshape(test_dim), cmap=\"jet\", origin='lower') plt.xlabel('X') plt.ylabel('Y') plt.title('Elevation Data Field') plt.colorbar(label='Normalized Elevation') plt.show() <pre>Optimized Hyperparameters\n\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.8371517535182331  \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [2.45802 2.50701]   \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.04342255274528023 \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> In\u00a0[4]: Copied! <pre>num_robots = 1\nnum_placements = 9\nnum_side = 5  # Use a 5x5 point FoV approximation\n\n# Setup the hyperparameters\n_, noise_variance, kernel = get_model_params(X_train, y_train, \n                                             lengthscales=[3.0, 3.0], \n                                             optimizer='scipy',\n                                             max_steps=0,\n                                             print_params=False)\n\n# Get initial inducing points\nXu_init = get_inducing_pts(X_train, num_placements)\n# Initalize the height dimension at 2.0 for all points\nXu_init = np.concatenate((Xu_init, np.ones((Xu_init.shape[0], 1))*2.0), axis=1)\nXu_init[:, :2] = run_tsp(Xu_init[:, :2])[0][0]\n\n# Initialize the non-point FoV IPP model and optimize it\ntransform = SquareHeightTransform(num_side=num_side)\nsgpr, _ = continuous_sgp(num_placements,\n                         X_train, \n                         noise_variance,\n                         kernel,\n                         transform=transform,\n                         Xu_init=Xu_init.copy())\nsgp_sol_sp = sgpr.inducing_variable.Z.numpy()\n</pre> num_robots = 1 num_placements = 9 num_side = 5  # Use a 5x5 point FoV approximation  # Setup the hyperparameters _, noise_variance, kernel = get_model_params(X_train, y_train,                                               lengthscales=[3.0, 3.0],                                               optimizer='scipy',                                              max_steps=0,                                              print_params=False)  # Get initial inducing points Xu_init = get_inducing_pts(X_train, num_placements) # Initalize the height dimension at 2.0 for all points Xu_init = np.concatenate((Xu_init, np.ones((Xu_init.shape[0], 1))*2.0), axis=1) Xu_init[:, :2] = run_tsp(Xu_init[:, :2])[0][0]  # Initialize the non-point FoV IPP model and optimize it transform = SquareHeightTransform(num_side=num_side) sgpr, _ = continuous_sgp(num_placements,                          X_train,                           noise_variance,                          kernel,                          transform=transform,                          Xu_init=Xu_init.copy()) sgp_sol_sp = sgpr.inducing_variable.Z.numpy() In\u00a0[5]: Copied! <pre>plot_results(Xu_init.reshape(-1, 3), \n             sgpr.transform.expand(Xu_init.reshape(-1, 3)).numpy())\n</pre> plot_results(Xu_init.reshape(-1, 3),               sgpr.transform.expand(Xu_init.reshape(-1, 3)).numpy()) In\u00a0[6]: Copied! <pre>plot_results(sgp_sol_sp.reshape(-1, 3), \n             sgpr.transform.expand(sgp_sol_sp.reshape(-1, 3)).numpy())\n</pre> plot_results(sgp_sol_sp.reshape(-1, 3),               sgpr.transform.expand(sgp_sol_sp.reshape(-1, 3)).numpy())"},{"location":"examples/non_point_FoV.html#ipp-with-non-point-fov","title":"IPP with Non-Point FoV\u00b6","text":""},{"location":"examples/non_point_FoV.html#generate-synthetic-data-and-initial-sgp-hyperparameters","title":"Generate synthetic data and initial SGP hyperparameters\u00b6","text":""},{"location":"examples/non_point_FoV.html#generate-the-ipp-solution","title":"Generate the IPP solution\u00b6","text":""},{"location":"examples/non_point_FoV.html#initial-solution","title":"Initial Solution\u00b6","text":""},{"location":"examples/non_point_FoV.html#optimized-solution","title":"Optimized Solution\u00b6","text":""},{"location":"examples/non_stationary_kernel.html","title":"Non-Stationary Kernel","text":"In\u00a0[\u00a0]: Copied! <pre>import tensorflow as tf\nimport numpy as np\nimport gpflow\ngpflow.config.set_default_float(np.float32)\n\nfrom sgptools.utils.metrics import *\nfrom sgptools.utils.misc import *\nfrom sgptools.models.continuous_sgp import *\nfrom sgptools.kernels.neural_kernel import init_neural_kernel\nfrom sgptools.utils.gpflow import get_model_params\n\nfrom sklearn.preprocessing import StandardScaler\nimport matplotlib.pyplot as plt\n\nnp.random.seed(10)\ntf.random.set_seed(10)\n</pre> import tensorflow as tf import numpy as np import gpflow gpflow.config.set_default_float(np.float32)  from sgptools.utils.metrics import * from sgptools.utils.misc import * from sgptools.models.continuous_sgp import * from sgptools.kernels.neural_kernel import init_neural_kernel from sgptools.utils.gpflow import get_model_params  from sklearn.preprocessing import StandardScaler import matplotlib.pyplot as plt  np.random.seed(10) tf.random.set_seed(10) In\u00a0[2]: Copied! <pre>def non_stationary_function(X, Y):\n    Z1 = np.sin(X/10) + np.cos(Y/10)\n    Z2 = np.cos(X/6) + np.cos(Y/6)\n    Z = np.concatenate([Z1[:, ::2], Z2[:, ::2]], \n                       axis=1)\n    return Z\n\n# Generate data\ntrain_data_dims = (40, 20)\nx = np.linspace(0, 200, train_data_dims[0])\ny = np.linspace(0, 100, train_data_dims[1])\nX, Y = np.meshgrid(x, y)\nZ = non_stationary_function(X, Y)\n\nX_train = np.stack([X.ravel(), Y.ravel()], \n                   axis=1).astype(np.float32)\nY_train = Z.ravel()[:, None].astype(np.float32)\nprint('Train Data:', X_train.shape, Y_train.shape)\n\ntest_data_dims = (100, 50)\nx = np.linspace(0, 200, test_data_dims[0])\ny = np.linspace(0, 100, test_data_dims[1])\nX, Y = np.meshgrid(x, y)\nZ = non_stationary_function(X, Y)\n\nX_test = np.stack([X.ravel(), Y.ravel()], \n                  axis=1).astype(np.float32)\nY_test = Z.ravel()[:, None].astype(np.float32)\nprint('Test Data:', X_test.shape, Y_test.shape)\n\n# Normalize the data\nXscalar = StandardScaler()\nX_train = Xscalar.fit_transform(X_train)\nX_test = Xscalar.transform(X_test)\n\nyScalar = StandardScaler()\nY_train = yScalar.fit_transform(Y_train)\nY_test = yScalar.transform(Y_test)\n\n# Plot the data\nplt.figure()\nplt.imshow(Y_train.reshape(train_data_dims[1], \n                           train_data_dims[0]), \n           cmap='jet')\nplt.gca().set_aspect('equal')\nplt.title('Train Data')\nplt.show()\n\nplt.figure()\nplt.imshow(Y_test.reshape(test_data_dims[1], \n                          test_data_dims[0]), \n           cmap='jet')\nplt.gca().set_aspect('equal')\nplt.title('Test Data')\nplt.show()\n</pre> def non_stationary_function(X, Y):     Z1 = np.sin(X/10) + np.cos(Y/10)     Z2 = np.cos(X/6) + np.cos(Y/6)     Z = np.concatenate([Z1[:, ::2], Z2[:, ::2]],                         axis=1)     return Z  # Generate data train_data_dims = (40, 20) x = np.linspace(0, 200, train_data_dims[0]) y = np.linspace(0, 100, train_data_dims[1]) X, Y = np.meshgrid(x, y) Z = non_stationary_function(X, Y)  X_train = np.stack([X.ravel(), Y.ravel()],                     axis=1).astype(np.float32) Y_train = Z.ravel()[:, None].astype(np.float32) print('Train Data:', X_train.shape, Y_train.shape)  test_data_dims = (100, 50) x = np.linspace(0, 200, test_data_dims[0]) y = np.linspace(0, 100, test_data_dims[1]) X, Y = np.meshgrid(x, y) Z = non_stationary_function(X, Y)  X_test = np.stack([X.ravel(), Y.ravel()],                    axis=1).astype(np.float32) Y_test = Z.ravel()[:, None].astype(np.float32) print('Test Data:', X_test.shape, Y_test.shape)  # Normalize the data Xscalar = StandardScaler() X_train = Xscalar.fit_transform(X_train) X_test = Xscalar.transform(X_test)  yScalar = StandardScaler() Y_train = yScalar.fit_transform(Y_train) Y_test = yScalar.transform(Y_test)  # Plot the data plt.figure() plt.imshow(Y_train.reshape(train_data_dims[1],                             train_data_dims[0]),             cmap='jet') plt.gca().set_aspect('equal') plt.title('Train Data') plt.show()  plt.figure() plt.imshow(Y_test.reshape(test_data_dims[1],                            test_data_dims[0]),             cmap='jet') plt.gca().set_aspect('equal') plt.title('Test Data') plt.show() <pre>Train Data: (800, 2) (800, 1)\nTest Data: (5000, 2) (5000, 1)\n</pre> In\u00a0[3]: Copied! <pre># Model / inference hyperparameters\nnum_inducing = 100\nlr = 1e-2\nmax_steps = 5000\nQ = 3\n\n# Initialize non-stationary kernel model\nprint('Initializing model...')\nZ = get_inducing_pts(X_train, num_inducing)\nmodel = init_neural_kernel(X_train, Y_train, \n                           n_inits=5,\n                           inducing_variable=Z,\n                           Q=Q, \n                           hidden_sizes=(4, 4))\n\n# Create optimizer\nlearning_rate = tf.keras.optimizers.schedules.ExponentialDecay(\n                      lr, \n                      decay_steps=50, \n                      decay_rate=0.98)\nloss = optimize_model(model, \n                      max_steps=max_steps,\n                      lr=learning_rate,\n                      optimizer='tf')\n\n# Plot loss\nplt.plot(loss)\nplt.xlabel('Iteration')\nplt.ylabel('Loss')\nplt.show()\n\nkernels = []\nnoice_vars = []\nlabels = ['Non Stationary Kernel', 'Stationary Kernel']\n\nkernels.append(model.kernel)\nnoice_vars.append(model.likelihood.variance.numpy())\n\n# Get stationary kernel\n_, noise_variance, kernel = get_model_params(X_train, Y_train, \n                                             lengthscales=[1.0, 0.5])\nkernels.append(kernel)\nnoice_vars.append(noise_variance)\n</pre> # Model / inference hyperparameters num_inducing = 100 lr = 1e-2 max_steps = 5000 Q = 3  # Initialize non-stationary kernel model print('Initializing model...') Z = get_inducing_pts(X_train, num_inducing) model = init_neural_kernel(X_train, Y_train,                             n_inits=5,                            inducing_variable=Z,                            Q=Q,                             hidden_sizes=(4, 4))  # Create optimizer learning_rate = tf.keras.optimizers.schedules.ExponentialDecay(                       lr,                        decay_steps=50,                        decay_rate=0.98) loss = optimize_model(model,                        max_steps=max_steps,                       lr=learning_rate,                       optimizer='tf')  # Plot loss plt.plot(loss) plt.xlabel('Iteration') plt.ylabel('Loss') plt.show()  kernels = [] noice_vars = [] labels = ['Non Stationary Kernel', 'Stationary Kernel']  kernels.append(model.kernel) noice_vars.append(model.likelihood.variance.numpy())  # Get stationary kernel _, noise_variance, kernel = get_model_params(X_train, Y_train,                                               lengthscales=[1.0, 0.5]) kernels.append(kernel) noice_vars.append(noise_variance) <pre>Initializing model...\nInitializing neural spectral kernel...\n</pre> <pre>2024-05-05 12:08:35.281864: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1960] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\n</pre> <pre>Best init: -1165.898315\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value             \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float32 \u2502 0.59679           \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 (2,)    \u2502 float32 \u2502 [1.16673 0.37082] \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float32 \u2502 0.52522           \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> In\u00a0[4]: Copied! <pre>for i, (kernel, noice_var) in enumerate(zip(kernels, noice_vars)):\n        print(labels[i])\n        for num_placements in [4, 9]:\n                # Get initial inducing points\n                Xu_init = get_inducing_pts(X_train, num_placements)*0.5\n                Xu_init.astype(np.float32)\n\n                # Setup SGP and optimize it\n                sgpr, _ = continuous_sgp(num_placements, \n                                         X_train,\n                                         noise_variance=noice_var,\n                                         kernel=kernel,\n                                         Xu_init=Xu_init)\n                sgp_sol_sp = sgpr.inducing_variable.Z.numpy()\n\n                # Get the GP predictions\n                Xu_X, Xu_y = cont2disc(sgp_sol_sp, candidates=X_test,\n                                       candidate_labels=Y_test)\n                gpr = gpflow.models.GPR((Xu_X, Xu_y),\n                                        noise_variance=noice_var,\n                                        kernel=kernel)\n                y_pred, y_var = gpr.predict_f(X_test)\n                y_pred = y_pred.numpy()\n\n                rmse = np.sqrt(np.mean((y_pred - Y_test)**2))\n\n                # Plot the results\n                plt.figure()\n                plt.imshow(y_pred.reshape(test_data_dims[1], test_data_dims[0]),\n                        cmap='jet', origin='lower',\n                        extent=[np.min(X_test[:, 0]), np.max(X_test[:, 0]),\n                                np.min(X_test[:, 1]), np.max(X_test[:, 1])])\n                plt.gca().set_aspect('equal')\n                plt.scatter(sgp_sol_sp[:, 0], sgp_sol_sp[:, 1], c='k', marker='p', s=100)\n                plt.title('Number of Sensors: {}    RMSE: {:.3f}'.format(num_placements, rmse))\n                plt.xlabel('X')\n                plt.ylabel('Y')\n                plt.show()\n</pre> for i, (kernel, noice_var) in enumerate(zip(kernels, noice_vars)):         print(labels[i])         for num_placements in [4, 9]:                 # Get initial inducing points                 Xu_init = get_inducing_pts(X_train, num_placements)*0.5                 Xu_init.astype(np.float32)                  # Setup SGP and optimize it                 sgpr, _ = continuous_sgp(num_placements,                                           X_train,                                          noise_variance=noice_var,                                          kernel=kernel,                                          Xu_init=Xu_init)                 sgp_sol_sp = sgpr.inducing_variable.Z.numpy()                  # Get the GP predictions                 Xu_X, Xu_y = cont2disc(sgp_sol_sp, candidates=X_test,                                        candidate_labels=Y_test)                 gpr = gpflow.models.GPR((Xu_X, Xu_y),                                         noise_variance=noice_var,                                         kernel=kernel)                 y_pred, y_var = gpr.predict_f(X_test)                 y_pred = y_pred.numpy()                  rmse = np.sqrt(np.mean((y_pred - Y_test)**2))                  # Plot the results                 plt.figure()                 plt.imshow(y_pred.reshape(test_data_dims[1], test_data_dims[0]),                         cmap='jet', origin='lower',                         extent=[np.min(X_test[:, 0]), np.max(X_test[:, 0]),                                 np.min(X_test[:, 1]), np.max(X_test[:, 1])])                 plt.gca().set_aspect('equal')                 plt.scatter(sgp_sol_sp[:, 0], sgp_sol_sp[:, 1], c='k', marker='p', s=100)                 plt.title('Number of Sensors: {}    RMSE: {:.3f}'.format(num_placements, rmse))                 plt.xlabel('X')                 plt.ylabel('Y')                 plt.show() <pre>Non Stationary Kernel\n</pre> <pre>Stationary Kernel\n</pre>"},{"location":"examples/non_stationary_kernel.html#non-stationary-kernel","title":"Non-Stationary Kernel\u00b6","text":""},{"location":"examples/non_stationary_kernel.html#generate-synthetic-non-stationary-data","title":"Generate synthetic non-stationary data\u00b6","text":""},{"location":"examples/non_stationary_kernel.html#learn-kernel-parameters-from-train-data","title":"Learn kernel parameters from train data\u00b6","text":""},{"location":"examples/non_stationary_kernel.html#get-continuous-sgp-solution-with-a-baseline-stationary-kernel-and-the-learned-neural-kernel-function","title":"Get Continuous-SGP solution with a baseline stationary kernel and the learned neural kernel function\u00b6","text":""},{"location":"examples/obstacles.html","title":"Environment with Obstacles","text":"In\u00a0[\u00a0]: Copied! <pre>import matplotlib.pyplot as plt\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom sgptools.utils.gpflow import get_model_params\nfrom sgptools.utils.data import remove_polygons, remove_circle_patches\nfrom sgptools.models.continuous_sgp import *\n\nnp.random.seed(0)\ntf.random.set_seed(0)\n</pre> import matplotlib.pyplot as plt  import numpy as np import tensorflow as tf  from sgptools.utils.gpflow import get_model_params from sgptools.utils.data import remove_polygons, remove_circle_patches from sgptools.models.continuous_sgp import *  np.random.seed(0) tf.random.set_seed(0) In\u00a0[2]: Copied! <pre># Generate synthetic training data\ndata_dims = (50, 25)\nx = np.linspace(0, 200, data_dims[0])\ny = np.linspace(0, 100, data_dims[1])\nX, Y = np.meshgrid(x, y)\n\nX, Y = remove_circle_patches(X, Y, [plt.Circle((30, 60), 15)])\nX, Y = remove_polygons(X, Y, [[[80, 20], [105, 20], [105, 60], [80, 60]],])\nX, Y = remove_polygons(X, Y, [[[130, 20], [180, 20], [160, 60]],])\n\nplt.figure(figsize=(10, 5))\nplt.scatter(X, Y, s=1)\n\nax = plt.gca()\nax.add_patch(plt.Polygon([[80, 20], [105, 20], [105, 60], [80, 60]], \n             fill=None, hatch='//', color='C1', label='Obstacle'))\nax.add_patch(plt.Polygon([[130, 20], [180, 20], [160, 60]], \n             fill=None, hatch='//', color='C1'))\nax.add_patch(plt.Circle((30, 60), 15, \n             fill=None, hatch='//', color='C1'))\nax.set_aspect('equal')\n\nplt.xlabel('x', fontsize=14)\nplt.ylabel('y', fontsize=14)\nplt.xlim(0, 200)\nplt.ylim(0, 100)\n\nplt.show()\n\nX_train = np.array([X, Y]).T\ny_train = np.zeros((X_train.shape[0], 1))\nX_train = X_train.astype(float)\ny_train = y_train.astype(float)\nprint(X_train.shape, y_train.shape)\n</pre> # Generate synthetic training data data_dims = (50, 25) x = np.linspace(0, 200, data_dims[0]) y = np.linspace(0, 100, data_dims[1]) X, Y = np.meshgrid(x, y)  X, Y = remove_circle_patches(X, Y, [plt.Circle((30, 60), 15)]) X, Y = remove_polygons(X, Y, [[[80, 20], [105, 20], [105, 60], [80, 60]],]) X, Y = remove_polygons(X, Y, [[[130, 20], [180, 20], [160, 60]],])  plt.figure(figsize=(10, 5)) plt.scatter(X, Y, s=1)  ax = plt.gca() ax.add_patch(plt.Polygon([[80, 20], [105, 20], [105, 60], [80, 60]],               fill=None, hatch='//', color='C1', label='Obstacle')) ax.add_patch(plt.Polygon([[130, 20], [180, 20], [160, 60]],               fill=None, hatch='//', color='C1')) ax.add_patch(plt.Circle((30, 60), 15,               fill=None, hatch='//', color='C1')) ax.set_aspect('equal')  plt.xlabel('x', fontsize=14) plt.ylabel('y', fontsize=14) plt.xlim(0, 200) plt.ylim(0, 100)  plt.show()  X_train = np.array([X, Y]).T y_train = np.zeros((X_train.shape[0], 1)) X_train = X_train.astype(float) y_train = y_train.astype(float) print(X_train.shape, y_train.shape) <pre>(1089, 2) (1089, 1)\n</pre> In\u00a0[3]: Copied! <pre>num_inducing = 200\n\n# Get initial GP parameters\n_, noise_variance, rbf = get_model_params(np.zeros((1, 2)), np.zeros((1, 1)), \n                             lengthscales=12.3254, \n                             variance=0.561265,\n                             noise_variance=2e-6,\n                             max_steps=0)\n\n# Optimize the inducing points/sensor locations\nsgpr, _ = continuous_sgp(num_inducing,\n                         X_train,\n                         kernel=rbf,\n                         noise_variance=noise_variance,\n                         lr=1e-2,\n                         max_steps=5000)\nsgp_sol_sp = sgpr.inducing_variable.Z.numpy()\n</pre> num_inducing = 200  # Get initial GP parameters _, noise_variance, rbf = get_model_params(np.zeros((1, 2)), np.zeros((1, 1)),                               lengthscales=12.3254,                               variance=0.561265,                              noise_variance=2e-6,                              max_steps=0)  # Optimize the inducing points/sensor locations sgpr, _ = continuous_sgp(num_inducing,                          X_train,                          kernel=rbf,                          noise_variance=noise_variance,                          lr=1e-2,                          max_steps=5000) sgp_sol_sp = sgpr.inducing_variable.Z.numpy() <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502     value \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502  0.561265 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 12.3254   \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502  2e-06    \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> In\u00a0[4]: Copied! <pre># Plot the sensor locations and the obstacles\nplt.figure(figsize=(10, 5))\nplt.scatter(sgp_sol_sp[:, 0], sgp_sol_sp[:, 1], \n            color='C0', \n            s=2, \n            label='Solution Placements')\n\nax = plt.gca()\nax.add_patch(plt.Polygon([[80, 20], [105, 20], [105, 60], [80, 60]], \n             fill=None, hatch='//', color='C1', label='Obstacle'))\nax.add_patch(plt.Polygon([[130, 20], [180, 20], [160, 60]], \n             fill=None, hatch='//', color='C1'))\nax.add_patch(plt.Circle((30, 60), 15, \n             fill=None, hatch='//', color='C1'))\nax.set_aspect('equal')\n\nplt.xlabel('x', fontsize=14)\nplt.ylabel('y', fontsize=14)\nplt.xlim(0, 200)\nplt.ylim(0, 100)\n\nplt.show()\n</pre> # Plot the sensor locations and the obstacles plt.figure(figsize=(10, 5)) plt.scatter(sgp_sol_sp[:, 0], sgp_sol_sp[:, 1],              color='C0',              s=2,              label='Solution Placements')  ax = plt.gca() ax.add_patch(plt.Polygon([[80, 20], [105, 20], [105, 60], [80, 60]],               fill=None, hatch='//', color='C1', label='Obstacle')) ax.add_patch(plt.Polygon([[130, 20], [180, 20], [160, 60]],               fill=None, hatch='//', color='C1')) ax.add_patch(plt.Circle((30, 60), 15,               fill=None, hatch='//', color='C1')) ax.set_aspect('equal')  plt.xlabel('x', fontsize=14) plt.ylabel('y', fontsize=14) plt.xlim(0, 200) plt.ylim(0, 100)  plt.show()"},{"location":"examples/obstacles.html#environment-with-obstacles","title":"Environment with Obstacles\u00b6","text":""},{"location":"examples/obstacles.html#note-that-all-the-solution-placements-avoid-the-obstacles","title":"Note that all the solution placements avoid the obstacles\u00b6","text":""}]}
\ No newline at end of file
+{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"index.html","title":"Home","text":"<p>SGP-Tools is a software suite for Sensor Placement and Informative Path Planning.</p> <p>The library includes python code for the following:</p> <ul> <li>Greedy algorithm-based approaches</li> <li>Bayesian optimization-based approaches</li> <li>Genetic algorithm-based approaches</li> <li>Sparse Gaussian process (SGP)-based approaches</li> </ul> <p> </p>"},{"location":"index.html#installation","title":"Installation","text":"<p>The library is available as a <code>pip</code> package. To install the package, run the following command:</p> <pre><code>python3 -m pip install sgptools\n</code></pre> <p>Installation from source:</p> <pre><code>git clone https://github.com/itskalvik/sgp-tools.git\ncd sgp-tools/\npython3 -m pip install -r requirements.txt\npython3 -m pip install -e .\n</code></pre> <p>Note: The requirements.txt file contains packages and their latest versions that were last verified to be working without any issues.</p>"},{"location":"index.html#quick-start","title":"Quick Start","text":"<p>Please refer to the example Jupyter notebooks demonstrating the methods included in the library \ud83d\ude04</p>"},{"location":"index.html#method-summary","title":"Method Summary","text":""},{"location":"index.html#datasets","title":"Datasets","text":"<ul> <li>High-resolution topography data can be downloaded from NOAA Digital Coast</li> <li>High-resolution bathymetry data can be downloaded from NOAA Digital Coast</li> </ul>"},{"location":"index.html#about","title":"About","text":"<p>Please consider citing the following papers if you use SGP-Tools in your academic work \ud83d\ude04</p> <pre><code>@misc{JakkalaA23SP,\nAUTHOR={Kalvik Jakkala and Srinivas Akella},\nTITLE={Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces},\nNOTE= {Preprint},\nYEAR={2023},\nURL={https://itskalvik.github.io/publication/sgp-sp},\n}\n\n@inproceedings{JakkalaA24IPP,\nAUTHOR={Kalvik Jakkala and Srinivas Akella},\nTITLE={Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes},\nbooktitle={IEEE International Conference on Robotics and Automation, {ICRA}},\nYEAR={2024},\nPUBLISHER = {{IEEE}},\nURL={https://itskalvik.github.io/publication/sgp-ipp}\n}\n</code></pre>"},{"location":"index.html#acknowledgements","title":"Acknowledgements","text":"<p>This work was funded in part by the UNC Charlotte Office of Research and Economic Development and by NSF under Award Number IIP-1919233.</p>"},{"location":"index.html#license","title":"License","text":"<p>The SGP-Tools software suite is licensed under the terms of the Apache License 2.0. See LICENSE for more information.</p>"},{"location":"blueos-sgp-tools.html","title":"BlueOS SGP-Tools","text":"<p>A BlueOS Extension for Autonomous Approximate Bathymetric Surveys</p>"},{"location":"blueos-sgp-tools.html#what-does-it-do","title":"What Does It Do?","text":"<p>Autonomous Surface Vehicles (ASVs), such as the BlueRobotics BlueBoat, are well-suited for bathymetric surveys. However, it is often the case that an exhaustive survey mapping the depth at every location in an area is infeasible or unnecessary. In such cases, we can leverage variations in the underwater terrain to determine a few critical locations for data collection, which would result in a good approximation of the area's bathymetry.</p> <p>The SGP-Tools python library provides path planners to address the above problem, known as the informative path planning problem. The BlueOS SGP-Tools extension uses this library to determine ideal locations for the ASV to collect data and controls the ASV to autonomously visit the selected locations.</p> <p>The following shows the path planner adaptively planning paths for four robots tasked with surveying a given area:</p> <p> </p>"},{"location":"blueos-sgp-tools.html#usage","title":"Usage","text":"<ol> <li> <p>First, we need to define the survey area and the robot launch location. The extension can read this data from QGC plan files. The survey area and launch position must be defined using a polygon-shaped geofence drawn in QGC and saved as <code>mission.plan</code>.</p> <p> </p> </li> <li> <p>Once you have the plan file, copy it to the robot using the <code>File Browser</code> feature in BlueOS's <code>Pirate Mode</code>. The <code>mission.plan</code> file should be uploaded to the following directory: <code>/extensions/sgptools/</code></p> <p>Once the <code>mission.plan</code> file is uploaded, restart the extension to ensure that the new file is used for the mission.</p> <p> </p> </li> <li> <p>Finally, use the terminal provided by the SGP-Tools extension to start the mission with the following command:     <pre><code>ros2 launch ros_sgp_tools single_robot.launch.py\n</code></pre></p> <p> </p> </li> <li> <p>The sensor data, along with the corresponding GPS coordinates, will be logged to an HDF5 file in the <code>DATA_FOLDER</code>, where the <code>mission.plan</code> was uploaded. </p> <p>We can estimate the bathymetry of the entire survey area using the collected data and visualize a normalized version with the following command (\u26a0\ufe0f Do not run this during the mission, as it will disrupt the path planner): <pre><code>ros2 launch ros_sgp_tools visualize_data.launch.py\n</code></pre></p> <p>The above command will publish a point cloud that can be viewed using foxglove. You can access it from a web browser at https://app.foxglove.dev/. Use the <code>open connection</code> feature and change the address from <code>localhost</code> to the IP address of the ASV.</p> <p> </p> <p>You can control the point cloud density using the <code>num_samples</code> parameter. You can set this from foxglove's <code>Parameters</code> panel.</p> <p>By default, the latest mission log will be visualized. You can visualize a specific mission log using the following command (replace <code>&lt;log folder name&gt;</code> with  the log folder name):</p> <pre><code>ros2 launch ros_sgp_tools visualize_data.launch.py mission_log:=&lt;log folder name&gt;\n</code></pre> </li> </ol>"},{"location":"blueos-sgp-tools.html#parameters","title":"Parameters","text":"<p>You can control the following extension parameters by running the following command in the terminal provided by the SGP-Tools extension:</p> <pre><code>export &lt;parameter_name&gt;=&lt;parameter_value&gt;\n</code></pre> <p>The parameters reset to their default values after rebooting. They can be made permanent by configuring the parameters using the app environment variables on the BlueOS extensions page in pirate mode.</p>"},{"location":"blueos-sgp-tools.html#available-parameters","title":"Available Parameters:","text":"<ul> <li> <p><code>PING2_PORT</code> (<code>default: /dev/ttyUSB0</code>): </p> <p>Specifies the device to which the Ping2 sonar is mounted. </p> </li> <li> <p><code>NUM_WAYPOINTS</code> (<code>default: 20</code>): </p> <p>The number of waypoints optimized by the path planner. Increasing the number of waypoints gives a more complex path which potentally covers a larger area but also requires more compute and could result in slower online path updates. </p> </li> <li> <p><code>SAMPLING_RATE</code> (<code>default: 2</code>): </p> <p>The number of points to sample along each edge of the path during path planning. Increasing the samping rate results in the planner being aware of the data being collected along the whole path instead of assuming that the data is collected only at the vertices. This method requires relatively less compute then increasing the number of waypints, but still adds to the overall compute cost.</p> </li> <li> <p><code>START_FOXGLOVE</code> (<code>default: False</code>): </p> <p>Enables foxglove, a web-based data visualization platform similar to RViz.</p> </li> <li> <p><code>ADAPTIVE_IPP</code> (<code>default: True</code>): </p> <p>Enables adaptive IPP, which uses the data streaming from the sonar to learn the correlations in the underwater bathymetry and further optimizes the future waypoints to maximize collecting new informative data.</p> </li> <li> <p><code>NAMESPACE</code> (<code>default: robot_0</code>): </p> <p>ROS2 namespace, useful when multiple ROS2 robots are operating on the same network.</p> </li> <li> <p><code>DATA_TYPE</code> (<code>default: Ping2</code>): </p> <p>Type of sensor to be used by the path planner. Currently, only the BlueRobotics Ping Sonar is supported.</p> </li> <li> <p><code>FAKE_DATA</code> (<code>default: False</code>):</p> <p>Enables a fake sensor data publisher used for testing. Requires <code>DATA_TYPE=SerialPing2</code>.</p> </li> </ul>"},{"location":"blueos-sgp-tools.html#hardware-configuration","title":"Hardware Configuration","text":"<ul> <li> <p>This extension works only on 64-bit operating systems. You can install the latest version of BlueOS on 64-bit Raspberry Pi OS Lite by running the following command on the Pi (ensure the username is set to <code>pi</code>):     <pre><code>sudo su -c 'curl -fsSL https://raw.githubusercontent.com/bluerobotics/blueos-docker/master/install/install.sh | bash'\n</code></pre></p> </li> <li> <p>The extension requires over 4GB of memory+swap. Please ensure that the swap size is large enough to accommodate the extension. The extension will copy the shell script <code>config_swap.sh</code> to <code>/usr/blueos/extensions/sgptools/</code> folder on the underlying device. You can use this script to increase the swap size before starting the path planner. </p> <p>You will have to use <code>Pirate Mode</code> to access BlueOS's built-in terminal and run the script on the underlying device via the <code>red-pill</code> utility. Use the following commands to enable <code>red-pill</code> and increase the swap size:  <pre><code>red-pill\nsudo bash /usr/blueos/extensions/sgptools/config_swap.sh\n</code></pre></p> <p> </p> </li> </ul>"},{"location":"blueos-sgp-tools.html#disclaimer","title":"Disclaimer \u26a0\ufe0f","text":"<p>This extension, when executed properly, will take control of the ASV and could potentially collide the vehicle with obstacles in the environment. Please use it with caution.</p>"},{"location":"docker-sgp-tools.html","title":"docker-sgp-tools","text":"<p>The docker-sgp-tools package provides the following docker-compose scripts:</p> <ul> <li><code>robot-compose.yml</code>: A minimal docker container used to run SGP-Tools ROS2 package on ArduPilot-based robots.</li> <li><code>sitl-compose.yml</code>: A GUI-based docker container with ROS2, Gazebo, ArduPilot SITL, and SGP-Tools used for simulating ArduPilot vehicles and testing SGP-Tools IPP code. </li> </ul>"},{"location":"docker-sgp-tools.html#prerequisites","title":"Prerequisites","text":"<ul> <li>docker</li> <li>docker-compose</li> <li>(Optional) WSLg</li> </ul>"},{"location":"docker-sgp-tools.html#getting-started","title":"Getting Started","text":""},{"location":"docker-sgp-tools.html#starting-the-containers","title":"Starting the Containers","text":"<p>Run the following commands to start the SITL docker container:</p> <pre><code>git clone --recurse-submodules https://github.com/itskalvik/docker-sgp-tools.git\ncd docker-sgp-tools\ndocker-compose -f sitl-compose.yml pull\ndocker-compose -f sitl-compose.yml up -d\ndocker-compose -f sitl-compose.yml exec sgptools bash\n</code></pre> <p>Use <code>robot-compose.yml</code> to run the minimal docker container. </p>"},{"location":"docker-sgp-tools.html#running-sgp-tools-onlineadaptive-ipp-with-gazeboardurover-simulator","title":"Running SGP-Tools Online/Adaptive IPP with Gazebo/ArduRover Simulator","text":"<p>Use <code>docker-compose -f sitl-compose.yml exec sgptools bash</code> to get a new terminal. Run the following commands in separate terminals in the docker container:</p> <ul> <li> <p>Launch Gazebo with the AION R1 UGV:     <pre><code>gz sim -v4 -r r1_rover_runway.sdf\n</code></pre>     To simulate a BlueBoat refer to this documentation. Note that the container already has Wave Sim installed in it.</p> </li> <li> <p>Launch ArduRover SITL:     <pre><code>sim_vehicle.py -v Rover -f rover-skid --model JSON --add-param-file=$HOME/SITL_Models/Gazebo/config/r1_rover.param --console --map -N -l 35.30371178789218,-80.73099267294185,0.,0.\n</code></pre>     Note: Restart sim_vechile.py if you get the following message: <code>paramftp: bad count 1294 should be 1284</code></p> </li> <li> <p>Launch the SGP-Tools Online/Adaptive IPP method:     <pre><code>ros2 launch ros_sgp_tools single_robot.launch.py\n</code></pre></p> </li> </ul>"},{"location":"docker-sgp-tools.html#building-the-docker-containers-from-scratch","title":"Building the Docker Containers from Scratch","text":"<p>First, setup buildx to build the containers for both arm64 and amd64 platforms:  <pre><code>docker buildx create --name multi-arch \\\n  --platform \"linux/arm64,linux/amd64\" \\\n  --driver \"docker-container\"\ndocker buildx use multi-arch\n</code></pre></p> <p>Next, clone the repo and build the container.  <pre><code>git clone --recurse-submodules https://github.com/itskalvik/docker-sgp-tools.git\ncd docker-sgp-tools\ndocker-compose -f sitl-compose.yml build \n</code></pre></p> <p>Use <code>robot-compose.yml</code> to build the minimal docker container.</p>"},{"location":"docker-sgp-tools.html#other-commands","title":"Other commands","text":"<ul> <li> <p>The docker-compose down command stops and removes containers, networks, volumes, and images, making it suitable for completely clearing all resources deployed by an application.</p> <pre><code>docker-compose -f sitl-compose.yml down\n</code></pre> </li> <li> <p>The docker-compose stop command just pauses running containers without removing them, which is ideal for temporary halts.</p> <pre><code>docker-compose -f sitl-compose.yml stop\n</code></pre> </li> </ul>"},{"location":"docker-sgp-tools.html#references","title":"References","text":"<p>Based on Dockerfiles from the following repos:</p> <ul> <li>https://github.com/Robotic-Decision-Making-Lab/blue</li> <li>https://github.com/ryomo/ros2-gazebo-docker</li> </ul>"},{"location":"ros_sgp_tools.html","title":"ros_sgp_tools","text":"<p>The ros_sgp_tools package provides a ROS2 companion package for the SGP-Tools python library that can be deployed on ArduPilot-based vehicles. </p> <ul> <li>The package can be used to run online/adaptive IPP on ArduPilot based UGVs and ASVs. </li> <li>The package can also be used with Gazebo/Ardupilot SITL.</li> <li>To use our Docker container with the preconfigured development environment, please refer to the documentation here. </li> </ul>"},{"location":"ros_sgp_tools.html#running-sgp-tools-onlineadaptive-ipp-with-gazeboardurover-simulator","title":"Running SGP-Tools Online/Adaptive IPP with Gazebo/ArduRover Simulator","text":"<p>Run the following commands in separate terminals:</p> <ul> <li> <p>Launch Gazebo with the AION R1 UGV:     <pre><code>gz sim -v4 -r r1_rover_runway.sdf\n</code></pre>     To simulate a BlueBoat refer to this documentation.</p> </li> <li> <p>Launch ArduRover SITL:     <pre><code>sim_vehicle.py -v Rover -f rover-skid --model JSON --add-param-file=$HOME/SITL_Models/Gazebo/config/r1_rover.param --console --map -N -l 35.30371178789218,-80.73099267294185,0.,0.\n</code></pre>     Note: </p> <ul> <li>Restart sim_vechile.py if you get the following message: <code>paramftp: bad count</code></li> <li>Ensure the MAV Console shows that the vehicle has a GPS lock before running the next command</li> <li>Ensure the MAV Map shows the vehicle before running the next command</li> </ul> </li> <li> <p>Launch the SGP-Tools Online/Adaptive IPP method:     <pre><code>ros2 launch ros_sgp_tools single_robot.launch.py\n</code></pre></p> </li> </ul>"},{"location":"ros_sgp_tools.html#environment-setup","title":"Environment setup","text":"<ul> <li>To use our Docker container with the preconfigured development environment, please refer to the documentation here. </li> </ul> <p>Alternatively, please follow the following instructions to configure the development envirnoment on your local machine. </p> <ul> <li>Install ROS 2 Humble <pre><code>sudo apt install ros-humble-desktop\necho \"source /opt/ros/humble/setup.bash\" &gt;&gt; ~/.bashrc\nsource ~/.bashrc\n</code></pre></li> <li>Create a ROS 2 workspace   <pre><code>mkdir -p ~/ros2_ws/src\ncd ~/ros2_ws/\ncolcon build --symlink-install\necho \"source $HOME/ros2_ws/install/setup.bash\" &gt;&gt; ~/.bashrc\nsource ~/.bashrc\n</code></pre></li> <li>Install Gazebo Garden</li> <li>Install ArduPilot SITL</li> <li>Install ardupilot_gazebo</li> <li>Get SITL_Models repo</li> <li>Setup environment variables</li> </ul> <pre><code>echo \"export GZ_VERSION=garden\" &gt;&gt; ~/.bashrc\necho \"export GZ_SIM_SYSTEM_PLUGIN_PATH=$HOME/ardupilot_gazebo/build:${GZ_SIM_SYSTEM_PLUGIN_PATH}\" &gt;&gt; ~/.bashrc\necho \"export GZ_SIM_RESOURCE_PATH=$HOME/ardupilot_gazebo/models:$HOME/ardupilot_gazebo/worlds:$HOME/SITL_Models/Gazebo/models:$HOME/SITL_Models/Gazebo/worlds:$GZ_SIM_RESOURCE_PATH\" &gt;&gt; ~/.bashrc\n</code></pre>"},{"location":"ros_sgp_tools.html#package-setup","title":"Package setup","text":"<ul> <li>Install apt dependencies    <pre><code>sudo apt install ros-humble-mavros* ros-$ROS_DISTRO-cv-bridge\n</code></pre></li> <li>Install pip dependencies    <pre><code>pip3 install -r requirements.txt\n</code></pre></li> <li>Clone repo and build package   <pre><code>cd ~/ros2_ws/src\ngit clone https://github.com/itskalvik/ros_sgp_tools.git\ncd ..\ncolcon build --symlink-install\n</code></pre></li> </ul>"},{"location":"api/kernels/index.html","title":"","text":""},{"location":"api/kernels/index.html#sgptools.kernels","title":"<code>sgptools.kernels</code>","text":"<p>Special kernel functions in this package:</p> <ul> <li><code>neural_kernel</code>: Provides a neural spectral kernel function that uses a mixture of multilayer perceptrons</li> </ul>"},{"location":"api/kernels/Neural-Kernel.html","title":"Neural Kernel","text":""},{"location":"api/kernels/Neural-Kernel.html#sgptools.kernels.neural_kernel","title":"<code>sgptools.kernels.neural_kernel</code>","text":"<p>Provides a neural spectral kernel function along with an initialization function</p>"},{"location":"api/kernels/Neural-Kernel.html#sgptools.kernels.neural_kernel.NeuralSpectralKernel","title":"<code>NeuralSpectralKernel</code>","text":"<p>               Bases: <code>Kernel</code></p> <p>Neural Spectral Kernel function (non-stationary kernel function).  Based on the implementation from the following repo</p> Refer to the following papers for more details <ul> <li>Neural Non-Stationary Spectral Kernel [Remes et al., 2018]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>input_dim</code> <code>int</code> <p>Number of data dimensions</p> required <code>active_dims</code> <code>int</code> <p>Number of data dimensions that are used for computing the covariances</p> <code>None</code> <code>Q</code> <code>int</code> <p>Number of MLP mixture components used in the kernel function</p> <code>1</code> <code>hidden_sizes</code> <code>list</code> <p>Number of hidden units in each MLP layer. Length of the list determines the number of layers.</p> <code>[32, 32]</code> Source code in <code>sgptools/kernels/neural_kernel.py</code> <pre><code>class NeuralSpectralKernel(gpflow.kernels.Kernel):\n    \"\"\"Neural Spectral Kernel function (non-stationary kernel function). \n    Based on the implementation from the following [repo](https://github.com/sremes/nssm-gp/tree/master?tab=readme-ov-file)\n\n    Refer to the following papers for more details:\n        - Neural Non-Stationary Spectral Kernel [Remes et al., 2018]\n\n    Args:\n        input_dim (int): Number of data dimensions\n        active_dims (int): Number of data dimensions that are used for computing the covariances\n        Q (int): Number of MLP mixture components used in the kernel function\n        hidden_sizes (list): Number of hidden units in each MLP layer. Length of the list determines the number of layers.\n    \"\"\"\n    def __init__(self, input_dim, active_dims=None, Q=1, hidden_sizes=[32, 32]):\n        super().__init__(active_dims=active_dims)\n\n        self.input_dim = input_dim\n        self.Q = Q\n        self.num_hidden = len(hidden_sizes)\n\n        self.freq = []\n        self.length = []\n        self.var = []\n        for q in range(self.Q):\n            freq = keras.Sequential([layers.Dense(hidden_sizes[i], activation='selu') for i in range(self.num_hidden)] + \n                                    [layers.Dense(input_dim, activation='softplus')])\n            length = keras.Sequential([layers.Dense(hidden_sizes[i], activation='selu') for i in range(self.num_hidden)] +\n                                   [layers.Dense(input_dim, activation='softplus')])\n            var = keras.Sequential([layers.Dense(hidden_sizes[i], activation='selu') for i in range(self.num_hidden)] +\n                                   [layers.Dense(1, activation='softplus')])\n            self.freq.append(freq)\n            self.length.append(length)\n            self.var.append(var)\n\n    def K(self, X, X2=None):\n        \"\"\"Computes the covariances between/amongst the input variables\n\n        Args:\n            X (ndarray): Variables to compute the covariance matrix\n            X2 (ndarray): If passed, the covariance between X and X2 is computed. Otherwise, \n                          the covariance between X and X is computed.\n\n        Returns:\n            cov (ndarray): covariance matrix\n        \"\"\"\n        if X2 is None:\n            X2 = X\n            equal = True\n        else:\n            equal = False\n\n        kern = 0.0\n        for q in range(self.Q):\n            # compute latent function values by the neural network\n            freq, freq2 = self.freq[q](X), self.freq[q](X2)\n            lens, lens2 = self.length[q](X), self.length[q](X2)\n            var, var2 = self.var[q](X), self.var[q](X2)\n\n            # compute length-scale term\n            Xr = tf.expand_dims(X, 1)  # N1 1 D\n            X2r = tf.expand_dims(X2, 0)  # 1 N2 D\n            l1 = tf.expand_dims(lens, 1)  # N1 1 D\n            l2 = tf.expand_dims(lens2, 0)  # 1 N2 D\n            L = tf.square(l1) + tf.square(l2)  # N1 N2 D\n            #D = tf.square((Xr - X2r) / L)  # N1 N2 D\n            D = tf.square(Xr - X2r) / L  # N1 N2 D\n            D = tf.reduce_sum(D, 2)  # N1 N2\n            det = tf.sqrt(2 * l1 * l2 / L)  # N1 N2 D\n            det = tf.reduce_prod(det, 2)  # N1 N2\n            E = det * tf.exp(-D)  # N1 N2\n\n            # compute cosine term\n            muX = (tf.reduce_sum(freq * X, 1, keepdims=True)\n                   - tf.transpose(tf.reduce_sum(freq2 * X2, 1, keepdims=True)))\n            COS = tf.cos(2 * np.pi * muX)\n\n            # compute kernel variance term\n            WW = tf.matmul(var, var2, transpose_b=True)  # w*w'^T\n\n            # compute the q'th kernel component\n            kern += WW * E * COS\n        if equal:\n            return robust_kernel(kern, tf.shape(X)[0])\n        else:\n            return kern\n\n    def K_diag(self, X):\n        kd = default_jitter()\n        for q in range(self.Q):\n            kd += tf.square(self.var[q](X))\n        return tf.squeeze(kd)\n</code></pre>"},{"location":"api/kernels/Neural-Kernel.html#sgptools.kernels.neural_kernel.NeuralSpectralKernel.K","title":"<code>K(X, X2=None)</code>","text":"<p>Computes the covariances between/amongst the input variables</p> <p>Parameters:</p> Name Type Description Default <code>X</code> <code>ndarray</code> <p>Variables to compute the covariance matrix</p> required <code>X2</code> <code>ndarray</code> <p>If passed, the covariance between X and X2 is computed. Otherwise,            the covariance between X and X is computed.</p> <code>None</code> <p>Returns:</p> Name Type Description <code>cov</code> <code>ndarray</code> <p>covariance matrix</p> Source code in <code>sgptools/kernels/neural_kernel.py</code> <pre><code>def K(self, X, X2=None):\n    \"\"\"Computes the covariances between/amongst the input variables\n\n    Args:\n        X (ndarray): Variables to compute the covariance matrix\n        X2 (ndarray): If passed, the covariance between X and X2 is computed. Otherwise, \n                      the covariance between X and X is computed.\n\n    Returns:\n        cov (ndarray): covariance matrix\n    \"\"\"\n    if X2 is None:\n        X2 = X\n        equal = True\n    else:\n        equal = False\n\n    kern = 0.0\n    for q in range(self.Q):\n        # compute latent function values by the neural network\n        freq, freq2 = self.freq[q](X), self.freq[q](X2)\n        lens, lens2 = self.length[q](X), self.length[q](X2)\n        var, var2 = self.var[q](X), self.var[q](X2)\n\n        # compute length-scale term\n        Xr = tf.expand_dims(X, 1)  # N1 1 D\n        X2r = tf.expand_dims(X2, 0)  # 1 N2 D\n        l1 = tf.expand_dims(lens, 1)  # N1 1 D\n        l2 = tf.expand_dims(lens2, 0)  # 1 N2 D\n        L = tf.square(l1) + tf.square(l2)  # N1 N2 D\n        #D = tf.square((Xr - X2r) / L)  # N1 N2 D\n        D = tf.square(Xr - X2r) / L  # N1 N2 D\n        D = tf.reduce_sum(D, 2)  # N1 N2\n        det = tf.sqrt(2 * l1 * l2 / L)  # N1 N2 D\n        det = tf.reduce_prod(det, 2)  # N1 N2\n        E = det * tf.exp(-D)  # N1 N2\n\n        # compute cosine term\n        muX = (tf.reduce_sum(freq * X, 1, keepdims=True)\n               - tf.transpose(tf.reduce_sum(freq2 * X2, 1, keepdims=True)))\n        COS = tf.cos(2 * np.pi * muX)\n\n        # compute kernel variance term\n        WW = tf.matmul(var, var2, transpose_b=True)  # w*w'^T\n\n        # compute the q'th kernel component\n        kern += WW * E * COS\n    if equal:\n        return robust_kernel(kern, tf.shape(X)[0])\n    else:\n        return kern\n</code></pre>"},{"location":"api/kernels/Neural-Kernel.html#sgptools.kernels.neural_kernel.init_neural_kernel","title":"<code>init_neural_kernel(x, y, inducing_variable, Q, n_inits=1, hidden_sizes=None)</code>","text":"<p>Helper function to initialize a Neural Spectral Kernel function (non-stationary kernel function).  Based on the implementation from the following repo</p> Refer to the following papers for more details <ul> <li>Neural Non-Stationary Spectral Kernel [Remes et al., 2018]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>ndarray</code> <p>(n, d); Input training set points</p> required <code>y</code> <code>ndarray</code> <p>(n, 1); Training set labels</p> required <code>inducing_variable</code> <code>ndarray</code> <p>(m, d); Initial inducing points</p> required <code>Q</code> <code>int</code> <p>Number of MLP mixture components used in the kernel function</p> required <code>n_inits</code> <code>int</code> <p>Number of times to initalize the kernel function (returns the best model)</p> <code>1</code> <code>hidden_sizes</code> <code>list</code> <p>Number of hidden units in each MLP layer. Length of the list determines the number of layers.</p> <code>None</code> Source code in <code>sgptools/kernels/neural_kernel.py</code> <pre><code>def init_neural_kernel(x, y, inducing_variable, Q, n_inits=1, hidden_sizes=None):\n    \"\"\"Helper function to initialize a Neural Spectral Kernel function (non-stationary kernel function). \n    Based on the implementation from the following [repo](https://github.com/sremes/nssm-gp/tree/master?tab=readme-ov-file)\n\n    Refer to the following papers for more details:\n        - Neural Non-Stationary Spectral Kernel [Remes et al., 2018]\n\n    Args:\n        x (ndarray): (n, d); Input training set points\n        y (ndarray): (n, 1); Training set labels\n        inducing_variable (ndarray): (m, d); Initial inducing points\n        Q (int): Number of MLP mixture components used in the kernel function\n        n_inits (int): Number of times to initalize the kernel function (returns the best model)\n        hidden_sizes (list): Number of hidden units in each MLP layer. Length of the list determines the number of layers.\n    \"\"\"\n    x, y = data_input_to_tensor((x, y))\n\n    print('Initializing neural spectral kernel...')\n    best_loglik = -np.inf\n    best_m = None\n    N, input_dim = x.shape\n\n    for k in range(n_inits):\n        # gpflow.reset_default_graph_and_session()\n        k = NeuralSpectralKernel(input_dim=input_dim, Q=Q, \n                                    hidden_sizes=hidden_sizes)\n        model = SGPR((x, y), inducing_variable=inducing_variable, \n                        kernel=k)\n        loglik = model.elbo()\n        if loglik &gt; best_loglik:\n            best_loglik = loglik\n            best_m = model\n        del model\n        gc.collect()\n    print('Best init: %f' % best_loglik)\n\n    return best_m\n</code></pre>"},{"location":"api/models/index.html","title":"","text":""},{"location":"api/models/index.html#sgptools.models","title":"<code>sgptools.models</code>","text":"<p>Sensor placement and informative path planning methods in this package:</p> <ul> <li><code>bo</code>: Provides a Bayesian optimization based approach that maximizes mutual-information to get sensor placements</li> <li><code>cma_es</code>: Provides a genetic algorithm (CMA-ES) based approach that maximizes mutual-information to get sensor placements</li> <li><code>continuous_sgp</code>: Provides an SGP-based sensor placement approach that is optimized using gradient descent</li> <li><code>greedy_mi</code>: Provides a greedy algorithm based approach that maximizes mutual-information to get sensor placements</li> <li><code>greedy_sgp</code>: Provides an SGP-based sensor placement approach that is optimized using a greedy algorithm</li> </ul>"},{"location":"api/models/Bayesian-Optimization.html","title":"Bayesian Optimization","text":""},{"location":"api/models/Bayesian-Optimization.html#sgptools.models.bo","title":"<code>sgptools.models.bo</code>","text":""},{"location":"api/models/Bayesian-Optimization.html#sgptools.models.bo.BayesianOpt","title":"<code>BayesianOpt</code>","text":"<p>Class for optimizing sensor placements using Bayesian Optimization</p> Refer to the following papers for more details <ul> <li>UAV route planning for active disease classification [Vivaldini et al., 2019]</li> <li>Occupancy map building through Bayesian exploration [Francis et al., 2019]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>X_train</code> <code>ndarray</code> <p>(n, d); Locations in the environment used to approximate the monitoring regions</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> Source code in <code>sgptools/models/bo.py</code> <pre><code>class BayesianOpt:\n    \"\"\"Class for optimizing sensor placements using Bayesian Optimization\n\n    Refer to the following papers for more details:\n        - UAV route planning for active disease classification [Vivaldini et al., 2019]\n        - Occupancy map building through Bayesian exploration [Francis et al., 2019]\n\n    Args:\n        X_train (ndarray): (n, d); Locations in the environment used to approximate the monitoring regions\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        transform (Transform): Transform object\n    \"\"\"\n    def __init__(self, X_train, noise_variance, kernel,\n                 transform=None):\n        self.X_train = X_train\n        self.noise_variance = noise_variance\n        self.kernel = kernel\n        self.num_dim = X_train.shape[-1]\n        self.transform = transform\n\n        # use the boundaries of the region as the search space\n        self.pbounds_dim = []\n        for i in range(self.num_dim):\n            self.pbounds_dim.append((np.min(X_train[:, i]), np.max(X_train[:, i])))\n\n    def objective(self, **kwargs):\n        \"\"\"Objective function (GP-based Mutual Information)\n\n        Args:\n            x&lt;i&gt; (ndarray): (1, d); Current solution sensor placement location i\n        \"\"\"\n        # MI does not depend on waypoint order (reshape to -1, num_dim)\n        X = []\n        for i in range(len(kwargs)):\n            X.append(kwargs['x{}'.format(i)])\n        X = np.array(X).reshape(-1, self.num_dim)\n        if self.transform is not None:\n            X = self.transform.expand(X)\n            constraints_loss = self.transform.constraints(X)\n\n        try:\n            mi = get_mi(X, self.X_train, self.noise_variance, self.kernel)\n            mi += constraints_loss\n            mi = mi.numpy()\n        except:\n            mi = -1e4 # if the cholskey decomposition fails\n        return mi\n\n    def optimize(self, \n                 num_sensors=10, \n                 max_steps=100,  \n                 X_init=None,\n                 init_points=10,\n                 verbose=0, \n                 seed=1234):\n        \"\"\"Optimizes the sensor placements using Bayesian Optimization without any constraints\n\n        Args:\n            num_sensors (int): Number of sensor locations to optimize.\n            max_steps (int): Maximum number of optimization steps. \n            X_init (ndarray): (m, d); Initial inducing points.\n            init_points (int): Number of random solutions used for initial exploration. \n                               Random exploration can help by diversifying the exploration space. \n            verbose (int): The level of verbosity.\n            seed (int): The algorithm will use it to seed the randomnumber generator, ensuring replicability.\n\n        Returns:\n            Xu (ndarray): (m, d); Solution sensor placement locations\n        \"\"\"\n        if X_init is None:\n            X_init = get_inducing_pts(self.X_train, num_sensors, random=True)\n        else:\n            num_sensors = len(X_init.reshape(-1, self.num_dim))\n        X_init = X_init.reshape(-1)\n\n        pbounds = {}\n        for i in range(self.num_dim*num_sensors):\n            pbounds['x{}'.format(i)] = self.pbounds_dim[i%self.num_dim]\n\n        optimizer = BayesianOptimization(f=self.objective,\n                                         pbounds=pbounds,\n                                         verbose=verbose,\n                                         random_state=seed,\n                                         allow_duplicate_points=True)\n        optimizer.maximize(init_points=init_points,\n                           n_iter=max_steps)\n\n        sol = []\n        for i in range(self.num_dim*num_sensors):\n            sol.append(optimizer.max['params']['x{}'.format(i)])\n        sol = np.array(sol).reshape(-1, self.num_dim)\n        if self.transform is not None:\n            sol = self.transform.expand(sol,\n                                        expand_sensor_model=False)\n            if not isinstance(sol, np.ndarray):\n                sol = sol.numpy()\n        return sol.reshape(-1, self.num_dim)\n</code></pre>"},{"location":"api/models/Bayesian-Optimization.html#sgptools.models.bo.BayesianOpt.objective","title":"<code>objective(**kwargs)</code>","text":"<p>Objective function (GP-based Mutual Information)</p> <p>Parameters:</p> Name Type Description Default <code>x&lt;i&gt;</code> <code>ndarray</code> <p>(1, d); Current solution sensor placement location i</p> required Source code in <code>sgptools/models/bo.py</code> <pre><code>def objective(self, **kwargs):\n    \"\"\"Objective function (GP-based Mutual Information)\n\n    Args:\n        x&lt;i&gt; (ndarray): (1, d); Current solution sensor placement location i\n    \"\"\"\n    # MI does not depend on waypoint order (reshape to -1, num_dim)\n    X = []\n    for i in range(len(kwargs)):\n        X.append(kwargs['x{}'.format(i)])\n    X = np.array(X).reshape(-1, self.num_dim)\n    if self.transform is not None:\n        X = self.transform.expand(X)\n        constraints_loss = self.transform.constraints(X)\n\n    try:\n        mi = get_mi(X, self.X_train, self.noise_variance, self.kernel)\n        mi += constraints_loss\n        mi = mi.numpy()\n    except:\n        mi = -1e4 # if the cholskey decomposition fails\n    return mi\n</code></pre>"},{"location":"api/models/Bayesian-Optimization.html#sgptools.models.bo.BayesianOpt.optimize","title":"<code>optimize(num_sensors=10, max_steps=100, X_init=None, init_points=10, verbose=0, seed=1234)</code>","text":"<p>Optimizes the sensor placements using Bayesian Optimization without any constraints</p> <p>Parameters:</p> Name Type Description Default <code>num_sensors</code> <code>int</code> <p>Number of sensor locations to optimize.</p> <code>10</code> <code>max_steps</code> <code>int</code> <p>Maximum number of optimization steps. </p> <code>100</code> <code>X_init</code> <code>ndarray</code> <p>(m, d); Initial inducing points.</p> <code>None</code> <code>init_points</code> <code>int</code> <p>Number of random solutions used for initial exploration.                 Random exploration can help by diversifying the exploration space. </p> <code>10</code> <code>verbose</code> <code>int</code> <p>The level of verbosity.</p> <code>0</code> <code>seed</code> <code>int</code> <p>The algorithm will use it to seed the randomnumber generator, ensuring replicability.</p> <code>1234</code> <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(m, d); Solution sensor placement locations</p> Source code in <code>sgptools/models/bo.py</code> <pre><code>def optimize(self, \n             num_sensors=10, \n             max_steps=100,  \n             X_init=None,\n             init_points=10,\n             verbose=0, \n             seed=1234):\n    \"\"\"Optimizes the sensor placements using Bayesian Optimization without any constraints\n\n    Args:\n        num_sensors (int): Number of sensor locations to optimize.\n        max_steps (int): Maximum number of optimization steps. \n        X_init (ndarray): (m, d); Initial inducing points.\n        init_points (int): Number of random solutions used for initial exploration. \n                           Random exploration can help by diversifying the exploration space. \n        verbose (int): The level of verbosity.\n        seed (int): The algorithm will use it to seed the randomnumber generator, ensuring replicability.\n\n    Returns:\n        Xu (ndarray): (m, d); Solution sensor placement locations\n    \"\"\"\n    if X_init is None:\n        X_init = get_inducing_pts(self.X_train, num_sensors, random=True)\n    else:\n        num_sensors = len(X_init.reshape(-1, self.num_dim))\n    X_init = X_init.reshape(-1)\n\n    pbounds = {}\n    for i in range(self.num_dim*num_sensors):\n        pbounds['x{}'.format(i)] = self.pbounds_dim[i%self.num_dim]\n\n    optimizer = BayesianOptimization(f=self.objective,\n                                     pbounds=pbounds,\n                                     verbose=verbose,\n                                     random_state=seed,\n                                     allow_duplicate_points=True)\n    optimizer.maximize(init_points=init_points,\n                       n_iter=max_steps)\n\n    sol = []\n    for i in range(self.num_dim*num_sensors):\n        sol.append(optimizer.max['params']['x{}'.format(i)])\n    sol = np.array(sol).reshape(-1, self.num_dim)\n    if self.transform is not None:\n        sol = self.transform.expand(sol,\n                                    expand_sensor_model=False)\n        if not isinstance(sol, np.ndarray):\n            sol = sol.numpy()\n    return sol.reshape(-1, self.num_dim)\n</code></pre>"},{"location":"api/models/Continuous-SGP.html","title":"Continuous SGP","text":""},{"location":"api/models/Continuous-SGP.html#sgptools.models.continuous_sgp","title":"<code>sgptools.models.continuous_sgp</code>","text":""},{"location":"api/models/Continuous-SGP.html#sgptools.models.continuous_sgp.continuous_sgp","title":"<code>continuous_sgp(num_inducing, X_train, noise_variance, kernel, transform=None, Xu_init=None, Xu_time=None, orientation=False, **kwargs)</code>","text":"<p>Get sensor placement solutions using the Continuous-SGP method</p> Refer to the following papers for more details <ul> <li>Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]</li> <li>Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>num_inducing</code> <code>int</code> <p>Number of inducing points</p> required <code>X_train</code> <code>ndarray</code> <p>(n, d); Unlabeled random sampled training points</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> <code>Xu_init</code> <code>ndarray</code> <p>(m, d); Initial inducing points</p> <code>None</code> <code>Xu_time</code> <code>ndarray</code> <p>(t, d); Temporal inducing points used in spatio-temporal models</p> <code>None</code> <code>orientation</code> <code>bool</code> <p>If True, a additionl dimension is added to the                  inducing points to represent the FoV orientation</p> <code>False</code> <p>Returns:</p> Name Type Description <code>sgpr</code> <code>AugmentedSGPR</code> <p>Optimized sparse Gaussian process model</p> <code>loss</code> <code>ndarray</code> <p>Loss values computed during training</p> Source code in <code>sgptools/models/continuous_sgp.py</code> <pre><code>def continuous_sgp(num_inducing, X_train, noise_variance, kernel, \n                   transform=None,\n                   Xu_init=None, \n                   Xu_time=None, \n                   orientation=False,\n                   **kwargs):\n    \"\"\"Get sensor placement solutions using the Continuous-SGP method\n\n    Refer to the following papers for more details:\n        - Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [[Jakkala and Akella, 2023](https://www.itskalvik.com/publication/sgp-sp/)]\n        - Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [[Jakkala and Akella, 2024](https://www.itskalvik.com/publication/sgp-ipp/)]\n\n    Args:\n        num_inducing (int): Number of inducing points\n        X_train (ndarray): (n, d); Unlabeled random sampled training points\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        transform (Transform): Transform object\n        Xu_init (ndarray): (m, d); Initial inducing points\n        Xu_time (ndarray): (t, d); Temporal inducing points used in spatio-temporal models\n        orientation (bool): If True, a additionl dimension is added to the \n                            inducing points to represent the FoV orientation\n\n    Returns:\n        sgpr (AugmentedSGPR): Optimized sparse Gaussian process model\n        loss (ndarray): Loss values computed during training\n    \"\"\"\n    # Generate init inducing points\n    if Xu_init is None:\n        Xu_init = get_inducing_pts(X_train, num_inducing, \n                                   orientation=orientation)\n\n    # Fit spare GP\n    sgpr = AugmentedSGPR((X_train, np.zeros((len(X_train), 1)).astype(X_train.dtype)),\n                         noise_variance=noise_variance,\n                         kernel=kernel, \n                         inducing_variable=Xu_init,\n                         inducing_variable_time=Xu_time,\n                         transform=transform)\n\n    # Train the mode\n    loss = optimize_model(sgpr,\n                          kernel_grad=False, \n                          **kwargs)\n\n    return sgpr, loss\n</code></pre>"},{"location":"api/models/Genetic-Algorithm.html","title":"Genetic Algorithm","text":""},{"location":"api/models/Genetic-Algorithm.html#sgptools.models.cma_es","title":"<code>sgptools.models.cma_es</code>","text":""},{"location":"api/models/Genetic-Algorithm.html#sgptools.models.cma_es.CMA_ES","title":"<code>CMA_ES</code>","text":"<p>Class for optimizing sensor placements using CMA-ES (a genetic algorithm)</p> Refer to the following paper for more details <ul> <li>Adaptive Continuous-Space Informative Path Planning for Online Environmental Monitoring [Hitz et al., 2017]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>X_train</code> <code>ndarray</code> <p>(n, d); Locations in the environment used to approximate the monitoring regions</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>distance_budget</code> <code>float</code> <p>Distance budget for when treating the inducing points                       as waypoints of a path</p> <code>None</code> <code>num_robots</code> <code>int</code> <p>Number of robots, used when modeling                multi-robot IPP with a distance budget</p> <code>1</code> <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> Source code in <code>sgptools/models/cma_es.py</code> <pre><code>class CMA_ES:\n    \"\"\"Class for optimizing sensor placements using CMA-ES (a genetic algorithm)\n\n    Refer to the following paper for more details:\n        - Adaptive Continuous-Space Informative Path Planning for Online Environmental Monitoring [Hitz et al., 2017]\n\n    Args:\n        X_train (ndarray): (n, d); Locations in the environment used to approximate the monitoring regions\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        distance_budget (float): Distance budget for when treating the inducing points \n                                 as waypoints of a path\n        num_robots (int): Number of robots, used when modeling \n                          multi-robot IPP with a distance budget\n        transform (Transform): Transform object\n    \"\"\"\n    def __init__(self, X_train, noise_variance, kernel,\n                 distance_budget=None,\n                 num_robots=1,\n                 transform=None):\n        self.boundaries = geometry.MultiPoint([[p[0], p[1]] for p in X_train]).convex_hull\n        self.X_train = X_train\n        self.noise_variance = noise_variance\n        self.kernel = kernel\n        self.num_dim = X_train.shape[-1]\n        self.distance_budget = distance_budget\n        self.num_robots = num_robots\n        self.transform = transform\n\n    def update(self, noise_variance, kernel):\n        \"\"\"Update GP noise variance and kernel function parameters\n\n        Args:\n            noise_variance (float): data variance\n            kernel (gpflow.kernels.Kernel): gpflow kernel function\n        \"\"\"\n        self.noise_variance = noise_variance\n        self.kernel = kernel\n\n    def objective(self, X):\n        \"\"\"Objective function (GP-based Mutual Information)\n\n        Args:\n            X (ndarray): (n, d); Current solution sensor placement locations\n        \"\"\"\n        # MI does not depend on waypoint order (reshape to -1, num_dim)\n        X = np.array(X).reshape(-1, self.num_dim)\n        constraints_loss = 0.0\n        if self.transform is not None:\n            X = self.transform.expand(X)\n            constraints_loss = self.transform.constraints(X)\n\n        try:\n            mi = -get_mi(X, self.X_train, self.noise_variance, self.kernel)\n            mi -= constraints_loss\n            mi = mi.numpy()\n        except:\n            mi = 0.0 # if the cholskey decomposition fails\n        return mi\n\n    def optimize(self, \n                 num_sensors=10, \n                 max_steps=5000, \n                 tol=1e-6, \n                 X_init=None,\n                 verbose=0,\n                 seed=1234):\n        \"\"\"Optimizes the sensor placements using CMA-ES without any constraints\n\n        Args:\n            num_sensors (int): Number of sensor locations to optimize\n            max_steps (int): Maximum number of optimization steps\n            tol (float): Convergence tolerance to decide when to stop optimization\n            X_init (ndarray): (m, d); Initial inducing points\n            verbose (int): The level of verbosity.\n            seed (int): The algorithm will use it to seed the randomnumber generator, ensuring replicability.\n\n        Returns:\n            Xu (ndarray): (m, d); Solution sensor placement locations\n        \"\"\"\n        sigma0 = 1.0\n\n        if X_init is None:\n            X_init = get_inducing_pts(self.X_train, num_sensors, random=True)\n        X_init = X_init.reshape(-1)\n\n        xopt, _ = cma.fmin2(self.objective, X_init, sigma0, \n                            options={'maxfevals': max_steps,\n                                     'verb_disp': verbose,\n                                     'tolfun': tol,\n                                     'seed': seed},\n                            restarts=5)\n\n        xopt = np.array(xopt).reshape(-1, self.num_dim)\n        if self.transform is not None:\n            xopt = self.transform.expand(xopt, \n                                         expand_sensor_model=False)\n            if not isinstance(xopt, np.ndarray):\n                xopt = xopt.numpy()\n        return xopt.reshape(-1, self.num_dim)\n</code></pre>"},{"location":"api/models/Genetic-Algorithm.html#sgptools.models.cma_es.CMA_ES.objective","title":"<code>objective(X)</code>","text":"<p>Objective function (GP-based Mutual Information)</p> <p>Parameters:</p> Name Type Description Default <code>X</code> <code>ndarray</code> <p>(n, d); Current solution sensor placement locations</p> required Source code in <code>sgptools/models/cma_es.py</code> <pre><code>def objective(self, X):\n    \"\"\"Objective function (GP-based Mutual Information)\n\n    Args:\n        X (ndarray): (n, d); Current solution sensor placement locations\n    \"\"\"\n    # MI does not depend on waypoint order (reshape to -1, num_dim)\n    X = np.array(X).reshape(-1, self.num_dim)\n    constraints_loss = 0.0\n    if self.transform is not None:\n        X = self.transform.expand(X)\n        constraints_loss = self.transform.constraints(X)\n\n    try:\n        mi = -get_mi(X, self.X_train, self.noise_variance, self.kernel)\n        mi -= constraints_loss\n        mi = mi.numpy()\n    except:\n        mi = 0.0 # if the cholskey decomposition fails\n    return mi\n</code></pre>"},{"location":"api/models/Genetic-Algorithm.html#sgptools.models.cma_es.CMA_ES.optimize","title":"<code>optimize(num_sensors=10, max_steps=5000, tol=1e-06, X_init=None, verbose=0, seed=1234)</code>","text":"<p>Optimizes the sensor placements using CMA-ES without any constraints</p> <p>Parameters:</p> Name Type Description Default <code>num_sensors</code> <code>int</code> <p>Number of sensor locations to optimize</p> <code>10</code> <code>max_steps</code> <code>int</code> <p>Maximum number of optimization steps</p> <code>5000</code> <code>tol</code> <code>float</code> <p>Convergence tolerance to decide when to stop optimization</p> <code>1e-06</code> <code>X_init</code> <code>ndarray</code> <p>(m, d); Initial inducing points</p> <code>None</code> <code>verbose</code> <code>int</code> <p>The level of verbosity.</p> <code>0</code> <code>seed</code> <code>int</code> <p>The algorithm will use it to seed the randomnumber generator, ensuring replicability.</p> <code>1234</code> <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(m, d); Solution sensor placement locations</p> Source code in <code>sgptools/models/cma_es.py</code> <pre><code>def optimize(self, \n             num_sensors=10, \n             max_steps=5000, \n             tol=1e-6, \n             X_init=None,\n             verbose=0,\n             seed=1234):\n    \"\"\"Optimizes the sensor placements using CMA-ES without any constraints\n\n    Args:\n        num_sensors (int): Number of sensor locations to optimize\n        max_steps (int): Maximum number of optimization steps\n        tol (float): Convergence tolerance to decide when to stop optimization\n        X_init (ndarray): (m, d); Initial inducing points\n        verbose (int): The level of verbosity.\n        seed (int): The algorithm will use it to seed the randomnumber generator, ensuring replicability.\n\n    Returns:\n        Xu (ndarray): (m, d); Solution sensor placement locations\n    \"\"\"\n    sigma0 = 1.0\n\n    if X_init is None:\n        X_init = get_inducing_pts(self.X_train, num_sensors, random=True)\n    X_init = X_init.reshape(-1)\n\n    xopt, _ = cma.fmin2(self.objective, X_init, sigma0, \n                        options={'maxfevals': max_steps,\n                                 'verb_disp': verbose,\n                                 'tolfun': tol,\n                                 'seed': seed},\n                        restarts=5)\n\n    xopt = np.array(xopt).reshape(-1, self.num_dim)\n    if self.transform is not None:\n        xopt = self.transform.expand(xopt, \n                                     expand_sensor_model=False)\n        if not isinstance(xopt, np.ndarray):\n            xopt = xopt.numpy()\n    return xopt.reshape(-1, self.num_dim)\n</code></pre>"},{"location":"api/models/Genetic-Algorithm.html#sgptools.models.cma_es.CMA_ES.update","title":"<code>update(noise_variance, kernel)</code>","text":"<p>Update GP noise variance and kernel function parameters</p> <p>Parameters:</p> Name Type Description Default <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required Source code in <code>sgptools/models/cma_es.py</code> <pre><code>def update(self, noise_variance, kernel):\n    \"\"\"Update GP noise variance and kernel function parameters\n\n    Args:\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n    \"\"\"\n    self.noise_variance = noise_variance\n    self.kernel = kernel\n</code></pre>"},{"location":"api/models/Greedy-MI.html","title":"Greedy MI","text":""},{"location":"api/models/Greedy-MI.html#sgptools.models.greedy_mi","title":"<code>sgptools.models.greedy_mi</code>","text":""},{"location":"api/models/Greedy-MI.html#sgptools.models.greedy_mi.GreedyMI","title":"<code>GreedyMI</code>","text":"<p>Helper class to compute mutual information using a Gaussian process for a given set of sensor locations. Used by <code>get_greedy_mi_sol</code> function to compute the solution sensor placements using the Greedy-MI method.</p> Refer to the following papers for more details <ul> <li>Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies [Krause et al., 2008]</li> <li>Data-driven learning and planning for environmental sampling [Ma et al., 2018]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>S</code> <code>ndarray</code> <p>(n, d); Candidate sensor placement locations</p> required <code>V</code> <code>ndarray</code> <p>(n, d); Locations in the environment used to approximate the monitoring regions</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> Source code in <code>sgptools/models/greedy_mi.py</code> <pre><code>class GreedyMI:\n    \"\"\"Helper class to compute mutual information using a Gaussian process for a given set of sensor locations.\n    Used by `get_greedy_mi_sol` function to compute the solution sensor placements using the Greedy-MI method.\n\n    Refer to the following papers for more details:\n        - Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies [Krause et al., 2008]\n        - Data-driven learning and planning for environmental sampling [Ma et al., 2018]\n\n    Args:\n        S (ndarray): (n, d); Candidate sensor placement locations\n        V (ndarray): (n, d); Locations in the environment used to approximate the monitoring regions\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        transform (Transform): Transform object\n    \"\"\"\n    def __init__(self, S, V, noise_variance, kernel, \n                 transform=None):\n        self.S = S\n        self.V = V\n        self.kernel = kernel\n        self.input_dim = S.shape[1]\n        self.noise_variance = noise_variance\n        self.transform = transform\n\n    def mutual_info(self, x):\n        \"\"\"Computes mutual information using the points `x` \n\n        Args:\n            x (ndarray): (n); Indices of the solution placement locations\n\n        Returns:\n            MI (float): Mutual information between the placement x and candidate locations\n        \"\"\"\n        x = np.array(x).reshape(-1).astype(int)\n        A = self.S[x[:-1]].reshape(-1, self.input_dim)\n        y = self.S[x[-1]].reshape(-1, self.input_dim)\n\n        if len(A) == 0:\n            sigma_a = 1.0\n        else:\n            if self.transform is not None:\n                A = self.transform.expand(A)\n            a_gp = GPR(data=(A, np.zeros((len(A), 1))),\n                       kernel=self.kernel,\n                       noise_variance=self.noise_variance)\n            _, sigma_a = a_gp.predict_f(y)\n\n        # Remove locations in A\u222ay from V to build A bar (Refer to Krause et al., 2008)\n        V_ = self.V.copy()\n        V_rows = V_.view([('', V_.dtype)] * V_.shape[1])\n\n        if self.transform is not None:\n            solution = self.S[x].reshape(-1, self.input_dim)\n            A_ = self.transform.expand(solution)\n        else:\n            A_ = self.S[x]\n        A_rows = A_.view([('', V_.dtype)] * A_.shape[1])\n\n        V_ = np.setdiff1d(V_rows, A_rows).view(V_.dtype).reshape(-1, V_.shape[1])\n\n        self.v_gp = GPR(data=(V_, np.zeros((len(V_), 1))), \n                        kernel=self.kernel,\n                        noise_variance=self.noise_variance)\n        _, sigma_v = self.v_gp.predict_f(y)\n\n        return (sigma_a/sigma_v).numpy().squeeze()\n</code></pre>"},{"location":"api/models/Greedy-MI.html#sgptools.models.greedy_mi.GreedyMI.mutual_info","title":"<code>mutual_info(x)</code>","text":"<p>Computes mutual information using the points <code>x</code> </p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>ndarray</code> <p>(n); Indices of the solution placement locations</p> required <p>Returns:</p> Name Type Description <code>MI</code> <code>float</code> <p>Mutual information between the placement x and candidate locations</p> Source code in <code>sgptools/models/greedy_mi.py</code> <pre><code>def mutual_info(self, x):\n    \"\"\"Computes mutual information using the points `x` \n\n    Args:\n        x (ndarray): (n); Indices of the solution placement locations\n\n    Returns:\n        MI (float): Mutual information between the placement x and candidate locations\n    \"\"\"\n    x = np.array(x).reshape(-1).astype(int)\n    A = self.S[x[:-1]].reshape(-1, self.input_dim)\n    y = self.S[x[-1]].reshape(-1, self.input_dim)\n\n    if len(A) == 0:\n        sigma_a = 1.0\n    else:\n        if self.transform is not None:\n            A = self.transform.expand(A)\n        a_gp = GPR(data=(A, np.zeros((len(A), 1))),\n                   kernel=self.kernel,\n                   noise_variance=self.noise_variance)\n        _, sigma_a = a_gp.predict_f(y)\n\n    # Remove locations in A\u222ay from V to build A bar (Refer to Krause et al., 2008)\n    V_ = self.V.copy()\n    V_rows = V_.view([('', V_.dtype)] * V_.shape[1])\n\n    if self.transform is not None:\n        solution = self.S[x].reshape(-1, self.input_dim)\n        A_ = self.transform.expand(solution)\n    else:\n        A_ = self.S[x]\n    A_rows = A_.view([('', V_.dtype)] * A_.shape[1])\n\n    V_ = np.setdiff1d(V_rows, A_rows).view(V_.dtype).reshape(-1, V_.shape[1])\n\n    self.v_gp = GPR(data=(V_, np.zeros((len(V_), 1))), \n                    kernel=self.kernel,\n                    noise_variance=self.noise_variance)\n    _, sigma_v = self.v_gp.predict_f(y)\n\n    return (sigma_a/sigma_v).numpy().squeeze()\n</code></pre>"},{"location":"api/models/Greedy-MI.html#sgptools.models.greedy_mi.get_greedy_mi_sol","title":"<code>get_greedy_mi_sol(num_sensors, candidates, X_train, noise_variance, kernel, transform=None, optimizer='naive')</code>","text":"<p>Get sensor placement solutions using the GP-based mutual information approach (submodular objective function).  Uses a greedy algorithm to select sensor placements from a given discrete set of candidates locations.</p> Refer to the following papers for more details <ul> <li>Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies [Krause et al., 2008]</li> <li>Data-driven learning and planning for environmental sampling [Ma et al., 2018]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>num_sensors</code> <code>int</code> <p>Number of sensor locations to optimize</p> required <code>candidates</code> <code>ndarray</code> <p>(n, d); Candidate sensor placement locations</p> required <code>X_train</code> <code>ndarray</code> <p>(n, d); Locations in the environment used to approximate the monitoring regions</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> <code>optimizer</code> <code>str</code> <p>Name of an optimizer available in the apricot library</p> <code>'naive'</code> <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(m, d); Solution sensor placement locations</p> Source code in <code>sgptools/models/greedy_mi.py</code> <pre><code>def get_greedy_mi_sol(num_sensors, candidates, X_train, noise_variance, kernel, \n                      transform=None, optimizer='naive'):\n    \"\"\"Get sensor placement solutions using the GP-based mutual information approach (submodular objective function). \n    Uses a greedy algorithm to select sensor placements from a given discrete set of candidates locations.\n\n    Refer to the following papers for more details:\n        - Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies [Krause et al., 2008]\n        - Data-driven learning and planning for environmental sampling [Ma et al., 2018]\n\n    Args:\n        num_sensors (int): Number of sensor locations to optimize\n        candidates (ndarray): (n, d); Candidate sensor placement locations\n        X_train (ndarray): (n, d); Locations in the environment used to approximate the monitoring regions\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        transform (Transform): Transform object\n        optimizer (str): Name of an optimizer available in the apricot library\n\n    Returns:\n        Xu (ndarray): (m, d); Solution sensor placement locations\n    \"\"\"\n    mi_model = GreedyMI(candidates, X_train, noise_variance, kernel, transform)\n    model = CustomSelection(num_sensors,\n                            mi_model.mutual_info,\n                            optimizer=optimizer,\n                            verbose=False)\n    sol = model.fit_transform(np.arange(len(candidates)).reshape(-1, 1))\n    return candidates[sol.reshape(-1)]\n</code></pre>"},{"location":"api/models/Greedy-SGP.html","title":"Greedy SGP","text":""},{"location":"api/models/Greedy-SGP.html#sgptools.models.greedy_sgp","title":"<code>sgptools.models.greedy_sgp</code>","text":""},{"location":"api/models/Greedy-SGP.html#sgptools.models.greedy_sgp.GreedySGP","title":"<code>GreedySGP</code>","text":"<p>Helper class to compute SGP's ELBO/optimization bound for a given set of sensor locations. Used by <code>get_greedy_sgp_sol</code> function to compute the solution sensor placements using the Greedy-SGP method.</p> Refer to the following papers for more details <ul> <li>Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>num_inducing</code> <code>int</code> <p>Number of inducing points</p> required <code>S</code> <code>ndarray</code> <p>(n, d); Candidate sensor placement locations</p> required <code>V</code> <code>ndarray</code> <p>(n, d); Locations in the environment used to approximate the monitoring regions</p> required <code>noise_variance</code> <code>float</code> <p>Data noise variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> Source code in <code>sgptools/models/greedy_sgp.py</code> <pre><code>class GreedySGP:\n    \"\"\"Helper class to compute SGP's ELBO/optimization bound for a given set of sensor locations.\n    Used by `get_greedy_sgp_sol` function to compute the solution sensor placements using the Greedy-SGP method.\n\n    Refer to the following papers for more details:\n        - Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [[Jakkala and Akella, 2023](https://www.itskalvik.com/publication/sgp-sp/)]\n\n    Args:\n        num_inducing (int): Number of inducing points\n        S (ndarray): (n, d); Candidate sensor placement locations\n        V (ndarray): (n, d); Locations in the environment used to approximate the monitoring regions\n        noise_variance (float): Data noise variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        transform (Transform): Transform object\n    \"\"\"\n    def __init__(self, num_inducing, S, V, noise_variance, kernel, \n                 transform=None):\n        self.sgp = AugmentedSGPR((V, np.zeros((len(V), 1))),\n                                 noise_variance=noise_variance,\n                                 kernel=kernel, \n                                 inducing_variable=S[:num_inducing],\n                                 transform=transform)\n        self.locs = S\n        self.num_inducing = num_inducing\n        self.inducing_dim = S.shape[1]\n\n    def bound(self, x):\n        \"\"\"Computes the SGP's optimization bound using the inducing points `x` \n\n        Args:\n            x (ndarray): (n); Indices of the solution placement locations\n\n        Returns:\n            elbo (float): Evidence lower bound/SGP's optimization bound value\n        \"\"\"\n        x = np.array(x).reshape(-1).astype(int)\n        Xu = np.ones((self.num_inducing, self.inducing_dim), dtype=np.float32)\n\n        # Initialize all inducing points at the first solution placement location.\n        # Ensures that the number of inducing points is always fixed and no additional\n        # information is passed to the SGP\n        Xu *= self.locs[x][0]\n\n        # Copy all given solution placements to the inducing points set\n        Xu[-len(x):] = self.locs[x]\n\n        # Update the SGP inducing points\n        self.sgp.inducing_variable.Z.assign(Xu)\n        return self.sgp.elbo().numpy() # return the ELBO\n</code></pre>"},{"location":"api/models/Greedy-SGP.html#sgptools.models.greedy_sgp.GreedySGP.bound","title":"<code>bound(x)</code>","text":"<p>Computes the SGP's optimization bound using the inducing points <code>x</code> </p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>ndarray</code> <p>(n); Indices of the solution placement locations</p> required <p>Returns:</p> Name Type Description <code>elbo</code> <code>float</code> <p>Evidence lower bound/SGP's optimization bound value</p> Source code in <code>sgptools/models/greedy_sgp.py</code> <pre><code>def bound(self, x):\n    \"\"\"Computes the SGP's optimization bound using the inducing points `x` \n\n    Args:\n        x (ndarray): (n); Indices of the solution placement locations\n\n    Returns:\n        elbo (float): Evidence lower bound/SGP's optimization bound value\n    \"\"\"\n    x = np.array(x).reshape(-1).astype(int)\n    Xu = np.ones((self.num_inducing, self.inducing_dim), dtype=np.float32)\n\n    # Initialize all inducing points at the first solution placement location.\n    # Ensures that the number of inducing points is always fixed and no additional\n    # information is passed to the SGP\n    Xu *= self.locs[x][0]\n\n    # Copy all given solution placements to the inducing points set\n    Xu[-len(x):] = self.locs[x]\n\n    # Update the SGP inducing points\n    self.sgp.inducing_variable.Z.assign(Xu)\n    return self.sgp.elbo().numpy() # return the ELBO\n</code></pre>"},{"location":"api/models/Greedy-SGP.html#sgptools.models.greedy_sgp.get_greedy_sgp_sol","title":"<code>get_greedy_sgp_sol(num_sensors, candidates, X_train, noise_variance, kernel, transform=None)</code>","text":"<p>Get sensor placement solutions using the Greedy-SGP method. Uses a greedy algorithm to  select sensor placements from a given discrete set of candidates locations.</p> Refer to the following papers for more details <ul> <li>Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>num_sensors</code> <code>int</code> <p>Number of sensor locations to optimize</p> required <code>candidates</code> <code>ndarray</code> <p>(n, d); Candidate sensor placement locations</p> required <code>X_train</code> <code>ndarray</code> <p>(n, d); Locations in the environment used to approximate the monitoring regions</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(m, d); Solution sensor placement locations</p> Source code in <code>sgptools/models/greedy_sgp.py</code> <pre><code>def get_greedy_sgp_sol(num_sensors, candidates, X_train, noise_variance, kernel, \n                       transform=None):\n    \"\"\"Get sensor placement solutions using the Greedy-SGP method. Uses a greedy algorithm to \n    select sensor placements from a given discrete set of candidates locations.\n\n    Refer to the following papers for more details:\n        - Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [[Jakkala and Akella, 2023](https://www.itskalvik.com/publication/sgp-sp/)]\n\n    Args:\n        num_sensors (int): Number of sensor locations to optimize\n        candidates (ndarray): (n, d); Candidate sensor placement locations\n        X_train (ndarray): (n, d); Locations in the environment used to approximate the monitoring regions\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        transform (Transform): Transform object\n\n    Returns:\n        Xu (ndarray): (m, d); Solution sensor placement locations\n    \"\"\"\n    sgp_model = GreedySGP(num_sensors, candidates, X_train, \n                          noise_variance, kernel, transform=transform)\n    model = CustomSelection(num_sensors,\n                            sgp_model.bound,\n                            optimizer='naive',\n                            verbose=False)\n    sol = model.fit_transform(np.arange(len(candidates)).reshape(-1, 1))\n    return candidates[sol.reshape(-1)]\n</code></pre>"},{"location":"api/models/core/index.html","title":"","text":""},{"location":"api/models/core/index.html#sgptools.models.core","title":"<code>sgptools.models.core</code>","text":"<p>Core modules in this package:</p> <ul> <li><code>augmented_gpr</code>: Provides a Gaussian process model with expand and aggregate functions</li> <li><code>augmented_sgpr</code>: Provides a sparse Gaussian process model with update, expand, and aggregate functions</li> <li><code>osgpr</code>: Provides a streaming sparse Gaussian process model along with initialization function</li> <li><code>transformations</code>: Provides transforms to model complex sensor field of views and handle informative path planning</li> </ul>"},{"location":"api/models/core/AugmentedGPR.html","title":"AugmentedGPR","text":""},{"location":"api/models/core/AugmentedGPR.html#sgptools.models.core.augmented_gpr","title":"<code>sgptools.models.core.augmented_gpr</code>","text":"<p>Provides a Gaussian process model with expand and aggregate functions</p>"},{"location":"api/models/core/AugmentedGPR.html#sgptools.models.core.augmented_gpr.AugmentedGPR","title":"<code>AugmentedGPR</code>","text":"<p>               Bases: <code>GPR</code></p> <p>GPR model from the GPFlow library augmented to use a transform object's expand and aggregate functions on the data points where necessary.  </p> Refer to the following papers for more details <ul> <li>Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]</li> <li>Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>tuple</code> <p>(X, y) ndarrays with inputs (n, d) and labels (n, 1)</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> Source code in <code>sgptools/models/core/augmented_gpr.py</code> <pre><code>class AugmentedGPR(GPR):\n    \"\"\"GPR model from the GPFlow library augmented to use a transform object's\n    expand and aggregate functions on the data points where necessary.  \n\n    Refer to the following papers for more details:\n        - Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]\n        - Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]\n\n    Args:\n        data (tuple): (X, y) ndarrays with inputs (n, d) and labels (n, 1)\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        noise_variance (float): data variance\n        transform (Transform): Transform object\n    \"\"\"\n    def __init__(\n        self,\n        *args,\n        transform=None,\n        **kwargs\n    ):\n        super().__init__(\n            *args,\n            **kwargs\n        )\n        if transform is None:\n            self.transform = Transform()\n        else:\n            self.transform = transform\n\n    def predict_f(\n        self, Xnew: InputData, \n        full_cov: bool = True, \n        full_output_cov: bool = False,\n        aggregate_train: bool = False,\n    ) -&gt; MeanAndVariance:\n        assert_params_false(self.predict_f, full_output_cov=full_output_cov)\n        if self.transform is not None:\n            Xnew = self.transform.expand(Xnew)\n\n        X, Y = self.data\n        err = Y - self.mean_function(X)\n\n        kmm = self.kernel(X)\n        knn = self.kernel(Xnew, full_cov=full_cov)\n        kmn = self.kernel(X, Xnew)\n        kmm_plus_s = add_likelihood_noise_cov(kmm, self.likelihood, X)\n\n        if self.transform is not None:\n            kmn = self.transform.aggregate(tf.transpose(kmn))\n            kmn = tf.transpose(kmn)\n            knn = self.transform.aggregate(knn)\n\n        if aggregate_train:\n            kmm_plus_s = self.transform.aggregate(kmm_plus_s)\n            err = self.transform.aggregate(err)\n            # reduce kmn only if it was not reduced before\n            # which can when train and test data are the same size\n            if kmn.shape[0] != kmn.shape[1]:\n                kmn = self.transform.aggregate(kmn)\n\n        conditional = gpflow.conditionals.base_conditional\n        f_mean_zero, f_var = conditional(\n            kmn, kmm_plus_s, knn, err, full_cov=full_cov, white=False\n        )  # [N, P], [N, P] or [P, N, N]\n        f_mean = f_mean_zero + self.mean_function(Xnew)\n        return f_mean, f_var\n</code></pre>"},{"location":"api/models/core/AugmentedSGPR.html","title":"AugmentedSGPR","text":""},{"location":"api/models/core/AugmentedSGPR.html#sgptools.models.core.augmented_sgpr","title":"<code>sgptools.models.core.augmented_sgpr</code>","text":"<p>Provides a sparse Gaussian process model with update, expand, and aggregate functions</p>"},{"location":"api/models/core/AugmentedSGPR.html#sgptools.models.core.augmented_sgpr.AugmentedSGPR","title":"<code>AugmentedSGPR</code>","text":"<p>               Bases: <code>SGPR</code></p> <p>SGPR model from the GPFlow library augmented to use a transform object's expand and aggregate functions on the inducing points where necessary. The object has an additional update function to update the kernel and noise variance parameters  (currently, the online updates part works only with RBF kernels).  </p> Refer to the following papers for more details <ul> <li>Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]</li> <li>Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>tuple</code> <p>(X, y) ndarrays with inputs (n, d) and labels (n, 1)</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>inducing_variable</code> <code>ndarray</code> <p>(m, d); Initial inducing points</p> required <code>transform</code> <code>Transform</code> <p>Transform object</p> <code>None</code> <code>inducing_variable_time</code> <code>ndarray</code> <p>(m, d); Temporal dimensions of the inducing points,                                  used when modeling spatio-temporal IPP</p> <code>None</code> Source code in <code>sgptools/models/core/augmented_sgpr.py</code> <pre><code>class AugmentedSGPR(SGPR):\n    \"\"\"SGPR model from the GPFlow library augmented to use a transform object's\n    expand and aggregate functions on the inducing points where necessary. The object\n    has an additional update function to update the kernel and noise variance parameters \n    (currently, the online updates part works only with RBF kernels).  \n\n\n    Refer to the following papers for more details:\n        - Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]\n        - Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]\n\n    Args:\n        data (tuple): (X, y) ndarrays with inputs (n, d) and labels (n, 1)\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        noise_variance (float): data variance\n        inducing_variable (ndarray): (m, d); Initial inducing points\n        transform (Transform): Transform object\n        inducing_variable_time (ndarray): (m, d); Temporal dimensions of the inducing points, \n                                            used when modeling spatio-temporal IPP\n    \"\"\"\n    def __init__(\n        self,\n        *args,\n        transform=None,\n        inducing_variable_time=None,\n        **kwargs\n    ):\n        super().__init__(\n            *args,\n            **kwargs\n        )\n        if transform is None:\n            self.transform = Transform()\n        else:\n            self.transform = transform\n\n        if inducing_variable_time is not None:\n            self.inducing_variable_time = inducingpoint_wrapper(inducing_variable_time)\n            self.transform.inducing_variable_time = self.inducing_variable_time\n        else:\n            self.inducing_variable_time = None\n\n    def update(self, noise_variance, kernel):\n        \"\"\"Update SGP noise variance and kernel function parameters\n\n        Args:\n            noise_variance (float): data variance\n            kernel (gpflow.kernels.Kernel): gpflow kernel function\n        \"\"\"\n        self.likelihood.variance.assign(noise_variance)\n        self.kernel.lengthscales.assign(kernel.lengthscales)\n        self.kernel.variance.assign(kernel.variance)\n\n    def _common_calculation(self) -&gt; \"SGPR.CommonTensors\":\n        \"\"\"\n        Matrices used in log-det calculation\n        :return: A , B, LB, AAT with :math:`LL\u1d40 = K\u1d64\u1d64 , A = L\u207b\u00b9K_{uf}/\u03c3, AAT = AA\u1d40,\n            B = AAT+I, LBLB\u1d40 = B`\n            A is M x N, B is M x M, LB is M x M, AAT is M x M\n        \"\"\"\n        x, _ = self.data\n\n        iv = self.inducing_variable.Z  # [M]\n        iv = self.transform.expand(iv)\n\n        kuf = self.kernel(iv, x)\n        kuf = self.transform.aggregate(kuf)\n\n        kuu = self.kernel(iv) + 1e-6 * tf.eye(tf.shape(iv)[0], dtype=iv.dtype)\n        kuu = self.transform.aggregate(kuu)\n\n        L = tf.linalg.cholesky(kuu)\n\n        sigma_sq = self.likelihood.variance\n        sigma = tf.sqrt(sigma_sq)\n\n        # Compute intermediate matrices\n        A = tf.linalg.triangular_solve(L, kuf, lower=True) / sigma\n        AAT = tf.linalg.matmul(A, A, transpose_b=True)\n        B = add_noise_cov(AAT, tf.cast(1.0, AAT.dtype))\n        LB = tf.linalg.cholesky(B)\n\n        return self.CommonTensors(sigma_sq, sigma, A, B, LB, AAT, L)\n\n    def elbo(self) -&gt; tf.Tensor:\n        \"\"\"\n        Construct a tensorflow function to compute the bound on the marginal\n        likelihood. For a derivation of the terms in here, see the associated\n        SGPR notebook.\n        \"\"\"\n        common = self._common_calculation()\n        output_shape = tf.shape(self.data[-1])\n        num_data = to_default_float(output_shape[0])\n        output_dim = to_default_float(output_shape[1])\n        const = -0.5 * num_data * output_dim * np.log(2 * np.pi)\n        logdet = self.logdet_term(common)\n        quad = self.quad_term(common)\n        constraints = self.transform.constraints(self.inducing_variable.Z)\n        return const + logdet + quad + constraints\n\n    def predict_f(\n        self, Xnew: InputData, full_cov: bool = False, full_output_cov: bool = False\n    ) -&gt; MeanAndVariance:\n\n        # could copy into posterior into a fused version\n        \"\"\"\n        Compute the mean and variance of the latent function at some new points\n        Xnew. For a derivation of the terms in here, see the associated SGPR\n        notebook.\n        \"\"\"\n        X_data, Y_data = self.data\n\n        iv = self.inducing_variable.Z\n        iv = self.transform.expand(iv)\n\n        num_inducing = tf.shape(iv)[0]\n\n        err = Y_data - self.mean_function(X_data)\n        kuf = self.kernel(iv, X_data)\n        kuu = self.kernel(iv) + 1e-6 * tf.eye(num_inducing, dtype=iv.dtype)\n        Kus = self.kernel(iv, Xnew)\n        sigma = tf.sqrt(self.likelihood.variance)\n        L = tf.linalg.cholesky(kuu)\n        A = tf.linalg.triangular_solve(L, kuf, lower=True) / sigma\n        B = tf.linalg.matmul(A, A, transpose_b=True) + tf.eye(\n            num_inducing, dtype=default_float()\n        )  # cache qinv\n        LB = tf.linalg.cholesky(B)\n        Aerr = tf.linalg.matmul(A, err)\n        c = tf.linalg.triangular_solve(LB, Aerr, lower=True) / sigma\n        tmp1 = tf.linalg.triangular_solve(L, Kus, lower=True)\n        tmp2 = tf.linalg.triangular_solve(LB, tmp1, lower=True)\n        mean = tf.linalg.matmul(tmp2, c, transpose_a=True)\n        if full_cov:\n            var = (\n                self.kernel(Xnew)\n                + tf.linalg.matmul(tmp2, tmp2, transpose_a=True)\n                - tf.linalg.matmul(tmp1, tmp1, transpose_a=True)\n            )\n            var = tf.tile(var[None, ...], [self.num_latent_gps, 1, 1])  # [P, N, N]\n        else:\n            var = (\n                self.kernel(Xnew, full_cov=False)\n                + tf.reduce_sum(tf.square(tmp2), 0)\n                - tf.reduce_sum(tf.square(tmp1), 0)\n            )\n            var = tf.tile(var[:, None], [1, self.num_latent_gps])\n\n        return mean + self.mean_function(Xnew), var\n</code></pre>"},{"location":"api/models/core/AugmentedSGPR.html#sgptools.models.core.augmented_sgpr.AugmentedSGPR.elbo","title":"<code>elbo()</code>","text":"<p>Construct a tensorflow function to compute the bound on the marginal likelihood. For a derivation of the terms in here, see the associated SGPR notebook.</p> Source code in <code>sgptools/models/core/augmented_sgpr.py</code> <pre><code>def elbo(self) -&gt; tf.Tensor:\n    \"\"\"\n    Construct a tensorflow function to compute the bound on the marginal\n    likelihood. For a derivation of the terms in here, see the associated\n    SGPR notebook.\n    \"\"\"\n    common = self._common_calculation()\n    output_shape = tf.shape(self.data[-1])\n    num_data = to_default_float(output_shape[0])\n    output_dim = to_default_float(output_shape[1])\n    const = -0.5 * num_data * output_dim * np.log(2 * np.pi)\n    logdet = self.logdet_term(common)\n    quad = self.quad_term(common)\n    constraints = self.transform.constraints(self.inducing_variable.Z)\n    return const + logdet + quad + constraints\n</code></pre>"},{"location":"api/models/core/AugmentedSGPR.html#sgptools.models.core.augmented_sgpr.AugmentedSGPR.predict_f","title":"<code>predict_f(Xnew, full_cov=False, full_output_cov=False)</code>","text":"<p>Compute the mean and variance of the latent function at some new points Xnew. For a derivation of the terms in here, see the associated SGPR notebook.</p> Source code in <code>sgptools/models/core/augmented_sgpr.py</code> <pre><code>def predict_f(\n    self, Xnew: InputData, full_cov: bool = False, full_output_cov: bool = False\n) -&gt; MeanAndVariance:\n\n    # could copy into posterior into a fused version\n    \"\"\"\n    Compute the mean and variance of the latent function at some new points\n    Xnew. For a derivation of the terms in here, see the associated SGPR\n    notebook.\n    \"\"\"\n    X_data, Y_data = self.data\n\n    iv = self.inducing_variable.Z\n    iv = self.transform.expand(iv)\n\n    num_inducing = tf.shape(iv)[0]\n\n    err = Y_data - self.mean_function(X_data)\n    kuf = self.kernel(iv, X_data)\n    kuu = self.kernel(iv) + 1e-6 * tf.eye(num_inducing, dtype=iv.dtype)\n    Kus = self.kernel(iv, Xnew)\n    sigma = tf.sqrt(self.likelihood.variance)\n    L = tf.linalg.cholesky(kuu)\n    A = tf.linalg.triangular_solve(L, kuf, lower=True) / sigma\n    B = tf.linalg.matmul(A, A, transpose_b=True) + tf.eye(\n        num_inducing, dtype=default_float()\n    )  # cache qinv\n    LB = tf.linalg.cholesky(B)\n    Aerr = tf.linalg.matmul(A, err)\n    c = tf.linalg.triangular_solve(LB, Aerr, lower=True) / sigma\n    tmp1 = tf.linalg.triangular_solve(L, Kus, lower=True)\n    tmp2 = tf.linalg.triangular_solve(LB, tmp1, lower=True)\n    mean = tf.linalg.matmul(tmp2, c, transpose_a=True)\n    if full_cov:\n        var = (\n            self.kernel(Xnew)\n            + tf.linalg.matmul(tmp2, tmp2, transpose_a=True)\n            - tf.linalg.matmul(tmp1, tmp1, transpose_a=True)\n        )\n        var = tf.tile(var[None, ...], [self.num_latent_gps, 1, 1])  # [P, N, N]\n    else:\n        var = (\n            self.kernel(Xnew, full_cov=False)\n            + tf.reduce_sum(tf.square(tmp2), 0)\n            - tf.reduce_sum(tf.square(tmp1), 0)\n        )\n        var = tf.tile(var[:, None], [1, self.num_latent_gps])\n\n    return mean + self.mean_function(Xnew), var\n</code></pre>"},{"location":"api/models/core/AugmentedSGPR.html#sgptools.models.core.augmented_sgpr.AugmentedSGPR.update","title":"<code>update(noise_variance, kernel)</code>","text":"<p>Update SGP noise variance and kernel function parameters</p> <p>Parameters:</p> Name Type Description Default <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required Source code in <code>sgptools/models/core/augmented_sgpr.py</code> <pre><code>def update(self, noise_variance, kernel):\n    \"\"\"Update SGP noise variance and kernel function parameters\n\n    Args:\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n    \"\"\"\n    self.likelihood.variance.assign(noise_variance)\n    self.kernel.lengthscales.assign(kernel.lengthscales)\n    self.kernel.variance.assign(kernel.variance)\n</code></pre>"},{"location":"api/models/core/OSGPR.html","title":"OSGPR","text":""},{"location":"api/models/core/OSGPR.html#sgptools.models.core.osgpr","title":"<code>sgptools.models.core.osgpr</code>","text":"<p>Provides a streaming sparse Gaussian process model along with initialization function</p>"},{"location":"api/models/core/OSGPR.html#sgptools.models.core.osgpr.OSGPR_VFE","title":"<code>OSGPR_VFE</code>","text":"<p>               Bases: <code>GPModel</code>, <code>InternalDataTrainingLossMixin</code></p> <p>Online Sparse Variational GP regression model from streaming_sparse_gp</p> Refer to the following paper for more details <ul> <li>Streaming Gaussian process approximations [Bui et al., 2017]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>tuple</code> <p>(X, y) ndarrays with inputs (n, d) and labels (n, 1)</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>mu_old</code> <code>ndarray</code> <p>mean of old <code>q(u)</code>; here <code>u</code> are the latents corresponding to the inducing points <code>Z_old</code></p> required <code>Su_old</code> <code>ndarray</code> <p>posterior covariance of old <code>q(u)</code></p> required <code>Kaa_old</code> <code>ndarray</code> <p>prior covariance of old <code>q(u)</code></p> required <code>Z_old</code> <code>ndarray</code> <p>(m_old, d): Old initial inducing points</p> required <code>Z</code> <code>ndarray</code> <p>(m_new, d): New initial inducing points</p> required <code>mean_function</code> <code>function</code> <p>GP mean function</p> <code>None</code> Source code in <code>sgptools/models/core/osgpr.py</code> <pre><code>class OSGPR_VFE(GPModel, InternalDataTrainingLossMixin):\n    \"\"\"Online Sparse Variational GP regression model from [streaming_sparse_gp](https://github.com/thangbui/streaming_sparse_gp/tree/master)\n\n    Refer to the following paper for more details:\n        - Streaming Gaussian process approximations [Bui et al., 2017]\n\n    Args:\n        data (tuple): (X, y) ndarrays with inputs (n, d) and labels (n, 1)\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        mu_old (ndarray): mean of old `q(u)`; here `u` are the latents corresponding to the inducing points `Z_old`\n        Su_old (ndarray): posterior covariance of old `q(u)`\n        Kaa_old (ndarray): prior covariance of old `q(u)`\n        Z_old (ndarray): (m_old, d): Old initial inducing points\n        Z (ndarray): (m_new, d): New initial inducing points\n        mean_function (function): GP mean function\n    \"\"\"\n    def __init__(self, data, kernel, mu_old, Su_old, Kaa_old, Z_old, Z, mean_function=None):\n        self.X, self.Y = self.data = gpflow.models.util.data_input_to_tensor(data)\n        likelihood = gpflow.likelihoods.Gaussian()\n        num_latent_gps = GPModel.calc_num_latent_gps_from_data(data, kernel, likelihood)\n        super().__init__(kernel, likelihood, mean_function, num_latent_gps)\n\n        self.inducing_variable = InducingPoints(Z)\n        self.num_data = self.X.shape[0]\n\n        self.mu_old = tf.Variable(mu_old, shape=tf.TensorShape(None), trainable=False)\n        self.M_old = Z_old.shape[0]\n        self.Su_old = tf.Variable(Su_old, shape=tf.TensorShape(None), trainable=False)\n        self.Kaa_old = tf.Variable(Kaa_old, shape=tf.TensorShape(None), trainable=False)\n        self.Z_old = tf.Variable(Z_old, shape=tf.TensorShape(None), trainable=False)\n\n    def update(self, data):\n        \"\"\"Configure the OSGPR to adapt to a new batch of data. \n        Note: The OSGPR needs to be trained using gradient-based approaches after update.\n\n        Args:\n            data (tuple): (X, y) ndarrays with new batch of inputs (n, d) and labels (n, 1)\n        \"\"\"\n        self.X, self.Y = self.data = gpflow.models.util.data_input_to_tensor(data)\n        self.num_data = self.X.shape[0]\n\n        self.Z_old = tf.Variable(self.inducing_variable.Z.numpy(), \n                                 shape=tf.TensorShape(None), \n                                 trainable=False)\n\n        # Get posterior mean and covariance for the old inducing points\n        mu_old, Su_old = self.predict_f(self.Z_old, full_cov=True)\n        self.mu_old = tf.Variable(mu_old, shape=tf.TensorShape(None), trainable=False)\n        self.Su_old = tf.Variable(Su_old, shape=tf.TensorShape(None), trainable=False)\n\n        # Get the prior covariance matrix for the old inducing points\n        Kaa_old = self.kernel(self.Z_old)\n        self.Kaa_old = tf.Variable(Kaa_old, shape=tf.TensorShape(None), trainable=False)\n\n    def _common_terms(self):\n        Mb = self.inducing_variable.num_inducing\n        Ma = self.M_old\n        # jitter = gpflow.default_jitter()\n        jitter = gpflow.utilities.to_default_float(1e-4)\n        sigma2 = self.likelihood.variance\n        sigma = tf.sqrt(sigma2)\n\n        Saa = self.Su_old\n        ma = self.mu_old\n\n        # a is old inducing points, b is new\n        # f is training points\n        # s is test points\n        Kbf = covariances.Kuf(self.inducing_variable, self.kernel, self.X)\n        Kbb = covariances.Kuu(self.inducing_variable, self.kernel, jitter=jitter)\n        Kba = covariances.Kuf(self.inducing_variable, self.kernel, self.Z_old)\n        Kaa_cur = gpflow.utilities.add_noise_cov(self.kernel(self.Z_old), jitter)\n        Kaa = gpflow.utilities.add_noise_cov(self.Kaa_old, jitter)\n\n        err = self.Y - self.mean_function(self.X)\n\n        Sainv_ma = tf.linalg.solve(Saa, ma)\n        Sinv_y = self.Y / sigma2\n        c1 = tf.matmul(Kbf, Sinv_y)\n        c2 = tf.matmul(Kba, Sainv_ma)\n        c = c1 + c2\n\n        Lb = tf.linalg.cholesky(Kbb)\n        Lbinv_c = tf.linalg.triangular_solve(Lb, c, lower=True)\n        Lbinv_Kba = tf.linalg.triangular_solve(Lb, Kba, lower=True)\n        Lbinv_Kbf = tf.linalg.triangular_solve(Lb, Kbf, lower=True) / sigma\n        d1 = tf.matmul(Lbinv_Kbf, Lbinv_Kbf, transpose_b=True)\n\n        LSa = tf.linalg.cholesky(Saa)\n        Kab_Lbinv = tf.linalg.matrix_transpose(Lbinv_Kba)\n        LSainv_Kab_Lbinv = tf.linalg.triangular_solve(\n            LSa, Kab_Lbinv, lower=True)\n        d2 = tf.matmul(LSainv_Kab_Lbinv, LSainv_Kab_Lbinv, transpose_a=True)\n\n        La = tf.linalg.cholesky(Kaa)\n        Lainv_Kab_Lbinv = tf.linalg.triangular_solve(\n            La, Kab_Lbinv, lower=True)\n        d3 = tf.matmul(Lainv_Kab_Lbinv, Lainv_Kab_Lbinv, transpose_a=True)\n\n        D = tf.eye(Mb, dtype=gpflow.default_float()) + d1 + d2 - d3\n        D = gpflow.utilities.add_noise_cov(D, jitter)\n        LD = tf.linalg.cholesky(D)\n\n        LDinv_Lbinv_c = tf.linalg.triangular_solve(LD, Lbinv_c, lower=True)\n\n        return (Kbf, Kba, Kaa, Kaa_cur, La, Kbb, Lb, D, LD,\n                Lbinv_Kba, LDinv_Lbinv_c, err, d1)\n\n    def maximum_log_likelihood_objective(self):\n        \"\"\"\n        Construct a tensorflow function to compute the bound on the marginal\n        likelihood. \n        \"\"\"\n\n        Mb = self.inducing_variable.num_inducing\n        Ma = self.M_old\n        jitter = gpflow.default_jitter()\n        # jitter = gpflow.utilities.to_default_float(1e-4)\n        sigma2 = self.likelihood.variance\n        sigma = tf.sqrt(sigma2)\n        N = self.num_data\n\n        Saa = self.Su_old\n        ma = self.mu_old\n\n        # a is old inducing points, b is new\n        # f is training points\n        Kfdiag = self.kernel(self.X, full_cov=False)\n        (Kbf, Kba, Kaa, Kaa_cur, La, Kbb, Lb, D, LD,\n            Lbinv_Kba, LDinv_Lbinv_c, err, Qff) = self._common_terms()\n\n        LSa = tf.linalg.cholesky(Saa)\n        Lainv_ma = tf.linalg.triangular_solve(LSa, ma, lower=True)\n\n        # constant term\n        bound = -0.5 * N * np.log(2 * np.pi)\n        # quadratic term\n        bound += -0.5 * tf.reduce_sum(tf.square(err)) / sigma2\n        # bound += -0.5 * tf.reduce_sum(ma * Sainv_ma)\n        bound += -0.5 * tf.reduce_sum(tf.square(Lainv_ma))\n        bound += 0.5 * tf.reduce_sum(tf.square(LDinv_Lbinv_c))\n        # log det term\n        bound += -0.5 * N * tf.reduce_sum(tf.math.log(sigma2))\n        bound += - tf.reduce_sum(tf.math.log(tf.linalg.diag_part(LD)))\n\n        # delta 1: trace term\n        bound += -0.5 * tf.reduce_sum(Kfdiag) / sigma2\n        bound += 0.5 * tf.reduce_sum(tf.linalg.diag_part(Qff))\n\n        # delta 2: a and b difference\n        bound += tf.reduce_sum(tf.math.log(tf.linalg.diag_part(La)))\n        bound += - tf.reduce_sum(tf.math.log(tf.linalg.diag_part(LSa)))\n\n        Kaadiff = Kaa_cur - tf.matmul(Lbinv_Kba, Lbinv_Kba, transpose_a=True)\n        Sainv_Kaadiff = tf.linalg.solve(Saa, Kaadiff)\n        Kainv_Kaadiff = tf.linalg.solve(Kaa, Kaadiff)\n\n        bound += -0.5 * tf.reduce_sum(\n            tf.linalg.diag_part(Sainv_Kaadiff) - tf.linalg.diag_part(Kainv_Kaadiff))\n\n        return bound\n\n    def predict_f(self, Xnew, full_cov=False):\n        \"\"\"\n        Compute the mean and variance of the latent function at some new points\n        Xnew. \n        \"\"\"\n\n        # jitter = gpflow.default_jitter()\n        jitter = gpflow.utilities.to_default_float(1e-4)\n\n        # a is old inducing points, b is new\n        # f is training points\n        # s is test points\n        Kbs = covariances.Kuf(self.inducing_variable, self.kernel, Xnew)\n        (Kbf, Kba, Kaa, Kaa_cur, La, Kbb, Lb, D, LD,\n            Lbinv_Kba, LDinv_Lbinv_c, err, Qff) = self._common_terms()\n\n        Lbinv_Kbs = tf.linalg.triangular_solve(Lb, Kbs, lower=True)\n        LDinv_Lbinv_Kbs = tf.linalg.triangular_solve(LD, Lbinv_Kbs, lower=True)\n        mean = tf.matmul(LDinv_Lbinv_Kbs, LDinv_Lbinv_c, transpose_a=True)\n\n        if full_cov:\n            Kss = self.kernel(Xnew) + jitter * tf.eye(tf.shape(Xnew)[0], dtype=gpflow.default_float())\n            var1 = Kss\n            var2 = - tf.matmul(Lbinv_Kbs, Lbinv_Kbs, transpose_a=True)\n            var3 = tf.matmul(LDinv_Lbinv_Kbs, LDinv_Lbinv_Kbs, transpose_a=True)\n            var = var1 + var2 + var3\n        else:\n            var1 = self.kernel(Xnew, full_cov=False)\n            var2 = -tf.reduce_sum(tf.square(Lbinv_Kbs), axis=0)\n            var3 = tf.reduce_sum(tf.square(LDinv_Lbinv_Kbs), axis=0)\n            var = var1 + var2 + var3\n\n        return mean + self.mean_function(Xnew), var\n</code></pre>"},{"location":"api/models/core/OSGPR.html#sgptools.models.core.osgpr.OSGPR_VFE.maximum_log_likelihood_objective","title":"<code>maximum_log_likelihood_objective()</code>","text":"<p>Construct a tensorflow function to compute the bound on the marginal likelihood.</p> Source code in <code>sgptools/models/core/osgpr.py</code> <pre><code>def maximum_log_likelihood_objective(self):\n    \"\"\"\n    Construct a tensorflow function to compute the bound on the marginal\n    likelihood. \n    \"\"\"\n\n    Mb = self.inducing_variable.num_inducing\n    Ma = self.M_old\n    jitter = gpflow.default_jitter()\n    # jitter = gpflow.utilities.to_default_float(1e-4)\n    sigma2 = self.likelihood.variance\n    sigma = tf.sqrt(sigma2)\n    N = self.num_data\n\n    Saa = self.Su_old\n    ma = self.mu_old\n\n    # a is old inducing points, b is new\n    # f is training points\n    Kfdiag = self.kernel(self.X, full_cov=False)\n    (Kbf, Kba, Kaa, Kaa_cur, La, Kbb, Lb, D, LD,\n        Lbinv_Kba, LDinv_Lbinv_c, err, Qff) = self._common_terms()\n\n    LSa = tf.linalg.cholesky(Saa)\n    Lainv_ma = tf.linalg.triangular_solve(LSa, ma, lower=True)\n\n    # constant term\n    bound = -0.5 * N * np.log(2 * np.pi)\n    # quadratic term\n    bound += -0.5 * tf.reduce_sum(tf.square(err)) / sigma2\n    # bound += -0.5 * tf.reduce_sum(ma * Sainv_ma)\n    bound += -0.5 * tf.reduce_sum(tf.square(Lainv_ma))\n    bound += 0.5 * tf.reduce_sum(tf.square(LDinv_Lbinv_c))\n    # log det term\n    bound += -0.5 * N * tf.reduce_sum(tf.math.log(sigma2))\n    bound += - tf.reduce_sum(tf.math.log(tf.linalg.diag_part(LD)))\n\n    # delta 1: trace term\n    bound += -0.5 * tf.reduce_sum(Kfdiag) / sigma2\n    bound += 0.5 * tf.reduce_sum(tf.linalg.diag_part(Qff))\n\n    # delta 2: a and b difference\n    bound += tf.reduce_sum(tf.math.log(tf.linalg.diag_part(La)))\n    bound += - tf.reduce_sum(tf.math.log(tf.linalg.diag_part(LSa)))\n\n    Kaadiff = Kaa_cur - tf.matmul(Lbinv_Kba, Lbinv_Kba, transpose_a=True)\n    Sainv_Kaadiff = tf.linalg.solve(Saa, Kaadiff)\n    Kainv_Kaadiff = tf.linalg.solve(Kaa, Kaadiff)\n\n    bound += -0.5 * tf.reduce_sum(\n        tf.linalg.diag_part(Sainv_Kaadiff) - tf.linalg.diag_part(Kainv_Kaadiff))\n\n    return bound\n</code></pre>"},{"location":"api/models/core/OSGPR.html#sgptools.models.core.osgpr.OSGPR_VFE.predict_f","title":"<code>predict_f(Xnew, full_cov=False)</code>","text":"<p>Compute the mean and variance of the latent function at some new points Xnew.</p> Source code in <code>sgptools/models/core/osgpr.py</code> <pre><code>def predict_f(self, Xnew, full_cov=False):\n    \"\"\"\n    Compute the mean and variance of the latent function at some new points\n    Xnew. \n    \"\"\"\n\n    # jitter = gpflow.default_jitter()\n    jitter = gpflow.utilities.to_default_float(1e-4)\n\n    # a is old inducing points, b is new\n    # f is training points\n    # s is test points\n    Kbs = covariances.Kuf(self.inducing_variable, self.kernel, Xnew)\n    (Kbf, Kba, Kaa, Kaa_cur, La, Kbb, Lb, D, LD,\n        Lbinv_Kba, LDinv_Lbinv_c, err, Qff) = self._common_terms()\n\n    Lbinv_Kbs = tf.linalg.triangular_solve(Lb, Kbs, lower=True)\n    LDinv_Lbinv_Kbs = tf.linalg.triangular_solve(LD, Lbinv_Kbs, lower=True)\n    mean = tf.matmul(LDinv_Lbinv_Kbs, LDinv_Lbinv_c, transpose_a=True)\n\n    if full_cov:\n        Kss = self.kernel(Xnew) + jitter * tf.eye(tf.shape(Xnew)[0], dtype=gpflow.default_float())\n        var1 = Kss\n        var2 = - tf.matmul(Lbinv_Kbs, Lbinv_Kbs, transpose_a=True)\n        var3 = tf.matmul(LDinv_Lbinv_Kbs, LDinv_Lbinv_Kbs, transpose_a=True)\n        var = var1 + var2 + var3\n    else:\n        var1 = self.kernel(Xnew, full_cov=False)\n        var2 = -tf.reduce_sum(tf.square(Lbinv_Kbs), axis=0)\n        var3 = tf.reduce_sum(tf.square(LDinv_Lbinv_Kbs), axis=0)\n        var = var1 + var2 + var3\n\n    return mean + self.mean_function(Xnew), var\n</code></pre>"},{"location":"api/models/core/OSGPR.html#sgptools.models.core.osgpr.OSGPR_VFE.update","title":"<code>update(data)</code>","text":"<p>Configure the OSGPR to adapt to a new batch of data.  Note: The OSGPR needs to be trained using gradient-based approaches after update.</p> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>tuple</code> <p>(X, y) ndarrays with new batch of inputs (n, d) and labels (n, 1)</p> required Source code in <code>sgptools/models/core/osgpr.py</code> <pre><code>def update(self, data):\n    \"\"\"Configure the OSGPR to adapt to a new batch of data. \n    Note: The OSGPR needs to be trained using gradient-based approaches after update.\n\n    Args:\n        data (tuple): (X, y) ndarrays with new batch of inputs (n, d) and labels (n, 1)\n    \"\"\"\n    self.X, self.Y = self.data = gpflow.models.util.data_input_to_tensor(data)\n    self.num_data = self.X.shape[0]\n\n    self.Z_old = tf.Variable(self.inducing_variable.Z.numpy(), \n                             shape=tf.TensorShape(None), \n                             trainable=False)\n\n    # Get posterior mean and covariance for the old inducing points\n    mu_old, Su_old = self.predict_f(self.Z_old, full_cov=True)\n    self.mu_old = tf.Variable(mu_old, shape=tf.TensorShape(None), trainable=False)\n    self.Su_old = tf.Variable(Su_old, shape=tf.TensorShape(None), trainable=False)\n\n    # Get the prior covariance matrix for the old inducing points\n    Kaa_old = self.kernel(self.Z_old)\n    self.Kaa_old = tf.Variable(Kaa_old, shape=tf.TensorShape(None), trainable=False)\n</code></pre>"},{"location":"api/models/core/OSGPR.html#sgptools.models.core.osgpr.init_osgpr","title":"<code>init_osgpr(X_train, num_inducing=10, lengthscales=1.0, variance=1.0, noise_variance=0.001, kernel=None)</code>","text":"<p>Initialize a VFE OSGPR model with an RBF kernel with  unit variance and lengthcales, and 0.001 noise variance. Used in the Online Continuous SGP approach. </p> <p>Parameters:</p> Name Type Description Default <code>X_train</code> <code>ndarray</code> <p>(n, d); Unlabeled random sampled training points.              They only effect the initial inducing point locations,              i.e., limits them to the bounds of the data</p> required <code>num_inducing</code> <code>int</code> <p>Number of inducing points</p> <code>10</code> <code>lengthscales</code> <code>float or list</code> <p>Kernel lengthscale(s), if passed as a list,                      each element corresponds to each data dimension</p> <code>1.0</code> <code>variance</code> <code>float</code> <p>Kernel variance</p> <code>1.0</code> <code>noise_variance</code> <code>float</code> <p>Data noise variance</p> <code>0.001</code> <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> <code>None</code> <p>Returns:</p> Name Type Description <code>online_param</code> <code>OSGPR_VFE</code> <p>Initialized online sparse Gaussian process model</p> Source code in <code>sgptools/models/core/osgpr.py</code> <pre><code>def init_osgpr(X_train, \n               num_inducing=10, \n               lengthscales=1.0, \n               variance=1.0,\n               noise_variance=0.001,\n               kernel=None):\n    \"\"\"Initialize a VFE OSGPR model with an RBF kernel with \n    unit variance and lengthcales, and 0.001 noise variance.\n    Used in the Online Continuous SGP approach. \n\n    Args:\n        X_train (ndarray): (n, d); Unlabeled random sampled training points. \n                        They only effect the initial inducing point locations, \n                        i.e., limits them to the bounds of the data\n        num_inducing (int): Number of inducing points\n        lengthscales (float or list): Kernel lengthscale(s), if passed as a list, \n                                each element corresponds to each data dimension\n        variance (float): Kernel variance\n        noise_variance (float): Data noise variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n\n    Returns:\n        online_param (OSGPR_VFE): Initialized online sparse Gaussian process model\n    \"\"\"\n\n    if kernel is None:\n        kernel = gpflow.kernels.SquaredExponential(lengthscales=lengthscales, \n                                                   variance=variance)\n\n    y_train = np.zeros((len(X_train), 1), dtype=X_train.dtype)\n    Z_init = get_inducing_pts(X_train, num_inducing)\n    init_param = gpflow.models.SGPR((X_train, y_train),\n                                    kernel, \n                                    inducing_variable=Z_init, \n                                    noise_variance=noise_variance)\n\n    # Initialize the OSGPR model using the parameters from the SGPR model\n    # The X_train and y_train here will be overwritten in the online phase \n    X_train = np.array([[0, 0], [0, 0]])\n    y_train = np.array([0, 0]).reshape(-1, 1)\n    Zopt = init_param.inducing_variable.Z.numpy()\n    mu, Su = init_param.predict_f(Zopt, full_cov=True)\n    Kaa = init_param.kernel(Zopt)\n    online_param = OSGPR_VFE((X_train[:2], y_train[:2]),\n                             init_param.kernel,\n                             mu, Su[0], Kaa,\n                             Zopt, Zopt)\n    online_param.likelihood.variance.assign(init_param.likelihood.variance)\n\n    return online_param\n</code></pre>"},{"location":"api/models/core/Transformations.html","title":"Transformations","text":""},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations","title":"<code>sgptools.models.core.transformations</code>","text":"<p>Provides transforms to model complex sensor field of views and handle informative path planning</p>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.IPPTransform","title":"<code>IPPTransform</code>","text":"<p>               Bases: <code>Transform</code></p> <p>Transform to model IPP problems</p> Usage details <ul> <li>For point sensing, set <code>sampling_rate = 2</code></li> <li>For continuous sensing, set <code>sampling_rate &gt; 2</code> (account for the information along the path)</li> <li>For continuous sensing with aggregation, set <code>sampling_rate &gt; 2</code> and <code>aggregate_fov = True</code> (faster but solution quality is a bit diminished)</li> <li>If using a non-point FoV model with continuous sampling, only the FoV inducing points are aggregated</li> <li>For multi-robot case, set <code>num_robots &gt; 1</code></li> <li>For onlineIPP use <code>update_fixed</code> to freeze the visited waypoints</li> </ul> <p>Parameters:</p> Name Type Description Default <code>sampling_rate</code> <code>int</code> <p>Number of points to sample between each pair of inducing points</p> <code>2</code> <code>distance_budget</code> <code>float</code> <p>Distance budget for the path</p> <code>None</code> <code>num_robots</code> <code>int</code> <p>Number of robots</p> <code>1</code> <code>Xu_fixed</code> <code>ndarray</code> <p>(num_robots, num_visited, num_dim); Visited waypoints that don't need to be optimized</p> <code>None</code> <code>num_dim</code> <code>int</code> <p>Number of dimensions of the inducing points</p> <code>2</code> <code>sensor_model</code> <code>Transform</code> <p>Transform object to expand each inducing point to <code>p</code> points                        approximating each sensor's FoV</p> <code>None</code> <code>aggregate_fov</code> <code>bool</code> <p>Used only when sampling_rate &gt; 2, i.e., when using a continuous sensing model.                    If <code>True</code>, covariances corresponding to interpolated inducing points along each edge                   of the path are aggregated to reduce the matrix inversion cost.</p> <code>False</code> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>class IPPTransform(Transform):\n    \"\"\"Transform to model IPP problems\n\n    Usage details: \n        * For point sensing, set `sampling_rate = 2`\n        * For continuous sensing, set `sampling_rate &gt; 2` (account for the information along the path)\n        * For continuous sensing with aggregation, set `sampling_rate &gt; 2` and `aggregate_fov = True` (faster but solution quality is a bit diminished)\n        * If using a non-point FoV model with continuous sampling, only the FoV inducing points are aggregated\n        * For multi-robot case, set `num_robots &gt; 1`\n        * For onlineIPP use `update_fixed` to freeze the visited waypoints\n\n    Args:\n        sampling_rate (int): Number of points to sample between each pair of inducing points\n        distance_budget (float): Distance budget for the path\n        num_robots (int): Number of robots\n        Xu_fixed (ndarray): (num_robots, num_visited, num_dim); Visited waypoints that don't need to be optimized\n        num_dim (int): Number of dimensions of the inducing points\n        sensor_model (Transform): Transform object to expand each inducing point to `p` points \n                                  approximating each sensor's FoV\n        aggregate_fov (bool): Used only when sampling_rate &gt; 2, i.e., when using a continuous sensing model. \n                              If `True`, covariances corresponding to interpolated inducing points along each edge\n                              of the path are aggregated to reduce the matrix inversion cost.\n    \"\"\"\n    def __init__(self, \n                 sampling_rate=2, \n                 distance_budget=None, \n                 num_robots=1,\n                 Xu_fixed=None,\n                 num_dim=2,\n                 sensor_model=None,\n                 aggregate_fov=False,\n                 **kwargs):\n        super().__init__(**kwargs)\n        if sampling_rate &lt; 2:\n            raise ValueError('Sampling rate must be greater than 2.')\n\n        self.sampling_rate = sampling_rate\n        self.distance_budget = distance_budget\n        self.num_robots = num_robots\n        self.num_dim = num_dim\n        self.sensor_model = sensor_model\n\n        # Set aggregation size to sampling rate if aggregate_fov is True\n        # and sampling rate is enabled (greater than 2)\n        if aggregate_fov:\n            if self.sensor_model is not None:\n                self.sensor_model.enable_aggregation()\n            elif sampling_rate &gt; 2:\n                self.aggregation_size = sampling_rate\n\n        # Initilize variable to store visited waypoints for onlineIPP\n        if Xu_fixed is not None:\n            self.Xu_fixed = tf.Variable(Xu_fixed, \n                                        shape=tf.TensorShape(None), \n                                        trainable=False)\n        else:\n            self.Xu_fixed = None\n\n    def update_Xu_fixed(self, Xu_fixed):\n        \"\"\"Function to update the visited waypoints\n\n        Args:\n            Xu_fixed (ndarray): numpy array (num_robots, num_visited_waypoints, num_dim)\n        \"\"\"\n        self.num_fixed = Xu_fixed.shape[1]\n        if self.Xu_fixed is not None:\n            self.Xu_fixed.assign(Xu_fixed)\n        else:\n            self.Xu_fixed = tf.Variable(Xu_fixed, \n                                        shape=tf.TensorShape(None), \n                                        trainable=False)\n\n    def expand(self, Xu, expand_sensor_model=True):\n        \"\"\"Sample points between each pair of inducing points to form the path\n\n        Args:\n            Xu (ndarray): (num_robots x num_inducing, num_dim); Inducing points in the num_dim dimensional space\n            expand_sensor_model (bool): Only add the fixed inducing points without other sensor/path transforms, \n                                        used for online IPP\n\n        Returns:\n            Xu (ndarray): Expansion transformed inducing points\n        \"\"\"\n        # If using single-robot offline IPP with point sensing, return inducing points as is.\n        if self.sampling_rate == 2 and self.Xu_fixed is None and self.sensor_model is None:\n            return Xu\n\n        Xu = tf.reshape(Xu, (self.num_robots, -1, self.num_dim))\n\n        # If using online IPP, add visited waypoints that won't be optimized anymore\n        if self.Xu_fixed is not None:\n            Xu = tf.concat([self.Xu_fixed, Xu[:, self.num_fixed:]], axis=1)\n\n        if not expand_sensor_model:\n            return tf.reshape(Xu, (-1, self.num_dim))\n\n        # Interpolate additional inducing points between waypoints to approximate \n        # the continuous data sensing model\n        if self.sampling_rate &gt; 2:\n            Xu = tf.linspace(Xu[:, :-1], Xu[:, 1:], self.sampling_rate)\n            Xu = tf.transpose(Xu, perm=[1, 2, 0, 3])\n            Xu = tf.reshape(Xu, (self.num_robots, -1, self.num_dim))\n\n        if self.sensor_model is not None:\n            Xu_ = []\n            for i in range(self.num_robots):\n                Xu_.append(self.sensor_model.expand(Xu[i]))\n            Xu = tf.concat(Xu_, axis=0)\n            return Xu\n\n        Xu = tf.reshape(Xu, (-1, self.num_dim))\n        return Xu\n\n    def aggregate(self, k):\n        \"\"\"Applies the aggregation transform to kernel matrices. Checks `sensor_model` \n           and uses the appropriate aggregation transform. \n\n        Args:\n            k (tensor): (mp, mp)/(mp, n); Kernel matrix. \n                        `m` is the number of inducing points,\n                        `p` is the number of points each inducing point is mapped,\n                        `n` is the number of training data points.\n\n        Returns:\n            k (tensor): (m, m)/(m, n); Aggregated kernel matrix\n        \"\"\"\n        if self.sensor_model is not None:\n            return self.sensor_model.aggregate(k)\n        else:\n            return super().aggregate(k)\n\n    def constraints(self, Xu):\n        \"\"\"Computes the distance constraint term that is added to the SGP's optimization function.\n        Each robot can be assigned a different distance budget.\n\n        Args:\n            Xu (ndarray): Inducing points from which to compute the distance constraints\n\n        Returns:\n            loss (float): distance constraint term\n        \"\"\"\n        if self.distance_budget is None:\n            return 0.\n        else:\n            # Only do fixed points expansion transform\n            Xu = self.expand(Xu, expand_sensor_model=False)\n            dist = self.distance(Xu)-self.distance_budget\n            dist = tf.reduce_sum(tf.nn.relu(dist))\n            loss = -dist*self.constraint_weight\n            return loss\n\n    def distance(self, Xu):\n        \"\"\"Computes the distance incured by sequentially visiting the inducing points\n        Args:\n            Xu (ndarray): (m, num_dim); Inducing points from which to compute the path lengths\n                          `m` is the number of inducing points\n                          `num_dim` dimension of the data collection environment\n        Returns:\n            dist (float or tensor of floats): path length(s)\n        \"\"\"\n        Xu = tf.reshape(Xu, (self.num_robots, -1, self.num_dim))\n        if self.sensor_model is not None:\n            dists = []\n            for i in range(self.num_robots):\n                dists.append(self.sensor_model.distance(Xu[i]))\n            dists = tf.concat(dists, axis=0)\n            return dists\n        else:\n            # Assumes 2D waypoints by default\n            dist = tf.norm(Xu[:, 1:, :2] - Xu[:, :-1, :2], axis=-1)\n            dist = tf.reduce_sum(dist, axis=1)\n            return dist\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.IPPTransform.aggregate","title":"<code>aggregate(k)</code>","text":"<p>Applies the aggregation transform to kernel matrices. Checks <code>sensor_model</code>     and uses the appropriate aggregation transform. </p> <p>Parameters:</p> Name Type Description Default <code>k</code> <code>tensor</code> <p>(mp, mp)/(mp, n); Kernel matrix.          <code>m</code> is the number of inducing points,         <code>p</code> is the number of points each inducing point is mapped,         <code>n</code> is the number of training data points.</p> required <p>Returns:</p> Name Type Description <code>k</code> <code>tensor</code> <p>(m, m)/(m, n); Aggregated kernel matrix</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def aggregate(self, k):\n    \"\"\"Applies the aggregation transform to kernel matrices. Checks `sensor_model` \n       and uses the appropriate aggregation transform. \n\n    Args:\n        k (tensor): (mp, mp)/(mp, n); Kernel matrix. \n                    `m` is the number of inducing points,\n                    `p` is the number of points each inducing point is mapped,\n                    `n` is the number of training data points.\n\n    Returns:\n        k (tensor): (m, m)/(m, n); Aggregated kernel matrix\n    \"\"\"\n    if self.sensor_model is not None:\n        return self.sensor_model.aggregate(k)\n    else:\n        return super().aggregate(k)\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.IPPTransform.constraints","title":"<code>constraints(Xu)</code>","text":"<p>Computes the distance constraint term that is added to the SGP's optimization function. Each robot can be assigned a different distance budget.</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>Inducing points from which to compute the distance constraints</p> required <p>Returns:</p> Name Type Description <code>loss</code> <code>float</code> <p>distance constraint term</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def constraints(self, Xu):\n    \"\"\"Computes the distance constraint term that is added to the SGP's optimization function.\n    Each robot can be assigned a different distance budget.\n\n    Args:\n        Xu (ndarray): Inducing points from which to compute the distance constraints\n\n    Returns:\n        loss (float): distance constraint term\n    \"\"\"\n    if self.distance_budget is None:\n        return 0.\n    else:\n        # Only do fixed points expansion transform\n        Xu = self.expand(Xu, expand_sensor_model=False)\n        dist = self.distance(Xu)-self.distance_budget\n        dist = tf.reduce_sum(tf.nn.relu(dist))\n        loss = -dist*self.constraint_weight\n        return loss\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.IPPTransform.distance","title":"<code>distance(Xu)</code>","text":"<p>Computes the distance incured by sequentially visiting the inducing points Args:     Xu (ndarray): (m, num_dim); Inducing points from which to compute the path lengths                   <code>m</code> is the number of inducing points                   <code>num_dim</code> dimension of the data collection environment Returns:     dist (float or tensor of floats): path length(s)</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def distance(self, Xu):\n    \"\"\"Computes the distance incured by sequentially visiting the inducing points\n    Args:\n        Xu (ndarray): (m, num_dim); Inducing points from which to compute the path lengths\n                      `m` is the number of inducing points\n                      `num_dim` dimension of the data collection environment\n    Returns:\n        dist (float or tensor of floats): path length(s)\n    \"\"\"\n    Xu = tf.reshape(Xu, (self.num_robots, -1, self.num_dim))\n    if self.sensor_model is not None:\n        dists = []\n        for i in range(self.num_robots):\n            dists.append(self.sensor_model.distance(Xu[i]))\n        dists = tf.concat(dists, axis=0)\n        return dists\n    else:\n        # Assumes 2D waypoints by default\n        dist = tf.norm(Xu[:, 1:, :2] - Xu[:, :-1, :2], axis=-1)\n        dist = tf.reduce_sum(dist, axis=1)\n        return dist\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.IPPTransform.expand","title":"<code>expand(Xu, expand_sensor_model=True)</code>","text":"<p>Sample points between each pair of inducing points to form the path</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(num_robots x num_inducing, num_dim); Inducing points in the num_dim dimensional space</p> required <code>expand_sensor_model</code> <code>bool</code> <p>Only add the fixed inducing points without other sensor/path transforms,                          used for online IPP</p> <code>True</code> <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>Expansion transformed inducing points</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def expand(self, Xu, expand_sensor_model=True):\n    \"\"\"Sample points between each pair of inducing points to form the path\n\n    Args:\n        Xu (ndarray): (num_robots x num_inducing, num_dim); Inducing points in the num_dim dimensional space\n        expand_sensor_model (bool): Only add the fixed inducing points without other sensor/path transforms, \n                                    used for online IPP\n\n    Returns:\n        Xu (ndarray): Expansion transformed inducing points\n    \"\"\"\n    # If using single-robot offline IPP with point sensing, return inducing points as is.\n    if self.sampling_rate == 2 and self.Xu_fixed is None and self.sensor_model is None:\n        return Xu\n\n    Xu = tf.reshape(Xu, (self.num_robots, -1, self.num_dim))\n\n    # If using online IPP, add visited waypoints that won't be optimized anymore\n    if self.Xu_fixed is not None:\n        Xu = tf.concat([self.Xu_fixed, Xu[:, self.num_fixed:]], axis=1)\n\n    if not expand_sensor_model:\n        return tf.reshape(Xu, (-1, self.num_dim))\n\n    # Interpolate additional inducing points between waypoints to approximate \n    # the continuous data sensing model\n    if self.sampling_rate &gt; 2:\n        Xu = tf.linspace(Xu[:, :-1], Xu[:, 1:], self.sampling_rate)\n        Xu = tf.transpose(Xu, perm=[1, 2, 0, 3])\n        Xu = tf.reshape(Xu, (self.num_robots, -1, self.num_dim))\n\n    if self.sensor_model is not None:\n        Xu_ = []\n        for i in range(self.num_robots):\n            Xu_.append(self.sensor_model.expand(Xu[i]))\n        Xu = tf.concat(Xu_, axis=0)\n        return Xu\n\n    Xu = tf.reshape(Xu, (-1, self.num_dim))\n    return Xu\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.IPPTransform.update_Xu_fixed","title":"<code>update_Xu_fixed(Xu_fixed)</code>","text":"<p>Function to update the visited waypoints</p> <p>Parameters:</p> Name Type Description Default <code>Xu_fixed</code> <code>ndarray</code> <p>numpy array (num_robots, num_visited_waypoints, num_dim)</p> required Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def update_Xu_fixed(self, Xu_fixed):\n    \"\"\"Function to update the visited waypoints\n\n    Args:\n        Xu_fixed (ndarray): numpy array (num_robots, num_visited_waypoints, num_dim)\n    \"\"\"\n    self.num_fixed = Xu_fixed.shape[1]\n    if self.Xu_fixed is not None:\n        self.Xu_fixed.assign(Xu_fixed)\n    else:\n        self.Xu_fixed = tf.Variable(Xu_fixed, \n                                    shape=tf.TensorShape(None), \n                                    trainable=False)\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareHeightTransform","title":"<code>SquareHeightTransform</code>","text":"<p>               Bases: <code>Transform</code></p> <p>Non-point Transform to model a height-dependent square FoV</p> <p>Parameters:</p> Name Type Description Default <code>num_side</code> <code>int</code> <p>Number of points along each side of the FoV</p> required <code>aggregate_fov</code> <code>bool</code> <p>If <code>True</code>, covariances corresponding to interpolated inducing points used to                    approximate the sensor FoV are aggregated to reduce the matrix inversion cost</p> <code>False</code> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>class SquareHeightTransform(Transform):\n    \"\"\"Non-point Transform to model a height-dependent square FoV\n\n    Args:\n        num_side (int): Number of points along each side of the FoV\n        aggregate_fov (bool): If `True`, covariances corresponding to interpolated inducing points used to \n                              approximate the sensor FoV are aggregated to reduce the matrix inversion cost\n    \"\"\"\n    def __init__(self, num_side, aggregate_fov=False, **kwargs):\n        super().__init__(**kwargs)\n        self.num_side = num_side\n\n        if aggregate_fov:\n            self.enable_aggregation()\n\n    def enable_aggregation(self, size=None):\n        \"\"\"Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the \n        covariance matrix size.\n\n        Args:\n            size (int): If None, all the interpolated inducing points within the FoV are aggregated. Alternatively, \n                        the number of inducing points to aggregate can be explicitly defined using this variable.\n        \"\"\"\n        if size is None:\n            self.aggregation_size = self.num_side**2\n        else:\n            self.aggregation_size = size\n\n    def expand(self, Xu):     \n        \"\"\"\n        Applies the expansion transform to the inducing points\n\n        Args:\n            Xu (ndarray): (m, 3); Inducing points in the 3D position space.\n                        `m` is the number of inducing points,\n                        `3` is the dimension of the space (x, y, z)\n\n        Returns:\n            Xu (ndarray): (mp, 2); Inducing points in input space.\n                        `p` is the number of points each inducing point is mapped \n                        to in order to form the FoV.\n        \"\"\"\n        x, y, h = tf.split(Xu, num_or_size_splits=3, axis=1)\n        x = tf.reshape(x, [-1,])\n        y = tf.reshape(y, [-1,])\n        h = tf.reshape(h, [-1,])\n\n        delta = h / (self.num_side - 1)\n\n        pts = []\n        for i in range(self.num_side):\n            pts.append(tf.linspace([x - h/2, y - (h/2) + (delta * i)], \n                                   [x + h/2, y - (h/2) + (delta * i)], \n                                   self.num_side, \n                                   axis=1))\n        xy = tf.concat(pts, axis=1)\n        xy = tf.transpose(xy, [2, 1, 0])\n        xy = tf.reshape(xy, [-1, 2])\n        xy = self._reshape(xy, tf.shape(Xu)[0])\n        return xy\n\n    def _reshape(self, X, num_inducing):\n        \"\"\"Reorder the inducing points to be in the correct order for aggregation with square height FoV\n\n        Args:\n            X (ndarray): (mp, 2); Inducing points in input space. `p` is the number of points each \n                        inducing point is mapped to in order to form the FoV.\n\n        Returns:\n            Xu (ndarray): (mp, 2); Reorder inducing points\n        \"\"\"\n        X = tf.reshape(X, (num_inducing, -1, self.num_side, self.num_side, 2))\n        X = tf.transpose(X, (0, 2, 1, 3, 4))\n        X = tf.reshape(X, (-1, 2))\n        return X\n\n    def distance(self, Xu):\n        \"\"\"Computes the distance incured by sequentially visiting the inducing points\n        Args:\n            Xu (ndarray): (m, 3); Inducing points from which to compute the path lengths.\n                          `m` is the number of inducing points.\n\n        Returns:\n            dist (float): path lengths\n        \"\"\"\n        Xu = tf.reshape(Xu, (-1, 3))\n        dist = tf.norm(Xu[1:] - Xu[:-1], axis=-1)\n        dist = tf.reduce_sum(dist, axis=0)\n        return dist\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareHeightTransform.distance","title":"<code>distance(Xu)</code>","text":"<p>Computes the distance incured by sequentially visiting the inducing points Args:     Xu (ndarray): (m, 3); Inducing points from which to compute the path lengths.                   <code>m</code> is the number of inducing points.</p> <p>Returns:</p> Name Type Description <code>dist</code> <code>float</code> <p>path lengths</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def distance(self, Xu):\n    \"\"\"Computes the distance incured by sequentially visiting the inducing points\n    Args:\n        Xu (ndarray): (m, 3); Inducing points from which to compute the path lengths.\n                      `m` is the number of inducing points.\n\n    Returns:\n        dist (float): path lengths\n    \"\"\"\n    Xu = tf.reshape(Xu, (-1, 3))\n    dist = tf.norm(Xu[1:] - Xu[:-1], axis=-1)\n    dist = tf.reduce_sum(dist, axis=0)\n    return dist\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareHeightTransform.enable_aggregation","title":"<code>enable_aggregation(size=None)</code>","text":"<p>Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the  covariance matrix size.</p> <p>Parameters:</p> Name Type Description Default <code>size</code> <code>int</code> <p>If None, all the interpolated inducing points within the FoV are aggregated. Alternatively,          the number of inducing points to aggregate can be explicitly defined using this variable.</p> <code>None</code> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def enable_aggregation(self, size=None):\n    \"\"\"Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the \n    covariance matrix size.\n\n    Args:\n        size (int): If None, all the interpolated inducing points within the FoV are aggregated. Alternatively, \n                    the number of inducing points to aggregate can be explicitly defined using this variable.\n    \"\"\"\n    if size is None:\n        self.aggregation_size = self.num_side**2\n    else:\n        self.aggregation_size = size\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareHeightTransform.expand","title":"<code>expand(Xu)</code>","text":"<p>Applies the expansion transform to the inducing points</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(m, 3); Inducing points in the 3D position space.         <code>m</code> is the number of inducing points,         <code>3</code> is the dimension of the space (x, y, z)</p> required <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(mp, 2); Inducing points in input space.         <code>p</code> is the number of points each inducing point is mapped          to in order to form the FoV.</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def expand(self, Xu):     \n    \"\"\"\n    Applies the expansion transform to the inducing points\n\n    Args:\n        Xu (ndarray): (m, 3); Inducing points in the 3D position space.\n                    `m` is the number of inducing points,\n                    `3` is the dimension of the space (x, y, z)\n\n    Returns:\n        Xu (ndarray): (mp, 2); Inducing points in input space.\n                    `p` is the number of points each inducing point is mapped \n                    to in order to form the FoV.\n    \"\"\"\n    x, y, h = tf.split(Xu, num_or_size_splits=3, axis=1)\n    x = tf.reshape(x, [-1,])\n    y = tf.reshape(y, [-1,])\n    h = tf.reshape(h, [-1,])\n\n    delta = h / (self.num_side - 1)\n\n    pts = []\n    for i in range(self.num_side):\n        pts.append(tf.linspace([x - h/2, y - (h/2) + (delta * i)], \n                               [x + h/2, y - (h/2) + (delta * i)], \n                               self.num_side, \n                               axis=1))\n    xy = tf.concat(pts, axis=1)\n    xy = tf.transpose(xy, [2, 1, 0])\n    xy = tf.reshape(xy, [-1, 2])\n    xy = self._reshape(xy, tf.shape(Xu)[0])\n    return xy\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareTransform","title":"<code>SquareTransform</code>","text":"<p>               Bases: <code>Transform</code></p> <p>Non-point Transform to model a square FoV. Only works for single robot cases. </p> <p>Parameters:</p> Name Type Description Default <code>length</code> <code>float</code> <p>Length of the square FoV</p> required <code>num_side</code> <code>int</code> <p>Number of points along each side of the FoV</p> required <code>aggregate_fov</code> <code>bool</code> <p>If <code>True</code>, covariances corresponding to interpolated inducing points used to                    approximate the sensor FoV are aggregated to reduce the matrix inversion cost</p> <code>False</code> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>class SquareTransform(Transform):\n    \"\"\"Non-point Transform to model a square FoV. Only works for single robot cases. \n\n    Args:\n        length (float): Length of the square FoV\n        num_side (int): Number of points along each side of the FoV\n        aggregate_fov (bool): If `True`, covariances corresponding to interpolated inducing points used to \n                              approximate the sensor FoV are aggregated to reduce the matrix inversion cost\n    \"\"\"\n    def __init__(self, length, num_side, aggregate_fov=False, **kwargs):\n        super().__init__(**kwargs)\n        self.length = length\n        self.num_side = num_side\n        self.length_factor=length/(self.num_side)\n        self.num_length = int(length/self.length_factor)\n\n        if aggregate_fov:\n            self.enable_aggregation()\n\n    def enable_aggregation(self, size=None):\n        \"\"\"Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the \n        covariance matrix size.\n\n        Args:\n            size (int): If None, all the interpolated inducing points within the FoV are aggregated. Alternatively, \n                        the number of inducing points to aggregate can be explicitly defined using this variable.\n        \"\"\"\n        if size is None:\n            self.aggregation_size = self.num_side**2\n        else:\n            self.aggregation_size = size\n\n    def expand(self, Xu):\n        \"\"\"Applies the expansion transformation to the inducing points\n\n        Args:\n            Xu (ndarray): (m, 3); Inducing points in the position and orientation space.\n                          `m` is the number of inducing points,\n                          `3` is the dimension of the space (x, y, angle in radians)\n\n        Returns:\n            Xu (ndarray): (mp, 2); Inducing points in input space.\n                        `p` is the number of points each inducing point is mapped \n                         to in order to form the FoV.\n        \"\"\"\n        x, y, theta = tf.split(Xu, num_or_size_splits=3, axis=1)\n        x     = tf.reshape(x, [-1,])\n        y     = tf.reshape(y, [-1,])\n        theta = tf.reshape(theta, [-1,])\n\n        points = []\n        for i in range(-int(np.floor((self.num_side)/2)), int(np.ceil((self.num_side)/2))):\n            points.append(tf.linspace([(x + (i * self.length_factor) * tf.cos(theta)) - self.length/2 * tf.cos(theta+np.pi/2), \n                                       (y + (i * self.length_factor) * tf.sin(theta)) - self.length/2 * tf.sin(theta+np.pi/2)], \n                                      [(x + (i * self.length_factor) * tf.cos(theta)) + self.length/2 * tf.cos(theta+np.pi/2), \n                                       (y + (i * self.length_factor) * tf.sin(theta)) + self.length/2 * tf.sin(theta+np.pi/2)], \n                                      self.num_side, axis=1))\n        xy = tf.concat(points, axis=1)\n        xy = tf.transpose(xy, [2, 1, 0])\n        xy = tf.reshape(xy, (-1, 2))\n        return xy\n\n    def distance(self, Xu):\n        \"\"\"Computes the distance incured by sequentially visiting the inducing points\n        Args:\n            Xu (ndarray): (m, 3); Inducing points from which to compute the path lengths.\n                          `m` is the number of inducing points.\n\n        Returns:\n            dist (float): path lengths\n        \"\"\"\n        Xu = tf.reshape(Xu, (-1, 3))[:, :2]\n        dist = tf.norm(Xu[1:] - Xu[:-1], axis=-1)\n        dist = tf.reduce_sum(dist, axis=0)\n        return dist\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareTransform.distance","title":"<code>distance(Xu)</code>","text":"<p>Computes the distance incured by sequentially visiting the inducing points Args:     Xu (ndarray): (m, 3); Inducing points from which to compute the path lengths.                   <code>m</code> is the number of inducing points.</p> <p>Returns:</p> Name Type Description <code>dist</code> <code>float</code> <p>path lengths</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def distance(self, Xu):\n    \"\"\"Computes the distance incured by sequentially visiting the inducing points\n    Args:\n        Xu (ndarray): (m, 3); Inducing points from which to compute the path lengths.\n                      `m` is the number of inducing points.\n\n    Returns:\n        dist (float): path lengths\n    \"\"\"\n    Xu = tf.reshape(Xu, (-1, 3))[:, :2]\n    dist = tf.norm(Xu[1:] - Xu[:-1], axis=-1)\n    dist = tf.reduce_sum(dist, axis=0)\n    return dist\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareTransform.enable_aggregation","title":"<code>enable_aggregation(size=None)</code>","text":"<p>Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the  covariance matrix size.</p> <p>Parameters:</p> Name Type Description Default <code>size</code> <code>int</code> <p>If None, all the interpolated inducing points within the FoV are aggregated. Alternatively,          the number of inducing points to aggregate can be explicitly defined using this variable.</p> <code>None</code> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def enable_aggregation(self, size=None):\n    \"\"\"Enable FoV covariance aggregation, which reduces the covariance matrix inversion cost by reducing the \n    covariance matrix size.\n\n    Args:\n        size (int): If None, all the interpolated inducing points within the FoV are aggregated. Alternatively, \n                    the number of inducing points to aggregate can be explicitly defined using this variable.\n    \"\"\"\n    if size is None:\n        self.aggregation_size = self.num_side**2\n    else:\n        self.aggregation_size = size\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.SquareTransform.expand","title":"<code>expand(Xu)</code>","text":"<p>Applies the expansion transformation to the inducing points</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(m, 3); Inducing points in the position and orientation space.           <code>m</code> is the number of inducing points,           <code>3</code> is the dimension of the space (x, y, angle in radians)</p> required <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(mp, 2); Inducing points in input space.         <code>p</code> is the number of points each inducing point is mapped           to in order to form the FoV.</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def expand(self, Xu):\n    \"\"\"Applies the expansion transformation to the inducing points\n\n    Args:\n        Xu (ndarray): (m, 3); Inducing points in the position and orientation space.\n                      `m` is the number of inducing points,\n                      `3` is the dimension of the space (x, y, angle in radians)\n\n    Returns:\n        Xu (ndarray): (mp, 2); Inducing points in input space.\n                    `p` is the number of points each inducing point is mapped \n                     to in order to form the FoV.\n    \"\"\"\n    x, y, theta = tf.split(Xu, num_or_size_splits=3, axis=1)\n    x     = tf.reshape(x, [-1,])\n    y     = tf.reshape(y, [-1,])\n    theta = tf.reshape(theta, [-1,])\n\n    points = []\n    for i in range(-int(np.floor((self.num_side)/2)), int(np.ceil((self.num_side)/2))):\n        points.append(tf.linspace([(x + (i * self.length_factor) * tf.cos(theta)) - self.length/2 * tf.cos(theta+np.pi/2), \n                                   (y + (i * self.length_factor) * tf.sin(theta)) - self.length/2 * tf.sin(theta+np.pi/2)], \n                                  [(x + (i * self.length_factor) * tf.cos(theta)) + self.length/2 * tf.cos(theta+np.pi/2), \n                                   (y + (i * self.length_factor) * tf.sin(theta)) + self.length/2 * tf.sin(theta+np.pi/2)], \n                                  self.num_side, axis=1))\n    xy = tf.concat(points, axis=1)\n    xy = tf.transpose(xy, [2, 1, 0])\n    xy = tf.reshape(xy, (-1, 2))\n    return xy\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.Transform","title":"<code>Transform</code>","text":"<p>Base class for transformations of the inducing points, including expansion and aggregation transforms.</p> Refer to the following papers for more details <ul> <li>Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]</li> <li>Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]</li> </ul> <p>Parameters:</p> Name Type Description Default <code>aggregation_size</code> <code>int</code> <p>Number of consecutive inducing points to aggregate</p> <code>None</code> <code>constraint_weight</code> <code>float</code> <p>Weight term that controls the importance of the                         constraint terms in the SGP's optimization objective</p> <code>1.0</code> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>class Transform:\n    \"\"\"Base class for transformations of the inducing points, including expansion and aggregation transforms.\n\n    Refer to the following papers for more details:\n        - Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [Jakkala and Akella, 2023]\n        - Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes [Jakkala and Akella, 2024]\n\n    Args:\n        aggregation_size (int): Number of consecutive inducing points to aggregate\n        constraint_weight (float): Weight term that controls the importance of the \n                                   constraint terms in the SGP's optimization objective \n    \"\"\"\n    def __init__(self, \n                 aggregation_size=None, \n                 constraint_weight=1.0,\n                 **kwargs):\n        self.aggregation_size = aggregation_size\n        self.constraint_weight = constraint_weight\n\n    def expand(self, Xu):\n        \"\"\"Applies the expansion transform to the inducing points\n\n        Args:\n            Xu (ndarray): Expansion transformed inducing points\n        \"\"\"\n        return Xu\n\n    def aggregate(self, k):\n        \"\"\"Applies the aggregation transform to kernel matrices\n\n        Args:\n            k (tensor): (mp, mp)/(mp, n); Kernel matrix. \n                        `m` is the number of inducing points,\n                        `p` is the number of points each inducing point is mapped,\n                        `n` is the number of training data points.\n\n        Returns:\n            k (tensor): (m, m)/(m, n); Aggregated kernel matrix\n        \"\"\"\n        if self.aggregation_size is None:\n            return k\n\n        if k.shape[0] == k.shape[1]:\n            # Handle Kuu which is a square matrix\n            k = tf.expand_dims(tf.expand_dims(k, axis=0), axis=-1)\n            k = tf.nn.avg_pool(k,\n                               ksize=[1, self.aggregation_size, self.aggregation_size, 1],\n                               strides=[1, self.aggregation_size, self.aggregation_size, 1],\n                               padding='VALID')\n            k = tf.squeeze(k, axis=[0, -1])\n        else:\n            # Handle Kuf which is a rectangular matrix\n            k = tf.expand_dims(k, axis=0)\n            k = tf.nn.avg_pool(k,\n                               ksize=[1, self.aggregation_size, 1],\n                               strides=[1, self.aggregation_size, 1],\n                               padding='VALID')\n            k = tf.squeeze(k, axis=[0])\n        return k\n\n    def constraints(self, Xu):\n        \"\"\"Computes the constraint terms that are added to the SGP's optimization function\n\n        Args:\n            Xu (ndarray): Inducing points from which to compute the constraints\n\n        Returns:\n            c (float): constraint terms (eg., distance constraint)\n        \"\"\"\n        return 0.\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.Transform.aggregate","title":"<code>aggregate(k)</code>","text":"<p>Applies the aggregation transform to kernel matrices</p> <p>Parameters:</p> Name Type Description Default <code>k</code> <code>tensor</code> <p>(mp, mp)/(mp, n); Kernel matrix.          <code>m</code> is the number of inducing points,         <code>p</code> is the number of points each inducing point is mapped,         <code>n</code> is the number of training data points.</p> required <p>Returns:</p> Name Type Description <code>k</code> <code>tensor</code> <p>(m, m)/(m, n); Aggregated kernel matrix</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def aggregate(self, k):\n    \"\"\"Applies the aggregation transform to kernel matrices\n\n    Args:\n        k (tensor): (mp, mp)/(mp, n); Kernel matrix. \n                    `m` is the number of inducing points,\n                    `p` is the number of points each inducing point is mapped,\n                    `n` is the number of training data points.\n\n    Returns:\n        k (tensor): (m, m)/(m, n); Aggregated kernel matrix\n    \"\"\"\n    if self.aggregation_size is None:\n        return k\n\n    if k.shape[0] == k.shape[1]:\n        # Handle Kuu which is a square matrix\n        k = tf.expand_dims(tf.expand_dims(k, axis=0), axis=-1)\n        k = tf.nn.avg_pool(k,\n                           ksize=[1, self.aggregation_size, self.aggregation_size, 1],\n                           strides=[1, self.aggregation_size, self.aggregation_size, 1],\n                           padding='VALID')\n        k = tf.squeeze(k, axis=[0, -1])\n    else:\n        # Handle Kuf which is a rectangular matrix\n        k = tf.expand_dims(k, axis=0)\n        k = tf.nn.avg_pool(k,\n                           ksize=[1, self.aggregation_size, 1],\n                           strides=[1, self.aggregation_size, 1],\n                           padding='VALID')\n        k = tf.squeeze(k, axis=[0])\n    return k\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.Transform.constraints","title":"<code>constraints(Xu)</code>","text":"<p>Computes the constraint terms that are added to the SGP's optimization function</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>Inducing points from which to compute the constraints</p> required <p>Returns:</p> Name Type Description <code>c</code> <code>float</code> <p>constraint terms (eg., distance constraint)</p> Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def constraints(self, Xu):\n    \"\"\"Computes the constraint terms that are added to the SGP's optimization function\n\n    Args:\n        Xu (ndarray): Inducing points from which to compute the constraints\n\n    Returns:\n        c (float): constraint terms (eg., distance constraint)\n    \"\"\"\n    return 0.\n</code></pre>"},{"location":"api/models/core/Transformations.html#sgptools.models.core.transformations.Transform.expand","title":"<code>expand(Xu)</code>","text":"<p>Applies the expansion transform to the inducing points</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>Expansion transformed inducing points</p> required Source code in <code>sgptools/models/core/transformations.py</code> <pre><code>def expand(self, Xu):\n    \"\"\"Applies the expansion transform to the inducing points\n\n    Args:\n        Xu (ndarray): Expansion transformed inducing points\n    \"\"\"\n    return Xu\n</code></pre>"},{"location":"api/utils/index.html","title":"","text":""},{"location":"api/utils/index.html#sgptools.utils","title":"<code>sgptools.utils</code>","text":"<p>General utilities to support the functionalities of this package:</p> <ul> <li><code>data</code>: Provides utilities to preprocess datasets</li> <li><code>gpflow</code>: Provides utilities to interface with GPflow</li> <li><code>metrics</code>: Provides utilities to quantify the solution quality</li> <li><code>misc</code>: Provides miscellaneous helper functions</li> <li><code>tsp</code>: Provides utilities to run TSP/VRP solver</li> </ul>"},{"location":"api/utils/Data.html","title":"Data","text":""},{"location":"api/utils/Data.html#sgptools.utils.data","title":"<code>sgptools.utils.data</code>","text":""},{"location":"api/utils/Data.html#sgptools.utils.data.get_dataset","title":"<code>get_dataset(dataset_path=None, num_train=1000, num_test=2500, num_candidates=150, **kwargs)</code>","text":"<p>Method to generate/load datasets and preprocess them for SP/IPP. The method uses kmeans to  generate train and test sets.</p> <p>Parameters:</p> Name Type Description Default <code>dataset_path</code> <code>str</code> <p>Path to a tif dataset file. If None, the method will generate synthetic data.</p> <code>None</code> <code>num_train</code> <code>int</code> <p>Number of training samples to generate.</p> <code>1000</code> <code>num_test</code> <code>int</code> <p>Number of testing samples to generate.</p> <code>2500</code> <code>num_candidates</code> <code>int</code> <p>Number of candidate locations to generate.</p> <code>150</code> <p>Returns:</p> Name Type Description <code>X_train</code> <code>ndarray</code> <p>(n, d); Training set inputs</p> <code>y_train</code> <code>ndarray</code> <p>(n, 1); Training set labels</p> <code>X_test</code> <code>ndarray</code> <p>(n, d); Testing set inputs</p> <code>y_test</code> <code>ndarray</code> <p>(n, 1); Testing set labels</p> <code>candidates</code> <code>ndarray</code> <p>(n, d); Candidate sensor placement locations</p> <code>X</code> <code>ndarray</code> <p>(n, d); Full dataset inputs</p> <code>y</code> <code>ndarray</code> <p>(n, 1); Full dataset labels</p> Source code in <code>sgptools/utils/data.py</code> <pre><code>def get_dataset(dataset_path=None,\n                num_train=1000,\n                num_test=2500, \n                num_candidates=150,\n                **kwargs):\n    \"\"\"Method to generate/load datasets and preprocess them for SP/IPP. The method uses kmeans to \n    generate train and test sets.\n\n    Args:\n        dataset_path (str): Path to a tif dataset file. If None, the method will generate synthetic data.\n        num_train (int): Number of training samples to generate.\n        num_test (int): Number of testing samples to generate.\n        num_candidates (int): Number of candidate locations to generate.\n\n    Returns:\n       X_train (ndarray): (n, d); Training set inputs\n       y_train (ndarray): (n, 1); Training set labels\n       X_test (ndarray): (n, d); Testing set inputs\n       y_test (ndarray): (n, 1); Testing set labels\n       candidates (ndarray): (n, d); Candidate sensor placement locations\n       X (ndarray): (n, d); Full dataset inputs\n       y (ndarray): (n, 1); Full dataset labels\n    \"\"\"\n    # Load the data\n    if dataset_path is not None:\n        X, y = prep_tif_dataset(dataset_path=dataset_path)\n    else:\n        X, y = prep_synthetic_dataset(**kwargs)\n\n    X_train = get_inducing_pts(X, num_train)\n    X_train, y_train = cont2disc(X_train, X, y)\n\n    X_test = get_inducing_pts(X, num_test)\n    X_test, y_test = cont2disc(X_test, X, y)\n\n    candidates = get_inducing_pts(X, num_candidates)\n    candidates = cont2disc(candidates, X)\n\n    # Standardize data\n    X_scaler = StandardScaler()\n    X_scaler.fit(X_train)\n    X_train = X_scaler.transform(X_train)*10.0\n    X_test = X_scaler.transform(X_test)*10.0\n    X = X_scaler.transform(X)*10.0\n    candidates = X_scaler.transform(candidates)*10.0\n\n    y_scaler = StandardScaler()\n    y_scaler.fit(y_train)\n    y_train = y_scaler.transform(y_train)\n    y_test = y_scaler.transform(y_test)\n    y = y_scaler.transform(y)\n\n    return X_train, y_train, X_test, y_test, candidates, X, y\n</code></pre>"},{"location":"api/utils/Data.html#sgptools.utils.data.point_pos","title":"<code>point_pos(point, d, theta)</code>","text":"<p>Generate a point at a distance d from a point at angle theta.</p> <p>Parameters:</p> Name Type Description Default <code>point</code> <code>ndarray</code> <p>(N, 2); array of points</p> required <code>d</code> <code>float</code> <p>distance</p> required <code>theta</code> <code>float</code> <p>angle in radians</p> required <p>Returns:</p> Name Type Description <code>X</code> <code>ndarray</code> <p>(N,); array of x-coordinate</p> <code>Y</code> <code>ndarray</code> <p>(N,); array of y-coordinate</p> Source code in <code>sgptools/utils/data.py</code> <pre><code>def point_pos(point, d, theta):\n    '''\n    Generate a point at a distance d from a point at angle theta.\n\n    Args:\n        point (ndarray): (N, 2); array of points\n        d (float): distance\n        theta (float): angle in radians\n\n    Returns:\n        X  (ndarray): (N,); array of x-coordinate\n        Y  (ndarray): (N,); array of y-coordinate\n    '''\n    return np.c_[point[:, 0] + d*np.cos(theta), point[:, 1] + d*np.sin(theta)]\n</code></pre>"},{"location":"api/utils/Data.html#sgptools.utils.data.prep_synthetic_dataset","title":"<code>prep_synthetic_dataset(shape=(50, 50), min_height=0.0, max_height=30.0, roughness=0.5, random_seed=None, **kwargs)</code>","text":"<p>Generates a 50x50 grid of synthetic elevation data using the diamond square algorithm.</p> Refer to the following repo for more details <ul> <li>https://github.com/buckinha/DiamondSquare</li> </ul> <p>Parameters:</p> Name Type Description Default <code>shape</code> <code>tuple</code> <p>(x, y); Grid size along the x and y axis</p> <code>(50, 50)</code> <code>min_height</code> <code>float</code> <p>Minimum allowed height in the sampled data</p> <code>0.0</code> <code>max_height</code> <code>float</code> <p>Maximum allowed height in the sampled data</p> <code>30.0</code> <code>roughness</code> <code>float</code> <p>Roughness of the sampled data</p> <code>0.5</code> <code>random_seed</code> <code>int</code> <p>Random seed for reproducibility</p> <code>None</code> <p>Returns:</p> Name Type Description <code>X</code> <code>ndarray</code> <p>(n, d); Dataset input features</p> <code>y</code> <code>ndarray</code> <p>(n, 1); Dataset labels</p> Source code in <code>sgptools/utils/data.py</code> <pre><code>def prep_synthetic_dataset(shape=(50, 50), \n                           min_height=0.0, \n                           max_height=30.0, \n                           roughness=0.5,\n                           random_seed=None,\n                           **kwargs):\n    '''Generates a 50x50 grid of synthetic elevation data using the diamond square algorithm.\n\n    Refer to the following repo for more details:\n        - [https://github.com/buckinha/DiamondSquare](https://github.com/buckinha/DiamondSquare)\n\n    Args:\n        shape (tuple): (x, y); Grid size along the x and y axis\n        min_height (float): Minimum allowed height in the sampled data\n        max_height (float): Maximum allowed height in the sampled data\n        roughness (float): Roughness of the sampled data\n        random_seed (int): Random seed for reproducibility\n\n    Returns:\n       X (ndarray): (n, d); Dataset input features\n       y (ndarray): (n, 1); Dataset labels\n    '''\n    data = diamond_square(shape=shape,\n                          min_height=min_height, \n                          max_height=max_height, \n                          roughness=roughness,\n                          random_seed=random_seed,\n                          **kwargs)\n\n    # create x and y coordinates from the extent\n    x_coords = np.arange(0, data.shape[0])/10\n    y_coords = np.arange(0, data.shape[1])/10\n    xx, yy = np.meshgrid(x_coords, y_coords)\n    X = np.c_[xx.ravel(), yy.ravel()]\n    y = data.ravel()\n    y = y.reshape(-1, 1)\n\n    return X.astype(float), y.astype(float)\n</code></pre>"},{"location":"api/utils/Data.html#sgptools.utils.data.prep_tif_dataset","title":"<code>prep_tif_dataset(dataset_path)</code>","text":"<p>Load and preprocess a dataset from a GeoTIFF file (.tif file). The input features  are set to the x and y pixel block coordinates and the labels are read from the file. The method also removes all invalid points.</p> <p>Large tif files  need to be downsampled using the following command:  <code>gdalwarp -tr 50 50 &lt;input&gt;.tif &lt;output&gt;.tif</code></p> <p>Args:     dataset_path (str): Path to the dataset file, used only when dataset_type is 'tif'.</p> <p>Returns:    X (ndarray): (n, d); Dataset input features    y (ndarray): (n, 1); Dataset labels</p> Source code in <code>sgptools/utils/data.py</code> <pre><code>def prep_tif_dataset(dataset_path):\n    '''Load and preprocess a dataset from a GeoTIFF file (.tif file). The input features \n    are set to the x and y pixel block coordinates and the labels are read from the file.\n    The method also removes all invalid points.\n\n    Large tif files \n    need to be downsampled using the following command: \n    ```gdalwarp -tr 50 50 &lt;input&gt;.tif &lt;output&gt;.tif```\n\n    Args:\n        dataset_path (str): Path to the dataset file, used only when dataset_type is 'tif'.\n\n    Returns:\n       X (ndarray): (n, d); Dataset input features\n       y (ndarray): (n, 1); Dataset labels\n    '''\n    data = PIL.Image.open(dataset_path)\n    data = np.array(data)\n\n    # create x and y coordinates from the extent\n    x_coords = np.arange(0, data.shape[1])/10\n    y_coords = np.arange(data.shape[0], 0, -1)/10\n    xx, yy = np.meshgrid(x_coords, y_coords)\n    X = np.c_[xx.ravel(), yy.ravel()]\n    y = data.ravel()\n\n    # Remove invalid labels\n    y[np.where(y==-999999.0)] = np.nan\n    X = X[~np.isnan(y)]\n    y = y[~np.isnan(y)]\n\n    X = X.reshape(-1, 2)\n    y = y.reshape(-1, 1)\n\n    return X.astype(float), y.astype(float)\n</code></pre>"},{"location":"api/utils/Data.html#sgptools.utils.data.remove_circle_patches","title":"<code>remove_circle_patches(X, Y, circle_patches)</code>","text":"<p>Remove points inside polycircle patchesgons.</p> <p>Parameters:</p> Name Type Description Default <code>X</code> <code> (ndarray</code> <p>(N,); array of x-coordinate</p> required <code>Y</code> <code> (ndarray</code> <p>(N,); array of y-coordinate</p> required <code>circle_patches</code> <code>list of matplotlib circle patches</code> <p>Circle patches to remove from the X, Y points</p> required <p>Returns:</p> Name Type Description <code>X</code> <code>ndarray</code> <p>(N,); array of x-coordinate</p> <code>Y</code> <code>ndarray</code> <p>(N,); array of y-coordinate</p> Source code in <code>sgptools/utils/data.py</code> <pre><code>def remove_circle_patches(X, Y, circle_patches):\n    '''\n    Remove points inside polycircle patchesgons.\n\n    Args:\n        X  (ndarray): (N,); array of x-coordinate\n        Y  (ndarray): (N,); array of y-coordinate\n        circle_patches (list of matplotlib circle patches): Circle patches to remove from the X, Y points\n\n    Returns:\n        X  (ndarray): (N,); array of x-coordinate\n        Y  (ndarray): (N,); array of y-coordinate\n    '''\n    points = np.array([X.flatten(), Y.flatten()]).T\n    for circle_patch in circle_patches:\n        points = points[~circle_patch.contains_points(points)]\n    return points[:, 0], points[:, 1]\n</code></pre>"},{"location":"api/utils/Data.html#sgptools.utils.data.remove_polygons","title":"<code>remove_polygons(X, Y, polygons)</code>","text":"<p>Remove points inside polygons.</p> <p>Parameters:</p> Name Type Description Default <code>X</code> <code> (ndarray</code> <p>(N,); array of x-coordinate</p> required <code>Y</code> <code> (ndarray</code> <p>(N,); array of y-coordinate</p> required <code>polygons</code> <code>list of matplotlib path polygon</code> <p>Polygons to remove from the X, Y points</p> required <p>Returns:</p> Name Type Description <code>X</code> <code>ndarray</code> <p>(N,); array of x-coordinate</p> <code>Y</code> <code>ndarray</code> <p>(N,); array of y-coordinate</p> Source code in <code>sgptools/utils/data.py</code> <pre><code>def remove_polygons(X, Y, polygons):\n    '''\n    Remove points inside polygons.\n\n    Args:\n        X  (ndarray): (N,); array of x-coordinate\n        Y  (ndarray): (N,); array of y-coordinate\n        polygons (list of matplotlib path polygon): Polygons to remove from the X, Y points\n\n    Returns:\n        X  (ndarray): (N,); array of x-coordinate\n        Y  (ndarray): (N,); array of y-coordinate\n    '''\n    points = np.array([X.flatten(), Y.flatten()]).T\n    for polygon in polygons:\n        p = path.Path(polygon)\n        points = points[~p.contains_points(points)]\n    return points[:, 0], points[:, 1]\n</code></pre>"},{"location":"api/utils/GPflow.html","title":"GPflow","text":""},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow","title":"<code>sgptools.utils.gpflow</code>","text":""},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow.TraceInducingPts","title":"<code>TraceInducingPts</code>","text":"<p>               Bases: <code>MonitorTask</code></p> <p>GPflow monitoring task, used to trace the inducing points states at every step during optimization. </p> <p>Parameters:</p> Name Type Description Default <code>model</code> <code>sgpr</code> <p>GPflow GP/SGP model</p> required Source code in <code>sgptools/utils/gpflow.py</code> <pre><code>class TraceInducingPts(gpflow.monitor.MonitorTask):\n    '''\n    GPflow monitoring task, used to trace the inducing points\n    states at every step during optimization. \n\n    Args:\n        model (gpflow.models.sgpr): GPflow GP/SGP model\n    '''\n    def __init__(self, model):\n        super().__init__()\n        self.trace = []\n        self.model = model\n\n    def run(self, **kwargs):\n        '''\n        Method used to extract the inducing points and \n        apply IPP fixed points transform if available\n        '''\n        Xu = self.model.inducing_variable.Z\n        Xu_exp = self.model.transform.expand(Xu, \n                            expand_sensor_model=False).numpy()\n        self.trace.append(Xu_exp)\n\n    def get_trace(self):\n        '''\n        Returns the inducing points collected at each optimization step\n\n        Returns:\n            trace (ndarray): (n, m, d); Array with the inducing points.\n                            `n` is the number of optimization steps;\n                            `m` is the number of inducing points;\n                            `d` is the dimension of the inducing points.\n        '''\n        return np.array(self.trace)\n</code></pre>"},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow.TraceInducingPts.get_trace","title":"<code>get_trace()</code>","text":"<p>Returns the inducing points collected at each optimization step</p> <p>Returns:</p> Name Type Description <code>trace</code> <code>ndarray</code> <p>(n, m, d); Array with the inducing points.             <code>n</code> is the number of optimization steps;             <code>m</code> is the number of inducing points;             <code>d</code> is the dimension of the inducing points.</p> Source code in <code>sgptools/utils/gpflow.py</code> <pre><code>def get_trace(self):\n    '''\n    Returns the inducing points collected at each optimization step\n\n    Returns:\n        trace (ndarray): (n, m, d); Array with the inducing points.\n                        `n` is the number of optimization steps;\n                        `m` is the number of inducing points;\n                        `d` is the dimension of the inducing points.\n    '''\n    return np.array(self.trace)\n</code></pre>"},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow.TraceInducingPts.run","title":"<code>run(**kwargs)</code>","text":"<p>Method used to extract the inducing points and  apply IPP fixed points transform if available</p> Source code in <code>sgptools/utils/gpflow.py</code> <pre><code>def run(self, **kwargs):\n    '''\n    Method used to extract the inducing points and \n    apply IPP fixed points transform if available\n    '''\n    Xu = self.model.inducing_variable.Z\n    Xu_exp = self.model.transform.expand(Xu, \n                        expand_sensor_model=False).numpy()\n    self.trace.append(Xu_exp)\n</code></pre>"},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow.get_model_params","title":"<code>get_model_params(X_train, y_train, max_steps=1500, lr=0.01, print_params=True, lengthscales=1.0, variance=1.0, noise_variance=0.1, kernel=None, return_gp=False, train_inducing_pts=False, num_inducing_pts=500, **kwargs)</code>","text":"<p>Train a GP on the given training set.  Trains a sparse GP if the training set is larger than 1000 samples.</p> <p>Parameters:</p> Name Type Description Default <code>X_train</code> <code>ndarray</code> <p>(n, d); Training set inputs</p> required <code>y_train</code> <code>ndarray</code> <p>(n, 1); Training set labels</p> required <code>max_steps</code> <code>int</code> <p>Maximum number of optimization steps</p> <code>1500</code> <code>lr</code> <code>float</code> <p>Optimization learning rate</p> <code>0.01</code> <code>print_params</code> <code>bool</code> <p>If True, prints the optimized GP parameters</p> <code>True</code> <code>lengthscales</code> <code>float or list</code> <p>Kernel lengthscale(s), if passed as a list,                      each element corresponds to each data dimension</p> <code>1.0</code> <code>variance</code> <code>float</code> <p>Kernel variance</p> <code>1.0</code> <code>noise_variance</code> <code>float</code> <p>Data noise variance</p> <code>0.1</code> <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> <code>None</code> <code>return_gp</code> <code>bool</code> <p>If True, returns the trained GP model</p> <code>False</code> <code>train_inducing_pts</code> <code>bool</code> <p>If True, trains the inducing points when                        using a sparse GP model</p> <code>False</code> <code>num_inducing_pts</code> <code>int</code> <p>Number of inducing points to use when training                     a sparse GP model</p> <code>500</code> <p>Returns:</p> Name Type Description <code>loss</code> <code>list</code> <p>Loss values obtained during training</p> <code>variance</code> <code>float</code> <p>Optimized data noise variance</p> <code>kernel</code> <code>Kernel</code> <p>Optimized gpflow kernel function</p> <code>gp</code> <code>GPR</code> <p>Optimized gpflow GP model.                      Returned only if <code>return_gp=True</code>.</p> Source code in <code>sgptools/utils/gpflow.py</code> <pre><code>def get_model_params(X_train, y_train, \n                     max_steps=1500, \n                     lr=1e-2, \n                     print_params=True, \n                     lengthscales=1.0, \n                     variance=1.0, \n                     noise_variance=0.1,\n                     kernel=None,\n                     return_gp=False,\n                     train_inducing_pts=False,\n                     num_inducing_pts=500,\n                     **kwargs):\n    \"\"\"Train a GP on the given training set. \n    Trains a sparse GP if the training set is larger than 1000 samples.\n\n    Args:\n        X_train (ndarray): (n, d); Training set inputs\n        y_train (ndarray): (n, 1); Training set labels\n        max_steps (int): Maximum number of optimization steps\n        lr (float): Optimization learning rate\n        print_params (bool): If True, prints the optimized GP parameters\n        lengthscales (float or list): Kernel lengthscale(s), if passed as a list, \n                                each element corresponds to each data dimension\n        variance (float): Kernel variance\n        noise_variance (float): Data noise variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        return_gp (bool): If True, returns the trained GP model\n        train_inducing_pts (bool): If True, trains the inducing points when\n                                   using a sparse GP model\n        num_inducing_pts (int): Number of inducing points to use when training\n                                a sparse GP model\n\n    Returns:\n        loss (list): Loss values obtained during training\n        variance (float): Optimized data noise variance\n        kernel (gpflow.kernels.Kernel): Optimized gpflow kernel function\n        gp (gpflow.models.GPR): Optimized gpflow GP model. \n                                Returned only if ```return_gp=True```.\n\n    \"\"\"\n    if kernel is None:\n        kernel = gpflow.kernels.SquaredExponential(lengthscales=lengthscales, \n                                                   variance=variance)\n\n    if len(X_train) &lt;= 1500:\n        gpr = gpflow.models.GPR(data=(X_train, y_train), \n                                kernel=kernel,\n                                noise_variance=noise_variance)\n        trainable_variables=gpr.trainable_variables\n    else:\n        inducing_pts = get_inducing_pts(X_train, num_inducing_pts)\n        gpr = gpflow.models.SGPR(data=(X_train, y_train), \n                                 kernel=kernel,\n                                 inducing_variable=inducing_pts,\n                                 noise_variance=noise_variance)\n        if train_inducing_pts:\n            trainable_variables=gpr.trainable_variables\n        else:\n            trainable_variables=gpr.trainable_variables[1:]\n\n    if max_steps &gt; 0:\n        loss = optimize_model(gpr, max_steps=max_steps, lr=lr, \n                              trainable_variables=trainable_variables,\n                              **kwargs)\n    else:\n        loss = 0\n\n    if print_params:\n        print_summary(gpr)\n\n    if return_gp:\n        return loss, gpr.likelihood.variance, kernel, gpr\n    else:\n        return loss, gpr.likelihood.variance, kernel\n</code></pre>"},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow.optimize_model","title":"<code>optimize_model(model, max_steps=2000, kernel_grad=True, lr=0.01, optimizer='scipy', method=None, verbose=False, trace_fn=None, convergence_criterion=True, trainable_variables=None, tol=None)</code>","text":"<p>Trains a GP/SGP model</p> <p>Parameters:</p> Name Type Description Default <code>model</code> <code>models</code> <p>GPflow GP/SGP model to train.</p> required <code>max_steps</code> <code>int</code> <p>Maximum number of training steps.</p> <code>2000</code> <code>kernel_grad</code> <code>bool</code> <p>If <code>False</code>, the kernel parameters will not be optimized.                  Ignored when <code>trainable_variables</code> are passed.</p> <code>True</code> <code>lr</code> <code>float</code> <p>Optimization learning rate.</p> <code>0.01</code> <code>optimizer</code> <code>str</code> <p>Optimizer to use for training (<code>scipy</code> or <code>tf</code>).</p> <code>'scipy'</code> <code>method</code> <code>str</code> <p>Optimization method refer to scipy minimize            and tf optimizers for full list</p> <code>None</code> <code>verbose</code> <code>bool</code> <p>If <code>True</code>, the training progress will be printed when using Scipy.</p> <code>False</code> <code>trace_fn</code> <code>str</code> <p>Function to trace metrics during training.              If <code>None</code>, the loss values are returned;             If <code>traceXu</code>, it the inducing points states at each optimization step are returned (increases computation time).</p> <code>None</code> <code>convergence_criterion</code> <code>bool</code> <p>If <code>True</code> and using a tensorflow optimizer, it                            enables early stopping when the loss plateaus.</p> <code>True</code> <code>trainable_variables</code> <code>list</code> <p>List of model variables to train.</p> <code>None</code> <code>tol</code> <code>float</code> <p>Convergence tolerance to decide when to stop optimization.</p> <code>None</code> Source code in <code>sgptools/utils/gpflow.py</code> <pre><code>def optimize_model(model, \n                   max_steps=2000, \n                   kernel_grad=True, \n                   lr=1e-2, \n                   optimizer='scipy', \n                   method=None,\n                   verbose=False, \n                   trace_fn=None,\n                   convergence_criterion=True,\n                   trainable_variables=None,\n                   tol=None):\n    \"\"\"\n    Trains a GP/SGP model\n\n    Args:\n        model (gpflow.models): GPflow GP/SGP model to train.\n        max_steps (int): Maximum number of training steps.\n        kernel_grad (bool): If `False`, the kernel parameters will not be optimized. \n                            Ignored when `trainable_variables` are passed.\n        lr (float): Optimization learning rate.\n        optimizer (str): Optimizer to use for training (`scipy` or `tf`).\n        method (str): Optimization method refer to [scipy minimize](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html#scipy.optimize.minimize) \n                      and [tf optimizers](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers) for full list\n        verbose (bool): If `True`, the training progress will be printed when using Scipy.\n        trace_fn (str): Function to trace metrics during training. \n                        If `None`, the loss values are returned;\n                        If `traceXu`, it the inducing points states at each optimization step are returned (increases computation time).\n        convergence_criterion (bool): If `True` and using a tensorflow optimizer, it \n                                      enables early stopping when the loss plateaus.\n        trainable_variables (list): List of model variables to train.\n        tol (float): Convergence tolerance to decide when to stop optimization.\n    \"\"\"\n    # Train all variables if trainable_variables are not provided\n    # If kernel_gradient is False, disable the kernel parameter gradient updates\n    if trainable_variables is None and kernel_grad:\n        trainable_variables=model.trainable_variables\n    elif trainable_variables is None and not kernel_grad:\n        trainable_variables=model.trainable_variables[:1]\n\n    if optimizer == 'scipy':\n        if method is None:\n            method = 'L-BFGS-B'\n\n        if trace_fn == 'traceXu':\n            execute_task = TraceInducingPts(model)\n            task_group = gpflow.monitor.MonitorTaskGroup(execute_task, \n                                                         period=1)\n            trace_fn = gpflow.monitor.Monitor(task_group)\n\n        opt = gpflow.optimizers.Scipy()\n        losses = opt.minimize(model.training_loss,\n                              trainable_variables,\n                              method=method,\n                              options=dict(disp=verbose, maxiter=max_steps),\n                              tol=tol,\n                              step_callback=trace_fn)\n        if trace_fn is None:\n            losses = losses.fun\n        else:\n            losses = trace_fn.task_groups[0].tasks[0].get_trace()\n    else:\n        if trace_fn is None:\n            trace_fn = lambda x: x.loss\n        elif trace_fn == 'traceXu':\n            def trace_fn(traceable_quantities):\n                return model.inducing_variable.Z.numpy()\n\n        if method is None:\n            method = 'adam'\n        opt = tf.keras.optimizers.get(method)\n        opt.learning_rate = lr\n        loss_fn = model.training_loss\n        if convergence_criterion:\n            convergence_criterion = tfp.optimizer.convergence_criteria.LossNotDecreasing(\n                                            atol=1e-5, \n                                            window_size=50,\n                                            min_num_steps=int(max_steps*0.1))\n        else:\n            convergence_criterion = None\n        losses = tfp.math.minimize(loss_fn,\n                                   trainable_variables=trainable_variables,\n                                   num_steps=max_steps,\n                                   optimizer=opt,\n                                   convergence_criterion=convergence_criterion,\n                                   trace_fn=trace_fn)\n        losses = losses.numpy()\n\n    return losses\n</code></pre>"},{"location":"api/utils/GPflow.html#sgptools.utils.gpflow.plot_loss","title":"<code>plot_loss(losses, save_file=None)</code>","text":"<p>Helper function to plot the training loss</p> <p>Parameters:</p> Name Type Description Default <code>losses</code> <code>list</code> <p>list of loss values</p> required <code>save_file</code> <code>str</code> <p>If passed, the loss plot will be saved to the <code>save_file</code></p> <code>None</code> Source code in <code>sgptools/utils/gpflow.py</code> <pre><code>def plot_loss(losses, save_file=None):\n    \"\"\"Helper function to plot the training loss\n\n    Args:\n        losses (list): list of loss values\n        save_file (str): If passed, the loss plot will be saved to the `save_file`\n    \"\"\"\n    plt.plot(losses)\n    plt.title('Log Likelihood')\n    plt.xlabel('Iteration')\n    plt.ylabel('Log Likelihood')\n    ax = plt.gca()\n    ax.ticklabel_format(useOffset=False)\n\n    if save_file is not None:\n        plt.savefig(save_file, bbox_inches='tight')\n        plt.close()\n    else:\n        plt.show()\n</code></pre>"},{"location":"api/utils/Metrics.html","title":"Metrics","text":""},{"location":"api/utils/Metrics.html#sgptools.utils.metrics","title":"<code>sgptools.utils.metrics</code>","text":""},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.gaussian_entropy","title":"<code>gaussian_entropy(K)</code>","text":"<p>Computes GP-based entropy from a kernel matrix</p> <p>Parameters:</p> Name Type Description Default <code>K</code> <code>ndarray</code> <p>(n, n); kernel matrix</p> required <p>Returns:</p> Name Type Description <code>entropy</code> <code>float</code> <p>Entropy computed from the kernel matrix</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def gaussian_entropy(K):\n    \"\"\"Computes GP-based entropy from a kernel matrix\n\n    Args:\n        K (ndarray): (n, n); kernel matrix\n\n    Returns:\n        entropy (float): Entropy computed from the kernel matrix\n    \"\"\"\n    return multivariate_normal(mean=None, cov=K, allow_singular=True).entropy()\n</code></pre>"},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.get_distance","title":"<code>get_distance(X)</code>","text":"<p>Compute the length of a path (L2-norm)</p> <p>Parameters:</p> Name Type Description Default <code>X</code> <code>ndarray</code> <p>(m, d); Waypoints of a path</p> required <p>Returns:</p> Name Type Description <code>dist</code> <code>float</code> <p>Total path length</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def get_distance(X):\n    \"\"\"Compute the length of a path (L2-norm)\n\n    Args:\n        X (ndarray): (m, d); Waypoints of a path\n\n    Returns:\n        dist (float): Total path length\n    \"\"\"\n    dist = np.linalg.norm(X[1:] - X[:-1], axis=-1)\n    dist = np.sum(dist)\n    return dist\n</code></pre>"},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.get_elbo","title":"<code>get_elbo(Xu, X_env, noise_variance, kernel, baseline=False)</code>","text":"<p>Computes the ELBO of the SGP, corrected to be positive</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(m, d); Sensing locations</p> required <code>X_env</code> <code>ndarray</code> <p>(n, d); Data points used to approximate the bounds of the environment</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <code>baseline</code> <code>bool</code> <p>If True, the ELBO is adjusted to be positive</p> <code>False</code> <p>Returns:</p> Name Type Description <code>elbo</code> <code>float</code> <p>ELBO of the SGP</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def get_elbo(Xu, X_env, noise_variance, kernel, baseline=False):\n    \"\"\"Computes the ELBO of the SGP, corrected to be positive\n\n    Args:\n        Xu (ndarray): (m, d); Sensing locations\n        X_env (ndarray): (n, d); Data points used to approximate the bounds of the environment\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n        baseline (bool): If True, the ELBO is adjusted to be positive\n\n    Returns:\n        elbo (float): ELBO of the SGP\n    \"\"\"\n    if baseline:\n        sgpr = gpflow.models.SGPR(X_env,\n                                  noise_variance=noise_variance,\n                                  kernel=kernel,\n                                  inducing_variable=[[0, 0]])\n        baseline = sgpr.elbo().numpy()\n    else:\n        baseline = 0.0\n\n    sgpr = gpflow.models.SGPR(X_env,\n                              noise_variance=noise_variance,\n                              kernel=kernel, \n                              inducing_variable=Xu)\n    return (sgpr.elbo() - baseline).numpy()\n</code></pre>"},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.get_kl","title":"<code>get_kl(Xu, X_env, noise_variance, kernel)</code>","text":"<p>Computes the KL divergence between the SGP and the GP</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(m, d); Sensing locations</p> required <code>X_env</code> <code>ndarray</code> <p>(n, d); Data points used to approximate the bounds of the environment</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <p>Returns:</p> Name Type Description <code>kl</code> <code>float</code> <p>KL divergence between the SGP and the GP</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def get_kl(Xu, X_env, noise_variance, kernel):\n    \"\"\"Computes the KL divergence between the SGP and the GP\n\n    Args:\n        Xu (ndarray): (m, d); Sensing locations\n        X_env (ndarray): (n, d); Data points used to approximate the bounds of the environment\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n\n    Returns:\n        kl (float): KL divergence between the SGP and the GP\n    \"\"\"\n    sgpr = gpflow.models.SGPR(X_env,\n                              noise_variance=noise_variance,\n                              kernel=kernel,\n                              inducing_variable=Xu)\n\n    common = sgpr._common_calculation()\n    sigma_sq = common.sigma_sq\n    AAT = common.AAT\n\n    x, _ = sgpr.data\n    kdiag = sgpr.kernel(x, full_cov=False)\n\n    # tr(K) / \u03c3\u00b2\n    trace_k = tf.reduce_sum(kdiag / sigma_sq)\n    # tr(Q) / \u03c3\u00b2\n    trace_q = tf.reduce_sum(tf.linalg.diag_part(AAT))\n    # tr(K - Q) / \u03c3\u00b2\n    trace = trace_k - trace_q\n    trace = 0.5 * trace\n\n    return float(trace.numpy())\n</code></pre>"},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.get_mi","title":"<code>get_mi(Xu, candidate_locs, noise_variance, kernel)</code>","text":"<p>Computes mutual information between the sensing locations and the candidate locations</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(m, d); Sensing locations</p> required <code>candidate_locs</code> <code>ndarray</code> <p>(n, d); Candidate sensing locations </p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <p>Returns:</p> Name Type Description <code>mi</code> <code>float</code> <p>Mutual information computed using a GP</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def get_mi(Xu, candidate_locs, noise_variance, kernel):\n    \"\"\"Computes mutual information between the sensing locations and the candidate locations\n\n    Args:\n        Xu (ndarray): (m, d); Sensing locations\n        candidate_locs (ndarray): (n, d); Candidate sensing locations \n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n\n    Returns:\n        mi (float): Mutual information computed using a GP\n    \"\"\"\n    Xu = np.array(Xu)\n    candidate_locs = np.array(candidate_locs)\n\n    gp = gpflow.models.GPR(data=(Xu, np.zeros((len(Xu), 1))),\n                           kernel=kernel,\n                           noise_variance=noise_variance)\n    _, sigma_a = gp.predict_f(candidate_locs, full_cov=True)\n    sigma_a = sigma_a.numpy()[0]\n    cond_entropy = gaussian_entropy(sigma_a)\n\n    K = kernel(candidate_locs, full_cov=True).numpy()\n    K += noise_variance * np.eye(len(candidate_locs))\n    entropy = gaussian_entropy(K)\n\n    return float(entropy - cond_entropy)\n</code></pre>"},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.get_reconstruction","title":"<code>get_reconstruction(sensor_data, X_test, noise_variance, kernel)</code>","text":"<p>Computes the GP-based data field estimates with the solution placements as the training set</p> <p>Parameters:</p> Name Type Description Default <code>sensor_data</code> <code>ndarray tuple</code> <p>((m, d), (m, 1)); Sensing locations' input                   and corresponding ground truth labels</p> required <code>X_test</code> <code>ndarray</code> <p>(n, d); Testing data input locations</p> required <code>noise_variance</code> <code>float</code> <p>data variance</p> required <code>kernel</code> <code>Kernel</code> <p>gpflow kernel function</p> required <p>Returns:</p> Name Type Description <code>y_pred</code> <code>ndarray</code> <p>(n, 1); Predicted data field estimates</p> <code>y_var</code> <code>ndarray</code> <p>(n, 1); Prediction variance at each location in the data field</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def get_reconstruction(sensor_data, X_test, noise_variance, kernel):\n    \"\"\"Computes the GP-based data field estimates with the solution placements as the training set\n\n    Args:\n        sensor_data (ndarray tuple): ((m, d), (m, 1)); Sensing locations' input \n                             and corresponding ground truth labels\n        X_test (ndarray): (n, d); Testing data input locations\n        noise_variance (float): data variance\n        kernel (gpflow.kernels.Kernel): gpflow kernel function\n\n    Returns:\n        y_pred (ndarray): (n, 1); Predicted data field estimates\n        y_var (ndarray): (n, 1); Prediction variance at each location in the data field\n    \"\"\"\n    Xu_X, Xu_y = sensor_data\n\n    # Get the GP predictions\n    gpr = gpflow.models.GPR((Xu_X, Xu_y),\n                            noise_variance=noise_variance,\n                            kernel=kernel)\n    y_pred, y_var = gpr.predict_f(X_test)\n    y_pred = y_pred.numpy().reshape(-1, 1)\n\n    return y_pred, y_var\n</code></pre>"},{"location":"api/utils/Metrics.html#sgptools.utils.metrics.get_rmse","title":"<code>get_rmse(y_pred, y_test)</code>","text":"<p>Computes the root-mean-square error between <code>y_pred</code> and <code>y_test</code></p> <p>Parameters:</p> Name Type Description Default <code>y_pred</code> <code>ndarray</code> <p>(n, 1); Predicted data field estimate</p> required <code>y_test</code> <code>ndarray</code> <p>(n, 1); Ground truth data field </p> required <p>Returns:</p> Name Type Description <code>rmse</code> <code>float</code> <p>Computed RMSE</p> Source code in <code>sgptools/utils/metrics.py</code> <pre><code>def get_rmse(y_pred, y_test):\n    \"\"\"Computes the root-mean-square error between `y_pred` and `y_test`\n\n    Args:\n        y_pred (ndarray): (n, 1); Predicted data field estimate\n        y_test (ndarray): (n, 1); Ground truth data field \n\n    Returns:\n        rmse (float): Computed RMSE\n    \"\"\"\n    return np.sqrt(np.mean(np.square(y_pred - y_test)))\n</code></pre>"},{"location":"api/utils/Misc.html","title":"Misc","text":""},{"location":"api/utils/Misc.html#sgptools.utils.misc","title":"<code>sgptools.utils.misc</code>","text":""},{"location":"api/utils/Misc.html#sgptools.utils.misc.cont2disc","title":"<code>cont2disc(Xu, candidates, candidate_labels=None)</code>","text":"<p>Map continuous space locations to a discrete set of candidate location</p> <p>Parameters:</p> Name Type Description Default <code>Xu</code> <code>ndarray</code> <p>(m, 2); Continuous space points</p> required <code>candidates</code> <code>ndarray</code> <p>(n, 2); Discrete set of candidate locations</p> required <code>candidate_labels</code> <code>ndarray</code> <p>(n, 1); Labels corresponding to the discrete set of candidate locations</p> <code>None</code> <p>Returns:</p> Name Type Description <code>Xu_x</code> <code>ndarray</code> <p>Discrete space points' locations </p> <code>Xu_y</code> <code>ndarray</code> <p>Labels of the discrete space points. Returned only if <code>candidate_labels</code>             was passed to the function</p> Source code in <code>sgptools/utils/misc.py</code> <pre><code>def cont2disc(Xu, candidates, candidate_labels=None):\n    \"\"\"Map continuous space locations to a discrete set of candidate location\n\n    Args:\n        Xu (ndarray): (m, 2); Continuous space points\n        candidates (ndarray): (n, 2); Discrete set of candidate locations\n        candidate_labels (ndarray): (n, 1); Labels corresponding to the discrete set of candidate locations\n\n    Returns:\n        Xu_x (ndarray): Discrete space points' locations \n        Xu_y (ndarray): Labels of the discrete space points. Returned only if `candidate_labels`\n                        was passed to the function\n\n    \"\"\"\n    # Sanity check to ensure that there are sensing locations and candidates to match\n    if len(candidates)==0 or len(Xu)==0:\n        if candidate_labels is not None:\n            return [], []\n        else:\n            return []\n\n    dists = pairwise_distances(candidates, Y=Xu, metric='euclidean')\n    row_ind, _ = linear_sum_assignment(dists)\n    Xu_X = candidates[row_ind].copy()\n    if candidate_labels is not None:\n        Xu_y = candidate_labels[row_ind].copy()\n        return Xu_X, Xu_y\n    else:\n        return Xu_X\n</code></pre>"},{"location":"api/utils/Misc.html#sgptools.utils.misc.get_inducing_pts","title":"<code>get_inducing_pts(data, num_inducing, orientation=False, random=False)</code>","text":"<p>Selects a subset of the data points to be used as inducing points.  The default approach uses kmeans to select the subset. </p> <p>Parameters:</p> Name Type Description Default <code>data</code> <code>ndarray</code> <p>(n, 2); Data points to select the inducing points from </p> required <code>num_inducing</code> <code>int</code> <p>Number of inducing points</p> required <code>orientation</code> <code>bool</code> <p>If True, add an additional dimension to model the sensor                  FoV rotation angle</p> <code>False</code> <code>random</code> <code>bool</code> <p>If True, the subset of inducing points are selected randomly             instead of using kmeans</p> <code>False</code> <p>Returns:</p> Name Type Description <code>Xu</code> <code>ndarray</code> <p>(m, d); Inducing points in the position and orientation space.             <code>m</code> is the number of inducing points,              <code>d</code> is the dimension of the space (x, y, optional - angle in radians)</p> Source code in <code>sgptools/utils/misc.py</code> <pre><code>def get_inducing_pts(data, num_inducing, orientation=False, random=False):\n    \"\"\"Selects a subset of the data points to be used as inducing points. \n    The default approach uses kmeans to select the subset. \n\n    Args:\n        data (ndarray): (n, 2); Data points to select the inducing points from \n        num_inducing (int): Number of inducing points\n        orientation (bool): If True, add an additional dimension to model the sensor \n                            FoV rotation angle\n        random (bool): If True, the subset of inducing points are selected randomly \n                       instead of using kmeans\n\n    Returns:\n        Xu (ndarray): (m, d); Inducing points in the position and orientation space.\n                        `m` is the number of inducing points, \n                        `d` is the dimension of the space (x, y, optional - angle in radians)\n    \"\"\"\n    if random:\n        idx = np.random.randint(len(data), size=num_inducing)\n        Xu = data[idx]\n    else:\n        Xu = kmeans2(data, num_inducing, minit=\"points\")[0]\n    if orientation:\n        thetas = np.random.uniform(0, 2 * np.pi, size=(Xu.shape[0], 1))\n        Xu = np.concatenate([Xu, thetas], axis=1)\n    return Xu\n</code></pre>"},{"location":"api/utils/Misc.html#sgptools.utils.misc.interpolate_path","title":"<code>interpolate_path(waypoints, sampling_rate=0.05)</code>","text":"<p>Interpolate additional points between the given waypoints to simulate continuous sensing robots</p> <p>Parameters:</p> Name Type Description Default <code>waypoints</code> <code>(n, d)</code> <p>Waypoints of the robot's path</p> required <code>sampling_rate</code> <code>float</code> <p>Distance between each pair of interpolated points</p> <code>0.05</code> <p>Returns:</p> Name Type Description <code>path</code> <code>ndarray</code> <p>(p, d) Interpolated path, <code>p</code> depends on the sampling_rate rate</p> Source code in <code>sgptools/utils/misc.py</code> <pre><code>def interpolate_path(waypoints, sampling_rate=0.05):\n    \"\"\"Interpolate additional points between the given waypoints to simulate continuous sensing robots\n\n    Args:\n        waypoints (n, d): Waypoints of the robot's path\n        sampling_rate (float): Distance between each pair of interpolated points\n\n    Returns:\n        path (ndarray): (p, d) Interpolated path, `p` depends on the sampling_rate rate\n    \"\"\"\n    interpolated_path = []\n    for i in range(2, len(waypoints)+1):\n        dist = get_distance(waypoints[i-2:i])\n        num_samples = int(dist / sampling_rate)\n        points = np.linspace(waypoints[i-1], waypoints[i-2], num_samples)\n        interpolated_path.extend(points)\n    return np.array(interpolated_path)\n</code></pre>"},{"location":"api/utils/Misc.html#sgptools.utils.misc.plot_paths","title":"<code>plot_paths(paths, candidates=None, title=None)</code>","text":"<p>Function to plot the IPP solution paths</p> <p>Parameters:</p> Name Type Description Default <code>paths</code> <code>ndarray</code> <p>(r, m, 2); <code>r</code> paths with <code>m</code> waypoints each</p> required <code>candidates</code> <code>ndarray</code> <p>(n, 2); Candidate unlabeled locations used in the SGP-based sensor placement approach</p> <code>None</code> <code>title</code> <code>str</code> <p>Title of the plot</p> <code>None</code> Source code in <code>sgptools/utils/misc.py</code> <pre><code>def plot_paths(paths, candidates=None, title=None):\n    \"\"\"Function to plot the IPP solution paths\n\n    Args:\n        paths (ndarray): (r, m, 2); `r` paths with `m` waypoints each\n        candidates (ndarray): (n, 2); Candidate unlabeled locations used in the SGP-based sensor placement approach\n        title (str): Title of the plot\n    \"\"\"\n    plt.figure()\n    for i, path in enumerate(paths):\n        plt.plot(path[:, 0], path[:, 1], \n                    c='r', label='Path', zorder=1, marker='o')\n        plt.scatter(path[0, 0], path[0, 1], \n                    c='g', label='Start', zorder=2, marker='o')\n        if candidates is not None:\n            plt.scatter(candidates[:, 0], candidates[:, 1], \n                        c='k', s=1, label='Unlabeled Train-Set Points', zorder=0)\n        if i==0:\n            plt.legend(bbox_to_anchor=(1.0, 1.02))\n    if title is not None:\n        plt.title(title)\n    plt.gca().set_aspect('equal')\n    plt.xlabel('X')\n    plt.ylabel('Y')\n</code></pre>"},{"location":"api/utils/Misc.html#sgptools.utils.misc.ploygon2candidats","title":"<code>ploygon2candidats(vertices, num_samples=5000, random_seed=2024)</code>","text":"<p>Sample unlabeled candidates within a polygon</p> <p>Parameters:</p> Name Type Description Default <code>vertices</code> <code>ndarray</code> <p>(v, 2) of vertices that define the polygon</p> required <code>num_samples</code> <code>int</code> <p>Number of samples to generate</p> <code>5000</code> <code>random_seed</code> <code>int</code> <p>Random seed for reproducibility</p> <code>2024</code> <p>Returns:</p> Name Type Description <code>candidates</code> <code>ndarray</code> <p>(n, 2); Candidate sensor placement locations</p> Source code in <code>sgptools/utils/misc.py</code> <pre><code>def ploygon2candidats(vertices, \n                      num_samples=5000, \n                      random_seed=2024):\n    \"\"\"Sample unlabeled candidates within a polygon\n\n    Args:\n        vertices (ndarray): (v, 2) of vertices that define the polygon\n        num_samples (int): Number of samples to generate\n        random_seed (int): Random seed for reproducibility\n\n    Returns:\n       candidates (ndarray): (n, 2); Candidate sensor placement locations\n    \"\"\"\n    poly = geometry.Polygon(vertices)\n    sampler = gpd.GeoSeries([poly])\n    candidates = sampler.sample_points(size=num_samples,\n                                       rng=random_seed)\n    candidates = candidates.get_coordinates().to_numpy()\n    return candidates\n</code></pre>"},{"location":"api/utils/Misc.html#sgptools.utils.misc.project_waypoints","title":"<code>project_waypoints(waypoints, candidates)</code>","text":"<p>Project the waypoints back to the candidate set while retaining the  waypoint visitation order.</p> <p>Parameters:</p> Name Type Description Default <code>waypoints</code> <code>(n, d)</code> <p>Waypoints of the robot's path</p> required <code>candidates</code> <code>ndarray</code> <p>(n, 2); Discrete set of candidate locations</p> required <p>Returns:</p> Name Type Description <code>waypoints</code> <code>(n, d)</code> <p>Projected waypoints of the robot's path</p> Source code in <code>sgptools/utils/misc.py</code> <pre><code>def project_waypoints(waypoints, candidates):\n    \"\"\"Project the waypoints back to the candidate set while retaining the \n    waypoint visitation order.\n\n    Args:\n        waypoints (n, d): Waypoints of the robot's path\n        candidates (ndarray): (n, 2); Discrete set of candidate locations\n\n    Returns:\n        waypoints (n, d): Projected waypoints of the robot's path\n    \"\"\"\n    waypoints_disc = cont2disc(waypoints, candidates)\n    waypoints_valid = _reoder_path(waypoints, waypoints_disc)\n    return waypoints_valid\n</code></pre>"},{"location":"api/utils/TSP.html","title":"TSP","text":""},{"location":"api/utils/TSP.html#sgptools.utils.tsp","title":"<code>sgptools.utils.tsp</code>","text":""},{"location":"api/utils/TSP.html#sgptools.utils.tsp.resample_path","title":"<code>resample_path(waypoints, num_inducing=10)</code>","text":"<p>Function to map path with arbitrary number of waypoints to  inducing points path with fixed number of waypoints</p> <p>Parameters:</p> Name Type Description Default <code>waypoints</code> <code>ndarray</code> <p>(num_waypoints, ndim); waypoints of path from vrp solver</p> required <code>num_inducing</code> <code>int</code> <p>Number of inducing points (waypoints) in the returned path</p> <code>10</code> <p>Returns:</p> Name Type Description <code>points</code> <code>ndarray</code> <p>(num_inducing, ndim); Resampled path</p> Source code in <code>sgptools/utils/tsp.py</code> <pre><code>def resample_path(waypoints, num_inducing=10):\n    \"\"\"Function to map path with arbitrary number of waypoints to \n    inducing points path with fixed number of waypoints\n\n    Args:\n        waypoints (ndarray): (num_waypoints, ndim); waypoints of path from vrp solver\n        num_inducing (int): Number of inducing points (waypoints) in the returned path\n\n    Returns:\n        points (ndarray): (num_inducing, ndim); Resampled path\n    \"\"\"\n    ndim = np.shape(waypoints)[-1]\n    if not (ndim==2 or ndim==3):\n        raise Exception(f\"ndim={ndim} is not supported for path resampling!\")\n    line = LineString(waypoints)\n    distances = np.linspace(0, line.length, num_inducing)\n    points = [line.interpolate(distance) for distance in distances]\n    if ndim==2:\n        points = np.array([[p.x, p.y] for p in points])\n    elif ndim==3:\n        points = np.array([[p.x, p.y, p.z] for p in points])\n    return points\n</code></pre>"},{"location":"api/utils/TSP.html#sgptools.utils.tsp.run_tsp","title":"<code>run_tsp(nodes, num_vehicles=1, max_dist=25, depth=1, resample=None, start_nodes=None, end_nodes=None, time_limit=10)</code>","text":"<p>Method to run TSP/VRP with arbitrary start and end nodes,  and without any distance constraint</p> <p>Parameters:</p> Name Type Description Default <code>nodes</code> <code>ndarray</code> <p>(# nodes, ndim); Nodes to visit </p> required <code>num_vehicles</code> <code>int</code> <p>Number of robots/vehicles</p> <code>1</code> <code>max_dist</code> <code>float</code> <p>Maximum distance allowed for each path when handling mutli-robot case</p> <code>25</code> <code>depth</code> <code>int</code> <p>Internal parameter used to track re-try recursion depth</p> <code>1</code> <code>resample</code> <code>int</code> <p>Each solution path will be resampled to have             <code>resample</code> number of points</p> <code>None</code> <code>start_nodes</code> <code>ndarray</code> <p>(# num_vehicles, ndim); Optionl array of start nodes from which                      to start each vehicle's solution path</p> <code>None</code> <code>end_nodes</code> <code>ndarray</code> <p>(# num_vehicles, ndim); Optionl array of end nodes at which                      to end each vehicle's solution path</p> <code>None</code> <code>time_limit</code> <code>int</code> <p>TSP runtime time limit in seconds</p> <code>10</code> <p>Returns:</p> Name Type Description <code>paths</code> <code>ndarray</code> <p>Solution paths</p> <code>distances</code> <code>list</code> <p>List of path lengths</p> Source code in <code>sgptools/utils/tsp.py</code> <pre><code>def run_tsp(nodes, \n            num_vehicles=1, \n            max_dist=25, \n            depth=1, \n            resample=None, \n            start_nodes=None,\n            end_nodes=None,\n            time_limit=10):\n    \"\"\"Method to run TSP/VRP with arbitrary start and end nodes, \n    and without any distance constraint\n\n    Args:\n        nodes (ndarray): (# nodes, ndim); Nodes to visit \n        num_vehicles (int): Number of robots/vehicles\n        max_dist (float): Maximum distance allowed for each path when handling mutli-robot case\n        depth (int): Internal parameter used to track re-try recursion depth\n        resample (int): Each solution path will be resampled to have\n                        `resample` number of points\n        start_nodes (ndarray): (# num_vehicles, ndim); Optionl array of start nodes from which \n                                to start each vehicle's solution path\n        end_nodes (ndarray): (# num_vehicles, ndim); Optionl array of end nodes at which \n                                to end each vehicle's solution path\n        time_limit (int): TSP runtime time limit in seconds\n\n    Returns:\n        paths (ndarray): Solution paths\n        distances (list): List of path lengths\n    \"\"\"\n    if depth &gt; 5:\n        print('Warning: Max depth reached')\n        return None, None\n\n    # Add the start and end nodes to the node list\n    if end_nodes is not None:\n        assert end_nodes.shape == (num_vehicles, nodes.shape[-1]), \\\n            \"Incorrect end_nodes shape, should be (num_vehicles, ndim)!\"\n        nodes = np.concatenate([end_nodes, nodes])\n    if start_nodes is not None:\n        assert start_nodes.shape == (num_vehicles, nodes.shape[-1]), \\\n            \"Incorrect start_nodes shape, should be (num_vehicles, ndim)!\"\n        nodes = np.concatenate([start_nodes, nodes])\n\n    # Add dummy 0 location to get arbitrary start and end node sols\n    if start_nodes is None or end_nodes is None:\n        distance_mat = np.zeros((len(nodes)+1, len(nodes)+1))\n        distance_mat[1:, 1:] = pairwise_distances(nodes, nodes)*1e4\n        trim_paths = True #shift to account for dummy node\n    else:\n        distance_mat = pairwise_distances(nodes, nodes)*1e4\n        trim_paths = False\n    distance_mat = distance_mat.astype(int)\n    max_dist = int(max_dist*1e4)\n\n    # Get start and end node indices for ortools\n    if start_nodes is None:\n        start_idx = np.zeros(num_vehicles, dtype=int)\n        num_start_nodes = 0\n    else:\n        start_idx = np.arange(num_vehicles)+int(trim_paths)\n        num_start_nodes = len(start_nodes)\n\n    if end_nodes is None:\n        end_idx = np.zeros(num_vehicles, dtype=int)\n    else:\n        end_idx = np.arange(num_vehicles)+num_start_nodes+int(trim_paths)\n\n    # used by ortools\n    def distance_callback(from_index, to_index):\n        from_node = manager.IndexToNode(from_index)\n        to_node = manager.IndexToNode(to_index)\n        return distance_mat[from_node][to_node]\n\n    # num_locations, num vehicles, start, end\n    manager = pywrapcp.RoutingIndexManager(len(distance_mat), \n                                           num_vehicles, \n                                           start_idx.tolist(),\n                                           end_idx.tolist())\n    routing = pywrapcp.RoutingModel(manager)\n    transit_callback_index = routing.RegisterTransitCallback(distance_callback)\n    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n\n    if num_vehicles &gt; 1:\n        # Dummy distaance constraint to ensure all paths have similar length\n        dimension_name = \"Distance\"\n        routing.AddDimension(\n            transit_callback_index,\n            0,  # no slack\n            max_dist,  # vehicle maximum travel distance\n            True,  # start cumul to zero\n            dimension_name,\n        )\n        distance_dimension = routing.GetDimensionOrDie(dimension_name)\n        distance_dimension.SetGlobalSpanCostCoefficient(100)\n\n    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n    search_parameters.first_solution_strategy = (\n        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n    )\n    search_parameters.local_search_metaheuristic = (\n        routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH\n    )\n    search_parameters.time_limit.seconds = time_limit\n    solution = routing.SolveWithParameters(search_parameters)\n\n    paths = None\n    if solution is not None:\n        paths, distances = get_routes(manager, routing, \n                                      solution, num_vehicles, \n                                      start_idx, end_idx, trim_paths)\n        for path in paths:\n            if len(path) &lt; 2:\n                print('TSP Warning: Empty path detected')\n                return run_tsp(nodes, num_vehicles, int(np.mean(distances)*(1.5/depth)), depth+1)\n    else:\n        print('TSP Warning: No solution found')\n        return run_tsp(nodes, num_vehicles, int(max_dist*1.5), depth+1)\n\n    # Map paths from node indices to node locations\n    paths = [nodes[path] for path in paths]\n\n    # Resample each solution path to have resample number of points\n    if resample is not None:\n        paths = np.array([resample_path(path, resample) for path in paths])\n\n    # Convert distances back to floats in the original scale of the nodes\n    distances = np.array(distances)/1e4\n    return paths, distances\n</code></pre>"},{"location":"examples/AIPP_non_point_FoV.html","title":"Adaptive-IPP with Non-Point FoV","text":"In\u00a0[\u00a0]: Copied! <pre>import os\nos.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"\n\nimport numpy as np\n\nfrom sgptools.utils.metrics import *\nfrom sgptools.utils.data import *\nfrom sgptools.utils.misc import *\nfrom sgptools.models.core.osgpr import *\nfrom sgptools.utils.tsp import run_tsp\nfrom sgptools.utils.gpflow import get_model_params\nfrom sgptools.models.continuous_sgp import *\nfrom sgptools.models.core.transformations import *\nfrom gpflow.utilities.traversal import print_summary\n\nfrom matplotlib import colors\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Polygon\nimport mpl_toolkits.mplot3d.art3d as art3d\nfrom scipy.optimize import linear_sum_assignment\nfrom sklearn.metrics import pairwise_distances\n\nfrom time import time\nfrom copy import deepcopy\n\nnp.random.seed(1234)\ntf.random.set_seed(1234)\n</pre> import os os.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"  import numpy as np  from sgptools.utils.metrics import * from sgptools.utils.data import * from sgptools.utils.misc import * from sgptools.models.core.osgpr import * from sgptools.utils.tsp import run_tsp from sgptools.utils.gpflow import get_model_params from sgptools.models.continuous_sgp import * from sgptools.models.core.transformations import * from gpflow.utilities.traversal import print_summary  from matplotlib import colors import matplotlib.pyplot as plt from matplotlib.patches import Polygon import mpl_toolkits.mplot3d.art3d as art3d from scipy.optimize import linear_sum_assignment from sklearn.metrics import pairwise_distances  from time import time from copy import deepcopy  np.random.seed(1234) tf.random.set_seed(1234) In\u00a0[2]: Copied! <pre>'''\nUtility to map the inducing points to FoV vertices\n'''\ndef get_vertices(Xu, X_fov):\n    X_fov = X_fov.reshape(len(Xu), -1, 2)\n    vertices = np.zeros((len(Xu), 5, 2))\n    vertices[:, 0] = X_fov.min(axis=1)\n    vertices[:, 1] = np.array([X_fov[:, :, 0].min(axis=1), \n                              X_fov[:, :, 1].max(axis=1)]).T\n    vertices[:, 2] = X_fov.max(axis=1)\n    vertices[:, 3] = np.array([X_fov[:, :, 0].max(axis=1), \n                              X_fov[:, :, 1].min(axis=1)]).T\n    vertices[:, 4] = X_fov.mean(axis=1)\n\n    dists = pairwise_distances(vertices[:, 4], Y=Xu[:, :2], \n                               metric='euclidean')\n    _, idx = linear_sum_assignment(dists)\n\n    vertices[:, 4] = Xu[idx][:, :2]\n    return vertices\n\n'''\nMethod to plot the solution with FoVs and environment reconstruction\n'''\ndef plot_results(X_inducing, X_fov, current_idx=0, fname=None):\n    # Setup 3D plot\n    fig = plt.figure(figsize=(5, 5))\n    ax = fig.add_subplot(111, projection='3d', computed_zorder=False)\n\n    current_idx += 1 # Increment by one for plotting\n\n    # Plot the reconstructed environment state\n    num_fov_pts = len(X_fov)//len(X_inducing)\n    sensor_data = cont2disc(X_fov[:num_fov_pts*(current_idx)], X, y)\n    y_pred, _ = get_reconstruction(sensor_data, X_test, \n                                   noise_variance_opt, kernel_opt)\n    norm = colors.Normalize(y_train.min(), y_train.max())\n    _ = ax.plot_surface(X_test[:, 0].reshape(test_dim),\n                        X_test[:, 1].reshape(test_dim),\n                        np.atleast_2d(-0.1),\n                        facecolors=plt.cm.jet(norm(y_pred.reshape(test_dim))),\n                        shade=False,\n                        alpha=0.8,\n                        zorder=0)\n\n    # Plot the solution path\n    ax.scatter(X_inducing[:current_idx, 0], \n               X_inducing[:current_idx, 1], \n               X_inducing[:current_idx, 2], c='C3')\n    ax.scatter(X_inducing[current_idx:, 0], \n               X_inducing[current_idx:, 1], \n               X_inducing[current_idx:, 2], c='C2')\n    ax.plot(X_inducing[:, 0], X_inducing[:, 1], X_inducing[:, 2], 'k-')\n\n    # Plot the FoV vertices\n    vertices = get_vertices(X_inducing, X_fov)\n    for i in range(vertices.shape[0]):\n        color = 'C3' if i &lt; current_idx else 'C2'\n        verts = []\n        verts.append([vertices[i, 0], vertices[i, 1], \n                      vertices[i, 2], vertices[i, 3]])\n        fov = Polygon(np.array(verts)[0, :, :2],\n                      linewidth=1.5, \n                      edgecolor=color,\n                      facecolor=color,\n                      fill=False,\n                      zorder=15)\n        ax.add_patch(fov)\n        art3d.pathpatch_2d_to_3d(fov)\n\n    # Configure other plot settings\n    ax.set_xlabel('X')\n    ax.set_ylabel('Y')\n    ax.set_zlabel('Z')\n\n    ax.set_xlim(np.min(X[:, 0])-0.5, np.max(X[:, 0])+0.5)\n    ax.set_ylim(np.min(X[:, 1])-0.5, np.max(X[:, 1])+0.5)\n    ax.set_zlim(0, 14)\n\n    ax.view_init(elev=30, azim=45+180)\n    ax.set_title(f'Waypoint: {current_idx}, RMSE: {get_rmse(y_pred, y_test):.2f}\\nData Field Reconstruction', y=0.99)\n\n    plt.tight_layout()\n    if fname is not None:\n        plt.savefig(fname, bbox_inches='tight')\n    plt.show()\n</pre> ''' Utility to map the inducing points to FoV vertices ''' def get_vertices(Xu, X_fov):     X_fov = X_fov.reshape(len(Xu), -1, 2)     vertices = np.zeros((len(Xu), 5, 2))     vertices[:, 0] = X_fov.min(axis=1)     vertices[:, 1] = np.array([X_fov[:, :, 0].min(axis=1),                                X_fov[:, :, 1].max(axis=1)]).T     vertices[:, 2] = X_fov.max(axis=1)     vertices[:, 3] = np.array([X_fov[:, :, 0].max(axis=1),                                X_fov[:, :, 1].min(axis=1)]).T     vertices[:, 4] = X_fov.mean(axis=1)      dists = pairwise_distances(vertices[:, 4], Y=Xu[:, :2],                                 metric='euclidean')     _, idx = linear_sum_assignment(dists)      vertices[:, 4] = Xu[idx][:, :2]     return vertices  ''' Method to plot the solution with FoVs and environment reconstruction ''' def plot_results(X_inducing, X_fov, current_idx=0, fname=None):     # Setup 3D plot     fig = plt.figure(figsize=(5, 5))     ax = fig.add_subplot(111, projection='3d', computed_zorder=False)      current_idx += 1 # Increment by one for plotting      # Plot the reconstructed environment state     num_fov_pts = len(X_fov)//len(X_inducing)     sensor_data = cont2disc(X_fov[:num_fov_pts*(current_idx)], X, y)     y_pred, _ = get_reconstruction(sensor_data, X_test,                                     noise_variance_opt, kernel_opt)     norm = colors.Normalize(y_train.min(), y_train.max())     _ = ax.plot_surface(X_test[:, 0].reshape(test_dim),                         X_test[:, 1].reshape(test_dim),                         np.atleast_2d(-0.1),                         facecolors=plt.cm.jet(norm(y_pred.reshape(test_dim))),                         shade=False,                         alpha=0.8,                         zorder=0)      # Plot the solution path     ax.scatter(X_inducing[:current_idx, 0],                 X_inducing[:current_idx, 1],                 X_inducing[:current_idx, 2], c='C3')     ax.scatter(X_inducing[current_idx:, 0],                 X_inducing[current_idx:, 1],                 X_inducing[current_idx:, 2], c='C2')     ax.plot(X_inducing[:, 0], X_inducing[:, 1], X_inducing[:, 2], 'k-')      # Plot the FoV vertices     vertices = get_vertices(X_inducing, X_fov)     for i in range(vertices.shape[0]):         color = 'C3' if i &lt; current_idx else 'C2'         verts = []         verts.append([vertices[i, 0], vertices[i, 1],                        vertices[i, 2], vertices[i, 3]])         fov = Polygon(np.array(verts)[0, :, :2],                       linewidth=1.5,                        edgecolor=color,                       facecolor=color,                       fill=False,                       zorder=15)         ax.add_patch(fov)         art3d.pathpatch_2d_to_3d(fov)      # Configure other plot settings     ax.set_xlabel('X')     ax.set_ylabel('Y')     ax.set_zlabel('Z')      ax.set_xlim(np.min(X[:, 0])-0.5, np.max(X[:, 0])+0.5)     ax.set_ylim(np.min(X[:, 1])-0.5, np.max(X[:, 1])+0.5)     ax.set_zlim(0, 14)      ax.view_init(elev=30, azim=45+180)     ax.set_title(f'Waypoint: {current_idx}, RMSE: {get_rmse(y_pred, y_test):.2f}\\nData Field Reconstruction', y=0.99)      plt.tight_layout()     if fname is not None:         plt.savefig(fname, bbox_inches='tight')     plt.show() In\u00a0[3]: Copied! <pre>'''\nMethod to run AIPP using the SGP-based approach\n'''\ndef run_aipp(X_train, ipp_model, Xu_init, path2data):\n    total_time_param = 0\n    total_time_ipp = 0\n    num_robots = Xu_init.shape[0]\n    num_waypoints = Xu_init.shape[1]\n    curr_sol = Xu_init\n    plot_results(ipp_model.transform.expand(curr_sol.reshape(-1, 3), \n                                            expand_sensor_model=False).numpy(),\n                 ipp_model.transform.expand(curr_sol.reshape(-1, 3)).numpy())\n\n    # Initialize the hyperparameters and SSGP\n    init_kernel = deepcopy(ipp_model.kernel)\n    init_noise_variance = ipp_model.likelihood.variance\n    param_model = init_osgpr(X_train, \n                             num_inducing=40, \n                             kernel=init_kernel,\n                             noise_variance=init_noise_variance)\n\n    sol_data_X = []\n    sol_data_y = []\n    for time_step in range(num_waypoints):\n        # Get a new batch of data from the last visited waypoint\n        last_visited = curr_sol[:, time_step].copy()\n        # Apply the sensor model expansion with fixed points IPP expansion\n        data_pts = ipp_model.transform.sensor_model.expand(last_visited).numpy()\n        data_X_batch, data_y_batch = path2data(data_pts)\n        sol_data_X.extend(data_X_batch)\n        sol_data_y.extend(data_y_batch)\n\n        # Skip param and path update for the last waypoint\n        if time_step == num_waypoints-1:\n            break\n\n        # Update the SSGP hyperparameter model\n        param_model.update((np.array(data_X_batch), \n                            np.array(data_y_batch)))\n        # Train only the kernel parameters and noise variance.\n        # Keeping the SSGP inducing points fixed results in better \n        # hyperparameter estimation\n        start_time = time()\n        optimize_model(param_model, \n                       trainable_variables=param_model.trainable_variables[1:], \n                       optimizer='scipy',\n                       method='CG')\n        end_time = time()\n        total_time_param += end_time - start_time\n        print_summary(param_model.kernel)\n\n        # Update the SGP-IPP model\n        Xu_visited = curr_sol.copy()[:, :time_step+1]\n        ipp_model.transform.update_Xu_fixed(Xu_visited)\n        ipp_model.update(param_model.likelihood.variance, \n                         param_model.kernel)\n        start_time = time()\n        _ = optimize_model(ipp_model,\n                           kernel_grad=False, \n                           optimizer='scipy',\n                           method='CG')\n        end_time = time()\n        total_time_ipp += end_time - start_time\n        curr_sol = ipp_model.inducing_variable.Z\n        curr_sol = ipp_model.transform.expand(curr_sol, \n                                              expand_sensor_model=False).numpy()\n        curr_sol = curr_sol.reshape(num_robots, num_waypoints, 3)\n\n        plot_results(ipp_model.transform.expand(curr_sol.reshape(-1, 3),\n                                                expand_sensor_model=False).numpy(),\n                     ipp_model.transform.expand(curr_sol.reshape(-1, 3)).numpy(),\n                     time_step+1)\n\n    return np.array(sol_data_X), np.array(sol_data_y), total_time_param, total_time_ipp\n</pre> ''' Method to run AIPP using the SGP-based approach ''' def run_aipp(X_train, ipp_model, Xu_init, path2data):     total_time_param = 0     total_time_ipp = 0     num_robots = Xu_init.shape[0]     num_waypoints = Xu_init.shape[1]     curr_sol = Xu_init     plot_results(ipp_model.transform.expand(curr_sol.reshape(-1, 3),                                              expand_sensor_model=False).numpy(),                  ipp_model.transform.expand(curr_sol.reshape(-1, 3)).numpy())      # Initialize the hyperparameters and SSGP     init_kernel = deepcopy(ipp_model.kernel)     init_noise_variance = ipp_model.likelihood.variance     param_model = init_osgpr(X_train,                               num_inducing=40,                               kernel=init_kernel,                              noise_variance=init_noise_variance)      sol_data_X = []     sol_data_y = []     for time_step in range(num_waypoints):         # Get a new batch of data from the last visited waypoint         last_visited = curr_sol[:, time_step].copy()         # Apply the sensor model expansion with fixed points IPP expansion         data_pts = ipp_model.transform.sensor_model.expand(last_visited).numpy()         data_X_batch, data_y_batch = path2data(data_pts)         sol_data_X.extend(data_X_batch)         sol_data_y.extend(data_y_batch)          # Skip param and path update for the last waypoint         if time_step == num_waypoints-1:             break          # Update the SSGP hyperparameter model         param_model.update((np.array(data_X_batch),                              np.array(data_y_batch)))         # Train only the kernel parameters and noise variance.         # Keeping the SSGP inducing points fixed results in better          # hyperparameter estimation         start_time = time()         optimize_model(param_model,                         trainable_variables=param_model.trainable_variables[1:],                         optimizer='scipy',                        method='CG')         end_time = time()         total_time_param += end_time - start_time         print_summary(param_model.kernel)          # Update the SGP-IPP model         Xu_visited = curr_sol.copy()[:, :time_step+1]         ipp_model.transform.update_Xu_fixed(Xu_visited)         ipp_model.update(param_model.likelihood.variance,                           param_model.kernel)         start_time = time()         _ = optimize_model(ipp_model,                            kernel_grad=False,                             optimizer='scipy',                            method='CG')         end_time = time()         total_time_ipp += end_time - start_time         curr_sol = ipp_model.inducing_variable.Z         curr_sol = ipp_model.transform.expand(curr_sol,                                                expand_sensor_model=False).numpy()         curr_sol = curr_sol.reshape(num_robots, num_waypoints, 3)          plot_results(ipp_model.transform.expand(curr_sol.reshape(-1, 3),                                                 expand_sensor_model=False).numpy(),                      ipp_model.transform.expand(curr_sol.reshape(-1, 3)).numpy(),                      time_step+1)      return np.array(sol_data_X), np.array(sol_data_y), total_time_param, total_time_ipp In\u00a0[4]: Copied! <pre># Get the synthetic elevation data\nX_train, y_train, X_test, y_test, candidates, X, y = get_dataset(random_seed=0)\ntrain_dim = np.sqrt(X_train.shape[0]).astype(int)\n\n# Train a GP and get the model parameters\nprint('Optimized Hyperparameters')\n_, noise_variance_opt, kernel_opt = get_model_params(X_train, y_train, \n                                                     lengthscales=[1.0, 1.0], \n                                                     optimizer='scipy')\n\n# Build a new test set on a grid for easy plotting in a 3D plot\ngpr = gpflow.models.GPR((X_test, y_test),\n                        noise_variance=noise_variance_opt,\n                        kernel=kernel_opt)\ntest_dim = (100, 100)\nX_test1 = np.linspace(np.min(X[:, 0]), np.max(X[:, 0]), test_dim[0])\nX_test2 = np.linspace(np.min(X[:, 1]), np.max(X[:, 1]), test_dim[1])\nX_test1, X_test2 = np.meshgrid(X_test1, X_test2)\nX_test = np.stack([X_test1.ravel(), X_test2.ravel()], axis=1)\ny_test, y_var = gpr.predict_f(X_test)\ny_test = y_test.numpy()\n\n# Plot the data field\nplt.imshow(y_test.reshape(test_dim), cmap=\"jet\", origin='lower')\nplt.xlabel('X')\nplt.ylabel('Y')\nplt.title('Elevation Data Field')\nplt.colorbar(label='Normalized Elevation')\nplt.show()\n</pre> # Get the synthetic elevation data X_train, y_train, X_test, y_test, candidates, X, y = get_dataset(random_seed=0) train_dim = np.sqrt(X_train.shape[0]).astype(int)  # Train a GP and get the model parameters print('Optimized Hyperparameters') _, noise_variance_opt, kernel_opt = get_model_params(X_train, y_train,                                                       lengthscales=[1.0, 1.0],                                                       optimizer='scipy')  # Build a new test set on a grid for easy plotting in a 3D plot gpr = gpflow.models.GPR((X_test, y_test),                         noise_variance=noise_variance_opt,                         kernel=kernel_opt) test_dim = (100, 100) X_test1 = np.linspace(np.min(X[:, 0]), np.max(X[:, 0]), test_dim[0]) X_test2 = np.linspace(np.min(X[:, 1]), np.max(X[:, 1]), test_dim[1]) X_test1, X_test2 = np.meshgrid(X_test1, X_test2) X_test = np.stack([X_test1.ravel(), X_test2.ravel()], axis=1) y_test, y_var = gpr.predict_f(X_test) y_test = y_test.numpy()  # Plot the data field plt.imshow(y_test.reshape(test_dim), cmap=\"jet\", origin='lower') plt.xlabel('X') plt.ylabel('Y') plt.title('Elevation Data Field') plt.colorbar(label='Normalized Elevation') plt.show() <pre>Optimized Hyperparameters\n\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.8371517535182331  \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [2.45802 2.50701]   \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.04342255274528023 \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> In\u00a0[5]: Copied! <pre>num_robots = 1\nnum_placements = 12\nnum_side = 5  # Use a 5x5 point FoV approximation\n\n# Setup the initial AIPP hyperparameters\nprint('Initial AIPP Hyperparameters')\n_, noise_variance, kernel = get_model_params(X_train, y_train, \n                                             lengthscales=[1.0, 1.0], \n                                             optimizer='scipy',\n                                             max_steps=0)\n\n# Get initial inducing points\nXu_init = get_inducing_pts(X_train, num_placements)\n# Initalize the height dimension at 2.0 for all points\nXu_init = np.concatenate((Xu_init, np.ones((Xu_init.shape[0], 1))*2.0), axis=1)\nXu_init[:, :2] = run_tsp(Xu_init[:, :2])[0][0]\n\n# Initialize the non-point FoV transform\nfov_transform = SquareHeightTransform(num_side=num_side)\ntransform = IPPTransform(num_dim=3,\n                         num_robots=1,\n                         sensor_model=fov_transform,\n                         aggregate_fov=True)\n\n# Initialize the IPP model\nsgpr, _ = continuous_sgp(num_placements,\n                         X_train, \n                         noise_variance,\n                         kernel,\n                         transform=transform,\n                         Xu_init=Xu_init,\n                         max_steps=0)\nsgp_sol_sp = sgpr.inducing_variable.Z.numpy()\n\n# Setup the data acquisition function to emulate the robot's sensor\npath2data = lambda x : cont2disc(x, X, y)\n\n# Generate the SGP-based AIPP solution\n_, _, param_time, ipp_time = run_aipp(X_train, \n                                      sgpr,\n                                      Xu_init.reshape(1, -1, 3), \n                                      path2data)\n\nprint(f'Total Hyperparameter Update Time:   {param_time:.2f}s')\nprint(f'Total IPP Update Time:              {ipp_time:.2f}s')\n\nprint(f'Average Hyperparameter Update Time: {param_time/num_placements:.2f}s')\nprint(f'Average IPP Update Time:            {ipp_time/num_placements:.2f}s')\n</pre> num_robots = 1 num_placements = 12 num_side = 5  # Use a 5x5 point FoV approximation  # Setup the initial AIPP hyperparameters print('Initial AIPP Hyperparameters') _, noise_variance, kernel = get_model_params(X_train, y_train,                                               lengthscales=[1.0, 1.0],                                               optimizer='scipy',                                              max_steps=0)  # Get initial inducing points Xu_init = get_inducing_pts(X_train, num_placements) # Initalize the height dimension at 2.0 for all points Xu_init = np.concatenate((Xu_init, np.ones((Xu_init.shape[0], 1))*2.0), axis=1) Xu_init[:, :2] = run_tsp(Xu_init[:, :2])[0][0]  # Initialize the non-point FoV transform fov_transform = SquareHeightTransform(num_side=num_side) transform = IPPTransform(num_dim=3,                          num_robots=1,                          sensor_model=fov_transform,                          aggregate_fov=True)  # Initialize the IPP model sgpr, _ = continuous_sgp(num_placements,                          X_train,                           noise_variance,                          kernel,                          transform=transform,                          Xu_init=Xu_init,                          max_steps=0) sgp_sol_sp = sgpr.inducing_variable.Z.numpy()  # Setup the data acquisition function to emulate the robot's sensor path2data = lambda x : cont2disc(x, X, y)  # Generate the SGP-based AIPP solution _, _, param_time, ipp_time = run_aipp(X_train,                                        sgpr,                                       Xu_init.reshape(1, -1, 3),                                        path2data)  print(f'Total Hyperparameter Update Time:   {param_time:.2f}s') print(f'Total IPP Update Time:              {ipp_time:.2f}s')  print(f'Average Hyperparameter Update Time: {param_time/num_placements:.2f}s') print(f'Average IPP Update Time:            {ipp_time/num_placements:.2f}s') <pre>Initial AIPP Hyperparameters\n\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 1.0                 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [1. 1.]             \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.09999999999999999 \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.02830909998623969 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [2.52935 4.99828]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.03608860733575918 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.66389 4.2298 ]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value                \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.047688497211455655 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.6262  4.20678]    \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value                \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.047733701955419004 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.09326 4.03863]    \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.05702257365194937 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.27988 4.50424]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.06604187384338019 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.3692  4.76543]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.09160363580094916 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.83409 5.53632]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.09052131666299722 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.84722 5.51582]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.08877117097798798 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.63615 4.87825]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value              \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.0936660767287354 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.72264 5.59398]  \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                            \u2502 class     \u2502 transform   \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 SquaredExponential.variance     \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.10593340089380918 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 SquaredExponential.lengthscales \u2502 Parameter \u2502 Softplus    \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3.63767 6.41376]   \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> <pre>Total Hyperparameter Update Time:   22.51s\nTotal IPP Update Time:              31.34s\nAverage Hyperparameter Update Time: 1.88s\nAverage IPP Update Time:            2.61s\n</pre>"},{"location":"examples/AIPP_non_point_FoV.html#adaptive-ipp-with-non-point-fov","title":"Adaptive-IPP with Non-Point FoV\u00b6","text":""},{"location":"examples/AIPP_non_point_FoV.html#generate-synthetic-data-and-initial-sgp-hyperparameters","title":"Generate synthetic data and initial SGP hyperparameters\u00b6","text":""},{"location":"examples/AIPP_non_point_FoV.html#generate-the-aipp-solution","title":"Generate the AIPP solution\u00b6","text":""},{"location":"examples/Baselines.html","title":"Baselines","text":"In\u00a0[\u00a0]: Copied! <pre>import os\nos.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"\n\nimport numpy as np\nfrom time import time\nimport tensorflow as tf\n\nfrom sgptools.utils.data import *\nfrom sgptools.utils.misc import *\nfrom sgptools.utils.metrics import *\nfrom sgptools.utils.tsp import run_tsp\nfrom sgptools.utils.gpflow import get_model_params\n\nfrom sgptools.models.bo import *\nfrom sgptools.models.cma_es import *\nfrom sgptools.models.greedy_mi import *\nfrom sgptools.models.greedy_sgp import *\nfrom sgptools.models.continuous_sgp import *\nfrom sgptools.models.core.transformations import IPPTransform\n\nimport matplotlib.pyplot as plt\n\nnp.random.seed(1234)\ntf.random.set_seed(1234)\n</pre> import os os.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"  import numpy as np from time import time import tensorflow as tf  from sgptools.utils.data import * from sgptools.utils.misc import * from sgptools.utils.metrics import * from sgptools.utils.tsp import run_tsp from sgptools.utils.gpflow import get_model_params  from sgptools.models.bo import * from sgptools.models.cma_es import * from sgptools.models.greedy_mi import * from sgptools.models.greedy_sgp import * from sgptools.models.continuous_sgp import * from sgptools.models.core.transformations import IPPTransform  import matplotlib.pyplot as plt  np.random.seed(1234) tf.random.set_seed(1234) In\u00a0[2]: Copied! <pre>num_robots = 2\nnum_placements = 10\n\n# Configure IPPTransform\ntransform = IPPTransform(num_robots=num_robots)\n\n# Get the synthetic elevation data\nX_train, y_train, X_test, y_test, candidates, X, y = get_dataset()\n\n# Train the GP and get the model parameters\n_, noise_variance, kernel = get_model_params(X_train, y_train, \n                                             lengthscales=[3.0, 3.0], \n                                             optimizer='scipy',\n                                             max_steps=0)\nplt.scatter(X[:, 0], X[:, 1], c=y, cmap='jet')\nplt.title(\"Ground Truth\")\nplt.xlabel('X')\nplt.ylabel('Y')\nplt.gca().set_aspect('equal')\nplt.show()\n\n# Get the initial waypoints\nXu_init = get_inducing_pts(X_train, num_placements*num_robots)\nXu_init, _ = run_tsp(Xu_init, \n                     num_vehicles=num_robots,\n                     max_dist=100,\n                     resample=num_placements)\nXu_init = Xu_init.reshape(-1, 2)\n</pre> num_robots = 2 num_placements = 10  # Configure IPPTransform transform = IPPTransform(num_robots=num_robots)  # Get the synthetic elevation data X_train, y_train, X_test, y_test, candidates, X, y = get_dataset()  # Train the GP and get the model parameters _, noise_variance, kernel = get_model_params(X_train, y_train,                                               lengthscales=[3.0, 3.0],                                               optimizer='scipy',                                              max_steps=0) plt.scatter(X[:, 0], X[:, 1], c=y, cmap='jet') plt.title(\"Ground Truth\") plt.xlabel('X') plt.ylabel('Y') plt.gca().set_aspect('equal') plt.show()  # Get the initial waypoints Xu_init = get_inducing_pts(X_train, num_placements*num_robots) Xu_init, _ = run_tsp(Xu_init,                       num_vehicles=num_robots,                      max_dist=100,                      resample=num_placements) Xu_init = Xu_init.reshape(-1, 2) <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 1.0                 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [3. 3.]             \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.09999999999999999 \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> In\u00a0[3]: Copied! <pre># Convert a given path to the data (X, y) alongst the path\ndef path2data(path, X, y):\n    data_X = []\n    data_y = []\n    num_robots, _, _ = path.shape\n    for r in range(num_robots):\n        X_new, y_new = cont2disc(path[r], X, y)\n        data_X.extend(X_new)\n        data_y.extend(y_new)\n    return np.array(data_X), np.array(data_y)\n\n# Plot the solution paths\ndef plot_paths(paths, title=None):\n    plt.figure()\n    for i, path in enumerate(paths):\n        plt.plot(path[:, 0], path[:, 1], \n                 c='r', label='Path', \n                 zorder=1, marker='o')\n    if title is not None:\n        plt.title(title)\n    plt.xlabel('X')\n    plt.ylabel('Y')\n</pre> # Convert a given path to the data (X, y) alongst the path def path2data(path, X, y):     data_X = []     data_y = []     num_robots, _, _ = path.shape     for r in range(num_robots):         X_new, y_new = cont2disc(path[r], X, y)         data_X.extend(X_new)         data_y.extend(y_new)     return np.array(data_X), np.array(data_y)  # Plot the solution paths def plot_paths(paths, title=None):     plt.figure()     for i, path in enumerate(paths):         plt.plot(path[:, 0], path[:, 1],                   c='r', label='Path',                   zorder=1, marker='o')     if title is not None:         plt.title(title)     plt.xlabel('X')     plt.ylabel('Y') In\u00a0[4]: Copied! <pre>start = time()\nmodel, _ = continuous_sgp(num_placements, \n                          X_train, \n                          noise_variance, \n                          kernel,\n                          transform,\n                          Xu_init=Xu_init.copy())\nend = time()\nrun_time = end - start\n\n# Get solution inducing points\nsol = model.inducing_variable.Z.numpy()\nsol_paths = sol.reshape(num_robots, num_placements, 2)\n\n# Convert solution path waypoints to sensing points along the whole path\nX_path, y_path = path2data(sol_paths, X, y)\n\n# Compute RMSE\ny_pred, _ = get_reconstruction((X_path, y_path), \n                                X, \n                                noise_variance, \n                                kernel)\nrmse = get_rmse(y_pred, y)\n\n# Plot the solution\nplot_paths(sol_paths, title=f\"Continuous-SGP | RMSE: {rmse:.2f} | Time: {run_time:.2f}s\")\nplt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet')\nplt.gca().set_aspect('equal')\n</pre> start = time() model, _ = continuous_sgp(num_placements,                            X_train,                            noise_variance,                            kernel,                           transform,                           Xu_init=Xu_init.copy()) end = time() run_time = end - start  # Get solution inducing points sol = model.inducing_variable.Z.numpy() sol_paths = sol.reshape(num_robots, num_placements, 2)  # Convert solution path waypoints to sensing points along the whole path X_path, y_path = path2data(sol_paths, X, y)  # Compute RMSE y_pred, _ = get_reconstruction((X_path, y_path),                                  X,                                  noise_variance,                                  kernel) rmse = get_rmse(y_pred, y)  # Plot the solution plot_paths(sol_paths, title=f\"Continuous-SGP | RMSE: {rmse:.2f} | Time: {run_time:.2f}s\") plt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet') plt.gca().set_aspect('equal') In\u00a0[5]: Copied! <pre>start = time()\nsol = get_greedy_sgp_sol(num_placements*num_robots, \n                         candidates, \n                         candidates, \n                         noise_variance, \n                         kernel,\n                         transform=transform)\nend = time()\nrun_time = end - start\nsol_paths = sol.reshape(num_robots, num_placements, 2)\n\n# Convert solution path waypoints to sensing points along the whole path\nX_path, y_path = path2data(sol_paths, X, y)\n\n# Compute RMSE\ny_pred, _ = get_reconstruction((X_path, y_path), \n                                X, \n                                noise_variance, \n                                kernel)\nrmse = get_rmse(y_pred, y)\n\n# Plot the solution\nplot_paths(sol_paths, title=f\"Discrete-SGP | RMSE: {rmse:.2f} | Time: {run_time:.2f}s\")\nplt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet')\nplt.gca().set_aspect('equal')\n</pre> start = time() sol = get_greedy_sgp_sol(num_placements*num_robots,                           candidates,                           candidates,                           noise_variance,                           kernel,                          transform=transform) end = time() run_time = end - start sol_paths = sol.reshape(num_robots, num_placements, 2)  # Convert solution path waypoints to sensing points along the whole path X_path, y_path = path2data(sol_paths, X, y)  # Compute RMSE y_pred, _ = get_reconstruction((X_path, y_path),                                  X,                                  noise_variance,                                  kernel) rmse = get_rmse(y_pred, y)  # Plot the solution plot_paths(sol_paths, title=f\"Discrete-SGP | RMSE: {rmse:.2f} | Time: {run_time:.2f}s\") plt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet') plt.gca().set_aspect('equal') In\u00a0[6]: Copied! <pre>start = time()\nsol = get_greedy_mi_sol(num_placements*num_robots, \n                        candidates, \n                        candidates, \n                        noise_variance, \n                        kernel,\n                        transform=transform)\nend = time()\nrun_time = end - start\nsol_paths = sol.reshape(num_robots, num_placements, 2)\n\n# Convert solution path waypoints to sensing points along the whole path\nX_path, y_path = path2data(sol_paths, X, y)\n\n# Compute RMSE\ny_pred, _ = get_reconstruction((X_path, y_path), \n                                X, \n                                noise_variance, \n                                kernel)\nrmes = get_rmse(y_pred, y)\n\n# Plot the solution\nplot_paths(sol_paths, title=f\"Greedy-MI | RMSE: {rmes:.2f} | Time: {run_time:.2f}s\")\nplt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet')\nplt.gca().set_aspect('equal')\n</pre> start = time() sol = get_greedy_mi_sol(num_placements*num_robots,                          candidates,                          candidates,                          noise_variance,                          kernel,                         transform=transform) end = time() run_time = end - start sol_paths = sol.reshape(num_robots, num_placements, 2)  # Convert solution path waypoints to sensing points along the whole path X_path, y_path = path2data(sol_paths, X, y)  # Compute RMSE y_pred, _ = get_reconstruction((X_path, y_path),                                  X,                                  noise_variance,                                  kernel) rmes = get_rmse(y_pred, y)  # Plot the solution plot_paths(sol_paths, title=f\"Greedy-MI | RMSE: {rmes:.2f} | Time: {run_time:.2f}s\") plt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet') plt.gca().set_aspect('equal') In\u00a0[7]: Copied! <pre>start = time()\nmodel = CMA_ES(candidates, \n               noise_variance, \n               kernel, \n               transform=transform)\nsol = model.optimize(num_sensors=num_placements*num_robots, \n                     X_init=Xu_init.copy())\nend = time()\nrun_time = end - start\nsol_paths = sol.reshape(num_robots, num_placements, 2)\n\n# Convert solution path waypoints to sensing points along the whole path\nX_path, y_path = path2data(sol_paths, X, y)\ny_pred, _ = get_reconstruction((X_path, y_path), \n                                X, \n                                noise_variance, \n                                kernel)\n\n# Compute RMSE\nrmes = get_rmse(y_pred, y)\n\n# Plot the solution\nplot_paths(sol_paths, title=f\"CMA-ES-MI | RMSE: {rmes:.2f} | Time: {run_time:.2f}s\")\nplt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet')\nplt.gca().set_aspect('equal')\n</pre> start = time() model = CMA_ES(candidates,                 noise_variance,                 kernel,                 transform=transform) sol = model.optimize(num_sensors=num_placements*num_robots,                       X_init=Xu_init.copy()) end = time() run_time = end - start sol_paths = sol.reshape(num_robots, num_placements, 2)  # Convert solution path waypoints to sensing points along the whole path X_path, y_path = path2data(sol_paths, X, y) y_pred, _ = get_reconstruction((X_path, y_path),                                  X,                                  noise_variance,                                  kernel)  # Compute RMSE rmes = get_rmse(y_pred, y)  # Plot the solution plot_paths(sol_paths, title=f\"CMA-ES-MI | RMSE: {rmes:.2f} | Time: {run_time:.2f}s\") plt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet') plt.gca().set_aspect('equal') In\u00a0[8]: Copied! <pre>start = time()\nmodel = BayesianOpt(candidates, \n                    noise_variance,\n                    kernel,\n                    transform=transform)\nsol = model.optimize(num_sensors=num_placements*num_robots, \n                     X_init=Xu_init.copy())\nend = time()\nrun_time = end - start\nsol_paths = sol.reshape(num_robots, num_placements, 2)\n\n# Convert solution path waypoints to sensing points along the whole path\nX_path, y_path = path2data(sol_paths, X, y)\ny_pred, _ = get_reconstruction((X_path, y_path), \n                                X, \n                                noise_variance, \n                                kernel)\n\n# Compute RMSE\nrmes = get_rmse(y_pred, y)\n\n# Plot the solution\nplot_paths(sol_paths, title=f\"BO-MI | RMSE: {rmes:.2f} | Time: {run_time:.2f}s\")\nplt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet')\nplt.gca().set_aspect('equal')\n</pre> start = time() model = BayesianOpt(candidates,                      noise_variance,                     kernel,                     transform=transform) sol = model.optimize(num_sensors=num_placements*num_robots,                       X_init=Xu_init.copy()) end = time() run_time = end - start sol_paths = sol.reshape(num_robots, num_placements, 2)  # Convert solution path waypoints to sensing points along the whole path X_path, y_path = path2data(sol_paths, X, y) y_pred, _ = get_reconstruction((X_path, y_path),                                  X,                                  noise_variance,                                  kernel)  # Compute RMSE rmes = get_rmse(y_pred, y)  # Plot the solution plot_paths(sol_paths, title=f\"BO-MI | RMSE: {rmes:.2f} | Time: {run_time:.2f}s\") plt.scatter(X[:, 0], X[:, 1], c=y_pred, zorder=0, cmap='jet') plt.gca().set_aspect('equal')"},{"location":"examples/Baselines.html#baselines","title":"Baselines\u00b6","text":""},{"location":"examples/Baselines.html#set-the-initial-parameters-and-generate-synthetic-data","title":"Set the initial parameters and generate synthetic data\u00b6","text":"<p>(no distance budget constraints)</p>"},{"location":"examples/Baselines.html#helper-functions","title":"Helper functions\u00b6","text":""},{"location":"examples/Baselines.html#continuous-sgp","title":"Continuous-SGP\u00b6","text":""},{"location":"examples/Baselines.html#discrete-sgp","title":"Discrete-SGP\u00b6","text":""},{"location":"examples/Baselines.html#greedy-mutual-information","title":"Greedy Mutual Information\u00b6","text":""},{"location":"examples/Baselines.html#cma-es-mutual-information-genetic-algorithm","title":"CMA-ES Mutual Information (Genetic Algorithm)\u00b6","text":""},{"location":"examples/Baselines.html#bayesian-optimization-mutual-information","title":"Bayesian Optimization Mutual Information\u00b6","text":""},{"location":"examples/IPP.html","title":"SGP-Based IPP","text":"In\u00a0[\u00a0]: Copied! <pre>import os\nos.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom sgptools.utils.metrics import *\nfrom sgptools.utils.data import *\nfrom sgptools.utils.misc import *\nfrom sgptools.utils.tsp import run_tsp\nfrom sgptools.utils.gpflow import get_model_params\nfrom sgptools.models.continuous_sgp import *\nfrom sgptools.models.core.transformations import *\n\nimport matplotlib.pyplot as plt\n\nnp.random.seed(1234)\ntf.random.set_seed(1234)\n</pre> import os os.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"  import numpy as np import tensorflow as tf  from sgptools.utils.metrics import * from sgptools.utils.data import * from sgptools.utils.misc import * from sgptools.utils.tsp import run_tsp from sgptools.utils.gpflow import get_model_params from sgptools.models.continuous_sgp import * from sgptools.models.core.transformations import *  import matplotlib.pyplot as plt  np.random.seed(1234) tf.random.set_seed(1234) In\u00a0[2]: Copied! <pre># Get the synthetic elevation data\nX_train, y_train, X_test, y_test, candidates, X, y = get_dataset()\n\n# Train the GP and get the model parameters\n_, noise_variance, kernel = get_model_params(X_train, y_train, \n                                             lengthscales=[1.0, 1.0], \n                                             optimizer='scipy')\nplt.scatter(X[:, 0], X[:, 1], c=y, cmap='jet')\nplt.title(\"Ground Truth\")\nplt.xlabel('X')\nplt.ylabel('Y')\nplt.gca().set_aspect('equal')\nplt.show()\n</pre> # Get the synthetic elevation data X_train, y_train, X_test, y_test, candidates, X, y = get_dataset()  # Train the GP and get the model parameters _, noise_variance, kernel = get_model_params(X_train, y_train,                                               lengthscales=[1.0, 1.0],                                               optimizer='scipy') plt.scatter(X[:, 0], X[:, 1], c=y, cmap='jet') plt.title(\"Ground Truth\") plt.xlabel('X') plt.ylabel('Y') plt.gca().set_aspect('equal') plt.show() <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.6576268242419765  \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [2.95895 2.25221]   \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.02576014673391919 \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> In\u00a0[3]: Copied! <pre>num_robots = 1\nnum_placements = 6\n\n# Initialize inducing points and get initial path\nXu_init = get_inducing_pts(X_train, \n                           num_placements*num_robots)\nXu_init, _ = run_tsp(Xu_init, \n                     num_vehicles=num_robots)\nXu_init = Xu_init[0]\n\n# Setup unconstrained IPP transform and optimize it\ntransform = IPPTransform(num_robots=num_robots, num_dim=2)\nsgpr, _ = continuous_sgp(num_placements, \n                         X_train, \n                         noise_variance, \n                         kernel,\n                         transform,\n                         Xu_init=Xu_init, \n                         max_steps=1000)\n\n# Get solution inducing points\nsgp_sol_sp = sgpr.inducing_variable.Z.numpy()\nsgp_paths = sgp_sol_sp.reshape(num_robots, num_placements, 2)\n\n# Plot the solution\nplot_paths(sgp_paths, candidates,\n           title=\"Offline Single-Robot IPP with Point Sensing\")\n</pre> num_robots = 1 num_placements = 6  # Initialize inducing points and get initial path Xu_init = get_inducing_pts(X_train,                             num_placements*num_robots) Xu_init, _ = run_tsp(Xu_init,                       num_vehicles=num_robots) Xu_init = Xu_init[0]  # Setup unconstrained IPP transform and optimize it transform = IPPTransform(num_robots=num_robots, num_dim=2) sgpr, _ = continuous_sgp(num_placements,                           X_train,                           noise_variance,                           kernel,                          transform,                          Xu_init=Xu_init,                           max_steps=1000)  # Get solution inducing points sgp_sol_sp = sgpr.inducing_variable.Z.numpy() sgp_paths = sgp_sol_sp.reshape(num_robots, num_placements, 2)  # Plot the solution plot_paths(sgp_paths, candidates,            title=\"Offline Single-Robot IPP with Point Sensing\") In\u00a0[4]: Copied! <pre>num_robots = 1\nnum_placements = 6\n\n# Initialize inducing points and get initial path\nXu_init = get_inducing_pts(X_train, num_placements*num_robots)\nXu_init, _ = run_tsp(Xu_init, \n                     num_vehicles=num_robots)\nXu_init = Xu_init[0]\n\n# Setup unconstrained IPP transform with sampling rate=5\n# and optimize it\ntransform = IPPTransform(num_robots=num_robots,\n                           num_dim=2,\n                           sampling_rate=5)\nsgpr, _ = continuous_sgp(num_placements, \n                         X_train, \n                         noise_variance, \n                         kernel,\n                         transform,\n                         Xu_init=Xu_init, \n                         max_steps=1000)\n\n# Get solution inducing points\nsgp_sol_sp = sgpr.inducing_variable.Z.numpy()\nsgp_paths = sgp_sol_sp.reshape(num_robots, num_placements, 2)\n\n# Plot the solution\nplot_paths(sgp_paths, candidates, \n           title=\"Offline Single-Robot IPP with Continuous Sensing\")\n</pre> num_robots = 1 num_placements = 6  # Initialize inducing points and get initial path Xu_init = get_inducing_pts(X_train, num_placements*num_robots) Xu_init, _ = run_tsp(Xu_init,                       num_vehicles=num_robots) Xu_init = Xu_init[0]  # Setup unconstrained IPP transform with sampling rate=5 # and optimize it transform = IPPTransform(num_robots=num_robots,                            num_dim=2,                            sampling_rate=5) sgpr, _ = continuous_sgp(num_placements,                           X_train,                           noise_variance,                           kernel,                          transform,                          Xu_init=Xu_init,                           max_steps=1000)  # Get solution inducing points sgp_sol_sp = sgpr.inducing_variable.Z.numpy() sgp_paths = sgp_sol_sp.reshape(num_robots, num_placements, 2)  # Plot the solution plot_paths(sgp_paths, candidates,             title=\"Offline Single-Robot IPP with Continuous Sensing\") In\u00a0[5]: Copied! <pre>num_robots = 1\nnum_placements = 4\n\n# Initialize inducing points and get initial path\nXu_init = get_inducing_pts(X_train, num_placements*num_robots)\nXu_init, _ = run_tsp(Xu_init, \n                     num_vehicles=num_robots)\nXu_init = Xu_init[0]\n\n# Add random angles to inducing points to initialize the rotation angle of the FoVs\nangles = np.random.uniform(-np.pi, np.pi, num_placements).reshape(-1, 1)\nXu_init = np.concatenate([Xu_init, angles], axis=1)\n\n# Setup unconstrained IPP transform with square FoV transform \n# with length 5 and 3x3 point approximation, \n# and continuous sensing model (sampling rate of 3), and optimize it\nsquare_transform = SquareTransform(length=5, num_side=3)\ntransform = IPPTransform(num_robots=num_robots,\n                         num_dim=3,\n                         sampling_rate=3,\n                         sensor_model=square_transform)\nsgpr, _ = continuous_sgp(num_placements, \n                         X_train, \n                         noise_variance, \n                         kernel,\n                         transform,\n                         Xu_init=Xu_init, \n                         max_steps=1000)\n\n# Get solution inducing points\nsgp_sol_sp = sgpr.inducing_variable.Z.numpy()[:, :2]\nsgp_paths = sgp_sol_sp.reshape(num_robots, -1 , 2)\n\n# Plot the solution path and the FoVs\nplot_paths(sgp_paths, candidates, \n           title=\"Offline Single-Robot IPP with Continuous Sensing\")\nsgp_sol_sp = sgpr.inducing_variable.Z\nsgp_sol_sp = sgpr.transform.expand(sgp_sol_sp,).numpy()\nplt.scatter(sgp_sol_sp[:, 0], sgp_sol_sp[:, 1], s=10, c='r')\n</pre> num_robots = 1 num_placements = 4  # Initialize inducing points and get initial path Xu_init = get_inducing_pts(X_train, num_placements*num_robots) Xu_init, _ = run_tsp(Xu_init,                       num_vehicles=num_robots) Xu_init = Xu_init[0]  # Add random angles to inducing points to initialize the rotation angle of the FoVs angles = np.random.uniform(-np.pi, np.pi, num_placements).reshape(-1, 1) Xu_init = np.concatenate([Xu_init, angles], axis=1)  # Setup unconstrained IPP transform with square FoV transform  # with length 5 and 3x3 point approximation,  # and continuous sensing model (sampling rate of 3), and optimize it square_transform = SquareTransform(length=5, num_side=3) transform = IPPTransform(num_robots=num_robots,                          num_dim=3,                          sampling_rate=3,                          sensor_model=square_transform) sgpr, _ = continuous_sgp(num_placements,                           X_train,                           noise_variance,                           kernel,                          transform,                          Xu_init=Xu_init,                           max_steps=1000)  # Get solution inducing points sgp_sol_sp = sgpr.inducing_variable.Z.numpy()[:, :2] sgp_paths = sgp_sol_sp.reshape(num_robots, -1 , 2)  # Plot the solution path and the FoVs plot_paths(sgp_paths, candidates,             title=\"Offline Single-Robot IPP with Continuous Sensing\") sgp_sol_sp = sgpr.inducing_variable.Z sgp_sol_sp = sgpr.transform.expand(sgp_sol_sp,).numpy() plt.scatter(sgp_sol_sp[:, 0], sgp_sol_sp[:, 1], s=10, c='r') Out[5]: <pre>&lt;matplotlib.collections.PathCollection at 0x7f5688999990&gt;</pre> In\u00a0[6]: Copied! <pre>num_robots = 3\nnum_placements = 6\n\n# Initialize inducing points and get initial path \n# resample to ensure same number of waypoints in each path\nXu_init = get_inducing_pts(X_train, num_placements*num_robots)\nXu_init, _ = run_tsp(Xu_init, \n                     num_vehicles=num_robots,\n                     max_dist=100,\n                     resample=num_placements)\nXu_init = Xu_init.reshape(-1, 2)\n\n# Setup distance constrained 3 robot IPP transform\n# with continuous sensing model (sampling rate of 5) and \n# a distance budget of 30.0 and optimize it\ntransform = IPPTransform(num_robots=num_robots,\n                         num_dim=2,\n                         sampling_rate=5,\n                         distance_budget=30.,\n                         constraint_weight=100.)\nsgpr, _ = continuous_sgp(num_placements, \n                         X_train, \n                         noise_variance, \n                         kernel,\n                         transform,\n                         Xu_init=Xu_init, \n                         max_steps=5000)\n\n# Get solution inducing points\nsol_sp = sgpr.inducing_variable.Z.numpy()\nsol_paths = sol_sp.reshape(num_robots, \n                           num_placements, 2)\n\n# Plot the solution path\nplot_paths(sol_paths, candidates, title=\"Optimized paths\")\nprint(\"Distances:\", transform.distance(sol_sp).numpy())\n</pre> num_robots = 3 num_placements = 6  # Initialize inducing points and get initial path  # resample to ensure same number of waypoints in each path Xu_init = get_inducing_pts(X_train, num_placements*num_robots) Xu_init, _ = run_tsp(Xu_init,                       num_vehicles=num_robots,                      max_dist=100,                      resample=num_placements) Xu_init = Xu_init.reshape(-1, 2)  # Setup distance constrained 3 robot IPP transform # with continuous sensing model (sampling rate of 5) and  # a distance budget of 30.0 and optimize it transform = IPPTransform(num_robots=num_robots,                          num_dim=2,                          sampling_rate=5,                          distance_budget=30.,                          constraint_weight=100.) sgpr, _ = continuous_sgp(num_placements,                           X_train,                           noise_variance,                           kernel,                          transform,                          Xu_init=Xu_init,                           max_steps=5000)  # Get solution inducing points sol_sp = sgpr.inducing_variable.Z.numpy() sol_paths = sol_sp.reshape(num_robots,                             num_placements, 2)  # Plot the solution path plot_paths(sol_paths, candidates, title=\"Optimized paths\") print(\"Distances:\", transform.distance(sol_sp).numpy()) <pre>Distances: [30.         29.9984054  29.98115566]\n</pre>"},{"location":"examples/IPP.html#sgp-based-ipp","title":"SGP-Based IPP\u00b6","text":""},{"location":"examples/IPP.html#get-the-data-and-initial-sgp-hyperparameters","title":"Get the data and initial SGP hyperparameters\u00b6","text":""},{"location":"examples/IPP.html#single-robot-ipp-with-point-sensing","title":"Single-Robot IPP with Point Sensing\u00b6","text":""},{"location":"examples/IPP.html#single-robot-ipp-with-continuous-sensing","title":"Single-Robot IPP with Continuous Sensing\u00b6","text":""},{"location":"examples/IPP.html#single-robot-ipp-with-continuous-non-point-fov-sensor","title":"Single-Robot IPP with Continuous Non-point FoV Sensor\u00b6","text":""},{"location":"examples/IPP.html#multi-robot-ipp-with-continuous-sensing-and-a-distance-budget","title":"Multi-Robot IPP with Continuous Sensing and a Distance Budget\u00b6","text":""},{"location":"examples/non_point_FoV.html","title":"IPP with Non-Point FoV","text":"In\u00a0[\u00a0]: Copied! <pre>import os\nos.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"\n\nimport numpy as np\n\nfrom sgptools.utils.metrics import *\nfrom sgptools.utils.data import *\nfrom sgptools.utils.misc import *\nfrom sgptools.models.core.osgpr import *\nfrom sgptools.utils.tsp import run_tsp\nfrom sgptools.utils.gpflow import get_model_params\nfrom sgptools.models.continuous_sgp import *\nfrom sgptools.models.core.transformations import *\nfrom gpflow.utilities.traversal import print_summary\n\nfrom matplotlib import colors\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Polygon\nimport mpl_toolkits.mplot3d.art3d as art3d\nfrom scipy.optimize import linear_sum_assignment\nfrom sklearn.metrics import pairwise_distances\n\nfrom time import time\nfrom copy import deepcopy\n\nnp.random.seed(1234)\ntf.random.set_seed(1234)\n</pre> import os os.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"  import numpy as np  from sgptools.utils.metrics import * from sgptools.utils.data import * from sgptools.utils.misc import * from sgptools.models.core.osgpr import * from sgptools.utils.tsp import run_tsp from sgptools.utils.gpflow import get_model_params from sgptools.models.continuous_sgp import * from sgptools.models.core.transformations import * from gpflow.utilities.traversal import print_summary  from matplotlib import colors import matplotlib.pyplot as plt from matplotlib.patches import Polygon import mpl_toolkits.mplot3d.art3d as art3d from scipy.optimize import linear_sum_assignment from sklearn.metrics import pairwise_distances  from time import time from copy import deepcopy  np.random.seed(1234) tf.random.set_seed(1234) In\u00a0[2]: Copied! <pre>'''\nUtility to map the inducing points to FoV vertices\n'''\ndef get_vertices(Xu, X_fov):\n    X_fov = X_fov.reshape(len(Xu), -1, 2)\n    vertices = np.zeros((len(Xu), 5, 2))\n    vertices[:, 0] = X_fov.min(axis=1)\n    vertices[:, 1] = np.array([X_fov[:, :, 0].min(axis=1), \n                              X_fov[:, :, 1].max(axis=1)]).T\n    vertices[:, 2] = X_fov.max(axis=1)\n    vertices[:, 3] = np.array([X_fov[:, :, 0].max(axis=1), \n                              X_fov[:, :, 1].min(axis=1)]).T\n    vertices[:, 4] = X_fov.mean(axis=1)\n\n    dists = pairwise_distances(vertices[:, 4], Y=Xu[:, :2], \n                               metric='euclidean')\n    _, idx = linear_sum_assignment(dists)\n\n    vertices[:, 4] = Xu[idx][:, :2]\n    return vertices\n\n'''\nMethod to plot the solution with FoVs and environment reconstruction\n'''\ndef plot_results(X_inducing, X_fov, fname=None):\n    # Setup 3D plot\n    fig = plt.figure(figsize=(5, 5))\n    ax = fig.add_subplot(111, projection='3d', computed_zorder=False)\n\n    # Plot the reconstructed environment state\n    sensor_data = cont2disc(X_fov, X, y)\n    y_pred, _ = get_reconstruction(sensor_data, X_test, \n                                   noise_variance_opt, kernel_opt)\n    norm = colors.Normalize(y_train.min(), y_train.max())\n    _ = ax.plot_surface(X_test[:, 0].reshape(test_dim),\n                        X_test[:, 1].reshape(test_dim),\n                        np.atleast_2d(-0.1),\n                        facecolors=plt.cm.jet(norm(y_pred.reshape(test_dim))),\n                        shade=False,\n                        alpha=0.8,\n                        zorder=0)\n\n    # Plot the solution path\n    ax.scatter(X_inducing[:, 0], \n               X_inducing[:, 1], \n               X_inducing[:, 2], c='C3')\n    ax.plot(X_inducing[:, 0], X_inducing[:, 1], X_inducing[:, 2], 'k-')\n\n    # Plot the FoV vertices\n    vertices = get_vertices(X_inducing, X_fov)\n    for i in range(vertices.shape[0]):\n        color = 'C3'\n        verts = []\n        verts.append([vertices[i, 0], vertices[i, 1], \n                      vertices[i, 2], vertices[i, 3]])\n        fov = Polygon(np.array(verts)[0, :, :2],\n                      linewidth=1.5, \n                      edgecolor=color,\n                      facecolor=color,\n                      fill=False,\n                      zorder=15)\n        ax.add_patch(fov)\n        art3d.pathpatch_2d_to_3d(fov)\n\n    # Configure other plot settings\n    ax.set_xlabel('X')\n    ax.set_ylabel('Y')\n    ax.set_zlabel('Z')\n\n    ax.set_xlim(np.min(X[:, 0])-0.5, np.max(X[:, 0])+0.5)\n    ax.set_ylim(np.min(X[:, 1])-0.5, np.max(X[:, 1])+0.5)\n    ax.set_zlim(0, 14)\n\n    ax.view_init(elev=30, azim=45+180)\n    ax.set_title(f'Num Waypoints: {num_placements}, RMSE: {get_rmse(y_pred, y_test):.2f}\\nData Field Reconstruction', y=0.99)\n\n    plt.tight_layout()\n    if fname is not None:\n        plt.savefig(fname, bbox_inches='tight')\n    plt.show()\n</pre> ''' Utility to map the inducing points to FoV vertices ''' def get_vertices(Xu, X_fov):     X_fov = X_fov.reshape(len(Xu), -1, 2)     vertices = np.zeros((len(Xu), 5, 2))     vertices[:, 0] = X_fov.min(axis=1)     vertices[:, 1] = np.array([X_fov[:, :, 0].min(axis=1),                                X_fov[:, :, 1].max(axis=1)]).T     vertices[:, 2] = X_fov.max(axis=1)     vertices[:, 3] = np.array([X_fov[:, :, 0].max(axis=1),                                X_fov[:, :, 1].min(axis=1)]).T     vertices[:, 4] = X_fov.mean(axis=1)      dists = pairwise_distances(vertices[:, 4], Y=Xu[:, :2],                                 metric='euclidean')     _, idx = linear_sum_assignment(dists)      vertices[:, 4] = Xu[idx][:, :2]     return vertices  ''' Method to plot the solution with FoVs and environment reconstruction ''' def plot_results(X_inducing, X_fov, fname=None):     # Setup 3D plot     fig = plt.figure(figsize=(5, 5))     ax = fig.add_subplot(111, projection='3d', computed_zorder=False)      # Plot the reconstructed environment state     sensor_data = cont2disc(X_fov, X, y)     y_pred, _ = get_reconstruction(sensor_data, X_test,                                     noise_variance_opt, kernel_opt)     norm = colors.Normalize(y_train.min(), y_train.max())     _ = ax.plot_surface(X_test[:, 0].reshape(test_dim),                         X_test[:, 1].reshape(test_dim),                         np.atleast_2d(-0.1),                         facecolors=plt.cm.jet(norm(y_pred.reshape(test_dim))),                         shade=False,                         alpha=0.8,                         zorder=0)      # Plot the solution path     ax.scatter(X_inducing[:, 0],                 X_inducing[:, 1],                 X_inducing[:, 2], c='C3')     ax.plot(X_inducing[:, 0], X_inducing[:, 1], X_inducing[:, 2], 'k-')      # Plot the FoV vertices     vertices = get_vertices(X_inducing, X_fov)     for i in range(vertices.shape[0]):         color = 'C3'         verts = []         verts.append([vertices[i, 0], vertices[i, 1],                        vertices[i, 2], vertices[i, 3]])         fov = Polygon(np.array(verts)[0, :, :2],                       linewidth=1.5,                        edgecolor=color,                       facecolor=color,                       fill=False,                       zorder=15)         ax.add_patch(fov)         art3d.pathpatch_2d_to_3d(fov)      # Configure other plot settings     ax.set_xlabel('X')     ax.set_ylabel('Y')     ax.set_zlabel('Z')      ax.set_xlim(np.min(X[:, 0])-0.5, np.max(X[:, 0])+0.5)     ax.set_ylim(np.min(X[:, 1])-0.5, np.max(X[:, 1])+0.5)     ax.set_zlim(0, 14)      ax.view_init(elev=30, azim=45+180)     ax.set_title(f'Num Waypoints: {num_placements}, RMSE: {get_rmse(y_pred, y_test):.2f}\\nData Field Reconstruction', y=0.99)      plt.tight_layout()     if fname is not None:         plt.savefig(fname, bbox_inches='tight')     plt.show() In\u00a0[3]: Copied! <pre># Get the synthetic elevation data\nX_train, y_train, X_test, y_test, candidates, X, y = get_dataset(random_seed=0)\ntrain_dim = np.sqrt(X_train.shape[0]).astype(int)\n\n# Train a GP and get the model parameters\nprint('Optimized Hyperparameters')\n_, noise_variance_opt, kernel_opt = get_model_params(X_train, y_train, \n                                                     lengthscales=[1.0, 1.0], \n                                                     optimizer='scipy')\n\n# Build a new test set on a grid for easy plotting in a 3D plot\ngpr = gpflow.models.GPR((X_test, y_test),\n                        noise_variance=noise_variance_opt,\n                        kernel=kernel_opt)\ntest_dim = (100, 100)\nX_test1 = np.linspace(np.min(X[:, 0]), np.max(X[:, 0]), test_dim[0])\nX_test2 = np.linspace(np.min(X[:, 1]), np.max(X[:, 1]), test_dim[1])\nX_test1, X_test2 = np.meshgrid(X_test1, X_test2)\nX_test = np.stack([X_test1.ravel(), X_test2.ravel()], axis=1)\ny_test, y_var = gpr.predict_f(X_test)\ny_test = y_test.numpy()\n\n# Plot the data field\nplt.imshow(y_test.reshape(test_dim), cmap=\"jet\", origin='lower')\nplt.xlabel('X')\nplt.ylabel('Y')\nplt.title('Elevation Data Field')\nplt.colorbar(label='Normalized Elevation')\nplt.show()\n</pre> # Get the synthetic elevation data X_train, y_train, X_test, y_test, candidates, X, y = get_dataset(random_seed=0) train_dim = np.sqrt(X_train.shape[0]).astype(int)  # Train a GP and get the model parameters print('Optimized Hyperparameters') _, noise_variance_opt, kernel_opt = get_model_params(X_train, y_train,                                                       lengthscales=[1.0, 1.0],                                                       optimizer='scipy')  # Build a new test set on a grid for easy plotting in a 3D plot gpr = gpflow.models.GPR((X_test, y_test),                         noise_variance=noise_variance_opt,                         kernel=kernel_opt) test_dim = (100, 100) X_test1 = np.linspace(np.min(X[:, 0]), np.max(X[:, 0]), test_dim[0]) X_test2 = np.linspace(np.min(X[:, 1]), np.max(X[:, 1]), test_dim[1]) X_test1, X_test2 = np.meshgrid(X_test1, X_test2) X_test = np.stack([X_test1.ravel(), X_test2.ravel()], axis=1) y_test, y_var = gpr.predict_f(X_test) y_test = y_test.numpy()  # Plot the data field plt.imshow(y_test.reshape(test_dim), cmap=\"jet\", origin='lower') plt.xlabel('X') plt.ylabel('Y') plt.title('Elevation Data Field') plt.colorbar(label='Normalized Elevation') plt.show() <pre>Optimized Hyperparameters\n\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value               \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.8371517535182331  \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 (2,)    \u2502 float64 \u2502 [2.45802 2.50701]   \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 0.04342255274528023 \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> In\u00a0[4]: Copied! <pre>num_robots = 1\nnum_placements = 9\nnum_side = 5  # Use a 5x5 point FoV approximation\n\n# Setup the hyperparameters\n_, noise_variance, kernel = get_model_params(X_train, y_train, \n                                             lengthscales=[3.0, 3.0], \n                                             optimizer='scipy',\n                                             max_steps=0,\n                                             print_params=False)\n\n# Get initial inducing points\nXu_init = get_inducing_pts(X_train, num_placements)\n# Initalize the height dimension at 2.0 for all points\nXu_init = np.concatenate((Xu_init, np.ones((Xu_init.shape[0], 1))*2.0), axis=1)\nXu_init[:, :2] = run_tsp(Xu_init[:, :2])[0][0]\n\n# Initialize the non-point FoV IPP model and optimize it\ntransform = SquareHeightTransform(num_side=num_side)\nsgpr, _ = continuous_sgp(num_placements,\n                         X_train, \n                         noise_variance,\n                         kernel,\n                         transform=transform,\n                         Xu_init=Xu_init.copy())\nsgp_sol_sp = sgpr.inducing_variable.Z.numpy()\n</pre> num_robots = 1 num_placements = 9 num_side = 5  # Use a 5x5 point FoV approximation  # Setup the hyperparameters _, noise_variance, kernel = get_model_params(X_train, y_train,                                               lengthscales=[3.0, 3.0],                                               optimizer='scipy',                                              max_steps=0,                                              print_params=False)  # Get initial inducing points Xu_init = get_inducing_pts(X_train, num_placements) # Initalize the height dimension at 2.0 for all points Xu_init = np.concatenate((Xu_init, np.ones((Xu_init.shape[0], 1))*2.0), axis=1) Xu_init[:, :2] = run_tsp(Xu_init[:, :2])[0][0]  # Initialize the non-point FoV IPP model and optimize it transform = SquareHeightTransform(num_side=num_side) sgpr, _ = continuous_sgp(num_placements,                          X_train,                           noise_variance,                          kernel,                          transform=transform,                          Xu_init=Xu_init.copy()) sgp_sol_sp = sgpr.inducing_variable.Z.numpy() In\u00a0[5]: Copied! <pre>plot_results(Xu_init.reshape(-1, 3), \n             sgpr.transform.expand(Xu_init.reshape(-1, 3)).numpy())\n</pre> plot_results(Xu_init.reshape(-1, 3),               sgpr.transform.expand(Xu_init.reshape(-1, 3)).numpy()) In\u00a0[6]: Copied! <pre>plot_results(sgp_sol_sp.reshape(-1, 3), \n             sgpr.transform.expand(sgp_sol_sp.reshape(-1, 3)).numpy())\n</pre> plot_results(sgp_sol_sp.reshape(-1, 3),               sgpr.transform.expand(sgp_sol_sp.reshape(-1, 3)).numpy())"},{"location":"examples/non_point_FoV.html#ipp-with-non-point-fov","title":"IPP with Non-Point FoV\u00b6","text":""},{"location":"examples/non_point_FoV.html#generate-synthetic-data-and-initial-sgp-hyperparameters","title":"Generate synthetic data and initial SGP hyperparameters\u00b6","text":""},{"location":"examples/non_point_FoV.html#generate-the-ipp-solution","title":"Generate the IPP solution\u00b6","text":""},{"location":"examples/non_point_FoV.html#initial-solution","title":"Initial Solution\u00b6","text":""},{"location":"examples/non_point_FoV.html#optimized-solution","title":"Optimized Solution\u00b6","text":""},{"location":"examples/non_stationary_kernel.html","title":"Non-Stationary Kernel","text":"In\u00a0[\u00a0]: Copied! <pre>import tensorflow as tf\nimport numpy as np\nimport gpflow\ngpflow.config.set_default_float(np.float32)\n\nfrom sgptools.utils.metrics import *\nfrom sgptools.utils.misc import *\nfrom sgptools.models.continuous_sgp import *\nfrom sgptools.kernels.neural_kernel import init_neural_kernel\nfrom sgptools.utils.gpflow import get_model_params\n\nfrom sklearn.preprocessing import StandardScaler\nimport matplotlib.pyplot as plt\n\nnp.random.seed(10)\ntf.random.set_seed(10)\n</pre> import tensorflow as tf import numpy as np import gpflow gpflow.config.set_default_float(np.float32)  from sgptools.utils.metrics import * from sgptools.utils.misc import * from sgptools.models.continuous_sgp import * from sgptools.kernels.neural_kernel import init_neural_kernel from sgptools.utils.gpflow import get_model_params  from sklearn.preprocessing import StandardScaler import matplotlib.pyplot as plt  np.random.seed(10) tf.random.set_seed(10) In\u00a0[2]: Copied! <pre>def non_stationary_function(X, Y):\n    Z1 = np.sin(X/10) + np.cos(Y/10)\n    Z2 = np.cos(X/6) + np.cos(Y/6)\n    Z = np.concatenate([Z1[:, ::2], Z2[:, ::2]], \n                       axis=1)\n    return Z\n\n# Generate data\ntrain_data_dims = (40, 20)\nx = np.linspace(0, 200, train_data_dims[0])\ny = np.linspace(0, 100, train_data_dims[1])\nX, Y = np.meshgrid(x, y)\nZ = non_stationary_function(X, Y)\n\nX_train = np.stack([X.ravel(), Y.ravel()], \n                   axis=1).astype(np.float32)\nY_train = Z.ravel()[:, None].astype(np.float32)\nprint('Train Data:', X_train.shape, Y_train.shape)\n\ntest_data_dims = (100, 50)\nx = np.linspace(0, 200, test_data_dims[0])\ny = np.linspace(0, 100, test_data_dims[1])\nX, Y = np.meshgrid(x, y)\nZ = non_stationary_function(X, Y)\n\nX_test = np.stack([X.ravel(), Y.ravel()], \n                  axis=1).astype(np.float32)\nY_test = Z.ravel()[:, None].astype(np.float32)\nprint('Test Data:', X_test.shape, Y_test.shape)\n\n# Normalize the data\nXscalar = StandardScaler()\nX_train = Xscalar.fit_transform(X_train)\nX_test = Xscalar.transform(X_test)\n\nyScalar = StandardScaler()\nY_train = yScalar.fit_transform(Y_train)\nY_test = yScalar.transform(Y_test)\n\n# Plot the data\nplt.figure()\nplt.imshow(Y_train.reshape(train_data_dims[1], \n                           train_data_dims[0]), \n           cmap='jet')\nplt.gca().set_aspect('equal')\nplt.title('Train Data')\nplt.show()\n\nplt.figure()\nplt.imshow(Y_test.reshape(test_data_dims[1], \n                          test_data_dims[0]), \n           cmap='jet')\nplt.gca().set_aspect('equal')\nplt.title('Test Data')\nplt.show()\n</pre> def non_stationary_function(X, Y):     Z1 = np.sin(X/10) + np.cos(Y/10)     Z2 = np.cos(X/6) + np.cos(Y/6)     Z = np.concatenate([Z1[:, ::2], Z2[:, ::2]],                         axis=1)     return Z  # Generate data train_data_dims = (40, 20) x = np.linspace(0, 200, train_data_dims[0]) y = np.linspace(0, 100, train_data_dims[1]) X, Y = np.meshgrid(x, y) Z = non_stationary_function(X, Y)  X_train = np.stack([X.ravel(), Y.ravel()],                     axis=1).astype(np.float32) Y_train = Z.ravel()[:, None].astype(np.float32) print('Train Data:', X_train.shape, Y_train.shape)  test_data_dims = (100, 50) x = np.linspace(0, 200, test_data_dims[0]) y = np.linspace(0, 100, test_data_dims[1]) X, Y = np.meshgrid(x, y) Z = non_stationary_function(X, Y)  X_test = np.stack([X.ravel(), Y.ravel()],                    axis=1).astype(np.float32) Y_test = Z.ravel()[:, None].astype(np.float32) print('Test Data:', X_test.shape, Y_test.shape)  # Normalize the data Xscalar = StandardScaler() X_train = Xscalar.fit_transform(X_train) X_test = Xscalar.transform(X_test)  yScalar = StandardScaler() Y_train = yScalar.fit_transform(Y_train) Y_test = yScalar.transform(Y_test)  # Plot the data plt.figure() plt.imshow(Y_train.reshape(train_data_dims[1],                             train_data_dims[0]),             cmap='jet') plt.gca().set_aspect('equal') plt.title('Train Data') plt.show()  plt.figure() plt.imshow(Y_test.reshape(test_data_dims[1],                            test_data_dims[0]),             cmap='jet') plt.gca().set_aspect('equal') plt.title('Test Data') plt.show() <pre>Train Data: (800, 2) (800, 1)\nTest Data: (5000, 2) (5000, 1)\n</pre> In\u00a0[3]: Copied! <pre># Model / inference hyperparameters\nnum_inducing = 100\nlr = 1e-2\nmax_steps = 5000\nQ = 3\n\n# Initialize non-stationary kernel model\nprint('Initializing model...')\nZ = get_inducing_pts(X_train, num_inducing)\nmodel = init_neural_kernel(X_train, Y_train, \n                           n_inits=5,\n                           inducing_variable=Z,\n                           Q=Q, \n                           hidden_sizes=(4, 4))\n\n# Create optimizer\nlearning_rate = tf.keras.optimizers.schedules.ExponentialDecay(\n                      lr, \n                      decay_steps=50, \n                      decay_rate=0.98)\nloss = optimize_model(model, \n                      max_steps=max_steps,\n                      lr=learning_rate,\n                      optimizer='tf')\n\n# Plot loss\nplt.plot(loss)\nplt.xlabel('Iteration')\nplt.ylabel('Loss')\nplt.show()\n\nkernels = []\nnoice_vars = []\nlabels = ['Non Stationary Kernel', 'Stationary Kernel']\n\nkernels.append(model.kernel)\nnoice_vars.append(model.likelihood.variance.numpy())\n\n# Get stationary kernel\n_, noise_variance, kernel = get_model_params(X_train, Y_train, \n                                             lengthscales=[1.0, 0.5])\nkernels.append(kernel)\nnoice_vars.append(noise_variance)\n</pre> # Model / inference hyperparameters num_inducing = 100 lr = 1e-2 max_steps = 5000 Q = 3  # Initialize non-stationary kernel model print('Initializing model...') Z = get_inducing_pts(X_train, num_inducing) model = init_neural_kernel(X_train, Y_train,                             n_inits=5,                            inducing_variable=Z,                            Q=Q,                             hidden_sizes=(4, 4))  # Create optimizer learning_rate = tf.keras.optimizers.schedules.ExponentialDecay(                       lr,                        decay_steps=50,                        decay_rate=0.98) loss = optimize_model(model,                        max_steps=max_steps,                       lr=learning_rate,                       optimizer='tf')  # Plot loss plt.plot(loss) plt.xlabel('Iteration') plt.ylabel('Loss') plt.show()  kernels = [] noice_vars = [] labels = ['Non Stationary Kernel', 'Stationary Kernel']  kernels.append(model.kernel) noice_vars.append(model.likelihood.variance.numpy())  # Get stationary kernel _, noise_variance, kernel = get_model_params(X_train, Y_train,                                               lengthscales=[1.0, 0.5]) kernels.append(kernel) noice_vars.append(noise_variance) <pre>Initializing model...\nInitializing neural spectral kernel...\n</pre> <pre>2024-05-05 12:08:35.281864: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1960] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\n</pre> <pre>Best init: -1165.898315\n</pre> <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502 value             \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float32 \u2502 0.59679           \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 (2,)    \u2502 float32 \u2502 [1.16673 0.37082] \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float32 \u2502 0.52522           \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> In\u00a0[4]: Copied! <pre>for i, (kernel, noice_var) in enumerate(zip(kernels, noice_vars)):\n        print(labels[i])\n        for num_placements in [4, 9]:\n                # Get initial inducing points\n                Xu_init = get_inducing_pts(X_train, num_placements)*0.5\n                Xu_init.astype(np.float32)\n\n                # Setup SGP and optimize it\n                sgpr, _ = continuous_sgp(num_placements, \n                                         X_train,\n                                         noise_variance=noice_var,\n                                         kernel=kernel,\n                                         Xu_init=Xu_init)\n                sgp_sol_sp = sgpr.inducing_variable.Z.numpy()\n\n                # Get the GP predictions\n                Xu_X, Xu_y = cont2disc(sgp_sol_sp, candidates=X_test,\n                                       candidate_labels=Y_test)\n                gpr = gpflow.models.GPR((Xu_X, Xu_y),\n                                        noise_variance=noice_var,\n                                        kernel=kernel)\n                y_pred, y_var = gpr.predict_f(X_test)\n                y_pred = y_pred.numpy()\n\n                rmse = np.sqrt(np.mean((y_pred - Y_test)**2))\n\n                # Plot the results\n                plt.figure()\n                plt.imshow(y_pred.reshape(test_data_dims[1], test_data_dims[0]),\n                        cmap='jet', origin='lower',\n                        extent=[np.min(X_test[:, 0]), np.max(X_test[:, 0]),\n                                np.min(X_test[:, 1]), np.max(X_test[:, 1])])\n                plt.gca().set_aspect('equal')\n                plt.scatter(sgp_sol_sp[:, 0], sgp_sol_sp[:, 1], c='k', marker='p', s=100)\n                plt.title('Number of Sensors: {}    RMSE: {:.3f}'.format(num_placements, rmse))\n                plt.xlabel('X')\n                plt.ylabel('Y')\n                plt.show()\n</pre> for i, (kernel, noice_var) in enumerate(zip(kernels, noice_vars)):         print(labels[i])         for num_placements in [4, 9]:                 # Get initial inducing points                 Xu_init = get_inducing_pts(X_train, num_placements)*0.5                 Xu_init.astype(np.float32)                  # Setup SGP and optimize it                 sgpr, _ = continuous_sgp(num_placements,                                           X_train,                                          noise_variance=noice_var,                                          kernel=kernel,                                          Xu_init=Xu_init)                 sgp_sol_sp = sgpr.inducing_variable.Z.numpy()                  # Get the GP predictions                 Xu_X, Xu_y = cont2disc(sgp_sol_sp, candidates=X_test,                                        candidate_labels=Y_test)                 gpr = gpflow.models.GPR((Xu_X, Xu_y),                                         noise_variance=noice_var,                                         kernel=kernel)                 y_pred, y_var = gpr.predict_f(X_test)                 y_pred = y_pred.numpy()                  rmse = np.sqrt(np.mean((y_pred - Y_test)**2))                  # Plot the results                 plt.figure()                 plt.imshow(y_pred.reshape(test_data_dims[1], test_data_dims[0]),                         cmap='jet', origin='lower',                         extent=[np.min(X_test[:, 0]), np.max(X_test[:, 0]),                                 np.min(X_test[:, 1]), np.max(X_test[:, 1])])                 plt.gca().set_aspect('equal')                 plt.scatter(sgp_sol_sp[:, 0], sgp_sol_sp[:, 1], c='k', marker='p', s=100)                 plt.title('Number of Sensors: {}    RMSE: {:.3f}'.format(num_placements, rmse))                 plt.xlabel('X')                 plt.ylabel('Y')                 plt.show() <pre>Non Stationary Kernel\n</pre> <pre>Stationary Kernel\n</pre>"},{"location":"examples/non_stationary_kernel.html#non-stationary-kernel","title":"Non-Stationary Kernel\u00b6","text":""},{"location":"examples/non_stationary_kernel.html#generate-synthetic-non-stationary-data","title":"Generate synthetic non-stationary data\u00b6","text":""},{"location":"examples/non_stationary_kernel.html#learn-kernel-parameters-from-train-data","title":"Learn kernel parameters from train data\u00b6","text":""},{"location":"examples/non_stationary_kernel.html#get-continuous-sgp-solution-with-a-baseline-stationary-kernel-and-the-learned-neural-kernel-function","title":"Get Continuous-SGP solution with a baseline stationary kernel and the learned neural kernel function\u00b6","text":""},{"location":"examples/obstacles.html","title":"Environment with Obstacles","text":"In\u00a0[\u00a0]: Copied! <pre>import matplotlib.pyplot as plt\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom sgptools.utils.gpflow import get_model_params\nfrom sgptools.utils.data import remove_polygons, remove_circle_patches\nfrom sgptools.models.continuous_sgp import *\n\nnp.random.seed(0)\ntf.random.set_seed(0)\n</pre> import matplotlib.pyplot as plt  import numpy as np import tensorflow as tf  from sgptools.utils.gpflow import get_model_params from sgptools.utils.data import remove_polygons, remove_circle_patches from sgptools.models.continuous_sgp import *  np.random.seed(0) tf.random.set_seed(0) In\u00a0[2]: Copied! <pre># Generate synthetic training data\ndata_dims = (50, 25)\nx = np.linspace(0, 200, data_dims[0])\ny = np.linspace(0, 100, data_dims[1])\nX, Y = np.meshgrid(x, y)\n\nX, Y = remove_circle_patches(X, Y, [plt.Circle((30, 60), 15)])\nX, Y = remove_polygons(X, Y, [[[80, 20], [105, 20], [105, 60], [80, 60]],])\nX, Y = remove_polygons(X, Y, [[[130, 20], [180, 20], [160, 60]],])\n\nplt.figure(figsize=(10, 5))\nplt.scatter(X, Y, s=1)\n\nax = plt.gca()\nax.add_patch(plt.Polygon([[80, 20], [105, 20], [105, 60], [80, 60]], \n             fill=None, hatch='//', color='C1', label='Obstacle'))\nax.add_patch(plt.Polygon([[130, 20], [180, 20], [160, 60]], \n             fill=None, hatch='//', color='C1'))\nax.add_patch(plt.Circle((30, 60), 15, \n             fill=None, hatch='//', color='C1'))\nax.set_aspect('equal')\n\nplt.xlabel('x', fontsize=14)\nplt.ylabel('y', fontsize=14)\nplt.xlim(0, 200)\nplt.ylim(0, 100)\n\nplt.show()\n\nX_train = np.array([X, Y]).T\ny_train = np.zeros((X_train.shape[0], 1))\nX_train = X_train.astype(float)\ny_train = y_train.astype(float)\nprint(X_train.shape, y_train.shape)\n</pre> # Generate synthetic training data data_dims = (50, 25) x = np.linspace(0, 200, data_dims[0]) y = np.linspace(0, 100, data_dims[1]) X, Y = np.meshgrid(x, y)  X, Y = remove_circle_patches(X, Y, [plt.Circle((30, 60), 15)]) X, Y = remove_polygons(X, Y, [[[80, 20], [105, 20], [105, 60], [80, 60]],]) X, Y = remove_polygons(X, Y, [[[130, 20], [180, 20], [160, 60]],])  plt.figure(figsize=(10, 5)) plt.scatter(X, Y, s=1)  ax = plt.gca() ax.add_patch(plt.Polygon([[80, 20], [105, 20], [105, 60], [80, 60]],               fill=None, hatch='//', color='C1', label='Obstacle')) ax.add_patch(plt.Polygon([[130, 20], [180, 20], [160, 60]],               fill=None, hatch='//', color='C1')) ax.add_patch(plt.Circle((30, 60), 15,               fill=None, hatch='//', color='C1')) ax.set_aspect('equal')  plt.xlabel('x', fontsize=14) plt.ylabel('y', fontsize=14) plt.xlim(0, 200) plt.ylim(0, 100)  plt.show()  X_train = np.array([X, Y]).T y_train = np.zeros((X_train.shape[0], 1)) X_train = X_train.astype(float) y_train = y_train.astype(float) print(X_train.shape, y_train.shape) <pre>(1089, 2) (1089, 1)\n</pre> In\u00a0[3]: Copied! <pre>num_inducing = 200\n\n# Get initial GP parameters\n_, noise_variance, rbf = get_model_params(np.zeros((1, 2)), np.zeros((1, 1)), \n                             lengthscales=12.3254, \n                             variance=0.561265,\n                             noise_variance=2e-6,\n                             max_steps=0)\n\n# Optimize the inducing points/sensor locations\nsgpr, _ = continuous_sgp(num_inducing,\n                         X_train,\n                         kernel=rbf,\n                         noise_variance=noise_variance,\n                         lr=1e-2,\n                         max_steps=5000)\nsgp_sol_sp = sgpr.inducing_variable.Z.numpy()\n</pre> num_inducing = 200  # Get initial GP parameters _, noise_variance, rbf = get_model_params(np.zeros((1, 2)), np.zeros((1, 1)),                               lengthscales=12.3254,                               variance=0.561265,                              noise_variance=2e-6,                              max_steps=0)  # Optimize the inducing points/sensor locations sgpr, _ = continuous_sgp(num_inducing,                          X_train,                          kernel=rbf,                          noise_variance=noise_variance,                          lr=1e-2,                          max_steps=5000) sgp_sol_sp = sgpr.inducing_variable.Z.numpy() <pre>\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 name                    \u2502 class     \u2502 transform        \u2502 prior   \u2502 trainable   \u2502 shape   \u2502 dtype   \u2502     value \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 GPR.kernel.variance     \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502  0.561265 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.kernel.lengthscales \u2502 Parameter \u2502 Softplus         \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502 12.3254   \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 GPR.likelihood.variance \u2502 Parameter \u2502 Softplus + Shift \u2502         \u2502 True        \u2502 ()      \u2502 float64 \u2502  2e-06    \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n</pre> In\u00a0[4]: Copied! <pre># Plot the sensor locations and the obstacles\nplt.figure(figsize=(10, 5))\nplt.scatter(sgp_sol_sp[:, 0], sgp_sol_sp[:, 1], \n            color='C0', \n            s=2, \n            label='Solution Placements')\n\nax = plt.gca()\nax.add_patch(plt.Polygon([[80, 20], [105, 20], [105, 60], [80, 60]], \n             fill=None, hatch='//', color='C1', label='Obstacle'))\nax.add_patch(plt.Polygon([[130, 20], [180, 20], [160, 60]], \n             fill=None, hatch='//', color='C1'))\nax.add_patch(plt.Circle((30, 60), 15, \n             fill=None, hatch='//', color='C1'))\nax.set_aspect('equal')\n\nplt.xlabel('x', fontsize=14)\nplt.ylabel('y', fontsize=14)\nplt.xlim(0, 200)\nplt.ylim(0, 100)\n\nplt.show()\n</pre> # Plot the sensor locations and the obstacles plt.figure(figsize=(10, 5)) plt.scatter(sgp_sol_sp[:, 0], sgp_sol_sp[:, 1],              color='C0',              s=2,              label='Solution Placements')  ax = plt.gca() ax.add_patch(plt.Polygon([[80, 20], [105, 20], [105, 60], [80, 60]],               fill=None, hatch='//', color='C1', label='Obstacle')) ax.add_patch(plt.Polygon([[130, 20], [180, 20], [160, 60]],               fill=None, hatch='//', color='C1')) ax.add_patch(plt.Circle((30, 60), 15,               fill=None, hatch='//', color='C1')) ax.set_aspect('equal')  plt.xlabel('x', fontsize=14) plt.ylabel('y', fontsize=14) plt.xlim(0, 200) plt.ylim(0, 100)  plt.show()"},{"location":"examples/obstacles.html#environment-with-obstacles","title":"Environment with Obstacles\u00b6","text":""},{"location":"examples/obstacles.html#note-that-all-the-solution-placements-avoid-the-obstacles","title":"Note that all the solution placements avoid the obstacles\u00b6","text":""}]}
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index e3455cc..b4542db 100644
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     <url>
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     <url>
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     <url>
          <loc>https://www.itskalvik.com/sgp-tools/api/models/core/Transformations.html</loc>
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     <url>
          <loc>https://www.itskalvik.com/sgp-tools/api/utils/index.html</loc>
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     <url>
          <loc>https://www.itskalvik.com/sgp-tools/api/utils/Data.html</loc>
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     <url>
          <loc>https://www.itskalvik.com/sgp-tools/api/utils/GPflow.html</loc>
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     <url>
          <loc>https://www.itskalvik.com/sgp-tools/api/utils/Metrics.html</loc>
-         <lastmod>2024-12-27</lastmod>
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     <url>
          <loc>https://www.itskalvik.com/sgp-tools/api/utils/Misc.html</loc>
-         <lastmod>2024-12-27</lastmod>
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     <url>
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     <url>
          <loc>https://www.itskalvik.com/sgp-tools/examples/AIPP_non_point_FoV.html</loc>
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     </url>
     <url>
          <loc>https://www.itskalvik.com/sgp-tools/examples/Baselines.html</loc>
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     <url>
          <loc>https://www.itskalvik.com/sgp-tools/examples/non_point_FoV.html</loc>
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+         <changefreq>daily</changefreq>
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     <url>
          <loc>https://www.itskalvik.com/sgp-tools/examples/non_stationary_kernel.html</loc>
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     <url>
          <loc>https://www.itskalvik.com/sgp-tools/examples/obstacles.html</loc>
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\ No newline at end of file
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