diff --git a/404.html b/404.html
index 12b9e8d83..a4e685e5a 100644
--- a/404.html
+++ b/404.html
@@ -10,8 +10,9 @@
       
       
       
+      
       <link rel="icon" href="/sbi/assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.1.9">
+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.4.8">
     
     
       
@@ -19,14 +20,17 @@
       
     
     
-      <link rel="stylesheet" href="/sbi/assets/stylesheets/main.85bb2934.min.css">
+      <link rel="stylesheet" href="/sbi/assets/stylesheets/main.4b4a2bd9.min.css">
       
         
-        <link rel="stylesheet" href="/sbi/assets/stylesheets/palette.a6bdf11c.min.css">
+        <link rel="stylesheet" href="/sbi/assets/stylesheets/palette.356b1318.min.css">
       
       
 
+
+    
     
+      
     
     
       
@@ -84,6 +88,7 @@
 
     </a>
     <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 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
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     <div class="md-header__title" data-md-component="header-title">
@@ -108,6 +113,7 @@
     
     
       <label class="md-header__button md-icon" for="__search">
+        
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@@ -116,12 +122,15 @@
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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-.27A6.516 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>
       <nav class="md-search__options" aria-label="Search">
         
         <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 12 19 6.41Z"/></svg>
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@@ -145,7 +154,7 @@
         <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -175,6 +184,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href="/sbi/." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -189,7 +199,7 @@
       <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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     sbi-dev/sbi
@@ -201,14 +211,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="/sbi/." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
       </a>
     </li>
   
@@ -216,14 +230,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="/sbi/install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
       </a>
     </li>
   
@@ -231,534 +249,724 @@
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
-          Tutorials and Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3">
-          <span class="md-nav__icon md-icon"></span>
-          Tutorials and Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Tutorials and Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            Tutorials and Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
-          Introduction
-          <span class="md-nav__icon md-icon"></span>
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-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_1">
-          <span class="md-nav__icon md-icon"></span>
-          Introduction
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="/sbi/tutorial/00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="/sbi/tutorial/02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="/sbi/tutorial/01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="/sbi/tutorial/16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
-          Advanced
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_2">
-          <span class="md-nav__icon md-icon"></span>
-          Advanced
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Advanced
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="/sbi/tutorial/03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="/sbi/tutorial/11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="/sbi/tutorial/04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="/sbi/tutorial/05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="/sbi/tutorial/14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="/sbi/tutorial/08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="/sbi/tutorial/10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
-          Diagnostics
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_3">
-          <span class="md-nav__icon md-icon"></span>
-          Diagnostics
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Diagnostics
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="/sbi/tutorial/12_diagnostics_posterior_predictive_check/" class="md-nav__link">
-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="/sbi/tutorial/13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="/sbi/tutorial/15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
-          Analysis
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-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
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-          <span class="md-nav__icon md-icon"></span>
-          Analysis
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Analysis
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Analysis
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="/sbi/tutorial/07_conditional_distributions/" class="md-nav__link">
-        Conditional distributions
+        
+  
+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="/sbi/tutorial/09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
+  
+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
-          Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_5">
-          <span class="md-nav__icon md-icon"></span>
-          Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
+        
           
+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_5">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="/sbi/examples/00_HH_simulator/" class="md-nav__link">
-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="/sbi/examples/01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
-          Contributing
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_4">
-          <span class="md-nav__icon md-icon"></span>
-          Contributing
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Contributing
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Contributing
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="/sbi/contribute/" class="md-nav__link">
-        Guide
+        
+  
+  <span class="md-ellipsis">
+    Guide
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="/sbi/code_of_conduct/" class="md-nav__link">
-        Code of Conduct
+        
+  
+  <span class="md-ellipsis">
+    Code of Conduct
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="/sbi/reference/" class="md-nav__link">
-        API Reference
+        
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -766,14 +974,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="/sbi/faq/" class="md-nav__link">
-        FAQ
+        
+  
+  <span class="md-ellipsis">
+    FAQ
+  </span>
+  
+
       </a>
     </li>
   
@@ -781,14 +993,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="/sbi/credits/" class="md-nav__link">
-        Credits
+        
+  
+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
       </a>
     </li>
   
@@ -856,7 +1072,7 @@ <h1>404 - Not found</h1>
       
     
     <a href="https://github.com/sbi-dev/sbi" target="_blank" rel="noopener" title="github.com" class="md-social__link">
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
     </a>
   
 </div>
@@ -870,10 +1086,11 @@ <h1>404 - Not found</h1>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "/sbi/", "features": [], "search": "/sbi/assets/javascripts/workers/search.208ed371.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": "/sbi/", "features": [], "search": "/sbi/assets/javascripts/workers/search.f886a092.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="/sbi/assets/javascripts/bundle.fac441b0.min.js"></script>
+      <script src="/sbi/assets/javascripts/bundle.81fa17fe.min.js"></script>
       
         <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML"></script>
       
diff --git a/assets/_mkdocstrings.css b/assets/_mkdocstrings.css
index a65078d02..049a254b9 100644
--- a/assets/_mkdocstrings.css
+++ b/assets/_mkdocstrings.css
@@ -1,18 +1,13 @@
 
-/* Don't capitalize names. */
-h5.doc-heading {
-  text-transform: none !important;
-}
-
-/* Avoid breaking parameters name, etc. in table cells. */
+/* Avoid breaking parameter names, etc. in table cells. */
 .doc-contents td code {
   word-break: normal !important;
 }
 
-/* For pieces of Markdown rendered in table cells. */
-.doc-contents td p {
-  margin-top: 0 !important;
-  margin-bottom: 0 !important;
+/* No line break before first paragraph of descriptions. */
+.doc-md-description,
+.doc-md-description>p:first-child {
+  display: inline;
 }
 
 /* Max width for docstring sections tables. */
@@ -21,16 +16,49 @@ h5.doc-heading {
   display: table !important;
   width: 100%;
 }
+
 .doc .md-typeset__table tr {
   display: table-row;
 }
 
-/* Avoid line breaks in rendered fields. */
-.field-body p {
-  display: inline;
-}
-
 /* Defaults in Spacy table style. */
 .doc-param-default {
   float: right;
 }
+
+/* Keep headings consistent. */
+h1.doc-heading,
+h2.doc-heading,
+h3.doc-heading,
+h4.doc-heading,
+h5.doc-heading,
+h6.doc-heading {
+  font-weight: 400;
+  line-height: 1.5;
+  color: inherit;
+  text-transform: none;
+}
+
+h1.doc-heading {
+  font-size: 1.6rem;
+}
+
+h2.doc-heading {
+  font-size: 1.2rem;
+}
+
+h3.doc-heading {
+  font-size: 1.15rem;
+}
+
+h4.doc-heading {
+  font-size: 1.10rem;
+}
+
+h5.doc-heading {
+  font-size: 1.05rem;
+}
+
+h6.doc-heading {
+  font-size: 1rem;
+}
\ No newline at end of file
diff --git a/assets/javascripts/bundle.81fa17fe.min.js b/assets/javascripts/bundle.81fa17fe.min.js
new file mode 100644
index 000000000..885b8708b
--- /dev/null
+++ b/assets/javascripts/bundle.81fa17fe.min.js
@@ -0,0 +1,29 @@
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+ * clipboard.js v2.0.11
+ * https://clipboardjs.com/
+ *
+ * Licensed MIT © Zeno Rocha
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diff --git a/assets/javascripts/bundle.81fa17fe.min.js.map b/assets/javascripts/bundle.81fa17fe.min.js.map
new file mode 100644
index 000000000..582e52559
--- /dev/null
+++ b/assets/javascripts/bundle.81fa17fe.min.js.map
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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) 2016-2023 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  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$\n  .pipe(\n    filter(({ mode }) => mode === \"global\")\n  )\n    .subscribe(key => {\n      switch (key.type) {\n\n        /* Go to previous page */\n        case \"p\":\n        case \",\":\n          const prev = getOptionalElement<HTMLLinkElement>(\"link[rel=prev]\")\n          if (typeof prev !== \"undefined\")\n            setLocation(prev)\n          break\n\n        /* Go to next page */\n        case \"n\":\n        case \".\":\n          const next = getOptionalElement<HTMLLinkElement>(\"link[rel=next]\")\n          if (typeof next !== \"undefined\")\n            setLocation(next)\n          break\n\n        /* Expand navigation, see https://bit.ly/3ZjG5io */\n        case \"Enter\":\n          const active = getActiveElement()\n          if (active instanceof HTMLLabelElement)\n            active.click()\n      }\n    })\n\n/* Set up patches */\npatchIndeterminate({ document$, 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 { 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 { 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 { 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 { 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 { innerFrom } from '../observable/innerFrom';\nimport { MonoTypeOperatorFunction, ObservableInput } from '../types';\nimport { operate } from '../util/lift';\nimport { noop } from '../util/noop';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * Emits the most recently emitted value from the source Observable whenever\n * another Observable, the `notifier`, emits.\n *\n * <span class=\"informal\">It's like {@link sampleTime}, but samples whenever\n * the `notifier` `ObservableInput` emits something.</span>\n *\n * ![](sample.png)\n *\n * Whenever the `notifier` `ObservableInput` emits a value, `sample`\n * looks at the source Observable and emits whichever value it has most recently\n * emitted since the previous sampling, unless the source has not emitted\n * anything since the previous sampling. The `notifier` is subscribed to as soon\n * as the output Observable is subscribed.\n *\n * ## Example\n *\n * On every click, sample the most recent `seconds` timer\n *\n * ```ts\n * import { fromEvent, interval, sample } from 'rxjs';\n *\n * const seconds = interval(1000);\n * const clicks = fromEvent(document, 'click');\n * const result = seconds.pipe(sample(clicks));\n *\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link audit}\n * @see {@link debounce}\n * @see {@link sampleTime}\n * @see {@link throttle}\n *\n * @param notifier The `ObservableInput` to use for sampling the\n * source Observable.\n * @return A function that returns an Observable that emits the results of\n * sampling the values emitted by the source Observable whenever the notifier\n * Observable emits value or completes.\n */\nexport function sample<T>(notifier: ObservableInput<any>): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    let hasValue = false;\n    let lastValue: T | null = null;\n    source.subscribe(\n      createOperatorSubscriber(subscriber, (value) => {\n        hasValue = true;\n        lastValue = value;\n      })\n    );\n    innerFrom(notifier).subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        () => {\n          if (hasValue) {\n            hasValue = false;\n            const value = lastValue!;\n            lastValue = null;\n            subscriber.next(value);\n          }\n        },\n        noop\n      )\n    );\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) 2016-2023 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) 2016-2023 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 document.activeElement instanceof HTMLElement\n    ? document.activeElement || undefined\n    : undefined\n}\n", "/*\n * Copyright (c) 2016-2023 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) 2016-2023 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\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\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 * Copyright (c) 2016-2023 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, \"resize\")\n  )\n    .pipe(\n      auditTime(0, animationFrameScheduler),\n      map(() => getElementContentOffset(el)),\n      startWith(getElementContentOffset(el))\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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      for (const entry of entries)\n        entry$.next(entry)\n    })),\n    switchMap(observer => merge(NEVER, of(observer))\n      .pipe(\n        finalize(() => observer.disconnect())\n      )\n    ),\n    shareReplay(1)\n  )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element size\n *\n * @param el - Element\n *\n * @returns Element size\n */\nexport function getElementSize(\n  el: HTMLElement\n): ElementSize {\n  return {\n    width:  el.offsetWidth,\n    height: el.offsetHeight\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element size\n *\n * This function returns an observable that subscribes to a single internal\n * instance of `ResizeObserver` upon subscription, and emit resize events until\n * termination. Note that this function should not be called with the same\n * element twice, as the first unsubscription will terminate observation.\n *\n * Sadly, we can't use the `DOMRect` objects returned by the observer, because\n * we need the emitted values to be consistent with `getElementSize`, which will\n * return the used values (rounded) and not actual values (unrounded). Thus, we\n * use the `offset*` properties. See the linked GitHub issue.\n *\n * @see https://bit.ly/3m0k3he - GitHub issue\n *\n * @param el - Element\n *\n * @returns Element size observable\n */\nexport function watchElementSize(\n  el: HTMLElement\n): Observable<ElementSize> {\n  return observer$\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(() => getElementSize(el))\n        )\n      ),\n      startWith(getElementSize(el))\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 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 * Copyright (c) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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$?: Subject<number>          // Progress subject\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the given URL\n *\n * If the request fails (e.g. when dispatched from `file://` locations), the\n * observable will complete without emitting a value.\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Response observable\n */\nexport function request(\n  url: URL | string, options?: Options\n): Observable<Blob> {\n  return new Observable<Blob>(observer => {\n    const req = new XMLHttpRequest()\n    req.open(\"GET\",  `${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      } 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.error(new Error(\"Request aborted\"))\n    })\n\n    // Handle download progress\n    if (typeof options?.progress$ !== \"undefined\") {\n      req.addEventListener(\"progress\", event => {\n        options.progress$!.next((event.loaded / event.total) * 100)\n      })\n\n      // Immediately set progress to 5% to indicate that we're loading\n      options.progress$.next(5)\n    }\n\n    // Send request\n    req.send()\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 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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  | \"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}\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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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 tooltip\n *\n * @param id - Tooltip identifier\n *\n * @returns Element\n */\nexport function renderTooltip(id?: string): HTMLElement {\n  return (\n    <div class=\"md-tooltip\" id={id}>\n      <div class=\"md-tooltip__inner md-typeset\"></div>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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\n */\nexport interface Version {\n  version: string                      /* Version identifier */\n  title: string                        /* Version title */\n  aliases: string[]                    /* Version aliases */\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      </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  return (\n    <div class=\"md-version\">\n      <button\n        class=\"md-version__current\"\n        aria-label={translation(\"select.version\")}\n      >\n        {active.title}\n      </button>\n      <ul class=\"md-version__list\">\n        {versions.map(renderVersion)}\n      </ul>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2023 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$.complete()),\n        share()\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { EMPTY, Observable, defer } from \"rxjs\"\n\nimport { Component } from \"../../../_\"\nimport { Annotation } from \"../_\"\nimport { mountAnnotationList } from \"../list\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: 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) 2016-2023 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  tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  getElementContentSize,\n  watchElementSize,\n  watchElementVisibility\n} from \"~/browser\"\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\n */\nexport interface CodeBlock {\n  scrollable: boolean                  /* Code block overflows */\n}\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<CodeBlock> {\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 | Annotation>> {\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<CodeBlock>()\n    push$.subscribe(({ scrollable }) => {\n      if (scrollable && hover)\n        el.setAttribute(\"tabindex\", \"0\")\n      else\n        el.removeAttribute(\"tabindex\")\n    })\n\n    /* Render button for Clipboard.js integration */\n    if (ClipboardJS.isSupported()) {\n      if (el.closest(\".copy\") || (\n        feature(\"content.code.copy\") && !el.closest(\".no-copy\")\n      )) {\n        const parent = el.closest(\"pre\")!\n        parent.id = `__code_${sequence++}`\n        parent.insertBefore(\n          renderClipboardButton(parent.id),\n          el\n        )\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\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(\n              watchElementSize(container)\n                .pipe(\n                  map(({ width, height }) => width && height),\n                  distinctUntilChanged(),\n                  switchMap(active => active ? annotations$ : EMPTY)\n                )\n            )\n          )\n      }\n    }\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      )\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) 2016-2023 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{color:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.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) 2016-2023 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@9.4.3/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$.subscribe(() => {\n    el.classList.add(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n    const id = `__mermaid_${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    mermaid.mermaidAPI.render(id, text, (svg: string, fn: Function) => {\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\n  /* Create and return component */\n  return mermaid$\n    .pipe(\n      map(() => ({ ref: el }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 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) 2016-2023 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  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} from \"~/browser\"\nimport { renderTabbedControl } from \"~/templates\"\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}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch content tabs\n *\n * @param el - Content tabs element\n *\n * @returns Content tabs observable\n */\nexport function watchContentTabs(\n  el: HTMLElement\n): Observable<ContentTabs> {\n  const inputs = getElements<HTMLInputElement>(\":scope > input\", el)\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 * This function scrolls the active tab into view. While this functionality is\n * provided by browsers as part of `scrollInfoView`, browsers will always also\n * scroll the vertical axis, which we do not want. Thus, we decided to provide\n * this functionality ourselves.\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$ }: MountOptions\n): Observable<Component<ContentTabs>> {\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  const container = getElement(\".tabbed-labels\", el)\n  return defer(() => {\n    const push$ = new Subject<ContentTabs>()\n    const done$ = push$.pipe(ignoreElements(), endWith(true))\n    combineLatest([push$, watchElementSize(el)])\n      .pipe(\n        auditTime(1, animationFrameScheduler),\n        takeUntil(done$)\n      )\n        .subscribe({\n\n          /* Handle emission */\n          next([{ active }, size]) {\n            const offset = getElementOffset(active)\n            const { width } = getElementSize(active)\n\n            /* Set tab indicator offset and width */\n            el.style.setProperty(\"--md-indicator-x\", `${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$)\n      )\n        .subscribe(direction => {\n          const { width } = getElementSize(container)\n          container.scrollBy({\n            left: width * direction,\n            behavior: \"smooth\"\n          })\n        })\n\n    /* Set up linking of content tabs, if enabled */\n    if (feature(\"content.tabs.link\"))\n      push$.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(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) 2016-2023 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 { Viewport, getElements } from \"~/browser\"\n\nimport { Component } from \"../../_\"\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\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$ }))\n  )\n}\n", "/*\n * Copyright (c) 2016-2023 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) 2016-2023 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  ignoreElements,\n  map,\n  of,\n  shareReplay,\n  startWith,\n  switchMap,\n  takeUntil\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  watchElementSize,\n  watchToggle\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Main } from \"../../main\"\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>> {\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$)\n      )\n        .subscribe(([{ active }, { hidden }]) => {\n          el.classList.toggle(\"md-header--shadow\", active && !hidden)\n          el.hidden = hidden\n        })\n\n    /* Link to main area */\n    main$.subscribe(push$)\n\n    /* Create and return component */\n    return header$\n      .pipe(\n        takeUntil(done$),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2023 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) 2016-2023 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) 2016-2023 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  finalize,\n  fromEvent,\n  map,\n  mergeMap,\n  observeOn,\n  of,\n  shareReplay,\n  startWith,\n  tap\n} from \"rxjs\"\n\nimport { getElements } 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  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  return of(...inputs)\n    .pipe(\n      mergeMap(input => fromEvent(input, \"change\")\n        .pipe(\n          map(() => input)\n        )\n      ),\n      startWith(inputs[Math.max(0, current.index)]),\n      map(input => ({\n        index: inputs.indexOf(input),\n        color: {\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 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  return defer(() => {\n    const push$ = new Subject<Palette>()\n    push$.subscribe(palette => {\n      document.body.setAttribute(\"data-md-color-switching\", \"\")\n\n      /* Set color palette */\n      for (const [key, value] of Object.entries(palette.color))\n        document.body.setAttribute(`data-md-color-${key}`, value)\n\n      /* 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    /* 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$.pipe(observeOn(asyncScheduler))\n      .subscribe(() => {\n        document.body.removeAttribute(\"data-md-color-switching\")\n      })\n\n    /* Create and return component */\n    const inputs = getElements<HTMLInputElement>(\"input\", el)\n    return watchPalette(inputs)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  defer,\n  finalize,\n  map,\n  tap\n} from \"rxjs\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Progress indicator\n */\nexport interface Progress {\n  value: number                        // Progress value\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  progress$: 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$.complete()),\n        map(value => ({ ref: el, value }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n  Observable,\n  Subject,\n  map,\n  tap\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport { getElement } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n  alert$: 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\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) 2016-2023 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  catchError,\n  defaultIfEmpty,\n  map,\n  of,\n  tap\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport { getElements, requestXML } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sitemap, i.e. a list of URLs\n */\nexport type Sitemap = string[]\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Preprocess a list of URLs\n *\n * This function replaces the `site_url` in the sitemap with the actual base\n * URL, to allow instant navigation to work in occasions like Netlify previews.\n *\n * @param urls - URLs\n *\n * @returns URL path parts\n */\nfunction preprocess(urls: Sitemap): Sitemap {\n  if (urls.length < 2)\n    return [\"\"]\n\n  /* Take the first two URLs and remove everything after the last slash */\n  const [root, next] = [...urls]\n    .sort((a, b) => a.length - b.length)\n    .map(url => url.replace(/[^/]+$/, \"\"))\n\n  /* Compute common prefix */\n  let index = 0\n  if (root === next)\n    index = root.length\n  else\n    while (root.charCodeAt(index) === next.charCodeAt(index))\n      index++\n\n  /* Remove common prefix and return in original order */\n  return urls.map(url => url.replace(root.slice(0, index), \"\"))\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the sitemap for the given base URL\n *\n * @param base - Base URL\n *\n * @returns Sitemap observable\n */\nexport function fetchSitemap(base?: URL): Observable<Sitemap> {\n  const cached = __md_get<Sitemap>(\"__sitemap\", sessionStorage, base)\n  if (cached) {\n    return of(cached)\n  } else {\n    const config = configuration()\n    return requestXML(new URL(\"sitemap.xml\", base || config.base))\n      .pipe(\n        map(sitemap => preprocess(getElements(\"loc\", sitemap)\n          .map(node => node.textContent!)\n        )),\n        catchError(() => EMPTY), // @todo refactor instant loading\n        defaultIfEmpty([]),\n        tap(sitemap => __md_set(\"__sitemap\", sitemap, sessionStorage, base))\n      )\n  }\n}\n", "/*\n * Copyright (c) 2016-2023 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  catchError,\n  concat,\n  debounceTime,\n  distinctUntilKeyChanged,\n  endWith,\n  filter,\n  fromEvent,\n  ignoreElements,\n  map,\n  of,\n  sample,\n  share,\n  skip,\n  startWith,\n  switchMap,\n  take,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { configuration, feature } from \"~/_\"\nimport {\n  Viewport,\n  getElements,\n  getLocation,\n  getOptionalElement,\n  request,\n  setLocation,\n  setLocationHash\n} from \"~/browser\"\nimport { getComponentElement } from \"~/components\"\n\nimport { 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$: Subject<number>           // Progress suject\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a map of head elements for lookup and replacement\n *\n * @param head - Document head\n *\n * @returns Element map\n */\nfunction lookup(head: HTMLHeadElement): Map<string, HTMLElement> {\n\n  // @todo When resolving URLs, we must make sure to use the correct base for\n  // resolution. The next time we refactor instant loading, we should use the\n  // location subject as a source, which is also used for anchor links tracking,\n  // but for now we just rely on canonical.\n  const canonical = getOptionalElement<HTMLLinkElement>(\"[rel=canonical]\", head)\n  if (typeof canonical !== \"undefined\")\n    canonical.href = canonical.href.replace(\"//localhost:\", \"//127.0.0.1:\")\n\n  // Create tag map and index elements in head\n  const tags = new Map<string, HTMLElement>()\n  for (const el of getElements(\":scope > *\", head)) {\n    let html = el.outerHTML\n\n    // If the current element is a style sheet or script, we must resolve the\n    // URL relative to the current location and make it absolute, so it's easy\n    // to deduplicate it later on by comparing the outer HTML of tags. We must\n    // keep identical style sheets and scripts without replacing them.\n    for (const key of [\"href\", \"src\"]) {\n      const value = el.getAttribute(key)!\n      if (value === null)\n        continue\n\n      // Resolve URL relative to current location\n      const url = new URL(value, canonical?.href)\n      const ref = el.cloneNode() as HTMLElement\n\n      // Set resolved URL and retrieve HTML for deduplication\n      ref.setAttribute(key, `${url}`)\n      html = ref.outerHTML\n      break\n    }\n\n    // Index element in tag map\n    tags.set(html, el)\n  }\n\n  // Return tag map\n  return tags\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 instant navigation request, and canonicalize URLs to the current\n  // base URL. The base URL will remain stable in between loads, as it's only\n  // read at the first initialization of the application.\n  const sitemap$ = fetchSitemap()\n    .pipe(\n      map(paths => paths.map(path => `${new URL(path, config.base)}`))\n    )\n\n  // Intercept inter-site navigation - to keep the number of event listeners\n  // low we use the fact that uncaptured events bubble up to the body. This also\n  // has the nice property that we don't need to detach and then again attach\n  // event listeners when instant navigation occurs.\n  const instant$ = fromEvent<MouseEvent>(document.body, \"click\")\n    .pipe(\n      withLatestFrom(sitemap$),\n      switchMap(([ev, sitemap]) => {\n        if (!(ev.target instanceof Element))\n          return EMPTY\n\n        // Skip, as target is not within a link - clicks on non-link elements\n        // are also captured, which we need to exclude from processing\n        const el = ev.target.closest(\"a\")\n        if (el === null)\n          return EMPTY\n\n        // Skip, as link opens in new window - we now know we have captured a\n        // click on a link, but the link either has a `target` property defined,\n        // or the user pressed the `meta` or `ctrl` key to open it in a new\n        // window. Thus, we need to filter those events, too.\n        if (el.target || ev.metaKey || ev.ctrlKey)\n          return EMPTY\n\n        // Next, we must check if the URL is relevant for us, i.e., if it's an\n        // internal link to a page that is managed by MkDocs. Only then we can\n        // be sure that the structure of the page to be loaded adheres to the\n        // current document structure and can subsequently be injected into it\n        // without doing a full reload. For this reason, we must canonicalize\n        // the URL by removing all search parameters and hash fragments.\n        const url = new URL(el.href)\n        url.search = url.hash = \"\"\n\n        // Skip, if URL is not included in the sitemap - this could be the case\n        // when linking between versions or languages, or to another page that\n        // the author included as part of the build, but that is not managed by\n        // MkDocs. In that case we must not continue with instant navigation.\n        if (!sitemap.includes(`${url}`))\n          return EMPTY\n\n        // We now know that we have a link to an internal page, so we prevent\n        // the browser from navigation and emit the URL for instant navigation.\n        // Note that this also includes anchor links, which means we need to\n        // implement anchor positioning ourselves. The reason for this is that\n        // if we wouldn't manage anchor links as well, scroll restoration will\n        // not work correctly (e.g. following an anchor link and scrolling).\n        ev.preventDefault()\n        return of(new URL(el.href))\n      }),\n      share()\n    )\n\n  // Before fetching for the first time, resolve the absolute favicon position,\n  // as the browser will try to fetch the icon immediately\n  instant$.pipe(take(1))\n    .subscribe(() => {\n      const favicon = getOptionalElement<HTMLLinkElement>(\"link[rel=icon]\")\n      if (typeof favicon !== \"undefined\")\n        favicon.href = favicon.href\n    })\n\n  // Enable scroll restoration before window unloads - this is essential to\n  // ensure that full reloads (F5) restore the viewport offset correctly. If\n  // only popstate events wouldn't reset the scroll position prior to their\n  // emission, we could just reset this in popstate. Meh.\n  fromEvent(window, \"beforeunload\")\n    .subscribe(() => {\n      history.scrollRestoration = \"auto\"\n    })\n\n  // When an instant navigation event occurs, disable scroll restoration, since\n  // we must normalize and synchronize the behavior across all browsers. For\n  // instance, when the user clicks the back or forward button, the browser\n  // would immediately jump to the position of the previous document.\n  instant$.pipe(withLatestFrom(viewport$))\n    .subscribe(([url, { offset }]) => {\n      history.scrollRestoration = \"manual\"\n\n      // While it would be better UX to defer the history state change until the\n      // document was fully fetched and parsed, we must schedule it here, since\n      // popstate events are emitted when history state changes happen. Moreover\n      // we need to back up the current viewport offset, so we can restore it\n      // when popstate events occur, e.g., when the browser's back and forward\n      // buttons are used for navigation.\n      history.replaceState(offset, \"\")\n      history.pushState(null, \"\", url)\n    })\n\n  // Emit URL that should be fetched via instant navigation on location subject,\n  // which was passed into this function. Instant navigation can be intercepted\n  // by other parts of the application, which can synchronously back up or\n  // restore state before instant navigation happens.\n  instant$.subscribe(location$)\n\n  // Fetch document - when fetching, we could use `responseType: document`, but\n  // since all MkDocs links are relative, we need to make sure that the current\n  // location matches the document we just loaded. Otherwise any relative links\n  // in the document might use the old location. If the request fails for some\n  // reason, we fall back to regular navigation and set the location explicitly,\n  // which will force-load the page. Furthermore, we must pre-warm the buffer\n  // for the duplicate check, or the first click on an anchor link will also\n  // trigger an instant navigation event, which doesn't make sense.\n  const response$ = location$\n    .pipe(\n      startWith(getLocation()),\n      distinctUntilKeyChanged(\"pathname\"),\n      skip(1),\n      switchMap(url => request(url, { progress$ })\n        .pipe(\n          catchError(() => {\n            setLocation(url, true)\n            return EMPTY\n          })\n        )\n      )\n    )\n\n  // Initialize the DOM parser, parse the returned HTML, and replace selected\n  // components before handing control down to the application\n  const dom = new DOMParser()\n  const document$ = response$\n    .pipe(\n      switchMap(res => res.text()),\n      switchMap(res => {\n        const next = dom.parseFromString(res, \"text/html\")\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 source = lookup(document.head)\n        const target = lookup(next.head)\n        for (const [html, el] of target) {\n\n          // Hack: skip stylesheets and scripts until we manage to replace them\n          // entirely in order to omit flashes of white content @todo refactor\n          if (\n            el.getAttribute(\"rel\") === \"stylesheet\" ||\n            el.hasAttribute(\"src\")\n          )\n            continue\n\n          if (source.has(html)) {\n            source.delete(html)\n          } else {\n            document.head.appendChild(el)\n          }\n        }\n\n        // Remove meta tags that are not present in the new document\n        for (const el of source.values())\n\n          // Hack: skip stylesheets and scripts until we manage to replace them\n          // entirely in order to omit flashes of white content @todo refactor\n          if (\n            el.getAttribute(\"rel\") === \"stylesheet\" ||\n            el.hasAttribute(\"src\")\n          )\n            continue\n          else\n            el.remove()\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(next)\n          )\n      }),\n      share()\n    )\n\n  // Intercept popstate events, e.g. when using the browser's back and forward\n  // buttons, and emit new location for fetching and parsing\n  const popstate$ = fromEvent<PopStateEvent>(window, \"popstate\")\n  popstate$.pipe(map(getLocation))\n    .subscribe(location$)\n\n  // Intercept clicks on anchor links, and scroll document into position - as\n  // we disabled scroll restoration, we need to do this manually here\n  location$\n    .pipe(\n      startWith(getLocation()),\n      bufferCount(2, 1),\n      filter(([prev, next]) => (\n        prev.pathname === next.pathname &&\n        prev.hash     !== next.hash\n      )),\n      map(([, next]) => next)\n    )\n      .subscribe(url => {\n        if (history.state !== null || !url.hash) {\n          window.scrollTo(0, history.state?.y ?? 0)\n        } else {\n          history.scrollRestoration = \"auto\"\n          setLocationHash(url.hash)\n          history.scrollRestoration = \"manual\"\n        }\n      })\n\n  // Intercept clicks on the same anchor link - we must use a distinct pipeline\n  // for this, or we'd end up in a loop, setting the hash again and again\n  location$\n    .pipe(\n      sample(instant$),\n      startWith(getLocation()),\n      bufferCount(2, 1),\n      filter(([prev, next]) => (\n        prev.pathname === next.pathname &&\n        prev.hash     === next.hash\n      )),\n      map(([, next]) => next)\n    )\n      .subscribe(url => {\n        history.scrollRestoration = \"auto\"\n        setLocationHash(url.hash)\n        history.scrollRestoration = \"manual\"\n\n        // Hack: we need to make sure that we don't end up with multiple history\n        // entries for the same anchor link, so we just remove the last entry\n        history.back()\n      })\n\n  // After parsing the document, check if the current history entry has a state.\n  // This may happen when users press the back or forward button to visit a page\n  // that was already seen. If there's no state, it means a new page was visited\n  // and we should scroll to the top, unless an anchor is given.\n  document$.pipe(withLatestFrom(location$))\n    .subscribe(([, url]) => {\n      if (history.state !== null || !url.hash) {\n        window.scrollTo(0, history.state?.y ?? 0)\n      } else {\n        setLocationHash(url.hash)\n      }\n    })\n\n  // If the current history is not empty, register an event listener updating\n  // the current history state whenever the scroll position changes. This must\n  // be debounced and cannot be done in popstate, as popstate has already\n  // removed the entry from the history.\n  viewport$\n    .pipe(\n      distinctUntilKeyChanged(\"offset\"),\n      debounceTime(100)\n    )\n      .subscribe(({ offset }) => {\n        history.replaceState(offset, \"\")\n      })\n\n  // Return document\n  return document$\n}\n", "/*\n * Copyright (c) 2016-2023 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) 2016-2023 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) 2016-2023 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$\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Subject,\n  catchError,\n  combineLatest,\n  filter,\n  fromEvent,\n  map,\n  of,\n  switchMap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n  getElement,\n  getLocation,\n  requestJSON,\n  setLocation\n} from \"~/browser\"\nimport { getComponentElements } from \"~/components\"\nimport {\n  Version,\n  renderVersionSelector\n} from \"~/templates\"\n\nimport { fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n  document$: 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            const { version } = urls.get(url)!\n            return fetchSitemap(new URL(url))\n              .pipe(\n                map(sitemap => {\n                  const location = getLocation()\n                  const path = location.href.replace(config.base, \"\")\n                  return sitemap.includes(path.split(\"#\")[0])\n                    ? new URL(`../${version}/${path}`, config.base)\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 alias of current.aliases)\n            if (new RegExp(ignore, \"i\").test(alias)) {\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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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>(\"[href^=\\\\#]\", 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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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t=`__mermaid_${qa++}`,r=M("div",{class:"mermaid"}),n=e.textContent;mermaid.mermaidAPI.render(t,n,(o,i)=>{let s=r.attachShadow({mode:"closed"});s.innerHTML=o,e.replaceWith(r),i==null||i(s)})}),Gr.pipe(l(()=>({ref:e})))}function Qa(e,{target$:t,print$:r}){let n=!0;return L(t.pipe(l(o=>o.closest("details:not([open])")),A(o=>e===o),l(()=>({action:"open",reveal:!0}))),r.pipe(A(o=>o||!n),S(()=>n=e.open),l(o=>({action:o?"open":"close"}))))}function $o(e,t){return $(()=>{let r=new x;return r.subscribe(({action:n,reveal:o})=>{e.toggleAttribute("open",n==="open"),o&&e.scrollIntoView()}),Qa(e,t).pipe(S(n=>r.next(n)),R(()=>r.complete()),l(n=>P({ref:e},n)))})}var Io=M("table");function jo(e){return e.replaceWith(Io),Io.replaceWith(Oo(e)),k({ref:e})}function Ya(e){let t=K(":scope > input",e),r=t.find(n=>n.checked)||t[0];return L(...t.map(n=>b(n,"change").pipe(l(()=>z(`label[for="${n.id}"]`))))).pipe(V(z(`label[for="${r.id}"]`)),l(n=>({active:n})))}function Fo(e,{viewport$:t}){let r=Yr("prev");e.append(r);let n=Yr("next");e.append(n);let o=z(".tabbed-labels",e);return $(()=>{let i=new x,s=i.pipe(Z(),te(!0));return Y([i,ye(e)]).pipe(Ce(1,Oe),G(s)).subscribe({next([{active:a},f]){let c=Xe(a),{width:u}=he(a);e.style.setProperty("--md-indicator-x",`${c.x}px`),e.style.setProperty("--md-indicator-width",`${u}px`);let p=rr(o);(c.x<p.x||c.x+u>p.x+f.width)&&o.scrollTo({left:Math.max(0,c.x-16),behavior:"smooth"})},complete(){e.style.removeProperty("--md-indicator-x"),e.style.removeProperty("--md-indicator-width")}}),Y([dt(o),ye(o)]).pipe(G(s)).subscribe(([a,f])=>{let c=bt(o);r.hidden=a.x<16,n.hidden=a.x>c.width-f.width-16}),L(b(r,"click").pipe(l(()=>-1)),b(n,"click").pipe(l(()=>1))).pipe(G(s)).subscribe(a=>{let{width:f}=he(o);o.scrollBy({left:f*a,behavior:"smooth"})}),re("content.tabs.link")&&i.pipe(Fe(1),ne(t)).subscribe(([{active:a},{offset:f}])=>{let c=a.innerText.trim();if(a.hasAttribute("data-md-switching"))a.removeAttribute("data-md-switching");else{let 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u,p;history.state!==null||!c.hash?window.scrollTo(0,(p=(u=history.state)==null?void 0:u.y)!=null?p:0):(history.scrollRestoration="auto",Dr(c.hash),history.scrollRestoration="manual")}),a.pipe(ne(e)).subscribe(([,c])=>{var u,p;history.state!==null||!c.hash?window.scrollTo(0,(p=(u=history.state)==null?void 0:u.y)!=null?p:0):Dr(c.hash)}),a.pipe(g(()=>t),ee("offset"),ke(100)).subscribe(({offset:c})=>{history.replaceState(c,"")}),a}var Bo=yt(Go());function Jo(e){let t=e.separator.split("|").map(o=>o.replace(/(\(\?[!=][^)]+\))/g,"").length===0?"\uFFFD":o).join("|"),r=new RegExp(t,"img"),n=(o,i,s)=>`${i}<mark data-md-highlight>${s}</mark>`;return o=>{o=o.replace(/[\s*+\-:~^]+/g," ").trim();let i=new RegExp(`(^|${e.separator}|)(${o.replace(/[|\\{}()[\]^$+*?.-]/g,"\\$&").replace(r,"|")})`,"img");return s=>(0,Bo.default)(s).replace(i,n).replace(/<\/mark>(\s+)<mark[^>]*>/img,"$1")}}function Lt(e){return e.type===1}function dr(e){return e.type===3}function Xo(e,t){let r=vo(e);return 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m=p.firstElementChild;u.set(p,parseFloat(m.getAttribute("data-md-score")))}if(u.size){let[[p]]=[...u].sort(([,m],[,d])=>d-m);p.click()}f.claim()}break;case"Escape":case"Tab":Ke("search",!1),i.blur();break;case"ArrowUp":case"ArrowDown":if(typeof c=="undefined")i.focus();else{let u=[i,...K(":not(details) > [href], summary, details[open] [href]",s)],p=Math.max(0,(Math.max(0,u.indexOf(c))+u.length+(f.type==="ArrowUp"?-1:1))%u.length);u[p].focus()}f.claim();break;default:i!==_e()&&i.focus()}}),r.pipe(A(({mode:f})=>f==="global")).subscribe(f=>{switch(f.type){case"f":case"s":case"/":i.focus(),i.select(),f.claim();break}});let a=ei(i,{worker$:o});return L(a,ti(s,{worker$:o,query$:a})).pipe(qe(...oe("search-share",e).map(f=>ri(f,{query$:a})),...oe("search-suggest",e).map(f=>ni(f,{worker$:o,keyboard$:r}))))}catch(o){return e.hidden=!0,ze}}function ii(e,{index$:t,location$:r}){return Y([t,r.pipe(V(le()),A(n=>!!n.searchParams.get("h")))]).pipe(l(([n,o])=>Jo(n.config)(o.searchParams.get("h"))),l(n=>{var s;let o=new Map,i=document.createNodeIterator(e,NodeFilter.SHOW_TEXT);for(let a=i.nextNode();a;a=i.nextNode())if((s=a.parentElement)!=null&&s.offsetHeight){let f=a.textContent,c=n(f);c.length>f.length&&o.set(a,c)}for(let[a,f]of o){let{childNodes:c}=M("span",null,f);a.replaceWith(...Array.from(c))}return{ref:e,nodes:o}}))}function ss(e,{viewport$:t,main$:r}){let n=e.parentElement,o=n.offsetTop-n.parentElement.offsetTop;return Y([r,t]).pipe(l(([{offset:i,height:s},{offset:{y:a}}])=>(s=s+Math.min(o,Math.max(0,a-i))-o,{height:s,locked:a>=i+o})),J((i,s)=>i.height===s.height&&i.locked===s.locked))}function Jr(e,n){var o=n,{header$:t}=o,r=on(o,["header$"]);let i=z(".md-sidebar__scrollwrap",e),{y:s}=Xe(i);return $(()=>{let a=new x,f=a.pipe(Z(),te(!0)),c=a.pipe(Ce(0,Oe));return 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d=p.offsetParent;for(;d;d=d.offsetParent)m+=d.offsetTop;return c.set([...f=[...f,u]].reverse(),m)},new Map))}).pipe(l(f=>new Map([...f].sort(([,c],[,u])=>c-u))),Je(i),g(([f,c])=>t.pipe(Pr(([u,p],{offset:{y:m},size:d})=>{let h=m+d.height>=Math.floor(a.height);for(;p.length;){let[,v]=p[0];if(v-c<m||h)u=[...u,p.shift()];else break}for(;u.length;){let[,v]=u[u.length-1];if(v-c>=m&&!h)p=[u.pop(),...p];else break}return[u,p]},[[],[...f]]),J((u,p)=>u[0]===p[0]&&u[1]===p[1])))))).pipe(l(([a,f])=>({prev:a.map(([c])=>c),next:f.map(([c])=>c)})),V({prev:[],next:[]}),Re(2,1),l(([a,f])=>a.prev.length<f.prev.length?{prev:f.prev.slice(Math.max(0,a.prev.length-1),f.prev.length),next:[]}:{prev:f.prev.slice(-1),next:f.next.slice(0,f.next.length-a.next.length)}))}function pi(e,{viewport$:t,header$:r,main$:n,target$:o}){return $(()=>{let i=new x,s=i.pipe(Z(),te(!0));if(i.subscribe(({prev:a,next:f})=>{for(let[c]of 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diff --git a/assets/javascripts/bundle.fac441b0.min.js.map b/assets/javascripts/bundle.fac441b0.min.js.map
deleted file mode 100644
index 1b87b2119..000000000
--- a/assets/javascripts/bundle.fac441b0.min.js.map
+++ /dev/null
@@ -1,8 +0,0 @@
-{
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"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/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/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", 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"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/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/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", 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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", "(function(global) {\r\n  /**\r\n   * Polyfill URLSearchParams\r\n   *\r\n   * Inspired from : https://github.com/WebReflection/url-search-params/blob/master/src/url-search-params.js\r\n   */\r\n\r\n  var checkIfIteratorIsSupported = function() {\r\n    try {\r\n      return !!Symbol.iterator;\r\n    } catch (error) {\r\n      return false;\r\n    }\r\n  };\r\n\r\n\r\n  var iteratorSupported = checkIfIteratorIsSupported();\r\n\r\n  var createIterator = function(items) {\r\n    var iterator = {\r\n      next: function() {\r\n        var value = items.shift();\r\n        return { done: value === void 0, value: value };\r\n      }\r\n    };\r\n\r\n    if (iteratorSupported) {\r\n      iterator[Symbol.iterator] = function() {\r\n        return iterator;\r\n      };\r\n    }\r\n\r\n    return iterator;\r\n  };\r\n\r\n  /**\r\n   * Search param name and values should be encoded according to https://url.spec.whatwg.org/#urlencoded-serializing\r\n   * encodeURIComponent() produces the same result except encoding spaces as `%20` instead of `+`.\r\n   */\r\n  var serializeParam = function(value) {\r\n    return encodeURIComponent(value).replace(/%20/g, '+');\r\n  };\r\n\r\n  var deserializeParam = function(value) {\r\n    return decodeURIComponent(String(value).replace(/\\+/g, ' '));\r\n  };\r\n\r\n  var polyfillURLSearchParams = function() {\r\n\r\n    var URLSearchParams = function(searchString) {\r\n      Object.defineProperty(this, '_entries', { writable: true, value: {} });\r\n      var typeofSearchString = typeof searchString;\r\n\r\n      if (typeofSearchString === 'undefined') {\r\n        // do nothing\r\n      } else if (typeofSearchString === 'string') {\r\n        if (searchString !== '') {\r\n          this._fromString(searchString);\r\n        }\r\n      } else if (searchString instanceof URLSearchParams) {\r\n        var _this = this;\r\n        searchString.forEach(function(value, name) {\r\n          _this.append(name, value);\r\n        });\r\n      } else if ((searchString !== null) && (typeofSearchString === 'object')) {\r\n        if (Object.prototype.toString.call(searchString) === '[object Array]') {\r\n          for (var i = 0; i < searchString.length; i++) {\r\n            var entry = searchString[i];\r\n            if ((Object.prototype.toString.call(entry) === '[object Array]') || (entry.length !== 2)) {\r\n              this.append(entry[0], entry[1]);\r\n            } else {\r\n              throw new TypeError('Expected [string, any] as entry at index ' + i + ' of URLSearchParams\\'s input');\r\n            }\r\n          }\r\n        } else {\r\n          for (var key in searchString) {\r\n            if (searchString.hasOwnProperty(key)) {\r\n              this.append(key, searchString[key]);\r\n            }\r\n          }\r\n        }\r\n      } else {\r\n        throw new TypeError('Unsupported input\\'s type for URLSearchParams');\r\n      }\r\n    };\r\n\r\n    var proto = URLSearchParams.prototype;\r\n\r\n    proto.append = function(name, value) {\r\n      if (name in this._entries) {\r\n        this._entries[name].push(String(value));\r\n      } else {\r\n        this._entries[name] = [String(value)];\r\n      }\r\n    };\r\n\r\n    proto.delete = function(name) {\r\n      delete this._entries[name];\r\n    };\r\n\r\n    proto.get = function(name) {\r\n      return (name in this._entries) ? this._entries[name][0] : null;\r\n    };\r\n\r\n    proto.getAll = function(name) {\r\n      return (name in this._entries) ? this._entries[name].slice(0) : [];\r\n    };\r\n\r\n    proto.has = function(name) {\r\n      return (name in this._entries);\r\n    };\r\n\r\n    proto.set = function(name, value) {\r\n      this._entries[name] = [String(value)];\r\n    };\r\n\r\n    proto.forEach = function(callback, thisArg) {\r\n      var entries;\r\n      for (var name in this._entries) {\r\n        if (this._entries.hasOwnProperty(name)) {\r\n          entries = this._entries[name];\r\n          for (var i = 0; i < entries.length; i++) {\r\n            callback.call(thisArg, entries[i], name, this);\r\n          }\r\n        }\r\n      }\r\n    };\r\n\r\n    proto.keys = function() {\r\n      var items = [];\r\n      this.forEach(function(value, name) {\r\n        items.push(name);\r\n      });\r\n      return createIterator(items);\r\n    };\r\n\r\n    proto.values = function() {\r\n      var items = [];\r\n      this.forEach(function(value) {\r\n        items.push(value);\r\n      });\r\n      return createIterator(items);\r\n    };\r\n\r\n    proto.entries = function() {\r\n      var items = [];\r\n      this.forEach(function(value, name) {\r\n        items.push([name, value]);\r\n      });\r\n      return createIterator(items);\r\n    };\r\n\r\n    if (iteratorSupported) {\r\n      proto[Symbol.iterator] = proto.entries;\r\n    }\r\n\r\n    proto.toString = function() {\r\n      var searchArray = [];\r\n      this.forEach(function(value, name) {\r\n        searchArray.push(serializeParam(name) + '=' + serializeParam(value));\r\n      });\r\n      return searchArray.join('&');\r\n    };\r\n\r\n\r\n    global.URLSearchParams = URLSearchParams;\r\n  };\r\n\r\n  var checkIfURLSearchParamsSupported = function() {\r\n    try {\r\n      var URLSearchParams = global.URLSearchParams;\r\n\r\n      return (\r\n        (new URLSearchParams('?a=1').toString() === 'a=1') &&\r\n        (typeof URLSearchParams.prototype.set === 'function') &&\r\n        (typeof URLSearchParams.prototype.entries === 'function')\r\n      );\r\n    } catch (e) {\r\n      return false;\r\n    }\r\n  };\r\n\r\n  if (!checkIfURLSearchParamsSupported()) {\r\n    polyfillURLSearchParams();\r\n  }\r\n\r\n  var proto = global.URLSearchParams.prototype;\r\n\r\n  if (typeof proto.sort !== 'function') {\r\n    proto.sort = function() {\r\n      var _this = this;\r\n      var items = [];\r\n      this.forEach(function(value, name) {\r\n        items.push([name, value]);\r\n        if (!_this._entries) {\r\n          _this.delete(name);\r\n        }\r\n      });\r\n      items.sort(function(a, b) {\r\n        if (a[0] < b[0]) {\r\n          return -1;\r\n        } else if (a[0] > b[0]) {\r\n          return +1;\r\n        } else {\r\n          return 0;\r\n        }\r\n      });\r\n      if (_this._entries) { // force reset because IE keeps keys index\r\n        _this._entries = {};\r\n      }\r\n      for (var i = 0; i < items.length; i++) {\r\n        this.append(items[i][0], items[i][1]);\r\n      }\r\n    };\r\n  }\r\n\r\n  if (typeof proto._fromString !== 'function') {\r\n    Object.defineProperty(proto, '_fromString', {\r\n      enumerable: false,\r\n      configurable: false,\r\n      writable: false,\r\n      value: function(searchString) {\r\n        if (this._entries) {\r\n          this._entries = {};\r\n        } else {\r\n          var keys = [];\r\n          this.forEach(function(value, name) {\r\n            keys.push(name);\r\n          });\r\n          for (var i = 0; i < keys.length; i++) {\r\n            this.delete(keys[i]);\r\n          }\r\n        }\r\n\r\n        searchString = searchString.replace(/^\\?/, '');\r\n        var attributes = searchString.split('&');\r\n        var attribute;\r\n        for (var i = 0; i < attributes.length; i++) {\r\n          attribute = attributes[i].split('=');\r\n          this.append(\r\n            deserializeParam(attribute[0]),\r\n            (attribute.length > 1) ? deserializeParam(attribute[1]) : ''\r\n          );\r\n        }\r\n      }\r\n    });\r\n  }\r\n\r\n  // HTMLAnchorElement\r\n\r\n})(\r\n  (typeof global !== 'undefined') ? global\r\n    : ((typeof window !== 'undefined') ? window\r\n    : ((typeof self !== 'undefined') ? self : this))\r\n);\r\n\r\n(function(global) {\r\n  /**\r\n   * Polyfill URL\r\n   *\r\n   * Inspired from : https://github.com/arv/DOM-URL-Polyfill/blob/master/src/url.js\r\n   */\r\n\r\n  var checkIfURLIsSupported = function() {\r\n    try {\r\n      var u = new global.URL('b', 'http://a');\r\n      u.pathname = 'c d';\r\n      return (u.href === 'http://a/c%20d') && u.searchParams;\r\n    } catch (e) {\r\n      return false;\r\n    }\r\n  };\r\n\r\n\r\n  var polyfillURL = function() {\r\n    var _URL = global.URL;\r\n\r\n    var URL = function(url, base) {\r\n      if (typeof url !== 'string') url = String(url);\r\n      if (base && typeof base !== 'string') base = String(base);\r\n\r\n      // Only create another document if the base is different from current location.\r\n      var doc = document, baseElement;\r\n      if (base && (global.location === void 0 || base !== global.location.href)) {\r\n        base = base.toLowerCase();\r\n        doc = document.implementation.createHTMLDocument('');\r\n        baseElement = doc.createElement('base');\r\n        baseElement.href = base;\r\n        doc.head.appendChild(baseElement);\r\n        try {\r\n          if (baseElement.href.indexOf(base) !== 0) throw new Error(baseElement.href);\r\n        } catch (err) {\r\n          throw new Error('URL unable to set base ' + base + ' due to ' + err);\r\n        }\r\n      }\r\n\r\n      var anchorElement = doc.createElement('a');\r\n      anchorElement.href = url;\r\n      if (baseElement) {\r\n        doc.body.appendChild(anchorElement);\r\n        anchorElement.href = anchorElement.href; // force href to refresh\r\n      }\r\n\r\n      var inputElement = doc.createElement('input');\r\n      inputElement.type = 'url';\r\n      inputElement.value = url;\r\n\r\n      if (anchorElement.protocol === ':' || !/:/.test(anchorElement.href) || (!inputElement.checkValidity() && !base)) {\r\n        throw new TypeError('Invalid URL');\r\n      }\r\n\r\n      Object.defineProperty(this, '_anchorElement', {\r\n        value: anchorElement\r\n      });\r\n\r\n\r\n      // create a linked searchParams which reflect its changes on URL\r\n      var searchParams = new global.URLSearchParams(this.search);\r\n      var enableSearchUpdate = true;\r\n      var enableSearchParamsUpdate = true;\r\n      var _this = this;\r\n      ['append', 'delete', 'set'].forEach(function(methodName) {\r\n        var method = searchParams[methodName];\r\n        searchParams[methodName] = function() {\r\n          method.apply(searchParams, arguments);\r\n          if (enableSearchUpdate) {\r\n            enableSearchParamsUpdate = false;\r\n            _this.search = searchParams.toString();\r\n            enableSearchParamsUpdate = true;\r\n          }\r\n        };\r\n      });\r\n\r\n      Object.defineProperty(this, 'searchParams', {\r\n        value: searchParams,\r\n        enumerable: true\r\n      });\r\n\r\n      var search = void 0;\r\n      Object.defineProperty(this, '_updateSearchParams', {\r\n        enumerable: false,\r\n        configurable: false,\r\n        writable: false,\r\n        value: function() {\r\n          if (this.search !== search) {\r\n            search = this.search;\r\n            if (enableSearchParamsUpdate) {\r\n              enableSearchUpdate = false;\r\n              this.searchParams._fromString(this.search);\r\n              enableSearchUpdate = true;\r\n            }\r\n          }\r\n        }\r\n      });\r\n    };\r\n\r\n    var proto = URL.prototype;\r\n\r\n    var linkURLWithAnchorAttribute = function(attributeName) {\r\n      Object.defineProperty(proto, attributeName, {\r\n        get: function() {\r\n          return this._anchorElement[attributeName];\r\n        },\r\n        set: function(value) {\r\n          this._anchorElement[attributeName] = value;\r\n        },\r\n        enumerable: true\r\n      });\r\n    };\r\n\r\n    ['hash', 'host', 'hostname', 'port', 'protocol']\r\n      .forEach(function(attributeName) {\r\n        linkURLWithAnchorAttribute(attributeName);\r\n      });\r\n\r\n    Object.defineProperty(proto, 'search', {\r\n      get: function() {\r\n        return this._anchorElement['search'];\r\n      },\r\n      set: function(value) {\r\n        this._anchorElement['search'] = value;\r\n        this._updateSearchParams();\r\n      },\r\n      enumerable: true\r\n    });\r\n\r\n    Object.defineProperties(proto, {\r\n\r\n      'toString': {\r\n        get: function() {\r\n          var _this = this;\r\n          return function() {\r\n            return _this.href;\r\n          };\r\n        }\r\n      },\r\n\r\n      'href': {\r\n        get: function() {\r\n          return this._anchorElement.href.replace(/\\?$/, '');\r\n        },\r\n        set: function(value) {\r\n          this._anchorElement.href = value;\r\n          this._updateSearchParams();\r\n        },\r\n        enumerable: true\r\n      },\r\n\r\n      'pathname': {\r\n        get: function() {\r\n          return this._anchorElement.pathname.replace(/(^\\/?)/, '/');\r\n        },\r\n        set: function(value) {\r\n          this._anchorElement.pathname = value;\r\n        },\r\n        enumerable: true\r\n      },\r\n\r\n      'origin': {\r\n        get: function() {\r\n          // get expected port from protocol\r\n          var expectedPort = { 'http:': 80, 'https:': 443, 'ftp:': 21 }[this._anchorElement.protocol];\r\n          // add port to origin if, expected port is different than actual port\r\n          // and it is not empty f.e http://foo:8080\r\n          // 8080 != 80 && 8080 != ''\r\n          var addPortToOrigin = this._anchorElement.port != expectedPort &&\r\n            this._anchorElement.port !== '';\r\n\r\n          return this._anchorElement.protocol +\r\n            '//' +\r\n            this._anchorElement.hostname +\r\n            (addPortToOrigin ? (':' + this._anchorElement.port) : '');\r\n        },\r\n        enumerable: true\r\n      },\r\n\r\n      'password': { // TODO\r\n        get: function() {\r\n          return '';\r\n        },\r\n        set: function(value) {\r\n        },\r\n        enumerable: true\r\n      },\r\n\r\n      'username': { // TODO\r\n        get: function() {\r\n          return '';\r\n        },\r\n        set: function(value) {\r\n        },\r\n        enumerable: true\r\n      },\r\n    });\r\n\r\n    URL.createObjectURL = function(blob) {\r\n      return _URL.createObjectURL.apply(_URL, arguments);\r\n    };\r\n\r\n    URL.revokeObjectURL = function(url) {\r\n      return _URL.revokeObjectURL.apply(_URL, arguments);\r\n    };\r\n\r\n    global.URL = URL;\r\n\r\n  };\r\n\r\n  if (!checkIfURLIsSupported()) {\r\n    polyfillURL();\r\n  }\r\n\r\n  if ((global.location !== void 0) && !('origin' in global.location)) {\r\n    var getOrigin = function() {\r\n      return global.location.protocol + '//' + global.location.hostname + (global.location.port ? (':' + global.location.port) : '');\r\n    };\r\n\r\n    try {\r\n      Object.defineProperty(global.location, 'origin', {\r\n        get: getOrigin,\r\n        enumerable: true\r\n      });\r\n    } catch (e) {\r\n      setInterval(function() {\r\n        global.location.origin = getOrigin();\r\n      }, 100);\r\n    }\r\n  }\r\n\r\n})(\r\n  (typeof global !== 'undefined') ? global\r\n    : ((typeof window !== 'undefined') ? window\r\n    : ((typeof self !== 'undefined') ? self : this))\r\n);\r\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", "Array.prototype.flat||Object.defineProperty(Array.prototype,\"flat\",{configurable:!0,value:function r(){var t=isNaN(arguments[0])?1:Number(arguments[0]);return t?Array.prototype.reduce.call(this,function(a,e){return Array.isArray(e)?a.push.apply(a,r.call(e,t-1)):a.push(e),a},[]):Array.prototype.slice.call(this)},writable:!0}),Array.prototype.flatMap||Object.defineProperty(Array.prototype,\"flatMap\",{configurable:!0,value:function(r){return Array.prototype.map.apply(this,arguments).flat()},writable:!0})\n", "/*\n * Copyright (c) 2016-2023 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 \"array-flat-polyfill\"\nimport \"focus-visible\"\nimport \"unfetch/polyfill\"\nimport \"url-polyfill\"\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  mountSearch,\n  mountSearchHiglight,\n  mountSidebar,\n  mountSource,\n  mountTableOfContents,\n  mountTabs,\n  watchHeader,\n  watchMain\n} from \"./components\"\nimport {\n  SearchIndex,\n  setupClipboardJS,\n  setupInstantLoading,\n  setupVersionSelector\n} from \"./integrations\"\nimport {\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 instant loading, if enabled */\nif (feature(\"navigation.instant\"))\n  setupInstantLoading({ location$, viewport$ })\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$\n  .pipe(\n    filter(({ mode }) => mode === \"global\")\n  )\n    .subscribe(key => {\n      switch (key.type) {\n\n        /* Go to previous page */\n        case \"p\":\n        case \",\":\n          const prev = getOptionalElement<HTMLLinkElement>(\"link[rel=prev]\")\n          if (typeof prev !== \"undefined\")\n            setLocation(prev)\n          break\n\n        /* Go to next page */\n        case \"n\":\n        case \".\":\n          const next = getOptionalElement<HTMLLinkElement>(\"link[rel=next]\")\n          if (typeof next !== \"undefined\")\n            setLocation(next)\n          break\n\n        /* Expand navigation, see https://bit.ly/3ZjG5io */\n        case \"Enter\":\n          const active = getActiveElement()\n          if (active instanceof HTMLLabelElement)\n            active.click()\n      }\n    })\n\n/* Set up patches */\npatchIndeterminate({ document$, 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  /* 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.component$ = component$         /* Component observable */\n", "self.fetch||(self.fetch=function(e,n){return n=n||{},new Promise(function(t,r){var s=new XMLHttpRequest,o=[],u={},a=function e(){return{ok:2==(s.status/100|0),statusText:s.statusText,status:s.status,url:s.responseURL,text:function(){return Promise.resolve(s.responseText)},json:function(){return Promise.resolve(s.responseText).then(JSON.parse)},blob:function(){return Promise.resolve(new Blob([s.response]))},clone:e,headers:{keys:function(){return o},entries:function(){return o.map(function(e){return[e,s.getResponseHeader(e)]})},get:function(e){return s.getResponseHeader(e)},has:function(e){return null!=s.getResponseHeader(e)}}}};for(var i in s.open(n.method||\"get\",e,!0),s.onload=function(){s.getAllResponseHeaders().toLowerCase().replace(/^(.+?):/gm,function(e,n){u[n]||o.push(u[n]=n)}),t(a())},s.onerror=r,s.withCredentials=\"include\"==n.credentials,n.headers)s.setRequestHeader(i,n.headers[i]);s.send(n.body||null)})});\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 { 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 { 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 { 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 { 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) 2016-2023 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) 2016-2023 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 document.activeElement instanceof HTMLElement\n    ? document.activeElement || undefined\n    : undefined\n}\n", "/*\n * Copyright (c) 2016-2023 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  startWith\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element focus\n *\n * Previously, this function 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 *\n * @param el - Element\n *\n * @returns Element focus observable\n */\nexport function watchElementFocus(\n  el: HTMLElement\n): Observable<boolean> {\n  return merge(\n    fromEvent(document.body, \"focusin\"),\n    fromEvent(document.body, \"focusout\")\n  )\n    .pipe(\n      debounceTime(1),\n      map(() => {\n        const active = getActiveElement()\n        return typeof active !== \"undefined\"\n          ? el.contains(active)\n          : false\n      }),\n      startWith(el === getActiveElement()),\n      distinctUntilChanged()\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 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\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\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 * Copyright (c) 2016-2023 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, \"resize\")\n  )\n    .pipe(\n      auditTime(0, animationFrameScheduler),\n      map(() => getElementContentOffset(el)),\n      startWith(getElementContentOffset(el))\n    )\n}\n", "/**\r\n * A collection of shims that provide minimal functionality of the ES6 collections.\r\n *\r\n * These implementations are not meant to be used outside of the ResizeObserver\r\n * modules as they cover only a limited range of use cases.\r\n */\r\n/* eslint-disable require-jsdoc, valid-jsdoc */\r\nvar MapShim = (function () {\r\n    if (typeof Map !== 'undefined') {\r\n        return Map;\r\n    }\r\n    /**\r\n     * Returns index in provided array that matches the specified key.\r\n     *\r\n     * @param {Array<Array>} arr\r\n     * @param {*} key\r\n     * @returns {number}\r\n     */\r\n    function getIndex(arr, key) {\r\n        var result = -1;\r\n        arr.some(function (entry, index) {\r\n            if (entry[0] === key) {\r\n                result = index;\r\n                return true;\r\n            }\r\n            return false;\r\n        });\r\n        return result;\r\n    }\r\n    return /** @class */ (function () {\r\n        function class_1() {\r\n            this.__entries__ = [];\r\n        }\r\n        Object.defineProperty(class_1.prototype, \"size\", {\r\n            /**\r\n             * @returns {boolean}\r\n             */\r\n            get: function () {\r\n                return this.__entries__.length;\r\n            },\r\n            enumerable: true,\r\n            configurable: true\r\n        });\r\n        /**\r\n         * @param {*} key\r\n         * @returns {*}\r\n         */\r\n        class_1.prototype.get = function (key) {\r\n            var index = getIndex(this.__entries__, key);\r\n            var entry = this.__entries__[index];\r\n            return entry && entry[1];\r\n        };\r\n        /**\r\n         * @param {*} key\r\n         * @param {*} value\r\n         * @returns {void}\r\n         */\r\n        class_1.prototype.set = function (key, value) {\r\n            var index = getIndex(this.__entries__, key);\r\n            if (~index) {\r\n                this.__entries__[index][1] = value;\r\n            }\r\n            else {\r\n                this.__entries__.push([key, value]);\r\n            }\r\n        };\r\n        /**\r\n         * @param {*} key\r\n         * @returns {void}\r\n         */\r\n        class_1.prototype.delete = function (key) {\r\n            var entries = this.__entries__;\r\n            var index = getIndex(entries, key);\r\n            if (~index) {\r\n                entries.splice(index, 1);\r\n            }\r\n        };\r\n        /**\r\n         * @param {*} key\r\n         * @returns {void}\r\n         */\r\n        class_1.prototype.has = function (key) {\r\n            return !!~getIndex(this.__entries__, key);\r\n        };\r\n        /**\r\n         * @returns {void}\r\n         */\r\n        class_1.prototype.clear = function () {\r\n            this.__entries__.splice(0);\r\n        };\r\n        /**\r\n         * @param {Function} callback\r\n         * @param {*} [ctx=null]\r\n         * @returns {void}\r\n         */\r\n        class_1.prototype.forEach = function (callback, ctx) {\r\n            if (ctx === void 0) { ctx = null; }\r\n            for (var _i = 0, _a = this.__entries__; _i < _a.length; _i++) {\r\n                var entry = _a[_i];\r\n                callback.call(ctx, entry[1], entry[0]);\r\n            }\r\n        };\r\n        return class_1;\r\n    }());\r\n})();\n\n/**\r\n * Detects whether window and document objects are available in current environment.\r\n */\r\nvar isBrowser = typeof window !== 'undefined' && typeof document !== 'undefined' && window.document === document;\n\n// Returns global object of a current environment.\r\nvar global$1 = (function () {\r\n    if (typeof global !== 'undefined' && global.Math === Math) {\r\n        return global;\r\n    }\r\n    if (typeof self !== 'undefined' && self.Math === Math) {\r\n        return self;\r\n    }\r\n    if (typeof window !== 'undefined' && window.Math === Math) {\r\n        return window;\r\n    }\r\n    // eslint-disable-next-line no-new-func\r\n    return Function('return this')();\r\n})();\n\n/**\r\n * A shim for the requestAnimationFrame which falls back to the setTimeout if\r\n * first one is not supported.\r\n *\r\n * @returns {number} Requests' identifier.\r\n */\r\nvar requestAnimationFrame$1 = (function () {\r\n    if (typeof requestAnimationFrame === 'function') {\r\n        // It's required to use a bounded function because IE sometimes throws\r\n        // an \"Invalid calling object\" error if rAF is invoked without the global\r\n        // object on the left hand side.\r\n        return requestAnimationFrame.bind(global$1);\r\n    }\r\n    return function (callback) { return setTimeout(function () { return callback(Date.now()); }, 1000 / 60); };\r\n})();\n\n// Defines minimum timeout before adding a trailing call.\r\nvar trailingTimeout = 2;\r\n/**\r\n * Creates a wrapper function which ensures that provided callback will be\r\n * invoked only once during the specified delay period.\r\n *\r\n * @param {Function} callback - Function to be invoked after the delay period.\r\n * @param {number} delay - Delay after which to invoke callback.\r\n * @returns {Function}\r\n */\r\nfunction throttle (callback, delay) {\r\n    var leadingCall = false, trailingCall = false, lastCallTime = 0;\r\n    /**\r\n     * Invokes the original callback function and schedules new invocation if\r\n     * the \"proxy\" was called during current request.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    function resolvePending() {\r\n        if (leadingCall) {\r\n            leadingCall = false;\r\n            callback();\r\n        }\r\n        if (trailingCall) {\r\n            proxy();\r\n        }\r\n    }\r\n    /**\r\n     * Callback invoked after the specified delay. It will further postpone\r\n     * invocation of the original function delegating it to the\r\n     * requestAnimationFrame.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    function timeoutCallback() {\r\n        requestAnimationFrame$1(resolvePending);\r\n    }\r\n    /**\r\n     * Schedules invocation of the original function.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    function proxy() {\r\n        var timeStamp = Date.now();\r\n        if (leadingCall) {\r\n            // Reject immediately following calls.\r\n            if (timeStamp - lastCallTime < trailingTimeout) {\r\n                return;\r\n            }\r\n            // Schedule new call to be in invoked when the pending one is resolved.\r\n            // This is important for \"transitions\" which never actually start\r\n            // immediately so there is a chance that we might miss one if change\r\n            // happens amids the pending invocation.\r\n            trailingCall = true;\r\n        }\r\n        else {\r\n            leadingCall = true;\r\n            trailingCall = false;\r\n            setTimeout(timeoutCallback, delay);\r\n        }\r\n        lastCallTime = timeStamp;\r\n    }\r\n    return proxy;\r\n}\n\n// Minimum delay before invoking the update of observers.\r\nvar REFRESH_DELAY = 20;\r\n// A list of substrings of CSS properties used to find transition events that\r\n// might affect dimensions of observed elements.\r\nvar transitionKeys = ['top', 'right', 'bottom', 'left', 'width', 'height', 'size', 'weight'];\r\n// Check if MutationObserver is available.\r\nvar mutationObserverSupported = typeof MutationObserver !== 'undefined';\r\n/**\r\n * Singleton controller class which handles updates of ResizeObserver instances.\r\n */\r\nvar ResizeObserverController = /** @class */ (function () {\r\n    /**\r\n     * Creates a new instance of ResizeObserverController.\r\n     *\r\n     * @private\r\n     */\r\n    function ResizeObserverController() {\r\n        /**\r\n         * Indicates whether DOM listeners have been added.\r\n         *\r\n         * @private {boolean}\r\n         */\r\n        this.connected_ = false;\r\n        /**\r\n         * Tells that controller has subscribed for Mutation Events.\r\n         *\r\n         * @private {boolean}\r\n         */\r\n        this.mutationEventsAdded_ = false;\r\n        /**\r\n         * Keeps reference to the instance of MutationObserver.\r\n         *\r\n         * @private {MutationObserver}\r\n         */\r\n        this.mutationsObserver_ = null;\r\n        /**\r\n         * A list of connected observers.\r\n         *\r\n         * @private {Array<ResizeObserverSPI>}\r\n         */\r\n        this.observers_ = [];\r\n        this.onTransitionEnd_ = this.onTransitionEnd_.bind(this);\r\n        this.refresh = throttle(this.refresh.bind(this), REFRESH_DELAY);\r\n    }\r\n    /**\r\n     * Adds observer to observers list.\r\n     *\r\n     * @param {ResizeObserverSPI} observer - Observer to be added.\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.addObserver = function (observer) {\r\n        if (!~this.observers_.indexOf(observer)) {\r\n            this.observers_.push(observer);\r\n        }\r\n        // Add listeners if they haven't been added yet.\r\n        if (!this.connected_) {\r\n            this.connect_();\r\n        }\r\n    };\r\n    /**\r\n     * Removes observer from observers list.\r\n     *\r\n     * @param {ResizeObserverSPI} observer - Observer to be removed.\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.removeObserver = function (observer) {\r\n        var observers = this.observers_;\r\n        var index = observers.indexOf(observer);\r\n        // Remove observer if it's present in registry.\r\n        if (~index) {\r\n            observers.splice(index, 1);\r\n        }\r\n        // Remove listeners if controller has no connected observers.\r\n        if (!observers.length && this.connected_) {\r\n            this.disconnect_();\r\n        }\r\n    };\r\n    /**\r\n     * Invokes the update of observers. It will continue running updates insofar\r\n     * it detects changes.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.refresh = function () {\r\n        var changesDetected = this.updateObservers_();\r\n        // Continue running updates if changes have been detected as there might\r\n        // be future ones caused by CSS transitions.\r\n        if (changesDetected) {\r\n            this.refresh();\r\n        }\r\n    };\r\n    /**\r\n     * Updates every observer from observers list and notifies them of queued\r\n     * entries.\r\n     *\r\n     * @private\r\n     * @returns {boolean} Returns \"true\" if any observer has detected changes in\r\n     *      dimensions of it's elements.\r\n     */\r\n    ResizeObserverController.prototype.updateObservers_ = function () {\r\n        // Collect observers that have active observations.\r\n        var activeObservers = this.observers_.filter(function (observer) {\r\n            return observer.gatherActive(), observer.hasActive();\r\n        });\r\n        // Deliver notifications in a separate cycle in order to avoid any\r\n        // collisions between observers, e.g. when multiple instances of\r\n        // ResizeObserver are tracking the same element and the callback of one\r\n        // of them changes content dimensions of the observed target. Sometimes\r\n        // this may result in notifications being blocked for the rest of observers.\r\n        activeObservers.forEach(function (observer) { return observer.broadcastActive(); });\r\n        return activeObservers.length > 0;\r\n    };\r\n    /**\r\n     * Initializes DOM listeners.\r\n     *\r\n     * @private\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.connect_ = function () {\r\n        // Do nothing if running in a non-browser environment or if listeners\r\n        // have been already added.\r\n        if (!isBrowser || this.connected_) {\r\n            return;\r\n        }\r\n        // Subscription to the \"Transitionend\" event is used as a workaround for\r\n        // delayed transitions. This way it's possible to capture at least the\r\n        // final state of an element.\r\n        document.addEventListener('transitionend', this.onTransitionEnd_);\r\n        window.addEventListener('resize', this.refresh);\r\n        if (mutationObserverSupported) {\r\n            this.mutationsObserver_ = new MutationObserver(this.refresh);\r\n            this.mutationsObserver_.observe(document, {\r\n                attributes: true,\r\n                childList: true,\r\n                characterData: true,\r\n                subtree: true\r\n            });\r\n        }\r\n        else {\r\n            document.addEventListener('DOMSubtreeModified', this.refresh);\r\n            this.mutationEventsAdded_ = true;\r\n        }\r\n        this.connected_ = true;\r\n    };\r\n    /**\r\n     * Removes DOM listeners.\r\n     *\r\n     * @private\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.disconnect_ = function () {\r\n        // Do nothing if running in a non-browser environment or if listeners\r\n        // have been already removed.\r\n        if (!isBrowser || !this.connected_) {\r\n            return;\r\n        }\r\n        document.removeEventListener('transitionend', this.onTransitionEnd_);\r\n        window.removeEventListener('resize', this.refresh);\r\n        if (this.mutationsObserver_) {\r\n            this.mutationsObserver_.disconnect();\r\n        }\r\n        if (this.mutationEventsAdded_) {\r\n            document.removeEventListener('DOMSubtreeModified', this.refresh);\r\n        }\r\n        this.mutationsObserver_ = null;\r\n        this.mutationEventsAdded_ = false;\r\n        this.connected_ = false;\r\n    };\r\n    /**\r\n     * \"Transitionend\" event handler.\r\n     *\r\n     * @private\r\n     * @param {TransitionEvent} event\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.onTransitionEnd_ = function (_a) {\r\n        var _b = _a.propertyName, propertyName = _b === void 0 ? '' : _b;\r\n        // Detect whether transition may affect dimensions of an element.\r\n        var isReflowProperty = transitionKeys.some(function (key) {\r\n            return !!~propertyName.indexOf(key);\r\n        });\r\n        if (isReflowProperty) {\r\n            this.refresh();\r\n        }\r\n    };\r\n    /**\r\n     * Returns instance of the ResizeObserverController.\r\n     *\r\n     * @returns {ResizeObserverController}\r\n     */\r\n    ResizeObserverController.getInstance = function () {\r\n        if (!this.instance_) {\r\n            this.instance_ = new ResizeObserverController();\r\n        }\r\n        return this.instance_;\r\n    };\r\n    /**\r\n     * Holds reference to the controller's instance.\r\n     *\r\n     * @private {ResizeObserverController}\r\n     */\r\n    ResizeObserverController.instance_ = null;\r\n    return ResizeObserverController;\r\n}());\n\n/**\r\n * Defines non-writable/enumerable properties of the provided target object.\r\n *\r\n * @param {Object} target - Object for which to define properties.\r\n * @param {Object} props - Properties to be defined.\r\n * @returns {Object} Target object.\r\n */\r\nvar defineConfigurable = (function (target, props) {\r\n    for (var _i = 0, _a = Object.keys(props); _i < _a.length; _i++) {\r\n        var key = _a[_i];\r\n        Object.defineProperty(target, key, {\r\n            value: props[key],\r\n            enumerable: false,\r\n            writable: false,\r\n            configurable: true\r\n        });\r\n    }\r\n    return target;\r\n});\n\n/**\r\n * Returns the global object associated with provided element.\r\n *\r\n * @param {Object} target\r\n * @returns {Object}\r\n */\r\nvar getWindowOf = (function (target) {\r\n    // Assume that the element is an instance of Node, which means that it\r\n    // has the \"ownerDocument\" property from which we can retrieve a\r\n    // corresponding global object.\r\n    var ownerGlobal = target && target.ownerDocument && target.ownerDocument.defaultView;\r\n    // Return the local global object if it's not possible extract one from\r\n    // provided element.\r\n    return ownerGlobal || global$1;\r\n});\n\n// Placeholder of an empty content rectangle.\r\nvar emptyRect = createRectInit(0, 0, 0, 0);\r\n/**\r\n * Converts provided string to a number.\r\n *\r\n * @param {number|string} value\r\n * @returns {number}\r\n */\r\nfunction toFloat(value) {\r\n    return parseFloat(value) || 0;\r\n}\r\n/**\r\n * Extracts borders size from provided styles.\r\n *\r\n * @param {CSSStyleDeclaration} styles\r\n * @param {...string} positions - Borders positions (top, right, ...)\r\n * @returns {number}\r\n */\r\nfunction getBordersSize(styles) {\r\n    var positions = [];\r\n    for (var _i = 1; _i < arguments.length; _i++) {\r\n        positions[_i - 1] = arguments[_i];\r\n    }\r\n    return positions.reduce(function (size, position) {\r\n        var value = styles['border-' + position + '-width'];\r\n        return size + toFloat(value);\r\n    }, 0);\r\n}\r\n/**\r\n * Extracts paddings sizes from provided styles.\r\n *\r\n * @param {CSSStyleDeclaration} styles\r\n * @returns {Object} Paddings box.\r\n */\r\nfunction getPaddings(styles) {\r\n    var positions = ['top', 'right', 'bottom', 'left'];\r\n    var paddings = {};\r\n    for (var _i = 0, positions_1 = positions; _i < positions_1.length; _i++) {\r\n        var position = positions_1[_i];\r\n        var value = styles['padding-' + position];\r\n        paddings[position] = toFloat(value);\r\n    }\r\n    return paddings;\r\n}\r\n/**\r\n * Calculates content rectangle of provided SVG element.\r\n *\r\n * @param {SVGGraphicsElement} target - Element content rectangle of which needs\r\n *      to be calculated.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getSVGContentRect(target) {\r\n    var bbox = target.getBBox();\r\n    return createRectInit(0, 0, bbox.width, bbox.height);\r\n}\r\n/**\r\n * Calculates content rectangle of provided HTMLElement.\r\n *\r\n * @param {HTMLElement} target - Element for which to calculate the content rectangle.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getHTMLElementContentRect(target) {\r\n    // Client width & height properties can't be\r\n    // used exclusively as they provide rounded values.\r\n    var clientWidth = target.clientWidth, clientHeight = target.clientHeight;\r\n    // By this condition we can catch all non-replaced inline, hidden and\r\n    // detached elements. Though elements with width & height properties less\r\n    // than 0.5 will be discarded as well.\r\n    //\r\n    // Without it we would need to implement separate methods for each of\r\n    // those cases and it's not possible to perform a precise and performance\r\n    // effective test for hidden elements. E.g. even jQuery's ':visible' filter\r\n    // gives wrong results for elements with width & height less than 0.5.\r\n    if (!clientWidth && !clientHeight) {\r\n        return emptyRect;\r\n    }\r\n    var styles = getWindowOf(target).getComputedStyle(target);\r\n    var paddings = getPaddings(styles);\r\n    var horizPad = paddings.left + paddings.right;\r\n    var vertPad = paddings.top + paddings.bottom;\r\n    // Computed styles of width & height are being used because they are the\r\n    // only dimensions available to JS that contain non-rounded values. It could\r\n    // be possible to utilize the getBoundingClientRect if only it's data wasn't\r\n    // affected by CSS transformations let alone paddings, borders and scroll bars.\r\n    var width = toFloat(styles.width), height = toFloat(styles.height);\r\n    // Width & height include paddings and borders when the 'border-box' box\r\n    // model is applied (except for IE).\r\n    if (styles.boxSizing === 'border-box') {\r\n        // Following conditions are required to handle Internet Explorer which\r\n        // doesn't include paddings and borders to computed CSS dimensions.\r\n        //\r\n        // We can say that if CSS dimensions + paddings are equal to the \"client\"\r\n        // properties then it's either IE, and thus we don't need to subtract\r\n        // anything, or an element merely doesn't have paddings/borders styles.\r\n        if (Math.round(width + horizPad) !== clientWidth) {\r\n            width -= getBordersSize(styles, 'left', 'right') + horizPad;\r\n        }\r\n        if (Math.round(height + vertPad) !== clientHeight) {\r\n            height -= getBordersSize(styles, 'top', 'bottom') + vertPad;\r\n        }\r\n    }\r\n    // Following steps can't be applied to the document's root element as its\r\n    // client[Width/Height] properties represent viewport area of the window.\r\n    // Besides, it's as well not necessary as the <html> itself neither has\r\n    // rendered scroll bars nor it can be clipped.\r\n    if (!isDocumentElement(target)) {\r\n        // In some browsers (only in Firefox, actually) CSS width & height\r\n        // include scroll bars size which can be removed at this step as scroll\r\n        // bars are the only difference between rounded dimensions + paddings\r\n        // and \"client\" properties, though that is not always true in Chrome.\r\n        var vertScrollbar = Math.round(width + horizPad) - clientWidth;\r\n        var horizScrollbar = Math.round(height + vertPad) - clientHeight;\r\n        // Chrome has a rather weird rounding of \"client\" properties.\r\n        // E.g. for an element with content width of 314.2px it sometimes gives\r\n        // the client width of 315px and for the width of 314.7px it may give\r\n        // 314px. And it doesn't happen all the time. So just ignore this delta\r\n        // as a non-relevant.\r\n        if (Math.abs(vertScrollbar) !== 1) {\r\n            width -= vertScrollbar;\r\n        }\r\n        if (Math.abs(horizScrollbar) !== 1) {\r\n            height -= horizScrollbar;\r\n        }\r\n    }\r\n    return createRectInit(paddings.left, paddings.top, width, height);\r\n}\r\n/**\r\n * Checks whether provided element is an instance of the SVGGraphicsElement.\r\n *\r\n * @param {Element} target - Element to be checked.\r\n * @returns {boolean}\r\n */\r\nvar isSVGGraphicsElement = (function () {\r\n    // Some browsers, namely IE and Edge, don't have the SVGGraphicsElement\r\n    // interface.\r\n    if (typeof SVGGraphicsElement !== 'undefined') {\r\n        return function (target) { return target instanceof getWindowOf(target).SVGGraphicsElement; };\r\n    }\r\n    // If it's so, then check that element is at least an instance of the\r\n    // SVGElement and that it has the \"getBBox\" method.\r\n    // eslint-disable-next-line no-extra-parens\r\n    return function (target) { return (target instanceof getWindowOf(target).SVGElement &&\r\n        typeof target.getBBox === 'function'); };\r\n})();\r\n/**\r\n * Checks whether provided element is a document element (<html>).\r\n *\r\n * @param {Element} target - Element to be checked.\r\n * @returns {boolean}\r\n */\r\nfunction isDocumentElement(target) {\r\n    return target === getWindowOf(target).document.documentElement;\r\n}\r\n/**\r\n * Calculates an appropriate content rectangle for provided html or svg element.\r\n *\r\n * @param {Element} target - Element content rectangle of which needs to be calculated.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getContentRect(target) {\r\n    if (!isBrowser) {\r\n        return emptyRect;\r\n    }\r\n    if (isSVGGraphicsElement(target)) {\r\n        return getSVGContentRect(target);\r\n    }\r\n    return getHTMLElementContentRect(target);\r\n}\r\n/**\r\n * Creates rectangle with an interface of the DOMRectReadOnly.\r\n * Spec: https://drafts.fxtf.org/geometry/#domrectreadonly\r\n *\r\n * @param {DOMRectInit} rectInit - Object with rectangle's x/y coordinates and dimensions.\r\n * @returns {DOMRectReadOnly}\r\n */\r\nfunction createReadOnlyRect(_a) {\r\n    var x = _a.x, y = _a.y, width = _a.width, height = _a.height;\r\n    // If DOMRectReadOnly is available use it as a prototype for the rectangle.\r\n    var Constr = typeof DOMRectReadOnly !== 'undefined' ? DOMRectReadOnly : Object;\r\n    var rect = Object.create(Constr.prototype);\r\n    // Rectangle's properties are not writable and non-enumerable.\r\n    defineConfigurable(rect, {\r\n        x: x, y: y, width: width, height: height,\r\n        top: y,\r\n        right: x + width,\r\n        bottom: height + y,\r\n        left: x\r\n    });\r\n    return rect;\r\n}\r\n/**\r\n * Creates DOMRectInit object based on the provided dimensions and the x/y coordinates.\r\n * Spec: https://drafts.fxtf.org/geometry/#dictdef-domrectinit\r\n *\r\n * @param {number} x - X coordinate.\r\n * @param {number} y - Y coordinate.\r\n * @param {number} width - Rectangle's width.\r\n * @param {number} height - Rectangle's height.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction createRectInit(x, y, width, height) {\r\n    return { x: x, y: y, width: width, height: height };\r\n}\n\n/**\r\n * Class that is responsible for computations of the content rectangle of\r\n * provided DOM element and for keeping track of it's changes.\r\n */\r\nvar ResizeObservation = /** @class */ (function () {\r\n    /**\r\n     * Creates an instance of ResizeObservation.\r\n     *\r\n     * @param {Element} target - Element to be observed.\r\n     */\r\n    function ResizeObservation(target) {\r\n        /**\r\n         * Broadcasted width of content rectangle.\r\n         *\r\n         * @type {number}\r\n         */\r\n        this.broadcastWidth = 0;\r\n        /**\r\n         * Broadcasted height of content rectangle.\r\n         *\r\n         * @type {number}\r\n         */\r\n        this.broadcastHeight = 0;\r\n        /**\r\n         * Reference to the last observed content rectangle.\r\n         *\r\n         * @private {DOMRectInit}\r\n         */\r\n        this.contentRect_ = createRectInit(0, 0, 0, 0);\r\n        this.target = target;\r\n    }\r\n    /**\r\n     * Updates content rectangle and tells whether it's width or height properties\r\n     * have changed since the last broadcast.\r\n     *\r\n     * @returns {boolean}\r\n     */\r\n    ResizeObservation.prototype.isActive = function () {\r\n        var rect = getContentRect(this.target);\r\n        this.contentRect_ = rect;\r\n        return (rect.width !== this.broadcastWidth ||\r\n            rect.height !== this.broadcastHeight);\r\n    };\r\n    /**\r\n     * Updates 'broadcastWidth' and 'broadcastHeight' properties with a data\r\n     * from the corresponding properties of the last observed content rectangle.\r\n     *\r\n     * @returns {DOMRectInit} Last observed content rectangle.\r\n     */\r\n    ResizeObservation.prototype.broadcastRect = function () {\r\n        var rect = this.contentRect_;\r\n        this.broadcastWidth = rect.width;\r\n        this.broadcastHeight = rect.height;\r\n        return rect;\r\n    };\r\n    return ResizeObservation;\r\n}());\n\nvar ResizeObserverEntry = /** @class */ (function () {\r\n    /**\r\n     * Creates an instance of ResizeObserverEntry.\r\n     *\r\n     * @param {Element} target - Element that is being observed.\r\n     * @param {DOMRectInit} rectInit - Data of the element's content rectangle.\r\n     */\r\n    function ResizeObserverEntry(target, rectInit) {\r\n        var contentRect = createReadOnlyRect(rectInit);\r\n        // According to the specification following properties are not writable\r\n        // and are also not enumerable in the native implementation.\r\n        //\r\n        // Property accessors are not being used as they'd require to define a\r\n        // private WeakMap storage which may cause memory leaks in browsers that\r\n        // don't support this type of collections.\r\n        defineConfigurable(this, { target: target, contentRect: contentRect });\r\n    }\r\n    return ResizeObserverEntry;\r\n}());\n\nvar ResizeObserverSPI = /** @class */ (function () {\r\n    /**\r\n     * Creates a new instance of ResizeObserver.\r\n     *\r\n     * @param {ResizeObserverCallback} callback - Callback function that is invoked\r\n     *      when one of the observed elements changes it's content dimensions.\r\n     * @param {ResizeObserverController} controller - Controller instance which\r\n     *      is responsible for the updates of observer.\r\n     * @param {ResizeObserver} callbackCtx - Reference to the public\r\n     *      ResizeObserver instance which will be passed to callback function.\r\n     */\r\n    function ResizeObserverSPI(callback, controller, callbackCtx) {\r\n        /**\r\n         * Collection of resize observations that have detected changes in dimensions\r\n         * of elements.\r\n         *\r\n         * @private {Array<ResizeObservation>}\r\n         */\r\n        this.activeObservations_ = [];\r\n        /**\r\n         * Registry of the ResizeObservation instances.\r\n         *\r\n         * @private {Map<Element, ResizeObservation>}\r\n         */\r\n        this.observations_ = new MapShim();\r\n        if (typeof callback !== 'function') {\r\n            throw new TypeError('The callback provided as parameter 1 is not a function.');\r\n        }\r\n        this.callback_ = callback;\r\n        this.controller_ = controller;\r\n        this.callbackCtx_ = callbackCtx;\r\n    }\r\n    /**\r\n     * Starts observing provided element.\r\n     *\r\n     * @param {Element} target - Element to be observed.\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.observe = function (target) {\r\n        if (!arguments.length) {\r\n            throw new TypeError('1 argument required, but only 0 present.');\r\n        }\r\n        // Do nothing if current environment doesn't have the Element interface.\r\n        if (typeof Element === 'undefined' || !(Element instanceof Object)) {\r\n            return;\r\n        }\r\n        if (!(target instanceof getWindowOf(target).Element)) {\r\n            throw new TypeError('parameter 1 is not of type \"Element\".');\r\n        }\r\n        var observations = this.observations_;\r\n        // Do nothing if element is already being observed.\r\n        if (observations.has(target)) {\r\n            return;\r\n        }\r\n        observations.set(target, new ResizeObservation(target));\r\n        this.controller_.addObserver(this);\r\n        // Force the update of observations.\r\n        this.controller_.refresh();\r\n    };\r\n    /**\r\n     * Stops observing provided element.\r\n     *\r\n     * @param {Element} target - Element to stop observing.\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.unobserve = function (target) {\r\n        if (!arguments.length) {\r\n            throw new TypeError('1 argument required, but only 0 present.');\r\n        }\r\n        // Do nothing if current environment doesn't have the Element interface.\r\n        if (typeof Element === 'undefined' || !(Element instanceof Object)) {\r\n            return;\r\n        }\r\n        if (!(target instanceof getWindowOf(target).Element)) {\r\n            throw new TypeError('parameter 1 is not of type \"Element\".');\r\n        }\r\n        var observations = this.observations_;\r\n        // Do nothing if element is not being observed.\r\n        if (!observations.has(target)) {\r\n            return;\r\n        }\r\n        observations.delete(target);\r\n        if (!observations.size) {\r\n            this.controller_.removeObserver(this);\r\n        }\r\n    };\r\n    /**\r\n     * Stops observing all elements.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.disconnect = function () {\r\n        this.clearActive();\r\n        this.observations_.clear();\r\n        this.controller_.removeObserver(this);\r\n    };\r\n    /**\r\n     * Collects observation instances the associated element of which has changed\r\n     * it's content rectangle.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.gatherActive = function () {\r\n        var _this = this;\r\n        this.clearActive();\r\n        this.observations_.forEach(function (observation) {\r\n            if (observation.isActive()) {\r\n                _this.activeObservations_.push(observation);\r\n            }\r\n        });\r\n    };\r\n    /**\r\n     * Invokes initial callback function with a list of ResizeObserverEntry\r\n     * instances collected from active resize observations.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.broadcastActive = function () {\r\n        // Do nothing if observer doesn't have active observations.\r\n        if (!this.hasActive()) {\r\n            return;\r\n        }\r\n        var ctx = this.callbackCtx_;\r\n        // Create ResizeObserverEntry instance for every active observation.\r\n        var entries = this.activeObservations_.map(function (observation) {\r\n            return new ResizeObserverEntry(observation.target, observation.broadcastRect());\r\n        });\r\n        this.callback_.call(ctx, entries, ctx);\r\n        this.clearActive();\r\n    };\r\n    /**\r\n     * Clears the collection of active observations.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.clearActive = function () {\r\n        this.activeObservations_.splice(0);\r\n    };\r\n    /**\r\n     * Tells whether observer has active observations.\r\n     *\r\n     * @returns {boolean}\r\n     */\r\n    ResizeObserverSPI.prototype.hasActive = function () {\r\n        return this.activeObservations_.length > 0;\r\n    };\r\n    return ResizeObserverSPI;\r\n}());\n\n// Registry of internal observers. If WeakMap is not available use current shim\r\n// for the Map collection as it has all required methods and because WeakMap\r\n// can't be fully polyfilled anyway.\r\nvar observers = typeof WeakMap !== 'undefined' ? new WeakMap() : new MapShim();\r\n/**\r\n * ResizeObserver API. Encapsulates the ResizeObserver SPI implementation\r\n * exposing only those methods and properties that are defined in the spec.\r\n */\r\nvar ResizeObserver = /** @class */ (function () {\r\n    /**\r\n     * Creates a new instance of ResizeObserver.\r\n     *\r\n     * @param {ResizeObserverCallback} callback - Callback that is invoked when\r\n     *      dimensions of the observed elements change.\r\n     */\r\n    function ResizeObserver(callback) {\r\n        if (!(this instanceof ResizeObserver)) {\r\n            throw new TypeError('Cannot call a class as a function.');\r\n        }\r\n        if (!arguments.length) {\r\n            throw new TypeError('1 argument required, but only 0 present.');\r\n        }\r\n        var controller = ResizeObserverController.getInstance();\r\n        var observer = new ResizeObserverSPI(callback, controller, this);\r\n        observers.set(this, observer);\r\n    }\r\n    return ResizeObserver;\r\n}());\r\n// Expose public methods of ResizeObserver.\r\n[\r\n    'observe',\r\n    'unobserve',\r\n    'disconnect'\r\n].forEach(function (method) {\r\n    ResizeObserver.prototype[method] = function () {\r\n        var _a;\r\n        return (_a = observers.get(this))[method].apply(_a, arguments);\r\n    };\r\n});\n\nvar index = (function () {\r\n    // Export existing implementation if available.\r\n    if (typeof global$1.ResizeObserver !== 'undefined') {\r\n        return global$1.ResizeObserver;\r\n    }\r\n    return ResizeObserver;\r\n})();\n\nexport default index;\n", "/*\n * Copyright (c) 2016-2023 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 ResizeObserver from \"resize-observer-polyfill\"\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\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 * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => of(\n  new ResizeObserver(entries => {\n    for (const entry of entries)\n      entry$.next(entry)\n  })\n))\n  .pipe(\n    switchMap(observer => merge(NEVER, of(observer))\n      .pipe(\n        finalize(() => observer.disconnect())\n      )\n    ),\n    shareReplay(1)\n  )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element size\n *\n * @param el - Element\n *\n * @returns Element size\n */\nexport function getElementSize(\n  el: HTMLElement\n): ElementSize {\n  return {\n    width:  el.offsetWidth,\n    height: el.offsetHeight\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element size\n *\n * This function returns an observable that subscribes to a single internal\n * instance of `ResizeObserver` upon subscription, and emit resize events until\n * termination. Note that this function should not be called with the same\n * element twice, as the first unsubscription will terminate observation.\n *\n * Sadly, we can't use the `DOMRect` objects returned by the observer, because\n * we need the emitted values to be consistent with `getElementSize`, which will\n * return the used values (rounded) and not actual values (unrounded). Thus, we\n * use the `offset*` properties. See the linked GitHub issue.\n *\n * @see https://bit.ly/3m0k3he - GitHub issue\n *\n * @param el - Element\n *\n * @returns Element size observable\n */\nexport function watchElementSize(\n  el: HTMLElement\n): Observable<ElementSize> {\n  return observer$\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(() => getElementSize(el))\n        )\n      ),\n      startWith(getElementSize(el))\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 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 * Copyright (c) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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\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 * @param url - URL to change to\n */\nexport function setLocation(url: URL | HTMLLinkElement): void {\n  location.href = url.href\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) 2016-2023 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) 2016-2023 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) 2016-2023 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.substring(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) 2016-2023 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) 2016-2023 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  catchError,\n  from,\n  map,\n  of,\n  shareReplay,\n  switchMap,\n  throwError\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the given URL\n *\n * If the request fails (e.g. when dispatched from `file://` locations), the\n * observable will complete without emitting a value.\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Response observable\n */\nexport function request(\n  url: URL | string, options: RequestInit = { credentials: \"same-origin\" }\n): Observable<Response> {\n  return from(fetch(`${url}`, options))\n    .pipe(\n      catchError(() => EMPTY),\n      switchMap(res => res.status !== 200\n        ? throwError(() => new Error(res.statusText))\n        : of(res)\n      )\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?: RequestInit\n): Observable<T> {\n  return request(url, options)\n    .pipe(\n      switchMap(res => res.json()),\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?: RequestInit\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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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$])\n    .pipe(\n      map(([{ height }, { offset, size }, { x, y }]) => ({\n        offset: {\n          x: offset.x - x,\n          y: offset.y - y + height\n        },\n        size\n      }))\n    )\n}\n", "\"use strict\";(()=>{function c(s,n){parent.postMessage(s,n||\"*\")}function d(...s){return s.reduce((n,e)=>n.then(()=>new Promise(r=>{let t=document.createElement(\"script\");t.src=e,t.onload=r,document.body.appendChild(t)})),Promise.resolve())}var o=class extends EventTarget{constructor(e){super();this.url=e;this.m=e=>{e.source===this.w&&(this.dispatchEvent(new MessageEvent(\"message\",{data:e.data})),this.onmessage&&this.onmessage(e))};this.e=(e,r,t,i,m)=>{if(r===`${this.url}`){let a=new ErrorEvent(\"error\",{message:e,filename:r,lineno:t,colno:i,error:m});this.dispatchEvent(a),this.onerror&&this.onerror(a)}};let r=document.createElement(\"iframe\");r.hidden=!0,document.body.appendChild(this.iframe=r),this.w.document.open(),this.w.document.write(`<html><body><script>postMessage=${c};importScripts=${d};addEventListener(\"error\",ev=>{parent.dispatchEvent(new ErrorEvent(\"error\",{filename:\"${e}\",error:ev.error}))})<\\/script><script src=${e}?${+Date.now()}><\\/script></body></html>`),this.w.document.close(),onmessage=this.m,onerror=this.e,this.r=new Promise((t,i)=>{this.w.onload=t,this.w.onerror=i})}terminate(){document.body.removeChild(this.iframe),onmessage=onerror=null}postMessage(e){this.r.catch().then(()=>{this.w.dispatchEvent(new MessageEvent(\"message\",{data:e}))})}get w(){return this.iframe.contentWindow}};window.IFrameWorker=o;location.protocol===\"file:\"&&(window.Worker=o);})();\n", "/*\n * Copyright (c) 2016-2023 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 \"iframe-worker/shim\"\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) 2016-2023 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  | \"header.autohide\"                  /* Hide header */\n  | \"navigation.expand\"                /* Automatic expansion */\n  | \"navigation.indexes\"               /* Section pages */\n  | \"navigation.instant\"               /* Instant loading */\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                     /* Default version */\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) 2016-2023 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  | \"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  \"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) 2016-2023 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  /* 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) 2016-2023 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) 2016-2023 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  tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  getElementContentSize,\n  watchElementSize,\n  watchElementVisibility\n} from \"~/browser\"\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\n */\nexport interface CodeBlock {\n  scrollable: boolean                  /* Code block overflows */\n}\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<CodeBlock> {\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 | Annotation>> {\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<CodeBlock>()\n    push$.subscribe(({ scrollable }) => {\n      if (scrollable && hover)\n        el.setAttribute(\"tabindex\", \"0\")\n      else\n        el.removeAttribute(\"tabindex\")\n    })\n\n    /* Render button for Clipboard.js integration */\n    if (ClipboardJS.isSupported()) {\n      if (el.closest(\".copy\") || (\n        feature(\"content.code.copy\") && !el.closest(\".no-copy\")\n      )) {\n        const parent = el.closest(\"pre\")!\n        parent.id = `__code_${sequence++}`\n        parent.insertBefore(\n          renderClipboardButton(parent.id),\n          el\n        )\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\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(\n              watchElementSize(container)\n                .pipe(\n                  map(({ width, height }) => width && height),\n                  distinctUntilChanged(),\n                  switchMap(active => active ? annotations$ : EMPTY)\n                )\n            )\n          )\n      }\n    }\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      )\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) 2016-2023 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 tooltip\n *\n * @param id - Tooltip identifier\n *\n * @returns Element\n */\nexport function renderTooltip(id?: string): HTMLElement {\n  return (\n    <div class=\"md-tooltip\" id={id}>\n      <div class=\"md-tooltip__inner md-typeset\"></div>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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\n */\nexport interface Version {\n  version: string                      /* Version identifier */\n  title: string                        /* Version title */\n  aliases: string[]                    /* Version aliases */\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      </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  return (\n    <div class=\"md-version\">\n      <button\n        class=\"md-version__current\"\n        aria-label={translation(\"select.version\")}\n      >\n        {active.title}\n      </button>\n      <ul class=\"md-version__list\">\n        {versions.map(renderVersion)}\n      </ul>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2023 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$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  endWith,\n  finalize,\n  ignoreElements,\n  merge,\n  share,\n  takeUntil\n} from \"rxjs\"\n\nimport {\n  getElement,\n  getElements,\n  getOptionalElement\n} from \"~/browser\"\nimport { renderAnnotation } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n  Annotation,\n  mountAnnotation\n} from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: 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 markers in the given code block\n *\n * @param container - Containing element\n *\n * @returns Annotation markers\n */\nfunction findAnnotationMarkers(container: HTMLElement): Text[] {\n  const markers: Text[] = []\n  for (const el of getElements(\".c, .c1, .cm\", 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 findAnnotationMarkers(container)) {\n    const [, id] = marker.textContent!.match(/\\((\\d+)\\)/)!\n    if (getOptionalElement(`li:nth-child(${id})`, el)) {\n      annotations.set(id, renderAnnotation(id, prefix))\n      marker.replaceWith(annotations.get(id)!)\n    }\n  }\n\n  /* Keep list if there are no annotations to render */\n  if (annotations.size === 0)\n    return EMPTY\n\n  /* Mount component on subscription */\n  return defer(() => {\n    const done$ = new Subject()\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(`li:nth-child(${id})`, el)\n      ])\n\n    /* Handle print mode - see https://bit.ly/3rgPdpt */\n    print$\n      .pipe(\n        takeUntil(done$.pipe(ignoreElements(), endWith(true)))\n      )\n        .subscribe(active => {\n          el.hidden = !active\n\n          /* Show annotations in code block or list (print) */\n          for (const [inner, child] of pairs)\n            if (!active)\n              swap(child, inner)\n            else\n              swap(inner, child)\n        })\n\n    /* Create and return component */\n    return merge(...[...annotations]\n      .map(([, annotation]) => (\n        mountAnnotation(annotation, container, { target$ })\n      ))\n    )\n      .pipe(\n        finalize(() => done$.complete()),\n        share()\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  map,\n  of,\n  shareReplay,\n  tap\n} from \"rxjs\"\n\nimport { watchScript } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../../_\"\n\nimport themeCSS from \"./index.css\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid diagram\n */\nexport interface Mermaid {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid instance observable\n */\nlet mermaid$: 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@9.4.3/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$.subscribe(() => {\n    el.classList.add(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n    const id = `__mermaid_${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    mermaid.mermaidAPI.render(id, text, (svg: string, fn: Function) => {\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\n  /* Create and return component */\n  return mermaid$\n    .pipe(\n      map(() => ({ ref: el }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 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", "/*\n * Copyright (c) 2016-2023 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) 2016-2023 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  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} from \"~/browser\"\nimport { renderTabbedControl } from \"~/templates\"\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}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch content tabs\n *\n * @param el - Content tabs element\n *\n * @returns Content tabs observable\n */\nexport function watchContentTabs(\n  el: HTMLElement\n): Observable<ContentTabs> {\n  const inputs = getElements<HTMLInputElement>(\":scope > input\", el)\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 * This function scrolls the active tab into view. While this functionality is\n * provided by browsers as part of `scrollInfoView`, browsers will always also\n * scroll the vertical axis, which we do not want. Thus, we decided to provide\n * this functionality ourselves.\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$ }: MountOptions\n): Observable<Component<ContentTabs>> {\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  const container = getElement(\".tabbed-labels\", el)\n  return defer(() => {\n    const push$ = new Subject<ContentTabs>()\n    const done$ = push$.pipe(ignoreElements(), endWith(true))\n    combineLatest([push$, watchElementSize(el)])\n      .pipe(\n        auditTime(1, animationFrameScheduler),\n        takeUntil(done$)\n      )\n        .subscribe({\n\n          /* Handle emission */\n          next([{ active }, size]) {\n            const offset = getElementOffset(active)\n            const { width } = getElementSize(active)\n\n            /* Set tab indicator offset and width */\n            el.style.setProperty(\"--md-indicator-x\", `${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$)\n      )\n        .subscribe(direction => {\n          const { width } = getElementSize(container)\n          container.scrollBy({\n            left: width * direction,\n            behavior: \"smooth\"\n          })\n        })\n\n    /* Set up linking of content tabs, if enabled */\n    if (feature(\"content.tabs.link\"))\n      push$.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    /* Create and return component */\n    return watchContentTabs(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) 2016-2023 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 { Viewport, getElements } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Annotation } from \"../annotation\"\nimport {\n  CodeBlock,\n  Mermaid,\n  mountCodeBlock,\n  mountMermaid\n} from \"../code\"\nimport {\n  Details,\n  mountDetails\n} from \"../details\"\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  | ContentTabs\n  | CodeBlock\n  | Mermaid\n  | DataTable\n  | Details\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    /* 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$ }))\n  )\n}\n", "/*\n * Copyright (c) 2016-2023 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) 2016-2023 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  ignoreElements,\n  map,\n  of,\n  shareReplay,\n  startWith,\n  switchMap,\n  takeUntil\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  watchElementSize,\n  watchToggle\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Main } from \"../../main\"\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>> {\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$)\n      )\n        .subscribe(([{ active }, { hidden }]) => {\n          el.classList.toggle(\"md-header--shadow\", active && !hidden)\n          el.hidden = hidden\n        })\n\n    /* Link to main area */\n    main$.subscribe(push$)\n\n    /* Create and return component */\n    return header$\n      .pipe(\n        takeUntil(done$),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2023 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$.subscribe({\n\n      /* Handle emission */\n      next({ active }) {\n        el.classList.toggle(\"md-header__title--active\", active)\n      },\n\n      /* Handle complete */\n      complete() {\n        el.classList.remove(\"md-header__title--active\")\n      }\n    })\n\n    /* Obtain headline, if any */\n    const heading = getOptionalElement(\"article h1\")\n    if (typeof heading === \"undefined\")\n      return EMPTY\n\n    /* Create and return component */\n    return watchHeaderTitle(heading, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2023 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) 2016-2023 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  finalize,\n  fromEvent,\n  map,\n  mergeMap,\n  observeOn,\n  of,\n  shareReplay,\n  startWith,\n  tap\n} from \"rxjs\"\n\nimport { getElements } 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  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  return of(...inputs)\n    .pipe(\n      mergeMap(input => fromEvent(input, \"change\")\n        .pipe(\n          map(() => input)\n        )\n      ),\n      startWith(inputs[Math.max(0, current.index)]),\n      map(input => ({\n        index: inputs.indexOf(input),\n        color: {\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 meta = h(\"meta\", { name: \"theme-color\" })\n  document.head.appendChild(meta)\n\n  /* Mount component on subscription */\n  return defer(() => {\n    const push$ = new Subject<Palette>()\n    push$.subscribe(palette => {\n      document.body.setAttribute(\"data-md-color-switching\", \"\")\n\n      /* Set color palette */\n      for (const [key, value] of Object.entries(palette.color))\n        document.body.setAttribute(`data-md-color-${key}`, value)\n\n      /* 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    /* Update theme-color meta tag */\n    push$\n      .pipe(\n        map(() => {\n          const header = getComponentElement(\"header\")\n          const { backgroundColor } = window.getComputedStyle(header)\n\n          /* Return color in hexadecimal format */\n          return backgroundColor.match(/\\d+/g)!\n            .map(value => (+value).toString(16).padStart(2, \"0\"))\n            .join(\"\")\n        })\n      )\n        .subscribe(color => meta.content = `#${color}`)\n\n    /* Revert transition durations after color switch */\n    push$.pipe(observeOn(asyncScheduler))\n      .subscribe(() => {\n        document.body.removeAttribute(\"data-md-color-switching\")\n      })\n\n    /* Create and return component */\n    const inputs = getElements<HTMLInputElement>(\"input\", el)\n    return watchPalette(inputs)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n  Observable,\n  Subject,\n  map,\n  tap\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport { getElement } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n  alert$: 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 text = el.innerText\n  el.removeAttribute(\"data-md-copying\")\n  return text\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) 2016-2023 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  catchError,\n  defaultIfEmpty,\n  map,\n  of,\n  tap\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport { getElements, requestXML } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sitemap, i.e. a list of URLs\n */\nexport type Sitemap = string[]\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Preprocess a list of URLs\n *\n * This function replaces the `site_url` in the sitemap with the actual base\n * URL, to allow instant loading to work in occasions like Netlify previews.\n *\n * @param urls - URLs\n *\n * @returns URL path parts\n */\nfunction preprocess(urls: Sitemap): Sitemap {\n  if (urls.length < 2)\n    return [\"\"]\n\n  /* Take the first two URLs and remove everything after the last slash */\n  const [root, next] = [...urls]\n    .sort((a, b) => a.length - b.length)\n    .map(url => url.replace(/[^/]+$/, \"\"))\n\n  /* Compute common prefix */\n  let index = 0\n  if (root === next)\n    index = root.length\n  else\n    while (root.charCodeAt(index) === next.charCodeAt(index))\n      index++\n\n  /* Remove common prefix and return in original order */\n  return urls.map(url => url.replace(root.slice(0, index), \"\"))\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the sitemap for the given base URL\n *\n * @param base - Base URL\n *\n * @returns Sitemap observable\n */\nexport function fetchSitemap(base?: URL): Observable<Sitemap> {\n  const cached = __md_get<Sitemap>(\"__sitemap\", sessionStorage, base)\n  if (cached) {\n    return of(cached)\n  } else {\n    const config = configuration()\n    return requestXML(new URL(\"sitemap.xml\", base || config.base))\n      .pipe(\n        map(sitemap => preprocess(getElements(\"loc\", sitemap)\n          .map(node => node.textContent!)\n        )),\n        catchError(() => EMPTY), // @todo refactor instant loading\n        defaultIfEmpty([]),\n        tap(sitemap => __md_set(\"__sitemap\", sitemap, sessionStorage, base))\n      )\n  }\n}\n", "/*\n * Copyright (c) 2016-2023 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  catchError,\n  concat,\n  debounceTime,\n  distinctUntilKeyChanged,\n  endWith,\n  fromEvent,\n  ignoreElements,\n  map,\n  of,\n  share,\n  skip,\n  startWith,\n  switchMap,\n  take,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { configuration, feature } from \"~/_\"\nimport {\n  Viewport,\n  getElements,\n  getLocation,\n  getOptionalElement,\n  request,\n  setLocation,\n  setLocationHash\n} from \"~/browser\"\nimport { getComponentElement } from \"~/components\"\n\nimport { 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}\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 setupInstantLoading(\n  { location$, viewport$ }: 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 instant navigation request, and canonicalize URLs to the current\n  // base URL. The base URL will remain stable in between loads, as it's only\n  // read at the first initialization of the application.\n  const sitemap$ = fetchSitemap()\n    .pipe(\n      map(paths => paths.map(path => `${new URL(path, config.base)}`))\n    )\n\n  // Intercept inter-site navigation - to keep the number of event listeners\n  // low we use the fact that uncaptured events bubble up to the body. This also\n  // has the nice property that we don't need to detach and then again attach\n  // event listeners when instant navigation occurs.\n  const instant$ = fromEvent<MouseEvent>(document.body, \"click\")\n    .pipe(\n      withLatestFrom(sitemap$),\n      switchMap(([ev, sitemap]) => {\n        if (!(ev.target instanceof Element))\n          return EMPTY\n\n        // Skip, as target is not within a link - clicks on non-link elements\n        // are also captured, which we need to exclude from processing.\n        const el = ev.target.closest(\"a\")\n        if (el === null)\n          return EMPTY\n\n        // Skip, as link opens in new window - we now know we have captured a\n        // click on a link, but the link either has a `target` property defined,\n        // or the user pressed the `meta` or `ctrl` key to open it in a new\n        // window. Thus, we need to filter those events, too.\n        if (el.target || ev.metaKey || ev.ctrlKey)\n          return EMPTY\n\n        // Next, we must check if the URL is relevant for us, i.e., if it's an\n        // internal link to a page that is managed by MkDocs. Only then we can\n        // be sure that the structure of the page to be loaded adheres to the\n        // current document structure and can subsequently be injected into it\n        // without doing a full reload. For this reason, we must canonicalize\n        // the URL by removing all search parameters and hash fragments.\n        const url = new URL(el.href)\n        url.search = url.hash = \"\"\n\n        // Skip, if URL is not included in the sitemap - this could be the case\n        // when linking between versions or languages, or to another page that\n        // the author included as part of the build, but that is not managed by\n        // MkDocs. In that case we must not continue with instant navigation.\n        if (!sitemap.includes(`${url}`))\n          return EMPTY\n\n        // We now know that we have a link to an internal page, so we prevent\n        // the browser from navigation and emit the URL for instant navigation.\n        // Note that this also includes anchor links, which means we need to\n        // implement anchor positioning ourselves. The reason for this is that\n        // if we wouldn't manage anchor links as well, scroll restoration will\n        // not work correctly (e.g. following an anchor link and scrolling).\n        ev.preventDefault()\n        return of(new URL(el.href))\n      }),\n      share()\n    )\n\n  // Before fetching for the first time, resolve the absolute favicon position,\n  // as the browser will try to fetch the icon immediately.\n  instant$.pipe(take(1))\n    .subscribe(() => {\n      const favicon = getOptionalElement<HTMLLinkElement>(\"link[rel=icon]\")\n      if (typeof favicon !== \"undefined\")\n        favicon.href = favicon.href\n    })\n\n  // Enable scroll restoration before window unloads - this is essential to\n  // ensure that full reloads (F5) restore the viewport offset correctly. If\n  // only popstate events wouldn't reset the scroll position prior to their\n  // emission, we could just reset this in popstate. Meh.\n  fromEvent(window, \"beforeunload\")\n    .subscribe(() => {\n      history.scrollRestoration = \"auto\"\n    })\n\n  // When an instant navigation event occurs, disable scroll restoration, since\n  // we must normalize and synchronize the behavior across all browsers. For\n  // instance, when the user clicks the back or forward button, the browser\n  // would immediately jump to the position of the previous document.\n  instant$.pipe(withLatestFrom(viewport$))\n    .subscribe(([url, { offset }]) => {\n      history.scrollRestoration = \"manual\"\n\n      // While it would be better UX to defer the history state change until the\n      // document was fully fetched and parsed, we must schedule it here, since\n      // popstate events are emitted when history state changes happen. Moreover\n      // we need to back up the current viewport offset, so we can restore it\n      // when popstate events occur, e.g., when the browser's back and forward\n      // buttons are used for navigation.\n      history.replaceState(offset, \"\")\n      history.pushState(null, \"\", url)\n    })\n\n  // Emit URL that should be fetched via instant loading on location subject,\n  // which was passed into this function. The idea is that instant loading can\n  // be intercepted by other parts of the application, which can synchronously\n  // back up or restore state before instant loading happens.\n  instant$.subscribe(location$)\n\n  // Fetch document - when fetching, we could use `responseType: document`, but\n  // since all MkDocs links are relative, we need to make sure that the current\n  // location matches the document we just loaded. Otherwise any relative links\n  // in the document might use the old location. If the request fails for some\n  // reason, we fall back to regular navigation and set the location explicitly,\n  // which will force-load the page. Furthermore, we must pre-warm the buffer\n  // for the duplicate check, or the first click on an anchor link will also\n  // trigger an instant loading event, which doesn't make sense.\n  const response$ = location$\n    .pipe(\n      startWith(getLocation()),\n      distinctUntilKeyChanged(\"pathname\"),\n      skip(1),\n      switchMap(url => request(url)\n        .pipe(\n          catchError(() => {\n            setLocation(url)\n            return EMPTY\n          })\n        )\n      )\n    )\n\n  // Initialize the DOM parser, parse the returned HTML, and replace selected\n  // meta tags and components before handing control down to the application.\n  const dom = new DOMParser()\n  const document$ = response$\n    .pipe(\n      switchMap(res => res.text()),\n      switchMap(res => {\n        const document = dom.parseFromString(res, \"text/html\")\n        for (const selector of [\n\n          // Meta tags\n          \"title\",\n          \"link[rel=canonical]\",\n          \"meta[name=author]\",\n          \"meta[name=description]\",\n\n          // Components\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, document)\n          if (\n            typeof source !== \"undefined\" &&\n            typeof target !== \"undefined\"\n          ) {\n            source.replaceWith(target)\n          }\n        }\n\n        // After meta tags and components 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 = document.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      share()\n    )\n\n  // Intercept popstate events, e.g. when using the browser's back and forward\n  // buttons, and emit new location for fetching and parsing.\n  const popstate$ = fromEvent<PopStateEvent>(window, \"popstate\")\n  popstate$.pipe(map(getLocation))\n    .subscribe(location$)\n\n  // Intercept clicks on anchor links, and scroll document into position. As\n  // we disabled scroll restoration, we need to do this manually here.\n  location$\n    .pipe(\n      startWith(getLocation()),\n      bufferCount(2, 1),\n      switchMap(([prev, next]) => (\n        prev.pathname === next.pathname &&\n        prev.hash     !== next.hash\n      )\n        ? of(next)\n        : EMPTY\n      )\n    )\n      .subscribe(url => {\n        if (history.state !== null || !url.hash) {\n          window.scrollTo(0, history.state?.y ?? 0)\n        } else {\n          history.scrollRestoration = \"auto\"\n          setLocationHash(url.hash)\n          history.scrollRestoration = \"manual\"\n        }\n      })\n\n  // After parsing the document, check if the current history entry has a state.\n  // This may happen when users press the back or forward button to visit a page\n  // that was already seen. If there's no state, it means a new page was visited\n  // and we should scroll to the top, unless an anchor is given.\n  document$.pipe(withLatestFrom(location$))\n    .subscribe(([, url]) => {\n      if (history.state !== null || !url.hash) {\n        window.scrollTo(0, history.state?.y ?? 0)\n      } else {\n        setLocationHash(url.hash)\n      }\n    })\n\n  // If the current history is not empty, register an event listener updating\n  // the current history state whenever the scroll position changes. This must\n  // be debounced and cannot be done in popstate, as popstate has already\n  // removed the entry from the history.\n  document$\n    .pipe(\n      switchMap(() => viewport$),\n      distinctUntilKeyChanged(\"offset\"),\n      debounceTime(100)\n    )\n      .subscribe(({ offset }) => {\n        history.replaceState(offset, \"\")\n      })\n\n  // Return document\n  return document$\n}\n", "/*\n * Copyright (c) 2016-2023 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\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) 2016-2023 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) 2016-2023 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$\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Subject,\n  catchError,\n  combineLatest,\n  filter,\n  fromEvent,\n  map,\n  of,\n  switchMap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n  getElement,\n  getLocation,\n  requestJSON,\n  setLocation\n} from \"~/browser\"\nimport { getComponentElements } from \"~/components\"\nimport {\n  Version,\n  renderVersionSelector\n} from \"~/templates\"\n\nimport { fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n  document$: 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 loading. We need to refactor this\n                // at some point together with instant loading.\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            const { version } = urls.get(url)!\n            return fetchSitemap(new URL(url))\n              .pipe(\n                map(sitemap => {\n                  const location = getLocation()\n                  const path = location.href.replace(config.base, \"\")\n                  return sitemap.includes(path.split(\"#\")[0])\n                    ? new URL(`../${version}/${path}`, config.base)\n                    : new URL(url)\n                })\n              )\n          })\n        )\n      )\n    )\n      .subscribe(url => setLocation(url))\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 loading */\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        /* Check if version is considered a default */\n        const ignored = [config.version?.default || [\"latest\"]].flat()\n        main: for (const ignore of ignored)\n          for (const alias of current.aliases)\n            if (new RegExp(ignore, \"i\").test(alias)) {\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) 2016-2023 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  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 = new URL(location.href)\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  /* 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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  defer,\n  distinctUntilChanged,\n  endWith,\n  finalize,\n  first,\n  from,\n  fromEvent,\n  ignoreElements,\n  map,\n  mergeMap,\n  takeUntil,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  getElement,\n  getElementContainer,\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.parentElement!\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          const container = getElementContainer(item)\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            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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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>(\"[href^=\\\\#]\", 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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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-}
diff --git a/assets/javascripts/lunr/min/lunr.el.min.js b/assets/javascripts/lunr/min/lunr.el.min.js
new file mode 100644
index 000000000..ace017bd6
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.el.min.js
@@ -0,0 +1 @@
+!function(e,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.el=function(){this.pipeline.reset(),void 0===this.searchPipeline&&this.pipeline.add(e.el.trimmer,e.el.normilizer),this.pipeline.add(e.el.stopWordFilter,e.el.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.el.stemmer))},e.el.wordCharacters="A-Za-zΑαΒβΓγΔδΕεΖζΗηΘθΙιΚκΛλΜμΝνΞξΟοΠπΡρΣσςΤτΥυΦφΧχΨψΩωΆάΈέΉήΊίΌόΎύΏώΪΐΫΰΐΰ",e.el.trimmer=e.trimmerSupport.generateTrimmer(e.el.wordCharacters),e.Pipeline.registerFunction(e.el.trimmer,"trimmer-el"),e.el.stemmer=function(){function e(e){return s.test(e)}function t(e){return/[ΑΕΗΙΟΥΩ]$/.test(e)}function r(e){return/[ΑΕΗΙΟΩ]$/.test(e)}function n(n){var s=n;if(n.length<3)return s;if(!e(n))return s;if(i.indexOf(n)>=0)return s;var u=new RegExp("(.*)("+Object.keys(l).join("|")+")$"),o=u.exec(s);return null!==o&&(s=o[1]+l[o[2]]),null!==(o=/^(.+?)(ΑΔΕΣ|ΑΔΩΝ)$/.exec(s))&&(s=o[1],/(ΟΚ|ΜΑΜ|ΜΑΝ|ΜΠΑΜΠ|ΠΑΤΕΡ|ΓΙΑΓΙ|ΝΤΑΝΤ|ΚΥΡ|ΘΕΙ|ΠΕΘΕΡ|ΜΟΥΣΑΜ|ΚΑΠΛΑΜ|ΠΑΡ|ΨΑΡ|ΤΖΟΥΡ|ΤΑΜΠΟΥΡ|ΓΑΛΑΤ|ΦΑΦΛΑΤ)$/.test(o[1])||(s+="ΑΔ")),null!==(o=/^(.+?)(ΕΔΕΣ|ΕΔΩΝ)$/.exec(s))&&(s=o[1],/(ΟΠ|ΙΠ|ΕΜΠ|ΥΠ|ΓΗΠ|ΔΑΠ|ΚΡΑΣΠ|ΜΙΛ)$/.test(o[1])&&(s+="ΕΔ")),null!==(o=/^(.+?)(ΟΥΔΕΣ|ΟΥΔΩΝ)$/.exec(s))&&(s=o[1],/(ΑΡΚ|ΚΑΛΙΑΚ|ΠΕΤΑΛ|ΛΙΧ|ΠΛΕΞ|ΣΚ|Σ|ΦΛ|ΦΡ|ΒΕΛ|ΛΟΥΛ|ΧΝ|ΣΠ|ΤΡΑΓ|ΦΕ)$/.test(o[1])&&(s+="ΟΥΔ")),null!==(o=/^(.+?)(ΕΩΣ|ΕΩΝ|ΕΑΣ|ΕΑ)$/.exec(s))&&(s=o[1],/^(Θ|Δ|ΕΛ|ΓΑΛ|Ν|Π|ΙΔ|ΠΑΡ|ΣΤΕΡ|ΟΡΦ|ΑΝΔΡ|ΑΝΤΡ)$/.test(o[1])&&(s+="Ε")),null!==(o=/^(.+?)(ΕΙΟ|ΕΙΟΣ|ΕΙΟΙ|ΕΙΑ|ΕΙΑΣ|ΕΙΕΣ|ΕΙΟΥ|ΕΙΟΥΣ|ΕΙΩΝ)$/.exec(s))&&o[1].length>4&&(s=o[1]),null!==(o=/^(.+?)(ΙΟΥΣ|ΙΑΣ|ΙΕΣ|ΙΟΣ|ΙΟΥ|ΙΟΙ|ΙΩΝ|ΙΟΝ|ΙΑ|ΙΟ)$/.exec(s))&&(s=o[1],(t(s)||s.length<2||/^(ΑΓ|ΑΓΓΕΛ|ΑΓΡ|ΑΕΡ|ΑΘΛ|ΑΚΟΥΣ|ΑΞ|ΑΣ|Β|ΒΙΒΛ|ΒΥΤ|Γ|ΓΙΑΓ|ΓΩΝ|Δ|ΔΑΝ|ΔΗΛ|ΔΗΜ|ΔΟΚΙΜ|ΕΛ|ΖΑΧΑΡ|ΗΛ|ΗΠ|ΙΔ|ΙΣΚ|ΙΣΤ|ΙΟΝ|ΙΩΝ|ΚΙΜΩΛ|ΚΟΛΟΝ|ΚΟΡ|ΚΤΗΡ|ΚΥΡ|ΛΑΓ|ΛΟΓ|ΜΑΓ|ΜΠΑΝ|ΜΠΡ|ΝΑΥΤ|ΝΟΤ|ΟΠΑΛ|ΟΞ|ΟΡ|ΟΣ|ΠΑΝΑΓ|ΠΑΤΡ|ΠΗΛ|ΠΗΝ|ΠΛΑΙΣ|ΠΟΝΤ|ΡΑΔ|ΡΟΔ|ΣΚ|ΣΚΟΡΠ|ΣΟΥΝ|ΣΠΑΝ|ΣΤΑΔ|ΣΥΡ|ΤΗΛ|ΤΙΜ|ΤΟΚ|ΤΟΠ|ΤΡΟΧ|ΦΙΛ|ΦΩΤ|Χ|ΧΙΛ|ΧΡΩΜ|ΧΩΡ)$/.test(o[1]))&&(s+="Ι"),/^(ΠΑΛ)$/.test(o[1])&&(s+="ΑΙ")),null!==(o=/^(.+?)(ΙΚΟΣ|ΙΚΟΝ|ΙΚΕΙΣ|ΙΚΟΙ|ΙΚΕΣ|ΙΚΟΥΣ|ΙΚΗ|ΙΚΗΣ|ΙΚΟ|ΙΚΑ|ΙΚΟΥ|ΙΚΩΝ|ΙΚΩΣ)$/.exec(s))&&(s=o[1],(t(s)||/^(ΑΔ|ΑΛ|ΑΜΑΝ|ΑΜΕΡ|ΑΜΜΟΧΑΛ|ΑΝΗΘ|ΑΝΤΙΔ|ΑΠΛ|ΑΤΤ|ΑΦΡ|ΒΑΣ|ΒΡΩΜ|ΓΕΝ|ΓΕΡ|Δ|ΔΙΚΑΝ|ΔΥΤ|ΕΙΔ|ΕΝΔ|ΕΞΩΔ|ΗΘ|ΘΕΤ|ΚΑΛΛΙΝ|ΚΑΛΠ|ΚΑΤΑΔ|ΚΟΥΖΙΝ|ΚΡ|ΚΩΔ|ΛΟΓ|Μ|ΜΕΡ|ΜΟΝΑΔ|ΜΟΥΛ|ΜΟΥΣ|ΜΠΑΓΙΑΤ|ΜΠΑΝ|ΜΠΟΛ|ΜΠΟΣ|ΜΥΣΤ|Ν|ΝΙΤ|ΞΙΚ|ΟΠΤ|ΠΑΝ|ΠΕΤΣ|ΠΙΚΑΝΤ|ΠΙΤΣ|ΠΛΑΣΤ|ΠΛΙΑΤΣ|ΠΟΝΤ|ΠΟΣΤΕΛΝ|ΠΡΩΤΟΔ|ΣΕΡΤ|ΣΗΜΑΝΤ|ΣΤΑΤ|ΣΥΝΑΔ|ΣΥΝΟΜΗΛ|ΤΕΛ|ΤΕΧΝ|ΤΡΟΠ|ΤΣΑΜ|ΥΠΟΔ|Φ|ΦΙΛΟΝ|ΦΥΛΟΔ|ΦΥΣ|ΧΑΣ)$/.test(o[1])||/(ΦΟΙΝ)$/.test(o[1]))&&(s+="ΙΚ")),"ΑΓΑΜΕ"===s&&(s="ΑΓΑΜ"),null!==(o=/^(.+?)(ΑΓΑΜΕ|ΗΣΑΜΕ|ΟΥΣΑΜΕ|ΗΚΑΜΕ|ΗΘΗΚΑΜΕ)$/.exec(s))&&(s=o[1]),null!==(o=/^(.+?)(ΑΜΕ)$/.exec(s))&&(s=o[1],/^(ΑΝΑΠ|ΑΠΟΘ|ΑΠΟΚ|ΑΠΟΣΤ|ΒΟΥΒ|ΞΕΘ|ΟΥΛ|ΠΕΘ|ΠΙΚΡ|ΠΟΤ|ΣΙΧ|Χ)$/.test(o[1])&&(s+="ΑΜ")),null!==(o=/^(.+?)(ΑΓΑΝΕ|ΗΣΑΝΕ|ΟΥΣΑΝΕ|ΙΟΝΤΑΝΕ|ΙΟΤΑΝΕ|ΙΟΥΝΤΑΝΕ|ΟΝΤΑΝΕ|ΟΤΑΝΕ|ΟΥΝΤΑΝΕ|ΗΚΑΝΕ|ΗΘΗΚΑΝΕ)$/.exec(s))&&(s=o[1],/^(ΤΡ|ΤΣ)$/.test(o[1])&&(s+="ΑΓΑΝ")),null!==(o=/^(.+?)(ΑΝΕ)$/.exec(s))&&(s=o[1],(r(s)||/^(ΒΕΤΕΡ|ΒΟΥΛΚ|ΒΡΑΧΜ|Γ|ΔΡΑΔΟΥΜ|Θ|ΚΑΛΠΟΥΖ|ΚΑΣΤΕΛ|ΚΟΡΜΟΡ|ΛΑΟΠΛ|ΜΩΑΜΕΘ|Μ|ΜΟΥΣΟΥΛΜΑΝ|ΟΥΛ|Π|ΠΕΛΕΚ|ΠΛ|ΠΟΛΙΣ|ΠΟΡΤΟΛ|ΣΑΡΑΚΑΤΣ|ΣΟΥΛΤ|ΤΣΑΡΛΑΤ|ΟΡΦ|ΤΣΙΓΓ|ΤΣΟΠ|ΦΩΤΟΣΤΕΦ|Χ|ΨΥΧΟΠΛ|ΑΓ|ΟΡΦ|ΓΑΛ|ΓΕΡ|ΔΕΚ|ΔΙΠΛ|ΑΜΕΡΙΚΑΝ|ΟΥΡ|ΠΙΘ|ΠΟΥΡΙΤ|Σ|ΖΩΝΤ|ΙΚ|ΚΑΣΤ|ΚΟΠ|ΛΙΧ|ΛΟΥΘΗΡ|ΜΑΙΝΤ|ΜΕΛ|ΣΙΓ|ΣΠ|ΣΤΕΓ|ΤΡΑΓ|ΤΣΑΓ|Φ|ΕΡ|ΑΔΑΠ|ΑΘΙΓΓ|ΑΜΗΧ|ΑΝΙΚ|ΑΝΟΡΓ|ΑΠΗΓ|ΑΠΙΘ|ΑΤΣΙΓΓ|ΒΑΣ|ΒΑΣΚ|ΒΑΘΥΓΑΛ|ΒΙΟΜΗΧ|ΒΡΑΧΥΚ|ΔΙΑΤ|ΔΙΑΦ|ΕΝΟΡΓ|ΘΥΣ|ΚΑΠΝΟΒΙΟΜΗΧ|ΚΑΤΑΓΑΛ|ΚΛΙΒ|ΚΟΙΛΑΡΦ|ΛΙΒ|ΜΕΓΛΟΒΙΟΜΗΧ|ΜΙΚΡΟΒΙΟΜΗΧ|ΝΤΑΒ|ΞΗΡΟΚΛΙΒ|ΟΛΙΓΟΔΑΜ|ΟΛΟΓΑΛ|ΠΕΝΤΑΡΦ|ΠΕΡΗΦ|ΠΕΡΙΤΡ|ΠΛΑΤ|ΠΟΛΥΔΑΠ|ΠΟΛΥΜΗΧ|ΣΤΕΦ|ΤΑΒ|ΤΕΤ|ΥΠΕΡΗΦ|ΥΠΟΚΟΠ|ΧΑΜΗΛΟΔΑΠ|ΨΗΛΟΤΑΒ)$/.test(o[1]))&&(s+="ΑΝ")),null!==(o=/^(.+?)(ΗΣΕΤΕ)$/.exec(s))&&(s=o[1]),null!==(o=/^(.+?)(ΕΤΕ)$/.exec(s))&&(s=o[1],(r(s)||/(ΟΔ|ΑΙΡ|ΦΟΡ|ΤΑΘ|ΔΙΑΘ|ΣΧ|ΕΝΔ|ΕΥΡ|ΤΙΘ|ΥΠΕΡΘ|ΡΑΘ|ΕΝΘ|ΡΟΘ|ΣΘ|ΠΥΡ|ΑΙΝ|ΣΥΝΔ|ΣΥΝ|ΣΥΝΘ|ΧΩΡ|ΠΟΝ|ΒΡ|ΚΑΘ|ΕΥΘ|ΕΚΘ|ΝΕΤ|ΡΟΝ|ΑΡΚ|ΒΑΡ|ΒΟΛ|ΩΦΕΛ)$/.test(o[1])||/^(ΑΒΑΡ|ΒΕΝ|ΕΝΑΡ|ΑΒΡ|ΑΔ|ΑΘ|ΑΝ|ΑΠΛ|ΒΑΡΟΝ|ΝΤΡ|ΣΚ|ΚΟΠ|ΜΠΟΡ|ΝΙΦ|ΠΑΓ|ΠΑΡΑΚΑΛ|ΣΕΡΠ|ΣΚΕΛ|ΣΥΡΦ|ΤΟΚ|Υ|Δ|ΕΜ|ΘΑΡΡ|Θ)$/.test(o[1]))&&(s+="ΕΤ")),null!==(o=/^(.+?)(ΟΝΤΑΣ|ΩΝΤΑΣ)$/.exec(s))&&(s=o[1],/^ΑΡΧ$/.test(o[1])&&(s+="ΟΝΤ"),/ΚΡΕ$/.test(o[1])&&(s+="ΩΝΤ")),null!==(o=/^(.+?)(ΟΜΑΣΤΕ|ΙΟΜΑΣΤΕ)$/.exec(s))&&(s=o[1],/^ΟΝ$/.test(o[1])&&(s+="ΟΜΑΣΤ")),null!==(o=/^(.+?)(ΙΕΣΤΕ)$/.exec(s))&&(s=o[1],/^(Π|ΑΠ|ΣΥΜΠ|ΑΣΥΜΠ|ΑΚΑΤΑΠ|ΑΜΕΤΑΜΦ)$/.test(o[1])&&(s+="ΙΕΣΤ")),null!==(o=/^(.+?)(ΕΣΤΕ)$/.exec(s))&&(s=o[1],/^(ΑΛ|ΑΡ|ΕΚΤΕΛ|Ζ|Μ|Ξ|ΠΑΡΑΚΑΛ|ΠΡΟ|ΝΙΣ)$/.test(o[1])&&(s+="ΕΣΤ")),null!==(o=/^(.+?)(ΗΘΗΚΑ|ΗΘΗΚΕΣ|ΗΘΗΚΕ)$/.exec(s))&&(s=o[1]),null!==(o=/^(.+?)(ΗΚΑ|ΗΚΕΣ|ΗΚΕ)$/.exec(s))&&(s=o[1],(/(ΣΚΩΛ|ΣΚΟΥΛ|ΝΑΡΘ|ΣΦ|ΟΘ|ΠΙΘ)$/.test(o[1])||/^(ΔΙΑΘ|Θ|ΠΑΡΑΚΑΤΑΘ|ΠΡΟΣΘ|ΣΥΝΘ)$/.test(o[1]))&&(s+="ΗΚ")),null!==(o=/^(.+?)(ΟΥΣΑ|ΟΥΣΕΣ|ΟΥΣΕ)$/.exec(s))&&(s=o[1],(t(s)||/^(ΦΑΡΜΑΚ|ΧΑΔ|ΑΓΚ|ΑΝΑΡΡ|ΒΡΟΜ|ΕΚΛΙΠ|ΛΑΜΠΙΔ|ΛΕΧ|Μ|ΠΑΤ|Ρ|Λ|ΜΕΔ|ΜΕΣΑΖ|ΥΠΟΤΕΙΝ|ΑΜ|ΑΙΘ|ΑΝΗΚ|ΔΕΣΠΟΖ|ΕΝΔΙΑΦΕΡ)$/.test(o[1])||/(ΠΟΔΑΡ|ΒΛΕΠ|ΠΑΝΤΑΧ|ΦΡΥΔ|ΜΑΝΤΙΛ|ΜΑΛΛ|ΚΥΜΑΤ|ΛΑΧ|ΛΗΓ|ΦΑΓ|ΟΜ|ΠΡΩΤ)$/.test(o[1]))&&(s+="ΟΥΣ")),null!==(o=/^(.+?)(ΑΓΑ|ΑΓΕΣ|ΑΓΕ)$/.exec(s))&&(s=o[1],(/^(ΑΒΑΣΤ|ΠΟΛΥΦ|ΑΔΗΦ|ΠΑΜΦ|Ρ|ΑΣΠ|ΑΦ|ΑΜΑΛ|ΑΜΑΛΛΙ|ΑΝΥΣΤ|ΑΠΕΡ|ΑΣΠΑΡ|ΑΧΑΡ|ΔΕΡΒΕΝ|ΔΡΟΣΟΠ|ΞΕΦ|ΝΕΟΠ|ΝΟΜΟΤ|ΟΛΟΠ|ΟΜΟΤ|ΠΡΟΣΤ|ΠΡΟΣΩΠΟΠ|ΣΥΜΠ|ΣΥΝΤ|Τ|ΥΠΟΤ|ΧΑΡ|ΑΕΙΠ|ΑΙΜΟΣΤ|ΑΝΥΠ|ΑΠΟΤ|ΑΡΤΙΠ|ΔΙΑΤ|ΕΝ|ΕΠΙΤ|ΚΡΟΚΑΛΟΠ|ΣΙΔΗΡΟΠ|Λ|ΝΑΥ|ΟΥΛΑΜ|ΟΥΡ|Π|ΤΡ|Μ)$/.test(o[1])||/(ΟΦ|ΠΕΛ|ΧΟΡΤ|ΛΛ|ΣΦ|ΡΠ|ΦΡ|ΠΡ|ΛΟΧ|ΣΜΗΝ)$/.test(o[1])&&!/^(ΨΟΦ|ΝΑΥΛΟΧ)$/.test(o[1])||/(ΚΟΛΛ)$/.test(o[1]))&&(s+="ΑΓ")),null!==(o=/^(.+?)(ΗΣΕ|ΗΣΟΥ|ΗΣΑ)$/.exec(s))&&(s=o[1],/^(Ν|ΧΕΡΣΟΝ|ΔΩΔΕΚΑΝ|ΕΡΗΜΟΝ|ΜΕΓΑΛΟΝ|ΕΠΤΑΝ|Ι)$/.test(o[1])&&(s+="ΗΣ")),null!==(o=/^(.+?)(ΗΣΤΕ)$/.exec(s))&&(s=o[1],/^(ΑΣΒ|ΣΒ|ΑΧΡ|ΧΡ|ΑΠΛ|ΑΕΙΜΝ|ΔΥΣΧΡ|ΕΥΧΡ|ΚΟΙΝΟΧΡ|ΠΑΛΙΜΨ)$/.test(o[1])&&(s+="ΗΣΤ")),null!==(o=/^(.+?)(ΟΥΝΕ|ΗΣΟΥΝΕ|ΗΘΟΥΝΕ)$/.exec(s))&&(s=o[1],/^(Ν|Ρ|ΣΠΙ|ΣΤΡΑΒΟΜΟΥΤΣ|ΚΑΚΟΜΟΥΤΣ|ΕΞΩΝ)$/.test(o[1])&&(s+="ΟΥΝ")),null!==(o=/^(.+?)(ΟΥΜΕ|ΗΣΟΥΜΕ|ΗΘΟΥΜΕ)$/.exec(s))&&(s=o[1],/^(ΠΑΡΑΣΟΥΣ|Φ|Χ|ΩΡΙΟΠΛ|ΑΖ|ΑΛΛΟΣΟΥΣ|ΑΣΟΥΣ)$/.test(o[1])&&(s+="ΟΥΜ")),null!=(o=/^(.+?)(ΜΑΤΟΙ|ΜΑΤΟΥΣ|ΜΑΤΟ|ΜΑΤΑ|ΜΑΤΩΣ|ΜΑΤΩΝ|ΜΑΤΟΣ|ΜΑΤΕΣ|ΜΑΤΗ|ΜΑΤΗΣ|ΜΑΤΟΥ)$/.exec(s))&&(s=o[1]+"Μ",/^(ΓΡΑΜ)$/.test(o[1])?s+="Α":/^(ΓΕ|ΣΤΑ)$/.test(o[1])&&(s+="ΑΤ")),null!==(o=/^(.+?)(ΟΥΑ)$/.exec(s))&&(s=o[1]+"ΟΥ"),n.length===s.length&&null!==(o=/^(.+?)(Α|ΑΓΑΤΕ|ΑΓΑΝ|ΑΕΙ|ΑΜΑΙ|ΑΝ|ΑΣ|ΑΣΑΙ|ΑΤΑΙ|ΑΩ|Ε|ΕΙ|ΕΙΣ|ΕΙΤΕ|ΕΣΑΙ|ΕΣ|ΕΤΑΙ|Ι|ΙΕΜΑΙ|ΙΕΜΑΣΤΕ|ΙΕΤΑΙ|ΙΕΣΑΙ|ΙΕΣΑΣΤΕ|ΙΟΜΑΣΤΑΝ|ΙΟΜΟΥΝ|ΙΟΜΟΥΝΑ|ΙΟΝΤΑΝ|ΙΟΝΤΟΥΣΑΝ|ΙΟΣΑΣΤΑΝ|ΙΟΣΑΣΤΕ|ΙΟΣΟΥΝ|ΙΟΣΟΥΝΑ|ΙΟΤΑΝ|ΙΟΥΜΑ|ΙΟΥΜΑΣΤΕ|ΙΟΥΝΤΑΙ|ΙΟΥΝΤΑΝ|Η|ΗΔΕΣ|ΗΔΩΝ|ΗΘΕΙ|ΗΘΕΙΣ|ΗΘΕΙΤΕ|ΗΘΗΚΑΤΕ|ΗΘΗΚΑΝ|ΗΘΟΥΝ|ΗΘΩ|ΗΚΑΤΕ|ΗΚΑΝ|ΗΣ|ΗΣΑΝ|ΗΣΑΤΕ|ΗΣΕΙ|ΗΣΕΣ|ΗΣΟΥΝ|ΗΣΩ|Ο|ΟΙ|ΟΜΑΙ|ΟΜΑΣΤΑΝ|ΟΜΟΥΝ|ΟΜΟΥΝΑ|ΟΝΤΑΙ|ΟΝΤΑΝ|ΟΝΤΟΥΣΑΝ|ΟΣ|ΟΣΑΣΤΑΝ|ΟΣΑΣΤΕ|ΟΣΟΥΝ|ΟΣΟΥΝΑ|ΟΤΑΝ|ΟΥ|ΟΥΜΑΙ|ΟΥΜΑΣΤΕ|ΟΥΝ|ΟΥΝΤΑΙ|ΟΥΝΤΑΝ|ΟΥΣ|ΟΥΣΑΝ|ΟΥΣΑΤΕ|Υ||ΥΑ|ΥΣ|Ω|ΩΝ|ΟΙΣ)$/.exec(s))&&(s=o[1]),null!=(o=/^(.+?)(ΕΣΤΕΡ|ΕΣΤΑΤ|ΟΤΕΡ|ΟΤΑΤ|ΥΤΕΡ|ΥΤΑΤ|ΩΤΕΡ|ΩΤΑΤ)$/.exec(s))&&(/^(ΕΞ|ΕΣ|ΑΝ|ΚΑΤ|Κ|ΠΡ)$/.test(o[1])||(s=o[1]),/^(ΚΑ|Μ|ΕΛΕ|ΛΕ|ΔΕ)$/.test(o[1])&&(s+="ΥΤ")),s}var l={"ΦΑΓΙΑ":"ΦΑ","ΦΑΓΙΟΥ":"ΦΑ","ΦΑΓΙΩΝ":"ΦΑ","ΣΚΑΓΙΑ":"ΣΚΑ","ΣΚΑΓΙΟΥ":"ΣΚΑ","ΣΚΑΓΙΩΝ":"ΣΚΑ","ΣΟΓΙΟΥ":"ΣΟ","ΣΟΓΙΑ":"ΣΟ","ΣΟΓΙΩΝ":"ΣΟ","ΤΑΤΟΓΙΑ":"ΤΑΤΟ","ΤΑΤΟΓΙΟΥ":"ΤΑΤΟ","ΤΑΤΟΓΙΩΝ":"ΤΑΤΟ","ΚΡΕΑΣ":"ΚΡΕ","ΚΡΕΑΤΟΣ":"ΚΡΕ","ΚΡΕΑΤΑ":"ΚΡΕ","ΚΡΕΑΤΩΝ":"ΚΡΕ","ΠΕΡΑΣ":"ΠΕΡ","ΠΕΡΑΤΟΣ":"ΠΕΡ","ΠΕΡΑΤΑ":"ΠΕΡ","ΠΕΡΑΤΩΝ":"ΠΕΡ","ΤΕΡΑΣ":"ΤΕΡ","ΤΕΡΑΤΟΣ":"ΤΕΡ","ΤΕΡΑΤΑ":"ΤΕΡ","ΤΕΡΑΤΩΝ":"ΤΕΡ","ΦΩΣ":"ΦΩ","ΦΩΤΟΣ":"ΦΩ","ΦΩΤΑ":"ΦΩ","ΦΩΤΩΝ":"ΦΩ","ΚΑΘΕΣΤΩΣ":"ΚΑΘΕΣΤ","ΚΑΘΕΣΤΩΤΟΣ":"ΚΑΘΕΣΤ","ΚΑΘΕΣΤΩΤΑ":"ΚΑΘΕΣΤ","ΚΑΘΕΣΤΩΤΩΝ":"ΚΑΘΕΣΤ","ΓΕΓΟΝΟΣ":"ΓΕΓΟΝ","ΓΕΓΟΝΟΤΟΣ":"ΓΕΓΟΝ","ΓΕΓΟΝΟΤΑ":"ΓΕΓΟΝ","ΓΕΓΟΝΟΤΩΝ":"ΓΕΓΟΝ","ΕΥΑ":"ΕΥ"},i=["ΑΚΡΙΒΩΣ","ΑΛΑ","ΑΛΛΑ","ΑΛΛΙΩΣ","ΑΛΛΟΤΕ","ΑΜΑ","ΑΝΩ","ΑΝΑ","ΑΝΑΜΕΣΑ","ΑΝΑΜΕΤΑΞΥ","ΑΝΕΥ","ΑΝΤΙ","ΑΝΤΙΠΕΡΑ","ΑΝΤΙΟ","ΑΞΑΦΝΑ","ΑΠΟ","ΑΠΟΨΕ","ΑΡΑ","ΑΡΑΓΕ","ΑΥΡΙΟ","ΑΦΟΙ","ΑΦΟΥ","ΑΦΟΤΟΥ","ΒΡΕ","ΓΕΙΑ","ΓΙΑ","ΓΙΑΤΙ","ΓΡΑΜΜΑ","ΔΕΗ","ΔΕΝ","ΔΗΛΑΔΗ","ΔΙΧΩΣ","ΔΥΟ","ΕΑΝ","ΕΓΩ","ΕΔΩ","ΕΔΑ","ΕΙΘΕ","ΕΙΜΑΙ","ΕΙΜΑΣΤΕ","ΕΙΣΑΙ","ΕΙΣΑΣΤΕ","ΕΙΝΑΙ","ΕΙΣΤΕ","ΕΙΤΕ","ΕΚΕΙ","ΕΚΟ","ΕΛΑ","ΕΜΑΣ","ΕΜΕΙΣ","ΕΝΤΕΛΩΣ","ΕΝΤΟΣ","ΕΝΤΩΜΕΤΑΞΥ","ΕΝΩ","ΕΞΙ","ΕΞΙΣΟΥ","ΕΞΗΣ","ΕΞΩ","ΕΟΚ","ΕΠΑΝΩ","ΕΠΕΙΔΗ","ΕΠΕΙΤΑ","ΕΠΙ","ΕΠΙΣΗΣ","ΕΠΟΜΕΝΩΣ","ΕΠΤΑ","ΕΣΑΣ","ΕΣΕΙΣ","ΕΣΤΩ","ΕΣΥ","ΕΣΩ","ΕΤΣΙ","ΕΥΓΕ","ΕΦΕ","ΕΦΕΞΗΣ","ΕΧΤΕΣ","ΕΩΣ","ΗΔΗ","ΗΜΙ","ΗΠΑ","ΗΤΟΙ","ΘΕΣ","ΙΔΙΩΣ","ΙΔΗ","ΙΚΑ","ΙΣΩΣ","ΚΑΘΕ","ΚΑΘΕΤΙ","ΚΑΘΟΛΟΥ","ΚΑΘΩΣ","ΚΑΙ","ΚΑΝ","ΚΑΠΟΤΕ","ΚΑΠΟΥ","ΚΑΤΑ","ΚΑΤΙ","ΚΑΤΟΠΙΝ","ΚΑΤΩ","ΚΕΙ","ΚΙΧ","ΚΚΕ","ΚΟΛΑΝ","ΚΥΡΙΩΣ","ΚΩΣ","ΜΑΚΑΡΙ","ΜΑΛΙΣΤΑ","ΜΑΛΛΟΝ","ΜΑΙ","ΜΑΟ","ΜΑΟΥΣ","ΜΑΣ","ΜΕΘΑΥΡΙΟ","ΜΕΣ","ΜΕΣΑ","ΜΕΤΑ","ΜΕΤΑΞΥ","ΜΕΧΡΙ","ΜΗΔΕ","ΜΗΝ","ΜΗΠΩΣ","ΜΗΤΕ","ΜΙΑ","ΜΙΑΣ","ΜΙΣ","ΜΜΕ","ΜΟΛΟΝΟΤΙ","ΜΟΥ","ΜΠΑ","ΜΠΑΣ","ΜΠΟΥΦΑΝ","ΜΠΡΟΣ","ΝΑΙ","ΝΕΣ","ΝΤΑ","ΝΤΕ","ΞΑΝΑ","ΟΗΕ","ΟΚΤΩ","ΟΜΩΣ","ΟΝΕ","ΟΠΑ","ΟΠΟΥ","ΟΠΩΣ","ΟΣΟ","ΟΤΑΝ","ΟΤΕ","ΟΤΙ","ΟΥΤΕ","ΟΧΙ","ΠΑΛΙ","ΠΑΝ","ΠΑΝΟ","ΠΑΝΤΟΤΕ","ΠΑΝΤΟΥ","ΠΑΝΤΩΣ","ΠΑΝΩ","ΠΑΡΑ","ΠΕΡΑ","ΠΕΡΙ","ΠΕΡΙΠΟΥ","ΠΙΑ","ΠΙΟ","ΠΙΣΩ","ΠΛΑΙ","ΠΛΕΟΝ","ΠΛΗΝ","ΠΟΤΕ","ΠΟΥ","ΠΡΟ","ΠΡΟΣ","ΠΡΟΧΤΕΣ","ΠΡΟΧΘΕΣ","ΡΟΔΙ","ΠΩΣ","ΣΑΙ","ΣΑΣ","ΣΑΝ","ΣΕΙΣ","ΣΙΑ","ΣΚΙ","ΣΟΙ","ΣΟΥ","ΣΡΙ","ΣΥΝ","ΣΥΝΑΜΑ","ΣΧΕΔΟΝ","ΤΑΔΕ","ΤΑΞΙ","ΤΑΧΑ","ΤΕΙ","ΤΗΝ","ΤΗΣ","ΤΙΠΟΤΑ","ΤΙΠΟΤΕ","ΤΙΣ","ΤΟΝ","ΤΟΤΕ","ΤΟΥ","ΤΟΥΣ","ΤΣΑ","ΤΣΕ","ΤΣΙ","ΤΣΟΥ","ΤΩΝ","ΥΠΟ","ΥΠΟΨΗ","ΥΠΟΨΙΝ","ΥΣΤΕΡΑ","ΦΕΤΟΣ","ΦΙΣ","ΦΠΑ","ΧΑΦ","ΧΘΕΣ","ΧΤΕΣ","ΧΩΡΙΣ","ΩΣ","ΩΣΑΝ","ΩΣΟΤΟΥ","ΩΣΠΟΥ","ΩΣΤΕ","ΩΣΤΟΣΟ"],s=new RegExp("^[ΑΒΓΔΕΖΗΘΙΚΛΜΝΞΟΠΡΣΤΥΦΧΨΩ]+$");return function(e){return"function"==typeof e.update?e.update(function(e){return n(e.toUpperCase()).toLowerCase()}):n(e.toUpperCase()).toLowerCase()}}(),e.Pipeline.registerFunction(e.el.stemmer,"stemmer-el"),e.el.stopWordFilter=e.generateStopWordFilter("αλλα αν αντι απο αυτα αυτεσ αυτη αυτο αυτοι αυτοσ αυτουσ αυτων για δε δεν εαν ειμαι ειμαστε ειναι εισαι ειστε εκεινα εκεινεσ εκεινη εκεινο εκεινοι εκεινοσ εκεινουσ εκεινων ενω επι η θα ισωσ κ και κατα κι μα με μετα μη μην να ο οι ομωσ οπωσ οσο οτι παρα ποια ποιεσ ποιο ποιοι ποιοσ ποιουσ ποιων που προσ πωσ σε στη στην στο στον τα την τησ το τον τοτε του των ωσ".split(" ")),e.Pipeline.registerFunction(e.el.stopWordFilter,"stopWordFilter-el"),e.el.normilizer=function(){var e={"Ά":"Α","ά":"α","Έ":"Ε","έ":"ε","Ή":"Η","ή":"η","Ί":"Ι","ί":"ι","Ό":"Ο","ο":"ο","Ύ":"Υ","ύ":"υ","Ώ":"Ω","ώ":"ω","Ϊ":"Ι","ϊ":"ι","Ϋ":"Υ","ϋ":"υ","ΐ":"ι","ΰ":"υ"};return function(t){if("function"==typeof t.update)return t.update(function(t){for(var r="",n=0;n<t.length;n++)r+=e[t.charAt(n)]||t.charAt(n);return r});for(var r="",n=0;n<t.length;n++)r+=e[t.charAt(n)]||t.charAt(n);return r}}(),e.Pipeline.registerFunction(e.el.normilizer,"normilizer-el")}});
\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.he.min.js b/assets/javascripts/lunr/min/lunr.he.min.js
new file mode 100644
index 000000000..b863d3eae
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.he.min.js
@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.he=function(){this.pipeline.reset(),this.pipeline.add(e.he.trimmer,e.he.stopWordFilter,e.he.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.he.stemmer))},e.he.wordCharacters="֑-״א-תa-zA-Zａ-ｚＡ-Ｚ0-9０-９",e.he.trimmer=e.trimmerSupport.generateTrimmer(e.he.wordCharacters),e.Pipeline.registerFunction(e.he.trimmer,"trimmer-he"),e.he.stemmer=function(){var e=this;return e.result=!1,e.preRemoved=!1,e.sufRemoved=!1,e.pre={pre1:"ה ו י ת",pre2:"ב כ ל מ ש כש",pre3:"הב הכ הל המ הש בש לכ",pre4:"וב וכ ול ומ וש",pre5:"מה שה כל",pre6:"מב מכ מל ממ מש",pre7:"בה בו בי בת כה כו כי כת לה לו לי לת",pre8:"ובה ובו ובי ובת וכה וכו וכי וכת ולה ולו ולי ולת"},e.suf={suf1:"ך כ ם ן נ",suf2:"ים ות וך וכ ום ון ונ הם הן יכ יך ינ ים",suf3:"תי תך תכ תם תן תנ",suf4:"ותי ותך ותכ ותם ותן ותנ",suf5:"נו כם כן הם הן",suf6:"ונו וכם וכן והם והן",suf7:"תכם תכן תנו תהם תהן",suf8:"הוא היא הם הן אני אתה את אנו אתם אתן",suf9:"ני נו כי כו כם כן תי תך תכ תם תן",suf10:"י ך כ ם ן נ ת"},e.patterns=JSON.parse('{"hebrewPatterns": [{"pt1": [{"c": "ה", "l": 0}]}, {"pt2": [{"c": "ו", "l": 0}]}, {"pt3": [{"c": "י", "l": 0}]}, {"pt4": [{"c": "ת", "l": 0}]}, {"pt5": [{"c": "מ", "l": 0}]}, {"pt6": [{"c": "ל", "l": 0}]}, {"pt7": [{"c": "ב", "l": 0}]}, {"pt8": [{"c": "כ", "l": 0}]}, {"pt9": [{"c": "ש", "l": 0}]}, {"pt10": [{"c": "כש", "l": 0}]}, {"pt11": [{"c": "בה", "l": 0}]}, {"pt12": [{"c": "וב", "l": 0}]}, {"pt13": [{"c": "וכ", "l": 0}]}, {"pt14": [{"c": "ול", "l": 0}]}, {"pt15": [{"c": "ומ", "l": 0}]}, {"pt16": [{"c": "וש", "l": 0}]}, {"pt17": [{"c": "הב", "l": 0}]}, {"pt18": [{"c": "הכ", "l": 0}]}, {"pt19": [{"c": "הל", "l": 0}]}, {"pt20": [{"c": "המ", "l": 0}]}, {"pt21": [{"c": "הש", "l": 0}]}, {"pt22": [{"c": "מה", "l": 0}]}, {"pt23": [{"c": "שה", "l": 0}]}, {"pt24": [{"c": "כל", "l": 0}]}]}'),e.execArray=["cleanWord","removeDiacritics","removeStopWords","normalizeHebrewCharacters"],e.stem=function(){var r=0;for(e.result=!1,e.preRemoved=!1,e.sufRemoved=!1;r<e.execArray.length&&1!=e.result;)e.result=e[e.execArray[r]](),r++},e.setCurrent=function(r){e.word=r},e.getCurrent=function(){return e.word},e.cleanWord=function(){return!!new RegExp("[^֑-״א-ת]").test("")},e.removeDiacritics=function(){var r=new RegExp("[ְ-ֿ]","g");return e.word=e.word.replace(r,""),!1},e.removeStopWords=function(){if("אבל או אולי אותו אותי אותך אותם אותן אותנו אז אחר אחרות אחרי אחריכן אחרים אחרת אי איזה איך אין איפה אל אלה אלו אם אנחנו אני אף אפשר את אתה אתכם אתכן אתם אתן באיזה באיזו בגלל בין בלבד בעבור בעזרת בכל בכן בלי במידה במקום שבו ברוב בשביל בשעה ש בתוך גם דרך הוא היא היה היי היכן היתה היתי הם הן הנה הסיבה שבגללה הרי ואילו ואת זאת זה זות יהיה יוכל יוכלו יותר מדי יכול יכולה יכולות יכולים יכל יכלה יכלו יש כאן כאשר כולם כולן כזה כי כיצד כך כל כלל כמו כן כפי כש לא לאו לאיזותך לאן לבין לה להיות להם להן לו לזה לזות לי לך לכם לכן למה למעלה למעלה מ למטה למטה מ למעט למקום שבו למרות לנו לעבר לעיכן לפיכך לפני מאד מאחורי מאיזו סיבה מאין מאיפה מבלי מבעד מדוע מה מהיכן מול מחוץ מי מידע מכאן מכל מכן מלבד מן מנין מסוגל מעט מעטים מעל מצד מקום בו מתחת מתי נגד נגר נו עד עז על עלי עליו עליה עליהם עליך עלינו עם עצמה עצמהם עצמהן עצמו עצמי עצמם עצמן עצמנו פה רק שוב של שלה שלהם שלהן שלו שלי שלך שלכה שלכם שלכן שלנו שם תהיה תחת".split(" ").indexOf(e.word)>=0)return!0},e.normalizeHebrewCharacters=function(){return e.word=e.word.replace("ך","כ"),e.word=e.word.replace("ם","מ"),e.word=e.word.replace("ן","נ"),e.word=e.word.replace("ף","פ"),e.word=e.word.replace("ץ","צ"),!1},function(r){return"function"==typeof r.update?r.update(function(r){return e.setCurrent(r),e.stem(),e.getCurrent()}):(e.setCurrent(r),e.stem(),e.getCurrent())}}(),e.Pipeline.registerFunction(e.he.stemmer,"stemmer-he"),e.he.stopWordFilter=e.generateStopWordFilter("אבל או אולי אותו אותי אותך אותם אותן אותנו אז אחר אחרות אחרי אחריכן אחרים אחרת אי איזה איך אין איפה אל אלה אלו אם אנחנו אני אף אפשר את אתה אתכם אתכן אתם אתן באיזה באיזו בגלל בין בלבד בעבור בעזרת בכל בכן בלי במידה במקום שבו ברוב בשביל בשעה ש בתוך גם דרך הוא היא היה היי היכן היתה היתי הם הן הנה הסיבה שבגללה הרי ואילו ואת זאת זה זות יהיה יוכל יוכלו יותר מדי יכול יכולה יכולות יכולים יכל יכלה יכלו יש כאן כאשר כולם כולן כזה כי כיצד כך כל כלל כמו כן כפי כש לא לאו לאיזותך לאן לבין לה להיות להם להן לו לזה לזות לי לך לכם לכן למה למעלה למעלה מ למטה למטה מ למעט למקום שבו למרות לנו לעבר לעיכן לפיכך לפני מאד מאחורי מאיזו סיבה מאין מאיפה מבלי מבעד מדוע מה מהיכן מול מחוץ מי מידע מכאן מכל מכן מלבד מן מנין מסוגל מעט מעטים מעל מצד מקום בו מתחת מתי נגד נגר נו עד עז על עלי עליו עליה עליהם עליך עלינו עם עצמה עצמהם עצמהן עצמו עצמי עצמם עצמן עצמנו פה רק שוב של שלה שלהם שלהן שלו שלי שלך שלכה שלכם שלכן שלנו שם תהיה תחת".split(" ")),e.Pipeline.registerFunction(e.he.stopWordFilter,"stopWordFilter-he")}});
\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.kn.min.js b/assets/javascripts/lunr/min/lunr.kn.min.js
new file mode 100644
index 000000000..1cef9befd
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.kn.min.js
@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.kn=function(){this.pipeline.reset(),this.pipeline.add(e.kn.trimmer,e.kn.stopWordFilter,e.kn.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.kn.stemmer))},e.kn.wordCharacters="ಀ-಄ಅ-ಔಕ-ಹಾ-ೌ಼-ಽೕ-ೖೝ-ೞೠ-ೡೢ-ೣ೤೥೦-೯ೱ-ೳ",e.kn.trimmer=e.trimmerSupport.generateTrimmer(e.kn.wordCharacters),e.Pipeline.registerFunction(e.kn.trimmer,"trimmer-kn"),e.kn.stopWordFilter=e.generateStopWordFilter("ಮತ್ತು ಈ ಒಂದು ರಲ್ಲಿ ಹಾಗೂ ಎಂದು ಅಥವಾ ಇದು ರ ಅವರು ಎಂಬ ಮೇಲೆ ಅವರ ತನ್ನ ಆದರೆ ತಮ್ಮ ನಂತರ ಮೂಲಕ ಹೆಚ್ಚು ನ ಆ ಕೆಲವು ಅನೇಕ ಎರಡು ಹಾಗು ಪ್ರಮುಖ ಇದನ್ನು ಇದರ ಸುಮಾರು ಅದರ ಅದು ಮೊದಲ ಬಗ್ಗೆ ನಲ್ಲಿ ರಂದು ಇತರ ಅತ್ಯಂತ ಹೆಚ್ಚಿನ ಸಹ ಸಾಮಾನ್ಯವಾಗಿ ನೇ ಹಲವಾರು ಹೊಸ ದಿ ಕಡಿಮೆ ಯಾವುದೇ ಹೊಂದಿದೆ ದೊಡ್ಡ ಅನ್ನು ಇವರು ಪ್ರಕಾರ ಇದೆ ಮಾತ್ರ ಕೂಡ ಇಲ್ಲಿ ಎಲ್ಲಾ ವಿವಿಧ ಅದನ್ನು ಹಲವು ರಿಂದ ಕೇವಲ ದ ದಕ್ಷಿಣ ಗೆ ಅವನ ಅತಿ ನೆಯ ಬಹಳ ಕೆಲಸ ಎಲ್ಲ ಪ್ರತಿ ಇತ್ಯಾದಿ ಇವು ಬೇರೆ ಹೀಗೆ ನಡುವೆ ಇದಕ್ಕೆ ಎಸ್ ಇವರ ಮೊದಲು ಶ್ರೀ ಮಾಡುವ ಇದರಲ್ಲಿ ರೀತಿಯ ಮಾಡಿದ ಕಾಲ ಅಲ್ಲಿ ಮಾಡಲು ಅದೇ ಈಗ ಅವು ಗಳು ಎ ಎಂಬುದು ಅವನು ಅಂದರೆ ಅವರಿಗೆ ಇರುವ ವಿಶೇಷ ಮುಂದೆ ಅವುಗಳ ಮುಂತಾದ ಮೂಲ ಬಿ ಮೀ ಒಂದೇ ಇನ್ನೂ ಹೆಚ್ಚಾಗಿ ಮಾಡಿ ಅವರನ್ನು ಇದೇ ಯ ರೀತಿಯಲ್ಲಿ ಜೊತೆ ಅದರಲ್ಲಿ ಮಾಡಿದರು ನಡೆದ ಆಗ ಮತ್ತೆ ಪೂರ್ವ ಆತ ಬಂದ ಯಾವ ಒಟ್ಟು ಇತರೆ ಹಿಂದೆ ಪ್ರಮಾಣದ ಗಳನ್ನು ಕುರಿತು ಯು ಆದ್ದರಿಂದ ಅಲ್ಲದೆ ನಗರದ ಮೇಲಿನ ಏಕೆಂದರೆ ರಷ್ಟು ಎಂಬುದನ್ನು ಬಾರಿ ಎಂದರೆ ಹಿಂದಿನ ಆದರೂ ಆದ ಸಂಬಂಧಿಸಿದ ಮತ್ತೊಂದು ಸಿ ಆತನ ".split(" ")),e.kn.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var r=e.wordcut;r.init(),e.kn.tokenizer=function(t){if(!arguments.length||null==t||void 0==t)return[];if(Array.isArray(t))return t.map(function(r){return isLunr2?new e.Token(r.toLowerCase()):r.toLowerCase()});var n=t.toString().toLowerCase().replace(/^\s+/,"");return r.cut(n).split("|")},e.Pipeline.registerFunction(e.kn.stemmer,"stemmer-kn"),e.Pipeline.registerFunction(e.kn.stopWordFilter,"stopWordFilter-kn")}});
\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.sa.min.js b/assets/javascripts/lunr/min/lunr.sa.min.js
new file mode 100644
index 000000000..50ee56420
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.sa.min.js
@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.sa=function(){this.pipeline.reset(),this.pipeline.add(e.sa.trimmer,e.sa.stopWordFilter,e.sa.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.sa.stemmer))},e.sa.wordCharacters="ऀ-ःऄ-एऐ-टठ-यर-िी-ॏॐ-य़ॠ-९॰-ॿ꣠-꣱ꣲ-ꣷ꣸-ꣻ꣼-ꣽꣾ-ꣿᆰ0-ᆰ9",e.sa.trimmer=e.trimmerSupport.generateTrimmer(e.sa.wordCharacters),e.Pipeline.registerFunction(e.sa.trimmer,"trimmer-sa"),e.sa.stopWordFilter=e.generateStopWordFilter('तथा अयम्‌ एकम्‌ इत्यस्मिन्‌ तथा तत्‌ वा अयम्‌ इत्यस्य ते आहूत उपरि तेषाम्‌  किन्तु तेषाम्‌ तदा इत्यनेन अधिकः इत्यस्य तत्‌ केचन बहवः द्वि तथा महत्वपूर्णः अयम्‌ अस्य  विषये अयं अस्ति तत्‌ प्रथमः विषये इत्युपरि इत्युपरि इतर अधिकतमः अधिकः अपि सामान्यतया ठ इतरेतर नूतनम्‌ द  न्यूनम्‌ कश्चित्‌ वा विशालः द  सः अस्ति तदनुसारम् तत्र अस्ति केवलम्‌ अपि अत्र सर्वे विविधाः तत्‌ बहवः यतः इदानीम्‌ द  दक्षिण इत्यस्मै तस्य उपरि नथ अतीव कार्यम्‌ सर्वे एकैकम्‌ इत्यादि। एते सन्ति  उत इत्थम्‌ मध्ये एतदर्थं . स कस्य प्रथमः श्री. करोति अस्मिन् प्रकारः निर्मिता कालः तत्र कर्तुं  समान अधुना ते सन्ति स एकः अस्ति सः अर्थात् तेषां कृते . स्थितम्  विशेषः अग्रिम तेषाम्‌ समान स्रोतः ख म समान इदानीमपि अधिकतया करोतु ते समान इत्यस्य वीथी सह यस्मिन्  कृतवान्‌ धृतः तदा पुनः पूर्वं सः आगतः किम्‌ कुल इतर पुरा  मात्रा स विषये उ अतएव अपि नगरस्य  उपरि यतः प्रतिशतं  कतरः कालः साधनानि भूत तथापि जात सम्बन्धि अन्यत्‌ ग अतः अस्माकं स्वकीयाः अस्माकं इदानीं अन्तः इत्यादयः भवन्तः इत्यादयः एते एताः तस्य अस्य इदम् एते तेषां तेषां तेषां तान् तेषां तेषां तेषां समानः सः एकः च तादृशाः बहवः अन्ये च वदन्ति यत् कियत् कस्मै  कस्मै  यस्मै  यस्मै  यस्मै  यस्मै न अतिनीचः किन्तु प्रथमं सम्पूर्णतया  ततः चिरकालानन्तरं पुस्तकं सम्पूर्णतया अन्तः  किन्तु अत्र वा इह इव श्रद्धाय अवशिष्यते  परन्तु अन्ये वर्गाः सन्ति ते सन्ति शक्नुवन्ति सर्वे मिलित्वा सर्वे एकत्र"'.split(" ")),e.sa.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var r=e.wordcut;r.init(),e.sa.tokenizer=function(t){if(!arguments.length||null==t||void 0==t)return[];if(Array.isArray(t))return t.map(function(r){return isLunr2?new e.Token(r.toLowerCase()):r.toLowerCase()});var i=t.toString().toLowerCase().replace(/^\s+/,"");return r.cut(i).split("|")},e.Pipeline.registerFunction(e.sa.stemmer,"stemmer-sa"),e.Pipeline.registerFunction(e.sa.stopWordFilter,"stopWordFilter-sa")}});
\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.te.min.js b/assets/javascripts/lunr/min/lunr.te.min.js
new file mode 100644
index 000000000..9fa7a93b9
--- /dev/null
+++ b/assets/javascripts/lunr/min/lunr.te.min.js
@@ -0,0 +1 @@
+!function(e,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.te=function(){this.pipeline.reset(),this.pipeline.add(e.te.trimmer,e.te.stopWordFilter,e.te.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.te.stemmer))},e.te.wordCharacters="ఀ-ఄఅ-ఔక-హా-ౌౕ-ౖౘ-ౚౠ-ౡౢ-ౣ౦-౯౸-౿఼ఽ్ౝ౷౤౥",e.te.trimmer=e.trimmerSupport.generateTrimmer(e.te.wordCharacters),e.Pipeline.registerFunction(e.te.trimmer,"trimmer-te"),e.te.stopWordFilter=e.generateStopWordFilter("అందరూ అందుబాటులో అడగండి అడగడం అడ్డంగా అనుగుణంగా అనుమతించు అనుమతిస్తుంది అయితే ఇప్పటికే ఉన్నారు ఎక్కడైనా ఎప్పుడు ఎవరైనా ఎవరో ఏ ఏదైనా ఏమైనప్పటికి ఒక ఒకరు కనిపిస్తాయి కాదు కూడా గా గురించి చుట్టూ చేయగలిగింది తగిన తర్వాత దాదాపు దూరంగా నిజంగా పై ప్రకారం ప్రక్కన మధ్య మరియు మరొక మళ్ళీ మాత్రమే మెచ్చుకో వద్ద వెంట వేరుగా వ్యతిరేకంగా సంబంధం".split(" ")),e.te.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var t=e.wordcut;t.init(),e.te.tokenizer=function(r){if(!arguments.length||null==r||void 0==r)return[];if(Array.isArray(r))return r.map(function(t){return isLunr2?new e.Token(t.toLowerCase()):t.toLowerCase()});var i=r.toString().toLowerCase().replace(/^\s+/,"");return t.cut(i).split("|")},e.Pipeline.registerFunction(e.te.stemmer,"stemmer-te"),e.Pipeline.registerFunction(e.te.stopWordFilter,"stopWordFilter-te")}});
\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.zh.min.js b/assets/javascripts/lunr/min/lunr.zh.min.js
index 9838ef96d..fda66e9c5 100644
--- a/assets/javascripts/lunr/min/lunr.zh.min.js
+++ b/assets/javascripts/lunr/min/lunr.zh.min.js
@@ -1 +1 @@
-!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r(require("@node-rs/jieba")):r()(e.lunr)}(this,function(e){return function(r,t){if(void 0===r)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===r.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var i="2"==r.version[0];r.zh=function(){this.pipeline.reset(),this.pipeline.add(r.zh.trimmer,r.zh.stopWordFilter,r.zh.stemmer),i?this.tokenizer=r.zh.tokenizer:(r.tokenizer&&(r.tokenizer=r.zh.tokenizer),this.tokenizerFn&&(this.tokenizerFn=r.zh.tokenizer))},r.zh.tokenizer=function(n){if(!arguments.length||null==n||void 0==n)return[];if(Array.isArray(n))return n.map(function(e){return i?new r.Token(e.toLowerCase()):e.toLowerCase()});t&&e.load(t);var o=n.toString().trim().toLowerCase(),s=[];e.cut(o,!0).forEach(function(e){s=s.concat(e.split(" "))}),s=s.filter(function(e){return!!e});var u=0;return s.map(function(e,t){if(i){var n=o.indexOf(e,u),s={};return s.position=[n,e.length],s.index=t,u=n,new r.Token(e,s)}return e})},r.zh.wordCharacters="\\w一-龥",r.zh.trimmer=r.trimmerSupport.generateTrimmer(r.zh.wordCharacters),r.Pipeline.registerFunction(r.zh.trimmer,"trimmer-zh"),r.zh.stemmer=function(){return function(e){return e}}(),r.Pipeline.registerFunction(r.zh.stemmer,"stemmer-zh"),r.zh.stopWordFilter=r.generateStopWordFilter("的 一 不 在 人 有 是 为 以 于 上 他 而 后 之 来 及 了 因 下 可 到 由 这 与 也 此 但 并 个 其 已 无 小 我 们 起 最 再 今 去 好 只 又 或 很 亦 某 把 那 你 乃 它 吧 被 比 别 趁 当 从 到 得 打 凡 儿 尔 该 各 给 跟 和 何 还 即 几 既 看 据 距 靠 啦 了 另 么 每 们 嘛 拿 哪 那 您 凭 且 却 让 仍 啥 如 若 使 谁 虽 随 同 所 她 哇 嗡 往 哪 些 向 沿 哟 用 于 咱 则 怎 曾 至 致 着 诸 自".split(" ")),r.Pipeline.registerFunction(r.zh.stopWordFilter,"stopWordFilter-zh")}});
\ No newline at end of file
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r(require("@node-rs/jieba")):r()(e.lunr)}(this,function(e){return function(r,t){if(void 0===r)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===r.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var i="2"==r.version[0];r.zh=function(){this.pipeline.reset(),this.pipeline.add(r.zh.trimmer,r.zh.stopWordFilter,r.zh.stemmer),i?this.tokenizer=r.zh.tokenizer:(r.tokenizer&&(r.tokenizer=r.zh.tokenizer),this.tokenizerFn&&(this.tokenizerFn=r.zh.tokenizer))},r.zh.tokenizer=function(n){if(!arguments.length||null==n||void 0==n)return[];if(Array.isArray(n))return n.map(function(e){return i?new r.Token(e.toLowerCase()):e.toLowerCase()});t&&e.load(t);var o=n.toString().trim().toLowerCase(),s=[];e.cut(o,!0).forEach(function(e){s=s.concat(e.split(" "))}),s=s.filter(function(e){return!!e});var u=0;return s.map(function(e,t){if(i){var n=o.indexOf(e,u),s={};return s.position=[n,e.length],s.index=t,u=n,new r.Token(e,s)}return e})},r.zh.wordCharacters="\\w一-龥",r.zh.trimmer=r.trimmerSupport.generateTrimmer(r.zh.wordCharacters),r.Pipeline.registerFunction(r.zh.trimmer,"trimmer-zh"),r.zh.stemmer=function(){return function(e){return e}}(),r.Pipeline.registerFunction(r.zh.stemmer,"stemmer-zh"),r.zh.stopWordFilter=r.generateStopWordFilter("的 一 不 在 人 有 是 为 為 以 于 於 上 他 而 后 後 之 来 來 及 了 因 下 可 到 由 这 這 与 與 也 此 但 并 並 个 個 其 已 无 無 小 我 们 們 起 最 再 今 去 好 只 又 或 很 亦 某 把 那 你 乃 它 吧 被 比 别 趁 当 當 从 從 得 打 凡 儿 兒 尔 爾 该 該 各 给 給 跟 和 何 还 還 即 几 幾 既 看 据 據 距 靠 啦 另 么 麽 每 嘛 拿 哪 您 凭 憑 且 却 卻 让 讓 仍 啥 如 若 使 谁 誰 虽 雖 随 隨 同 所 她 哇 嗡 往 些 向 沿 哟 喲 用 咱 则 則 怎 曾 至 致 着 著 诸 諸 自".split(" ")),r.Pipeline.registerFunction(r.zh.stopWordFilter,"stopWordFilter-zh")}});
\ No newline at end of file
diff --git a/assets/javascripts/workers/search.208ed371.min.js b/assets/javascripts/workers/search.f886a092.min.js
similarity index 66%
rename from assets/javascripts/workers/search.208ed371.min.js
rename to assets/javascripts/workers/search.f886a092.min.js
index 8b0e66b67..283b855c3 100644
--- a/assets/javascripts/workers/search.208ed371.min.js
+++ b/assets/javascripts/workers/search.f886a092.min.js
@@ -1,4 +1,4 @@
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+"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
@@ -11,18 +11,18 @@
  */t.Set=function(e){if(this.elements=Object.create(null),e){this.length=e.length;for(var r=0;r<this.length;r++)this.elements[e[r]]=!0}else this.length=0},t.Set.complete={intersect:function(e){return e},union:function(){return this},contains:function(){return!0}},t.Set.empty={intersect:function(){return this},union:function(e){return e},contains:function(){return!1}},t.Set.prototype.contains=function(e){return!!this.elements[e]},t.Set.prototype.intersect=function(e){var r,n,i,s=[];if(e===t.Set.complete)return this;if(e===t.Set.empty)return e;this.length<e.length?(r=this,n=e):(r=e,n=this),i=Object.keys(r.elements);for(var o=0;o<i.length;o++){var a=i[o];a in n.elements&&s.push(a)}return new t.Set(s)},t.Set.prototype.union=function(e){return e===t.Set.complete?t.Set.complete:e===t.Set.empty?this:new t.Set(Object.keys(this.elements).concat(Object.keys(e.elements)))},t.idf=function(e,r){var n=0;for(var i in e)i!="_index"&&(n+=Object.keys(e[i]).length);var s=(r-n+.5)/(n+.5);return Math.log(1+Math.abs(s))},t.Token=function(e,r){this.str=e||"",this.metadata=r||{}},t.Token.prototype.toString=function(){return this.str},t.Token.prototype.update=function(e){return this.str=e(this.str,this.metadata),this},t.Token.prototype.clone=function(e){return e=e||function(r){return r},new t.Token(e(this.str,this.metadata),this.metadata)};/*!
  * lunr.tokenizer
  * Copyright (C) 2020 Oliver Nightingale
- */t.tokenizer=function(e,r){if(e==null||e==null)return[];if(Array.isArray(e))return e.map(function(g){return new t.Token(t.utils.asString(g).toLowerCase(),t.utils.clone(r))});for(var n=e.toString().toLowerCase(),i=n.length,s=[],o=0,a=0;o<=i;o++){var c=n.charAt(o),u=o-a;if(c.match(t.tokenizer.separator)||o==i){if(u>0){var l=t.utils.clone(r)||{};l.position=[a,u],l.index=s.length,s.push(new t.Token(n.slice(a,o),l))}a=o+1}}return s},t.tokenizer.separator=/[\s\-]+/;/*!
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  * lunr.Pipeline
  * Copyright (C) 2020 Oliver Nightingale
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  * lunr.Vector
  * Copyright (C) 2020 Oliver Nightingale
- */t.Vector=function(e){this._magnitude=0,this.elements=e||[]},t.Vector.prototype.positionForIndex=function(e){if(this.elements.length==0)return 0;for(var r=0,n=this.elements.length/2,i=n-r,s=Math.floor(i/2),o=this.elements[s*2];i>1&&(o<e&&(r=s),o>e&&(n=s),o!=e);)i=n-r,s=r+Math.floor(i/2),o=this.elements[s*2];if(o==e||o>e)return s*2;if(o<e)return(s+1)*2},t.Vector.prototype.insert=function(e,r){this.upsert(e,r,function(){throw"duplicate index"})},t.Vector.prototype.upsert=function(e,r,n){this._magnitude=0;var i=this.positionForIndex(e);this.elements[i]==e?this.elements[i+1]=n(this.elements[i+1],r):this.elements.splice(i,0,e,r)},t.Vector.prototype.magnitude=function(){if(this._magnitude)return this._magnitude;for(var e=0,r=this.elements.length,n=1;n<r;n+=2){var i=this.elements[n];e+=i*i}return this._magnitude=Math.sqrt(e)},t.Vector.prototype.dot=function(e){for(var r=0,n=this.elements,i=e.elements,s=n.length,o=i.length,a=0,c=0,u=0,l=0;u<s&&l<o;)a=n[u],c=i[l],a<c?u+=2:a>c?l+=2:a==c&&(r+=n[u+1]*i[l+1],u+=2,l+=2);return r},t.Vector.prototype.similarity=function(e){return this.dot(e)/this.magnitude()||0},t.Vector.prototype.toArray=function(){for(var e=new Array(this.elements.length/2),r=1,n=0;r<this.elements.length;r+=2,n++)e[n]=this.elements[r];return e},t.Vector.prototype.toJSON=function(){return this.elements};/*!
+ */t.Vector=function(e){this._magnitude=0,this.elements=e||[]},t.Vector.prototype.positionForIndex=function(e){if(this.elements.length==0)return 0;for(var r=0,n=this.elements.length/2,i=n-r,s=Math.floor(i/2),o=this.elements[s*2];i>1&&(o<e&&(r=s),o>e&&(n=s),o!=e);)i=n-r,s=r+Math.floor(i/2),o=this.elements[s*2];if(o==e||o>e)return s*2;if(o<e)return(s+1)*2},t.Vector.prototype.insert=function(e,r){this.upsert(e,r,function(){throw"duplicate index"})},t.Vector.prototype.upsert=function(e,r,n){this._magnitude=0;var i=this.positionForIndex(e);this.elements[i]==e?this.elements[i+1]=n(this.elements[i+1],r):this.elements.splice(i,0,e,r)},t.Vector.prototype.magnitude=function(){if(this._magnitude)return this._magnitude;for(var e=0,r=this.elements.length,n=1;n<r;n+=2){var i=this.elements[n];e+=i*i}return this._magnitude=Math.sqrt(e)},t.Vector.prototype.dot=function(e){for(var r=0,n=this.elements,i=e.elements,s=n.length,o=i.length,a=0,u=0,c=0,f=0;c<s&&f<o;)a=n[c],u=i[f],a<u?c+=2:a>u?f+=2:a==u&&(r+=n[c+1]*i[f+1],c+=2,f+=2);return r},t.Vector.prototype.similarity=function(e){return this.dot(e)/this.magnitude()||0},t.Vector.prototype.toArray=function(){for(var e=new Array(this.elements.length/2),r=1,n=0;r<this.elements.length;r+=2,n++)e[n]=this.elements[r];return e},t.Vector.prototype.toJSON=function(){return this.elements};/*!
  * lunr.stemmer
  * Copyright (C) 2020 Oliver Nightingale
  * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt
- */t.stemmer=function(){var e={ational:"ate",tional:"tion",enci:"ence",anci:"ance",izer:"ize",bli:"ble",alli:"al",entli:"ent",eli:"e",ousli:"ous",ization:"ize",ation:"ate",ator:"ate",alism:"al",iveness:"ive",fulness:"ful",ousness:"ous",aliti:"al",iviti:"ive",biliti:"ble",logi:"log"},r={icate:"ic",ative:"",alize:"al",iciti:"ic",ical:"ic",ful:"",ness:""},n="[^aeiou]",i="[aeiouy]",s=n+"[^aeiouy]*",o=i+"[aeiou]*",a="^("+s+")?"+o+s,c="^("+s+")?"+o+s+"("+o+")?$",u="^("+s+")?"+o+s+o+s,l="^("+s+")?"+i,g=new RegExp(a),f=new RegExp(u),v=new RegExp(c),m=new RegExp(l),x=/^(.+?)(ss|i)es$/,d=/^(.+?)([^s])s$/,y=/^(.+?)eed$/,b=/^(.+?)(ed|ing)$/,E=/.$/,w=/(at|bl|iz)$/,R=new RegExp("([^aeiouylsz])\\1$"),j=new RegExp("^"+s+i+"[^aeiouwxy]$"),_=/^(.+?[^aeiou])y$/,D=/^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/,N=/^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/,C=/^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/,V=/^(.+?)(s|t)(ion)$/,P=/^(.+?)e$/,z=/ll$/,$=new RegExp("^"+s+i+"[^aeiouwxy]$"),M=function(h){var S,k,L,p,T,O,F;if(h.length<3)return h;if(L=h.substr(0,1),L=="y"&&(h=L.toUpperCase()+h.substr(1)),p=x,T=d,p.test(h)?h=h.replace(p,"$1$2"):T.test(h)&&(h=h.replace(T,"$1$2")),p=y,T=b,p.test(h)){var Q=p.exec(h);p=g,p.test(Q[1])&&(p=E,h=h.replace(p,""))}else if(T.test(h)){var Q=T.exec(h);S=Q[1],T=m,T.test(S)&&(h=S,T=w,O=R,F=j,T.test(h)?h=h+"e":O.test(h)?(p=E,h=h.replace(p,"")):F.test(h)&&(h=h+"e"))}if(p=_,p.test(h)){var Q=p.exec(h);S=Q[1],h=S+"i"}if(p=D,p.test(h)){var Q=p.exec(h);S=Q[1],k=Q[2],p=g,p.test(S)&&(h=S+e[k])}if(p=N,p.test(h)){var Q=p.exec(h);S=Q[1],k=Q[2],p=g,p.test(S)&&(h=S+r[k])}if(p=C,T=V,p.test(h)){var Q=p.exec(h);S=Q[1],p=f,p.test(S)&&(h=S)}else if(T.test(h)){var Q=T.exec(h);S=Q[1]+Q[2],T=f,T.test(S)&&(h=S)}if(p=P,p.test(h)){var Q=p.exec(h);S=Q[1],p=f,T=v,O=$,(p.test(S)||T.test(S)&&!O.test(S))&&(h=S)}return p=z,T=f,p.test(h)&&T.test(h)&&(p=E,h=h.replace(p,"")),L=="y"&&(h=L.toLowerCase()+h.substr(1)),h};return function(I){return I.update(M)}}(),t.Pipeline.registerFunction(t.stemmer,"stemmer");/*!
+ */t.stemmer=function(){var e={ational:"ate",tional:"tion",enci:"ence",anci:"ance",izer:"ize",bli:"ble",alli:"al",entli:"ent",eli:"e",ousli:"ous",ization:"ize",ation:"ate",ator:"ate",alism:"al",iveness:"ive",fulness:"ful",ousness:"ous",aliti:"al",iviti:"ive",biliti:"ble",logi:"log"},r={icate:"ic",ative:"",alize:"al",iciti:"ic",ical:"ic",ful:"",ness:""},n="[^aeiou]",i="[aeiouy]",s=n+"[^aeiouy]*",o=i+"[aeiou]*",a="^("+s+")?"+o+s,u="^("+s+")?"+o+s+"("+o+")?$",c="^("+s+")?"+o+s+o+s,f="^("+s+")?"+i,g=new RegExp(a),l=new RegExp(c),m=new RegExp(u),x=new RegExp(f),v=/^(.+?)(ss|i)es$/,d=/^(.+?)([^s])s$/,y=/^(.+?)eed$/,b=/^(.+?)(ed|ing)$/,E=/.$/,w=/(at|bl|iz)$/,R=new RegExp("([^aeiouylsz])\\1$"),j=new RegExp("^"+s+i+"[^aeiouwxy]$"),_=/^(.+?[^aeiou])y$/,D=/^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/,N=/^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/,C=/^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/,V=/^(.+?)(s|t)(ion)$/,P=/^(.+?)e$/,z=/ll$/,$=new RegExp("^"+s+i+"[^aeiouwxy]$"),M=function(h){var S,k,L,p,T,O,F;if(h.length<3)return h;if(L=h.substr(0,1),L=="y"&&(h=L.toUpperCase()+h.substr(1)),p=v,T=d,p.test(h)?h=h.replace(p,"$1$2"):T.test(h)&&(h=h.replace(T,"$1$2")),p=y,T=b,p.test(h)){var Q=p.exec(h);p=g,p.test(Q[1])&&(p=E,h=h.replace(p,""))}else if(T.test(h)){var Q=T.exec(h);S=Q[1],T=x,T.test(S)&&(h=S,T=w,O=R,F=j,T.test(h)?h=h+"e":O.test(h)?(p=E,h=h.replace(p,"")):F.test(h)&&(h=h+"e"))}if(p=_,p.test(h)){var Q=p.exec(h);S=Q[1],h=S+"i"}if(p=D,p.test(h)){var Q=p.exec(h);S=Q[1],k=Q[2],p=g,p.test(S)&&(h=S+e[k])}if(p=N,p.test(h)){var Q=p.exec(h);S=Q[1],k=Q[2],p=g,p.test(S)&&(h=S+r[k])}if(p=C,T=V,p.test(h)){var Q=p.exec(h);S=Q[1],p=l,p.test(S)&&(h=S)}else if(T.test(h)){var Q=T.exec(h);S=Q[1]+Q[2],T=l,T.test(S)&&(h=S)}if(p=P,p.test(h)){var Q=p.exec(h);S=Q[1],p=l,T=m,O=$,(p.test(S)||T.test(S)&&!O.test(S))&&(h=S)}return p=z,T=l,p.test(h)&&T.test(h)&&(p=E,h=h.replace(p,"")),L=="y"&&(h=L.toLowerCase()+h.substr(1)),h};return function(I){return I.update(M)}}(),t.Pipeline.registerFunction(t.stemmer,"stemmer");/*!
  * lunr.stopWordFilter
  * Copyright (C) 2020 Oliver Nightingale
  */t.generateStopWordFilter=function(e){var r=e.reduce(function(n,i){return n[i]=i,n},{});return function(n){if(n&&r[n.toString()]!==n.toString())return n}},t.stopWordFilter=t.generateStopWordFilter(["a","able","about","across","after","all","almost","also","am","among","an","and","any","are","as","at","be","because","been","but","by","can","cannot","could","dear","did","do","does","either","else","ever","every","for","from","get","got","had","has","have","he","her","hers","him","his","how","however","i","if","in","into","is","it","its","just","least","let","like","likely","may","me","might","most","must","my","neither","no","nor","not","of","off","often","on","only","or","other","our","own","rather","said","say","says","she","should","since","so","some","than","that","the","their","them","then","there","these","they","this","tis","to","too","twas","us","wants","was","we","were","what","when","where","which","while","who","whom","why","will","with","would","yet","you","your"]),t.Pipeline.registerFunction(t.stopWordFilter,"stopWordFilter");/*!
@@ -31,12 +31,12 @@
  */t.trimmer=function(e){return e.update(function(r){return r.replace(/^\W+/,"").replace(/\W+$/,"")})},t.Pipeline.registerFunction(t.trimmer,"trimmer");/*!
  * lunr.TokenSet
  * Copyright (C) 2020 Oliver Nightingale
- */t.TokenSet=function(){this.final=!1,this.edges={},this.id=t.TokenSet._nextId,t.TokenSet._nextId+=1},t.TokenSet._nextId=1,t.TokenSet.fromArray=function(e){for(var r=new t.TokenSet.Builder,n=0,i=e.length;n<i;n++)r.insert(e[n]);return r.finish(),r.root},t.TokenSet.fromClause=function(e){return"editDistance"in e?t.TokenSet.fromFuzzyString(e.term,e.editDistance):t.TokenSet.fromString(e.term)},t.TokenSet.fromFuzzyString=function(e,r){for(var n=new t.TokenSet,i=[{node:n,editsRemaining:r,str:e}];i.length;){var s=i.pop();if(s.str.length>0){var o=s.str.charAt(0),a;o in s.node.edges?a=s.node.edges[o]:(a=new t.TokenSet,s.node.edges[o]=a),s.str.length==1&&(a.final=!0),i.push({node:a,editsRemaining:s.editsRemaining,str:s.str.slice(1)})}if(s.editsRemaining!=0){if("*"in s.node.edges)var c=s.node.edges["*"];else{var c=new t.TokenSet;s.node.edges["*"]=c}if(s.str.length==0&&(c.final=!0),i.push({node:c,editsRemaining:s.editsRemaining-1,str:s.str}),s.str.length>1&&i.push({node:s.node,editsRemaining:s.editsRemaining-1,str:s.str.slice(1)}),s.str.length==1&&(s.node.final=!0),s.str.length>=1){if("*"in s.node.edges)var u=s.node.edges["*"];else{var u=new t.TokenSet;s.node.edges["*"]=u}s.str.length==1&&(u.final=!0),i.push({node:u,editsRemaining:s.editsRemaining-1,str:s.str.slice(1)})}if(s.str.length>1){var l=s.str.charAt(0),g=s.str.charAt(1),f;g in s.node.edges?f=s.node.edges[g]:(f=new t.TokenSet,s.node.edges[g]=f),s.str.length==1&&(f.final=!0),i.push({node:f,editsRemaining:s.editsRemaining-1,str:l+s.str.slice(2)})}}}return n},t.TokenSet.fromString=function(e){for(var r=new t.TokenSet,n=r,i=0,s=e.length;i<s;i++){var o=e[i],a=i==s-1;if(o=="*")r.edges[o]=r,r.final=a;else{var c=new t.TokenSet;c.final=a,r.edges[o]=c,r=c}}return n},t.TokenSet.prototype.toArray=function(){for(var e=[],r=[{prefix:"",node:this}];r.length;){var n=r.pop(),i=Object.keys(n.node.edges),s=i.length;n.node.final&&(n.prefix.charAt(0),e.push(n.prefix));for(var o=0;o<s;o++){var a=i[o];r.push({prefix:n.prefix.concat(a),node:n.node.edges[a]})}}return e},t.TokenSet.prototype.toString=function(){if(this._str)return this._str;for(var e=this.final?"1":"0",r=Object.keys(this.edges).sort(),n=r.length,i=0;i<n;i++){var s=r[i],o=this.edges[s];e=e+s+o.id}return e},t.TokenSet.prototype.intersect=function(e){for(var r=new t.TokenSet,n=void 0,i=[{qNode:e,output:r,node:this}];i.length;){n=i.pop();for(var s=Object.keys(n.qNode.edges),o=s.length,a=Object.keys(n.node.edges),c=a.length,u=0;u<o;u++)for(var l=s[u],g=0;g<c;g++){var f=a[g];if(f==l||l=="*"){var v=n.node.edges[f],m=n.qNode.edges[l],x=v.final&&m.final,d=void 0;f in n.output.edges?(d=n.output.edges[f],d.final=d.final||x):(d=new t.TokenSet,d.final=x,n.output.edges[f]=d),i.push({qNode:m,output:d,node:v})}}}return r},t.TokenSet.Builder=function(){this.previousWord="",this.root=new t.TokenSet,this.uncheckedNodes=[],this.minimizedNodes={}},t.TokenSet.Builder.prototype.insert=function(e){var r,n=0;if(e<this.previousWord)throw new Error("Out of order word insertion");for(var i=0;i<e.length&&i<this.previousWord.length&&e[i]==this.previousWord[i];i++)n++;this.minimize(n),this.uncheckedNodes.length==0?r=this.root:r=this.uncheckedNodes[this.uncheckedNodes.length-1].child;for(var i=n;i<e.length;i++){var s=new t.TokenSet,o=e[i];r.edges[o]=s,this.uncheckedNodes.push({parent:r,char:o,child:s}),r=s}r.final=!0,this.previousWord=e},t.TokenSet.Builder.prototype.finish=function(){this.minimize(0)},t.TokenSet.Builder.prototype.minimize=function(e){for(var r=this.uncheckedNodes.length-1;r>=e;r--){var n=this.uncheckedNodes[r],i=n.child.toString();i in this.minimizedNodes?n.parent.edges[n.char]=this.minimizedNodes[i]:(n.child._str=i,this.minimizedNodes[i]=n.child),this.uncheckedNodes.pop()}};/*!
+ */t.TokenSet=function(){this.final=!1,this.edges={},this.id=t.TokenSet._nextId,t.TokenSet._nextId+=1},t.TokenSet._nextId=1,t.TokenSet.fromArray=function(e){for(var r=new t.TokenSet.Builder,n=0,i=e.length;n<i;n++)r.insert(e[n]);return r.finish(),r.root},t.TokenSet.fromClause=function(e){return"editDistance"in e?t.TokenSet.fromFuzzyString(e.term,e.editDistance):t.TokenSet.fromString(e.term)},t.TokenSet.fromFuzzyString=function(e,r){for(var n=new t.TokenSet,i=[{node:n,editsRemaining:r,str:e}];i.length;){var s=i.pop();if(s.str.length>0){var o=s.str.charAt(0),a;o in s.node.edges?a=s.node.edges[o]:(a=new t.TokenSet,s.node.edges[o]=a),s.str.length==1&&(a.final=!0),i.push({node:a,editsRemaining:s.editsRemaining,str:s.str.slice(1)})}if(s.editsRemaining!=0){if("*"in s.node.edges)var u=s.node.edges["*"];else{var u=new t.TokenSet;s.node.edges["*"]=u}if(s.str.length==0&&(u.final=!0),i.push({node:u,editsRemaining:s.editsRemaining-1,str:s.str}),s.str.length>1&&i.push({node:s.node,editsRemaining:s.editsRemaining-1,str:s.str.slice(1)}),s.str.length==1&&(s.node.final=!0),s.str.length>=1){if("*"in s.node.edges)var c=s.node.edges["*"];else{var c=new t.TokenSet;s.node.edges["*"]=c}s.str.length==1&&(c.final=!0),i.push({node:c,editsRemaining:s.editsRemaining-1,str:s.str.slice(1)})}if(s.str.length>1){var f=s.str.charAt(0),g=s.str.charAt(1),l;g in s.node.edges?l=s.node.edges[g]:(l=new t.TokenSet,s.node.edges[g]=l),s.str.length==1&&(l.final=!0),i.push({node:l,editsRemaining:s.editsRemaining-1,str:f+s.str.slice(2)})}}}return n},t.TokenSet.fromString=function(e){for(var r=new t.TokenSet,n=r,i=0,s=e.length;i<s;i++){var o=e[i],a=i==s-1;if(o=="*")r.edges[o]=r,r.final=a;else{var u=new t.TokenSet;u.final=a,r.edges[o]=u,r=u}}return n},t.TokenSet.prototype.toArray=function(){for(var e=[],r=[{prefix:"",node:this}];r.length;){var n=r.pop(),i=Object.keys(n.node.edges),s=i.length;n.node.final&&(n.prefix.charAt(0),e.push(n.prefix));for(var o=0;o<s;o++){var a=i[o];r.push({prefix:n.prefix.concat(a),node:n.node.edges[a]})}}return e},t.TokenSet.prototype.toString=function(){if(this._str)return this._str;for(var e=this.final?"1":"0",r=Object.keys(this.edges).sort(),n=r.length,i=0;i<n;i++){var s=r[i],o=this.edges[s];e=e+s+o.id}return e},t.TokenSet.prototype.intersect=function(e){for(var r=new t.TokenSet,n=void 0,i=[{qNode:e,output:r,node:this}];i.length;){n=i.pop();for(var s=Object.keys(n.qNode.edges),o=s.length,a=Object.keys(n.node.edges),u=a.length,c=0;c<o;c++)for(var f=s[c],g=0;g<u;g++){var l=a[g];if(l==f||f=="*"){var m=n.node.edges[l],x=n.qNode.edges[f],v=m.final&&x.final,d=void 0;l in n.output.edges?(d=n.output.edges[l],d.final=d.final||v):(d=new t.TokenSet,d.final=v,n.output.edges[l]=d),i.push({qNode:x,output:d,node:m})}}}return r},t.TokenSet.Builder=function(){this.previousWord="",this.root=new t.TokenSet,this.uncheckedNodes=[],this.minimizedNodes={}},t.TokenSet.Builder.prototype.insert=function(e){var r,n=0;if(e<this.previousWord)throw new Error("Out of order word insertion");for(var i=0;i<e.length&&i<this.previousWord.length&&e[i]==this.previousWord[i];i++)n++;this.minimize(n),this.uncheckedNodes.length==0?r=this.root:r=this.uncheckedNodes[this.uncheckedNodes.length-1].child;for(var i=n;i<e.length;i++){var s=new t.TokenSet,o=e[i];r.edges[o]=s,this.uncheckedNodes.push({parent:r,char:o,child:s}),r=s}r.final=!0,this.previousWord=e},t.TokenSet.Builder.prototype.finish=function(){this.minimize(0)},t.TokenSet.Builder.prototype.minimize=function(e){for(var r=this.uncheckedNodes.length-1;r>=e;r--){var n=this.uncheckedNodes[r],i=n.child.toString();i in this.minimizedNodes?n.parent.edges[n.char]=this.minimizedNodes[i]:(n.child._str=i,this.minimizedNodes[i]=n.child),this.uncheckedNodes.pop()}};/*!
  * lunr.Index
  * Copyright (C) 2020 Oliver Nightingale
- */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),c=0;c<this.fields.length;c++)i[this.fields[c]]=new t.Vector;e.call(r,r);for(var c=0;c<r.clauses.length;c++){var u=r.clauses[c],l=null,g=t.Set.empty;u.usePipeline?l=this.pipeline.runString(u.term,{fields:u.fields}):l=[u.term];for(var f=0;f<l.length;f++){var v=l[f];u.term=v;var m=t.TokenSet.fromClause(u),x=this.tokenSet.intersect(m).toArray();if(x.length===0&&u.presence===t.Query.presence.REQUIRED){for(var d=0;d<u.fields.length;d++){var y=u.fields[d];o[y]=t.Set.empty}break}for(var b=0;b<x.length;b++)for(var E=x[b],w=this.invertedIndex[E],R=w._index,d=0;d<u.fields.length;d++){var y=u.fields[d],j=w[y],_=Object.keys(j),D=E+"/"+y,N=new t.Set(_);if(u.presence==t.Query.presence.REQUIRED&&(g=g.union(N),o[y]===void 0&&(o[y]=t.Set.complete)),u.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,u.boost,function(ge,ye){return ge+ye}),!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(u.presence===t.Query.presence.REQUIRED)for(var d=0;d<u.fields.length;d++){var y=u.fields[d];o[y]=o[y].intersect(g)}}for(var M=t.Set.complete,I=t.Set.empty,c=0;c<this.fields.length;c++){var y=this.fields[c];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 c=0;c<h.length;c++){var P=h[c],L=t.FieldRef.fromString(P);n[P]=new t.MatchData}}for(var c=0;c<h.length;c++){var L=t.FieldRef.fromString(h[c]),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(de,pe){return pe.score-de.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,c=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 u=0;u<i.length;u++){var l=i[u],g=l[0],f=l[1];n[g]=new t.Vector(f)}for(var u=0;u<o.length;u++){var l=o[u],v=l[0],m=l[1];a.insert(v),s[v]=m}return a.finish(),r.fields=e.fields,r.fieldVectors=n,r.invertedIndex=s,r.tokenSet=a.root,r.pipeline=c,new t.Index(r)};/*!
+ */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,c=a?a(e):e[o],u=this.tokenizer(c,{fields:[o]}),l=this.pipeline.run(u),g=new t.FieldRef(n,o),f=Object.create(null);this.fieldTermFrequencies[g]=f,this.fieldLengths[g]=0,this.fieldLengths[g]+=l.length;for(var v=0;v<l.length;v++){var m=l[v];if(f[m]==null&&(f[m]=0),f[m]+=1,this.invertedIndex[m]==null){var x=Object.create(null);x._index=this.termIndex,this.termIndex+=1;for(var d=0;d<i.length;d++)x[i[d]]=Object.create(null);this.invertedIndex[m]=x}this.invertedIndex[m][o][n]==null&&(this.invertedIndex[m][o][n]=Object.create(null));for(var y=0;y<this.metadataWhitelist.length;y++){var b=this.metadataWhitelist[y],E=m.metadata[b];this.invertedIndex[m][o][n][b]==null&&(this.invertedIndex[m][o][n][b]=[]),this.invertedIndex[m][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 c=Object.keys(this._fields),s=0;s<c.length;s++){var u=c[s];n[u]=n[u]/i[u]}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,c=this.fieldLengths[o],u=new t.Vector,l=this.fieldTermFrequencies[o],g=Object.keys(l),f=g.length,v=this._fields[a].boost||1,m=this._documents[o.docRef].boost||1,x=0;x<f;x++){var d=g[x],y=l[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*(c/this.averageFieldLength[a]))+y),w*=v,w*=m,R=Math.round(w*1e3)/1e3,u.insert(b,R)}e[o]=u}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],c=Object.keys(e.metadata[i][a]);this.metadata[i][a]==null&&(this.metadata[i][a]=Object.create(null));for(var u=0;u<c.length;u++){var 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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 e=this.next(),r=e.charCodeAt(0);while(r>47&&r<58);e!=t.QueryLexer.EOS&&this.backup()},t.QueryLexer.prototype.more=function(){return this.pos<this.length},t.QueryLexer.EOS="EOS",t.QueryLexer.FIELD="FIELD",t.QueryLexer.TERM="TERM",t.QueryLexer.EDIT_DISTANCE="EDIT_DISTANCE",t.QueryLexer.BOOST="BOOST",t.QueryLexer.PRESENCE="PRESENCE",t.QueryLexer.lexField=function(e){return e.backup(),e.emit(t.QueryLexer.FIELD),e.ignore(),t.QueryLexer.lexText},t.QueryLexer.lexTerm=function(e){if(e.width()>1&&(e.backup(),e.emit(t.QueryLexer.TERM)),e.ignore(),e.more())return t.QueryLexer.lexText},t.QueryLexer.lexEditDistance=function(e){return e.ignore(),e.acceptDigitRun(),e.emit(t.QueryLexer.EDIT_DISTANCE),t.QueryLexer.lexText},t.QueryLexer.lexBoost=function(e){return 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 r=e.consumeLexeme();if(r!=null){var n=parseInt(r.str,10);if(isNaN(n)){var i="edit distance must be numeric";throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.editDistance=n;var s=e.peekLexeme();if(s==null){e.nextClause();return}switch(s.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 '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},t.QueryParser.parseBoost=function(e){var r=e.consumeLexeme();if(r!=null){var n=parseInt(r.str,10);if(isNaN(n)){var i="boost must be numeric";throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.boost=n;var s=e.peekLexeme();if(s==null){e.nextClause();return}switch(s.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 '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},function(e,r){typeof define=="function"&&define.amd?define(r):typeof K=="object"?ee.exports=r():e.lunr=r()}(this,function(){return t})})()});var de=Pe(te());function re(t,e=document){let r=ke(t,e);if(typeof r=="undefined")throw new ReferenceError(`Missing element: expected "${t}" to be present`);return r}function ke(t,e=document){return e.querySelector(t)||void 0}Object.entries||(Object.entries=function(t){let e=[];for(let r of Object.keys(t))e.push([r,t[r]]);return e});Object.values||(Object.values=function(t){let e=[];for(let r of 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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.f886a092.min.js.map
 
diff --git a/assets/javascripts/workers/search.208ed371.min.js.map b/assets/javascripts/workers/search.f886a092.min.js.map
similarity index 62%
rename from assets/javascripts/workers/search.208ed371.min.js.map
rename to assets/javascripts/workers/search.f886a092.min.js.map
index 75dc4f987..9115984a7 100644
--- a/assets/javascripts/workers/search.208ed371.min.js.map
+++ b/assets/javascripts/workers/search.f886a092.min.js.map
@@ -1,8 +1,7 @@
 {
   "version": 3,
-  "sources": ["node_modules/lunr/lunr.js", "src/assets/javascripts/integrations/search/worker/main/index.ts", "src/assets/javascripts/browser/element/_/index.ts", "src/assets/javascripts/polyfills/index.ts", "src/assets/javascripts/integrations/search/config/index.ts", "src/assets/javascripts/integrations/search/internal/_/index.ts", "src/assets/javascripts/integrations/search/internal/extract/index.ts", "src/assets/javascripts/integrations/search/internal/highlight/index.ts", "src/assets/javascripts/integrations/search/internal/tokenize/index.ts", "src/assets/javascripts/integrations/search/query/transform/index.ts", "src/assets/javascripts/integrations/search/query/_/index.ts", "src/assets/javascripts/integrations/search/_/index.ts"],
-  "sourceRoot": "../../../..",
-  "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) 2016-2023 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) 2016-2023 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 document.activeElement instanceof HTMLElement\n    ? document.activeElement || undefined\n    : undefined\n}\n", "/*\n * Copyright (c) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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 *\n * @returns Highlighted string value\n */\nexport function highlight(\n  input: string, table: PositionTable, positions: Position[]\n): string {\n  return highlightAll([input], table, positions).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 *\n * @returns Highlighted string values\n */\nexport function highlightAll(\n  inputs: string[], table: PositionTable, positions: Position[]\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\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      /* 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      /* Append slice and abort if we have two */\n      if (slices.push(slice) === 2)\n        break\n    }\n\n    /* Return highlighted slices */\n    return slices.join(\"\")\n  })\n}\n", "/*\n * Copyright (c) 2016-2023 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) 2016-2023 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+|$)/g, \"\")\n\n    /* => 4 */\n    .split(/\\s+/g)\n      .flatMap(fn)\n      .map(term => /([~^]$)/.test(term) ? `${term}1` : term)\n      .map(term => /(^[+-]|[~^]\\d+$)/.test(term) ? term : `${term}*`)\n      .join(\" \")\n}\n", "/*\n * Copyright (c) 2016-2023 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) 2016-2023 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  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    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)\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"],
-  "mappings": 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+  "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) 2016-2023 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) 2016-2023 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 document.activeElement instanceof HTMLElement\n    ? document.activeElement || undefined\n    : undefined\n}\n", "/*\n * Copyright (c) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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) 2016-2023 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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new file mode 100644
index 000000000..55c3549e1
--- /dev/null
+++ b/assets/stylesheets/main.4b4a2bd9.min.css
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diff --git a/assets/stylesheets/main.85bb2934.min.css b/assets/stylesheets/main.85bb2934.min.css
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index 0cbca8652..000000000
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index b920fdc15..000000000
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\ No newline at end of file
diff --git a/assets/stylesheets/palette.356b1318.min.css b/assets/stylesheets/palette.356b1318.min.css
new file mode 100644
index 000000000..141eb5f9d
--- /dev/null
+++ b/assets/stylesheets/palette.356b1318.min.css
@@ -0,0 +1 @@
+@media screen{[data-md-color-scheme=slate]{--md-default-fg-color:hsla(var(--md-hue),15%,90%,0.82);--md-default-fg-color--light:hsla(var(--md-hue),15%,90%,0.56);--md-default-fg-color--lighter:hsla(var(--md-hue),15%,90%,0.32);--md-default-fg-color--lightest:hsla(var(--md-hue),15%,90%,0.12);--md-default-bg-color:hsla(var(--md-hue),15%,14%,1);--md-default-bg-color--light:hsla(var(--md-hue),15%,14%,0.54);--md-default-bg-color--lighter:hsla(var(--md-hue),15%,14%,0.26);--md-default-bg-color--lightest:hsla(var(--md-hue),15%,14%,0.07);--md-code-fg-color:hsla(var(--md-hue),18%,86%,0.82);--md-code-bg-color:hsla(var(--md-hue),15%,18%,1);--md-code-hl-color--light:#4287ff26;--md-code-hl-number-color:#e6695b;--md-code-hl-special-color:#f06090;--md-code-hl-function-color:#c973d9;--md-code-hl-constant-color:#9383e2;--md-code-hl-keyword-color:#6791e0;--md-code-hl-string-color:#2fb170;--md-code-hl-name-color:var(--md-code-fg-color);--md-code-hl-operator-color:var(--md-default-fg-color--light);--md-code-hl-punctuation-color:var(--md-default-fg-color--light);--md-code-hl-comment-color:var(--md-default-fg-color--light);--md-code-hl-generic-color:var(--md-default-fg-color--light);--md-code-hl-variable-color:var(--md-default-fg-color--light);--md-typeset-color:var(--md-default-fg-color);--md-typeset-a-color:var(--md-primary-fg-color);--md-typeset-kbd-color:hsla(var(--md-hue),15%,90%,0.12);--md-typeset-kbd-accent-color:hsla(var(--md-hue),15%,90%,0.2);--md-typeset-kbd-border-color:hsla(var(--md-hue),15%,14%,1);--md-typeset-mark-color:#4287ff4d;--md-typeset-table-color:hsla(var(--md-hue),15%,95%,0.12);--md-typeset-table-color--light:hsla(var(--md-hue),15%,95%,0.035);--md-admonition-fg-color:var(--md-default-fg-color);--md-admonition-bg-color:var(--md-default-bg-color);--md-footer-bg-color:hsla(var(--md-hue),15%,10%,0.87);--md-footer-bg-color--dark:hsla(var(--md-hue),15%,8%,1);--md-shadow-z1:0 0.2rem 0.5rem #0000000d,0 0 0.05rem #0000001a;--md-shadow-z2:0 0.2rem 0.5rem #00000040,0 0 0.05rem #00000040;--md-shadow-z3:0 0.2rem 0.5rem #0006,0 0 0.05rem #00000059;color-scheme:dark}[data-md-color-scheme=slate] img[src$="#gh-light-mode-only"],[data-md-color-scheme=slate] 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index bfcf15dc4..000000000
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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>
       </label>
       <div class="md-search" data-md-component="search" role="dialog">
@@ -123,12 +129,15 @@
     <form class="md-search__form" name="search">
       <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-.27A6.516 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 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
         <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 12 19 6.41Z"/></svg>
         </button>
       </nav>
@@ -152,7 +161,7 @@
         <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -182,6 +191,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href=".." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -196,7 +206,7 @@
       <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -208,14 +218,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href=".." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
       </a>
     </li>
   
@@ -223,14 +237,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
       </a>
     </li>
   
@@ -238,534 +256,724 @@
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
-          Tutorials and Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3">
-          <span class="md-nav__icon md-icon"></span>
-          Tutorials and Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Tutorials and Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            Tutorials and Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
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-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
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-          <span class="md-nav__icon md-icon"></span>
-          Introduction
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
-          Advanced
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-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_2">
-          <span class="md-nav__icon md-icon"></span>
-          Advanced
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Advanced
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
-          Diagnostics
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_3">
-          <span class="md-nav__icon md-icon"></span>
-          Diagnostics
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Diagnostics
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/12_diagnostics_posterior_predictive_check/" class="md-nav__link">
-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
-          Analysis
-          <span class="md-nav__icon md-icon"></span>
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-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
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-          <span class="md-nav__icon md-icon"></span>
-          Analysis
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Analysis
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Analysis
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/07_conditional_distributions/" class="md-nav__link">
-        Conditional distributions
+        
+  
+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
+  
+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
-          Examples
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-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_5">
-          <span class="md-nav__icon md-icon"></span>
-          Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
+        
           
+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_5">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../examples/00_HH_simulator/" class="md-nav__link">
-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../examples/01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
-          Contributing
-          <span class="md-nav__icon md-icon"></span>
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-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_4">
-          <span class="md-nav__icon md-icon"></span>
-          Contributing
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Contributing
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Contributing
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../contribute/" class="md-nav__link">
-        Guide
+        
+  
+  <span class="md-ellipsis">
+    Guide
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../code_of_conduct/" class="md-nav__link">
-        Code of Conduct
+        
+  
+  <span class="md-ellipsis">
+    Code of Conduct
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../reference/" class="md-nav__link">
-        API Reference
+        
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -773,14 +981,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../faq/" class="md-nav__link">
-        FAQ
+        
+  
+  <span class="md-ellipsis">
+    FAQ
+  </span>
+  
+
       </a>
     </li>
   
@@ -788,14 +1000,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../credits/" class="md-nav__link">
-        Credits
+        
+  
+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
       </a>
     </li>
   
@@ -892,7 +1108,7 @@ <h1 id="citation">Citation<a class="headerlink" href="#citation" title="Permanen
       
     
     <a href="https://github.com/sbi-dev/sbi" target="_blank" rel="noopener" title="github.com" class="md-social__link">
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+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
     </a>
   
 </div>
@@ -906,10 +1122,11 @@ <h1 id="citation">Citation<a class="headerlink" href="#citation" title="Permanen
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.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": "../assets/javascripts/workers/search.f886a092.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.fac441b0.min.js"></script>
+      <script src="../assets/javascripts/bundle.81fa17fe.min.js"></script>
       
         <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML"></script>
       
diff --git a/code_of_conduct/index.html b/code_of_conduct/index.html
index 78bb4dbec..ce0bc34b1 100644
--- a/code_of_conduct/index.html
+++ b/code_of_conduct/index.html
@@ -16,8 +16,9 @@
       
         <link rel="next" href="../reference/">
       
+      
       <link rel="icon" href="../assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.1.9">
+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.4.8">
     
     
       
@@ -25,15 +26,18 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.85bb2934.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.4b4a2bd9.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.a6bdf11c.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.356b1318.min.css">
       
       
 
+
     
     
+      
+    
     
       
         
@@ -95,6 +99,7 @@
 
     </a>
     <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 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
     <div class="md-header__title" data-md-component="header-title">
@@ -119,6 +124,7 @@
     
     
       <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-.27A6.516 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>
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@@ -127,12 +133,15 @@
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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-.27A6.516 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 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
         <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 12 19 6.41Z"/></svg>
         </button>
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@@ -156,7 +165,7 @@
         <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -186,6 +195,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href=".." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -200,7 +210,7 @@
       <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -212,14 +222,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href=".." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
       </a>
     </li>
   
@@ -227,14 +241,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
       </a>
     </li>
   
@@ -242,508 +260,687 @@
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
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-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
-          Tutorials and Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3">
-          <span class="md-nav__icon md-icon"></span>
-          Tutorials and Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Tutorials and Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            Tutorials and Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
-          Introduction
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_1">
-          <span class="md-nav__icon md-icon"></span>
-          Introduction
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
-      
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-        <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
-          Advanced
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_2">
-          <span class="md-nav__icon md-icon"></span>
-          Advanced
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Advanced
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
-          Diagnostics
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_3">
-          <span class="md-nav__icon md-icon"></span>
-          Diagnostics
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Diagnostics
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/12_diagnostics_posterior_predictive_check/" class="md-nav__link">
-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
-          Analysis
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_4">
-          <span class="md-nav__icon md-icon"></span>
-          Analysis
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Analysis
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Analysis
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/07_conditional_distributions/" class="md-nav__link">
-        Conditional distributions
+        
+  
+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
+  
+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
-          Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_5">
-          <span class="md-nav__icon md-icon"></span>
-          Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
+        
           
+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_5">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../examples/00_HH_simulator/" class="md-nav__link">
-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../examples/01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
     
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" checked>
-      
-      
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
-          Contributing
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="true">
-        <label class="md-nav__title" for="__nav_4">
-          <span class="md-nav__icon md-icon"></span>
-          Contributing
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" checked>
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Contributing
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Contributing
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../contribute/" class="md-nav__link">
-        Guide
+        
+  
+  <span class="md-ellipsis">
+    Guide
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
     
@@ -758,12 +955,24 @@
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          Code of Conduct
+          
+  
+  <span class="md-ellipsis">
+    Code of Conduct
+  </span>
+  
+
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <a href="./" class="md-nav__link md-nav__link--active">
-        Code of Conduct
+        
+  
+  <span class="md-ellipsis">
+    Code of Conduct
+  </span>
+  
+
       </a>
       
         
@@ -871,24 +1080,29 @@
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../reference/" class="md-nav__link">
-        API Reference
+        
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -896,14 +1110,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../faq/" class="md-nav__link">
-        FAQ
+        
+  
+  <span class="md-ellipsis">
+    FAQ
+  </span>
+  
+
       </a>
     </li>
   
@@ -911,14 +1129,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../credits/" class="md-nav__link">
-        Credits
+        
+  
+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
       </a>
     </li>
   
@@ -1184,7 +1406,7 @@ <h2 id="attribution">Attribution<a class="headerlink" href="#attribution" title=
       
     
     <a href="https://github.com/sbi-dev/sbi" target="_blank" rel="noopener" title="github.com" class="md-social__link">
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
     </a>
   
 </div>
@@ -1198,10 +1420,11 @@ <h2 id="attribution">Attribution<a class="headerlink" href="#attribution" title=
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
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+    
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.f886a092.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>
     
     
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+      <script src="../assets/javascripts/bundle.81fa17fe.min.js"></script>
       
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diff --git a/contribute/index.html b/contribute/index.html
index 3f502c09d..c5d3ce206 100644
--- a/contribute/index.html
+++ b/contribute/index.html
@@ -16,8 +16,9 @@
       
         <link rel="next" href="../code_of_conduct/">
       
+      
       <link rel="icon" href="../assets/images/favicon.png">
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+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.4.8">
     
     
       
@@ -25,15 +26,18 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.85bb2934.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.4b4a2bd9.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.a6bdf11c.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.356b1318.min.css">
       
       
 
+
     
     
+      
+    
     
       
         
@@ -95,6 +99,7 @@
 
     </a>
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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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@@ -119,6 +124,7 @@
     
     
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+        
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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-.27A6.516 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>
       <nav class="md-search__options" aria-label="Search">
         
         <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 12 19 6.41Z"/></svg>
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@@ -156,7 +165,7 @@
         <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -186,6 +195,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href=".." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -200,7 +210,7 @@
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   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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@@ -212,14 +222,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href=".." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
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+  
+
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@@ -227,14 +241,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
       </a>
     </li>
   
@@ -242,494 +260,667 @@
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
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-      
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-        <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
-          Tutorials and Examples
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
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-          <span class="md-nav__icon md-icon"></span>
-          Tutorials and Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Tutorials and Examples
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+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            Tutorials and Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     
+    
+    
+    
+    
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-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
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-        <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
-          Introduction
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-          <span class="md-nav__icon md-icon"></span>
-          Introduction
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-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
       </a>
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+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
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-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
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-          <span class="md-nav__icon md-icon"></span>
-          Advanced
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-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Advanced
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+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
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     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
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+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
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-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
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-      
-      
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-          <span class="md-nav__icon md-icon"></span>
-          Diagnostics
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-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Diagnostics
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
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       <a href="../tutorial/12_diagnostics_posterior_predictive_check/" class="md-nav__link">
-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
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       <a href="../tutorial/15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
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+              
             
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-      </nav>
+          </ul>
+        </nav>
+      
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+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
-          Analysis
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_4">
-          <span class="md-nav__icon md-icon"></span>
-          Analysis
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Analysis
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Analysis
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/07_conditional_distributions/" class="md-nav__link">
-        Conditional distributions
+        
+  
+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
+  
+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
-          Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_5">
-          <span class="md-nav__icon md-icon"></span>
-          Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
+        
           
+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_5">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../examples/00_HH_simulator/" class="md-nav__link">
-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../examples/01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
     
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" checked>
-      
-      
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
-          Contributing
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="true">
-        <label class="md-nav__title" for="__nav_4">
-          <span class="md-nav__icon md-icon"></span>
-          Contributing
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" checked>
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Contributing
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Contributing
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
     
@@ -742,12 +933,24 @@
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          Guide
+          
+  
+  <span class="md-ellipsis">
+    Guide
+  </span>
+  
+
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <a href="./" class="md-nav__link md-nav__link--active">
-        Guide
+        
+  
+  <span class="md-ellipsis">
+    Guide
+  </span>
+  
+
       </a>
       
         
@@ -811,38 +1014,49 @@
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../code_of_conduct/" class="md-nav__link">
-        Code of Conduct
+        
+  
+  <span class="md-ellipsis">
+    Code of Conduct
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../reference/" class="md-nav__link">
-        API Reference
+        
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -850,14 +1064,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../faq/" class="md-nav__link">
-        FAQ
+        
+  
+  <span class="md-ellipsis">
+    FAQ
+  </span>
+  
+
       </a>
     </li>
   
@@ -865,14 +1083,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../credits/" class="md-nav__link">
-        Credits
+        
+  
+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
       </a>
     </li>
   
@@ -1043,7 +1265,7 @@ <h2 id="online-documentation">Online documentation<a class="headerlink" href="#o
       
     
     <a href="https://github.com/sbi-dev/sbi" target="_blank" rel="noopener" title="github.com" class="md-social__link">
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
     </a>
   
 </div>
@@ -1057,10 +1279,11 @@ <h2 id="online-documentation">Online documentation<a class="headerlink" href="#o
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.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": "../assets/javascripts/workers/search.f886a092.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.fac441b0.min.js"></script>
+      <script src="../assets/javascripts/bundle.81fa17fe.min.js"></script>
       
         <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML"></script>
       
diff --git a/credits/index.html b/credits/index.html
index 78bdc2ed3..b9b7a33d4 100644
--- a/credits/index.html
+++ b/credits/index.html
@@ -14,8 +14,9 @@
         <link rel="prev" href="../faq/">
       
       
+      
       <link rel="icon" href="../assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.1.9">
+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.4.8">
     
     
       
@@ -23,15 +24,18 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.85bb2934.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.4b4a2bd9.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.a6bdf11c.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.356b1318.min.css">
       
       
 
+
     
     
+      
+    
     
       
         
@@ -93,6 +97,7 @@
 
     </a>
     <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 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
     <div class="md-header__title" data-md-component="header-title">
@@ -117,6 +122,7 @@
     
     
       <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-.27A6.516 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>
       <div class="md-search" data-md-component="search" role="dialog">
@@ -125,12 +131,15 @@
     <form class="md-search__form" name="search">
       <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-.27A6.516 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 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
         <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 12 19 6.41Z"/></svg>
         </button>
       </nav>
@@ -154,7 +163,7 @@
         <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -184,6 +193,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href=".." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -198,7 +208,7 @@
       <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -210,14 +220,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href=".." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
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     </li>
   
@@ -225,14 +239,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
       </a>
     </li>
   
@@ -240,534 +258,724 @@
     
       
       
-      
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+    
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-        <label class="md-nav__title" for="__nav_3">
-          <span class="md-nav__icon md-icon"></span>
-          Tutorials and Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Tutorials and Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            Tutorials and Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
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-          <span class="md-nav__icon md-icon"></span>
-          Introduction
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
-          Advanced
-          <span class="md-nav__icon md-icon"></span>
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-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_2">
-          <span class="md-nav__icon md-icon"></span>
-          Advanced
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Advanced
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
-          Diagnostics
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_3">
-          <span class="md-nav__icon md-icon"></span>
-          Diagnostics
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Diagnostics
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/12_diagnostics_posterior_predictive_check/" class="md-nav__link">
-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
-          Analysis
-          <span class="md-nav__icon md-icon"></span>
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-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_4">
-          <span class="md-nav__icon md-icon"></span>
-          Analysis
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Analysis
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Analysis
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/07_conditional_distributions/" class="md-nav__link">
-        Conditional distributions
+        
+  
+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
+  
+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
-          Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_5">
-          <span class="md-nav__icon md-icon"></span>
-          Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
+        
           
+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_5">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../examples/00_HH_simulator/" class="md-nav__link">
-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../examples/01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
-      
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-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
-          Contributing
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_4">
-          <span class="md-nav__icon md-icon"></span>
-          Contributing
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Contributing
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Contributing
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../contribute/" class="md-nav__link">
-        Guide
+        
+  
+  <span class="md-ellipsis">
+    Guide
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../code_of_conduct/" class="md-nav__link">
-        Code of Conduct
+        
+  
+  <span class="md-ellipsis">
+    Code of Conduct
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../reference/" class="md-nav__link">
-        API Reference
+        
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -775,14 +983,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../faq/" class="md-nav__link">
-        FAQ
+        
+  
+  <span class="md-ellipsis">
+    FAQ
+  </span>
+  
+
       </a>
     </li>
   
@@ -790,8 +1002,6 @@
     
       
       
-      
-
   
   
     
@@ -806,12 +1016,24 @@
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          Credits
+          
+  
+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <a href="./" class="md-nav__link md-nav__link--active">
-        Credits
+        
+  
+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
       </a>
       
         
@@ -1003,7 +1225,7 @@ <h2 id="important-dependencies-and-prior-art">Important dependencies and prior a
       
     
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diff --git a/examples/00_HH_simulator/index.html b/examples/00_HH_simulator/index.html
index 8ffc7456c..b3634e663 100644
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+++ b/examples/00_HH_simulator/index.html
@@ -16,8 +16,9 @@
       
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-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
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+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
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-        Posterior sensitivity analysis
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         <label class="md-nav__link md-nav__link--active" for="__toc">
-          Hodgkin-Huxley example
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+  
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+  
+
           <span class="md-nav__icon md-icon"></span>
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-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
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@@ -768,108 +950,140 @@
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+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
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+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
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+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
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-        Guide
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+    Guide
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-        FAQ
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+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
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+    
+    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.f886a092.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>
     
     
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diff --git a/examples/01_decision_making_model/index.html b/examples/01_decision_making_model/index.html
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-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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   <div class="md-source__repository">
     sbi-dev/sbi
@@ -186,6 +195,7 @@
                     
 
 
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   <label class="md-nav__title" for="__drawer">
     <a href="../.." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -200,7 +210,7 @@
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+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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     sbi-dev/sbi
@@ -212,14 +222,18 @@
     
       
       
-      
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       <a href="../.." class="md-nav__link">
-        Home
+        
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+  <span class="md-ellipsis">
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@@ -227,14 +241,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
       </a>
     </li>
   
@@ -242,440 +260,598 @@
     
       
       
-      
-
   
   
     
   
   
     
+    
+    
+    
+    
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-      
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-          Tutorials and Examples
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+        
+        
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+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
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+  <span class="md-ellipsis">
+    Tutorials and Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            Tutorials and Examples
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+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     
+    
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-      
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-          Introduction
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+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
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+  <span class="md-ellipsis">
+    Introduction
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+  
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+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
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+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
       </a>
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+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
-      
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-      
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-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
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-          <span class="md-nav__icon md-icon"></span>
-          Advanced
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Advanced
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
       </a>
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
       </a>
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
       </a>
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
       </a>
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
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-          
+              
             
               
+                
   
   
   
     
+    
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+    
+    
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-      
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-      
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-          Diagnostics
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+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Diagnostics
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
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       <a href="../../tutorial/12_diagnostics_posterior_predictive_check/" class="md-nav__link">
-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
       </a>
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
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-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
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-          Analysis
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+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
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+  <span class="md-ellipsis">
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+          </label>
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+            Analysis
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+                
   
   
   
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-        Conditional distributions
+        
+  
+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
+  
+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
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+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
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+    
+    
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+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" checked>
+        
           
+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
+            
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+  <span class="md-ellipsis">
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+          <label class="md-nav__title" for="__nav_3_5">
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       <a href="../00_HH_simulator/" class="md-nav__link">
-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
     
@@ -690,12 +866,24 @@
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          Decision making model
+          
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
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+
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-        Decision making model
+        
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
       </a>
       
         
@@ -795,94 +983,120 @@
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
-          Contributing
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_4">
-          <span class="md-nav__icon md-icon"></span>
-          Contributing
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Contributing
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Contributing
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../contribute/" class="md-nav__link">
-        Guide
+        
+  
+  <span class="md-ellipsis">
+    Guide
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../code_of_conduct/" class="md-nav__link">
-        Code of Conduct
+        
+  
+  <span class="md-ellipsis">
+    Code of Conduct
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../reference/" class="md-nav__link">
-        API Reference
+        
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -890,14 +1104,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../faq/" class="md-nav__link">
-        FAQ
+        
+  
+  <span class="md-ellipsis">
+    FAQ
+  </span>
+  
+
       </a>
     </li>
   
@@ -905,14 +1123,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../credits/" class="md-nav__link">
-        Credits
+        
+  
+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
       </a>
     </li>
   
@@ -1489,7 +1711,7 @@ <h3 id="construct-conditional-potential-function">Construct conditional potentia
       
     
     <a href="https://github.com/sbi-dev/sbi" target="_blank" rel="noopener" title="github.com" class="md-social__link">
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
     </a>
   
 </div>
@@ -1503,10 +1725,11 @@ <h3 id="construct-conditional-potential-function">Construct conditional potentia
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.208ed371.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": "../../assets/javascripts/workers/search.f886a092.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.fac441b0.min.js"></script>
+      <script src="../../assets/javascripts/bundle.81fa17fe.min.js"></script>
       
         <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML"></script>
       
diff --git a/faq/index.html b/faq/index.html
index 377481da6..19e843f8f 100644
--- a/faq/index.html
+++ b/faq/index.html
@@ -16,8 +16,9 @@
       
         <link rel="next" href="../credits/">
       
+      
       <link rel="icon" href="../assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.1.9">
+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.4.8">
     
     
       
@@ -25,14 +26,17 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.85bb2934.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.4b4a2bd9.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.a6bdf11c.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.356b1318.min.css">
       
       
 
+
+    
     
+      
     
     
       
@@ -95,6 +99,7 @@
 
     </a>
     <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 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
     <div class="md-header__title" data-md-component="header-title">
@@ -119,6 +124,7 @@
     
     
       <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-.27A6.516 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>
       <div class="md-search" data-md-component="search" role="dialog">
@@ -127,12 +133,15 @@
     <form class="md-search__form" name="search">
       <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-.27A6.516 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 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
         <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 12 19 6.41Z"/></svg>
         </button>
       </nav>
@@ -156,7 +165,7 @@
         <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -186,6 +195,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href=".." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -200,7 +210,7 @@
       <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -212,14 +222,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href=".." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
       </a>
     </li>
   
@@ -227,14 +241,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
       </a>
     </li>
   
@@ -242,534 +260,724 @@
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
-          Tutorials and Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3">
-          <span class="md-nav__icon md-icon"></span>
-          Tutorials and Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Tutorials and Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            Tutorials and Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
-          Introduction
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_1">
-          <span class="md-nav__icon md-icon"></span>
-          Introduction
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
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-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
-      
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-        <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
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-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_2">
-          <span class="md-nav__icon md-icon"></span>
-          Advanced
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Advanced
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
-      
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-        <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
-          Diagnostics
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_3">
-          <span class="md-nav__icon md-icon"></span>
-          Diagnostics
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Diagnostics
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/12_diagnostics_posterior_predictive_check/" class="md-nav__link">
-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
-          Analysis
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_4">
-          <span class="md-nav__icon md-icon"></span>
-          Analysis
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Analysis
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Analysis
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/07_conditional_distributions/" class="md-nav__link">
-        Conditional distributions
+        
+  
+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
+  
+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
-      
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-        <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
-          Examples
-          <span class="md-nav__icon md-icon"></span>
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-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
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-          <span class="md-nav__icon md-icon"></span>
-          Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
+        
           
+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_5">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../examples/00_HH_simulator/" class="md-nav__link">
-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../examples/01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
-      
-      
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-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
-          Contributing
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_4">
-          <span class="md-nav__icon md-icon"></span>
-          Contributing
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Contributing
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Contributing
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../contribute/" class="md-nav__link">
-        Guide
+        
+  
+  <span class="md-ellipsis">
+    Guide
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../code_of_conduct/" class="md-nav__link">
-        Code of Conduct
+        
+  
+  <span class="md-ellipsis">
+    Code of Conduct
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../reference/" class="md-nav__link">
-        API Reference
+        
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -777,8 +985,6 @@
     
       
       
-      
-
   
   
     
@@ -793,7 +999,13 @@
       
       
       <a href="./" class="md-nav__link md-nav__link--active">
-        FAQ
+        
+  
+  <span class="md-ellipsis">
+    FAQ
+  </span>
+  
+
       </a>
       
     </li>
@@ -802,14 +1014,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../credits/" class="md-nav__link">
-        Credits
+        
+  
+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
       </a>
     </li>
   
@@ -897,7 +1113,7 @@ <h1 id="frequently-asked-questions">Frequently asked questions<a class="headerli
       
     
     <a href="https://github.com/sbi-dev/sbi" target="_blank" rel="noopener" title="github.com" class="md-social__link">
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+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
     </a>
   
 </div>
@@ -911,10 +1127,11 @@ <h1 id="frequently-asked-questions">Frequently asked questions<a class="headerli
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.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": "../assets/javascripts/workers/search.f886a092.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.fac441b0.min.js"></script>
+      <script src="../assets/javascripts/bundle.81fa17fe.min.js"></script>
       
         <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML"></script>
       
diff --git a/faq/question_01/index.html b/faq/question_01/index.html
index 09c669505..350070418 100644
--- a/faq/question_01/index.html
+++ b/faq/question_01/index.html
@@ -12,8 +12,9 @@
       
       
       
+      
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+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.4.8">
     
     
       
@@ -21,14 +22,17 @@
       
     
     
-      <link rel="stylesheet" href="../../assets/stylesheets/main.85bb2934.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.4b4a2bd9.min.css">
       
         
-        <link rel="stylesheet" href="../../assets/stylesheets/palette.a6bdf11c.min.css">
+        <link rel="stylesheet" href="../../assets/stylesheets/palette.356b1318.min.css">
       
       
 
+
+    
     
+      
     
     
       
@@ -91,6 +95,7 @@
 
     </a>
     <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 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
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     <div class="md-header__title" data-md-component="header-title">
@@ -115,6 +120,7 @@
     
     
       <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-.27A6.516 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>
       <div class="md-search" data-md-component="search" role="dialog">
@@ -123,12 +129,15 @@
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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-.27A6.516 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 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
         <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 12 19 6.41Z"/></svg>
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@@ -152,7 +161,7 @@
         <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -182,6 +191,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href="../.." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -196,7 +206,7 @@
       <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -208,14 +218,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../.." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
       </a>
     </li>
   
@@ -223,14 +237,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
       </a>
     </li>
   
@@ -238,534 +256,724 @@
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
-      
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-        <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
-          Tutorials and Examples
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-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3">
-          <span class="md-nav__icon md-icon"></span>
-          Tutorials and Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Tutorials and Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            Tutorials and Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
-      
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-        <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
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-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
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-          <span class="md-nav__icon md-icon"></span>
-          Introduction
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
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-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
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-          <span class="md-nav__icon md-icon"></span>
-          Advanced
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Advanced
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
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+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
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-      
-      
-      
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-      
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-          <span class="md-nav__icon md-icon"></span>
-          Diagnostics
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Diagnostics
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
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       <a href="../../tutorial/12_diagnostics_posterior_predictive_check/" class="md-nav__link">
-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
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     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
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-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
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-      
-      
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-      
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+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
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+  <span class="md-ellipsis">
+    Analysis
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Analysis
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
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       <a href="../../tutorial/07_conditional_distributions/" class="md-nav__link">
-        Conditional distributions
+        
+  
+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
+  
+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
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     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
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-      
-      
-      
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-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
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-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_5">
-          <span class="md-nav__icon md-icon"></span>
-          Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
+        
           
+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_5">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/00_HH_simulator/" class="md-nav__link">
-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
-          Contributing
-          <span class="md-nav__icon md-icon"></span>
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-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_4">
-          <span class="md-nav__icon md-icon"></span>
-          Contributing
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Contributing
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Contributing
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../contribute/" class="md-nav__link">
-        Guide
+        
+  
+  <span class="md-ellipsis">
+    Guide
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../code_of_conduct/" class="md-nav__link">
-        Code of Conduct
+        
+  
+  <span class="md-ellipsis">
+    Code of Conduct
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../reference/" class="md-nav__link">
-        API Reference
+        
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -773,14 +981,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../" class="md-nav__link">
-        FAQ
+        
+  
+  <span class="md-ellipsis">
+    FAQ
+  </span>
+  
+
       </a>
     </li>
   
@@ -788,14 +1000,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../credits/" class="md-nav__link">
-        Credits
+        
+  
+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
       </a>
     </li>
   
@@ -904,7 +1120,7 @@ <h1 id="what-should-i-do-when-my-posterior-samples-are-outside-of-the-prior-supp
       
     
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+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
     </a>
   
 </div>
@@ -918,10 +1134,11 @@ <h1 id="what-should-i-do-when-my-posterior-samples-are-outside-of-the-prior-supp
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.208ed371.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": "../../assets/javascripts/workers/search.f886a092.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.fac441b0.min.js"></script>
+      <script src="../../assets/javascripts/bundle.81fa17fe.min.js"></script>
       
         <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML"></script>
       
diff --git a/faq/question_02/index.html b/faq/question_02/index.html
index 2263f170e..7f6825bb5 100644
--- a/faq/question_02/index.html
+++ b/faq/question_02/index.html
@@ -12,8 +12,9 @@
       
       
       
+      
       <link rel="icon" href="../../assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.1.9">
+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.4.8">
     
     
       
@@ -21,14 +22,17 @@
       
     
     
-      <link rel="stylesheet" href="../../assets/stylesheets/main.85bb2934.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.4b4a2bd9.min.css">
       
         
-        <link rel="stylesheet" href="../../assets/stylesheets/palette.a6bdf11c.min.css">
+        <link rel="stylesheet" href="../../assets/stylesheets/palette.356b1318.min.css">
       
       
 
+
+    
     
+      
     
     
       
@@ -91,6 +95,7 @@
 
     </a>
     <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 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
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     <div class="md-header__title" data-md-component="header-title">
@@ -115,6 +120,7 @@
     
     
       <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-.27A6.516 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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@@ -123,12 +129,15 @@
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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-.27A6.516 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 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
         <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 12 19 6.41Z"/></svg>
         </button>
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@@ -152,7 +161,7 @@
         <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -182,6 +191,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href="../.." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -196,7 +206,7 @@
       <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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   <div class="md-source__repository">
     sbi-dev/sbi
@@ -208,14 +218,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../.." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
       </a>
     </li>
   
@@ -223,14 +237,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
       </a>
     </li>
   
@@ -238,534 +256,724 @@
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
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-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
-          Tutorials and Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3">
-          <span class="md-nav__icon md-icon"></span>
-          Tutorials and Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Tutorials and Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            Tutorials and Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
-      
-      
-      
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@@ -892,7 +1108,7 @@ <h1 id="can-the-algorithms-deal-with-invalid-data-eg-nan-or-inf">Can the algorit
       
     
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@@ -906,10 +1122,11 @@ <h1 id="can-the-algorithms-deal-with-invalid-data-eg-nan-or-inf">Can the algorit
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+    
+    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.f886a092.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.fac441b0.min.js"></script>
+      <script src="../../assets/javascripts/bundle.81fa17fe.min.js"></script>
       
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diff --git a/faq/question_03/index.html b/faq/question_03/index.html
index 8e330f2d4..6c9790adf 100644
--- a/faq/question_03/index.html
+++ b/faq/question_03/index.html
@@ -12,8 +12,9 @@
       
       
       
+      
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-      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.1.9">
+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.4.8">
     
     
       
@@ -21,14 +22,17 @@
       
     
     
-      <link rel="stylesheet" href="../../assets/stylesheets/main.85bb2934.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.4b4a2bd9.min.css">
       
         
-        <link rel="stylesheet" href="../../assets/stylesheets/palette.a6bdf11c.min.css">
+        <link rel="stylesheet" href="../../assets/stylesheets/palette.356b1318.min.css">
       
       
 
+
+    
     
+      
     
     
       
@@ -91,6 +95,7 @@
 
     </a>
     <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 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
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@@ -115,6 +120,7 @@
     
     
       <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-.27A6.516 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-.27A6.516 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>
       <nav class="md-search__options" aria-label="Search">
         
         <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 12 19 6.41Z"/></svg>
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@@ -152,7 +161,7 @@
         <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -182,6 +191,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href="../.." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -196,7 +206,7 @@
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   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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@@ -208,14 +218,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../.." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
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@@ -223,14 +237,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
       </a>
     </li>
   
@@ -238,534 +256,724 @@
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
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-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
-      
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-        <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
-          Tutorials and Examples
-          <span class="md-nav__icon md-icon"></span>
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-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
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-          <span class="md-nav__icon md-icon"></span>
-          Tutorials and Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Tutorials and Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            Tutorials and Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
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-        <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
-          Introduction
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-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
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-          <span class="md-nav__icon md-icon"></span>
-          Introduction
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
-      
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-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
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-          <span class="md-nav__icon md-icon"></span>
-          Advanced
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Advanced
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
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+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
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-      
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-          <span class="md-nav__icon md-icon"></span>
-          Diagnostics
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-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Diagnostics
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
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       <a href="../../tutorial/12_diagnostics_posterior_predictive_check/" class="md-nav__link">
-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
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       <a href="../../tutorial/15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
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+              
             
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-      </nav>
+          </ul>
+        </nav>
+      
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+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
-          Analysis
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_4">
-          <span class="md-nav__icon md-icon"></span>
-          Analysis
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Analysis
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Analysis
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/07_conditional_distributions/" class="md-nav__link">
-        Conditional distributions
+        
+  
+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
+  
+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
-          Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_5">
-          <span class="md-nav__icon md-icon"></span>
-          Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
+        
           
+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_5">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/00_HH_simulator/" class="md-nav__link">
-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
-          Contributing
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_4">
-          <span class="md-nav__icon md-icon"></span>
-          Contributing
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Contributing
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Contributing
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../contribute/" class="md-nav__link">
-        Guide
+        
+  
+  <span class="md-ellipsis">
+    Guide
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../code_of_conduct/" class="md-nav__link">
-        Code of Conduct
+        
+  
+  <span class="md-ellipsis">
+    Code of Conduct
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../reference/" class="md-nav__link">
-        API Reference
+        
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -773,14 +981,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../" class="md-nav__link">
-        FAQ
+        
+  
+  <span class="md-ellipsis">
+    FAQ
+  </span>
+  
+
       </a>
     </li>
   
@@ -788,14 +1000,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../credits/" class="md-nav__link">
-        Credits
+        
+  
+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
       </a>
     </li>
   
@@ -934,7 +1150,7 @@ <h3 id="some-more-background">Some more background<a class="headerlink" href="#s
       
     
     <a href="https://github.com/sbi-dev/sbi" target="_blank" rel="noopener" title="github.com" class="md-social__link">
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
     </a>
   
 </div>
@@ -948,10 +1164,11 @@ <h3 id="some-more-background">Some more background<a class="headerlink" href="#s
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.208ed371.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": "../../assets/javascripts/workers/search.f886a092.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.fac441b0.min.js"></script>
+      <script src="../../assets/javascripts/bundle.81fa17fe.min.js"></script>
       
         <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML"></script>
       
diff --git a/faq/question_04/index.html b/faq/question_04/index.html
index 70cfba44f..ade9c0bdf 100644
--- a/faq/question_04/index.html
+++ b/faq/question_04/index.html
@@ -12,8 +12,9 @@
       
       
       
+      
       <link rel="icon" href="../../assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.1.9">
+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.4.8">
     
     
       
@@ -21,14 +22,17 @@
       
     
     
-      <link rel="stylesheet" href="../../assets/stylesheets/main.85bb2934.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.4b4a2bd9.min.css">
       
         
-        <link rel="stylesheet" href="../../assets/stylesheets/palette.a6bdf11c.min.css">
+        <link rel="stylesheet" href="../../assets/stylesheets/palette.356b1318.min.css">
       
       
 
+
+    
     
+      
     
     
       
@@ -91,6 +95,7 @@
 
     </a>
     <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 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
     <div class="md-header__title" data-md-component="header-title">
@@ -115,6 +120,7 @@
     
     
       <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-.27A6.516 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>
       <div class="md-search" data-md-component="search" role="dialog">
@@ -123,12 +129,15 @@
     <form class="md-search__form" name="search">
       <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-.27A6.516 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 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
         <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 12 19 6.41Z"/></svg>
         </button>
       </nav>
@@ -152,7 +161,7 @@
         <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -182,6 +191,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href="../.." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -196,7 +206,7 @@
       <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -208,14 +218,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../.." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
       </a>
     </li>
   
@@ -223,14 +237,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
       </a>
     </li>
   
@@ -238,534 +256,724 @@
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
-          Tutorials and Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3">
-          <span class="md-nav__icon md-icon"></span>
-          Tutorials and Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Tutorials and Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            Tutorials and Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
-          Introduction
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-        <label class="md-nav__title" for="__nav_3_1">
-          <span class="md-nav__icon md-icon"></span>
-          Introduction
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
-          Advanced
-          <span class="md-nav__icon md-icon"></span>
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-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_2">
-          <span class="md-nav__icon md-icon"></span>
-          Advanced
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Advanced
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
-          Diagnostics
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_3">
-          <span class="md-nav__icon md-icon"></span>
-          Diagnostics
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Diagnostics
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/12_diagnostics_posterior_predictive_check/" class="md-nav__link">
-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
-          Analysis
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_4">
-          <span class="md-nav__icon md-icon"></span>
-          Analysis
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Analysis
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Analysis
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/07_conditional_distributions/" class="md-nav__link">
-        Conditional distributions
+        
+  
+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
+  
+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
-          Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_5">
-          <span class="md-nav__icon md-icon"></span>
-          Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
+        
           
+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_5">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/00_HH_simulator/" class="md-nav__link">
-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
-          Contributing
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_4">
-          <span class="md-nav__icon md-icon"></span>
-          Contributing
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Contributing
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Contributing
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../contribute/" class="md-nav__link">
-        Guide
+        
+  
+  <span class="md-ellipsis">
+    Guide
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../code_of_conduct/" class="md-nav__link">
-        Code of Conduct
+        
+  
+  <span class="md-ellipsis">
+    Code of Conduct
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../reference/" class="md-nav__link">
-        API Reference
+        
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -773,14 +981,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../" class="md-nav__link">
-        FAQ
+        
+  
+  <span class="md-ellipsis">
+    FAQ
+  </span>
+  
+
       </a>
     </li>
   
@@ -788,14 +1000,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../credits/" class="md-nav__link">
-        Credits
+        
+  
+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
       </a>
     </li>
   
@@ -915,7 +1131,7 @@ <h2 id="performance">Performance<a class="headerlink" href="#performance" title=
       
     
     <a href="https://github.com/sbi-dev/sbi" target="_blank" rel="noopener" title="github.com" class="md-social__link">
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
     </a>
   
 </div>
@@ -929,10 +1145,11 @@ <h2 id="performance">Performance<a class="headerlink" href="#performance" title=
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.208ed371.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": "../../assets/javascripts/workers/search.f886a092.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.fac441b0.min.js"></script>
+      <script src="../../assets/javascripts/bundle.81fa17fe.min.js"></script>
       
         <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML"></script>
       
diff --git a/faq/question_05/index.html b/faq/question_05/index.html
index 443d4fa97..cb7f7ec3e 100644
--- a/faq/question_05/index.html
+++ b/faq/question_05/index.html
@@ -12,8 +12,9 @@
       
       
       
+      
       <link rel="icon" href="../../assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.1.9">
+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.4.8">
     
     
       
@@ -21,14 +22,17 @@
       
     
     
-      <link rel="stylesheet" href="../../assets/stylesheets/main.85bb2934.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.4b4a2bd9.min.css">
       
         
-        <link rel="stylesheet" href="../../assets/stylesheets/palette.a6bdf11c.min.css">
+        <link rel="stylesheet" href="../../assets/stylesheets/palette.356b1318.min.css">
       
       
 
+
+    
     
+      
     
     
       
@@ -91,6 +95,7 @@
 
     </a>
     <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 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
     <div class="md-header__title" data-md-component="header-title">
@@ -115,6 +120,7 @@
     
     
       <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-.27A6.516 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>
       <div class="md-search" data-md-component="search" role="dialog">
@@ -123,12 +129,15 @@
     <form class="md-search__form" name="search">
       <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-.27A6.516 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 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
         <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 12 19 6.41Z"/></svg>
         </button>
       </nav>
@@ -152,7 +161,7 @@
         <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -182,6 +191,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href="../.." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -196,7 +206,7 @@
       <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -208,14 +218,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../.." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
       </a>
     </li>
   
@@ -223,14 +237,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
       </a>
     </li>
   
@@ -238,534 +256,724 @@
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
-          Tutorials and Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3">
-          <span class="md-nav__icon md-icon"></span>
-          Tutorials and Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Tutorials and Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            Tutorials and Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
-          Introduction
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_1">
-          <span class="md-nav__icon md-icon"></span>
-          Introduction
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
-          Advanced
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_2">
-          <span class="md-nav__icon md-icon"></span>
-          Advanced
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Advanced
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
-          Diagnostics
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_3">
-          <span class="md-nav__icon md-icon"></span>
-          Diagnostics
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Diagnostics
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/12_diagnostics_posterior_predictive_check/" class="md-nav__link">
-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
-          Analysis
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_4">
-          <span class="md-nav__icon md-icon"></span>
-          Analysis
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Analysis
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Analysis
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/07_conditional_distributions/" class="md-nav__link">
-        Conditional distributions
+        
+  
+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
+  
+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
-          Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_5">
-          <span class="md-nav__icon md-icon"></span>
-          Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
+        
           
+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_5">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/00_HH_simulator/" class="md-nav__link">
-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
-      
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-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
-          Contributing
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_4">
-          <span class="md-nav__icon md-icon"></span>
-          Contributing
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Contributing
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Contributing
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../contribute/" class="md-nav__link">
-        Guide
+        
+  
+  <span class="md-ellipsis">
+    Guide
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../code_of_conduct/" class="md-nav__link">
-        Code of Conduct
+        
+  
+  <span class="md-ellipsis">
+    Code of Conduct
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../reference/" class="md-nav__link">
-        API Reference
+        
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -773,14 +981,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../" class="md-nav__link">
-        FAQ
+        
+  
+  <span class="md-ellipsis">
+    FAQ
+  </span>
+  
+
       </a>
     </li>
   
@@ -788,14 +1000,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../credits/" class="md-nav__link">
-        Credits
+        
+  
+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
       </a>
     </li>
   
@@ -905,7 +1121,7 @@ <h1 id="how-should-i-save-and-load-objects-in-sbi">How should I save and load ob
       
     
     <a href="https://github.com/sbi-dev/sbi" target="_blank" rel="noopener" title="github.com" class="md-social__link">
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
     </a>
   
 </div>
@@ -919,10 +1135,11 @@ <h1 id="how-should-i-save-and-load-objects-in-sbi">How should I save and load ob
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.208ed371.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": "../../assets/javascripts/workers/search.f886a092.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.fac441b0.min.js"></script>
+      <script src="../../assets/javascripts/bundle.81fa17fe.min.js"></script>
       
         <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML"></script>
       
diff --git a/faq/question_06/index.html b/faq/question_06/index.html
index 9261f218f..a419b5557 100644
--- a/faq/question_06/index.html
+++ b/faq/question_06/index.html
@@ -12,8 +12,9 @@
       
       
       
+      
       <link rel="icon" href="../../assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.1.9">
+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.4.8">
     
     
       
@@ -21,14 +22,17 @@
       
     
     
-      <link rel="stylesheet" href="../../assets/stylesheets/main.85bb2934.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.4b4a2bd9.min.css">
       
         
-        <link rel="stylesheet" href="../../assets/stylesheets/palette.a6bdf11c.min.css">
+        <link rel="stylesheet" href="../../assets/stylesheets/palette.356b1318.min.css">
       
       
 
+
+    
     
+      
     
     
       
@@ -91,6 +95,7 @@
 
     </a>
     <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 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
     <div class="md-header__title" data-md-component="header-title">
@@ -115,6 +120,7 @@
     
     
       <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-.27A6.516 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>
       <div class="md-search" data-md-component="search" role="dialog">
@@ -123,12 +129,15 @@
     <form class="md-search__form" name="search">
       <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-.27A6.516 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 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
         <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 12 19 6.41Z"/></svg>
         </button>
       </nav>
@@ -152,7 +161,7 @@
         <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -182,6 +191,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href="../.." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -196,7 +206,7 @@
       <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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   <div class="md-source__repository">
     sbi-dev/sbi
@@ -208,14 +218,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../.." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
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@@ -223,14 +237,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
       </a>
     </li>
   
@@ -238,534 +256,724 @@
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
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-          <span class="md-nav__icon md-icon"></span>
-          Tutorials and Examples
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-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
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+  <span class="md-ellipsis">
+    Tutorials and Examples
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+  
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+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            Tutorials and Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     
+    
+    
+    
+    
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-      
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-          Introduction
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-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
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+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
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+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
       </a>
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
       </a>
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+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
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-          <span class="md-nav__icon md-icon"></span>
-          Advanced
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-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Advanced
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
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       <a href="../../tutorial/03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
       </a>
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
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+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
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-      
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-          <span class="md-nav__icon md-icon"></span>
-          Diagnostics
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-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Diagnostics
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
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       <a href="../../tutorial/12_diagnostics_posterior_predictive_check/" class="md-nav__link">
-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
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+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
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-            
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+              
             
               
+                
   
   
   
     
+    
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+    
+    
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-      
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-          Analysis
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+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
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+  <span class="md-ellipsis">
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+  </span>
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+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Analysis
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
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-        Conditional distributions
+        
+  
+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
+  
+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
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+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
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-            
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+              
             
               
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+    
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+        
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+        
           
+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
+            
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+            Examples
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-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
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-            
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+              
             
               
+                
   
   
   
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       <a href="../../examples/01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
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+              
             
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+          </ul>
+        </nav>
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+          </ul>
+        </nav>
+      
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+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
-          Contributing
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_4">
-          <span class="md-nav__icon md-icon"></span>
-          Contributing
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Contributing
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Contributing
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../contribute/" class="md-nav__link">
-        Guide
+        
+  
+  <span class="md-ellipsis">
+    Guide
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../code_of_conduct/" class="md-nav__link">
-        Code of Conduct
+        
+  
+  <span class="md-ellipsis">
+    Code of Conduct
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../reference/" class="md-nav__link">
-        API Reference
+        
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -773,14 +981,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../" class="md-nav__link">
-        FAQ
+        
+  
+  <span class="md-ellipsis">
+    FAQ
+  </span>
+  
+
       </a>
     </li>
   
@@ -788,14 +1000,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../credits/" class="md-nav__link">
-        Credits
+        
+  
+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
       </a>
     </li>
   
@@ -890,7 +1106,7 @@ <h1 id="can-i-stop-neural-network-training-and-resume-it-later">Can I stop neura
       
     
     <a href="https://github.com/sbi-dev/sbi" target="_blank" rel="noopener" title="github.com" class="md-social__link">
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
     </a>
   
 </div>
@@ -904,10 +1120,11 @@ <h1 id="can-i-stop-neural-network-training-and-resume-it-later">Can I stop neura
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.208ed371.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": "../../assets/javascripts/workers/search.f886a092.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.fac441b0.min.js"></script>
+      <script src="../../assets/javascripts/bundle.81fa17fe.min.js"></script>
       
         <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML"></script>
       
diff --git a/faq/question_07/index.html b/faq/question_07/index.html
index fd083bc6a..bf520a965 100644
--- a/faq/question_07/index.html
+++ b/faq/question_07/index.html
@@ -12,8 +12,9 @@
       
       
       
+      
       <link rel="icon" href="../../assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.1.9">
+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.4.8">
     
     
       
@@ -21,14 +22,17 @@
       
     
     
-      <link rel="stylesheet" href="../../assets/stylesheets/main.85bb2934.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.4b4a2bd9.min.css">
       
         
-        <link rel="stylesheet" href="../../assets/stylesheets/palette.a6bdf11c.min.css">
+        <link rel="stylesheet" href="../../assets/stylesheets/palette.356b1318.min.css">
       
       
 
+
+    
     
+      
     
     
       
@@ -91,6 +95,7 @@
 
     </a>
     <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 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
     <div class="md-header__title" data-md-component="header-title">
@@ -115,6 +120,7 @@
     
     
       <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-.27A6.516 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>
       <div class="md-search" data-md-component="search" role="dialog">
@@ -123,12 +129,15 @@
     <form class="md-search__form" name="search">
       <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-.27A6.516 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 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
         <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 12 19 6.41Z"/></svg>
         </button>
       </nav>
@@ -152,7 +161,7 @@
         <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -182,6 +191,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href="../.." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -196,7 +206,7 @@
       <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -208,14 +218,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../.." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
       </a>
     </li>
   
@@ -223,14 +237,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
       </a>
     </li>
   
@@ -238,534 +256,724 @@
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
-          Tutorials and Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3">
-          <span class="md-nav__icon md-icon"></span>
-          Tutorials and Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Tutorials and Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            Tutorials and Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
-          Introduction
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_1">
-          <span class="md-nav__icon md-icon"></span>
-          Introduction
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
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-          Advanced
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-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_2">
-          <span class="md-nav__icon md-icon"></span>
-          Advanced
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Advanced
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
       </a>
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
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+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
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-      
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-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
-      
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-        <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
-          Diagnostics
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_3">
-          <span class="md-nav__icon md-icon"></span>
-          Diagnostics
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Diagnostics
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/12_diagnostics_posterior_predictive_check/" class="md-nav__link">
-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
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     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
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     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
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-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
-      
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-        <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
-          Analysis
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_4">
-          <span class="md-nav__icon md-icon"></span>
-          Analysis
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Analysis
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Analysis
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/07_conditional_distributions/" class="md-nav__link">
-        Conditional distributions
+        
+  
+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../tutorial/09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
+  
+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
-      
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-        <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
-          Examples
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-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
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-          <span class="md-nav__icon md-icon"></span>
-          Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
+        
           
+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_5">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/00_HH_simulator/" class="md-nav__link">
-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
-      
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-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
-          Contributing
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
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-        <label class="md-nav__title" for="__nav_4">
-          <span class="md-nav__icon md-icon"></span>
-          Contributing
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Contributing
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Contributing
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../contribute/" class="md-nav__link">
-        Guide
+        
+  
+  <span class="md-ellipsis">
+    Guide
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../code_of_conduct/" class="md-nav__link">
-        Code of Conduct
+        
+  
+  <span class="md-ellipsis">
+    Code of Conduct
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../reference/" class="md-nav__link">
-        API Reference
+        
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -773,14 +981,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../" class="md-nav__link">
-        FAQ
+        
+  
+  <span class="md-ellipsis">
+    FAQ
+  </span>
+  
+
       </a>
     </li>
   
@@ -788,14 +1000,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../credits/" class="md-nav__link">
-        Credits
+        
+  
+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
       </a>
     </li>
   
@@ -925,7 +1141,7 @@ <h1 id="can-i-use-a-custom-prior-with-sbi">Can I use a custom prior with sbi?<a
       
     
     <a href="https://github.com/sbi-dev/sbi" target="_blank" rel="noopener" title="github.com" class="md-social__link">
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
     </a>
   
 </div>
@@ -939,10 +1155,11 @@ <h1 id="can-i-use-a-custom-prior-with-sbi">Can I use a custom prior with sbi?<a
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.208ed371.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": "../../assets/javascripts/workers/search.f886a092.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.fac441b0.min.js"></script>
+      <script src="../../assets/javascripts/bundle.81fa17fe.min.js"></script>
       
         <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML"></script>
       
diff --git a/index.html b/index.html
index d36940064..f4f3c3717 100644
--- a/index.html
+++ b/index.html
@@ -14,8 +14,9 @@
       
         <link rel="next" href="install/">
       
+      
       <link rel="icon" href="assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.1.9">
+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.4.8">
     
     
       
@@ -23,15 +24,18 @@
       
     
     
-      <link rel="stylesheet" href="assets/stylesheets/main.85bb2934.min.css">
+      <link rel="stylesheet" href="assets/stylesheets/main.4b4a2bd9.min.css">
       
         
-        <link rel="stylesheet" href="assets/stylesheets/palette.a6bdf11c.min.css">
+        <link rel="stylesheet" href="assets/stylesheets/palette.356b1318.min.css">
       
       
 
+
     
     
+      
+    
     
       
         
@@ -93,6 +97,7 @@
 
     </a>
     <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 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
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     <div class="md-header__title" data-md-component="header-title">
@@ -117,6 +122,7 @@
     
     
       <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-.27A6.516 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>
       <div class="md-search" data-md-component="search" role="dialog">
@@ -125,12 +131,15 @@
     <form class="md-search__form" name="search">
       <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-.27A6.516 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 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
         <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 12 19 6.41Z"/></svg>
         </button>
       </nav>
@@ -154,7 +163,7 @@
         <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -184,6 +193,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href="." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -198,7 +208,7 @@
       <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -210,8 +220,6 @@
     
       
       
-      
-
   
   
     
@@ -226,12 +234,24 @@
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          Home
+          
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <a href="." class="md-nav__link md-nav__link--active">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
       </a>
       
         
@@ -314,14 +334,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
       </a>
     </li>
   
@@ -329,534 +353,724 @@
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
-      
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-      
-        <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
-          Tutorials and Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3">
-          <span class="md-nav__icon md-icon"></span>
-          Tutorials and Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Tutorials and Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            Tutorials and Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
-      
-      
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-        <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
-          Introduction
-          <span class="md-nav__icon md-icon"></span>
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-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_1">
-          <span class="md-nav__icon md-icon"></span>
-          Introduction
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="tutorial/00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="tutorial/02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="tutorial/01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="tutorial/16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
-          Advanced
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_2">
-          <span class="md-nav__icon md-icon"></span>
-          Advanced
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Advanced
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="tutorial/03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="tutorial/11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="tutorial/04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="tutorial/05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="tutorial/14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="tutorial/08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="tutorial/10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
-          Diagnostics
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_3">
-          <span class="md-nav__icon md-icon"></span>
-          Diagnostics
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Diagnostics
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="tutorial/12_diagnostics_posterior_predictive_check/" class="md-nav__link">
-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="tutorial/13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="tutorial/15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
-          Analysis
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-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
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-          <span class="md-nav__icon md-icon"></span>
-          Analysis
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Analysis
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Analysis
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="tutorial/07_conditional_distributions/" class="md-nav__link">
-        Conditional distributions
+        
+  
+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="tutorial/09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
+  
+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
-          Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_5">
-          <span class="md-nav__icon md-icon"></span>
-          Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
+        
           
+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_5">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="examples/00_HH_simulator/" class="md-nav__link">
-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="examples/01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
-          Contributing
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_4">
-          <span class="md-nav__icon md-icon"></span>
-          Contributing
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Contributing
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Contributing
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="contribute/" class="md-nav__link">
-        Guide
+        
+  
+  <span class="md-ellipsis">
+    Guide
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="code_of_conduct/" class="md-nav__link">
-        Code of Conduct
+        
+  
+  <span class="md-ellipsis">
+    Code of Conduct
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="reference/" class="md-nav__link">
-        API Reference
+        
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -864,14 +1078,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="faq/" class="md-nav__link">
-        FAQ
+        
+  
+  <span class="md-ellipsis">
+    FAQ
+  </span>
+  
+
       </a>
     </li>
   
@@ -879,14 +1097,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="credits/" class="md-nav__link">
-        Credits
+        
+  
+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
       </a>
     </li>
   
@@ -1161,7 +1383,7 @@ <h3 id="utilities">Utilities<a class="headerlink" href="#utilities" title="Perma
       
     
     <a href="https://github.com/sbi-dev/sbi" target="_blank" rel="noopener" title="github.com" class="md-social__link">
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
     </a>
   
 </div>
@@ -1175,10 +1397,11 @@ <h3 id="utilities">Utilities<a class="headerlink" href="#utilities" title="Perma
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": ".", "features": [], "search": "assets/javascripts/workers/search.208ed371.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": "assets/javascripts/workers/search.f886a092.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.fac441b0.min.js"></script>
+      <script src="assets/javascripts/bundle.81fa17fe.min.js"></script>
       
         <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML"></script>
       
diff --git a/install/index.html b/install/index.html
index c0c79694a..ebafb58d2 100644
--- a/install/index.html
+++ b/install/index.html
@@ -16,8 +16,9 @@
       
         <link rel="next" href="../tutorial/00_getting_started/">
       
+      
       <link rel="icon" href="../assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.1.9">
+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.4.8">
     
     
       
@@ -25,14 +26,17 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.85bb2934.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.4b4a2bd9.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.a6bdf11c.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.356b1318.min.css">
       
       
 
+
+    
     
+      
     
     
       
@@ -95,6 +99,7 @@
 
     </a>
     <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 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
     <div class="md-header__title" data-md-component="header-title">
@@ -119,6 +124,7 @@
     
     
       <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-.27A6.516 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>
       <div class="md-search" data-md-component="search" role="dialog">
@@ -127,12 +133,15 @@
     <form class="md-search__form" name="search">
       <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-.27A6.516 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 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
         <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 12 19 6.41Z"/></svg>
         </button>
       </nav>
@@ -156,7 +165,7 @@
         <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -186,6 +195,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href=".." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -200,7 +210,7 @@
       <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -212,14 +222,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href=".." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
       </a>
     </li>
   
@@ -227,8 +241,6 @@
     
       
       
-      
-
   
   
     
@@ -243,7 +255,13 @@
       
       
       <a href="./" class="md-nav__link md-nav__link--active">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
       </a>
       
     </li>
@@ -252,534 +270,724 @@
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
-          Tutorials and Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3">
-          <span class="md-nav__icon md-icon"></span>
-          Tutorials and Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Tutorials and Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            Tutorials and Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
-          Introduction
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_1">
-          <span class="md-nav__icon md-icon"></span>
-          Introduction
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
-          Advanced
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_2">
-          <span class="md-nav__icon md-icon"></span>
-          Advanced
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Advanced
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
-          Diagnostics
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_3">
-          <span class="md-nav__icon md-icon"></span>
-          Diagnostics
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Diagnostics
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/12_diagnostics_posterior_predictive_check/" class="md-nav__link">
-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
-          Analysis
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_4">
-          <span class="md-nav__icon md-icon"></span>
-          Analysis
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Analysis
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Analysis
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/07_conditional_distributions/" class="md-nav__link">
-        Conditional distributions
+        
+  
+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
+  
+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
-          Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_5">
-          <span class="md-nav__icon md-icon"></span>
-          Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
+        
           
+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_5">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../examples/00_HH_simulator/" class="md-nav__link">
-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../examples/01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
-          Contributing
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_4">
-          <span class="md-nav__icon md-icon"></span>
-          Contributing
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Contributing
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Contributing
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../contribute/" class="md-nav__link">
-        Guide
+        
+  
+  <span class="md-ellipsis">
+    Guide
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../code_of_conduct/" class="md-nav__link">
-        Code of Conduct
+        
+  
+  <span class="md-ellipsis">
+    Code of Conduct
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../reference/" class="md-nav__link">
-        API Reference
+        
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -787,14 +995,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../faq/" class="md-nav__link">
-        FAQ
+        
+  
+  <span class="md-ellipsis">
+    FAQ
+  </span>
+  
+
       </a>
     </li>
   
@@ -802,14 +1014,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../credits/" class="md-nav__link">
-        Credits
+        
+  
+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
       </a>
     </li>
   
@@ -904,7 +1120,7 @@ <h1 id="installation">Installation<a class="headerlink" href="#installation" tit
       
     
     <a href="https://github.com/sbi-dev/sbi" target="_blank" rel="noopener" title="github.com" class="md-social__link">
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     </a>
   
 </div>
@@ -918,10 +1134,11 @@ <h1 id="installation">Installation<a class="headerlink" href="#installation" tit
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     </div>
     
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+    
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.f886a092.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>
     
     
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diff --git a/objects.inv b/objects.inv
index ed1e5e8c8..f844ba395 100644
Binary files a/objects.inv and b/objects.inv differ
diff --git a/reference/index.html b/reference/index.html
index 224928a98..e19e17b33 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -16,8 +16,9 @@
       
         <link rel="next" href="../faq/">
       
+      
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+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.4.8">
     
     
       
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-      <link rel="stylesheet" href="../assets/stylesheets/main.85bb2934.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.4b4a2bd9.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.a6bdf11c.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.356b1318.min.css">
       
       
 
+
     
     
+      
+    
     
       
         
@@ -95,6 +99,7 @@
 
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@@ -119,6 +124,7 @@
     
     
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+        
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         <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 12 19 6.41Z"/></svg>
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@@ -156,7 +165,7 @@
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-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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   <div class="md-source__repository">
     sbi-dev/sbi
@@ -186,6 +195,7 @@
                     
 
 
+
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   <label class="md-nav__title" for="__drawer">
     <a href=".." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -200,7 +210,7 @@
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-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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@@ -212,14 +222,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
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-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
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+  
+
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@@ -227,14 +241,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
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@@ -242,528 +260,712 @@
     
       
       
-      
-
   
   
   
     
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
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-          Tutorials and Examples
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+        
+        
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+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
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+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Tutorials and Examples
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+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            Tutorials and Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     
+    
+    
+    
+    
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-      
-      
-      
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-          <span class="md-nav__icon md-icon"></span>
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+        
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+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
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+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
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+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
       </a>
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+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
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-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
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-      
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-          <span class="md-nav__icon md-icon"></span>
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+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
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+  
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+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
       </a>
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
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+              
             
-          
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-      </nav>
+          </ul>
+        </nav>
+      
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+    
+    
+    
+    
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+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Diagnostics
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
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-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
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       <a href="../tutorial/13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
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-            
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-        Density plots and MCMC diagnostics with ArviZ
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+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
+      </a>
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+              
             
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-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
-          Analysis
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_4">
-          <span class="md-nav__icon md-icon"></span>
-          Analysis
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Analysis
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Analysis
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/07_conditional_distributions/" class="md-nav__link">
-        Conditional distributions
+        
+  
+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../tutorial/09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
+  
+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
-          Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_5">
-          <span class="md-nav__icon md-icon"></span>
-          Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
+        
           
+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_5">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../examples/00_HH_simulator/" class="md-nav__link">
-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../examples/01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
-          Contributing
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_4">
-          <span class="md-nav__icon md-icon"></span>
-          Contributing
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Contributing
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Contributing
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../contribute/" class="md-nav__link">
-        Guide
+        
+  
+  <span class="md-ellipsis">
+    Guide
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../code_of_conduct/" class="md-nav__link">
-        Code of Conduct
+        
+  
+  <span class="md-ellipsis">
+    Code of Conduct
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
     
@@ -778,12 +980,24 @@
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          API Reference
+          
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <a href="./" class="md-nav__link md-nav__link--active">
-        API Reference
+        
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
       </a>
       
         
@@ -806,461 +1020,1225 @@
     Inference
   </a>
   
-    <nav class="md-nav" aria-label="Inference">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
+</li>
+      
+        <li class="md-nav__item">
   <a href="#sbi.inference.base.infer" class="md-nav__link">
-    infer()
+    infer
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
+      
+        <li class="md-nav__item">
   <a href="#sbi.utils.user_input_checks.prepare_for_sbi" class="md-nav__link">
-    prepare_for_sbi()
+    prepare_for_sbi
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
+      
+        <li class="md-nav__item">
   <a href="#sbi.inference.base.simulate_for_sbi" class="md-nav__link">
-    simulate_for_sbi()
+    simulate_for_sbi
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
+      
+        <li class="md-nav__item">
   <a href="#sbi.inference.snpe.snpe_a.SNPE_A" class="md-nav__link">
     SNPE_A
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.snpe.snpe_c.SNPE_C" class="md-nav__link">
-    SNPE_C
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snpe.snpe_a.SNPE_A.__init__" class="md-nav__link">
+    __init__()
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.snle.snle_a.SNLE_A" class="md-nav__link">
-    SNLE_A
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snpe.snpe_a.SNPE_A.build_posterior" class="md-nav__link">
+    build_posterior()
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.snre.snre_a.SNRE_A" class="md-nav__link">
-    SNRE_A
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snpe.snpe_a.SNPE_A.correct_for_proposal" class="md-nav__link">
+    correct_for_proposal()
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.snre.snre_b.SNRE_B" class="md-nav__link">
-    SNRE_B
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snpe.snpe_a.SNPE_A.train" class="md-nav__link">
+    train()
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.snre.snre_c.SNRE_C" class="md-nav__link">
-    SNRE_C
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snpe.snpe_c.SNPE_C" class="md-nav__link">
+    SNPE_C
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.snre.bnre.BNRE" class="md-nav__link">
-    BNRE
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snpe.snpe_c.SNPE_C.__init__" class="md-nav__link">
+    __init__()
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.abc.mcabc.MCABC" class="md-nav__link">
-    MCABC
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snpe.snpe_c.SNPE_C.train" class="md-nav__link">
+    train()
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.abc.smcabc.SMCABC" class="md-nav__link">
-    SMCABC
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snle.snle_a.SNLE_A" class="md-nav__link">
+    SNLE_A
   </a>
   
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
         <li class="md-nav__item">
-  <a href="#posteriors" class="md-nav__link">
-    Posteriors
+  <a href="#sbi.inference.snle.snle_a.SNLE_A.__init__" class="md-nav__link">
+    __init__()
   </a>
   
-    <nav class="md-nav" aria-label="Posteriors">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.posteriors.direct_posterior.DirectPosterior" class="md-nav__link">
-    DirectPosterior
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snre.snre_a.SNRE_A" class="md-nav__link">
+    SNRE_A
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior" class="md-nav__link">
-    ImportanceSamplingPosterior
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snre.snre_a.SNRE_A.__init__" class="md-nav__link">
+    __init__()
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior" class="md-nav__link">
-    MCMCPosterior
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snre.snre_a.SNRE_A.train" class="md-nav__link">
+    train()
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.posteriors.rejection_posterior.RejectionPosterior" class="md-nav__link">
-    RejectionPosterior
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snre.snre_b.SNRE_B" class="md-nav__link">
+    SNRE_B
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snre.snre_b.SNRE_B.__init__" class="md-nav__link">
+    __init__()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snre.snre_b.SNRE_B.train" class="md-nav__link">
+    train()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snre.snre_c.SNRE_C" class="md-nav__link">
+    SNRE_C
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snre.snre_c.SNRE_C.__init__" class="md-nav__link">
+    __init__()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snre.snre_c.SNRE_C.train" class="md-nav__link">
+    train()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snre.bnre.BNRE" class="md-nav__link">
+    BNRE
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snre.bnre.BNRE.__init__" class="md-nav__link">
+    __init__()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snre.bnre.BNRE.train" class="md-nav__link">
+    train()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.abc.mcabc.MCABC" class="md-nav__link">
+    MCABC
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.abc.mcabc.MCABC.__call__" class="md-nav__link">
+    __call__()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.abc.mcabc.MCABC.__init__" class="md-nav__link">
+    __init__()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.abc.smcabc.SMCABC" class="md-nav__link">
+    SMCABC
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.abc.smcabc.SMCABC.__call__" class="md-nav__link">
+    __call__()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.abc.smcabc.SMCABC.__init__" class="md-nav__link">
+    __init__()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.abc.smcabc.SMCABC.get_new_kernel" class="md-nav__link">
+    get_new_kernel()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.abc.smcabc.SMCABC.get_particle_ranges" class="md-nav__link">
+    get_particle_ranges()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.abc.smcabc.SMCABC.resample_if_ess_too_small" class="md-nav__link">
+    resample_if_ess_too_small()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.abc.smcabc.SMCABC.run_lra_update_weights" class="md-nav__link">
+    run_lra_update_weights()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.abc.smcabc.SMCABC.run_sass_set_xo" class="md-nav__link">
+    run_sass_set_xo()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.abc.smcabc.SMCABC.sample_from_population_with_weights" class="md-nav__link">
+    sample_from_population_with_weights()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#posteriors" class="md-nav__link">
+    Posteriors
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.direct_posterior.DirectPosterior" class="md-nav__link">
+    DirectPosterior
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.direct_posterior.DirectPosterior.__init__" class="md-nav__link">
+    __init__()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.direct_posterior.DirectPosterior.leakage_correction" class="md-nav__link">
+    leakage_correction()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.direct_posterior.DirectPosterior.log_prob" class="md-nav__link">
+    log_prob()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.direct_posterior.DirectPosterior.map" class="md-nav__link">
+    map()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.direct_posterior.DirectPosterior.sample" class="md-nav__link">
+    sample()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior" class="md-nav__link">
+    ImportanceSamplingPosterior
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.__init__" class="md-nav__link">
+    __init__()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.estimate_normalization_constant" class="md-nav__link">
+    estimate_normalization_constant()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.log_prob" class="md-nav__link">
+    log_prob()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.map" class="md-nav__link">
+    map()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.sample" class="md-nav__link">
+    sample()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior" class="md-nav__link">
+    MCMCPosterior
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.mcmc_method" class="md-nav__link">
+    mcmc_method
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.posterior_sampler" class="md-nav__link">
+    posterior_sampler
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.__init__" class="md-nav__link">
+    __init__()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.get_arviz_inference_data" class="md-nav__link">
+    get_arviz_inference_data()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.log_prob" class="md-nav__link">
+    log_prob()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.map" class="md-nav__link">
+    map()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.sample" class="md-nav__link">
+    sample()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.set_mcmc_method" class="md-nav__link">
+    set_mcmc_method()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.rejection_posterior.RejectionPosterior" class="md-nav__link">
+    RejectionPosterior
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.rejection_posterior.RejectionPosterior.__init__" class="md-nav__link">
+    __init__()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.rejection_posterior.RejectionPosterior.log_prob" class="md-nav__link">
+    log_prob()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.rejection_posterior.RejectionPosterior.map" class="md-nav__link">
+    map()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.rejection_posterior.RejectionPosterior.sample" class="md-nav__link">
+    sample()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
   <a href="#sbi.inference.posteriors.vi_posterior.VIPosterior" class="md-nav__link">
     VIPosterior
   </a>
   
 </li>
-        
-      </ul>
-    </nav>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.q" class="md-nav__link">
+    q
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.vi_method" class="md-nav__link">
+    vi_method
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.__init__" class="md-nav__link">
+    __init__()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.evaluate" class="md-nav__link">
+    evaluate()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.log_prob" class="md-nav__link">
+    log_prob()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.map" class="md-nav__link">
+    map()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.sample" class="md-nav__link">
+    sample()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.set_q" class="md-nav__link">
+    set_q()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.set_vi_method" class="md-nav__link">
+    set_vi_method()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.train" class="md-nav__link">
+    train()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#models" class="md-nav__link">
+    Models
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.utils.get_nn_models.posterior_nn" class="md-nav__link">
+    posterior_nn
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.utils.get_nn_models.likelihood_nn" class="md-nav__link">
+    likelihood_nn
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.utils.get_nn_models.classifier_nn" class="md-nav__link">
+    classifier_nn
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#potentials" class="md-nav__link">
+    Potentials
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.potentials.posterior_based_potential.posterior_estimator_based_potential" class="md-nav__link">
+    posterior_estimator_based_potential
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.potentials.likelihood_based_potential.likelihood_estimator_based_potential" class="md-nav__link">
+    likelihood_estimator_based_potential
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.potentials.ratio_based_potential.ratio_estimator_based_potential" class="md-nav__link">
+    ratio_estimator_based_potential
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#analysis" class="md-nav__link">
+    Analysis
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.analysis.plot.pairplot" class="md-nav__link">
+    pairplot
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.analysis.plot.marginal_plot" class="md-nav__link">
+    marginal_plot
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.analysis.plot.conditional_pairplot" class="md-nav__link">
+    conditional_pairplot
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.analysis.conditional_density.conditional_corrcoeff" class="md-nav__link">
+    conditional_corrcoeff
+  </a>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../faq/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    FAQ
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../credits/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+    
+  </ul>
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+            
+              
+              <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+
+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+  
+  
+  
+    
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      Table of contents
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#inference" class="md-nav__link">
+    Inference
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.base.infer" class="md-nav__link">
+    infer
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.utils.user_input_checks.prepare_for_sbi" class="md-nav__link">
+    prepare_for_sbi
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.base.simulate_for_sbi" class="md-nav__link">
+    simulate_for_sbi
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snpe.snpe_a.SNPE_A" class="md-nav__link">
+    SNPE_A
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snpe.snpe_a.SNPE_A.__init__" class="md-nav__link">
+    __init__()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snpe.snpe_a.SNPE_A.build_posterior" class="md-nav__link">
+    build_posterior()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snpe.snpe_a.SNPE_A.correct_for_proposal" class="md-nav__link">
+    correct_for_proposal()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snpe.snpe_a.SNPE_A.train" class="md-nav__link">
+    train()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snpe.snpe_c.SNPE_C" class="md-nav__link">
+    SNPE_C
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snpe.snpe_c.SNPE_C.__init__" class="md-nav__link">
+    __init__()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snpe.snpe_c.SNPE_C.train" class="md-nav__link">
+    train()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snle.snle_a.SNLE_A" class="md-nav__link">
+    SNLE_A
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snle.snle_a.SNLE_A.__init__" class="md-nav__link">
+    __init__()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snre.snre_a.SNRE_A" class="md-nav__link">
+    SNRE_A
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snre.snre_a.SNRE_A.__init__" class="md-nav__link">
+    __init__()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snre.snre_a.SNRE_A.train" class="md-nav__link">
+    train()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snre.snre_b.SNRE_B" class="md-nav__link">
+    SNRE_B
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snre.snre_b.SNRE_B.__init__" class="md-nav__link">
+    __init__()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snre.snre_b.SNRE_B.train" class="md-nav__link">
+    train()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snre.snre_c.SNRE_C" class="md-nav__link">
+    SNRE_C
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snre.snre_c.SNRE_C.__init__" class="md-nav__link">
+    __init__()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snre.snre_c.SNRE_C.train" class="md-nav__link">
+    train()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snre.bnre.BNRE" class="md-nav__link">
+    BNRE
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snre.bnre.BNRE.__init__" class="md-nav__link">
+    __init__()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.snre.bnre.BNRE.train" class="md-nav__link">
+    train()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.abc.mcabc.MCABC" class="md-nav__link">
+    MCABC
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.abc.mcabc.MCABC.__call__" class="md-nav__link">
+    __call__()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.abc.mcabc.MCABC.__init__" class="md-nav__link">
+    __init__()
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.abc.smcabc.SMCABC" class="md-nav__link">
+    SMCABC
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.abc.smcabc.SMCABC.__call__" class="md-nav__link">
+    __call__()
+  </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#models" class="md-nav__link">
-    Models
+  <a href="#sbi.inference.abc.smcabc.SMCABC.__init__" class="md-nav__link">
+    __init__()
   </a>
   
-    <nav class="md-nav" aria-label="Models">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#sbi.utils.get_nn_models.posterior_nn" class="md-nav__link">
-    posterior_nn()
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.abc.smcabc.SMCABC.get_new_kernel" class="md-nav__link">
+    get_new_kernel()
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.utils.get_nn_models.likelihood_nn" class="md-nav__link">
-    likelihood_nn()
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.abc.smcabc.SMCABC.get_particle_ranges" class="md-nav__link">
+    get_particle_ranges()
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.utils.get_nn_models.classifier_nn" class="md-nav__link">
-    classifier_nn()
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.abc.smcabc.SMCABC.resample_if_ess_too_small" class="md-nav__link">
+    resample_if_ess_too_small()
   </a>
   
 </li>
-        
-      </ul>
-    </nav>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.abc.smcabc.SMCABC.run_lra_update_weights" class="md-nav__link">
+    run_lra_update_weights()
+  </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#potentials" class="md-nav__link">
-    Potentials
+  <a href="#sbi.inference.abc.smcabc.SMCABC.run_sass_set_xo" class="md-nav__link">
+    run_sass_set_xo()
   </a>
   
-    <nav class="md-nav" aria-label="Potentials">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.potentials.posterior_based_potential.posterior_estimator_based_potential" class="md-nav__link">
-    posterior_estimator_based_potential()
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.abc.smcabc.SMCABC.sample_from_population_with_weights" class="md-nav__link">
+    sample_from_population_with_weights()
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.potentials.likelihood_based_potential.likelihood_estimator_based_potential" class="md-nav__link">
-    likelihood_estimator_based_potential()
+      
+        <li class="md-nav__item">
+  <a href="#posteriors" class="md-nav__link">
+    Posteriors
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.potentials.ratio_based_potential.ratio_estimator_based_potential" class="md-nav__link">
-    ratio_estimator_based_potential()
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.direct_posterior.DirectPosterior" class="md-nav__link">
+    DirectPosterior
   </a>
   
 </li>
-        
-      </ul>
-    </nav>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.direct_posterior.DirectPosterior.__init__" class="md-nav__link">
+    __init__()
+  </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#analysis" class="md-nav__link">
-    Analysis
+  <a href="#sbi.inference.posteriors.direct_posterior.DirectPosterior.leakage_correction" class="md-nav__link">
+    leakage_correction()
   </a>
   
-    <nav class="md-nav" aria-label="Analysis">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#sbi.analysis.plot.pairplot" class="md-nav__link">
-    pairplot()
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.direct_posterior.DirectPosterior.log_prob" class="md-nav__link">
+    log_prob()
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.analysis.plot.marginal_plot" class="md-nav__link">
-    marginal_plot()
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.direct_posterior.DirectPosterior.map" class="md-nav__link">
+    map()
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.analysis.plot.conditional_pairplot" class="md-nav__link">
-    conditional_pairplot()
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.direct_posterior.DirectPosterior.sample" class="md-nav__link">
+    sample()
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.analysis.conditional_density.conditional_corrcoeff" class="md-nav__link">
-    conditional_corrcoeff()
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior" class="md-nav__link">
+    ImportanceSamplingPosterior
   </a>
   
 </li>
-        
-      </ul>
-    </nav>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.__init__" class="md-nav__link">
+    __init__()
+  </a>
   
 </li>
       
-    </ul>
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.estimate_normalization_constant" class="md-nav__link">
+    estimate_normalization_constant()
+  </a>
   
-</nav>
+</li>
       
-    </li>
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.log_prob" class="md-nav__link">
+    log_prob()
+  </a>
   
-
-    
-      
-      
+</li>
       
-
-  
-  
-  
-    <li class="md-nav__item">
-      <a href="../faq/" class="md-nav__link">
-        FAQ
-      </a>
-    </li>
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.map" class="md-nav__link">
+    map()
+  </a>
   
-
-    
-      
-      
+</li>
       
-
-  
-  
-  
-    <li class="md-nav__item">
-      <a href="../credits/" class="md-nav__link">
-        Credits
-      </a>
-    </li>
-  
-
-    
-  </ul>
-</nav>
-                  </div>
-                </div>
-              </div>
-            
-            
-              
-              <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
-                <div class="md-sidebar__scrollwrap">
-                  <div class="md-sidebar__inner">
-                    
-
-<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.sample" class="md-nav__link">
+    sample()
+  </a>
   
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior" class="md-nav__link">
+    MCMCPosterior
+  </a>
   
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.mcmc_method" class="md-nav__link">
+    mcmc_method
+  </a>
   
-    
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.posterior_sampler" class="md-nav__link">
+    posterior_sampler
+  </a>
   
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.__init__" class="md-nav__link">
+    __init__()
+  </a>
   
-    <label class="md-nav__title" for="__toc">
-      <span class="md-nav__icon md-icon"></span>
-      Table of contents
-    </label>
-    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+</li>
       
         <li class="md-nav__item">
-  <a href="#inference" class="md-nav__link">
-    Inference
+  <a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.get_arviz_inference_data" class="md-nav__link">
+    get_arviz_inference_data()
   </a>
   
-    <nav class="md-nav" aria-label="Inference">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.base.infer" class="md-nav__link">
-    infer()
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.log_prob" class="md-nav__link">
+    log_prob()
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.utils.user_input_checks.prepare_for_sbi" class="md-nav__link">
-    prepare_for_sbi()
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.map" class="md-nav__link">
+    map()
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.base.simulate_for_sbi" class="md-nav__link">
-    simulate_for_sbi()
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.sample" class="md-nav__link">
+    sample()
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.snpe.snpe_a.SNPE_A" class="md-nav__link">
-    SNPE_A
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.set_mcmc_method" class="md-nav__link">
+    set_mcmc_method()
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.snpe.snpe_c.SNPE_C" class="md-nav__link">
-    SNPE_C
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.rejection_posterior.RejectionPosterior" class="md-nav__link">
+    RejectionPosterior
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.snle.snle_a.SNLE_A" class="md-nav__link">
-    SNLE_A
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.rejection_posterior.RejectionPosterior.__init__" class="md-nav__link">
+    __init__()
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.snre.snre_a.SNRE_A" class="md-nav__link">
-    SNRE_A
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.rejection_posterior.RejectionPosterior.log_prob" class="md-nav__link">
+    log_prob()
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.snre.snre_b.SNRE_B" class="md-nav__link">
-    SNRE_B
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.rejection_posterior.RejectionPosterior.map" class="md-nav__link">
+    map()
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.snre.snre_c.SNRE_C" class="md-nav__link">
-    SNRE_C
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.rejection_posterior.RejectionPosterior.sample" class="md-nav__link">
+    sample()
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.snre.bnre.BNRE" class="md-nav__link">
-    BNRE
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.vi_posterior.VIPosterior" class="md-nav__link">
+    VIPosterior
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.abc.mcabc.MCABC" class="md-nav__link">
-    MCABC
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.q" class="md-nav__link">
+    q
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.abc.smcabc.SMCABC" class="md-nav__link">
-    SMCABC
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.vi_method" class="md-nav__link">
+    vi_method
   </a>
   
 </li>
-        
-      </ul>
-    </nav>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.__init__" class="md-nav__link">
+    __init__()
+  </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#posteriors" class="md-nav__link">
-    Posteriors
+  <a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.evaluate" class="md-nav__link">
+    evaluate()
   </a>
   
-    <nav class="md-nav" aria-label="Posteriors">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.posteriors.direct_posterior.DirectPosterior" class="md-nav__link">
-    DirectPosterior
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.log_prob" class="md-nav__link">
+    log_prob()
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior" class="md-nav__link">
-    ImportanceSamplingPosterior
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.map" class="md-nav__link">
+    map()
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior" class="md-nav__link">
-    MCMCPosterior
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.sample" class="md-nav__link">
+    sample()
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.posteriors.rejection_posterior.RejectionPosterior" class="md-nav__link">
-    RejectionPosterior
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.set_q" class="md-nav__link">
+    set_q()
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
-  <a href="#sbi.inference.posteriors.vi_posterior.VIPosterior" class="md-nav__link">
-    VIPosterior
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.set_vi_method" class="md-nav__link">
+    set_vi_method()
   </a>
   
 </li>
-        
-      </ul>
-    </nav>
+      
+        <li class="md-nav__item">
+  <a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.train" class="md-nav__link">
+    train()
+  </a>
   
 </li>
       
@@ -1269,33 +2247,27 @@
     Models
   </a>
   
-    <nav class="md-nav" aria-label="Models">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
+</li>
+      
+        <li class="md-nav__item">
   <a href="#sbi.utils.get_nn_models.posterior_nn" class="md-nav__link">
-    posterior_nn()
+    posterior_nn
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
+      
+        <li class="md-nav__item">
   <a href="#sbi.utils.get_nn_models.likelihood_nn" class="md-nav__link">
-    likelihood_nn()
+    likelihood_nn
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
+      
+        <li class="md-nav__item">
   <a href="#sbi.utils.get_nn_models.classifier_nn" class="md-nav__link">
-    classifier_nn()
+    classifier_nn
   </a>
   
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
         <li class="md-nav__item">
@@ -1303,33 +2275,27 @@
     Potentials
   </a>
   
-    <nav class="md-nav" aria-label="Potentials">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
+</li>
+      
+        <li class="md-nav__item">
   <a href="#sbi.inference.potentials.posterior_based_potential.posterior_estimator_based_potential" class="md-nav__link">
-    posterior_estimator_based_potential()
+    posterior_estimator_based_potential
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
+      
+        <li class="md-nav__item">
   <a href="#sbi.inference.potentials.likelihood_based_potential.likelihood_estimator_based_potential" class="md-nav__link">
-    likelihood_estimator_based_potential()
+    likelihood_estimator_based_potential
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
+      
+        <li class="md-nav__item">
   <a href="#sbi.inference.potentials.ratio_based_potential.ratio_estimator_based_potential" class="md-nav__link">
-    ratio_estimator_based_potential()
+    ratio_estimator_based_potential
   </a>
   
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
         <li class="md-nav__item">
@@ -1337,40 +2303,34 @@
     Analysis
   </a>
   
-    <nav class="md-nav" aria-label="Analysis">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
+</li>
+      
+        <li class="md-nav__item">
   <a href="#sbi.analysis.plot.pairplot" class="md-nav__link">
-    pairplot()
+    pairplot
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
+      
+        <li class="md-nav__item">
   <a href="#sbi.analysis.plot.marginal_plot" class="md-nav__link">
-    marginal_plot()
+    marginal_plot
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
+      
+        <li class="md-nav__item">
   <a href="#sbi.analysis.plot.conditional_pairplot" class="md-nav__link">
-    conditional_pairplot()
+    conditional_pairplot
   </a>
   
 </li>
-        
-          <li class="md-nav__item">
+      
+        <li class="md-nav__item">
   <a href="#sbi.analysis.conditional_density.conditional_corrcoeff" class="md-nav__link">
-    conditional_corrcoeff()
+    conditional_corrcoeff
   </a>
   
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -1395,16 +2355,12 @@ <h1 id="api-reference">API Reference<a class="headerlink" href="#api-reference"
 <h2 id="inference">Inference<a class="headerlink" href="#inference" title="Permanent link">&para;</a></h2>
 
 
-<div class="doc doc-object doc-function">
-
 
+<div class="doc doc-object doc-function">
 
-<h3 id="sbi.inference.base.infer" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">sbi</span><span class="o">.</span><span class="n">inference</span><span class="o">.</span><span class="n">base</span><span class="o">.</span><span class="n">infer</span><span class="p">(</span><span class="n">simulator</span><span class="p">,</span> <span class="n">prior</span><span class="p">,</span> <span class="n">method</span><span class="p">,</span> <span class="n">num_simulations</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span></code>
-
-<a href="#sbi.inference.base.infer" class="headerlink" title="Permanent link">&para;</a></h3>
 
 
+<a id="sbi.inference.base.infer"></a>
   <div class="doc doc-contents first">
   
       <p>Runs simulation-based inference and returns the posterior.</p>
@@ -1416,6 +2372,8 @@ <h3 id="sbi.inference.base.infer" class="doc doc-heading">
 use the flexible interface described here:
 <a href="https://www.mackelab.org/sbi/tutorial/02_flexible_interface/">https://www.mackelab.org/sbi/tutorial/02_flexible_interface/</a></p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -1432,10 +2390,14 @@ <h3 id="sbi.inference.base.infer" class="doc doc-heading">
           <td>
                 <code><span title="typing.Callable">Callable</span></code>
           </td>
-          <td><p>A function that takes parameters <span class="arithmatex">\(\theta\)</span> and maps them to
+          <td>
+            <div class="doc-md-description">
+              <p>A function that takes parameters <span class="arithmatex">\(\theta\)</span> and maps them to
 simulations, or observations, <code>x</code>, <span class="arithmatex">\(\mathrm{sim}(\theta)\to x\)</span>. Any
 regular Python callable (i.e. function or class with <code>__call__</code> method)
-can be used.</p></td>
+can be used.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -1445,10 +2407,14 @@ <h3 id="sbi.inference.base.infer" class="doc doc-heading">
           <td>
                 <code><span title="torch.distributions.Distribution">Distribution</span></code>
           </td>
-          <td><p>A probability distribution that expresses prior knowledge about the
+          <td>
+            <div class="doc-md-description">
+              <p>A probability distribution that expresses prior knowledge about the
 parameters, e.g. which ranges are meaningful for them. Any
 object with <code>.log_prob()</code>and <code>.sample()</code> (for example, a PyTorch
-distribution) can be used.</p></td>
+distribution) can be used.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -1458,7 +2424,11 @@ <h3 id="sbi.inference.base.infer" class="doc doc-heading">
           <td>
                 <code>str</code>
           </td>
-          <td><p>What inference method to use. Either of SNPE, SNLE or SNRE.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>What inference method to use. Either of SNPE, SNLE or SNRE.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -1468,8 +2438,12 @@ <h3 id="sbi.inference.base.infer" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of simulation calls. More simulations means a longer
-runtime, but a better posterior estimate.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of simulation calls. More simulations means a longer
+runtime, but a better posterior estimate.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -1479,17 +2453,22 @@ <h3 id="sbi.inference.base.infer" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of parallel workers to use for simulations.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of parallel workers to use for simulations.</p>
+            </div>
+          </td>
           <td>
                 <code>1</code>
           </td>
         </tr>
     </tbody>
   </table>
+      <p>Returns: Posterior over parameters conditional on observations (amortized).</p>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/base.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">27</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/base.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">27</span>
 <span class="normal">28</span>
 <span class="normal">29</span>
 <span class="normal">30</span>
@@ -1599,21 +2578,17 @@ <h3 id="sbi.inference.base.infer" class="doc doc-heading">
 
     <span class="k">return</span> <span class="n">posterior</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
-<div class="doc doc-object doc-function">
-
-
 
-<h3 id="sbi.utils.user_input_checks.prepare_for_sbi" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">sbi</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">user_input_checks</span><span class="o">.</span><span class="n">prepare_for_sbi</span><span class="p">(</span><span class="n">simulator</span><span class="p">,</span> <span class="n">prior</span><span class="p">)</span></code>
+<div class="doc doc-object doc-function">
 
-<a href="#sbi.utils.user_input_checks.prepare_for_sbi" class="headerlink" title="Permanent link">&para;</a></h3>
 
 
+<a id="sbi.utils.user_input_checks.prepare_for_sbi"></a>
   <div class="doc doc-contents first">
   
       <p>Prepare simulator and prior for usage in sbi.</p>
@@ -1628,6 +2603,8 @@ <h3 id="sbi.utils.user_input_checks.prepare_for_sbi" class="doc doc-heading">
 </ul>
 <p>If this is not possible, a suitable exception will be raised.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -1644,7 +2621,11 @@ <h3 id="sbi.utils.user_input_checks.prepare_for_sbi" class="doc doc-heading">
           <td>
                 <code><span title="typing.Callable">Callable</span></code>
           </td>
-          <td><p>Simulator as provided by the user.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Simulator as provided by the user.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -1653,7 +2634,11 @@ <h3 id="sbi.utils.user_input_checks.prepare_for_sbi" class="doc doc-heading">
           <td><code>prior</code></td>
           <td>
           </td>
-          <td><p>Prior as provided by the user.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Prior as provided by the user.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -1661,6 +2646,8 @@ <h3 id="sbi.utils.user_input_checks.prepare_for_sbi" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -1674,14 +2661,18 @@ <h3 id="sbi.utils.user_input_checks.prepare_for_sbi" class="doc doc-heading">
           <td>
                 <code><span title="typing.Tuple">Tuple</span>[<span title="typing.Callable">Callable</span>, <span title="torch.distributions.Distribution">Distribution</span>]</code>
           </td>
-          <td><p>Tuple (simulator, prior) checked and matching the requirements of sbi.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Tuple (simulator, prior) checked and matching the requirements of sbi.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/utils/user_input_checks.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">608</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/utils/user_input_checks.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">608</span>
 <span class="normal">609</span>
 <span class="normal">610</span>
 <span class="normal">611</span>
@@ -1747,21 +2738,17 @@ <h3 id="sbi.utils.user_input_checks.prepare_for_sbi" class="doc doc-heading">
 
     <span class="k">return</span> <span class="n">simulator</span><span class="p">,</span> <span class="n">prior</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
-<div class="doc doc-object doc-function">
-
 
+<div class="doc doc-object doc-function">
 
-<h3 id="sbi.inference.base.simulate_for_sbi" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">sbi</span><span class="o">.</span><span class="n">inference</span><span class="o">.</span><span class="n">base</span><span class="o">.</span><span class="n">simulate_for_sbi</span><span class="p">(</span><span class="n">simulator</span><span class="p">,</span> <span class="n">proposal</span><span class="p">,</span> <span class="n">num_simulations</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">simulation_batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_progress_bar</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
-
-<a href="#sbi.inference.base.simulate_for_sbi" class="headerlink" title="Permanent link">&para;</a></h3>
 
 
+<a id="sbi.inference.base.simulate_for_sbi"></a>
   <div class="doc doc-contents first">
   
       <p>Returns (<span class="arithmatex">\(\theta, x\)</span>) pairs obtained from sampling the proposal and simulating.</p>
@@ -1771,6 +2758,8 @@ <h3 id="sbi.inference.base.simulate_for_sbi" class="doc doc-heading">
 <li>Simulate these parameters to obtain <span class="arithmatex">\(x\)</span>.</li>
 </ul>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -1787,10 +2776,14 @@ <h3 id="sbi.inference.base.simulate_for_sbi" class="doc doc-heading">
           <td>
                 <code><span title="typing.Callable">Callable</span></code>
           </td>
-          <td><p>A function that takes parameters <span class="arithmatex">\(\theta\)</span> and maps them to
+          <td>
+            <div class="doc-md-description">
+              <p>A function that takes parameters <span class="arithmatex">\(\theta\)</span> and maps them to
 simulations, or observations, <code>x</code>, <span class="arithmatex">\(\text{sim}(\theta)\to x\)</span>. Any
 regular Python callable (i.e. function or class with <code>__call__</code> method)
-can be used.</p></td>
+can be used.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -1800,8 +2793,12 @@ <h3 id="sbi.inference.base.simulate_for_sbi" class="doc doc-heading">
           <td>
                 <code><span title="typing.Any">Any</span></code>
           </td>
-          <td><p>Probability distribution that the parameters <span class="arithmatex">\(\theta\)</span> are sampled
-from.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Probability distribution that the parameters <span class="arithmatex">\(\theta\)</span> are sampled
+from.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -1811,7 +2808,11 @@ <h3 id="sbi.inference.base.simulate_for_sbi" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of simulations that are run.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of simulations that are run.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -1821,7 +2822,11 @@ <h3 id="sbi.inference.base.simulate_for_sbi" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of parallel workers to use for simulations.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of parallel workers to use for simulations.</p>
+            </div>
+          </td>
           <td>
                 <code>1</code>
           </td>
@@ -1831,10 +2836,14 @@ <h3 id="sbi.inference.base.simulate_for_sbi" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of parameter sets that the simulator
+          <td>
+            <div class="doc-md-description">
+              <p>Number of parameter sets that the simulator
 maps to data x at once. If None, we simulate all parameter sets at the
 same time. If &gt;= 1, the simulator has to process data of shape
-(simulation_batch_size, parameter_dimension).</p></td>
+(simulation_batch_size, parameter_dimension).</p>
+            </div>
+          </td>
           <td>
                 <code>1</code>
           </td>
@@ -1844,7 +2853,11 @@ <h3 id="sbi.inference.base.simulate_for_sbi" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[int]</code>
           </td>
-          <td><p>Seed for reproducibility.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Seed for reproducibility.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -1854,19 +2867,24 @@ <h3 id="sbi.inference.base.simulate_for_sbi" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to show a progress bar for simulating. This will not
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to show a progress bar for simulating. This will not
 affect whether there will be a progressbar while drawing samples from the
-proposal.</p></td>
+proposal.</p>
+            </div>
+          </td>
           <td>
                 <code>True</code>
           </td>
         </tr>
     </tbody>
   </table>
+      <p>Returns: Sampled parameters <span class="arithmatex">\(\theta\)</span> and simulation-outputs <span class="arithmatex">\(x\)</span>.</p>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/base.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">456</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/base.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">456</span>
 <span class="normal">457</span>
 <span class="normal">458</span>
 <span class="normal">459</span>
@@ -1964,7 +2982,7 @@ <h3 id="sbi.inference.base.simulate_for_sbi" class="doc doc-heading">
 
     <span class="k">return</span> <span class="n">theta</span><span class="p">,</span> <span class="n">x</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
@@ -1973,22 +2991,15 @@ <h3 id="sbi.inference.base.simulate_for_sbi" class="doc doc-heading">
 
 
 
-<h3 id="sbi.inference.snpe.snpe_a.SNPE_A" class="doc doc-heading">
-        <code>sbi.inference.snpe.snpe_a.SNPE_A</code>
-
-
-<a href="#sbi.inference.snpe.snpe_a.SNPE_A" class="headerlink" title="Permanent link">&para;</a></h3>
-
-
+<a id="sbi.inference.snpe.snpe_a.SNPE_A"></a>
   <div class="doc doc-contents first">
-      <p class="doc doc-class-bases">
-        Bases: <code><span title="sbi.inference.snpe.snpe_base.PosteriorEstimator">PosteriorEstimator</span></code></p>
-
+          <p class="doc doc-class-bases">
+            Bases: <code><span title="sbi.inference.snpe.snpe_base.PosteriorEstimator">PosteriorEstimator</span></code></p>
 
 
-        <details class="quote">
-          <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/snpe/snpe_a.py</code></summary>
-          <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 24</span>
+            <details class="quote">
+              <summary>Source code in <code>sbi/inference/snpe/snpe_a.py</code></summary>
+              <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 24</span>
 <span class="normal"> 25</span>
 <span class="normal"> 26</span>
 <span class="normal"> 27</span>
@@ -2662,7 +3673,7 @@ <h3 id="sbi.inference.snpe.snpe_a.SNPE_A" class="doc doc-heading">
                     <span class="n">param</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">add_</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn_like</span><span class="p">(</span><span class="n">param</span><span class="o">.</span><span class="n">data</span><span class="p">)</span> <span class="o">*</span> <span class="n">eps</span><span class="p">)</span>
                     <span class="n">param</span><span class="o">.</span><span class="n">grad</span> <span class="o">=</span> <span class="kc">None</span>  <span class="c1"># let autograd construct a new gradient</span>
 </code></pre></div></td></tr></table></div>
-        </details>
+            </details>
 
   
 
@@ -2676,14 +3687,16 @@ <h3 id="sbi.inference.snpe.snpe_a.SNPE_A" class="doc doc-heading">
 
 
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.snpe.snpe_a.SNPE_A.__init__" class="doc doc-heading">
-<code class="codehilite language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">prior</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">density_estimator</span><span class="o">=</span><span class="s1">&#39;mdn_snpe_a&#39;</span><span class="p">,</span> <span class="n">num_components</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">,</span> <span class="n">logging_level</span><span class="o">=</span><span class="s1">&#39;WARNING&#39;</span><span class="p">,</span> <span class="n">summary_writer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.snpe.snpe_a.SNPE_A.__init__" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.snpe.snpe_a.SNPE_A.__init__" class="doc doc-heading">
+          <code class=" language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">prior</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">density_estimator</span><span class="o">=</span><span class="s1">&#39;mdn_snpe_a&#39;</span><span class="p">,</span> <span class="n">num_components</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">,</span> <span class="n">logging_level</span><span class="o">=</span><span class="s1">&#39;WARNING&#39;</span><span class="p">,</span> <span class="n">summary_writer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.snpe.snpe_a.SNPE_A.__init__" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -2699,6 +3712,8 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.__init__" class="doc doc-heading">
 analytically. Thus, SNPE-A is limited to Gaussian distributions for all but the
 last round. In the last round, SNPE-A can use a Mixture of Gaussians.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -2715,10 +3730,14 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="torch.distributions.Distribution">Distribution</span>]</code>
           </td>
-          <td><p>A probability distribution that expresses prior knowledge about the
+          <td>
+            <div class="doc-md-description">
+              <p>A probability distribution that expresses prior knowledge about the
 parameters, e.g. which ranges are meaningful for them. Any
 object with <code>.log_prob()</code>and <code>.sample()</code> (for example, a PyTorch
-distribution) can be used.</p></td>
+distribution) can be used.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -2728,7 +3747,9 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Union">Union</span>[str, <span title="typing.Callable">Callable</span>]</code>
           </td>
-          <td><p>If it is a string (only &ldquo;mdn_snpe_a&rdquo; is valid), use a
+          <td>
+            <div class="doc-md-description">
+              <p>If it is a string (only &ldquo;mdn_snpe_a&rdquo; is valid), use a
 pre-configured mixture of densities network. Alternatively, a function
 that builds a custom neural network can be provided. The function will
 be called with the first batch of simulations (theta, x), which can
@@ -2740,7 +3761,9 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.__init__" class="doc doc-heading">
 Algorithm 1 in [1]). In the last round, this component is replicated
 <code>num_components</code> times, its parameters are perturbed with a very small
 noise, and then the last training round is done with the expanded
-Gaussian mixture as estimator for the proposal posterior.</p></td>
+Gaussian mixture as estimator for the proposal posterior.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;mdn_snpe_a&#39;</code>
           </td>
@@ -2750,9 +3773,13 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.__init__" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of components of the mixture of Gaussians in the
+          <td>
+            <div class="doc-md-description">
+              <p>Number of components of the mixture of Gaussians in the
 last round. This overrides the <code>num_components</code> value passed to
-<code>posterior_nn()</code>.</p></td>
+<code>posterior_nn()</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>10</code>
           </td>
@@ -2762,7 +3789,11 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.__init__" class="doc doc-heading">
           <td>
                 <code>str</code>
           </td>
-          <td><p>Training device, e.g., &ldquo;cpu&rdquo;, &ldquo;cuda&rdquo; or &ldquo;cuda:{0, 1, &hellip;}&rdquo;.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Training device, e.g., &ldquo;cpu&rdquo;, &ldquo;cuda&rdquo; or &ldquo;cuda:{0, 1, &hellip;}&rdquo;.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;cpu&#39;</code>
           </td>
@@ -2772,8 +3803,12 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Union">Union</span>[int, str]</code>
           </td>
-          <td><p>Minimum severity of messages to log. One of the strings
-INFO, WARNING, DEBUG, ERROR and CRITICAL.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Minimum severity of messages to log. One of the strings
+INFO, WARNING, DEBUG, ERROR and CRITICAL.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;WARNING&#39;</code>
           </td>
@@ -2783,8 +3818,12 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="sbi.types.TensorboardSummaryWriter">TensorboardSummaryWriter</span>]</code>
           </td>
-          <td><p>A tensorboard <code>SummaryWriter</code> to control, among others, log
-file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>A tensorboard <code>SummaryWriter</code> to control, among others, log
+file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -2794,7 +3833,11 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.__init__" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to show a progressbar during training.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to show a progressbar during training.</p>
+            </div>
+          </td>
           <td>
                 <code>True</code>
           </td>
@@ -2802,9 +3845,9 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.__init__" class="doc doc-heading">
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/snpe/snpe_a.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">25</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/snpe/snpe_a.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">25</span>
 <span class="normal">26</span>
 <span class="normal">27</span>
 <span class="normal">28</span>
@@ -2948,19 +3991,21 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.__init__" class="doc doc-heading">
     <span class="p">)</span>
     <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.snpe.snpe_a.SNPE_A.build_posterior" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">build_posterior</span><span class="p">(</span><span class="n">density_estimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">prior</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.snpe.snpe_a.SNPE_A.build_posterior" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.snpe.snpe_a.SNPE_A.build_posterior" class="doc doc-heading">
+          <code class=" language-python"><span class="n">build_posterior</span><span class="p">(</span><span class="n">density_estimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">prior</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.snpe.snpe_a.SNPE_A.build_posterior" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -2969,6 +4014,8 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.build_posterior" class="doc doc-heading
 <p>This method first corrects the estimated density with <code>correct_for_proposal</code>
 and then returns a <code>DirectPosterior</code>.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -2985,8 +4032,12 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.build_posterior" class="doc doc-heading
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="sbi.types.TorchModule">TorchModule</span>]</code>
           </td>
-          <td><p>The density estimator that the posterior is based on.
-If <code>None</code>, use the latest neural density estimator that was trained.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The density estimator that the posterior is based on.
+If <code>None</code>, use the latest neural density estimator that was trained.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -2996,7 +4047,11 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.build_posterior" class="doc doc-heading
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="torch.distributions.Distribution">Distribution</span>]</code>
           </td>
-          <td><p>Prior distribution.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Prior distribution.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -3004,6 +4059,8 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.build_posterior" class="doc doc-heading
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -3017,14 +4074,18 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.build_posterior" class="doc doc-heading
           <td>
                 <code><a class="autorefs autorefs-internal" title="sbi.inference.posteriors.direct_posterior.DirectPosterior" href="#sbi.inference.posteriors.direct_posterior.DirectPosterior">DirectPosterior</a></code>
           </td>
-          <td><p>Posterior <span class="arithmatex">\(p(\theta|x)\)</span>  with <code>.sample()</code> and <code>.log_prob()</code> methods.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Posterior <span class="arithmatex">\(p(\theta|x)\)</span>  with <code>.sample()</code> and <code>.log_prob()</code> methods.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/snpe/snpe_a.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">274</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/snpe/snpe_a.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">274</span>
 <span class="normal">275</span>
 <span class="normal">276</span>
 <span class="normal">277</span>
@@ -3092,19 +4153,21 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.build_posterior" class="doc doc-heading
     <span class="p">)</span>
     <span class="k">return</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_posterior</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.snpe.snpe_a.SNPE_A.correct_for_proposal" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">correct_for_proposal</span><span class="p">(</span><span class="n">density_estimator</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.snpe.snpe_a.SNPE_A.correct_for_proposal" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.snpe.snpe_a.SNPE_A.correct_for_proposal" class="doc doc-heading">
+          <code class=" language-python"><span class="n">correct_for_proposal</span><span class="p">(</span><span class="n">density_estimator</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.snpe.snpe_a.SNPE_A.correct_for_proposal" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -3113,6 +4176,8 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.correct_for_proposal" class="doc doc-he
 <p>Returns a <code>SNPE_A_MDN</code> object, which applies the posthoc-correction required in
 SNPE-A.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -3129,8 +4194,12 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.correct_for_proposal" class="doc doc-he
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="sbi.types.TorchModule">TorchModule</span>]</code>
           </td>
-          <td><p>The density estimator that the posterior is based on.
-If <code>None</code>, use the latest neural density estimator that was trained.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The density estimator that the posterior is based on.
+If <code>None</code>, use the latest neural density estimator that was trained.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -3138,6 +4207,8 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.correct_for_proposal" class="doc doc-he
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -3151,14 +4222,18 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.correct_for_proposal" class="doc doc-he
           <td>
                 <code><span title="sbi.inference.snpe.snpe_a.SNPE_A_MDN">SNPE_A_MDN</span></code>
           </td>
-          <td><p>Posterior <span class="arithmatex">\(p(\theta|x)\)</span>  with <code>.sample()</code> and <code>.log_prob()</code> methods.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Posterior <span class="arithmatex">\(p(\theta|x)\)</span>  with <code>.sample()</code> and <code>.log_prob()</code> methods.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/snpe/snpe_a.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">220</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/snpe/snpe_a.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">220</span>
 <span class="normal">221</span>
 <span class="normal">222</span>
 <span class="normal">223</span>
@@ -3264,19 +4339,21 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.correct_for_proposal" class="doc doc-he
     <span class="p">)</span>
     <span class="k">return</span> <span class="n">wrapped_density_estimator</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.snpe.snpe_a.SNPE_A.train" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">train</span><span class="p">(</span><span class="n">final_round</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">training_batch_size</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.0005</span><span class="p">,</span> <span class="n">validation_fraction</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">stop_after_epochs</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">max_num_epochs</span><span class="o">=</span><span class="mi">2</span> <span class="o">**</span> <span class="mi">31</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">clip_max_norm</span><span class="o">=</span><span class="mf">5.0</span><span class="p">,</span> <span class="n">calibration_kernel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">resume_training</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">retrain_from_scratch</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">show_train_summary</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dataloader_kwargs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">component_perturbation</span><span class="o">=</span><span class="mf">0.005</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.snpe.snpe_a.SNPE_A.train" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.snpe.snpe_a.SNPE_A.train" class="doc doc-heading">
+          <code class=" language-python"><span class="n">train</span><span class="p">(</span><span class="n">final_round</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">training_batch_size</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.0005</span><span class="p">,</span> <span class="n">validation_fraction</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">stop_after_epochs</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">max_num_epochs</span><span class="o">=</span><span class="mi">2</span> <span class="o">**</span> <span class="mi">31</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">clip_max_norm</span><span class="o">=</span><span class="mf">5.0</span><span class="p">,</span> <span class="n">calibration_kernel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">resume_training</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">retrain_from_scratch</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">show_train_summary</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dataloader_kwargs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">component_perturbation</span><span class="o">=</span><span class="mf">0.005</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.snpe.snpe_a.SNPE_A.train" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -3288,6 +4365,8 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.train" class="doc doc-heading">
 <p>Training is performed with maximum likelihood on samples from the latest round,
 which leads the algorithm to converge to the proposal posterior.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -3304,9 +4383,13 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.train" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether we are in the last round of training or not. For all
+          <td>
+            <div class="doc-md-description">
+              <p>Whether we are in the last round of training or not. For all
 but the last round, Algorithm 1 from [1] is executed. In last the
-round, Algorithm 2 from [1] is executed once.</p></td>
+round, Algorithm 2 from [1] is executed once.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -3316,7 +4399,11 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.train" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Training batch size.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Training batch size.</p>
+            </div>
+          </td>
           <td>
                 <code>50</code>
           </td>
@@ -3326,7 +4413,11 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.train" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Learning rate for Adam optimizer.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Learning rate for Adam optimizer.</p>
+            </div>
+          </td>
           <td>
                 <code>0.0005</code>
           </td>
@@ -3336,7 +4427,11 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.train" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>The fraction of data to use for validation.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The fraction of data to use for validation.</p>
+            </div>
+          </td>
           <td>
                 <code>0.1</code>
           </td>
@@ -3346,8 +4441,12 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.train" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>The number of epochs to wait for improvement on the
-validation set before terminating training.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The number of epochs to wait for improvement on the
+validation set before terminating training.</p>
+            </div>
+          </td>
           <td>
                 <code>20</code>
           </td>
@@ -3357,9 +4456,13 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.train" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Maximum number of epochs to run. If reached, we stop
+          <td>
+            <div class="doc-md-description">
+              <p>Maximum number of epochs to run. If reached, we stop
 training even when the validation loss is still decreasing. Otherwise,
-we train until validation loss increases (see also <code>stop_after_epochs</code>).</p></td>
+we train until validation loss increases (see also <code>stop_after_epochs</code>).</p>
+            </div>
+          </td>
           <td>
                 <code>2 ** 31 - 1</code>
           </td>
@@ -3369,8 +4472,12 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.train" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[float]</code>
           </td>
-          <td><p>Value at which to clip the total gradient norm in order to
-prevent exploding gradients. Use None for no clipping.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Value at which to clip the total gradient norm in order to
+prevent exploding gradients. Use None for no clipping.</p>
+            </div>
+          </td>
           <td>
                 <code>5.0</code>
           </td>
@@ -3380,8 +4487,12 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.train" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="typing.Callable">Callable</span>]</code>
           </td>
-          <td><p>A function to calibrate the loss with respect to the
-simulations <code>x</code>. See Lueckmann, Gonçalves et al., NeurIPS 2017.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>A function to calibrate the loss with respect to the
+simulations <code>x</code>. See Lueckmann, Gonçalves et al., NeurIPS 2017.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -3391,10 +4502,14 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.train" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Can be used in case training time is limited, e.g. on a
+          <td>
+            <div class="doc-md-description">
+              <p>Can be used in case training time is limited, e.g. on a
 cluster. If <code>True</code>, the split between train and validation set, the
 optimizer, the number of epochs, and the best validation log-prob will
-be restored from the last time <code>.train()</code> was called.</p></td>
+be restored from the last time <code>.train()</code> was called.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -3403,9 +4518,13 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.train" class="doc doc-heading">
           <td><code>force_first_round_loss</code></td>
           <td>
           </td>
-          <td><p>If <code>True</code>, train with maximum likelihood,
+          <td>
+            <div class="doc-md-description">
+              <p>If <code>True</code>, train with maximum likelihood,
 i.e., potentially ignoring the correction for using a proposal
-distribution different from the prior.</p></td>
+distribution different from the prior.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -3415,9 +4534,13 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.train" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to retrain the conditional density
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to retrain the conditional density
 estimator for the posterior from scratch each round. Not supported for
-SNPE-A.</p></td>
+SNPE-A.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -3427,8 +4550,12 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.train" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to print the number of epochs and validation
-loss and leakage after the training.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to print the number of epochs and validation
+loss and leakage after the training.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -3438,8 +4565,12 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.train" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="typing.Dict">Dict</span>]</code>
           </td>
-          <td><p>Additional or updated kwargs to be passed to the training
-and validation dataloaders (like, e.g., a collate_fn)</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Additional or updated kwargs to be passed to the training
+and validation dataloaders (like, e.g., a collate_fn)</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -3449,10 +4580,14 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.train" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>The standard deviation applied to all weights and
+          <td>
+            <div class="doc-md-description">
+              <p>The standard deviation applied to all weights and
 biases when, in the last round, the Mixture of Gaussians is build from
 a single Gaussian. This value can be problem-specific and also depends
-on the number of mixture components.</p></td>
+on the number of mixture components.</p>
+            </div>
+          </td>
           <td>
                 <code>0.005</code>
           </td>
@@ -3460,6 +4595,8 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.train" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -3471,16 +4608,20 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.train" class="doc doc-heading">
     <tbody>
         <tr>
           <td>
-                <code><span title="torch.nn">nn</span>.<span title="torch.nn.Module">Module</span></code>
+                <code><span title="torch.Module">Module</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Density estimator that approximates the distribution <span class="arithmatex">\(p(\theta|x)\)</span>.</p>
+            </div>
           </td>
-          <td><p>Density estimator that approximates the distribution <span class="arithmatex">\(p(\theta|x)\)</span>.</p></td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/snpe/snpe_a.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 98</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/snpe/snpe_a.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 98</span>
 <span class="normal"> 99</span>
 <span class="normal">100</span>
 <span class="normal">101</span>
@@ -3722,7 +4863,7 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.train" class="doc doc-heading">
 
     <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
@@ -3739,22 +4880,15 @@ <h4 id="sbi.inference.snpe.snpe_a.SNPE_A.train" class="doc doc-heading">
 
 
 
-<h3 id="sbi.inference.snpe.snpe_c.SNPE_C" class="doc doc-heading">
-        <code>sbi.inference.snpe.snpe_c.SNPE_C</code>
-
-
-<a href="#sbi.inference.snpe.snpe_c.SNPE_C" class="headerlink" title="Permanent link">&para;</a></h3>
-
-
+<a id="sbi.inference.snpe.snpe_c.SNPE_C"></a>
   <div class="doc doc-contents first">
-      <p class="doc doc-class-bases">
-        Bases: <code><span title="sbi.inference.snpe.snpe_base.PosteriorEstimator">PosteriorEstimator</span></code></p>
+          <p class="doc doc-class-bases">
+            Bases: <code><span title="sbi.inference.snpe.snpe_base.PosteriorEstimator">PosteriorEstimator</span></code></p>
 
 
-
-        <details class="quote">
-          <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/snpe/snpe_c.py</code></summary>
-          <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 27</span>
+            <details class="quote">
+              <summary>Source code in <code>sbi/inference/snpe/snpe_c.py</code></summary>
+              <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 27</span>
 <span class="normal"> 28</span>
 <span class="normal"> 29</span>
 <span class="normal"> 30</span>
@@ -4976,7 +6110,7 @@ <h3 id="sbi.inference.snpe.snpe_c.SNPE_C" class="doc doc-heading">
 
         <span class="k">return</span> <span class="n">theta</span>
 </code></pre></div></td></tr></table></div>
-        </details>
+            </details>
 
   
 
@@ -4990,14 +6124,16 @@ <h3 id="sbi.inference.snpe.snpe_c.SNPE_C" class="doc doc-heading">
 
 
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.snpe.snpe_c.SNPE_C.__init__" class="doc doc-heading">
-<code class="codehilite language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">prior</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">density_estimator</span><span class="o">=</span><span class="s1">&#39;maf&#39;</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">,</span> <span class="n">logging_level</span><span class="o">=</span><span class="s1">&#39;WARNING&#39;</span><span class="p">,</span> <span class="n">summary_writer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.snpe.snpe_c.SNPE_C.__init__" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.snpe.snpe_c.SNPE_C.__init__" class="doc doc-heading">
+          <code class=" language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">prior</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">density_estimator</span><span class="o">=</span><span class="s1">&#39;maf&#39;</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">,</span> <span class="n">logging_level</span><span class="o">=</span><span class="s1">&#39;WARNING&#39;</span><span class="p">,</span> <span class="n">summary_writer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.snpe.snpe_c.SNPE_C.__init__" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -5025,6 +6161,8 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.__init__" class="doc doc-heading">
 not trigger the non-atomic loss, and the algorithm will fall back onto using
 the atomic loss.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -5041,8 +6179,12 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="torch.distributions.Distribution">Distribution</span>]</code>
           </td>
-          <td><p>A probability distribution that expresses prior knowledge about the
-parameters, e.g. which ranges are meaningful for them.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>A probability distribution that expresses prior knowledge about the
+parameters, e.g. which ranges are meaningful for them.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -5052,14 +6194,18 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Union">Union</span>[str, <span title="typing.Callable">Callable</span>]</code>
           </td>
-          <td><p>If it is a string, use a pre-configured network of the
+          <td>
+            <div class="doc-md-description">
+              <p>If it is a string, use a pre-configured network of the
 provided type (one of nsf, maf, mdn, made). Alternatively, a function
 that builds a custom neural network can be provided. The function will
 be called with the first batch of simulations (theta, x), which can
 thus be used for shape inference and potentially for z-scoring. It
 needs to return a PyTorch <code>nn.Module</code> implementing the density
 estimator. The density estimator needs to provide the methods
-<code>.log_prob</code> and <code>.sample()</code>.</p></td>
+<code>.log_prob</code> and <code>.sample()</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;maf&#39;</code>
           </td>
@@ -5069,7 +6215,11 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.__init__" class="doc doc-heading">
           <td>
                 <code>str</code>
           </td>
-          <td><p>Training device, e.g., &ldquo;cpu&rdquo;, &ldquo;cuda&rdquo; or &ldquo;cuda:{0, 1, &hellip;}&rdquo;.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Training device, e.g., &ldquo;cpu&rdquo;, &ldquo;cuda&rdquo; or &ldquo;cuda:{0, 1, &hellip;}&rdquo;.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;cpu&#39;</code>
           </td>
@@ -5079,8 +6229,12 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Union">Union</span>[int, str]</code>
           </td>
-          <td><p>Minimum severity of messages to log. One of the strings
-INFO, WARNING, DEBUG, ERROR and CRITICAL.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Minimum severity of messages to log. One of the strings
+INFO, WARNING, DEBUG, ERROR and CRITICAL.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;WARNING&#39;</code>
           </td>
@@ -5090,8 +6244,12 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="sbi.types.TensorboardSummaryWriter">TensorboardSummaryWriter</span>]</code>
           </td>
-          <td><p>A tensorboard <code>SummaryWriter</code> to control, among others, log
-file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>A tensorboard <code>SummaryWriter</code> to control, among others, log
+file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -5101,7 +6259,11 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.__init__" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to show a progressbar during training.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to show a progressbar during training.</p>
+            </div>
+          </td>
           <td>
                 <code>True</code>
           </td>
@@ -5109,9 +6271,9 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.__init__" class="doc doc-heading">
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/snpe/snpe_c.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">28</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/snpe/snpe_c.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">28</span>
 <span class="normal">29</span>
 <span class="normal">30</span>
 <span class="normal">31</span>
@@ -5225,25 +6387,29 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.__init__" class="doc doc-heading">
     <span class="n">kwargs</span> <span class="o">=</span> <span class="n">del_entries</span><span class="p">(</span><span class="nb">locals</span><span class="p">(),</span> <span class="n">entries</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;self&quot;</span><span class="p">,</span> <span class="s2">&quot;__class__&quot;</span><span class="p">))</span>
     <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.snpe.snpe_c.SNPE_C.train" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">train</span><span class="p">(</span><span class="n">num_atoms</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">training_batch_size</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.0005</span><span class="p">,</span> <span class="n">validation_fraction</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">stop_after_epochs</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">max_num_epochs</span><span class="o">=</span><span class="mi">2</span> <span class="o">**</span> <span class="mi">31</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">clip_max_norm</span><span class="o">=</span><span class="mf">5.0</span><span class="p">,</span> <span class="n">calibration_kernel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">resume_training</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">force_first_round_loss</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">discard_prior_samples</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">use_combined_loss</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">retrain_from_scratch</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">show_train_summary</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dataloader_kwargs</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.snpe.snpe_c.SNPE_C.train" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.snpe.snpe_c.SNPE_C.train" class="doc doc-heading">
+          <code class=" language-python"><span class="n">train</span><span class="p">(</span><span class="n">num_atoms</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">training_batch_size</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.0005</span><span class="p">,</span> <span class="n">validation_fraction</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">stop_after_epochs</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">max_num_epochs</span><span class="o">=</span><span class="mi">2</span> <span class="o">**</span> <span class="mi">31</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">clip_max_norm</span><span class="o">=</span><span class="mf">5.0</span><span class="p">,</span> <span class="n">calibration_kernel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">resume_training</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">force_first_round_loss</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">discard_prior_samples</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">use_combined_loss</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">retrain_from_scratch</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">show_train_summary</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dataloader_kwargs</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.snpe.snpe_c.SNPE_C.train" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
   
       <p>Return density estimator that approximates the distribution <span class="arithmatex">\(p(\theta|x)\)</span>.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -5260,7 +6426,11 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.train" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of atoms to use for classification.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of atoms to use for classification.</p>
+            </div>
+          </td>
           <td>
                 <code>10</code>
           </td>
@@ -5270,7 +6440,11 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.train" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Training batch size.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Training batch size.</p>
+            </div>
+          </td>
           <td>
                 <code>50</code>
           </td>
@@ -5280,7 +6454,11 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.train" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Learning rate for Adam optimizer.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Learning rate for Adam optimizer.</p>
+            </div>
+          </td>
           <td>
                 <code>0.0005</code>
           </td>
@@ -5290,7 +6468,11 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.train" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>The fraction of data to use for validation.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The fraction of data to use for validation.</p>
+            </div>
+          </td>
           <td>
                 <code>0.1</code>
           </td>
@@ -5300,8 +6482,12 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.train" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>The number of epochs to wait for improvement on the
-validation set before terminating training.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The number of epochs to wait for improvement on the
+validation set before terminating training.</p>
+            </div>
+          </td>
           <td>
                 <code>20</code>
           </td>
@@ -5311,9 +6497,13 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.train" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Maximum number of epochs to run. If reached, we stop
+          <td>
+            <div class="doc-md-description">
+              <p>Maximum number of epochs to run. If reached, we stop
 training even when the validation loss is still decreasing. Otherwise,
-we train until validation loss increases (see also <code>stop_after_epochs</code>).</p></td>
+we train until validation loss increases (see also <code>stop_after_epochs</code>).</p>
+            </div>
+          </td>
           <td>
                 <code>2 ** 31 - 1</code>
           </td>
@@ -5323,8 +6513,12 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.train" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[float]</code>
           </td>
-          <td><p>Value at which to clip the total gradient norm in order to
-prevent exploding gradients. Use None for no clipping.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Value at which to clip the total gradient norm in order to
+prevent exploding gradients. Use None for no clipping.</p>
+            </div>
+          </td>
           <td>
                 <code>5.0</code>
           </td>
@@ -5334,8 +6528,12 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.train" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="typing.Callable">Callable</span>]</code>
           </td>
-          <td><p>A function to calibrate the loss with respect to the
-simulations <code>x</code>. See Lueckmann, Gonçalves et al., NeurIPS 2017.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>A function to calibrate the loss with respect to the
+simulations <code>x</code>. See Lueckmann, Gonçalves et al., NeurIPS 2017.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -5345,10 +6543,14 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.train" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Can be used in case training time is limited, e.g. on a
+          <td>
+            <div class="doc-md-description">
+              <p>Can be used in case training time is limited, e.g. on a
 cluster. If <code>True</code>, the split between train and validation set, the
 optimizer, the number of epochs, and the best validation log-prob will
-be restored from the last time <code>.train()</code> was called.</p></td>
+be restored from the last time <code>.train()</code> was called.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -5358,9 +6560,13 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.train" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>If <code>True</code>, train with maximum likelihood,
+          <td>
+            <div class="doc-md-description">
+              <p>If <code>True</code>, train with maximum likelihood,
 i.e., potentially ignoring the correction for using a proposal
-distribution different from the prior.</p></td>
+distribution different from the prior.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -5370,9 +6576,13 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.train" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to discard samples simulated in round 1, i.e.
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to discard samples simulated in round 1, i.e.
 from the prior. Training may be sped up by ignoring such less targeted
-samples.</p></td>
+samples.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -5382,10 +6592,14 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.train" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to train the neural net also on prior samples
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to train the neural net also on prior samples
 using maximum likelihood in addition to training it on all samples using
 atomic loss. The extra MLE loss helps prevent density leaking with
-bounded priors.</p></td>
+bounded priors.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -5395,8 +6609,12 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.train" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to retrain the conditional density
-estimator for the posterior from scratch each round.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to retrain the conditional density
+estimator for the posterior from scratch each round.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -5406,8 +6624,12 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.train" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to print the number of epochs and validation
-loss and leakage after the training.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to print the number of epochs and validation
+loss and leakage after the training.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -5417,8 +6639,12 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.train" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="typing.Dict">Dict</span>]</code>
           </td>
-          <td><p>Additional or updated kwargs to be passed to the training
-and validation dataloaders (like, e.g., a collate_fn)</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Additional or updated kwargs to be passed to the training
+and validation dataloaders (like, e.g., a collate_fn)</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -5426,6 +6652,8 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.train" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -5437,16 +6665,20 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.train" class="doc doc-heading">
     <tbody>
         <tr>
           <td>
-                <code><span title="torch.nn">nn</span>.<span title="torch.nn.Module">Module</span></code>
+                <code><span title="torch.nn.Module">Module</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Density estimator that approximates the distribution <span class="arithmatex">\(p(\theta|x)\)</span>.</p>
+            </div>
           </td>
-          <td><p>Density estimator that approximates the distribution <span class="arithmatex">\(p(\theta|x)\)</span>.</p></td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/snpe/snpe_c.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 86</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/snpe/snpe_c.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 86</span>
 <span class="normal"> 87</span>
 <span class="normal"> 88</span>
 <span class="normal"> 89</span>
@@ -5636,7 +6868,7 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.train" class="doc doc-heading">
 
     <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
@@ -5653,22 +6885,15 @@ <h4 id="sbi.inference.snpe.snpe_c.SNPE_C.train" class="doc doc-heading">
 
 
 
-<h3 id="sbi.inference.snle.snle_a.SNLE_A" class="doc doc-heading">
-        <code>sbi.inference.snle.snle_a.SNLE_A</code>
-
-
-<a href="#sbi.inference.snle.snle_a.SNLE_A" class="headerlink" title="Permanent link">&para;</a></h3>
-
-
+<a id="sbi.inference.snle.snle_a.SNLE_A"></a>
   <div class="doc doc-contents first">
-      <p class="doc doc-class-bases">
-        Bases: <code><span title="sbi.inference.snle.snle_base.LikelihoodEstimator">LikelihoodEstimator</span></code></p>
-
+          <p class="doc doc-class-bases">
+            Bases: <code><span title="sbi.inference.snle.snle_base.LikelihoodEstimator">LikelihoodEstimator</span></code></p>
 
 
-        <details class="quote">
-          <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/snle/snle_a.py</code></summary>
-          <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">14</span>
+            <details class="quote">
+              <summary>Source code in <code>sbi/inference/snle/snle_a.py</code></summary>
+              <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">14</span>
 <span class="normal">15</span>
 <span class="normal">16</span>
 <span class="normal">17</span>
@@ -5746,7 +6971,7 @@ <h3 id="sbi.inference.snle.snle_a.SNLE_A" class="doc doc-heading">
         <span class="n">kwargs</span> <span class="o">=</span> <span class="n">del_entries</span><span class="p">(</span><span class="nb">locals</span><span class="p">(),</span> <span class="n">entries</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;self&quot;</span><span class="p">,</span> <span class="s2">&quot;__class__&quot;</span><span class="p">))</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-        </details>
+            </details>
 
   
 
@@ -5760,14 +6985,16 @@ <h3 id="sbi.inference.snle.snle_a.SNLE_A" class="doc doc-heading">
 
 
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.snle.snle_a.SNLE_A.__init__" class="doc doc-heading">
-<code class="codehilite language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">prior</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">density_estimator</span><span class="o">=</span><span class="s1">&#39;maf&#39;</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">,</span> <span class="n">logging_level</span><span class="o">=</span><span class="s1">&#39;WARNING&#39;</span><span class="p">,</span> <span class="n">summary_writer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.snle.snle_a.SNLE_A.__init__" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.snle.snle_a.SNLE_A.__init__" class="doc doc-heading">
+          <code class=" language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">prior</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">density_estimator</span><span class="o">=</span><span class="s1">&#39;maf&#39;</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">,</span> <span class="n">logging_level</span><span class="o">=</span><span class="s1">&#39;WARNING&#39;</span><span class="p">,</span> <span class="n">summary_writer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.snle.snle_a.SNLE_A.__init__" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -5777,6 +7004,8 @@ <h4 id="sbi.inference.snle.snle_a.SNLE_A.__init__" class="doc doc-heading">
 Autoregressive Flows_, Papamakarios et al., AISTATS 2019,
 <a href="https://arxiv.org/abs/1805.07226">https://arxiv.org/abs/1805.07226</a></p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -5793,9 +7022,13 @@ <h4 id="sbi.inference.snle.snle_a.SNLE_A.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="torch.distributions.Distribution">Distribution</span>]</code>
           </td>
-          <td><p>A probability distribution that expresses prior knowledge about the
+          <td>
+            <div class="doc-md-description">
+              <p>A probability distribution that expresses prior knowledge about the
 parameters, e.g. which ranges are meaningful for them. If <code>None</code>, the
-prior must be passed to <code>.build_posterior()</code>.</p></td>
+prior must be passed to <code>.build_posterior()</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -5805,14 +7038,18 @@ <h4 id="sbi.inference.snle.snle_a.SNLE_A.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Union">Union</span>[str, <span title="typing.Callable">Callable</span>]</code>
           </td>
-          <td><p>If it is a string, use a pre-configured network of the
+          <td>
+            <div class="doc-md-description">
+              <p>If it is a string, use a pre-configured network of the
 provided type (one of nsf, maf, mdn, made). Alternatively, a function
 that builds a custom neural network can be provided. The function will
 be called with the first batch of simulations (theta, x), which can
 thus be used for shape inference and potentially for z-scoring. It
 needs to return a PyTorch <code>nn.Module</code> implementing the density
 estimator. The density estimator needs to provide the methods
-<code>.log_prob</code> and <code>.sample()</code>.</p></td>
+<code>.log_prob</code> and <code>.sample()</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;maf&#39;</code>
           </td>
@@ -5822,7 +7059,11 @@ <h4 id="sbi.inference.snle.snle_a.SNLE_A.__init__" class="doc doc-heading">
           <td>
                 <code>str</code>
           </td>
-          <td><p>Training device, e.g., &ldquo;cpu&rdquo;, &ldquo;cuda&rdquo; or &ldquo;cuda:{0, 1, &hellip;}&rdquo;.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Training device, e.g., &ldquo;cpu&rdquo;, &ldquo;cuda&rdquo; or &ldquo;cuda:{0, 1, &hellip;}&rdquo;.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;cpu&#39;</code>
           </td>
@@ -5832,8 +7073,12 @@ <h4 id="sbi.inference.snle.snle_a.SNLE_A.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Union">Union</span>[int, str]</code>
           </td>
-          <td><p>Minimum severity of messages to log. One of the strings
-INFO, WARNING, DEBUG, ERROR and CRITICAL.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Minimum severity of messages to log. One of the strings
+INFO, WARNING, DEBUG, ERROR and CRITICAL.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;WARNING&#39;</code>
           </td>
@@ -5843,8 +7088,12 @@ <h4 id="sbi.inference.snle.snle_a.SNLE_A.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="sbi.types.TensorboardSummaryWriter">TensorboardSummaryWriter</span>]</code>
           </td>
-          <td><p>A tensorboard <code>SummaryWriter</code> to control, among others, log
-file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>A tensorboard <code>SummaryWriter</code> to control, among others, log
+file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -5854,8 +7103,12 @@ <h4 id="sbi.inference.snle.snle_a.SNLE_A.__init__" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to show a progressbar during simulation and
-sampling.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to show a progressbar during simulation and
+sampling.</p>
+            </div>
+          </td>
           <td>
                 <code>True</code>
           </td>
@@ -5863,9 +7116,9 @@ <h4 id="sbi.inference.snle.snle_a.SNLE_A.__init__" class="doc doc-heading">
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/snle/snle_a.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">15</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/snle/snle_a.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">15</span>
 <span class="normal">16</span>
 <span class="normal">17</span>
 <span class="normal">18</span>
@@ -5941,7 +7194,7 @@ <h4 id="sbi.inference.snle.snle_a.SNLE_A.__init__" class="doc doc-heading">
     <span class="n">kwargs</span> <span class="o">=</span> <span class="n">del_entries</span><span class="p">(</span><span class="nb">locals</span><span class="p">(),</span> <span class="n">entries</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;self&quot;</span><span class="p">,</span> <span class="s2">&quot;__class__&quot;</span><span class="p">))</span>
     <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
@@ -5958,22 +7211,15 @@ <h4 id="sbi.inference.snle.snle_a.SNLE_A.__init__" class="doc doc-heading">
 
 
 
-<h3 id="sbi.inference.snre.snre_a.SNRE_A" class="doc doc-heading">
-        <code>sbi.inference.snre.snre_a.SNRE_A</code>
-
-
-<a href="#sbi.inference.snre.snre_a.SNRE_A" class="headerlink" title="Permanent link">&para;</a></h3>
-
-
+<a id="sbi.inference.snre.snre_a.SNRE_A"></a>
   <div class="doc doc-contents first">
-      <p class="doc doc-class-bases">
-        Bases: <code><span title="sbi.inference.snre.snre_base.RatioEstimator">RatioEstimator</span></code></p>
+          <p class="doc doc-class-bases">
+            Bases: <code><span title="sbi.inference.snre.snre_base.RatioEstimator">RatioEstimator</span></code></p>
 
 
-
-        <details class="quote">
-          <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/snre/snre_a.py</code></summary>
-          <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 12</span>
+            <details class="quote">
+              <summary>Source code in <code>sbi/inference/snre/snre_a.py</code></summary>
+              <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 12</span>
 <span class="normal"> 13</span>
 <span class="normal"> 14</span>
 <span class="normal"> 15</span>
@@ -6199,7 +7445,7 @@ <h3 id="sbi.inference.snre.snre_a.SNRE_A" class="doc doc-heading">
         <span class="c1"># Binary cross entropy to learn the likelihood (AALR-specific)</span>
         <span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">BCELoss</span><span class="p">()(</span><span class="n">likelihood</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-        </details>
+            </details>
 
   
 
@@ -6213,14 +7459,16 @@ <h3 id="sbi.inference.snre.snre_a.SNRE_A" class="doc doc-heading">
 
 
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.snre.snre_a.SNRE_A.__init__" class="doc doc-heading">
-<code class="codehilite language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">prior</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">classifier</span><span class="o">=</span><span class="s1">&#39;resnet&#39;</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">,</span> <span class="n">logging_level</span><span class="o">=</span><span class="s1">&#39;warning&#39;</span><span class="p">,</span> <span class="n">summary_writer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.snre.snre_a.SNRE_A.__init__" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.snre.snre_a.SNRE_A.__init__" class="doc doc-heading">
+          <code class=" language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">prior</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">classifier</span><span class="o">=</span><span class="s1">&#39;resnet&#39;</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">,</span> <span class="n">logging_level</span><span class="o">=</span><span class="s1">&#39;warning&#39;</span><span class="p">,</span> <span class="n">summary_writer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.snre.snre_a.SNRE_A.__init__" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -6229,6 +7477,8 @@ <h4 id="sbi.inference.snre.snre_a.SNRE_A.__init__" class="doc doc-heading">
 <p>[1] <em>Likelihood-free MCMC with Amortized Approximate Likelihood Ratios</em>, Hermans
     et al., ICML 2020, <a href="https://arxiv.org/abs/1903.04057">https://arxiv.org/abs/1903.04057</a></p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -6245,9 +7495,13 @@ <h4 id="sbi.inference.snre.snre_a.SNRE_A.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="torch.distributions.Distribution">Distribution</span>]</code>
           </td>
-          <td><p>A probability distribution that expresses prior knowledge about the
+          <td>
+            <div class="doc-md-description">
+              <p>A probability distribution that expresses prior knowledge about the
 parameters, e.g. which ranges are meaningful for them. If <code>None</code>, the
-prior must be passed to <code>.build_posterior()</code>.</p></td>
+prior must be passed to <code>.build_posterior()</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -6257,13 +7511,17 @@ <h4 id="sbi.inference.snre.snre_a.SNRE_A.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Union">Union</span>[str, <span title="typing.Callable">Callable</span>]</code>
           </td>
-          <td><p>Classifier trained to approximate likelihood ratios. If it is
+          <td>
+            <div class="doc-md-description">
+              <p>Classifier trained to approximate likelihood ratios. If it is
 a string, use a pre-configured network of the provided type (one of
 linear, mlp, resnet). Alternatively, a function that builds a custom
 neural network can be provided. The function will be called with the
 first batch of simulations (theta, x), which can thus be used for shape
 inference and potentially for z-scoring. It needs to return a PyTorch
-<code>nn.Module</code> implementing the classifier.</p></td>
+<code>nn.Module</code> implementing the classifier.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;resnet&#39;</code>
           </td>
@@ -6273,7 +7531,11 @@ <h4 id="sbi.inference.snre.snre_a.SNRE_A.__init__" class="doc doc-heading">
           <td>
                 <code>str</code>
           </td>
-          <td><p>Training device, e.g., &ldquo;cpu&rdquo;, &ldquo;cuda&rdquo; or &ldquo;cuda:{0, 1, &hellip;}&rdquo;.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Training device, e.g., &ldquo;cpu&rdquo;, &ldquo;cuda&rdquo; or &ldquo;cuda:{0, 1, &hellip;}&rdquo;.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;cpu&#39;</code>
           </td>
@@ -6283,8 +7545,12 @@ <h4 id="sbi.inference.snre.snre_a.SNRE_A.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Union">Union</span>[int, str]</code>
           </td>
-          <td><p>Minimum severity of messages to log. One of the strings
-INFO, WARNING, DEBUG, ERROR and CRITICAL.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Minimum severity of messages to log. One of the strings
+INFO, WARNING, DEBUG, ERROR and CRITICAL.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;warning&#39;</code>
           </td>
@@ -6294,8 +7560,12 @@ <h4 id="sbi.inference.snre.snre_a.SNRE_A.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="sbi.types.TensorboardSummaryWriter">TensorboardSummaryWriter</span>]</code>
           </td>
-          <td><p>A tensorboard <code>SummaryWriter</code> to control, among others, log
-file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>A tensorboard <code>SummaryWriter</code> to control, among others, log
+file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -6305,8 +7575,12 @@ <h4 id="sbi.inference.snre.snre_a.SNRE_A.__init__" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to show a progressbar during simulation and
-sampling.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to show a progressbar during simulation and
+sampling.</p>
+            </div>
+          </td>
           <td>
                 <code>True</code>
           </td>
@@ -6314,9 +7588,9 @@ <h4 id="sbi.inference.snre.snre_a.SNRE_A.__init__" class="doc doc-heading">
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/snre/snre_a.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">13</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/snre/snre_a.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">13</span>
 <span class="normal">14</span>
 <span class="normal">15</span>
 <span class="normal">16</span>
@@ -6388,25 +7662,29 @@ <h4 id="sbi.inference.snre.snre_a.SNRE_A.__init__" class="doc doc-heading">
     <span class="n">kwargs</span> <span class="o">=</span> <span class="n">del_entries</span><span class="p">(</span><span class="nb">locals</span><span class="p">(),</span> <span class="n">entries</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;self&quot;</span><span class="p">,</span> <span class="s2">&quot;__class__&quot;</span><span class="p">))</span>
     <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.snre.snre_a.SNRE_A.train" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">train</span><span class="p">(</span><span class="n">training_batch_size</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.0005</span><span class="p">,</span> <span class="n">validation_fraction</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">stop_after_epochs</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">max_num_epochs</span><span class="o">=</span><span class="mi">2</span> <span class="o">**</span> <span class="mi">31</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">clip_max_norm</span><span class="o">=</span><span class="mf">5.0</span><span class="p">,</span> <span class="n">resume_training</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">discard_prior_samples</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">retrain_from_scratch</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">show_train_summary</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dataloader_kwargs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">loss_kwargs</span><span class="o">=</span><span class="p">{})</span></code>
 
-<a href="#sbi.inference.snre.snre_a.SNRE_A.train" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.snre.snre_a.SNRE_A.train" class="doc doc-heading">
+          <code class=" language-python"><span class="n">train</span><span class="p">(</span><span class="n">training_batch_size</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.0005</span><span class="p">,</span> <span class="n">validation_fraction</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">stop_after_epochs</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">max_num_epochs</span><span class="o">=</span><span class="mi">2</span> <span class="o">**</span> <span class="mi">31</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">clip_max_norm</span><span class="o">=</span><span class="mf">5.0</span><span class="p">,</span> <span class="n">resume_training</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">discard_prior_samples</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">retrain_from_scratch</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">show_train_summary</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dataloader_kwargs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">loss_kwargs</span><span class="o">=</span><span class="p">{})</span></code>
+
+<a href="#sbi.inference.snre.snre_a.SNRE_A.train" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
   
       <p>Return classifier that approximates the ratio <span class="arithmatex">\(p(\theta,x)/p(\theta)p(x)\)</span>.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -6423,7 +7701,11 @@ <h4 id="sbi.inference.snre.snre_a.SNRE_A.train" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Training batch size.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Training batch size.</p>
+            </div>
+          </td>
           <td>
                 <code>50</code>
           </td>
@@ -6433,7 +7715,11 @@ <h4 id="sbi.inference.snre.snre_a.SNRE_A.train" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Learning rate for Adam optimizer.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Learning rate for Adam optimizer.</p>
+            </div>
+          </td>
           <td>
                 <code>0.0005</code>
           </td>
@@ -6443,7 +7729,11 @@ <h4 id="sbi.inference.snre.snre_a.SNRE_A.train" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>The fraction of data to use for validation.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The fraction of data to use for validation.</p>
+            </div>
+          </td>
           <td>
                 <code>0.1</code>
           </td>
@@ -6453,8 +7743,12 @@ <h4 id="sbi.inference.snre.snre_a.SNRE_A.train" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>The number of epochs to wait for improvement on the
-validation set before terminating training.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The number of epochs to wait for improvement on the
+validation set before terminating training.</p>
+            </div>
+          </td>
           <td>
                 <code>20</code>
           </td>
@@ -6464,9 +7758,13 @@ <h4 id="sbi.inference.snre.snre_a.SNRE_A.train" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Maximum number of epochs to run. If reached, we stop
+          <td>
+            <div class="doc-md-description">
+              <p>Maximum number of epochs to run. If reached, we stop
 training even when the validation loss is still decreasing. Otherwise,
-we train until validation loss increases (see also <code>stop_after_epochs</code>).</p></td>
+we train until validation loss increases (see also <code>stop_after_epochs</code>).</p>
+            </div>
+          </td>
           <td>
                 <code>2 ** 31 - 1</code>
           </td>
@@ -6476,8 +7774,12 @@ <h4 id="sbi.inference.snre.snre_a.SNRE_A.train" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[float]</code>
           </td>
-          <td><p>Value at which to clip the total gradient norm in order to
-prevent exploding gradients. Use None for no clipping.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Value at which to clip the total gradient norm in order to
+prevent exploding gradients. Use None for no clipping.</p>
+            </div>
+          </td>
           <td>
                 <code>5.0</code>
           </td>
@@ -6487,10 +7789,14 @@ <h4 id="sbi.inference.snre.snre_a.SNRE_A.train" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Can be used in case training time is limited, e.g. on a
+          <td>
+            <div class="doc-md-description">
+              <p>Can be used in case training time is limited, e.g. on a
 cluster. If <code>True</code>, the split between train and validation set, the
 optimizer, the number of epochs, and the best validation log-prob will
-be restored from the last time <code>.train()</code> was called.</p></td>
+be restored from the last time <code>.train()</code> was called.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -6500,9 +7806,13 @@ <h4 id="sbi.inference.snre.snre_a.SNRE_A.train" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to discard samples simulated in round 1, i.e.
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to discard samples simulated in round 1, i.e.
 from the prior. Training may be sped up by ignoring such less targeted
-samples.</p></td>
+samples.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -6512,8 +7822,12 @@ <h4 id="sbi.inference.snre.snre_a.SNRE_A.train" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to retrain the conditional density
-estimator for the posterior from scratch each round.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to retrain the conditional density
+estimator for the posterior from scratch each round.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -6523,8 +7837,12 @@ <h4 id="sbi.inference.snre.snre_a.SNRE_A.train" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to print the number of epochs and validation
-loss and leakage after the training.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to print the number of epochs and validation
+loss and leakage after the training.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -6534,8 +7852,12 @@ <h4 id="sbi.inference.snre.snre_a.SNRE_A.train" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="typing.Dict">Dict</span>]</code>
           </td>
-          <td><p>Additional or updated kwargs to be passed to the training
-and validation dataloaders (like, e.g., a collate_fn)</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Additional or updated kwargs to be passed to the training
+and validation dataloaders (like, e.g., a collate_fn)</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -6545,7 +7867,11 @@ <h4 id="sbi.inference.snre.snre_a.SNRE_A.train" class="doc doc-heading">
           <td>
                 <code><span title="typing.Dict">Dict</span>[str, <span title="typing.Any">Any</span>]</code>
           </td>
-          <td><p>Additional or updated kwargs to be passed to the self._loss fn.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Additional or updated kwargs to be passed to the self._loss fn.</p>
+            </div>
+          </td>
           <td>
                 <code>{}</code>
           </td>
@@ -6553,6 +7879,8 @@ <h4 id="sbi.inference.snre.snre_a.SNRE_A.train" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -6564,16 +7892,20 @@ <h4 id="sbi.inference.snre.snre_a.SNRE_A.train" class="doc doc-heading">
     <tbody>
         <tr>
           <td>
-                <code><span title="torch.nn">nn</span>.<span title="torch.nn.Module">Module</span></code>
+                <code><span title="torch.nn.Module">Module</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Classifier that approximates the ratio <span class="arithmatex">\(p(\theta,x)/p(\theta)p(x)\)</span>.</p>
+            </div>
           </td>
-          <td><p>Classifier that approximates the ratio <span class="arithmatex">\(p(\theta,x)/p(\theta)p(x)\)</span>.</p></td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/snre/snre_a.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 50</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/snre/snre_a.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 50</span>
 <span class="normal"> 51</span>
 <span class="normal"> 52</span>
 <span class="normal"> 53</span>
@@ -6675,7 +8007,7 @@ <h4 id="sbi.inference.snre.snre_a.SNRE_A.train" class="doc doc-heading">
     <span class="n">kwargs</span> <span class="o">=</span> <span class="n">del_entries</span><span class="p">(</span><span class="nb">locals</span><span class="p">(),</span> <span class="n">entries</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;self&quot;</span><span class="p">,</span> <span class="s2">&quot;__class__&quot;</span><span class="p">))</span>
     <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">,</span> <span class="n">num_atoms</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
@@ -6692,22 +8024,15 @@ <h4 id="sbi.inference.snre.snre_a.SNRE_A.train" class="doc doc-heading">
 
 
 
-<h3 id="sbi.inference.snre.snre_b.SNRE_B" class="doc doc-heading">
-        <code>sbi.inference.snre.snre_b.SNRE_B</code>
-
-
-<a href="#sbi.inference.snre.snre_b.SNRE_B" class="headerlink" title="Permanent link">&para;</a></h3>
-
-
+<a id="sbi.inference.snre.snre_b.SNRE_B"></a>
   <div class="doc doc-contents first">
-      <p class="doc doc-class-bases">
-        Bases: <code><span title="sbi.inference.snre.snre_base.RatioEstimator">RatioEstimator</span></code></p>
+          <p class="doc doc-class-bases">
+            Bases: <code><span title="sbi.inference.snre.snre_base.RatioEstimator">RatioEstimator</span></code></p>
 
 
-
-        <details class="quote">
-          <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/snre/snre_b.py</code></summary>
-          <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 12</span>
+            <details class="quote">
+              <summary>Source code in <code>sbi/inference/snre/snre_b.py</code></summary>
+              <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 12</span>
 <span class="normal"> 13</span>
 <span class="normal"> 14</span>
 <span class="normal"> 15</span>
@@ -6929,7 +8254,7 @@ <h3 id="sbi.inference.snre.snre_b.SNRE_B" class="doc doc-heading">
 
         <span class="k">return</span> <span class="o">-</span><span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">log_prob</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-        </details>
+            </details>
 
   
 
@@ -6943,14 +8268,16 @@ <h3 id="sbi.inference.snre.snre_b.SNRE_B" class="doc doc-heading">
 
 
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.snre.snre_b.SNRE_B.__init__" class="doc doc-heading">
-<code class="codehilite language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">prior</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">classifier</span><span class="o">=</span><span class="s1">&#39;resnet&#39;</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">,</span> <span class="n">logging_level</span><span class="o">=</span><span class="s1">&#39;warning&#39;</span><span class="p">,</span> <span class="n">summary_writer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.snre.snre_b.SNRE_B.__init__" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.snre.snre_b.SNRE_B.__init__" class="doc doc-heading">
+          <code class=" language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">prior</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">classifier</span><span class="o">=</span><span class="s1">&#39;resnet&#39;</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">,</span> <span class="n">logging_level</span><span class="o">=</span><span class="s1">&#39;warning&#39;</span><span class="p">,</span> <span class="n">summary_writer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.snre.snre_b.SNRE_B.__init__" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -6959,6 +8286,8 @@ <h4 id="sbi.inference.snre.snre_b.SNRE_B.__init__" class="doc doc-heading">
 <p>[1] <em>On Contrastive Learning for Likelihood-free Inference</em>, Durkan et al.,
     ICML 2020, <a href="https://arxiv.org/pdf/2002.03712">https://arxiv.org/pdf/2002.03712</a></p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -6975,9 +8304,13 @@ <h4 id="sbi.inference.snre.snre_b.SNRE_B.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="torch.distributions.Distribution">Distribution</span>]</code>
           </td>
-          <td><p>A probability distribution that expresses prior knowledge about the
+          <td>
+            <div class="doc-md-description">
+              <p>A probability distribution that expresses prior knowledge about the
 parameters, e.g. which ranges are meaningful for them. If <code>None</code>, the
-prior must be passed to <code>.build_posterior()</code>.</p></td>
+prior must be passed to <code>.build_posterior()</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -6987,13 +8320,17 @@ <h4 id="sbi.inference.snre.snre_b.SNRE_B.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Union">Union</span>[str, <span title="typing.Callable">Callable</span>]</code>
           </td>
-          <td><p>Classifier trained to approximate likelihood ratios. If it is
+          <td>
+            <div class="doc-md-description">
+              <p>Classifier trained to approximate likelihood ratios. If it is
 a string, use a pre-configured network of the provided type (one of
 linear, mlp, resnet). Alternatively, a function that builds a custom
 neural network can be provided. The function will be called with the
 first batch of simulations (theta, x), which can thus be used for shape
 inference and potentially for z-scoring. It needs to return a PyTorch
-<code>nn.Module</code> implementing the classifier.</p></td>
+<code>nn.Module</code> implementing the classifier.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;resnet&#39;</code>
           </td>
@@ -7003,7 +8340,11 @@ <h4 id="sbi.inference.snre.snre_b.SNRE_B.__init__" class="doc doc-heading">
           <td>
                 <code>str</code>
           </td>
-          <td><p>Training device, e.g., &ldquo;cpu&rdquo;, &ldquo;cuda&rdquo; or &ldquo;cuda:{0, 1, &hellip;}&rdquo;.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Training device, e.g., &ldquo;cpu&rdquo;, &ldquo;cuda&rdquo; or &ldquo;cuda:{0, 1, &hellip;}&rdquo;.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;cpu&#39;</code>
           </td>
@@ -7013,8 +8354,12 @@ <h4 id="sbi.inference.snre.snre_b.SNRE_B.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Union">Union</span>[int, str]</code>
           </td>
-          <td><p>Minimum severity of messages to log. One of the strings
-INFO, WARNING, DEBUG, ERROR and CRITICAL.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Minimum severity of messages to log. One of the strings
+INFO, WARNING, DEBUG, ERROR and CRITICAL.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;warning&#39;</code>
           </td>
@@ -7024,8 +8369,12 @@ <h4 id="sbi.inference.snre.snre_b.SNRE_B.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="sbi.types.TensorboardSummaryWriter">TensorboardSummaryWriter</span>]</code>
           </td>
-          <td><p>A tensorboard <code>SummaryWriter</code> to control, among others, log
-file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>A tensorboard <code>SummaryWriter</code> to control, among others, log
+file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -7035,8 +8384,12 @@ <h4 id="sbi.inference.snre.snre_b.SNRE_B.__init__" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to show a progressbar during simulation and
-sampling.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to show a progressbar during simulation and
+sampling.</p>
+            </div>
+          </td>
           <td>
                 <code>True</code>
           </td>
@@ -7044,9 +8397,9 @@ <h4 id="sbi.inference.snre.snre_b.SNRE_B.__init__" class="doc doc-heading">
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/snre/snre_b.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">13</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/snre/snre_b.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">13</span>
 <span class="normal">14</span>
 <span class="normal">15</span>
 <span class="normal">16</span>
@@ -7118,25 +8471,29 @@ <h4 id="sbi.inference.snre.snre_b.SNRE_B.__init__" class="doc doc-heading">
     <span class="n">kwargs</span> <span class="o">=</span> <span class="n">del_entries</span><span class="p">(</span><span class="nb">locals</span><span class="p">(),</span> <span class="n">entries</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;self&quot;</span><span class="p">,</span> <span class="s2">&quot;__class__&quot;</span><span class="p">))</span>
     <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.snre.snre_b.SNRE_B.train" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">train</span><span class="p">(</span><span class="n">num_atoms</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">training_batch_size</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.0005</span><span class="p">,</span> <span class="n">validation_fraction</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">stop_after_epochs</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">max_num_epochs</span><span class="o">=</span><span class="mi">2</span> <span class="o">**</span> <span class="mi">31</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">clip_max_norm</span><span class="o">=</span><span class="mf">5.0</span><span class="p">,</span> <span class="n">resume_training</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">discard_prior_samples</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">retrain_from_scratch</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">show_train_summary</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dataloader_kwargs</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.snre.snre_b.SNRE_B.train" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.snre.snre_b.SNRE_B.train" class="doc doc-heading">
+          <code class=" language-python"><span class="n">train</span><span class="p">(</span><span class="n">num_atoms</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">training_batch_size</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.0005</span><span class="p">,</span> <span class="n">validation_fraction</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">stop_after_epochs</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">max_num_epochs</span><span class="o">=</span><span class="mi">2</span> <span class="o">**</span> <span class="mi">31</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">clip_max_norm</span><span class="o">=</span><span class="mf">5.0</span><span class="p">,</span> <span class="n">resume_training</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">discard_prior_samples</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">retrain_from_scratch</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">show_train_summary</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dataloader_kwargs</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.snre.snre_b.SNRE_B.train" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
   
       <p>Return classifier that approximates the ratio <span class="arithmatex">\(p(\theta,x)/p(\theta)p(x)\)</span>.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -7153,7 +8510,11 @@ <h4 id="sbi.inference.snre.snre_b.SNRE_B.train" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of atoms to use for classification.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of atoms to use for classification.</p>
+            </div>
+          </td>
           <td>
                 <code>10</code>
           </td>
@@ -7163,7 +8524,11 @@ <h4 id="sbi.inference.snre.snre_b.SNRE_B.train" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Training batch size.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Training batch size.</p>
+            </div>
+          </td>
           <td>
                 <code>50</code>
           </td>
@@ -7173,7 +8538,11 @@ <h4 id="sbi.inference.snre.snre_b.SNRE_B.train" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Learning rate for Adam optimizer.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Learning rate for Adam optimizer.</p>
+            </div>
+          </td>
           <td>
                 <code>0.0005</code>
           </td>
@@ -7183,7 +8552,11 @@ <h4 id="sbi.inference.snre.snre_b.SNRE_B.train" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>The fraction of data to use for validation.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The fraction of data to use for validation.</p>
+            </div>
+          </td>
           <td>
                 <code>0.1</code>
           </td>
@@ -7193,8 +8566,12 @@ <h4 id="sbi.inference.snre.snre_b.SNRE_B.train" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>The number of epochs to wait for improvement on the
-validation set before terminating training.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The number of epochs to wait for improvement on the
+validation set before terminating training.</p>
+            </div>
+          </td>
           <td>
                 <code>20</code>
           </td>
@@ -7204,9 +8581,13 @@ <h4 id="sbi.inference.snre.snre_b.SNRE_B.train" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Maximum number of epochs to run. If reached, we stop
+          <td>
+            <div class="doc-md-description">
+              <p>Maximum number of epochs to run. If reached, we stop
 training even when the validation loss is still decreasing. Otherwise,
-we train until validation loss increases (see also <code>stop_after_epochs</code>).</p></td>
+we train until validation loss increases (see also <code>stop_after_epochs</code>).</p>
+            </div>
+          </td>
           <td>
                 <code>2 ** 31 - 1</code>
           </td>
@@ -7216,8 +8597,12 @@ <h4 id="sbi.inference.snre.snre_b.SNRE_B.train" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[float]</code>
           </td>
-          <td><p>Value at which to clip the total gradient norm in order to
-prevent exploding gradients. Use None for no clipping.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Value at which to clip the total gradient norm in order to
+prevent exploding gradients. Use None for no clipping.</p>
+            </div>
+          </td>
           <td>
                 <code>5.0</code>
           </td>
@@ -7227,10 +8612,14 @@ <h4 id="sbi.inference.snre.snre_b.SNRE_B.train" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Can be used in case training time is limited, e.g. on a
+          <td>
+            <div class="doc-md-description">
+              <p>Can be used in case training time is limited, e.g. on a
 cluster. If <code>True</code>, the split between train and validation set, the
 optimizer, the number of epochs, and the best validation log-prob will
-be restored from the last time <code>.train()</code> was called.</p></td>
+be restored from the last time <code>.train()</code> was called.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -7240,9 +8629,13 @@ <h4 id="sbi.inference.snre.snre_b.SNRE_B.train" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to discard samples simulated in round 1, i.e.
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to discard samples simulated in round 1, i.e.
 from the prior. Training may be sped up by ignoring such less targeted
-samples.</p></td>
+samples.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -7252,8 +8645,12 @@ <h4 id="sbi.inference.snre.snre_b.SNRE_B.train" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to retrain the conditional density
-estimator for the posterior from scratch each round.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to retrain the conditional density
+estimator for the posterior from scratch each round.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -7263,8 +8660,12 @@ <h4 id="sbi.inference.snre.snre_b.SNRE_B.train" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to print the number of epochs and validation
-loss and leakage after the training.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to print the number of epochs and validation
+loss and leakage after the training.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -7274,8 +8675,12 @@ <h4 id="sbi.inference.snre.snre_b.SNRE_B.train" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="typing.Dict">Dict</span>]</code>
           </td>
-          <td><p>Additional or updated kwargs to be passed to the training
-and validation dataloaders (like, e.g., a collate_fn)</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Additional or updated kwargs to be passed to the training
+and validation dataloaders (like, e.g., a collate_fn)</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -7283,6 +8688,8 @@ <h4 id="sbi.inference.snre.snre_b.SNRE_B.train" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -7294,16 +8701,20 @@ <h4 id="sbi.inference.snre.snre_b.SNRE_B.train" class="doc doc-heading">
     <tbody>
         <tr>
           <td>
-                <code><span title="torch.nn">nn</span>.<span title="torch.nn.Module">Module</span></code>
+                <code><span title="torch.nn.Module">Module</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Classifier that approximates the ratio <span class="arithmatex">\(p(\theta,x)/p(\theta)p(x)\)</span>.</p>
+            </div>
           </td>
-          <td><p>Classifier that approximates the ratio <span class="arithmatex">\(p(\theta,x)/p(\theta)p(x)\)</span>.</p></td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/snre/snre_b.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">50</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/snre/snre_b.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">50</span>
 <span class="normal">51</span>
 <span class="normal">52</span>
 <span class="normal">53</span>
@@ -7399,7 +8810,7 @@ <h4 id="sbi.inference.snre.snre_b.SNRE_B.train" class="doc doc-heading">
     <span class="n">kwargs</span> <span class="o">=</span> <span class="n">del_entries</span><span class="p">(</span><span class="nb">locals</span><span class="p">(),</span> <span class="n">entries</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;self&quot;</span><span class="p">,</span> <span class="s2">&quot;__class__&quot;</span><span class="p">))</span>
     <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
@@ -7416,22 +8827,15 @@ <h4 id="sbi.inference.snre.snre_b.SNRE_B.train" class="doc doc-heading">
 
 
 
-<h3 id="sbi.inference.snre.snre_c.SNRE_C" class="doc doc-heading">
-        <code>sbi.inference.snre.snre_c.SNRE_C</code>
-
-
-<a href="#sbi.inference.snre.snre_c.SNRE_C" class="headerlink" title="Permanent link">&para;</a></h3>
-
-
+<a id="sbi.inference.snre.snre_c.SNRE_C"></a>
   <div class="doc doc-contents first">
-      <p class="doc doc-class-bases">
-        Bases: <code><span title="sbi.inference.snre.snre_base.RatioEstimator">RatioEstimator</span></code></p>
+          <p class="doc doc-class-bases">
+            Bases: <code><span title="sbi.inference.snre.snre_base.RatioEstimator">RatioEstimator</span></code></p>
 
 
-
-        <details class="quote">
-          <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/snre/snre_c.py</code></summary>
-          <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 12</span>
+            <details class="quote">
+              <summary>Source code in <code>sbi/inference/snre/snre_c.py</code></summary>
+              <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 12</span>
 <span class="normal"> 13</span>
 <span class="normal"> 14</span>
 <span class="normal"> 15</span>
@@ -7815,7 +9219,7 @@ <h3 id="sbi.inference.snre.snre_c.SNRE_C" class="doc doc-heading">
         <span class="n">p_marginal</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">gamma</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">p_marginal</span><span class="p">,</span> <span class="n">p_joint</span>
 </code></pre></div></td></tr></table></div>
-        </details>
+            </details>
 
   
 
@@ -7829,14 +9233,16 @@ <h3 id="sbi.inference.snre.snre_c.SNRE_C" class="doc doc-heading">
 
 
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.snre.snre_c.SNRE_C.__init__" class="doc doc-heading">
-<code class="codehilite language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">prior</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">classifier</span><span class="o">=</span><span class="s1">&#39;resnet&#39;</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">,</span> <span class="n">logging_level</span><span class="o">=</span><span class="s1">&#39;warning&#39;</span><span class="p">,</span> <span class="n">summary_writer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.snre.snre_c.SNRE_C.__init__" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.snre.snre_c.SNRE_C.__init__" class="doc doc-heading">
+          <code class=" language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">prior</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">classifier</span><span class="o">=</span><span class="s1">&#39;resnet&#39;</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">,</span> <span class="n">logging_level</span><span class="o">=</span><span class="s1">&#39;warning&#39;</span><span class="p">,</span> <span class="n">summary_writer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.snre.snre_c.SNRE_C.__init__" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -7857,6 +9263,8 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.__init__" class="doc doc-heading">
 <p>[1] <em>Contrastive Neural Ratio Estimation</em>, Benajmin Kurt Miller, et. al.,
     NeurIPS 2022, <a href="https://arxiv.org/abs/2210.06170">https://arxiv.org/abs/2210.06170</a></p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -7873,9 +9281,13 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="torch.distributions.Distribution">Distribution</span>]</code>
           </td>
-          <td><p>A probability distribution that expresses prior knowledge about the
+          <td>
+            <div class="doc-md-description">
+              <p>A probability distribution that expresses prior knowledge about the
 parameters, e.g. which ranges are meaningful for them. If <code>None</code>, the
-prior must be passed to <code>.build_posterior()</code>.</p></td>
+prior must be passed to <code>.build_posterior()</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -7885,13 +9297,17 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Union">Union</span>[str, <span title="typing.Callable">Callable</span>]</code>
           </td>
-          <td><p>Classifier trained to approximate likelihood ratios. If it is
+          <td>
+            <div class="doc-md-description">
+              <p>Classifier trained to approximate likelihood ratios. If it is
 a string, use a pre-configured network of the provided type (one of
 linear, mlp, resnet). Alternatively, a function that builds a custom
 neural network can be provided. The function will be called with the
 first batch of simulations (theta, x), which can thus be used for shape
 inference and potentially for z-scoring. It needs to return a PyTorch
-<code>nn.Module</code> implementing the classifier.</p></td>
+<code>nn.Module</code> implementing the classifier.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;resnet&#39;</code>
           </td>
@@ -7901,7 +9317,11 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.__init__" class="doc doc-heading">
           <td>
                 <code>str</code>
           </td>
-          <td><p>Training device, e.g., &ldquo;cpu&rdquo;, &ldquo;cuda&rdquo; or &ldquo;cuda:{0, 1, &hellip;}&rdquo;.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Training device, e.g., &ldquo;cpu&rdquo;, &ldquo;cuda&rdquo; or &ldquo;cuda:{0, 1, &hellip;}&rdquo;.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;cpu&#39;</code>
           </td>
@@ -7911,8 +9331,12 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Union">Union</span>[int, str]</code>
           </td>
-          <td><p>Minimum severity of messages to log. One of the strings
-INFO, WARNING, DEBUG, ERROR and CRITICAL.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Minimum severity of messages to log. One of the strings
+INFO, WARNING, DEBUG, ERROR and CRITICAL.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;warning&#39;</code>
           </td>
@@ -7922,8 +9346,12 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="sbi.types.TensorboardSummaryWriter">TensorboardSummaryWriter</span>]</code>
           </td>
-          <td><p>A tensorboard <code>SummaryWriter</code> to control, among others, log
-file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>A tensorboard <code>SummaryWriter</code> to control, among others, log
+file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -7933,8 +9361,12 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.__init__" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to show a progressbar during simulation and
-sampling.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to show a progressbar during simulation and
+sampling.</p>
+            </div>
+          </td>
           <td>
                 <code>True</code>
           </td>
@@ -7942,9 +9374,9 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.__init__" class="doc doc-heading">
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/snre/snre_c.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">13</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/snre/snre_c.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">13</span>
 <span class="normal">14</span>
 <span class="normal">15</span>
 <span class="normal">16</span>
@@ -8044,25 +9476,29 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.__init__" class="doc doc-heading">
     <span class="n">kwargs</span> <span class="o">=</span> <span class="n">del_entries</span><span class="p">(</span><span class="nb">locals</span><span class="p">(),</span> <span class="n">entries</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;self&quot;</span><span class="p">,</span> <span class="s2">&quot;__class__&quot;</span><span class="p">))</span>
     <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.snre.snre_c.SNRE_C.train" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">train</span><span class="p">(</span><span class="n">num_classes</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">training_batch_size</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.0005</span><span class="p">,</span> <span class="n">validation_fraction</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">stop_after_epochs</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">max_num_epochs</span><span class="o">=</span><span class="mi">2</span> <span class="o">**</span> <span class="mi">31</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">clip_max_norm</span><span class="o">=</span><span class="mf">5.0</span><span class="p">,</span> <span class="n">resume_training</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">discard_prior_samples</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">retrain_from_scratch</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">show_train_summary</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dataloader_kwargs</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.snre.snre_c.SNRE_C.train" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.snre.snre_c.SNRE_C.train" class="doc doc-heading">
+          <code class=" language-python"><span class="n">train</span><span class="p">(</span><span class="n">num_classes</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">training_batch_size</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.0005</span><span class="p">,</span> <span class="n">validation_fraction</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">stop_after_epochs</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">max_num_epochs</span><span class="o">=</span><span class="mi">2</span> <span class="o">**</span> <span class="mi">31</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">clip_max_norm</span><span class="o">=</span><span class="mf">5.0</span><span class="p">,</span> <span class="n">resume_training</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">discard_prior_samples</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">retrain_from_scratch</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">show_train_summary</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dataloader_kwargs</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.snre.snre_c.SNRE_C.train" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
   
       <p>Return classifier that approximates the ratio <span class="arithmatex">\(p(\theta,x)/p(\theta)p(x)\)</span>.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -8079,11 +9515,15 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.train" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of theta to classify against, corresponds to <span class="arithmatex">\(K\)</span> in
+          <td>
+            <div class="doc-md-description">
+              <p>Number of theta to classify against, corresponds to <span class="arithmatex">\(K\)</span> in
 <em>Contrastive Neural Ratio Estimation</em>. Minimum value is 1. Similar to
 <code>num_atoms</code> for SNRE_B except SNRE_C has an additional independently
 drawn sample. The total number of alternative parameters <code>NRE-C</code> &ldquo;sees&rdquo;
-is <span class="arithmatex">\(2K-1\)</span> or <code>2 * num_classes - 1</code> divided between two loss terms.</p></td>
+is <span class="arithmatex">\(2K-1\)</span> or <code>2 * num_classes - 1</code> divided between two loss terms.</p>
+            </div>
+          </td>
           <td>
                 <code>5</code>
           </td>
@@ -8093,10 +9533,14 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.train" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Determines the relative weight of the sum of all <span class="arithmatex">\(K\)</span> dependently
+          <td>
+            <div class="doc-md-description">
+              <p>Determines the relative weight of the sum of all <span class="arithmatex">\(K\)</span> dependently
 drawn classes against the marginally drawn one. Specifically,
 <span class="arithmatex">\(p(y=k) :=p_K\)</span>, <span class="arithmatex">\(p(y=0) := p_0\)</span>, <span class="arithmatex">\(p_0 = 1 - K p_K\)</span>, and finally
-<span class="arithmatex">\(\gamma := K p_K / p_0\)</span>.</p></td>
+<span class="arithmatex">\(\gamma := K p_K / p_0\)</span>.</p>
+            </div>
+          </td>
           <td>
                 <code>1.0</code>
           </td>
@@ -8106,7 +9550,11 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.train" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Training batch size.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Training batch size.</p>
+            </div>
+          </td>
           <td>
                 <code>50</code>
           </td>
@@ -8116,7 +9564,11 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.train" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Learning rate for Adam optimizer.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Learning rate for Adam optimizer.</p>
+            </div>
+          </td>
           <td>
                 <code>0.0005</code>
           </td>
@@ -8124,9 +9576,13 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.train" class="doc doc-heading">
         <tr>
           <td><code>validation_fraction</code></td>
           <td>
-                <code>float</code>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>The fraction of data to use for validation.</p>
+            </div>
           </td>
-          <td><p>The fraction of data to use for validation.</p></td>
           <td>
                 <code>0.1</code>
           </td>
@@ -8136,8 +9592,12 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.train" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>The number of epochs to wait for improvement on the
-validation set before terminating training.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The number of epochs to wait for improvement on the
+validation set before terminating training.</p>
+            </div>
+          </td>
           <td>
                 <code>20</code>
           </td>
@@ -8147,9 +9607,13 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.train" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Maximum number of epochs to run. If reached, we stop
+          <td>
+            <div class="doc-md-description">
+              <p>Maximum number of epochs to run. If reached, we stop
 training even when the validation loss is still decreasing. Otherwise,
-we train until validation loss increases (see also <code>stop_after_epochs</code>).</p></td>
+we train until validation loss increases (see also <code>stop_after_epochs</code>).</p>
+            </div>
+          </td>
           <td>
                 <code>2 ** 31 - 1</code>
           </td>
@@ -8159,8 +9623,12 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.train" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[float]</code>
           </td>
-          <td><p>Value at which to clip the total gradient norm in order to
-prevent exploding gradients. Use None for no clipping.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Value at which to clip the total gradient norm in order to
+prevent exploding gradients. Use None for no clipping.</p>
+            </div>
+          </td>
           <td>
                 <code>5.0</code>
           </td>
@@ -8169,8 +9637,12 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.train" class="doc doc-heading">
           <td><code>exclude_invalid_x</code></td>
           <td>
           </td>
-          <td><p>Whether to exclude simulation outputs <code>x=NaN</code> or <code>x=±∞</code>
-during training. Expect errors, silent or explicit, when <code>False</code>.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to exclude simulation outputs <code>x=NaN</code> or <code>x=±∞</code>
+during training. Expect errors, silent or explicit, when <code>False</code>.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -8180,10 +9652,14 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.train" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Can be used in case training time is limited, e.g. on a
+          <td>
+            <div class="doc-md-description">
+              <p>Can be used in case training time is limited, e.g. on a
 cluster. If <code>True</code>, the split between train and validation set, the
 optimizer, the number of epochs, and the best validation log-prob will
-be restored from the last time <code>.train()</code> was called.</p></td>
+be restored from the last time <code>.train()</code> was called.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -8193,9 +9669,13 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.train" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to discard samples simulated in round 1, i.e.
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to discard samples simulated in round 1, i.e.
 from the prior. Training may be sped up by ignoring such less targeted
-samples.</p></td>
+samples.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -8205,8 +9685,12 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.train" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to retrain the conditional density
-estimator for the posterior from scratch each round.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to retrain the conditional density
+estimator for the posterior from scratch each round.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -8216,8 +9700,12 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.train" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to print the number of epochs and validation
-loss and leakage after the training.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to print the number of epochs and validation
+loss and leakage after the training.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -8227,8 +9715,12 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.train" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="typing.Dict">Dict</span>]</code>
           </td>
-          <td><p>Additional or updated kwargs to be passed to the training
-and validation dataloaders (like, e.g., a collate_fn)</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Additional or updated kwargs to be passed to the training
+and validation dataloaders (like, e.g., a collate_fn)</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -8236,6 +9728,8 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.train" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -8247,16 +9741,20 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.train" class="doc doc-heading">
     <tbody>
         <tr>
           <td>
-                <code><span title="torch.nn">nn</span>.<span title="torch.nn.Module">Module</span></code>
+                <code><span title="torch.nn.Module">Module</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Classifier that approximates the ratio <span class="arithmatex">\(p(\theta,x)/p(\theta)p(x)\)</span>.</p>
+            </div>
           </td>
-          <td><p>Classifier that approximates the ratio <span class="arithmatex">\(p(\theta,x)/p(\theta)p(x)\)</span>.</p></td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/snre/snre_c.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 64</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/snre/snre_c.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 64</span>
 <span class="normal"> 65</span>
 <span class="normal"> 66</span>
 <span class="normal"> 67</span>
@@ -8378,7 +9876,7 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.train" class="doc doc-heading">
     <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;loss_kwargs&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;gamma&quot;</span><span class="p">:</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;gamma&quot;</span><span class="p">)}</span>
     <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
@@ -8395,22 +9893,15 @@ <h4 id="sbi.inference.snre.snre_c.SNRE_C.train" class="doc doc-heading">
 
 
 
-<h3 id="sbi.inference.snre.bnre.BNRE" class="doc doc-heading">
-        <code>sbi.inference.snre.bnre.BNRE</code>
-
-
-<a href="#sbi.inference.snre.bnre.BNRE" class="headerlink" title="Permanent link">&para;</a></h3>
-
-
+<a id="sbi.inference.snre.bnre.BNRE"></a>
   <div class="doc doc-contents first">
-      <p class="doc doc-class-bases">
-        Bases: <code><a class="autorefs autorefs-internal" title="sbi.inference.snre.snre_a.SNRE_A" href="#sbi.inference.snre.snre_a.SNRE_A">SNRE_A</a></code></p>
+          <p class="doc doc-class-bases">
+            Bases: <code><a class="autorefs autorefs-internal" title="sbi.inference.snre.snre_a.SNRE_A" href="#sbi.inference.snre.snre_a.SNRE_A">SNRE_A</a></code></p>
 
 
-
-        <details class="quote">
-          <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/snre/bnre.py</code></summary>
-          <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 12</span>
+            <details class="quote">
+              <summary>Source code in <code>sbi/inference/snre/bnre.py</code></summary>
+              <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 12</span>
 <span class="normal"> 13</span>
 <span class="normal"> 14</span>
 <span class="normal"> 15</span>
@@ -8668,7 +10159,7 @@ <h3 id="sbi.inference.snre.bnre.BNRE" class="doc doc-heading">
 
         <span class="k">return</span> <span class="n">bce</span> <span class="o">+</span> <span class="n">regularization_strength</span> <span class="o">*</span> <span class="n">regularizer</span>
 </code></pre></div></td></tr></table></div>
-        </details>
+            </details>
 
   
 
@@ -8682,14 +10173,16 @@ <h3 id="sbi.inference.snre.bnre.BNRE" class="doc doc-heading">
 
 
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.snre.bnre.BNRE.__init__" class="doc doc-heading">
-<code class="codehilite language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">prior</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">classifier</span><span class="o">=</span><span class="s1">&#39;resnet&#39;</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">,</span> <span class="n">logging_level</span><span class="o">=</span><span class="s1">&#39;warning&#39;</span><span class="p">,</span> <span class="n">summary_writer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.snre.bnre.BNRE.__init__" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.snre.bnre.BNRE.__init__" class="doc doc-heading">
+          <code class=" language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">prior</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">classifier</span><span class="o">=</span><span class="s1">&#39;resnet&#39;</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">,</span> <span class="n">logging_level</span><span class="o">=</span><span class="s1">&#39;warning&#39;</span><span class="p">,</span> <span class="n">summary_writer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.snre.bnre.BNRE.__init__" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -8701,6 +10194,8 @@ <h4 id="sbi.inference.snre.bnre.BNRE.__init__" class="doc doc-heading">
 Estimation.
 NeurIPS 2022. <a href="https://arxiv.org/abs/2208.13624">https://arxiv.org/abs/2208.13624</a></p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -8717,9 +10212,13 @@ <h4 id="sbi.inference.snre.bnre.BNRE.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="torch.distributions.Distribution">Distribution</span>]</code>
           </td>
-          <td><p>A probability distribution that expresses prior knowledge about the
+          <td>
+            <div class="doc-md-description">
+              <p>A probability distribution that expresses prior knowledge about the
 parameters, e.g. which ranges are meaningful for them. If <code>None</code>, the
-prior must be passed to <code>.build_posterior()</code>.</p></td>
+prior must be passed to <code>.build_posterior()</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -8729,13 +10228,17 @@ <h4 id="sbi.inference.snre.bnre.BNRE.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Union">Union</span>[str, <span title="typing.Callable">Callable</span>]</code>
           </td>
-          <td><p>Classifier trained to approximate likelihood ratios. If it is
+          <td>
+            <div class="doc-md-description">
+              <p>Classifier trained to approximate likelihood ratios. If it is
 a string, use a pre-configured network of the provided type (one of
 linear, mlp, resnet). Alternatively, a function that builds a custom
 neural network can be provided. The function will be called with the
 first batch of simulations <span class="arithmatex">\((\theta, x)\)</span>, which can thus be used for
 shape inference and potentially for z-scoring. It needs to return a
-PyTorch <code>nn.Module</code> implementing the classifier.</p></td>
+PyTorch <code>nn.Module</code> implementing the classifier.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;resnet&#39;</code>
           </td>
@@ -8745,7 +10248,11 @@ <h4 id="sbi.inference.snre.bnre.BNRE.__init__" class="doc doc-heading">
           <td>
                 <code>str</code>
           </td>
-          <td><p>Training device, e.g., &ldquo;cpu&rdquo;, &ldquo;cuda&rdquo; or &ldquo;cuda:{0, 1, &hellip;}&rdquo;.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Training device, e.g., &ldquo;cpu&rdquo;, &ldquo;cuda&rdquo; or &ldquo;cuda:{0, 1, &hellip;}&rdquo;.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;cpu&#39;</code>
           </td>
@@ -8755,8 +10262,12 @@ <h4 id="sbi.inference.snre.bnre.BNRE.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Union">Union</span>[int, str]</code>
           </td>
-          <td><p>Minimum severity of messages to log. One of the strings
-INFO, WARNING, DEBUG, ERROR and CRITICAL.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Minimum severity of messages to log. One of the strings
+INFO, WARNING, DEBUG, ERROR and CRITICAL.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;warning&#39;</code>
           </td>
@@ -8766,8 +10277,12 @@ <h4 id="sbi.inference.snre.bnre.BNRE.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="sbi.types.TensorboardSummaryWriter">TensorboardSummaryWriter</span>]</code>
           </td>
-          <td><p>A tensorboard <code>SummaryWriter</code> to control, among others, log
-file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>A tensorboard <code>SummaryWriter</code> to control, among others, log
+file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -8777,8 +10292,12 @@ <h4 id="sbi.inference.snre.bnre.BNRE.__init__" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to show a progressbar during simulation and
-sampling.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to show a progressbar during simulation and
+sampling.</p>
+            </div>
+          </td>
           <td>
                 <code>True</code>
           </td>
@@ -8786,9 +10305,9 @@ <h4 id="sbi.inference.snre.bnre.BNRE.__init__" class="doc doc-heading">
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/snre/bnre.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">13</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/snre/bnre.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">13</span>
 <span class="normal">14</span>
 <span class="normal">15</span>
 <span class="normal">16</span>
@@ -8817,252 +10336,111 @@ <h4 id="sbi.inference.snre.bnre.BNRE.__init__" class="doc doc-heading">
 <span class="normal">39</span>
 <span class="normal">40</span>
 <span class="normal">41</span>
-<span class="normal">42</span>
-<span class="normal">43</span>
-<span class="normal">44</span>
-<span class="normal">45</span>
-<span class="normal">46</span>
-<span class="normal">47</span>
-<span class="normal">48</span>
-<span class="normal">49</span>
-<span class="normal">50</span>
-<span class="normal">51</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-    <span class="bp">self</span><span class="p">,</span>
-    <span class="n">prior</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Distribution</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">classifier</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;resnet&quot;</span><span class="p">,</span>
-    <span class="n">device</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;cpu&quot;</span><span class="p">,</span>
-    <span class="n">logging_level</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;warning&quot;</span><span class="p">,</span>
-    <span class="n">summary_writer</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">TensorboardSummaryWriter</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">show_progress_bars</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-<span class="p">):</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Balanced neural ratio estimation (BNRE)[1]. BNRE is a variation of NRE</span>
-<span class="sd">    aiming to produce more conservative posterior approximations</span>
-
-<span class="sd">    [1] Delaunoy, A., Hermans, J., Rozet, F., Wehenkel, A., &amp; Louppe, G..</span>
-<span class="sd">    Towards Reliable Simulation-Based Inference with Balanced Neural Ratio</span>
-<span class="sd">    Estimation.</span>
-<span class="sd">    NeurIPS 2022. https://arxiv.org/abs/2208.13624</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        prior: A probability distribution that expresses prior knowledge about the</span>
-<span class="sd">            parameters, e.g. which ranges are meaningful for them. If `None`, the</span>
-<span class="sd">            prior must be passed to `.build_posterior()`.</span>
-<span class="sd">        classifier: Classifier trained to approximate likelihood ratios. If it is</span>
-<span class="sd">            a string, use a pre-configured network of the provided type (one of</span>
-<span class="sd">            linear, mlp, resnet). Alternatively, a function that builds a custom</span>
-<span class="sd">            neural network can be provided. The function will be called with the</span>
-<span class="sd">            first batch of simulations $(\theta, x)$, which can thus be used for</span>
-<span class="sd">            shape inference and potentially for z-scoring. It needs to return a</span>
-<span class="sd">            PyTorch `nn.Module` implementing the classifier.</span>
-<span class="sd">        device: Training device, e.g., &quot;cpu&quot;, &quot;cuda&quot; or &quot;cuda:{0, 1, ...}&quot;.</span>
-<span class="sd">        logging_level: Minimum severity of messages to log. One of the strings</span>
-<span class="sd">            INFO, WARNING, DEBUG, ERROR and CRITICAL.</span>
-<span class="sd">        summary_writer: A tensorboard `SummaryWriter` to control, among others, log</span>
-<span class="sd">            file location (default is `&lt;current working directory&gt;/logs`.)</span>
-<span class="sd">        show_progress_bars: Whether to show a progressbar during simulation and</span>
-<span class="sd">            sampling.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">kwargs</span> <span class="o">=</span> <span class="n">del_entries</span><span class="p">(</span><span class="nb">locals</span><span class="p">(),</span> <span class="n">entries</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;self&quot;</span><span class="p">,</span> <span class="s2">&quot;__class__&quot;</span><span class="p">))</span>
-    <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-</code></pre></div></td></tr></table></div>
-      </details>
-  </div>
-
-</div>
-
-<div class="doc doc-object doc-function">
-
-
-
-<h4 id="sbi.inference.snre.bnre.BNRE.train" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">train</span><span class="p">(</span><span class="n">regularization_strength</span><span class="o">=</span><span class="mf">100.0</span><span class="p">,</span> <span class="n">training_batch_size</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.0005</span><span class="p">,</span> <span class="n">validation_fraction</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">stop_after_epochs</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">max_num_epochs</span><span class="o">=</span><span class="mi">2</span> <span class="o">**</span> <span class="mi">31</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">clip_max_norm</span><span class="o">=</span><span class="mf">5.0</span><span class="p">,</span> <span class="n">resume_training</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">discard_prior_samples</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">retrain_from_scratch</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">show_train_summary</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dataloader_kwargs</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
-
-<a href="#sbi.inference.snre.bnre.BNRE.train" class="headerlink" title="Permanent link">&para;</a></h4>
-
-
-  <div class="doc doc-contents ">
-  
-      <p>Return classifier that approximates the ratio <span class="arithmatex">\(p(\theta,x)/p(\theta)p(x)\)</span>.</p>
-
-  <p><strong>Parameters:</strong></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr>
-          <td><code>regularization_strength</code></td>
-          <td>
-                <code>float</code>
-          </td>
-          <td><p>The multiplicative coefficient applied to the
-balancing regularizer (<span class="arithmatex">\(\lambda\)</span>).</p></td>
-          <td>
-                <code>100.0</code>
-          </td>
-        </tr>
-        <tr>
-          <td><code>training_batch_size</code></td>
-          <td>
-                <code>int</code>
-          </td>
-          <td><p>Training batch size.</p></td>
-          <td>
-                <code>50</code>
-          </td>
-        </tr>
-        <tr>
-          <td><code>learning_rate</code></td>
-          <td>
-                <code>float</code>
-          </td>
-          <td><p>Learning rate for Adam optimizer.</p></td>
-          <td>
-                <code>0.0005</code>
-          </td>
-        </tr>
-        <tr>
-          <td><code>validation_fraction</code></td>
-          <td>
-                <code>float</code>
-          </td>
-          <td><p>The fraction of data to use for validation.</p></td>
-          <td>
-                <code>0.1</code>
-          </td>
-        </tr>
-        <tr>
-          <td><code>stop_after_epochs</code></td>
-          <td>
-                <code>int</code>
-          </td>
-          <td><p>The number of epochs to wait for improvement on the
-validation set before terminating training.</p></td>
-          <td>
-                <code>20</code>
-          </td>
-        </tr>
-        <tr>
-          <td><code>max_num_epochs</code></td>
-          <td>
-                <code>int</code>
-          </td>
-          <td><p>Maximum number of epochs to run. If reached, we stop
-training even when the validation loss is still decreasing. Otherwise,
-we train until validation loss increases (see also <code>stop_after_epochs</code>).</p></td>
-          <td>
-                <code>2 ** 31 - 1</code>
-          </td>
-        </tr>
-        <tr>
-          <td><code>clip_max_norm</code></td>
-          <td>
-                <code><span title="typing.Optional">Optional</span>[float]</code>
-          </td>
-          <td><p>Value at which to clip the total gradient norm in order to
-prevent exploding gradients. Use None for no clipping.</p></td>
-          <td>
-                <code>5.0</code>
-          </td>
-        </tr>
-        <tr>
-          <td><code>exclude_invalid_x</code></td>
-          <td>
-          </td>
-          <td><p>Whether to exclude simulation outputs <code>x=NaN</code> or <code>x=±∞</code>
-during training. Expect errors, silent or explicit, when <code>False</code>.</p></td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr>
-          <td><code>resume_training</code></td>
-          <td>
-                <code>bool</code>
-          </td>
-          <td><p>Can be used in case training time is limited, e.g. on a
-cluster. If <code>True</code>, the split between train and validation set, the
-optimizer, the number of epochs, and the best validation log-prob will
-be restored from the last time <code>.train()</code> was called.</p></td>
-          <td>
-                <code>False</code>
-          </td>
-        </tr>
-        <tr>
-          <td><code>discard_prior_samples</code></td>
-          <td>
-                <code>bool</code>
-          </td>
-          <td><p>Whether to discard samples simulated in round 1, i.e.
-from the prior. Training may be sped up by ignoring such less targeted
-samples.</p></td>
-          <td>
-                <code>False</code>
-          </td>
-        </tr>
-        <tr>
-          <td><code>retrain_from_scratch</code></td>
-          <td>
-                <code>bool</code>
-          </td>
-          <td><p>Whether to retrain the conditional density
-estimator for the posterior from scratch each round.</p></td>
-          <td>
-                <code>False</code>
-          </td>
-        </tr>
-        <tr>
-          <td><code>show_train_summary</code></td>
-          <td>
-                <code>bool</code>
-          </td>
-          <td><p>Whether to print the number of epochs and validation
-loss and leakage after the training.</p></td>
-          <td>
-                <code>False</code>
-          </td>
-        </tr>
-        <tr>
-          <td><code>dataloader_kwargs</code></td>
-          <td>
-                <code><span title="typing.Optional">Optional</span>[<span title="typing.Dict">Dict</span>]</code>
-          </td>
-          <td><p>Additional or updated kwargs to be passed to the training
-and validation dataloaders (like, e.g., a collate_fn)</p></td>
-          <td>
-                <code>None</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
+<span class="normal">42</span>
+<span class="normal">43</span>
+<span class="normal">44</span>
+<span class="normal">45</span>
+<span class="normal">46</span>
+<span class="normal">47</span>
+<span class="normal">48</span>
+<span class="normal">49</span>
+<span class="normal">50</span>
+<span class="normal">51</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+    <span class="bp">self</span><span class="p">,</span>
+    <span class="n">prior</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Distribution</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">classifier</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;resnet&quot;</span><span class="p">,</span>
+    <span class="n">device</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;cpu&quot;</span><span class="p">,</span>
+    <span class="n">logging_level</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;warning&quot;</span><span class="p">,</span>
+    <span class="n">summary_writer</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">TensorboardSummaryWriter</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">show_progress_bars</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Balanced neural ratio estimation (BNRE)[1]. BNRE is a variation of NRE</span>
+<span class="sd">    aiming to produce more conservative posterior approximations</span>
 
-  <p><strong>Returns:</strong></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Type</th>
-        <th>Description</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr>
-          <td>
-                <code><span title="torch.nn">nn</span>.<span title="torch.nn.Module">Module</span></code>
-          </td>
-          <td><p>Classifier that approximates the ratio <span class="arithmatex">\(p(\theta,x)/p(\theta)p(x)\)</span>.</p></td>
-        </tr>
-    </tbody>
-  </table>
+<span class="sd">    [1] Delaunoy, A., Hermans, J., Rozet, F., Wehenkel, A., &amp; Louppe, G..</span>
+<span class="sd">    Towards Reliable Simulation-Based Inference with Balanced Neural Ratio</span>
+<span class="sd">    Estimation.</span>
+<span class="sd">    NeurIPS 2022. https://arxiv.org/abs/2208.13624</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        prior: A probability distribution that expresses prior knowledge about the</span>
+<span class="sd">            parameters, e.g. which ranges are meaningful for them. If `None`, the</span>
+<span class="sd">            prior must be passed to `.build_posterior()`.</span>
+<span class="sd">        classifier: Classifier trained to approximate likelihood ratios. If it is</span>
+<span class="sd">            a string, use a pre-configured network of the provided type (one of</span>
+<span class="sd">            linear, mlp, resnet). Alternatively, a function that builds a custom</span>
+<span class="sd">            neural network can be provided. The function will be called with the</span>
+<span class="sd">            first batch of simulations $(\theta, x)$, which can thus be used for</span>
+<span class="sd">            shape inference and potentially for z-scoring. It needs to return a</span>
+<span class="sd">            PyTorch `nn.Module` implementing the classifier.</span>
+<span class="sd">        device: Training device, e.g., &quot;cpu&quot;, &quot;cuda&quot; or &quot;cuda:{0, 1, ...}&quot;.</span>
+<span class="sd">        logging_level: Minimum severity of messages to log. One of the strings</span>
+<span class="sd">            INFO, WARNING, DEBUG, ERROR and CRITICAL.</span>
+<span class="sd">        summary_writer: A tensorboard `SummaryWriter` to control, among others, log</span>
+<span class="sd">            file location (default is `&lt;current working directory&gt;/logs`.)</span>
+<span class="sd">        show_progress_bars: Whether to show a progressbar during simulation and</span>
+<span class="sd">            sampling.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">kwargs</span> <span class="o">=</span> <span class="n">del_entries</span><span class="p">(</span><span class="nb">locals</span><span class="p">(),</span> <span class="n">entries</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;self&quot;</span><span class="p">,</span> <span class="s2">&quot;__class__&quot;</span><span class="p">))</span>
+    <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/snre/bnre.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 53</span>
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="sbi.inference.snre.bnre.BNRE.train" class="doc doc-heading">
+          <code class=" language-python"><span class="n">train</span><span class="p">(</span><span class="n">regularization_strength</span><span class="o">=</span><span class="mf">100.0</span><span class="p">,</span> <span class="n">training_batch_size</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.0005</span><span class="p">,</span> <span class="n">validation_fraction</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">stop_after_epochs</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">max_num_epochs</span><span class="o">=</span><span class="mi">2</span> <span class="o">**</span> <span class="mi">31</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">clip_max_norm</span><span class="o">=</span><span class="mf">5.0</span><span class="p">,</span> <span class="n">resume_training</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">discard_prior_samples</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">retrain_from_scratch</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">show_train_summary</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dataloader_kwargs</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.snre.bnre.BNRE.train" class="headerlink" title="Permanent link">&para;</a></h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Return classifier that approximates the ratio <span class="arithmatex">\(p(\theta,x)/p(\theta)p(x)\)</span>.
+Args:</p>
+<div class="codehilite"><pre><span></span><code>regularization_strength: The multiplicative coefficient applied to the
+    balancing regularizer ($\lambda$).
+training_batch_size: Training batch size.
+learning_rate: Learning rate for Adam optimizer.
+validation_fraction: The fraction of data to use for validation.
+stop_after_epochs: The number of epochs to wait for improvement on the
+    validation set before terminating training.
+max_num_epochs: Maximum number of epochs to run. If reached, we stop
+    training even when the validation loss is still decreasing. Otherwise,
+    we train until validation loss increases (see also `stop_after_epochs`).
+clip_max_norm: Value at which to clip the total gradient norm in order to
+    prevent exploding gradients. Use None for no clipping.
+exclude_invalid_x: Whether to exclude simulation outputs `x=NaN` or `x=±∞`
+    during training. Expect errors, silent or explicit, when `False`.
+resume_training: Can be used in case training time is limited, e.g. on a
+    cluster. If `True`, the split between train and validation set, the
+    optimizer, the number of epochs, and the best validation log-prob will
+    be restored from the last time `.train()` was called.
+discard_prior_samples: Whether to discard samples simulated in round 1, i.e.
+    from the prior. Training may be sped up by ignoring such less targeted
+    samples.
+retrain_from_scratch: Whether to retrain the conditional density
+    estimator for the posterior from scratch each round.
+show_train_summary: Whether to print the number of epochs and validation
+    loss and leakage after the training.
+dataloader_kwargs: Additional or updated kwargs to be passed to the training
+    and validation dataloaders (like, e.g., a collate_fn)
+</code></pre></div>
+
+<p>Returns:
+    Classifier that approximates the ratio <span class="arithmatex">\(p(\theta,x)/p(\theta)p(x)\)</span>.</p>
+
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/snre/bnre.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 53</span>
 <span class="normal"> 54</span>
 <span class="normal"> 55</span>
 <span class="normal"> 56</span>
@@ -9168,7 +10546,7 @@ <h4 id="sbi.inference.snre.bnre.BNRE.train" class="doc doc-heading">
     <span class="p">}</span>
     <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
@@ -9185,22 +10563,15 @@ <h4 id="sbi.inference.snre.bnre.BNRE.train" class="doc doc-heading">
 
 
 
-<h3 id="sbi.inference.abc.mcabc.MCABC" class="doc doc-heading">
-        <code>sbi.inference.abc.mcabc.MCABC</code>
-
-
-<a href="#sbi.inference.abc.mcabc.MCABC" class="headerlink" title="Permanent link">&para;</a></h3>
-
-
+<a id="sbi.inference.abc.mcabc.MCABC"></a>
   <div class="doc doc-contents first">
-      <p class="doc doc-class-bases">
-        Bases: <code><span title="sbi.inference.abc.abc_base.ABCBASE">ABCBASE</span></code></p>
-
+          <p class="doc doc-class-bases">
+            Bases: <code><span title="sbi.inference.abc.abc_base.ABCBASE">ABCBASE</span></code></p>
 
 
-        <details class="quote">
-          <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/abc/mcabc.py</code></summary>
-          <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 11</span>
+            <details class="quote">
+              <summary>Source code in <code>sbi/inference/abc/mcabc.py</code></summary>
+              <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 11</span>
 <span class="normal"> 12</span>
 <span class="normal"> 13</span>
 <span class="normal"> 14</span>
@@ -9558,7 +10929,7 @@ <h3 id="sbi.inference.abc.mcabc.MCABC" class="doc doc-heading">
         <span class="k">else</span><span class="p">:</span>
             <span class="k">return</span> <span class="n">final_theta</span>
 </code></pre></div></td></tr></table></div>
-        </details>
+            </details>
 
   
 
@@ -9572,20 +10943,24 @@ <h3 id="sbi.inference.abc.mcabc.MCABC" class="doc doc-heading">
 
 
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.abc.mcabc.MCABC.__call__" class="doc doc-heading">
-<code class="codehilite language-python"><span class="fm">__call__</span><span class="p">(</span><span class="n">x_o</span><span class="p">,</span> <span class="n">num_simulations</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">quantile</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">lra</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">sass</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">sass_fraction</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">sass_expansion_degree</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">kde</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">kde_kwargs</span><span class="o">=</span><span class="p">{},</span> <span class="n">return_summary</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.abc.mcabc.MCABC.__call__" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.abc.mcabc.MCABC.__call__" class="doc doc-heading">
+          <code class=" language-python"><span class="fm">__call__</span><span class="p">(</span><span class="n">x_o</span><span class="p">,</span> <span class="n">num_simulations</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">quantile</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">lra</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">sass</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">sass_fraction</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">sass_expansion_degree</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">kde</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">kde_kwargs</span><span class="o">=</span><span class="p">{},</span> <span class="n">return_summary</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.abc.mcabc.MCABC.__call__" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
   
       <p>Run MCABC and return accepted parameters or KDE object fitted on them.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -9602,7 +10977,11 @@ <h4 id="sbi.inference.abc.mcabc.MCABC.__call__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Union">Union</span>[<span title="torch.Tensor">Tensor</span>, <span title="numpy.ndarray">ndarray</span>]</code>
           </td>
-          <td><p>Observed data.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Observed data.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -9612,7 +10991,11 @@ <h4 id="sbi.inference.abc.mcabc.MCABC.__call__" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of simulations to run.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of simulations to run.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -9622,8 +11005,12 @@ <h4 id="sbi.inference.abc.mcabc.MCABC.__call__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[float]</code>
           </td>
-          <td><p>Acceptance threshold <span class="arithmatex">\(\epsilon\)</span> for distance between observed and
-simulated data.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Acceptance threshold <span class="arithmatex">\(\epsilon\)</span> for distance between observed and
+simulated data.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -9633,9 +11020,13 @@ <h4 id="sbi.inference.abc.mcabc.MCABC.__call__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[float]</code>
           </td>
-          <td><p>Upper quantile of smallest distances for which the corresponding
+          <td>
+            <div class="doc-md-description">
+              <p>Upper quantile of smallest distances for which the corresponding
 parameters are returned, e.g, q=0.01 will return the top 1%. Exactly
-one of quantile or <code>eps</code> have to be passed.</p></td>
+one of quantile or <code>eps</code> have to be passed.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -9645,7 +11036,11 @@ <h4 id="sbi.inference.abc.mcabc.MCABC.__call__" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to run linear regression adjustment as in Beaumont et al. 2002</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to run linear regression adjustment as in Beaumont et al. 2002</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -9655,8 +11050,12 @@ <h4 id="sbi.inference.abc.mcabc.MCABC.__call__" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to determine semi-automatic summary statistics as in
-Fearnhead &amp; Prangle 2012.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to determine semi-automatic summary statistics as in
+Fearnhead &amp; Prangle 2012.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -9666,7 +11065,11 @@ <h4 id="sbi.inference.abc.mcabc.MCABC.__call__" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Fraction of simulation budget used for the initial sass run.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Fraction of simulation budget used for the initial sass run.</p>
+            </div>
+          </td>
           <td>
                 <code>0.25</code>
           </td>
@@ -9676,8 +11079,12 @@ <h4 id="sbi.inference.abc.mcabc.MCABC.__call__" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Degree of the polynomial feature expansion for the
-sass regression, default 1 - no expansion.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Degree of the polynomial feature expansion for the
+sass regression, default 1 - no expansion.</p>
+            </div>
+          </td>
           <td>
                 <code>1</code>
           </td>
@@ -9687,8 +11094,12 @@ <h4 id="sbi.inference.abc.mcabc.MCABC.__call__" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to run KDE on the accepted parameters to return a KDE
-object from which one can sample.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to run KDE on the accepted parameters to return a KDE
+object from which one can sample.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -9698,13 +11109,17 @@ <h4 id="sbi.inference.abc.mcabc.MCABC.__call__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Dict">Dict</span>[str, <span title="typing.Any">Any</span>]</code>
           </td>
-          <td><p>kwargs for performing KDE:
+          <td>
+            <div class="doc-md-description">
+              <p>kwargs for performing KDE:
 &lsquo;bandwidth=&rsquo;; either a float, or a string naming a bandwidth
 heuristics, e.g., &lsquo;cv&rsquo; (cross validation), &lsquo;silvermann&rsquo; or &lsquo;scott&rsquo;,
 default &lsquo;cv&rsquo;.
 &lsquo;transform&rsquo;: transform applied to the parameters before doing KDE.
 &lsquo;sample_weights&rsquo;: weights associated with samples. See &lsquo;get_kde&rsquo; for
-more details</p></td>
+more details</p>
+            </div>
+          </td>
           <td>
                 <code>{}</code>
           </td>
@@ -9714,8 +11129,12 @@ <h4 id="sbi.inference.abc.mcabc.MCABC.__call__" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to return the distances and data corresponding to
-the accepted parameters.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to return the distances and data corresponding to
+the accepted parameters.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -9723,6 +11142,8 @@ <h4 id="sbi.inference.abc.mcabc.MCABC.__call__" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -9736,28 +11157,40 @@ <h4 id="sbi.inference.abc.mcabc.MCABC.__call__" class="doc doc-heading">
 <td><code>theta</code></td>          <td>
                 <code>if kde False</code>
           </td>
-          <td><p>accepted parameters</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>accepted parameters</p>
+            </div>
+          </td>
         </tr>
         <tr>
 <td><code>kde</code></td>          <td>
                 <code>if kde True</code>
           </td>
-          <td><p>KDE object based on accepted parameters from which one
-can .sample() and .log_prob().</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>KDE object based on accepted parameters from which one
+can .sample() and .log_prob().</p>
+            </div>
+          </td>
         </tr>
         <tr>
 <td><code>summary</code></td>          <td>
                 <code>if summary True</code>
           </td>
-          <td><p>dictionary containing the accepted paramters (if
-kde True), distances and simulated data x.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>dictionary containing the accepted paramters (if
+kde True), distances and simulated data x.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/abc/mcabc.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 56</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/abc/mcabc.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 56</span>
 <span class="normal"> 57</span>
 <span class="normal"> 58</span>
 <span class="normal"> 59</span>
@@ -10025,19 +11458,21 @@ <h4 id="sbi.inference.abc.mcabc.MCABC.__call__" class="doc doc-heading">
     <span class="k">else</span><span class="p">:</span>
         <span class="k">return</span> <span class="n">final_theta</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.abc.mcabc.MCABC.__init__" class="doc doc-heading">
-<code class="codehilite language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">simulator</span><span class="p">,</span> <span class="n">prior</span><span class="p">,</span> <span class="n">distance</span><span class="o">=</span><span class="s1">&#39;l2&#39;</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">simulation_batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.abc.mcabc.MCABC.__init__" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.abc.mcabc.MCABC.__init__" class="doc doc-heading">
+          <code class=" language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">simulator</span><span class="p">,</span> <span class="n">prior</span><span class="p">,</span> <span class="n">distance</span><span class="o">=</span><span class="s1">&#39;l2&#39;</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">simulation_batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.abc.mcabc.MCABC.__init__" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -10047,6 +11482,8 @@ <h4 id="sbi.inference.abc.mcabc.MCABC.__init__" class="doc doc-heading">
 (1999). Population growth of human Y chromosomes: a study of Y chromosome
 microsatellites. Molecular biology and evolution, 16(12), 1791-1798.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -10063,10 +11500,14 @@ <h4 id="sbi.inference.abc.mcabc.MCABC.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Callable">Callable</span></code>
           </td>
-          <td><p>A function that takes parameters <span class="arithmatex">\(\theta\)</span> and maps them to
+          <td>
+            <div class="doc-md-description">
+              <p>A function that takes parameters <span class="arithmatex">\(\theta\)</span> and maps them to
 simulations, or observations, <code>x</code>, <span class="arithmatex">\(\mathrm{sim}(\theta)\to x\)</span>. Any
 regular Python callable (i.e. function or class with <code>__call__</code> method)
-can be used.</p></td>
+can be used.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -10075,10 +11516,14 @@ <h4 id="sbi.inference.abc.mcabc.MCABC.__init__" class="doc doc-heading">
           <td><code>prior</code></td>
           <td>
           </td>
-          <td><p>A probability distribution that expresses prior knowledge about the
+          <td>
+            <div class="doc-md-description">
+              <p>A probability distribution that expresses prior knowledge about the
 parameters, e.g. which ranges are meaningful for them. Any
 object with <code>.log_prob()</code>and <code>.sample()</code> (for example, a PyTorch
-distribution) can be used.</p></td>
+distribution) can be used.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -10088,8 +11533,12 @@ <h4 id="sbi.inference.abc.mcabc.MCABC.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Union">Union</span>[str, <span title="typing.Callable">Callable</span>]</code>
           </td>
-          <td><p>Distance function to compare observed and simulated data. Can be
-a custom function or one of <code>l1</code>, <code>l2</code>, <code>mse</code>.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Distance function to compare observed and simulated data. Can be
+a custom function or one of <code>l1</code>, <code>l2</code>, <code>mse</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;l2&#39;</code>
           </td>
@@ -10099,7 +11548,11 @@ <h4 id="sbi.inference.abc.mcabc.MCABC.__init__" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of parallel workers to use for simulations.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of parallel workers to use for simulations.</p>
+            </div>
+          </td>
           <td>
                 <code>1</code>
           </td>
@@ -10109,10 +11562,14 @@ <h4 id="sbi.inference.abc.mcabc.MCABC.__init__" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of parameter sets that the simulator
+          <td>
+            <div class="doc-md-description">
+              <p>Number of parameter sets that the simulator
 maps to data x at once. If None, we simulate all parameter sets at the
 same time. If &gt;= 1, the simulator has to process data of shape
-(simulation_batch_size, parameter_dimension).</p></td>
+(simulation_batch_size, parameter_dimension).</p>
+            </div>
+          </td>
           <td>
                 <code>1</code>
           </td>
@@ -10122,8 +11579,12 @@ <h4 id="sbi.inference.abc.mcabc.MCABC.__init__" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to show a progressbar during simulation and
-sampling.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to show a progressbar during simulation and
+sampling.</p>
+            </div>
+          </td>
           <td>
                 <code>True</code>
           </td>
@@ -10131,9 +11592,9 @@ <h4 id="sbi.inference.abc.mcabc.MCABC.__init__" class="doc doc-heading">
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/abc/mcabc.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">12</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/abc/mcabc.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">12</span>
 <span class="normal">13</span>
 <span class="normal">14</span>
 <span class="normal">15</span>
@@ -10219,7 +11680,7 @@ <h4 id="sbi.inference.abc.mcabc.MCABC.__init__" class="doc doc-heading">
         <span class="n">show_progress_bars</span><span class="o">=</span><span class="n">show_progress_bars</span><span class="p">,</span>
     <span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
@@ -10236,22 +11697,15 @@ <h4 id="sbi.inference.abc.mcabc.MCABC.__init__" class="doc doc-heading">
 
 
 
-<h3 id="sbi.inference.abc.smcabc.SMCABC" class="doc doc-heading">
-        <code>sbi.inference.abc.smcabc.SMCABC</code>
-
-
-<a href="#sbi.inference.abc.smcabc.SMCABC" class="headerlink" title="Permanent link">&para;</a></h3>
-
-
+<a id="sbi.inference.abc.smcabc.SMCABC"></a>
   <div class="doc doc-contents first">
-      <p class="doc doc-class-bases">
-        Bases: <code><span title="sbi.inference.abc.abc_base.ABCBASE">ABCBASE</span></code></p>
+          <p class="doc doc-class-bases">
+            Bases: <code><span title="sbi.inference.abc.abc_base.ABCBASE">ABCBASE</span></code></p>
 
 
-
-        <details class="quote">
-          <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/abc/smcabc.py</code></summary>
-          <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 15</span>
+            <details class="quote">
+              <summary>Source code in <code>sbi/inference/abc/smcabc.py</code></summary>
+              <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 15</span>
 <span class="normal"> 16</span>
 <span class="normal"> 17</span>
 <span class="normal"> 18</span>
@@ -11649,7 +13103,7 @@ <h3 id="sbi.inference.abc.smcabc.SMCABC" class="doc doc-heading">
         <span class="k">assert</span> <span class="n">particle_ranges</span><span class="o">.</span><span class="n">ndim</span> <span class="o">&lt;</span> <span class="mi">2</span>
         <span class="k">return</span> <span class="n">particle_ranges</span>
 </code></pre></div></td></tr></table></div>
-        </details>
+            </details>
 
   
 
@@ -11663,20 +13117,24 @@ <h3 id="sbi.inference.abc.smcabc.SMCABC" class="doc doc-heading">
 
 
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.abc.smcabc.SMCABC.__call__" class="doc doc-heading">
-<code class="codehilite language-python"><span class="fm">__call__</span><span class="p">(</span><span class="n">x_o</span><span class="p">,</span> <span class="n">num_particles</span><span class="p">,</span> <span class="n">num_initial_pop</span><span class="p">,</span> <span class="n">num_simulations</span><span class="p">,</span> <span class="n">epsilon_decay</span><span class="p">,</span> <span class="n">distance_based_decay</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">ess_min</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">kernel_variance_scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">use_last_pop_samples</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">return_summary</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">kde</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">kde_kwargs</span><span class="o">=</span><span class="p">{},</span> <span class="n">kde_sample_weights</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">lra</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">lra_with_weights</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">sass</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">sass_fraction</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">sass_expansion_degree</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.abc.smcabc.SMCABC.__call__" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.abc.smcabc.SMCABC.__call__" class="doc doc-heading">
+          <code class=" language-python"><span class="fm">__call__</span><span class="p">(</span><span class="n">x_o</span><span class="p">,</span> <span class="n">num_particles</span><span class="p">,</span> <span class="n">num_initial_pop</span><span class="p">,</span> <span class="n">num_simulations</span><span class="p">,</span> <span class="n">epsilon_decay</span><span class="p">,</span> <span class="n">distance_based_decay</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">ess_min</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">kernel_variance_scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">use_last_pop_samples</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">return_summary</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">kde</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">kde_kwargs</span><span class="o">=</span><span class="p">{},</span> <span class="n">kde_sample_weights</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">lra</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">lra_with_weights</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">sass</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">sass_fraction</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">sass_expansion_degree</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.abc.smcabc.SMCABC.__call__" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
   
       <p>Run SMCABC and return accepted parameters or KDE object fitted on them.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -11693,7 +13151,11 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__call__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Union">Union</span>[<span title="torch.Tensor">Tensor</span>, <span title="numpy.ndarray">ndarray</span>]</code>
           </td>
-          <td><p>Observed data.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Observed data.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -11703,7 +13165,11 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__call__" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of particles in each population.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of particles in each population.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -11713,7 +13179,11 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__call__" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of simulations used for initial population.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of simulations used for initial population.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -11723,7 +13193,11 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__call__" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Total number of possible simulations.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Total number of possible simulations.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -11733,7 +13207,11 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__call__" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Factor with which the acceptance threshold <span class="arithmatex">\(\epsilon\)</span> decays.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Factor with which the acceptance threshold <span class="arithmatex">\(\epsilon\)</span> decays.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -11743,9 +13221,13 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__call__" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether the <span class="arithmatex">\(\epsilon\)</span> decay is constant over
+          <td>
+            <div class="doc-md-description">
+              <p>Whether the <span class="arithmatex">\(\epsilon\)</span> decay is constant over
 populations or calculated from the previous populations distribution of
-distances.</p></td>
+distances.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -11755,8 +13237,12 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__call__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[float]</code>
           </td>
-          <td><p>Threshold of effective sampling size for resampling weights. Not
-used when None (default).</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Threshold of effective sampling size for resampling weights. Not
+used when None (default).</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -11766,7 +13252,11 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__call__" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Factor for scaling the perturbation kernel variance.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Factor for scaling the perturbation kernel variance.</p>
+            </div>
+          </td>
           <td>
                 <code>1.0</code>
           </td>
@@ -11776,10 +13266,14 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__call__" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to fill up the current population with
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to fill up the current population with
 samples from the previous population when the budget is used up. If
 False, the current population is discarded and the previous population
-is returned.</p></td>
+is returned.</p>
+            </div>
+          </td>
           <td>
                 <code>True</code>
           </td>
@@ -11789,7 +13283,11 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__call__" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to run linear regression adjustment as in Beaumont et al. 2002</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to run linear regression adjustment as in Beaumont et al. 2002</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -11799,8 +13297,12 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__call__" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to run lra as weighted linear regression with SMC
-weights</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to run lra as weighted linear regression with SMC
+weights</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -11810,8 +13312,12 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__call__" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to determine semi-automatic summary statistics as in
-Fearnhead &amp; Prangle 2012.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to determine semi-automatic summary statistics as in
+Fearnhead &amp; Prangle 2012.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -11821,7 +13327,11 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__call__" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Fraction of simulation budget used for the initial sass run.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Fraction of simulation budget used for the initial sass run.</p>
+            </div>
+          </td>
           <td>
                 <code>0.25</code>
           </td>
@@ -11831,8 +13341,12 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__call__" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Degree of the polynomial feature expansion for the
-sass regression, default 1 - no expansion.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Degree of the polynomial feature expansion for the
+sass regression, default 1 - no expansion.</p>
+            </div>
+          </td>
           <td>
                 <code>1</code>
           </td>
@@ -11842,8 +13356,12 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__call__" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to run KDE on the accepted parameters to return a KDE
-object from which one can sample.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to run KDE on the accepted parameters to return a KDE
+object from which one can sample.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -11853,13 +13371,17 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__call__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Dict">Dict</span>[str, <span title="typing.Any">Any</span>]</code>
           </td>
-          <td><p>kwargs for performing KDE:
+          <td>
+            <div class="doc-md-description">
+              <p>kwargs for performing KDE:
 &lsquo;bandwidth=&rsquo;; either a float, or a string naming a bandwidth
 heuristics, e.g., &lsquo;cv&rsquo; (cross validation), &lsquo;silvermann&rsquo; or &lsquo;scott&rsquo;,
 default &lsquo;cv&rsquo;.
 &lsquo;transform&rsquo;: transform applied to the parameters before doing KDE.
 &lsquo;sample_weights&rsquo;: weights associated with samples. See &lsquo;get_kde&rsquo; for
-more details</p></td>
+more details</p>
+            </div>
+          </td>
           <td>
                 <code>{}</code>
           </td>
@@ -11869,8 +13391,12 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__call__" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether perform weighted KDE with SMC weights or on raw
-particles.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether perform weighted KDE with SMC weights or on raw
+particles.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -11880,8 +13406,12 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__call__" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to return a dictionary with all accepted particles,
-weights, etc. at the end.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to return a dictionary with all accepted particles,
+weights, etc. at the end.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -11889,6 +13419,8 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__call__" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -11902,29 +13434,41 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__call__" class="doc doc-heading">
 <td><code>theta</code></td>          <td>
                 <code>if kde False</code>
           </td>
-          <td><p>accepted parameters of the last population.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>accepted parameters of the last population.</p>
+            </div>
+          </td>
         </tr>
         <tr>
 <td><code>kde</code></td>          <td>
                 <code>if kde True</code>
           </td>
-          <td><p>KDE object fitted on accepted parameters, from which one
-can .sample() and .log_prob().</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>KDE object fitted on accepted parameters, from which one
+can .sample() and .log_prob().</p>
+            </div>
+          </td>
         </tr>
         <tr>
 <td><code>summary</code></td>          <td>
                 <code>if return_summary True</code>
           </td>
-          <td><p>dictionary containing the accepted
+          <td>
+            <div class="doc-md-description">
+              <p>dictionary containing the accepted
 paramters (if kde True), distances and simulated data x of all
-populations.</p></td>
+populations.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/abc/smcabc.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 96</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/abc/smcabc.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 96</span>
 <span class="normal"> 97</span>
 <span class="normal"> 98</span>
 <span class="normal"> 99</span>
@@ -12346,19 +13890,21 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__call__" class="doc doc-heading">
     <span class="k">else</span><span class="p">:</span>
         <span class="k">return</span> <span class="n">final_particles</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.abc.smcabc.SMCABC.__init__" class="doc doc-heading">
-<code class="codehilite language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">simulator</span><span class="p">,</span> <span class="n">prior</span><span class="p">,</span> <span class="n">distance</span><span class="o">=</span><span class="s1">&#39;l2&#39;</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">simulation_batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;gaussian&#39;</span><span class="p">,</span> <span class="n">algorithm_variant</span><span class="o">=</span><span class="s1">&#39;C&#39;</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.abc.smcabc.SMCABC.__init__" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.abc.smcabc.SMCABC.__init__" class="doc doc-heading">
+          <code class=" language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">simulator</span><span class="p">,</span> <span class="n">prior</span><span class="p">,</span> <span class="n">distance</span><span class="o">=</span><span class="s1">&#39;l2&#39;</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">simulation_batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;gaussian&#39;</span><span class="p">,</span> <span class="n">algorithm_variant</span><span class="o">=</span><span class="s1">&#39;C&#39;</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.abc.smcabc.SMCABC.__init__" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -12379,6 +13925,8 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__init__" class="doc doc-heading">
         kernel: the kernel covariance is a scaled version of the covariance of
         the previous population.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -12395,10 +13943,14 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Callable">Callable</span></code>
           </td>
-          <td><p>A function that takes parameters <span class="arithmatex">\(\theta\)</span> and maps them to
+          <td>
+            <div class="doc-md-description">
+              <p>A function that takes parameters <span class="arithmatex">\(\theta\)</span> and maps them to
 simulations, or observations, <code>x</code>, <span class="arithmatex">\(\mathrm{sim}(\theta)\to x\)</span>. Any
 regular Python callable (i.e. function or class with <code>__call__</code> method)
-can be used.</p></td>
+can be used.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -12408,10 +13960,14 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__init__" class="doc doc-heading">
           <td>
                 <code><span title="torch.distributions.Distribution">Distribution</span></code>
           </td>
-          <td><p>A probability distribution that expresses prior knowledge about the
+          <td>
+            <div class="doc-md-description">
+              <p>A probability distribution that expresses prior knowledge about the
 parameters, e.g. which ranges are meaningful for them. Any
 object with <code>.log_prob()</code>and <code>.sample()</code> (for example, a PyTorch
-distribution) can be used.</p></td>
+distribution) can be used.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -12421,8 +13977,12 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Union">Union</span>[str, <span title="typing.Callable">Callable</span>]</code>
           </td>
-          <td><p>Distance function to compare observed and simulated data. Can be
-a custom function or one of <code>l1</code>, <code>l2</code>, <code>mse</code>.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Distance function to compare observed and simulated data. Can be
+a custom function or one of <code>l1</code>, <code>l2</code>, <code>mse</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;l2&#39;</code>
           </td>
@@ -12432,7 +13992,11 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__init__" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of parallel workers to use for simulations.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of parallel workers to use for simulations.</p>
+            </div>
+          </td>
           <td>
                 <code>1</code>
           </td>
@@ -12442,10 +14006,14 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__init__" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of parameter sets that the simulator
+          <td>
+            <div class="doc-md-description">
+              <p>Number of parameter sets that the simulator
 maps to data x at once. If None, we simulate all parameter sets at the
 same time. If &gt;= 1, the simulator has to process data of shape
-(simulation_batch_size, parameter_dimension).</p></td>
+(simulation_batch_size, parameter_dimension).</p>
+            </div>
+          </td>
           <td>
                 <code>1</code>
           </td>
@@ -12455,8 +14023,12 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__init__" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to show a progressbar during simulation and
-sampling.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to show a progressbar during simulation and
+sampling.</p>
+            </div>
+          </td>
           <td>
                 <code>True</code>
           </td>
@@ -12466,7 +14038,11 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__init__" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[str]</code>
           </td>
-          <td><p>Perturbation kernel.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Perturbation kernel.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;gaussian&#39;</code>
           </td>
@@ -12476,7 +14052,11 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__init__" class="doc doc-heading">
           <td>
                 <code>str</code>
           </td>
-          <td><p>Indicating the choice of algorithm variant, A, B, or C.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Indicating the choice of algorithm variant, A, B, or C.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;C&#39;</code>
           </td>
@@ -12484,9 +14064,9 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__init__" class="doc doc-heading">
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/abc/smcabc.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">16</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/abc/smcabc.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">16</span>
 <span class="normal">17</span>
 <span class="normal">18</span>
 <span class="normal">19</span>
@@ -12644,28 +14224,30 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.__init__" class="doc doc-heading">
 
     <span class="bp">self</span><span class="o">.</span><span class="n">_simulate_with_budget</span> <span class="o">=</span> <span class="n">simulate_with_budget</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.abc.smcabc.SMCABC.get_new_kernel" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">get_new_kernel</span><span class="p">(</span><span class="n">thetas</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.abc.smcabc.SMCABC.get_new_kernel" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.abc.smcabc.SMCABC.get_new_kernel" class="doc doc-heading">
+          <code class=" language-python"><span class="n">get_new_kernel</span><span class="p">(</span><span class="n">thetas</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.abc.smcabc.SMCABC.get_new_kernel" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
   
       <p>Return new kernel distribution for a given set of paramters.</p>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/abc/smcabc.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">590</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/abc/smcabc.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">590</span>
 <span class="normal">591</span>
 <span class="normal">592</span>
 <span class="normal">593</span>
@@ -12699,28 +14281,30 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.get_new_kernel" class="doc doc-heading">
     <span class="k">else</span><span class="p">:</span>
         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Kernel, &#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">kernel</span><span class="si">}</span><span class="s2">&#39; not supported.&quot;</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.abc.smcabc.SMCABC.get_particle_ranges" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">get_particle_ranges</span><span class="p">(</span><span class="n">particles</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">samples_per_dim</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.abc.smcabc.SMCABC.get_particle_ranges" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.abc.smcabc.SMCABC.get_particle_ranges" class="doc doc-heading">
+          <code class=" language-python"><span class="n">get_particle_ranges</span><span class="p">(</span><span class="n">particles</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">samples_per_dim</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.abc.smcabc.SMCABC.get_particle_ranges" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
   
       <p>Return range of particles in each parameter dimension.</p>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/abc/smcabc.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">698</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/abc/smcabc.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">698</span>
 <span class="normal">699</span>
 <span class="normal">700</span>
 <span class="normal">701</span>
@@ -12752,19 +14336,21 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.get_particle_ranges" class="doc doc-head
     <span class="k">assert</span> <span class="n">particle_ranges</span><span class="o">.</span><span class="n">ndim</span> <span class="o">&lt;</span> <span class="mi">2</span>
     <span class="k">return</span> <span class="n">particle_ranges</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.abc.smcabc.SMCABC.resample_if_ess_too_small" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">resample_if_ess_too_small</span><span class="p">(</span><span class="n">particles</span><span class="p">,</span> <span class="n">log_weights</span><span class="p">,</span> <span class="n">ess_min</span><span class="p">,</span> <span class="n">pop_idx</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.abc.smcabc.SMCABC.resample_if_ess_too_small" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.abc.smcabc.SMCABC.resample_if_ess_too_small" class="doc doc-heading">
+          <code class=" language-python"><span class="n">resample_if_ess_too_small</span><span class="p">(</span><span class="n">particles</span><span class="p">,</span> <span class="n">log_weights</span><span class="p">,</span> <span class="n">ess_min</span><span class="p">,</span> <span class="n">pop_idx</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.abc.smcabc.SMCABC.resample_if_ess_too_small" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -12772,9 +14358,9 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.resample_if_ess_too_small" class="doc do
       <p>Return resampled particles and uniform weights if effectice sampling size is
 too small.</p>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/abc/smcabc.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">608</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/abc/smcabc.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">608</span>
 <span class="normal">609</span>
 <span class="normal">610</span>
 <span class="normal">611</span>
@@ -12820,19 +14406,21 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.resample_if_ess_too_small" class="doc do
 
     <span class="k">return</span> <span class="n">particles</span><span class="p">,</span> <span class="n">log_weights</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.abc.smcabc.SMCABC.run_lra_update_weights" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">run_lra_update_weights</span><span class="p">(</span><span class="n">particles</span><span class="p">,</span> <span class="n">xs</span><span class="p">,</span> <span class="n">observation</span><span class="p">,</span> <span class="n">log_weights</span><span class="p">,</span> <span class="n">lra_with_weights</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.abc.smcabc.SMCABC.run_lra_update_weights" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.abc.smcabc.SMCABC.run_lra_update_weights" class="doc doc-heading">
+          <code class=" language-python"><span class="n">run_lra_update_weights</span><span class="p">(</span><span class="n">particles</span><span class="p">,</span> <span class="n">xs</span><span class="p">,</span> <span class="n">observation</span><span class="p">,</span> <span class="n">log_weights</span><span class="p">,</span> <span class="n">lra_with_weights</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.abc.smcabc.SMCABC.run_lra_update_weights" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -12842,9 +14430,9 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.run_lra_update_weights" class="doc doc-h
 particles.</p>
 <p>Updates the SMC weights according to the new particles.</p>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/abc/smcabc.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">632</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/abc/smcabc.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">632</span>
 <span class="normal">633</span>
 <span class="normal">634</span>
 <span class="normal">635</span>
@@ -12906,19 +14494,21 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.run_lra_update_weights" class="doc doc-h
 
     <span class="k">return</span> <span class="n">adjusted_particels</span><span class="p">,</span> <span class="n">adjusted_log_weights</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.abc.smcabc.SMCABC.run_sass_set_xo" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">run_sass_set_xo</span><span class="p">(</span><span class="n">num_particles</span><span class="p">,</span> <span class="n">num_pilot_simulations</span><span class="p">,</span> <span class="n">x_o</span><span class="p">,</span> <span class="n">lra</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">sass_expansion_degree</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.abc.smcabc.SMCABC.run_sass_set_xo" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.abc.smcabc.SMCABC.run_sass_set_xo" class="doc doc-heading">
+          <code class=" language-python"><span class="n">run_sass_set_xo</span><span class="p">(</span><span class="n">num_particles</span><span class="p">,</span> <span class="n">num_pilot_simulations</span><span class="p">,</span> <span class="n">x_o</span><span class="p">,</span> <span class="n">lra</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">sass_expansion_degree</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.abc.smcabc.SMCABC.run_sass_set_xo" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -12928,9 +14518,9 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.run_sass_set_xo" class="doc doc-heading"
 num_particles simulations to run the regression for sass.</p>
 <p>Sets self.x_o once the x_shape can be derived from simulations.</p>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/abc/smcabc.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">664</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/abc/smcabc.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">664</span>
 <span class="normal">665</span>
 <span class="normal">666</span>
 <span class="normal">667</span>
@@ -12996,32 +14586,34 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.run_sass_set_xo" class="doc doc-heading"
     <span class="p">)</span>
     <span class="k">return</span> <span class="n">sass_transform</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.abc.smcabc.SMCABC.sample_from_population_with_weights" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">sample_from_population_with_weights</span><span class="p">(</span><span class="n">particles</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">num_samples</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span></code>
+
+<h2 id="sbi.inference.abc.smcabc.SMCABC.sample_from_population_with_weights" class="doc doc-heading">
+          <code class=" language-python"><span class="n">sample_from_population_with_weights</span><span class="p">(</span><span class="n">particles</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">num_samples</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span></code>
   
   <span class="doc doc-labels">
       <small class="doc doc-label doc-label-staticmethod"><code>staticmethod</code></small>
   </span>
 
-<a href="#sbi.inference.abc.smcabc.SMCABC.sample_from_population_with_weights" class="headerlink" title="Permanent link">&para;</a></h4>
+<a href="#sbi.inference.abc.smcabc.SMCABC.sample_from_population_with_weights" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
   
       <p>Return samples from particles sampled with weights.</p>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/abc/smcabc.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">507</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/abc/smcabc.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">507</span>
 <span class="normal">508</span>
 <span class="normal">509</span>
 <span class="normal">510</span>
@@ -13049,7 +14641,7 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.sample_from_population_with_weights" cla
     <span class="c1"># return those indices from trace</span>
     <span class="k">return</span> <span class="n">particles</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
@@ -13067,16 +14659,10 @@ <h4 id="sbi.inference.abc.smcabc.SMCABC.sample_from_population_with_weights" cla
 
 
 
-<h3 id="sbi.inference.posteriors.direct_posterior.DirectPosterior" class="doc doc-heading">
-        <code>sbi.inference.posteriors.direct_posterior.DirectPosterior</code>
-
-
-<a href="#sbi.inference.posteriors.direct_posterior.DirectPosterior" class="headerlink" title="Permanent link">&para;</a></h3>
-
-
+<a id="sbi.inference.posteriors.direct_posterior.DirectPosterior"></a>
   <div class="doc doc-contents first">
-      <p class="doc doc-class-bases">
-        Bases: <code><span title="sbi.inference.posteriors.base_posterior.NeuralPosterior">NeuralPosterior</span></code></p>
+          <p class="doc doc-class-bases">
+            Bases: <code><span title="sbi.inference.posteriors.base_posterior.NeuralPosterior">NeuralPosterior</span></code></p>
 
   
       <p>Posterior <span class="arithmatex">\(p(\theta|x_o)\)</span> with <code>log_prob()</code> and <code>sample()</code> methods, only
@@ -13091,10 +14677,9 @@ <h3 id="sbi.inference.posteriors.direct_posterior.DirectPosterior" class="doc do
 - reject samples that lie outside of the prior bounds.<br/><br/>
 This class can not be used in combination with SNLE or SNRE.</p>
 
-
-        <details class="quote">
-          <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/direct_posterior.py</code></summary>
-          <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 20</span>
+            <details class="quote">
+              <summary>Source code in <code>sbi/inference/posteriors/direct_posterior.py</code></summary>
+              <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 20</span>
 <span class="normal"> 21</span>
 <span class="normal"> 22</span>
 <span class="normal"> 23</span>
@@ -13682,7 +15267,7 @@ <h3 id="sbi.inference.posteriors.direct_posterior.DirectPosterior" class="doc do
             <span class="n">force_update</span><span class="o">=</span><span class="n">force_update</span><span class="p">,</span>
         <span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-        </details>
+            </details>
 
   
 
@@ -13696,19 +15281,23 @@ <h3 id="sbi.inference.posteriors.direct_posterior.DirectPosterior" class="doc do
 
 
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.__init__" class="doc doc-heading">
-<code class="codehilite language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">posterior_estimator</span><span class="p">,</span> <span class="n">prior</span><span class="p">,</span> <span class="n">max_sampling_batch_size</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">x_shape</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">enable_transform</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.posteriors.direct_posterior.DirectPosterior.__init__" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.__init__" class="doc doc-heading">
+          <code class=" language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">posterior_estimator</span><span class="p">,</span> <span class="n">prior</span><span class="p">,</span> <span class="n">max_sampling_batch_size</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">x_shape</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">enable_transform</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.posteriors.direct_posterior.DirectPosterior.__init__" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
   
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -13725,7 +15314,11 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.__init__" clas
           <td>
                 <code><span title="torch.distributions.Distribution">Distribution</span></code>
           </td>
-          <td><p>Prior distribution with <code>.log_prob()</code> and <code>.sample()</code>.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Prior distribution with <code>.log_prob()</code> and <code>.sample()</code>.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -13733,9 +15326,13 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.__init__" clas
         <tr>
           <td><code>posterior_estimator</code></td>
           <td>
-                <code><span title="pyknos.nflows.flows">flows</span>.<span title="pyknos.nflows.flows.Flow">Flow</span></code>
+                <code><span title="pyknos.nflows.flows.Flow">Flow</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>The trained neural posterior.</p>
+            </div>
           </td>
-          <td><p>The trained neural posterior.</p></td>
           <td>
               <em>required</em>
           </td>
@@ -13745,8 +15342,12 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.__init__" clas
           <td>
                 <code>int</code>
           </td>
-          <td><p>Batchsize of samples being drawn from
-the proposal at every iteration.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Batchsize of samples being drawn from
+the proposal at every iteration.</p>
+            </div>
+          </td>
           <td>
                 <code>10000</code>
           </td>
@@ -13756,8 +15357,12 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.__init__" clas
           <td>
                 <code><span title="typing.Optional">Optional</span>[str]</code>
           </td>
-          <td><p>Training device, e.g., &ldquo;cpu&rdquo;, &ldquo;cuda&rdquo; or &ldquo;cuda:0&rdquo;. If None,
-<code>potential_fn.device</code> is used.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Training device, e.g., &ldquo;cpu&rdquo;, &ldquo;cuda&rdquo; or &ldquo;cuda:0&rdquo;. If None,
+<code>potential_fn.device</code> is used.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -13765,10 +15370,14 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.__init__" clas
         <tr>
           <td><code>x_shape</code></td>
           <td>
-                <code><span title="typing.Optional">Optional</span>[torch.<span title="torch.Size">Size</span>]</code>
+                <code><span title="typing.Optional">Optional</span>[<span title="torch.Size">Size</span>]</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Shape of a single simulator output. If passed, it is used to check
+the shape of the observed data and give a descriptive error.</p>
+            </div>
           </td>
-          <td><p>Shape of a single simulator output. If passed, it is used to check
-the shape of the observed data and give a descriptive error.</p></td>
           <td>
                 <code>None</code>
           </td>
@@ -13778,9 +15387,13 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.__init__" clas
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to transform parameters to unconstrained space
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to transform parameters to unconstrained space
 during MAP optimization. When False, an identity transform will be
-returned for <code>theta_transform</code>.</p></td>
+returned for <code>theta_transform</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>True</code>
           </td>
@@ -13788,9 +15401,9 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.__init__" clas
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/direct_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">34</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/direct_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">34</span>
 <span class="normal">35</span>
 <span class="normal">36</span>
 <span class="normal">37</span>
@@ -13888,19 +15501,21 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.__init__" clas
     <span class="bp">self</span><span class="o">.</span><span class="n">_purpose</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;It samples the posterior network and rejects samples that</span>
 <span class="s2">        lie outside of the prior bounds.&quot;&quot;&quot;</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.leakage_correction" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">leakage_correction</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">num_rejection_samples</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">force_update</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">rejection_sampling_batch_size</span><span class="o">=</span><span class="mi">10000</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.posteriors.direct_posterior.DirectPosterior.leakage_correction" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.leakage_correction" class="doc doc-heading">
+          <code class=" language-python"><span class="n">leakage_correction</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">num_rejection_samples</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">force_update</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">rejection_sampling_batch_size</span><span class="o">=</span><span class="mi">10000</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.posteriors.direct_posterior.DirectPosterior.leakage_correction" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -13913,6 +15528,8 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.leakage_correc
 is estimated only once for <code>self.default_x</code> and saved for later. We
 re-evaluate only whenever a new <code>x</code> is passed.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -13929,7 +15546,11 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.leakage_correc
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of samples used to estimate correction factor.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of samples used to estimate correction factor.</p>
+            </div>
+          </td>
           <td>
                 <code>10000</code>
           </td>
@@ -13939,7 +15560,11 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.leakage_correc
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to show a progress bar during sampling.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to show a progress bar during sampling.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -13949,7 +15574,11 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.leakage_correc
           <td>
                 <code>int</code>
           </td>
-          <td><p>Batch size for rejection sampling.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Batch size for rejection sampling.</p>
+            </div>
+          </td>
           <td>
                 <code>10000</code>
           </td>
@@ -13957,6 +15586,8 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.leakage_correc
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -13970,14 +15601,18 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.leakage_correc
           <td>
                 <code><span title="torch.Tensor">Tensor</span></code>
           </td>
-          <td><p>Saved or newly-estimated correction factor (as a scalar <code>Tensor</code>).</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Saved or newly-estimated correction factor (as a scalar <code>Tensor</code>).</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/direct_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">194</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/direct_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">194</span>
 <span class="normal">195</span>
 <span class="normal">196</span>
 <span class="normal">197</span>
@@ -14083,25 +15718,29 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.leakage_correc
 
     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_leakage_density_correction_factor</span>  <span class="c1"># type: ignore</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.log_prob" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">log_prob</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">norm_posterior</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">track_gradients</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">leakage_correction_params</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.posteriors.direct_posterior.DirectPosterior.log_prob" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.log_prob" class="doc doc-heading">
+          <code class=" language-python"><span class="n">log_prob</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">norm_posterior</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">track_gradients</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">leakage_correction_params</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.posteriors.direct_posterior.DirectPosterior.log_prob" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
   
       <p>Returns the log-probability of the posterior <span class="arithmatex">\(p(\theta|x)\)</span>.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -14118,7 +15757,11 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.log_prob" clas
           <td>
                 <code><span title="torch.Tensor">Tensor</span></code>
           </td>
-          <td><p>Parameters <span class="arithmatex">\(\theta\)</span>.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Parameters <span class="arithmatex">\(\theta\)</span>.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -14128,13 +15771,17 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.log_prob" clas
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to enforce a normalized posterior density.
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to enforce a normalized posterior density.
 Renormalization of the posterior is useful when some
 probability falls out or leaks out of the prescribed prior support.
 The normalizing factor is calculated via rejection sampling, so if you
 need speedier but unnormalized log posterior estimates set here
 <code>norm_posterior=False</code>. The returned log posterior is set to
--∞ outside of the prior support regardless of this setting.</p></td>
+-∞ outside of the prior support regardless of this setting.</p>
+            </div>
+          </td>
           <td>
                 <code>True</code>
           </td>
@@ -14144,9 +15791,13 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.log_prob" clas
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether the returned tensor supports tracking gradients.
+          <td>
+            <div class="doc-md-description">
+              <p>Whether the returned tensor supports tracking gradients.
 This can be helpful for e.g. sensitivity analysis, but increases memory
-consumption.</p></td>
+consumption.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -14156,11 +15807,15 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.log_prob" clas
           <td>
                 <code><span title="typing.Optional">Optional</span>[dict]</code>
           </td>
-          <td><p>A <code>dict</code> of keyword arguments to override the
+          <td>
+            <div class="doc-md-description">
+              <p>A <code>dict</code> of keyword arguments to override the
 default values of <code>leakage_correction()</code>. Possible options are:
 <code>num_rejection_samples</code>, <code>force_update</code>, <code>show_progress_bars</code>, and
 <code>rejection_sampling_batch_size</code>.
-These parameters only have an effect if <code>norm_posterior=True</code>.</p></td>
+These parameters only have an effect if <code>norm_posterior=True</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -14168,6 +15823,8 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.log_prob" clas
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -14181,20 +15838,28 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.log_prob" clas
           <td>
                 <code><span title="torch.Tensor">Tensor</span></code>
           </td>
-          <td><p><code>(len(θ),)</code>-shaped log posterior probability <span class="arithmatex">\(\log p(\theta|x)\)</span> for θ in the</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p><code>(len(θ),)</code>-shaped log posterior probability <span class="arithmatex">\(\log p(\theta|x)\)</span> for θ in the</p>
+            </div>
+          </td>
         </tr>
         <tr>
           <td>
                 <code><span title="torch.Tensor">Tensor</span></code>
           </td>
-          <td><p>support of the prior, -∞ (corresponding to 0 probability) outside.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>support of the prior, -∞ (corresponding to 0 probability) outside.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/direct_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">129</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/direct_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">129</span>
 <span class="normal">130</span>
 <span class="normal">131</span>
 <span class="normal">132</span>
@@ -14322,19 +15987,21 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.log_prob" clas
 
         <span class="k">return</span> <span class="n">masked_log_prob</span> <span class="o">-</span> <span class="n">log_factor</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.map" class="doc doc-heading">
-<code class="codehilite language-python"><span class="nb">map</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">num_to_optimize</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">init_method</span><span class="o">=</span><span class="s1">&#39;posterior&#39;</span><span class="p">,</span> <span class="n">num_init_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">save_best_every</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">force_update</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.posteriors.direct_posterior.DirectPosterior.map" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.map" class="doc doc-heading">
+          <code class=" language-python"><span class="nb">map</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">num_to_optimize</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">init_method</span><span class="o">=</span><span class="s1">&#39;posterior&#39;</span><span class="p">,</span> <span class="n">num_init_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">save_best_every</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">force_update</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.posteriors.direct_posterior.DirectPosterior.map" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -14351,6 +16018,8 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.map" class="do
 <p>For developers: if the prior is a <code>BoxUniform</code>, we carry out the optimization
 in unbounded space and transform the result back into bounded space.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -14367,7 +16036,11 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.map" class="do
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="torch.Tensor">Tensor</span>]</code>
           </td>
-          <td><p>Deprecated - use <code>.set_default_x()</code> prior to <code>.map()</code>.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Deprecated - use <code>.set_default_x()</code> prior to <code>.map()</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -14377,8 +16050,12 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.map" class="do
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of optimization steps that the algorithm takes
-to find the MAP.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of optimization steps that the algorithm takes
+to find the MAP.</p>
+            </div>
+          </td>
           <td>
                 <code>1000</code>
           </td>
@@ -14388,7 +16065,11 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.map" class="do
           <td>
                 <code>float</code>
           </td>
-          <td><p>Learning rate of the optimizer.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Learning rate of the optimizer.</p>
+            </div>
+          </td>
           <td>
                 <code>0.01</code>
           </td>
@@ -14398,10 +16079,14 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.map" class="do
           <td>
                 <code><span title="typing.Union">Union</span>[str, <span title="torch.Tensor">Tensor</span>]</code>
           </td>
-          <td><p>How to select the starting parameters for the optimization. If
+          <td>
+            <div class="doc-md-description">
+              <p>How to select the starting parameters for the optimization. If
 it is a string, it can be either [<code>posterior</code>, <code>prior</code>], which samples
 the respective distribution <code>num_init_samples</code> times. If it is a
-tensor, the tensor will be used as init locations.</p></td>
+tensor, the tensor will be used as init locations.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;posterior&#39;</code>
           </td>
@@ -14411,8 +16096,12 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.map" class="do
           <td>
                 <code>int</code>
           </td>
-          <td><p>Draw this number of samples from the posterior and
-evaluate the log-probability of all of them.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Draw this number of samples from the posterior and
+evaluate the log-probability of all of them.</p>
+            </div>
+          </td>
           <td>
                 <code>1000</code>
           </td>
@@ -14422,9 +16111,13 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.map" class="do
           <td>
                 <code>int</code>
           </td>
-          <td><p>From the drawn <code>num_init_samples</code>, use the
+          <td>
+            <div class="doc-md-description">
+              <p>From the drawn <code>num_init_samples</code>, use the
 <code>num_to_optimize</code> with highest log-probability as the initial points
-for the optimization.</p></td>
+for the optimization.</p>
+            </div>
+          </td>
           <td>
                 <code>100</code>
           </td>
@@ -14434,10 +16127,14 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.map" class="do
           <td>
                 <code>int</code>
           </td>
-          <td><p>The best log-probability is computed, saved in the
+          <td>
+            <div class="doc-md-description">
+              <p>The best log-probability is computed, saved in the
 <code>map</code>-attribute, and printed every <code>save_best_every</code>-th iteration.
 Computing the best log-probability creates a significant overhead
-(thus, the default is <code>10</code>.)</p></td>
+(thus, the default is <code>10</code>.)</p>
+            </div>
+          </td>
           <td>
                 <code>10</code>
           </td>
@@ -14447,8 +16144,12 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.map" class="do
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to show a progressbar during sampling from the
-posterior.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to show a progressbar during sampling from the
+posterior.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -14458,8 +16159,12 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.map" class="do
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to re-calculate the MAP when x is unchanged and
-have a cached value.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to re-calculate the MAP when x is unchanged and
+have a cached value.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -14468,8 +16173,12 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.map" class="do
           <td><code>log_prob_kwargs</code></td>
           <td>
           </td>
-          <td><p>Will be empty for SNLE and SNRE. Will contain
-{&lsquo;norm_posterior&rsquo;: True} for SNPE.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Will be empty for SNLE and SNRE. Will contain
+{&lsquo;norm_posterior&rsquo;: True} for SNPE.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -14477,6 +16186,8 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.map" class="do
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -14490,14 +16201,18 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.map" class="do
           <td>
                 <code><span title="torch.Tensor">Tensor</span></code>
           </td>
-          <td><p>The MAP estimate.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The MAP estimate.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/direct_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">248</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/direct_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">248</span>
 <span class="normal">249</span>
 <span class="normal">250</span>
 <span class="normal">251</span>
@@ -14629,25 +16344,29 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.map" class="do
         <span class="n">force_update</span><span class="o">=</span><span class="n">force_update</span><span class="p">,</span>
     <span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.sample" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">sample</span><span class="p">(</span><span class="n">sample_shape</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">(),</span> <span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_sampling_batch_size</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">sample_with</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.posteriors.direct_posterior.DirectPosterior.sample" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.sample" class="doc doc-heading">
+          <code class=" language-python"><span class="n">sample</span><span class="p">(</span><span class="n">sample_shape</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">(),</span> <span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_sampling_batch_size</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">sample_with</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.posteriors.direct_posterior.DirectPosterior.sample" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
   
       <p>Return samples from posterior distribution <span class="arithmatex">\(p(\theta|x)\)</span>.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -14664,11 +16383,15 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.sample" class=
           <td>
                 <code><span title="sbi.types.Shape">Shape</span></code>
           </td>
-          <td><p>Desired shape of samples that are drawn from posterior. If
+          <td>
+            <div class="doc-md-description">
+              <p>Desired shape of samples that are drawn from posterior. If
 sample_shape is multidimensional we simply draw <code>sample_shape.numel()</code>
-samples and then reshape into the desired shape.</p></td>
+samples and then reshape into the desired shape.</p>
+            </div>
+          </td>
           <td>
-                <code>torch.Size()</code>
+                <code><span title="torch.Size">Size</span>()</code>
           </td>
         </tr>
         <tr>
@@ -14676,8 +16399,12 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.sample" class=
           <td>
                 <code><span title="typing.Optional">Optional</span>[str]</code>
           </td>
-          <td><p>This argument only exists to keep backward-compatibility with
-<code>sbi</code> v0.17.2 or older. If it is set, we instantly raise an error.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>This argument only exists to keep backward-compatibility with
+<code>sbi</code> v0.17.2 or older. If it is set, we instantly raise an error.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -14687,7 +16414,11 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.sample" class=
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to show sampling progress monitor.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to show sampling progress monitor.</p>
+            </div>
+          </td>
           <td>
                 <code>True</code>
           </td>
@@ -14695,9 +16426,9 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.sample" class=
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/direct_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 84</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/direct_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 84</span>
 <span class="normal"> 85</span>
 <span class="normal"> 86</span>
 <span class="normal"> 87</span>
@@ -14785,7 +16516,7 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.sample" class=
     <span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
     <span class="k">return</span> <span class="n">samples</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
@@ -14802,16 +16533,10 @@ <h4 id="sbi.inference.posteriors.direct_posterior.DirectPosterior.sample" class=
 
 
 
-<h3 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior" class="doc doc-heading">
-        <code>sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior</code>
-
-
-<a href="#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior" class="headerlink" title="Permanent link">&para;</a></h3>
-
-
+<a id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior"></a>
   <div class="doc doc-contents first">
-      <p class="doc doc-class-bases">
-        Bases: <code><span title="sbi.inference.posteriors.base_posterior.NeuralPosterior">NeuralPosterior</span></code></p>
+          <p class="doc doc-class-bases">
+            Bases: <code><span title="sbi.inference.posteriors.base_posterior.NeuralPosterior">NeuralPosterior</span></code></p>
 
   
       <p>Provides importance sampling to sample from the posterior.<br/><br/>
@@ -14821,10 +16546,9 @@ <h3 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
 perform importance sampling (with <code>.sample()</code>) and to draw approximate samples with
 sampling-importance-resampling (SIR) (with <code>.sir_sample()</code>)</p>
 
-
-        <details class="quote">
-          <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/importance_posterior.py</code></summary>
-          <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 16</span>
+            <details class="quote">
+              <summary>Source code in <code>sbi/inference/posteriors/importance_posterior.py</code></summary>
+              <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 16</span>
 <span class="normal"> 17</span>
 <span class="normal"> 18</span>
 <span class="normal"> 19</span>
@@ -15448,7 +17172,7 @@ <h3 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
             <span class="n">force_update</span><span class="o">=</span><span class="n">force_update</span><span class="p">,</span>
         <span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-        </details>
+            </details>
 
   
 
@@ -15462,19 +17186,23 @@ <h3 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
 
 
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.__init__" class="doc doc-heading">
-<code class="codehilite language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">potential_fn</span><span class="p">,</span> <span class="n">proposal</span><span class="p">,</span> <span class="n">theta_transform</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;sir&#39;</span><span class="p">,</span> <span class="n">oversampling_factor</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">max_sampling_batch_size</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">x_shape</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.__init__" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.__init__" class="doc doc-heading">
+          <code class=" language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">potential_fn</span><span class="p">,</span> <span class="n">proposal</span><span class="p">,</span> <span class="n">theta_transform</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;sir&#39;</span><span class="p">,</span> <span class="n">oversampling_factor</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">max_sampling_batch_size</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">x_shape</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.__init__" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
   
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -15491,7 +17219,11 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
           <td>
                 <code><span title="typing.Callable">Callable</span></code>
           </td>
-          <td><p>The potential function from which to draw samples.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The potential function from which to draw samples.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -15501,7 +17233,11 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
           <td>
                 <code><span title="typing.Any">Any</span></code>
           </td>
-          <td><p>The proposal distribution.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The proposal distribution.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -15511,8 +17247,12 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="sbi.types.TorchTransform">TorchTransform</span>]</code>
           </td>
-          <td><p>Transformation that is applied to parameters. Is not used
-during but only when calling <code>.map()</code>.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Transformation that is applied to parameters. Is not used
+during but only when calling <code>.map()</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -15522,11 +17262,15 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
           <td>
                 <code>str</code>
           </td>
-          <td><p>Either of [<code>sir</code>|<code>importance</code>]. This sets the behavior of the
+          <td>
+            <div class="doc-md-description">
+              <p>Either of [<code>sir</code>|<code>importance</code>]. This sets the behavior of the
 <code>.sample()</code> method. With <code>sir</code>, approximate posterior samples are
 generated with sampling importance resampling (SIR). With
 <code>importance</code>, the <code>.sample()</code> method returns a tuple of samples and
-corresponding importance weights.</p></td>
+corresponding importance weights.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;sir&#39;</code>
           </td>
@@ -15536,8 +17280,12 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of proposed samples from which only one is
-selected based on its importance weight.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of proposed samples from which only one is
+selected based on its importance weight.</p>
+            </div>
+          </td>
           <td>
                 <code>32</code>
           </td>
@@ -15547,8 +17295,12 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
           <td>
                 <code>int</code>
           </td>
-          <td><p>The batch size of samples being drawn from the
-proposal at every iteration.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The batch size of samples being drawn from the
+proposal at every iteration.</p>
+            </div>
+          </td>
           <td>
                 <code>10000</code>
           </td>
@@ -15558,8 +17310,12 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
           <td>
                 <code><span title="typing.Optional">Optional</span>[str]</code>
           </td>
-          <td><p>Device on which to sample, e.g., &ldquo;cpu&rdquo;, &ldquo;cuda&rdquo; or &ldquo;cuda:0&rdquo;. If
-None, <code>potential_fn.device</code> is used.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Device on which to sample, e.g., &ldquo;cpu&rdquo;, &ldquo;cuda&rdquo; or &ldquo;cuda:0&rdquo;. If
+None, <code>potential_fn.device</code> is used.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -15567,10 +17323,14 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
         <tr>
           <td><code>x_shape</code></td>
           <td>
-                <code><span title="typing.Optional">Optional</span>[torch.<span title="torch.Size">Size</span>]</code>
+                <code><span title="typing.Optional">Optional</span>[<span title="torch.Size">Size</span>]</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Shape of a single simulator output. If passed, it is used to check
+the shape of the observed data and give a descriptive error.</p>
+            </div>
           </td>
-          <td><p>Shape of a single simulator output. If passed, it is used to check
-the shape of the observed data and give a descriptive error.</p></td>
           <td>
                 <code>None</code>
           </td>
@@ -15578,9 +17338,9 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/importance_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">25</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/importance_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">25</span>
 <span class="normal">26</span>
 <span class="normal">27</span>
 <span class="normal">28</span>
@@ -15680,25 +17440,29 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
         <span class="s2">&quot;.log_prob().&quot;</span>
     <span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.estimate_normalization_constant" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">estimate_normalization_constant</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">num_samples</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">force_update</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.estimate_normalization_constant" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.estimate_normalization_constant" class="doc doc-heading">
+          <code class=" language-python"><span class="n">estimate_normalization_constant</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">num_samples</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">force_update</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.estimate_normalization_constant" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
   
       <p>Returns the normalization constant via importance sampling.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -15715,7 +17479,11 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of importance samples used for the estimate.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of importance samples used for the estimate.</p>
+            </div>
+          </td>
           <td>
                 <code>10000</code>
           </td>
@@ -15725,8 +17493,12 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to re-calculate the normlization constant when x is
-unchanged and have a cached value.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to re-calculate the normlization constant when x is
+unchanged and have a cached value.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -15734,9 +17506,9 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/importance_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">118</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/importance_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">118</span>
 <span class="normal">119</span>
 <span class="normal">120</span>
 <span class="normal">121</span>
@@ -15806,19 +17578,21 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
 
     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_normalization_constant</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_device</span><span class="p">)</span>  <span class="c1"># type: ignore</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.log_prob" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">log_prob</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">track_gradients</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">normalization_constant_params</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.log_prob" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.log_prob" class="doc doc-heading">
+          <code class=" language-python"><span class="n">log_prob</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">track_gradients</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">normalization_constant_params</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.log_prob" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -15826,6 +17600,8 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
       <p>Returns the log-probability of theta under the posterior.</p>
 <p>The normalization constant is estimated with importance sampling.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -15842,7 +17618,11 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
           <td>
                 <code><span title="torch.Tensor">Tensor</span></code>
           </td>
-          <td><p>Parameters <span class="arithmatex">\(\theta\)</span>.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Parameters <span class="arithmatex">\(\theta\)</span>.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -15852,9 +17632,13 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether the returned tensor supports tracking gradients.
+          <td>
+            <div class="doc-md-description">
+              <p>Whether the returned tensor supports tracking gradients.
 This can be helpful for e.g. sensitivity analysis, but increases memory
-consumption.</p></td>
+consumption.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -15864,8 +17648,12 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
           <td>
                 <code><span title="typing.Optional">Optional</span>[dict]</code>
           </td>
-          <td><p>Parameters passed on to
-<code>estimate_normalization_constant()</code>.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Parameters passed on to
+<code>estimate_normalization_constant()</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -15873,6 +17661,8 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -15886,14 +17676,18 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
           <td>
                 <code><span title="torch.Tensor">Tensor</span></code>
           </td>
-          <td><p><code>len($\theta$)</code>-shaped log-probability.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p><code>len($\theta$)</code>-shaped log-probability.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/importance_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 76</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/importance_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 76</span>
 <span class="normal"> 77</span>
 <span class="normal"> 78</span>
 <span class="normal"> 79</span>
@@ -15975,19 +17769,21 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
             <span class="bp">self</span><span class="o">.</span><span class="n">_device</span>
         <span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.map" class="doc doc-heading">
-<code class="codehilite language-python"><span class="nb">map</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">num_to_optimize</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">init_method</span><span class="o">=</span><span class="s1">&#39;proposal&#39;</span><span class="p">,</span> <span class="n">num_init_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">save_best_every</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">force_update</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.map" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.map" class="doc doc-heading">
+          <code class=" language-python"><span class="nb">map</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">num_to_optimize</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">init_method</span><span class="o">=</span><span class="s1">&#39;proposal&#39;</span><span class="p">,</span> <span class="n">num_init_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">save_best_every</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">force_update</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.map" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -16004,6 +17800,8 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
 <p>For developers: if the prior is a <code>BoxUniform</code>, we carry out the optimization
 in unbounded space and transform the result back into bounded space.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -16020,7 +17818,11 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="torch.Tensor">Tensor</span>]</code>
           </td>
-          <td><p>Deprecated - use <code>.set_default_x()</code> prior to <code>.map()</code>.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Deprecated - use <code>.set_default_x()</code> prior to <code>.map()</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -16030,8 +17832,12 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of optimization steps that the algorithm takes
-to find the MAP.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of optimization steps that the algorithm takes
+to find the MAP.</p>
+            </div>
+          </td>
           <td>
                 <code>1000</code>
           </td>
@@ -16041,7 +17847,11 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
           <td>
                 <code>float</code>
           </td>
-          <td><p>Learning rate of the optimizer.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Learning rate of the optimizer.</p>
+            </div>
+          </td>
           <td>
                 <code>0.01</code>
           </td>
@@ -16051,10 +17861,14 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
           <td>
                 <code><span title="typing.Union">Union</span>[str, <span title="torch.Tensor">Tensor</span>]</code>
           </td>
-          <td><p>How to select the starting parameters for the optimization. If
+          <td>
+            <div class="doc-md-description">
+              <p>How to select the starting parameters for the optimization. If
 it is a string, it can be either [<code>posterior</code>, <code>prior</code>], which samples
 the respective distribution <code>num_init_samples</code> times. If it is a
-tensor, the tensor will be used as init locations.</p></td>
+tensor, the tensor will be used as init locations.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;proposal&#39;</code>
           </td>
@@ -16064,8 +17878,12 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
           <td>
                 <code>int</code>
           </td>
-          <td><p>Draw this number of samples from the posterior and
-evaluate the log-probability of all of them.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Draw this number of samples from the posterior and
+evaluate the log-probability of all of them.</p>
+            </div>
+          </td>
           <td>
                 <code>1000</code>
           </td>
@@ -16075,9 +17893,13 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
           <td>
                 <code>int</code>
           </td>
-          <td><p>From the drawn <code>num_init_samples</code>, use the
+          <td>
+            <div class="doc-md-description">
+              <p>From the drawn <code>num_init_samples</code>, use the
 <code>num_to_optimize</code> with highest log-probability as the initial points
-for the optimization.</p></td>
+for the optimization.</p>
+            </div>
+          </td>
           <td>
                 <code>100</code>
           </td>
@@ -16087,10 +17909,14 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
           <td>
                 <code>int</code>
           </td>
-          <td><p>The best log-probability is computed, saved in the
+          <td>
+            <div class="doc-md-description">
+              <p>The best log-probability is computed, saved in the
 <code>map</code>-attribute, and printed every <code>save_best_every</code>-th iteration.
 Computing the best log-probability creates a significant overhead
-(thus, the default is <code>10</code>.)</p></td>
+(thus, the default is <code>10</code>.)</p>
+            </div>
+          </td>
           <td>
                 <code>10</code>
           </td>
@@ -16100,8 +17926,12 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to show a progressbar during sampling from the
-posterior.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to show a progressbar during sampling from the
+posterior.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -16111,8 +17941,12 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to re-calculate the MAP when x is unchanged and
-have a cached value.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to re-calculate the MAP when x is unchanged and
+have a cached value.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -16121,8 +17955,12 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
           <td><code>log_prob_kwargs</code></td>
           <td>
           </td>
-          <td><p>Will be empty for SNLE and SNRE. Will contain
-{&lsquo;norm_posterior&rsquo;: True} for SNPE.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Will be empty for SNLE and SNRE. Will contain
+{&lsquo;norm_posterior&rsquo;: True} for SNPE.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -16130,6 +17968,8 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -16143,14 +17983,18 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
           <td>
                 <code><span title="torch.Tensor">Tensor</span></code>
           </td>
-          <td><p>The MAP estimate.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The MAP estimate.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/importance_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">262</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/importance_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">262</span>
 <span class="normal">263</span>
 <span class="normal">264</span>
 <span class="normal">265</span>
@@ -16282,25 +18126,29 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
         <span class="n">force_update</span><span class="o">=</span><span class="n">force_update</span><span class="p">,</span>
     <span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.sample" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">sample</span><span class="p">(</span><span class="n">sample_shape</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">(),</span> <span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">oversampling_factor</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">max_sampling_batch_size</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">sample_with</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.sample" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.sample" class="doc doc-heading">
+          <code class=" language-python"><span class="n">sample</span><span class="p">(</span><span class="n">sample_shape</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">(),</span> <span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">oversampling_factor</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">max_sampling_batch_size</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">sample_with</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.sample" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
   
       <p>Return samples from the approximate posterior distribution.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -16317,9 +18165,13 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
           <td>
                 <code><span title="sbi.types.Shape">Shape</span></code>
           </td>
-          <td><p><em>description</em></p></td>
           <td>
-                <code>torch.Size()</code>
+            <div class="doc-md-description">
+              <p><em>description</em></p>
+            </div>
+          </td>
+          <td>
+                <code><span title="torch.Size">Size</span>()</code>
           </td>
         </tr>
         <tr>
@@ -16327,7 +18179,11 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="torch.Tensor">Tensor</span>]</code>
           </td>
-          <td><p><em>description</em></p></td>
+          <td>
+            <div class="doc-md-description">
+              <p><em>description</em></p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -16335,9 +18191,9 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/importance_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">154</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/importance_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">154</span>
 <span class="normal">155</span>
 <span class="normal">156</span>
 <span class="normal">157</span>
@@ -16403,7 +18259,7 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
     <span class="k">else</span><span class="p">:</span>
         <span class="k">raise</span> <span class="ne">NameError</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
@@ -16420,26 +18276,19 @@ <h4 id="sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterio
 
 
 
-<h3 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior" class="doc doc-heading">
-        <code>sbi.inference.posteriors.mcmc_posterior.MCMCPosterior</code>
-
-
-<a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior" class="headerlink" title="Permanent link">&para;</a></h3>
-
-
+<a id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior"></a>
   <div class="doc doc-contents first">
-      <p class="doc doc-class-bases">
-        Bases: <code><span title="sbi.inference.posteriors.base_posterior.NeuralPosterior">NeuralPosterior</span></code></p>
+          <p class="doc doc-class-bases">
+            Bases: <code><span title="sbi.inference.posteriors.base_posterior.NeuralPosterior">NeuralPosterior</span></code></p>
 
   
       <p>Provides MCMC to sample from the posterior.<br/><br/>
 SNLE or SNRE train neural networks to approximate the likelihood(-ratios).
 <code>MCMCPosterior</code> allows to sample from the posterior with MCMC.</p>
 
-
-        <details class="quote">
-          <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/mcmc_posterior.py</code></summary>
-          <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 36</span>
+            <details class="quote">
+              <summary>Source code in <code>sbi/inference/posteriors/mcmc_posterior.py</code></summary>
+              <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 36</span>
 <span class="normal"> 37</span>
 <span class="normal"> 38</span>
 <span class="normal"> 39</span>
@@ -17769,7 +19618,7 @@ <h3 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior" class="doc doc-he
 
         <span class="k">return</span> <span class="n">inference_data</span>
 </code></pre></div></td></tr></table></div>
-        </details>
+            </details>
 
   
 
@@ -17785,15 +19634,16 @@ <h3 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior" class="doc doc-he
 
 
 
-<h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.mcmc_method" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">mcmc_method</span><span class="p">:</span> <span class="nb">str</span></code>
+
+<h2 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.mcmc_method" class="doc doc-heading">
+          <code class=" language-python"><span class="n">mcmc_method</span><span class="p">:</span> <span class="nb">str</span></code>
   
   <span class="doc doc-labels">
       <small class="doc doc-label doc-label-property"><code>property</code></small>
       <small class="doc doc-label doc-label-writable"><code>writable</code></small>
   </span>
 
-<a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.mcmc_method" class="headerlink" title="Permanent link">&para;</a></h4>
+<a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.mcmc_method" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -17807,14 +19657,15 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.mcmc_method" class
 
 
 
-<h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.posterior_sampler" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">posterior_sampler</span></code>
+
+<h2 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.posterior_sampler" class="doc doc-heading">
+          <code class=" language-python"><span class="n">posterior_sampler</span></code>
   
   <span class="doc doc-labels">
       <small class="doc doc-label doc-label-property"><code>property</code></small>
   </span>
 
-<a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.posterior_sampler" class="headerlink" title="Permanent link">&para;</a></h4>
+<a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.posterior_sampler" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -17826,19 +19677,23 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.posterior_sampler"
 
 
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.__init__" class="doc doc-heading">
-<code class="codehilite language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">potential_fn</span><span class="p">,</span> <span class="n">proposal</span><span class="p">,</span> <span class="n">theta_transform</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;slice_np&#39;</span><span class="p">,</span> <span class="n">thin</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">warmup_steps</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">num_chains</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">init_strategy</span><span class="o">=</span><span class="s1">&#39;resample&#39;</span><span class="p">,</span> <span class="n">init_strategy_parameters</span><span class="o">=</span><span class="p">{},</span> <span class="n">init_strategy_num_candidates</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">x_shape</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.__init__" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.__init__" class="doc doc-heading">
+          <code class=" language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">potential_fn</span><span class="p">,</span> <span class="n">proposal</span><span class="p">,</span> <span class="n">theta_transform</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;slice_np&#39;</span><span class="p">,</span> <span class="n">thin</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">warmup_steps</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">num_chains</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">init_strategy</span><span class="o">=</span><span class="s1">&#39;resample&#39;</span><span class="p">,</span> <span class="n">init_strategy_parameters</span><span class="o">=</span><span class="p">{},</span> <span class="n">init_strategy_num_candidates</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">x_shape</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.__init__" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
   
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -17855,7 +19710,11 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.__init__" class="d
           <td>
                 <code><span title="typing.Callable">Callable</span></code>
           </td>
-          <td><p>The potential function from which to draw samples.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The potential function from which to draw samples.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -17865,7 +19724,11 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.__init__" class="d
           <td>
                 <code><span title="typing.Any">Any</span></code>
           </td>
-          <td><p>Proposal distribution that is used to initialize the MCMC chain.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Proposal distribution that is used to initialize the MCMC chain.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -17875,8 +19738,12 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.__init__" class="d
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="sbi.types.TorchTransform">TorchTransform</span>]</code>
           </td>
-          <td><p>Transformation that will be applied during sampling.
-Allows to perform MCMC in unconstrained space.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Transformation that will be applied during sampling.
+Allows to perform MCMC in unconstrained space.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -17886,12 +19753,16 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.__init__" class="d
           <td>
                 <code>str</code>
           </td>
-          <td><p>Method used for MCMC sampling, one of <code>slice_np</code>,
+          <td>
+            <div class="doc-md-description">
+              <p>Method used for MCMC sampling, one of <code>slice_np</code>,
 <code>slice_np_vectorized</code>, <code>slice</code>, <code>hmc</code>, <code>nuts</code>. <code>slice_np</code> is a custom
 numpy implementation of slice sampling. <code>slice_np_vectorized</code> is
 identical to <code>slice_np</code>, but if <code>num_chains&gt;1</code>, the chains are
 vectorized for <code>slice_np_vectorized</code> whereas they are run sequentially
-for <code>slice_np</code>. The samplers <code>hmc</code>, <code>nuts</code> or <code>slice</code> sample with Pyro.</p></td>
+for <code>slice_np</code>. The samplers <code>hmc</code>, <code>nuts</code> or <code>slice</code> sample with Pyro.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;slice_np&#39;</code>
           </td>
@@ -17901,7 +19772,11 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.__init__" class="d
           <td>
                 <code>int</code>
           </td>
-          <td><p>The thinning factor for the chain.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The thinning factor for the chain.</p>
+            </div>
+          </td>
           <td>
                 <code>10</code>
           </td>
@@ -17911,7 +19786,11 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.__init__" class="d
           <td>
                 <code>int</code>
           </td>
-          <td><p>The initial number of samples to discard.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The initial number of samples to discard.</p>
+            </div>
+          </td>
           <td>
                 <code>10</code>
           </td>
@@ -17921,7 +19800,11 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.__init__" class="d
           <td>
                 <code>int</code>
           </td>
-          <td><p>The number of chains.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The number of chains.</p>
+            </div>
+          </td>
           <td>
                 <code>1</code>
           </td>
@@ -17931,14 +19814,18 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.__init__" class="d
           <td>
                 <code>str</code>
           </td>
-          <td><p>The initialisation strategy for chains; <code>proposal</code> will draw
+          <td>
+            <div class="doc-md-description">
+              <p>The initialisation strategy for chains; <code>proposal</code> will draw
 init locations from <code>proposal</code>, whereas <code>sir</code> will use Sequential-
 Importance-Resampling (SIR). SIR initially samples
 <code>init_strategy_num_candidates</code> from the <code>proposal</code>, evaluates all of
 them under the <code>potential_fn</code> and <code>proposal</code>, and then resamples the
 initial locations with weights proportional to <code>exp(potential_fn -
 proposal.log_prob</code>. <code>resample</code> is the same as <code>sir</code> but
-uses <code>exp(potential_fn)</code> as weights.</p></td>
+uses <code>exp(potential_fn)</code> as weights.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;resample&#39;</code>
           </td>
@@ -17948,10 +19835,14 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.__init__" class="d
           <td>
                 <code><span title="typing.Dict">Dict</span>[str, <span title="typing.Any">Any</span>]</code>
           </td>
-          <td><p>Dictionary of keyword arguments passed to the
+          <td>
+            <div class="doc-md-description">
+              <p>Dictionary of keyword arguments passed to the
 init strategy, e.g., for <code>init_strategy=sir</code> this could be
 <code>num_candidate_samples</code>, i.e., the number of candidates to to find init
-locations (internal default is <code>1000</code>), or <code>device</code>.</p></td>
+locations (internal default is <code>1000</code>), or <code>device</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>{}</code>
           </td>
@@ -17961,9 +19852,13 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.__init__" class="d
           <td>
                 <code><span title="typing.Optional">Optional</span>[int]</code>
           </td>
-          <td><p>Number of candidates to to find init
+          <td>
+            <div class="doc-md-description">
+              <p>Number of candidates to to find init
  locations in <code>init_strategy=sir</code> (deprecated, use
- init_strategy_parameters instead).</p></td>
+ init_strategy_parameters instead).</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -17973,7 +19868,11 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.__init__" class="d
           <td>
                 <code>int</code>
           </td>
-          <td><p>number of cpu cores used to parallelize mcmc</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>number of cpu cores used to parallelize mcmc</p>
+            </div>
+          </td>
           <td>
                 <code>1</code>
           </td>
@@ -17983,8 +19882,12 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.__init__" class="d
           <td>
                 <code><span title="typing.Optional">Optional</span>[str]</code>
           </td>
-          <td><p>Training device, e.g., &ldquo;cpu&rdquo;, &ldquo;cuda&rdquo; or &ldquo;cuda:0&rdquo;. If None,
-<code>potential_fn.device</code> is used.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Training device, e.g., &ldquo;cpu&rdquo;, &ldquo;cuda&rdquo; or &ldquo;cuda:0&rdquo;. If None,
+<code>potential_fn.device</code> is used.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -17992,10 +19895,14 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.__init__" class="d
         <tr>
           <td><code>x_shape</code></td>
           <td>
-                <code><span title="typing.Optional">Optional</span>[torch.<span title="torch.Size">Size</span>]</code>
+                <code><span title="typing.Optional">Optional</span>[<span title="torch.Size">Size</span>]</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Shape of a single simulator output. If passed, it is used to check
+the shape of the observed data and give a descriptive error.</p>
+            </div>
           </td>
-          <td><p>Shape of a single simulator output. If passed, it is used to check
-the shape of the observed data and give a descriptive error.</p></td>
           <td>
                 <code>None</code>
           </td>
@@ -18003,9 +19910,9 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.__init__" class="d
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/mcmc_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 42</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/mcmc_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 42</span>
 <span class="normal"> 43</span>
 <span class="normal"> 44</span>
 <span class="normal"> 45</span>
@@ -18181,19 +20088,21 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.__init__" class="d
         <span class="s2">&quot;can evaluate the _unnormalized_ posterior density with .log_prob().&quot;</span>
     <span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.get_arviz_inference_data" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">get_arviz_inference_data</span><span class="p">()</span></code>
 
-<a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.get_arviz_inference_data" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.get_arviz_inference_data" class="doc doc-heading">
+          <code class=" language-python"><span class="n">get_arviz_inference_data</span><span class="p">()</span></code>
+
+<a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.get_arviz_inference_data" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -18204,6 +20113,8 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.get_arviz_inferenc
 <p>For Pyro HMC and NUTS kernels InferenceData will contain diagnostics, for Pyro
 Slice or sbi slice sampling samples, only the samples are added.</p>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -18217,14 +20128,18 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.get_arviz_inferenc
 <td><code>inference_data</code></td>          <td>
                 <code><span title="arviz.data.InferenceData">InferenceData</span></code>
           </td>
-          <td><p>Arviz InferenceData object.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Arviz InferenceData object.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/mcmc_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">648</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/mcmc_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">648</span>
 <span class="normal">649</span>
 <span class="normal">650</span>
 <span class="normal">651</span>
@@ -18330,25 +20245,29 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.get_arviz_inferenc
 
     <span class="k">return</span> <span class="n">inference_data</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.log_prob" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">log_prob</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">track_gradients</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.log_prob" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.log_prob" class="doc doc-heading">
+          <code class=" language-python"><span class="n">log_prob</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">track_gradients</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.log_prob" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
   
       <p>Returns the log-probability of theta under the posterior.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -18365,7 +20284,11 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.log_prob" class="d
           <td>
                 <code><span title="torch.Tensor">Tensor</span></code>
           </td>
-          <td><p>Parameters <span class="arithmatex">\(\theta\)</span>.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Parameters <span class="arithmatex">\(\theta\)</span>.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -18375,9 +20298,13 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.log_prob" class="d
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether the returned tensor supports tracking gradients.
+          <td>
+            <div class="doc-md-description">
+              <p>Whether the returned tensor supports tracking gradients.
 This can be helpful for e.g. sensitivity analysis, but increases memory
-consumption.</p></td>
+consumption.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -18385,6 +20312,8 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.log_prob" class="d
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -18398,14 +20327,18 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.log_prob" class="d
           <td>
                 <code><span title="torch.Tensor">Tensor</span></code>
           </td>
-          <td><p><code>len($\theta$)</code>-shaped log-probability.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p><code>len($\theta$)</code>-shaped log-probability.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/mcmc_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">158</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/mcmc_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">158</span>
 <span class="normal">159</span>
 <span class="normal">160</span>
 <span class="normal">161</span>
@@ -18457,19 +20390,21 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.log_prob" class="d
         <span class="n">theta</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_device</span><span class="p">),</span> <span class="n">track_gradients</span><span class="o">=</span><span class="n">track_gradients</span>
     <span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.map" class="doc doc-heading">
-<code class="codehilite language-python"><span class="nb">map</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">num_to_optimize</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">init_method</span><span class="o">=</span><span class="s1">&#39;proposal&#39;</span><span class="p">,</span> <span class="n">num_init_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">save_best_every</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">force_update</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.map" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.map" class="doc doc-heading">
+          <code class=" language-python"><span class="nb">map</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">num_to_optimize</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">init_method</span><span class="o">=</span><span class="s1">&#39;proposal&#39;</span><span class="p">,</span> <span class="n">num_init_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">save_best_every</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">force_update</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.map" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -18486,6 +20421,8 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.map" class="doc do
 <p>For developers: if the prior is a <code>BoxUniform</code>, we carry out the optimization
 in unbounded space and transform the result back into bounded space.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -18502,7 +20439,11 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.map" class="doc do
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="torch.Tensor">Tensor</span>]</code>
           </td>
-          <td><p>Deprecated - use <code>.set_default_x()</code> prior to <code>.map()</code>.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Deprecated - use <code>.set_default_x()</code> prior to <code>.map()</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -18512,8 +20453,12 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.map" class="doc do
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of optimization steps that the algorithm takes
-to find the MAP.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of optimization steps that the algorithm takes
+to find the MAP.</p>
+            </div>
+          </td>
           <td>
                 <code>1000</code>
           </td>
@@ -18523,7 +20468,11 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.map" class="doc do
           <td>
                 <code>float</code>
           </td>
-          <td><p>Learning rate of the optimizer.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Learning rate of the optimizer.</p>
+            </div>
+          </td>
           <td>
                 <code>0.01</code>
           </td>
@@ -18533,10 +20482,14 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.map" class="doc do
           <td>
                 <code><span title="typing.Union">Union</span>[str, <span title="torch.Tensor">Tensor</span>]</code>
           </td>
-          <td><p>How to select the starting parameters for the optimization. If
+          <td>
+            <div class="doc-md-description">
+              <p>How to select the starting parameters for the optimization. If
 it is a string, it can be either [<code>posterior</code>, <code>prior</code>], which samples
 the respective distribution <code>num_init_samples</code> times. If it is a
-tensor, the tensor will be used as init locations.</p></td>
+tensor, the tensor will be used as init locations.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;proposal&#39;</code>
           </td>
@@ -18546,8 +20499,12 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.map" class="doc do
           <td>
                 <code>int</code>
           </td>
-          <td><p>Draw this number of samples from the posterior and
-evaluate the log-probability of all of them.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Draw this number of samples from the posterior and
+evaluate the log-probability of all of them.</p>
+            </div>
+          </td>
           <td>
                 <code>1000</code>
           </td>
@@ -18557,9 +20514,13 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.map" class="doc do
           <td>
                 <code>int</code>
           </td>
-          <td><p>From the drawn <code>num_init_samples</code>, use the
+          <td>
+            <div class="doc-md-description">
+              <p>From the drawn <code>num_init_samples</code>, use the
 <code>num_to_optimize</code> with highest log-probability as the initial points
-for the optimization.</p></td>
+for the optimization.</p>
+            </div>
+          </td>
           <td>
                 <code>100</code>
           </td>
@@ -18569,10 +20530,14 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.map" class="doc do
           <td>
                 <code>int</code>
           </td>
-          <td><p>The best log-probability is computed, saved in the
+          <td>
+            <div class="doc-md-description">
+              <p>The best log-probability is computed, saved in the
 <code>map</code>-attribute, and printed every <code>save_best_every</code>-th iteration.
 Computing the best log-probability creates a significant overhead
-(thus, the default is <code>10</code>.)</p></td>
+(thus, the default is <code>10</code>.)</p>
+            </div>
+          </td>
           <td>
                 <code>10</code>
           </td>
@@ -18582,8 +20547,12 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.map" class="doc do
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to show a progressbar during sampling from
-the posterior.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to show a progressbar during sampling from
+the posterior.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -18593,8 +20562,12 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.map" class="doc do
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to re-calculate the MAP when x is unchanged and
-have a cached value.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to re-calculate the MAP when x is unchanged and
+have a cached value.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -18603,8 +20576,12 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.map" class="doc do
           <td><code>log_prob_kwargs</code></td>
           <td>
           </td>
-          <td><p>Will be empty for SNLE and SNRE. Will contain
-{&lsquo;norm_posterior&rsquo;: True} for SNPE.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Will be empty for SNLE and SNRE. Will contain
+{&lsquo;norm_posterior&rsquo;: True} for SNPE.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -18612,6 +20589,8 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.map" class="doc do
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -18625,14 +20604,18 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.map" class="doc do
           <td>
                 <code><span title="torch.Tensor">Tensor</span></code>
           </td>
-          <td><p>The MAP estimate.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The MAP estimate.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/mcmc_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">581</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/mcmc_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">581</span>
 <span class="normal">582</span>
 <span class="normal">583</span>
 <span class="normal">584</span>
@@ -18764,19 +20747,21 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.map" class="doc do
         <span class="n">force_update</span><span class="o">=</span><span class="n">force_update</span><span class="p">,</span>
     <span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.sample" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">sample</span><span class="p">(</span><span class="n">sample_shape</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">(),</span> <span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">thin</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">warmup_steps</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_chains</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">init_strategy</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">init_strategy_parameters</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">init_strategy_num_candidates</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">mcmc_parameters</span><span class="o">=</span><span class="p">{},</span> <span class="n">mcmc_method</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sample_with</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.sample" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.sample" class="doc doc-heading">
+          <code class=" language-python"><span class="n">sample</span><span class="p">(</span><span class="n">sample_shape</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">(),</span> <span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">thin</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">warmup_steps</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_chains</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">init_strategy</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">init_strategy_parameters</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">init_strategy_num_candidates</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">mcmc_parameters</span><span class="o">=</span><span class="p">{},</span> <span class="n">mcmc_method</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sample_with</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.sample" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -18785,6 +20770,8 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.sample" class="doc
 <p>Check the <code>__init__()</code> method for a description of all arguments as well as
 their default values.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -18801,11 +20788,15 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.sample" class="doc
           <td>
                 <code><span title="sbi.types.Shape">Shape</span></code>
           </td>
-          <td><p>Desired shape of samples that are drawn from posterior. If
+          <td>
+            <div class="doc-md-description">
+              <p>Desired shape of samples that are drawn from posterior. If
 sample_shape is multidimensional we simply draw <code>sample_shape.numel()</code>
-samples and then reshape into the desired shape.</p></td>
+samples and then reshape into the desired shape.</p>
+            </div>
+          </td>
           <td>
-                <code>torch.Size()</code>
+                <code><span title="torch.Size">Size</span>()</code>
           </td>
         </tr>
         <tr>
@@ -18813,8 +20804,12 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.sample" class="doc
           <td>
                 <code><span title="typing.Dict">Dict</span></code>
           </td>
-          <td><p>Dictionary that is passed only to support the API of
-<code>sbi</code> v0.17.2 or older.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Dictionary that is passed only to support the API of
+<code>sbi</code> v0.17.2 or older.</p>
+            </div>
+          </td>
           <td>
                 <code>{}</code>
           </td>
@@ -18824,8 +20819,12 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.sample" class="doc
           <td>
                 <code><span title="typing.Optional">Optional</span>[str]</code>
           </td>
-          <td><p>This argument only exists to keep backward-compatibility with
-<code>sbi</code> v0.17.2 or older. Please use <code>method</code> instead.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>This argument only exists to keep backward-compatibility with
+<code>sbi</code> v0.17.2 or older. Please use <code>method</code> instead.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -18835,8 +20834,12 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.sample" class="doc
           <td>
                 <code><span title="typing.Optional">Optional</span>[str]</code>
           </td>
-          <td><p>This argument only exists to keep backward-compatibility with
-<code>sbi</code> v0.17.2 or older. If it is set, we instantly raise an error.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>This argument only exists to keep backward-compatibility with
+<code>sbi</code> v0.17.2 or older. If it is set, we instantly raise an error.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -18846,7 +20849,11 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.sample" class="doc
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to show sampling progress monitor.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to show sampling progress monitor.</p>
+            </div>
+          </td>
           <td>
                 <code>True</code>
           </td>
@@ -18854,6 +20861,8 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.sample" class="doc
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -18867,14 +20876,18 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.sample" class="doc
           <td>
                 <code><span title="torch.Tensor">Tensor</span></code>
           </td>
-          <td><p>Samples from posterior.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Samples from posterior.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/mcmc_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">185</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/mcmc_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">185</span>
 <span class="normal">186</span>
 <span class="normal">187</span>
 <span class="normal">188</span>
@@ -19132,25 +21145,29 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.sample" class="doc
 
     <span class="k">return</span> <span class="n">samples</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="o">*</span><span class="n">sample_shape</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span>  <span class="c1"># type: ignore</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.set_mcmc_method" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">set_mcmc_method</span><span class="p">(</span><span class="n">method</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.set_mcmc_method" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.set_mcmc_method" class="doc doc-heading">
+          <code class=" language-python"><span class="n">set_mcmc_method</span><span class="p">(</span><span class="n">method</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.set_mcmc_method" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
   
       <p>Sets sampling method to for MCMC and returns <code>NeuralPosterior</code>.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -19167,7 +21184,11 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.set_mcmc_method" c
           <td>
                 <code>str</code>
           </td>
-          <td><p>Method to use.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Method to use.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -19175,6 +21196,8 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.set_mcmc_method" c
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -19188,14 +21211,18 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.set_mcmc_method" c
           <td>
                 <code><span title="sbi.inference.posteriors.base_posterior.NeuralPosterior">NeuralPosterior</span></code>
           </td>
-          <td><p><code>NeuralPosterior</code> for chainable calls.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p><code>NeuralPosterior</code> for chainable calls.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/mcmc_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">146</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/mcmc_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">146</span>
 <span class="normal">147</span>
 <span class="normal">148</span>
 <span class="normal">149</span>
@@ -19217,7 +21244,7 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.set_mcmc_method" c
     <span class="bp">self</span><span class="o">.</span><span class="n">_mcmc_method</span> <span class="o">=</span> <span class="n">method</span>
     <span class="k">return</span> <span class="bp">self</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
@@ -19234,26 +21261,19 @@ <h4 id="sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.set_mcmc_method" c
 
 
 
-<h3 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior" class="doc doc-heading">
-        <code>sbi.inference.posteriors.rejection_posterior.RejectionPosterior</code>
-
-
-<a href="#sbi.inference.posteriors.rejection_posterior.RejectionPosterior" class="headerlink" title="Permanent link">&para;</a></h3>
-
-
+<a id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior"></a>
   <div class="doc doc-contents first">
-      <p class="doc doc-class-bases">
-        Bases: <code><span title="sbi.inference.posteriors.base_posterior.NeuralPosterior">NeuralPosterior</span></code></p>
+          <p class="doc doc-class-bases">
+            Bases: <code><span title="sbi.inference.posteriors.base_posterior.NeuralPosterior">NeuralPosterior</span></code></p>
 
   
       <p>Provides rejection sampling to sample from the posterior.<br/><br/>
 SNLE or SNRE train neural networks to approximate the likelihood(-ratios).
 <code>RejectionPosterior</code> allows to sample from the posterior with rejection sampling.</p>
 
-
-        <details class="quote">
-          <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/rejection_posterior.py</code></summary>
-          <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 17</span>
+            <details class="quote">
+              <summary>Source code in <code>sbi/inference/posteriors/rejection_posterior.py</code></summary>
+              <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 17</span>
 <span class="normal"> 18</span>
 <span class="normal"> 19</span>
 <span class="normal"> 20</span>
@@ -19681,7 +21701,7 @@ <h3 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior" class="
             <span class="n">force_update</span><span class="o">=</span><span class="n">force_update</span><span class="p">,</span>
         <span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-        </details>
+            </details>
 
   
 
@@ -19695,19 +21715,23 @@ <h3 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior" class="
 
 
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.__init__" class="doc doc-heading">
-<code class="codehilite language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">potential_fn</span><span class="p">,</span> <span class="n">proposal</span><span class="p">,</span> <span class="n">theta_transform</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_sampling_batch_size</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">num_samples_to_find_max</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">num_iter_to_find_max</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">m</span><span class="o">=</span><span class="mf">1.2</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">x_shape</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.posteriors.rejection_posterior.RejectionPosterior.__init__" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.__init__" class="doc doc-heading">
+          <code class=" language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">potential_fn</span><span class="p">,</span> <span class="n">proposal</span><span class="p">,</span> <span class="n">theta_transform</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_sampling_batch_size</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">num_samples_to_find_max</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">num_iter_to_find_max</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">m</span><span class="o">=</span><span class="mf">1.2</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">x_shape</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.posteriors.rejection_posterior.RejectionPosterior.__init__" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
   
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -19724,7 +21748,11 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.__init__
           <td>
                 <code><span title="typing.Callable">Callable</span></code>
           </td>
-          <td><p>The potential function from which to draw samples.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The potential function from which to draw samples.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -19734,7 +21762,11 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.__init__
           <td>
                 <code><span title="typing.Any">Any</span></code>
           </td>
-          <td><p>The proposal distribution.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The proposal distribution.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -19744,8 +21776,12 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.__init__
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="sbi.types.TorchTransform">TorchTransform</span>]</code>
           </td>
-          <td><p>Transformation that is applied to parameters. Is not used
-during but only when calling <code>.map()</code>.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Transformation that is applied to parameters. Is not used
+during but only when calling <code>.map()</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -19755,8 +21791,12 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.__init__
           <td>
                 <code>int</code>
           </td>
-          <td><p>The batchsize of samples being drawn from
-the proposal at every iteration.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The batchsize of samples being drawn from
+the proposal at every iteration.</p>
+            </div>
+          </td>
           <td>
                 <code>10000</code>
           </td>
@@ -19766,8 +21806,12 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.__init__
           <td>
                 <code>int</code>
           </td>
-          <td><p>The number of samples that are used to find the
-maximum of the <code>potential_fn / proposal</code> ratio.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The number of samples that are used to find the
+maximum of the <code>potential_fn / proposal</code> ratio.</p>
+            </div>
+          </td>
           <td>
                 <code>10000</code>
           </td>
@@ -19777,8 +21821,12 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.__init__
           <td>
                 <code>int</code>
           </td>
-          <td><p>The number of gradient ascent iterations to find the
-maximum of the <code>potential_fn / proposal</code> ratio.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The number of gradient ascent iterations to find the
+maximum of the <code>potential_fn / proposal</code> ratio.</p>
+            </div>
+          </td>
           <td>
                 <code>100</code>
           </td>
@@ -19788,7 +21836,11 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.__init__
           <td>
                 <code>float</code>
           </td>
-          <td><p>Multiplier to the <code>potential_fn / proposal</code> ratio.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Multiplier to the <code>potential_fn / proposal</code> ratio.</p>
+            </div>
+          </td>
           <td>
                 <code>1.2</code>
           </td>
@@ -19798,8 +21850,12 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.__init__
           <td>
                 <code><span title="typing.Optional">Optional</span>[str]</code>
           </td>
-          <td><p>Training device, e.g., &ldquo;cpu&rdquo;, &ldquo;cuda&rdquo; or &ldquo;cuda:0&rdquo;. If None,
-<code>potential_fn.device</code> is used.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Training device, e.g., &ldquo;cpu&rdquo;, &ldquo;cuda&rdquo; or &ldquo;cuda:0&rdquo;. If None,
+<code>potential_fn.device</code> is used.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -19807,10 +21863,14 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.__init__
         <tr>
           <td><code>x_shape</code></td>
           <td>
-                <code><span title="typing.Optional">Optional</span>[torch.<span title="torch.Size">Size</span>]</code>
+                <code><span title="typing.Optional">Optional</span>[<span title="torch.Size">Size</span>]</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Shape of a single simulator output. If passed, it is used to check
+the shape of the observed data and give a descriptive error.</p>
+            </div>
           </td>
-          <td><p>Shape of a single simulator output. If passed, it is used to check
-the shape of the observed data and give a descriptive error.</p></td>
           <td>
                 <code>None</code>
           </td>
@@ -19818,9 +21878,9 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.__init__
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/rejection_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">23</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/rejection_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">23</span>
 <span class="normal">24</span>
 <span class="normal">25</span>
 <span class="normal">26</span>
@@ -19914,25 +21974,29 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.__init__
         <span class="s2">&quot;can evaluate the _unnormalized_ posterior density with .log_prob().&quot;</span>
     <span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.log_prob" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">log_prob</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">track_gradients</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.posteriors.rejection_posterior.RejectionPosterior.log_prob" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.log_prob" class="doc doc-heading">
+          <code class=" language-python"><span class="n">log_prob</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">track_gradients</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.posteriors.rejection_posterior.RejectionPosterior.log_prob" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
   
       <p>Returns the log-probability of theta under the posterior.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -19949,7 +22013,11 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.log_prob
           <td>
                 <code><span title="torch.Tensor">Tensor</span></code>
           </td>
-          <td><p>Parameters <span class="arithmatex">\(\theta\)</span>.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Parameters <span class="arithmatex">\(\theta\)</span>.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -19959,9 +22027,13 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.log_prob
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether the returned tensor supports tracking gradients.
+          <td>
+            <div class="doc-md-description">
+              <p>Whether the returned tensor supports tracking gradients.
 This can be helpful for e.g. sensitivity analysis, but increases memory
-consumption.</p></td>
+consumption.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -19969,6 +22041,8 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.log_prob
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -19982,14 +22056,18 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.log_prob
           <td>
                 <code><span title="torch.Tensor">Tensor</span></code>
           </td>
-          <td><p><code>len($\theta$)</code>-shaped log-probability.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p><code>len($\theta$)</code>-shaped log-probability.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/rejection_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">71</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/rejection_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">71</span>
 <span class="normal">72</span>
 <span class="normal">73</span>
 <span class="normal">74</span>
@@ -20039,19 +22117,21 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.log_prob
         <span class="n">theta</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_device</span><span class="p">),</span> <span class="n">track_gradients</span><span class="o">=</span><span class="n">track_gradients</span>
     <span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.map" class="doc doc-heading">
-<code class="codehilite language-python"><span class="nb">map</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">num_to_optimize</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">init_method</span><span class="o">=</span><span class="s1">&#39;proposal&#39;</span><span class="p">,</span> <span class="n">num_init_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">save_best_every</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">force_update</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.posteriors.rejection_posterior.RejectionPosterior.map" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.map" class="doc doc-heading">
+          <code class=" language-python"><span class="nb">map</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">num_to_optimize</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">init_method</span><span class="o">=</span><span class="s1">&#39;proposal&#39;</span><span class="p">,</span> <span class="n">num_init_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">save_best_every</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">force_update</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.posteriors.rejection_posterior.RejectionPosterior.map" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -20068,6 +22148,8 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.map" cla
 <p>For developers: if the prior is a <code>BoxUniform</code>, we carry out the optimization
 in unbounded space and transform the result back into bounded space.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -20084,7 +22166,11 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.map" cla
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="torch.Tensor">Tensor</span>]</code>
           </td>
-          <td><p>Deprecated - use <code>.set_default_x()</code> prior to <code>.map()</code>.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Deprecated - use <code>.set_default_x()</code> prior to <code>.map()</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -20094,8 +22180,12 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.map" cla
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of optimization steps that the algorithm takes
-to find the MAP.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of optimization steps that the algorithm takes
+to find the MAP.</p>
+            </div>
+          </td>
           <td>
                 <code>1000</code>
           </td>
@@ -20105,7 +22195,11 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.map" cla
           <td>
                 <code>float</code>
           </td>
-          <td><p>Learning rate of the optimizer.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Learning rate of the optimizer.</p>
+            </div>
+          </td>
           <td>
                 <code>0.01</code>
           </td>
@@ -20115,10 +22209,14 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.map" cla
           <td>
                 <code><span title="typing.Union">Union</span>[str, <span title="torch.Tensor">Tensor</span>]</code>
           </td>
-          <td><p>How to select the starting parameters for the optimization. If
+          <td>
+            <div class="doc-md-description">
+              <p>How to select the starting parameters for the optimization. If
 it is a string, it can be either [<code>posterior</code>, <code>prior</code>], which samples
 the respective distribution <code>num_init_samples</code> times. If it is a
-tensor, the tensor will be used as init locations.</p></td>
+tensor, the tensor will be used as init locations.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;proposal&#39;</code>
           </td>
@@ -20128,8 +22226,12 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.map" cla
           <td>
                 <code>int</code>
           </td>
-          <td><p>Draw this number of samples from the posterior and
-evaluate the log-probability of all of them.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Draw this number of samples from the posterior and
+evaluate the log-probability of all of them.</p>
+            </div>
+          </td>
           <td>
                 <code>1000</code>
           </td>
@@ -20139,9 +22241,13 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.map" cla
           <td>
                 <code>int</code>
           </td>
-          <td><p>From the drawn <code>num_init_samples</code>, use the
+          <td>
+            <div class="doc-md-description">
+              <p>From the drawn <code>num_init_samples</code>, use the
 <code>num_to_optimize</code> with highest log-probability as the initial points
-for the optimization.</p></td>
+for the optimization.</p>
+            </div>
+          </td>
           <td>
                 <code>100</code>
           </td>
@@ -20151,10 +22257,14 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.map" cla
           <td>
                 <code>int</code>
           </td>
-          <td><p>The best log-probability is computed, saved in the
+          <td>
+            <div class="doc-md-description">
+              <p>The best log-probability is computed, saved in the
 <code>map</code>-attribute, and printed every <code>save_best_every</code>-th iteration.
 Computing the best log-probability creates a significant overhead
-(thus, the default is <code>10</code>.)</p></td>
+(thus, the default is <code>10</code>.)</p>
+            </div>
+          </td>
           <td>
                 <code>10</code>
           </td>
@@ -20164,8 +22274,12 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.map" cla
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to show a progressbar during sampling from
-the posterior.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to show a progressbar during sampling from
+the posterior.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -20175,8 +22289,12 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.map" cla
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to re-calculate the MAP when x is unchanged and
-have a cached value.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to re-calculate the MAP when x is unchanged and
+have a cached value.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -20185,8 +22303,12 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.map" cla
           <td><code>log_prob_kwargs</code></td>
           <td>
           </td>
-          <td><p>Will be empty for SNLE and SNRE. Will contain
-{&lsquo;norm_posterior&rsquo;: True} for SNPE.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Will be empty for SNLE and SNRE. Will contain
+{&lsquo;norm_posterior&rsquo;: True} for SNPE.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -20194,6 +22316,8 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.map" cla
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -20207,14 +22331,18 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.map" cla
           <td>
                 <code><span title="torch.Tensor">Tensor</span></code>
           </td>
-          <td><p>The MAP estimate.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The MAP estimate.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/rejection_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">165</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/rejection_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">165</span>
 <span class="normal">166</span>
 <span class="normal">167</span>
 <span class="normal">168</span>
@@ -20346,25 +22474,29 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.map" cla
         <span class="n">force_update</span><span class="o">=</span><span class="n">force_update</span><span class="p">,</span>
     <span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.sample" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">sample</span><span class="p">(</span><span class="n">sample_shape</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">(),</span> <span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_sampling_batch_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_samples_to_find_max</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_iter_to_find_max</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">m</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sample_with</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.posteriors.rejection_posterior.RejectionPosterior.sample" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.sample" class="doc doc-heading">
+          <code class=" language-python"><span class="n">sample</span><span class="p">(</span><span class="n">sample_shape</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">(),</span> <span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_sampling_batch_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_samples_to_find_max</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_iter_to_find_max</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">m</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sample_with</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.posteriors.rejection_posterior.RejectionPosterior.sample" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
   
       <p>Return samples from posterior <span class="arithmatex">\(p(\theta|x)\)</span> via rejection sampling.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -20381,11 +22513,15 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.sample"
           <td>
                 <code><span title="sbi.types.Shape">Shape</span></code>
           </td>
-          <td><p>Desired shape of samples that are drawn from posterior. If
+          <td>
+            <div class="doc-md-description">
+              <p>Desired shape of samples that are drawn from posterior. If
 sample_shape is multidimensional we simply draw <code>sample_shape.numel()</code>
-samples and then reshape into the desired shape.</p></td>
+samples and then reshape into the desired shape.</p>
+            </div>
+          </td>
           <td>
-                <code>torch.Size()</code>
+                <code><span title="torch.Size">Size</span>()</code>
           </td>
         </tr>
         <tr>
@@ -20393,8 +22529,12 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.sample"
           <td>
                 <code><span title="typing.Optional">Optional</span>[str]</code>
           </td>
-          <td><p>This argument only exists to keep backward-compatibility with
-<code>sbi</code> v0.17.2 or older. If it is set, we instantly raise an error.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>This argument only exists to keep backward-compatibility with
+<code>sbi</code> v0.17.2 or older. If it is set, we instantly raise an error.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -20404,7 +22544,11 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.sample"
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to show sampling progress monitor.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to show sampling progress monitor.</p>
+            </div>
+          </td>
           <td>
                 <code>True</code>
           </td>
@@ -20412,6 +22556,8 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.sample"
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -20424,14 +22570,18 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.sample"
         <tr>
           <td>
           </td>
-          <td><p>Samples from posterior.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Samples from posterior.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/rejection_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 97</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/rejection_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 97</span>
 <span class="normal"> 98</span>
 <span class="normal"> 99</span>
 <span class="normal">100</span>
@@ -20565,7 +22715,7 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.sample"
 
     <span class="k">return</span> <span class="n">samples</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="o">*</span><span class="n">sample_shape</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
@@ -20582,16 +22732,10 @@ <h4 id="sbi.inference.posteriors.rejection_posterior.RejectionPosterior.sample"
 
 
 
-<h3 id="sbi.inference.posteriors.vi_posterior.VIPosterior" class="doc doc-heading">
-        <code>sbi.inference.posteriors.vi_posterior.VIPosterior</code>
-
-
-<a href="#sbi.inference.posteriors.vi_posterior.VIPosterior" class="headerlink" title="Permanent link">&para;</a></h3>
-
-
+<a id="sbi.inference.posteriors.vi_posterior.VIPosterior"></a>
   <div class="doc doc-contents first">
-      <p class="doc doc-class-bases">
-        Bases: <code><span title="sbi.inference.posteriors.base_posterior.NeuralPosterior">NeuralPosterior</span></code></p>
+          <p class="doc doc-class-bases">
+            Bases: <code><span title="sbi.inference.posteriors.base_posterior.NeuralPosterior">NeuralPosterior</span></code></p>
 
   
       <p>Provides VI (Variational Inference) to sample from the posterior.<br/><br/>
@@ -20606,10 +22750,9 @@ <h3 id="sbi.inference.posteriors.vi_posterior.VIPosterior" class="doc doc-headin
 [2] Sequential Neural Posterior and Likelihood Approximation, Samuel Wiqvist, Jes
 Frellsen, Umberto Picchini, 2021, <a href="https://arxiv.org/abs/2102.06522">https://arxiv.org/abs/2102.06522</a></p>
 
-
-        <details class="quote">
-          <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/vi_posterior.py</code></summary>
-          <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 34</span>
+            <details class="quote">
+              <summary>Source code in <code>sbi/inference/posteriors/vi_posterior.py</code></summary>
+              <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 34</span>
 <span class="normal"> 35</span>
 <span class="normal"> 36</span>
 <span class="normal"> 37</span>
@@ -21669,7 +23812,7 @@ <h3 id="sbi.inference.posteriors.vi_posterior.VIPosterior" class="doc doc-headin
             <span class="n">force_update</span><span class="o">=</span><span class="n">force_update</span><span class="p">,</span>
         <span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-        </details>
+            </details>
 
   
 
@@ -21685,15 +23828,16 @@ <h3 id="sbi.inference.posteriors.vi_posterior.VIPosterior" class="doc doc-headin
 
 
 
-<h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.q" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">q</span><span class="p">:</span> <span class="n">Distribution</span></code>
+
+<h2 id="sbi.inference.posteriors.vi_posterior.VIPosterior.q" class="doc doc-heading">
+          <code class=" language-python"><span class="n">q</span><span class="p">:</span> <span class="n">Distribution</span></code>
   
   <span class="doc doc-labels">
       <small class="doc doc-label doc-label-property"><code>property</code></small>
       <small class="doc doc-label doc-label-writable"><code>writable</code></small>
   </span>
 
-<a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.q" class="headerlink" title="Permanent link">&para;</a></h4>
+<a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.q" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -21707,15 +23851,16 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.q" class="doc doc-head
 
 
 
-<h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.vi_method" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">vi_method</span><span class="p">:</span> <span class="nb">str</span></code>
+
+<h2 id="sbi.inference.posteriors.vi_posterior.VIPosterior.vi_method" class="doc doc-heading">
+          <code class=" language-python"><span class="n">vi_method</span><span class="p">:</span> <span class="nb">str</span></code>
   
   <span class="doc doc-labels">
       <small class="doc doc-label doc-label-property"><code>property</code></small>
       <small class="doc doc-label doc-label-writable"><code>writable</code></small>
   </span>
 
-<a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.vi_method" class="headerlink" title="Permanent link">&para;</a></h4>
+<a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.vi_method" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -21727,19 +23872,23 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.vi_method" class="doc
 
 
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.__init__" class="doc doc-heading">
-<code class="codehilite language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">potential_fn</span><span class="p">,</span> <span class="n">prior</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="s1">&#39;maf&#39;</span><span class="p">,</span> <span class="n">theta_transform</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">vi_method</span><span class="o">=</span><span class="s1">&#39;rKL&#39;</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">,</span> <span class="n">x_shape</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">parameters</span><span class="o">=</span><span class="p">[],</span> <span class="n">modules</span><span class="o">=</span><span class="p">[])</span></code>
 
-<a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.__init__" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.vi_posterior.VIPosterior.__init__" class="doc doc-heading">
+          <code class=" language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">potential_fn</span><span class="p">,</span> <span class="n">prior</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="s1">&#39;maf&#39;</span><span class="p">,</span> <span class="n">theta_transform</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">vi_method</span><span class="o">=</span><span class="s1">&#39;rKL&#39;</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">,</span> <span class="n">x_shape</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">parameters</span><span class="o">=</span><span class="p">[],</span> <span class="n">modules</span><span class="o">=</span><span class="p">[])</span></code>
+
+<a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.__init__" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
   
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -21756,7 +23905,11 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.__init__" class="doc d
           <td>
                 <code><span title="typing.Callable">Callable</span></code>
           </td>
-          <td><p>The potential function from which to draw samples.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The potential function from which to draw samples.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -21766,11 +23919,15 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.__init__" class="doc d
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="sbi.types.TorchDistribution">TorchDistribution</span>]</code>
           </td>
-          <td><p>This is the prior distribution. Note that this is only
+          <td>
+            <div class="doc-md-description">
+              <p>This is the prior distribution. Note that this is only
 used to check/construct the variational distribution or within some
 quality metrics. Please make sure that this matches with the prior
 within the potential_fn. If <code>None</code> is given, we will try to infer it
-from potential_fn or q, if this fails we raise an Error.</p></td>
+from potential_fn or q, if this fails we raise an Error.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -21780,7 +23937,9 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.__init__" class="doc d
           <td>
                 <code><span title="typing.Union">Union</span>[str, <span title="sbi.types.PyroTransformedDistribution">PyroTransformedDistribution</span>, <a class="autorefs autorefs-internal" title="sbi.inference.posteriors.vi_posterior.VIPosterior" href="#sbi.inference.posteriors.vi_posterior.VIPosterior">VIPosterior</a>, <span title="typing.Callable">Callable</span>]</code>
           </td>
-          <td><p>Variational distribution, either string, <code>TransformedDistribution</code>, or a
+          <td>
+            <div class="doc-md-description">
+              <p>Variational distribution, either string, <code>TransformedDistribution</code>, or a
 <code>VIPosterior</code> object. This specifies a parametric class of distribution
 over which the best possible posterior approximation is searched. For
 string input, we currently support [nsf, scf, maf, mcf, gaussian,
@@ -21791,7 +23950,9 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.__init__" class="doc d
 useful for setting the hyperparameters e.g. <code>num_transfroms</code> within the
 <code>get_flow_builder</code> method specifying the number of transformations
 within a normalizing flow. If q is already a <code>VIPosterior</code>, then the
-arguments will be copied from it (relevant for multi-round training).</p></td>
+arguments will be copied from it (relevant for multi-round training).</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;maf&#39;</code>
           </td>
@@ -21801,9 +23962,13 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.__init__" class="doc d
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="sbi.types.TorchTransform">TorchTransform</span>]</code>
           </td>
-          <td><p>Maps form prior support to unconstrained space. The
+          <td>
+            <div class="doc-md-description">
+              <p>Maps form prior support to unconstrained space. The
 inverse is used here to ensure that the posterior support is equal to
-that of the prior.</p></td>
+that of the prior.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -21813,12 +23978,16 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.__init__" class="doc d
           <td>
                 <code>str</code>
           </td>
-          <td><p>This specifies the variational methods which are used to fit q to
+          <td>
+            <div class="doc-md-description">
+              <p>This specifies the variational methods which are used to fit q to
 the posterior. We currently support [rKL, fKL, IW, alpha]. Note that
 some of the divergences are <code>mode seeking</code> i.e. they underestimate
 variance and collapse on multimodal targets (<code>rKL</code>, <code>alpha</code> for alpha &gt;
 1) and some are <code>mass covering</code> i.e. they overestimate variance but
-typically cover all modes (<code>fKL</code>, <code>IW</code>, <code>alpha</code> for alpha &lt; 1).</p></td>
+typically cover all modes (<code>fKL</code>, <code>IW</code>, <code>alpha</code> for alpha &lt; 1).</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;rKL&#39;</code>
           </td>
@@ -21828,8 +23997,12 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.__init__" class="doc d
           <td>
                 <code>str</code>
           </td>
-          <td><p>Training device, e.g., <code>cpu</code>, <code>cuda</code> or <code>cuda:0</code>. We will ensure
-that all other objects are also on this device.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Training device, e.g., <code>cpu</code>, <code>cuda</code> or <code>cuda:0</code>. We will ensure
+that all other objects are also on this device.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;cpu&#39;</code>
           </td>
@@ -21837,10 +24010,14 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.__init__" class="doc d
         <tr>
           <td><code>x_shape</code></td>
           <td>
-                <code><span title="typing.Optional">Optional</span>[torch.<span title="torch.Size">Size</span>]</code>
+                <code><span title="typing.Optional">Optional</span>[<span title="torch.Size">Size</span>]</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Shape of a single simulator output. If passed, it is used to check
+the shape of the observed data and give a descriptive error.</p>
+            </div>
           </td>
-          <td><p>Shape of a single simulator output. If passed, it is used to check
-the shape of the observed data and give a descriptive error.</p></td>
           <td>
                 <code>None</code>
           </td>
@@ -21850,9 +24027,13 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.__init__" class="doc d
           <td>
                 <code><span title="typing.Iterable">Iterable</span></code>
           </td>
-          <td><p>List of parameters of the variational posterior. This is only
+          <td>
+            <div class="doc-md-description">
+              <p>List of parameters of the variational posterior. This is only
 required for user-defined q i.e. if q does not have a <code>parameters</code>
-attribute.</p></td>
+attribute.</p>
+            </div>
+          </td>
           <td>
                 <code>[]</code>
           </td>
@@ -21862,9 +24043,13 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.__init__" class="doc d
           <td>
                 <code><span title="typing.Iterable">Iterable</span></code>
           </td>
-          <td><p>List of modules of the variational posterior. This is only
+          <td>
+            <div class="doc-md-description">
+              <p>List of modules of the variational posterior. This is only
 required for user-defined q i.e. if q does not have a <code>modules</code>
-attribute.</p></td>
+attribute.</p>
+            </div>
+          </td>
           <td>
                 <code>[]</code>
           </td>
@@ -21872,9 +24057,9 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.__init__" class="doc d
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/vi_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 48</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/vi_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 48</span>
 <span class="normal"> 49</span>
 <span class="normal"> 50</span>
 <span class="normal"> 51</span>
@@ -22054,19 +24239,21 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.__init__" class="doc d
         <span class="s2">&quot;can evaluate the _normalized_ posterior density with .log_prob().&quot;</span>
     <span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.evaluate" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">evaluate</span><span class="p">(</span><span class="n">quality_control_metric</span><span class="o">=</span><span class="s1">&#39;psis&#39;</span><span class="p">,</span> <span class="n">N</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="mf">50000.0</span><span class="p">))</span></code>
 
-<a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.evaluate" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.vi_posterior.VIPosterior.evaluate" class="doc doc-heading">
+          <code class=" language-python"><span class="n">evaluate</span><span class="p">(</span><span class="n">quality_control_metric</span><span class="o">=</span><span class="s1">&#39;psis&#39;</span><span class="p">,</span> <span class="n">N</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="mf">50000.0</span><span class="p">))</span></code>
+
+<a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.evaluate" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -22079,6 +24266,8 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.evaluate" class="doc d
 <p>NOTE: In our experience <code>prop</code> is sensitive to distinguish <code>good</code> from <code>ok</code>
 whereas <code>psis</code> is more sensitive in distinguishing <code>very bad</code> from <code>ok</code>.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -22095,8 +24284,12 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.evaluate" class="doc d
           <td>
                 <code>str</code>
           </td>
-          <td><p>The metric of choice, we currently support [psis,
-prop, prop_prior].</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The metric of choice, we currently support [psis,
+prop, prop_prior].</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;psis&#39;</code>
           </td>
@@ -22106,7 +24299,11 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.evaluate" class="doc d
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of samples which is used to evaluate the metric.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of samples which is used to evaluate the metric.</p>
+            </div>
+          </td>
           <td>
                 <code>int(50000.0)</code>
           </td>
@@ -22114,9 +24311,9 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.evaluate" class="doc d
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/vi_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">476</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/vi_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">476</span>
 <span class="normal">477</span>
 <span class="normal">478</span>
 <span class="normal">479</span>
@@ -22156,25 +24353,29 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.evaluate" class="doc d
     <span class="n">metric</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">quality_control_fn</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">N</span><span class="o">=</span><span class="n">N</span><span class="p">)),</span> <span class="mi">3</span><span class="p">)</span>
     <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Quality Score: </span><span class="si">{</span><span class="n">metric</span><span class="si">}</span><span class="s2"> &quot;</span> <span class="o">+</span> <span class="n">quality_control_msg</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.log_prob" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">log_prob</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">track_gradients</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.log_prob" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.vi_posterior.VIPosterior.log_prob" class="doc doc-heading">
+          <code class=" language-python"><span class="n">log_prob</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">track_gradients</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.log_prob" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
   
       <p>Returns the log-probability of theta under the variational posterior.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -22191,7 +24392,11 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.log_prob" class="doc d
           <td>
                 <code><span title="torch.Tensor">Tensor</span></code>
           </td>
-          <td><p>Parameters</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Parameters</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -22201,9 +24406,13 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.log_prob" class="doc d
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether the returned tensor supports tracking gradients.
+          <td>
+            <div class="doc-md-description">
+              <p>Whether the returned tensor supports tracking gradients.
 This can be helpful for e.g. sensitivity analysis but increases memory
-consumption.</p></td>
+consumption.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -22211,6 +24420,8 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.log_prob" class="doc d
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -22224,14 +24435,18 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.log_prob" class="doc d
           <td>
                 <code><span title="torch.Tensor">Tensor</span></code>
           </td>
-          <td><p><code>len($\theta$)</code>-shaped log-probability.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p><code>len($\theta$)</code>-shaped log-probability.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/vi_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">297</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/vi_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">297</span>
 <span class="normal">298</span>
 <span class="normal">299</span>
 <span class="normal">300</span>
@@ -22283,19 +24498,21 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.log_prob" class="doc d
         <span class="n">theta</span> <span class="o">=</span> <span class="n">ensure_theta_batched</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">theta</span><span class="p">))</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="o">.</span><span class="n">log_prob</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.map" class="doc doc-heading">
-<code class="codehilite language-python"><span class="nb">map</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">num_to_optimize</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">init_method</span><span class="o">=</span><span class="s1">&#39;proposal&#39;</span><span class="p">,</span> <span class="n">num_init_samples</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">save_best_every</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">force_update</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.map" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.vi_posterior.VIPosterior.map" class="doc doc-heading">
+          <code class=" language-python"><span class="nb">map</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">num_to_optimize</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">init_method</span><span class="o">=</span><span class="s1">&#39;proposal&#39;</span><span class="p">,</span> <span class="n">num_init_samples</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">save_best_every</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">force_update</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.map" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -22312,6 +24529,8 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.map" class="doc doc-he
 <p>For developers: if the prior is a <code>BoxUniform</code>, we carry out the optimization
 in unbounded space and transform the result back into bounded space.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -22328,7 +24547,11 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.map" class="doc doc-he
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="sbi.types.TorchTensor">TorchTensor</span>]</code>
           </td>
-          <td><p>Deprecated - use <code>.set_default_x()</code> prior to <code>.map()</code>.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Deprecated - use <code>.set_default_x()</code> prior to <code>.map()</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -22338,8 +24561,12 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.map" class="doc doc-he
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of optimization steps that the algorithm takes
-to find the MAP.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of optimization steps that the algorithm takes
+to find the MAP.</p>
+            </div>
+          </td>
           <td>
                 <code>1000</code>
           </td>
@@ -22349,7 +24576,11 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.map" class="doc doc-he
           <td>
                 <code>float</code>
           </td>
-          <td><p>Learning rate of the optimizer.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Learning rate of the optimizer.</p>
+            </div>
+          </td>
           <td>
                 <code>0.01</code>
           </td>
@@ -22359,10 +24590,14 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.map" class="doc doc-he
           <td>
                 <code><span title="typing.Union">Union</span>[str, <span title="sbi.types.TorchTensor">TorchTensor</span>]</code>
           </td>
-          <td><p>How to select the starting parameters for the optimization. If
+          <td>
+            <div class="doc-md-description">
+              <p>How to select the starting parameters for the optimization. If
 it is a string, it can be either [<code>posterior</code>, <code>prior</code>], which samples
 the respective distribution <code>num_init_samples</code> times. If it is a
-tensor, the tensor will be used as init locations.</p></td>
+tensor, the tensor will be used as init locations.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;proposal&#39;</code>
           </td>
@@ -22372,8 +24607,12 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.map" class="doc doc-he
           <td>
                 <code>int</code>
           </td>
-          <td><p>Draw this number of samples from the posterior and
-evaluate the log-probability of all of them.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Draw this number of samples from the posterior and
+evaluate the log-probability of all of them.</p>
+            </div>
+          </td>
           <td>
                 <code>10000</code>
           </td>
@@ -22383,9 +24622,13 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.map" class="doc doc-he
           <td>
                 <code>int</code>
           </td>
-          <td><p>From the drawn <code>num_init_samples</code>, use the
+          <td>
+            <div class="doc-md-description">
+              <p>From the drawn <code>num_init_samples</code>, use the
 <code>num_to_optimize</code> with highest log-probability as the initial points
-for the optimization.</p></td>
+for the optimization.</p>
+            </div>
+          </td>
           <td>
                 <code>100</code>
           </td>
@@ -22395,10 +24638,14 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.map" class="doc doc-he
           <td>
                 <code>int</code>
           </td>
-          <td><p>The best log-probability is computed, saved in the
+          <td>
+            <div class="doc-md-description">
+              <p>The best log-probability is computed, saved in the
 <code>map</code>-attribute, and printed every <code>save_best_every</code>-th iteration.
 Computing the best log-probability creates a significant overhead
-(thus, the default is <code>10</code>.)</p></td>
+(thus, the default is <code>10</code>.)</p>
+            </div>
+          </td>
           <td>
                 <code>10</code>
           </td>
@@ -22408,8 +24655,12 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.map" class="doc doc-he
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to show a progressbar during sampling from
-the posterior.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to show a progressbar during sampling from
+the posterior.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -22419,8 +24670,12 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.map" class="doc doc-he
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to re-calculate the MAP when x is unchanged and
-have a cached value.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to re-calculate the MAP when x is unchanged and
+have a cached value.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -22429,8 +24684,12 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.map" class="doc doc-he
           <td><code>log_prob_kwargs</code></td>
           <td>
           </td>
-          <td><p>Will be empty for SNLE and SNRE. Will contain
-{&lsquo;norm_posterior&rsquo;: True} for SNPE.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Will be empty for SNLE and SNRE. Will contain
+{&lsquo;norm_posterior&rsquo;: True} for SNPE.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -22438,6 +24697,8 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.map" class="doc doc-he
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -22451,14 +24712,18 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.map" class="doc doc-he
           <td>
                 <code><span title="torch.Tensor">Tensor</span></code>
           </td>
-          <td><p>The MAP estimate.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The MAP estimate.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/vi_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">497</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/vi_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">497</span>
 <span class="normal">498</span>
 <span class="normal">499</span>
 <span class="normal">500</span>
@@ -22592,25 +24857,29 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.map" class="doc doc-he
         <span class="n">force_update</span><span class="o">=</span><span class="n">force_update</span><span class="p">,</span>
     <span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.sample" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">sample</span><span class="p">(</span><span class="n">sample_shape</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">(),</span> <span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.sample" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.vi_posterior.VIPosterior.sample" class="doc doc-heading">
+          <code class=" language-python"><span class="n">sample</span><span class="p">(</span><span class="n">sample_shape</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">(),</span> <span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.sample" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
   
       <p>Samples from the variational posterior distribution.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -22627,14 +24896,20 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.sample" class="doc doc
           <td>
                 <code><span title="sbi.types.Shape">Shape</span></code>
           </td>
-          <td><p>Shape of samples</p></td>
           <td>
-                <code>torch.Size()</code>
+            <div class="doc-md-description">
+              <p>Shape of samples</p>
+            </div>
+          </td>
+          <td>
+                <code><span title="torch.Size">Size</span>()</code>
           </td>
         </tr>
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -22648,14 +24923,18 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.sample" class="doc doc
           <td>
                 <code><span title="torch.Tensor">Tensor</span></code>
           </td>
-          <td><p>Samples from posterior.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Samples from posterior.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/vi_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">274</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/vi_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">274</span>
 <span class="normal">275</span>
 <span class="normal">276</span>
 <span class="normal">277</span>
@@ -22699,19 +24978,21 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.sample" class="doc doc
     <span class="n">samples</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">(</span><span class="n">sample_shape</span><span class="p">))</span>
     <span class="k">return</span> <span class="n">samples</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="o">*</span><span class="n">sample_shape</span><span class="p">,</span> <span class="n">samples</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.set_q" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">set_q</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">parameters</span><span class="o">=</span><span class="p">[],</span> <span class="n">modules</span><span class="o">=</span><span class="p">[])</span></code>
 
-<a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.set_q" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.vi_posterior.VIPosterior.set_q" class="doc doc-heading">
+          <code class=" language-python"><span class="n">set_q</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">parameters</span><span class="o">=</span><span class="p">[],</span> <span class="n">modules</span><span class="o">=</span><span class="p">[])</span></code>
+
+<a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.set_q" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
@@ -22723,6 +25004,8 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.set_q" class="doc doc-
 Further, you can pass a function, which constructs a variational distribution
 if called.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -22739,7 +25022,9 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.set_q" class="doc doc-
           <td>
                 <code><span title="typing.Union">Union</span>[str, <span title="sbi.types.PyroTransformedDistribution">PyroTransformedDistribution</span>, <a class="autorefs autorefs-internal" title="sbi.inference.posteriors.vi_posterior.VIPosterior" href="#sbi.inference.posteriors.vi_posterior.VIPosterior">VIPosterior</a>, <span title="typing.Callable">Callable</span>]</code>
           </td>
-          <td><p>Variational distribution, either string, distribution, or a VIPosterior
+          <td>
+            <div class="doc-md-description">
+              <p>Variational distribution, either string, distribution, or a VIPosterior
 object. This specifies a parametric class of distribution over which
 the best possible posterior approximation is searched. For string input,
 we currently support [nsf, scf, maf, mcf, gaussian, gaussian_diag]. Of
@@ -22752,7 +25037,9 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.set_q" class="doc doc-
 useful for setting the hyperparameters e.g. <code>num_transfroms:int</code> by
 using the <code>get_flow_builder</code> method specifying the hyperparameters. If q
 is already a <code>VIPosterior</code>, then the arguments will be copied from it
-(relevant for multi-round training).</p></td>
+(relevant for multi-round training).</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -22762,7 +25049,11 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.set_q" class="doc doc-
           <td>
                 <code><span title="typing.Iterable">Iterable</span></code>
           </td>
-          <td><p>List of parameters associated with the distribution object.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>List of parameters associated with the distribution object.</p>
+            </div>
+          </td>
           <td>
                 <code>[]</code>
           </td>
@@ -22772,7 +25063,11 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.set_q" class="doc doc-
           <td>
                 <code><span title="typing.Iterable">Iterable</span></code>
           </td>
-          <td><p>List of modules associated with the distribution object.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>List of modules associated with the distribution object.</p>
+            </div>
+          </td>
           <td>
                 <code>[]</code>
           </td>
@@ -22780,9 +25075,9 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.set_q" class="doc doc-
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/vi_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">174</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/vi_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">174</span>
 <span class="normal">175</span>
 <span class="normal">176</span>
 <span class="normal">177</span>
@@ -22934,25 +25229,29 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.set_q" class="doc doc-
     <span class="n">check_variational_distribution</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prior</span><span class="p">)</span>
     <span class="bp">self</span><span class="o">.</span><span class="n">_q</span> <span class="o">=</span> <span class="n">q</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.set_vi_method" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">set_vi_method</span><span class="p">(</span><span class="n">method</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.set_vi_method" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.vi_posterior.VIPosterior.set_vi_method" class="doc doc-heading">
+          <code class=" language-python"><span class="n">set_vi_method</span><span class="p">(</span><span class="n">method</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.set_vi_method" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
   
       <p>Sets variational inference method.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -22969,7 +25268,11 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.set_vi_method" class="
           <td>
                 <code>str</code>
           </td>
-          <td><p>One of [rKL, fKL, IW, alpha].</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>One of [rKL, fKL, IW, alpha].</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -22977,6 +25280,8 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.set_vi_method" class="
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -22990,14 +25295,18 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.set_vi_method" class="
           <td>
                 <code><a class="autorefs autorefs-internal" title="sbi.inference.posteriors.vi_posterior.VIPosterior" href="#sbi.inference.posteriors.vi_posterior.VIPosterior">VIPosterior</a></code>
           </td>
-          <td><p><code>VIPosterior</code> for chainable calls.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p><code>VIPosterior</code> for chainable calls.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/vi_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">261</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/vi_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">261</span>
 <span class="normal">262</span>
 <span class="normal">263</span>
 <span class="normal">264</span>
@@ -23021,25 +25330,29 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.set_vi_method" class="
     <span class="bp">self</span><span class="o">.</span><span class="n">_optimizer_builder</span> <span class="o">=</span> <span class="n">get_VI_method</span><span class="p">(</span><span class="n">method</span><span class="p">)</span>
     <span class="k">return</span> <span class="bp">self</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.train" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">train</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_particles</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">0.999</span><span class="p">,</span> <span class="n">max_num_iters</span><span class="o">=</span><span class="mi">2000</span><span class="p">,</span> <span class="n">min_num_iters</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">clip_value</span><span class="o">=</span><span class="mf">10.0</span><span class="p">,</span> <span class="n">warm_up_rounds</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">retrain_from_scratch</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">reset_optimizer</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">show_progress_bar</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">check_for_convergence</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">quality_control</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">quality_control_metric</span><span class="o">=</span><span class="s1">&#39;psis&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
-<a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.train" class="headerlink" title="Permanent link">&para;</a></h4>
+<h2 id="sbi.inference.posteriors.vi_posterior.VIPosterior.train" class="doc doc-heading">
+          <code class=" language-python"><span class="n">train</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_particles</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">0.999</span><span class="p">,</span> <span class="n">max_num_iters</span><span class="o">=</span><span class="mi">2000</span><span class="p">,</span> <span class="n">min_num_iters</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">clip_value</span><span class="o">=</span><span class="mf">10.0</span><span class="p">,</span> <span class="n">warm_up_rounds</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">retrain_from_scratch</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">reset_optimizer</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">show_progress_bar</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">check_for_convergence</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">quality_control</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">quality_control_metric</span><span class="o">=</span><span class="s1">&#39;psis&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
+
+<a href="#sbi.inference.posteriors.vi_posterior.VIPosterior.train" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
   <div class="doc doc-contents ">
   
       <p>This method trains the variational posterior.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -23056,7 +25369,11 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.train" class="doc doc-
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="sbi.types.TorchTensor">TorchTensor</span>]</code>
           </td>
-          <td><p>The observation.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The observation.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -23066,9 +25383,13 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.train" class="doc doc-
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of samples to approximate expectations within the
+          <td>
+            <div class="doc-md-description">
+              <p>Number of samples to approximate expectations within the
 variational bounds. The larger the more accurate are gradient
-estimates, but the computational cost per iteration increases.</p></td>
+estimates, but the computational cost per iteration increases.</p>
+            </div>
+          </td>
           <td>
                 <code>256</code>
           </td>
@@ -23078,7 +25399,11 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.train" class="doc doc-
           <td>
                 <code>float</code>
           </td>
-          <td><p>Learning rate of the optimizer.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Learning rate of the optimizer.</p>
+            </div>
+          </td>
           <td>
                 <code>0.001</code>
           </td>
@@ -23088,8 +25413,12 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.train" class="doc doc-
           <td>
                 <code>float</code>
           </td>
-          <td><p>Learning rate decay per iteration. We use an exponential decay
-scheduler.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Learning rate decay per iteration. We use an exponential decay
+scheduler.</p>
+            </div>
+          </td>
           <td>
                 <code>0.999</code>
           </td>
@@ -23099,7 +25428,11 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.train" class="doc doc-
           <td>
                 <code>int</code>
           </td>
-          <td><p>Maximum number of iterations.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Maximum number of iterations.</p>
+            </div>
+          </td>
           <td>
                 <code>2000</code>
           </td>
@@ -23109,7 +25442,11 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.train" class="doc doc-
           <td>
                 <code>int</code>
           </td>
-          <td><p>Minimum number of iterations.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Minimum number of iterations.</p>
+            </div>
+          </td>
           <td>
                 <code>10</code>
           </td>
@@ -23119,8 +25456,12 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.train" class="doc doc-
           <td>
                 <code>float</code>
           </td>
-          <td><p>Gradient clipping value, decreasing may help if you see invalid
-values.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Gradient clipping value, decreasing may help if you see invalid
+values.</p>
+            </div>
+          </td>
           <td>
                 <code>10.0</code>
           </td>
@@ -23130,7 +25471,11 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.train" class="doc doc-
           <td>
                 <code>int</code>
           </td>
-          <td><p>Initialize the posterior as the prior.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Initialize the posterior as the prior.</p>
+            </div>
+          </td>
           <td>
                 <code>100</code>
           </td>
@@ -23140,7 +25485,11 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.train" class="doc doc-
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Retrain the variational distributions from scratch.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Retrain the variational distributions from scratch.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -23150,7 +25499,11 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.train" class="doc doc-
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Reset the divergence optimizer</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Reset the divergence optimizer</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -23160,7 +25513,11 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.train" class="doc doc-
           <td>
                 <code>bool</code>
           </td>
-          <td><p>If any progress report should be displayed.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>If any progress report should be displayed.</p>
+            </div>
+          </td>
           <td>
                 <code>True</code>
           </td>
@@ -23170,7 +25527,11 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.train" class="doc doc-
           <td>
                 <code>bool</code>
           </td>
-          <td><p>If False quality control is skipped.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>If False quality control is skipped.</p>
+            </div>
+          </td>
           <td>
                 <code>True</code>
           </td>
@@ -23180,7 +25541,11 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.train" class="doc doc-
           <td>
                 <code>str</code>
           </td>
-          <td><p>Which metric to use for evaluating the quality.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Which metric to use for evaluating the quality.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;psis&#39;</code>
           </td>
@@ -23189,7 +25554,9 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.train" class="doc doc-
           <td><code>kwargs</code></td>
           <td>
           </td>
-          <td><p>Hyperparameters check corresponding <code>DivergenceOptimizer</code> for detail
+          <td>
+            <div class="doc-md-description">
+              <p>Hyperparameters check corresponding <code>DivergenceOptimizer</code> for detail
 eps: Determines sensitivity of convergence check.
 retain_graph: Boolean which decides whether to retain the computation
     graph. This may be required for some <code>exotic</code> user-specified q&rsquo;s.
@@ -23203,35 +25570,21 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.train" class="doc doc-
 stick_the_landing: If one should use the STL estimator (only for rKL,
     IW, alpha).
 dreg: If one should use the DREG estimator (only for rKL, IW, alpha).
-weight_transform: Callable applied to importance weights (only for fKL)</p></td>
+weight_transform: Callable applied to importance weights (only for fKL)</p>
+            </div>
+          </td>
           <td>
                 <code>{}</code>
           </td>
         </tr>
     </tbody>
   </table>
+      <p>Returns:
+    VIPosterior: <code>VIPosterior</code> (can be used to chain calls).</p>
 
-  <p><strong>Returns:</strong></p>
-  <table>
-    <thead>
-      <tr>
-<th>Name</th>        <th>Type</th>
-        <th>Description</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr>
-<td><code>VIPosterior</code></td>          <td>
-                <code><a class="autorefs autorefs-internal" title="sbi.inference.posteriors.vi_posterior.VIPosterior" href="#sbi.inference.posteriors.vi_posterior.VIPosterior">VIPosterior</a></code>
-          </td>
-          <td><p><code>VIPosterior</code> (can be used to chain calls).</p></td>
-        </tr>
-    </tbody>
-  </table>
-
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/vi_posterior.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">324</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/posteriors/vi_posterior.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">324</span>
 <span class="normal">325</span>
 <span class="normal">326</span>
 <span class="normal">327</span>
@@ -23533,7 +25886,7 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.train" class="doc doc-
 
     <span class="k">return</span> <span class="bp">self</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
@@ -23547,22 +25900,20 @@ <h4 id="sbi.inference.posteriors.vi_posterior.VIPosterior.train" class="doc doc-
 </div><h2 id="models">Models<a class="headerlink" href="#models" title="Permanent link">&para;</a></h2>
 
 
-<div class="doc doc-object doc-function">
-
-
 
-<h3 id="sbi.utils.get_nn_models.posterior_nn" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">sbi</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">get_nn_models</span><span class="o">.</span><span class="n">posterior_nn</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">z_score_theta</span><span class="o">=</span><span class="s1">&#39;independent&#39;</span><span class="p">,</span> <span class="n">z_score_x</span><span class="o">=</span><span class="s1">&#39;independent&#39;</span><span class="p">,</span> <span class="n">hidden_features</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">num_transforms</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">num_bins</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">embedding_net</span><span class="o">=</span><span class="n">nn</span><span class="o">.</span><span class="n">Identity</span><span class="p">(),</span> <span class="n">num_components</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
+<div class="doc doc-object doc-function">
 
-<a href="#sbi.utils.get_nn_models.posterior_nn" class="headerlink" title="Permanent link">&para;</a></h3>
 
 
+<a id="sbi.utils.get_nn_models.posterior_nn"></a>
   <div class="doc doc-contents first">
   
       <p>Returns a function that builds a density estimator for learning the posterior.</p>
 <p>This function will usually be used for SNPE. The returned function is to be passed
 to the inference class when using the flexible interface.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -23579,8 +25930,12 @@ <h3 id="sbi.utils.get_nn_models.posterior_nn" class="doc doc-heading">
           <td>
                 <code>str</code>
           </td>
-          <td><p>The type of density estimator that will be created. One of [<code>mdn</code>,
-<code>made</code>, <code>maf</code>, <code>maf_rqs</code>, <code>nsf</code>].</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The type of density estimator that will be created. One of [<code>mdn</code>,
+<code>made</code>, <code>maf</code>, <code>maf_rqs</code>, <code>nsf</code>].</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -23590,13 +25945,17 @@ <h3 id="sbi.utils.get_nn_models.posterior_nn" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[str]</code>
           </td>
-          <td><p>Whether to z-score parameters <span class="arithmatex">\(\theta\)</span> before passing them into
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to z-score parameters <span class="arithmatex">\(\theta\)</span> before passing them into
 the network, can take one of the following:
 - <code>none</code>, or None: do not z-score.
 - <code>independent</code>: z-score each dimension independently.
 - <code>structured</code>: treat dimensions as related, therefore compute mean and std
 over the entire batch, instead of per-dimension. Should be used when each
-sample is, for example, a time series or an image.</p></td>
+sample is, for example, a time series or an image.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;independent&#39;</code>
           </td>
@@ -23606,8 +25965,12 @@ <h3 id="sbi.utils.get_nn_models.posterior_nn" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[str]</code>
           </td>
-          <td><p>Whether to z-score simulation outputs <span class="arithmatex">\(x\)</span> before passing them into
-the network, same options as z_score_theta.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to z-score simulation outputs <span class="arithmatex">\(x\)</span> before passing them into
+the network, same options as z_score_theta.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;independent&#39;</code>
           </td>
@@ -23617,7 +25980,11 @@ <h3 id="sbi.utils.get_nn_models.posterior_nn" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of hidden features.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of hidden features.</p>
+            </div>
+          </td>
           <td>
                 <code>50</code>
           </td>
@@ -23627,9 +25994,13 @@ <h3 id="sbi.utils.get_nn_models.posterior_nn" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of transforms when a flow is used. Only relevant if
+          <td>
+            <div class="doc-md-description">
+              <p>Number of transforms when a flow is used. Only relevant if
 density estimator is a normalizing flow (i.e. currently either a <code>maf</code> or a
-<code>nsf</code>). Ignored if density estimator is a <code>mdn</code> or <code>made</code>.</p></td>
+<code>nsf</code>). Ignored if density estimator is a <code>mdn</code> or <code>made</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>5</code>
           </td>
@@ -23639,8 +26010,12 @@ <h3 id="sbi.utils.get_nn_models.posterior_nn" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of bins used for the splines in <code>nsf</code>. Ignored if density
-estimator not <code>nsf</code>.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of bins used for the splines in <code>nsf</code>. Ignored if density
+estimator not <code>nsf</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>10</code>
           </td>
@@ -23648,13 +26023,17 @@ <h3 id="sbi.utils.get_nn_models.posterior_nn" class="doc doc-heading">
         <tr>
           <td><code>embedding_net</code></td>
           <td>
-                <code><span title="torch.nn">nn</span>.<span title="torch.nn.Module">Module</span></code>
+                <code><span title="torch.nn.Module">Module</span></code>
           </td>
-          <td><p>Optional embedding network for simulation outputs <span class="arithmatex">\(x\)</span>. This
+          <td>
+            <div class="doc-md-description">
+              <p>Optional embedding network for simulation outputs <span class="arithmatex">\(x\)</span>. This
 embedding net allows to learn features from potentially high-dimensional
-simulation outputs.</p></td>
+simulation outputs.</p>
+            </div>
+          </td>
           <td>
-                <code>nn.Identity()</code>
+                <code><span title="torch.nn.Identity">Identity</span>()</code>
           </td>
         </tr>
         <tr>
@@ -23662,8 +26041,12 @@ <h3 id="sbi.utils.get_nn_models.posterior_nn" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of mixture components for a mixture of Gaussians.
-Ignored if density estimator is not an mdn.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of mixture components for a mixture of Gaussians.
+Ignored if density estimator is not an mdn.</p>
+            </div>
+          </td>
           <td>
                 <code>10</code>
           </td>
@@ -23672,7 +26055,11 @@ <h3 id="sbi.utils.get_nn_models.posterior_nn" class="doc doc-heading">
           <td><code>kwargs</code></td>
           <td>
           </td>
-          <td><p>additional custom arguments passed to downstream build functions.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>additional custom arguments passed to downstream build functions.</p>
+            </div>
+          </td>
           <td>
                 <code>{}</code>
           </td>
@@ -23680,9 +26067,9 @@ <h3 id="sbi.utils.get_nn_models.posterior_nn" class="doc doc-heading">
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/utils/get_nn_models.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">177</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/utils/get_nn_models.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">177</span>
 <span class="normal">178</span>
 <span class="normal">179</span>
 <span class="normal">180</span>
@@ -23894,27 +26281,25 @@ <h3 id="sbi.utils.get_nn_models.posterior_nn" class="doc doc-heading">
 
     <span class="k">return</span> <span class="n">build_fn_snpe_a</span> <span class="k">if</span> <span class="n">model</span> <span class="o">==</span> <span class="s2">&quot;mdn_snpe_a&quot;</span> <span class="k">else</span> <span class="n">build_fn</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
-<div class="doc doc-object doc-function">
-
-
 
-<h3 id="sbi.utils.get_nn_models.likelihood_nn" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">sbi</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">get_nn_models</span><span class="o">.</span><span class="n">likelihood_nn</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">z_score_theta</span><span class="o">=</span><span class="s1">&#39;independent&#39;</span><span class="p">,</span> <span class="n">z_score_x</span><span class="o">=</span><span class="s1">&#39;independent&#39;</span><span class="p">,</span> <span class="n">hidden_features</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">num_transforms</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">num_bins</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">embedding_net</span><span class="o">=</span><span class="n">nn</span><span class="o">.</span><span class="n">Identity</span><span class="p">(),</span> <span class="n">num_components</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
+<div class="doc doc-object doc-function">
 
-<a href="#sbi.utils.get_nn_models.likelihood_nn" class="headerlink" title="Permanent link">&para;</a></h3>
 
 
+<a id="sbi.utils.get_nn_models.likelihood_nn"></a>
   <div class="doc doc-contents first">
   
       <p>Returns a function that builds a density estimator for learning the likelihood.</p>
 <p>This function will usually be used for SNLE. The returned function is to be passed
 to the inference class when using the flexible interface.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -23931,8 +26316,12 @@ <h3 id="sbi.utils.get_nn_models.likelihood_nn" class="doc doc-heading">
           <td>
                 <code>str</code>
           </td>
-          <td><p>The type of density estimator that will be created. One of [<code>mdn</code>,
-<code>made</code>, <code>maf</code>, <code>maf_rqs</code>, <code>nsf</code>].</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The type of density estimator that will be created. One of [<code>mdn</code>,
+<code>made</code>, <code>maf</code>, <code>maf_rqs</code>, <code>nsf</code>].</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -23942,13 +26331,17 @@ <h3 id="sbi.utils.get_nn_models.likelihood_nn" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[str]</code>
           </td>
-          <td><p>Whether to z-score parameters <span class="arithmatex">\(\theta\)</span> before passing them into
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to z-score parameters <span class="arithmatex">\(\theta\)</span> before passing them into
 the network, can take one of the following:
 - <code>none</code>, or None: do not z-score.
 - <code>independent</code>: z-score each dimension independently.
 - <code>structured</code>: treat dimensions as related, therefore compute mean and std
 over the entire batch, instead of per-dimension. Should be used when each
-sample is, for example, a time series or an image.</p></td>
+sample is, for example, a time series or an image.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;independent&#39;</code>
           </td>
@@ -23958,8 +26351,12 @@ <h3 id="sbi.utils.get_nn_models.likelihood_nn" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[str]</code>
           </td>
-          <td><p>Whether to z-score simulation outputs <span class="arithmatex">\(x\)</span> before passing them into
-the network, same options as z_score_theta.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to z-score simulation outputs <span class="arithmatex">\(x\)</span> before passing them into
+the network, same options as z_score_theta.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;independent&#39;</code>
           </td>
@@ -23969,7 +26366,11 @@ <h3 id="sbi.utils.get_nn_models.likelihood_nn" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of hidden features.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of hidden features.</p>
+            </div>
+          </td>
           <td>
                 <code>50</code>
           </td>
@@ -23979,9 +26380,13 @@ <h3 id="sbi.utils.get_nn_models.likelihood_nn" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of transforms when a flow is used. Only relevant if
+          <td>
+            <div class="doc-md-description">
+              <p>Number of transforms when a flow is used. Only relevant if
 density estimator is a normalizing flow (i.e. currently either a <code>maf</code> or a
-<code>nsf</code>). Ignored if density estimator is a <code>mdn</code> or <code>made</code>.</p></td>
+<code>nsf</code>). Ignored if density estimator is a <code>mdn</code> or <code>made</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>5</code>
           </td>
@@ -23991,8 +26396,12 @@ <h3 id="sbi.utils.get_nn_models.likelihood_nn" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of bins used for the splines in <code>nsf</code>. Ignored if density
-estimator not <code>nsf</code>.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of bins used for the splines in <code>nsf</code>. Ignored if density
+estimator not <code>nsf</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>10</code>
           </td>
@@ -24000,11 +26409,15 @@ <h3 id="sbi.utils.get_nn_models.likelihood_nn" class="doc doc-heading">
         <tr>
           <td><code>embedding_net</code></td>
           <td>
-                <code><span title="torch.nn">nn</span>.<span title="torch.nn.Module">Module</span></code>
+                <code><span title="torch.nn.Module">Module</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Optional embedding network for parameters <span class="arithmatex">\(\theta\)</span>.</p>
+            </div>
           </td>
-          <td><p>Optional embedding network for parameters <span class="arithmatex">\(\theta\)</span>.</p></td>
           <td>
-                <code>nn.Identity()</code>
+                <code><span title="torch.nn.Identity">Identity</span>()</code>
           </td>
         </tr>
         <tr>
@@ -24012,8 +26425,12 @@ <h3 id="sbi.utils.get_nn_models.likelihood_nn" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of mixture components for a mixture of Gaussians.
-Ignored if density estimator is not an mdn.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of mixture components for a mixture of Gaussians.
+Ignored if density estimator is not an mdn.</p>
+            </div>
+          </td>
           <td>
                 <code>10</code>
           </td>
@@ -24022,7 +26439,11 @@ <h3 id="sbi.utils.get_nn_models.likelihood_nn" class="doc doc-heading">
           <td><code>kwargs</code></td>
           <td>
           </td>
-          <td><p>additional custom arguments passed to downstream build functions.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>additional custom arguments passed to downstream build functions.</p>
+            </div>
+          </td>
           <td>
                 <code>{}</code>
           </td>
@@ -24030,9 +26451,9 @@ <h3 id="sbi.utils.get_nn_models.likelihood_nn" class="doc doc-heading">
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/utils/get_nn_models.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 93</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/utils/get_nn_models.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 93</span>
 <span class="normal"> 94</span>
 <span class="normal"> 95</span>
 <span class="normal"> 96</span>
@@ -24196,21 +26617,17 @@ <h3 id="sbi.utils.get_nn_models.likelihood_nn" class="doc doc-heading">
 
     <span class="k">return</span> <span class="n">build_fn</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
-<div class="doc doc-object doc-function">
-
 
+<div class="doc doc-object doc-function">
 
-<h3 id="sbi.utils.get_nn_models.classifier_nn" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">sbi</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">get_nn_models</span><span class="o">.</span><span class="n">classifier_nn</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">z_score_theta</span><span class="o">=</span><span class="s1">&#39;independent&#39;</span><span class="p">,</span> <span class="n">z_score_x</span><span class="o">=</span><span class="s1">&#39;independent&#39;</span><span class="p">,</span> <span class="n">hidden_features</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">embedding_net_theta</span><span class="o">=</span><span class="n">nn</span><span class="o">.</span><span class="n">Identity</span><span class="p">(),</span> <span class="n">embedding_net_x</span><span class="o">=</span><span class="n">nn</span><span class="o">.</span><span class="n">Identity</span><span class="p">(),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
-
-<a href="#sbi.utils.get_nn_models.classifier_nn" class="headerlink" title="Permanent link">&para;</a></h3>
 
 
+<a id="sbi.utils.get_nn_models.classifier_nn"></a>
   <div class="doc doc-contents first">
   
       <p>Returns a function that builds a classifier for learning density ratios.</p>
@@ -24218,6 +26635,8 @@ <h3 id="sbi.utils.get_nn_models.classifier_nn" class="doc doc-heading">
 to the inference class when using the flexible interface.</p>
 <p>Note that in the view of the SNRE classifier we build below, x=theta and y=x.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -24234,8 +26653,12 @@ <h3 id="sbi.utils.get_nn_models.classifier_nn" class="doc doc-heading">
           <td>
                 <code>str</code>
           </td>
-          <td><p>The type of classifier that will be created. One of [<code>linear</code>, <code>mlp</code>,
-<code>resnet</code>].</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The type of classifier that will be created. One of [<code>linear</code>, <code>mlp</code>,
+<code>resnet</code>].</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -24245,13 +26668,17 @@ <h3 id="sbi.utils.get_nn_models.classifier_nn" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[str]</code>
           </td>
-          <td><p>Whether to z-score parameters <span class="arithmatex">\(\theta\)</span> before passing them into
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to z-score parameters <span class="arithmatex">\(\theta\)</span> before passing them into
 the network, can take one of the following:
 - <code>none</code>, or None: do not z-score.
 - <code>independent</code>: z-score each dimension independently.
 - <code>structured</code>: treat dimensions as related, therefore compute mean and std
 over the entire batch, instead of per-dimension. Should be used when each
-sample is, for example, a time series or an image.</p></td>
+sample is, for example, a time series or an image.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;independent&#39;</code>
           </td>
@@ -24261,8 +26688,12 @@ <h3 id="sbi.utils.get_nn_models.classifier_nn" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[str]</code>
           </td>
-          <td><p>Whether to z-score simulation outputs <span class="arithmatex">\(x\)</span> before passing them into
-the network, same options as z_score_theta.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to z-score simulation outputs <span class="arithmatex">\(x\)</span> before passing them into
+the network, same options as z_score_theta.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;independent&#39;</code>
           </td>
@@ -24272,7 +26703,11 @@ <h3 id="sbi.utils.get_nn_models.classifier_nn" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of hidden features.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of hidden features.</p>
+            </div>
+          </td>
           <td>
                 <code>50</code>
           </td>
@@ -24280,30 +26715,42 @@ <h3 id="sbi.utils.get_nn_models.classifier_nn" class="doc doc-heading">
         <tr>
           <td><code>embedding_net_theta</code></td>
           <td>
-                <code><span title="torch.nn">nn</span>.<span title="torch.nn.Module">Module</span></code>
+                <code><span title="torch.nn.Module">Module</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Optional embedding network for parameters <span class="arithmatex">\(\theta\)</span>.</p>
+            </div>
           </td>
-          <td><p>Optional embedding network for parameters <span class="arithmatex">\(\theta\)</span>.</p></td>
           <td>
-                <code>nn.Identity()</code>
+                <code><span title="torch.nn.Identity">Identity</span>()</code>
           </td>
         </tr>
         <tr>
           <td><code>embedding_net_x</code></td>
           <td>
-                <code><span title="torch.nn">nn</span>.<span title="torch.nn.Module">Module</span></code>
+                <code><span title="torch.nn.Module">Module</span></code>
           </td>
-          <td><p>Optional embedding network for simulation outputs <span class="arithmatex">\(x\)</span>. This
+          <td>
+            <div class="doc-md-description">
+              <p>Optional embedding network for simulation outputs <span class="arithmatex">\(x\)</span>. This
 embedding net allows to learn features from potentially high-dimensional
-simulation outputs.</p></td>
+simulation outputs.</p>
+            </div>
+          </td>
           <td>
-                <code>nn.Identity()</code>
+                <code><span title="torch.nn.Identity">Identity</span>()</code>
           </td>
         </tr>
         <tr>
           <td><code>kwargs</code></td>
           <td>
           </td>
-          <td><p>additional custom arguments passed to downstream build functions.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>additional custom arguments passed to downstream build functions.</p>
+            </div>
+          </td>
           <td>
                 <code>{}</code>
           </td>
@@ -24311,9 +26758,9 @@ <h3 id="sbi.utils.get_nn_models.classifier_nn" class="doc doc-heading">
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/utils/get_nn_models.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">19</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/utils/get_nn_models.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">19</span>
 <span class="normal">20</span>
 <span class="normal">21</span>
 <span class="normal">22</span>
@@ -24457,22 +26904,18 @@ <h3 id="sbi.utils.get_nn_models.classifier_nn" class="doc doc-heading">
 
     <span class="k">return</span> <span class="n">build_fn</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div><h2 id="potentials">Potentials<a class="headerlink" href="#potentials" title="Permanent link">&para;</a></h2>
 
 
-<div class="doc doc-object doc-function">
-
-
 
-<h3 id="sbi.inference.potentials.posterior_based_potential.posterior_estimator_based_potential" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">sbi</span><span class="o">.</span><span class="n">inference</span><span class="o">.</span><span class="n">potentials</span><span class="o">.</span><span class="n">posterior_based_potential</span><span class="o">.</span><span class="n">posterior_estimator_based_potential</span><span class="p">(</span><span class="n">posterior_estimator</span><span class="p">,</span> <span class="n">prior</span><span class="p">,</span> <span class="n">x_o</span><span class="p">,</span> <span class="n">enable_transform</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
+<div class="doc doc-object doc-function">
 
-<a href="#sbi.inference.potentials.posterior_based_potential.posterior_estimator_based_potential" class="headerlink" title="Permanent link">&para;</a></h3>
 
 
+<a id="sbi.inference.potentials.posterior_based_potential.posterior_estimator_based_potential"></a>
   <div class="doc doc-contents first">
   
       <p>Returns the potential for posterior-based methods.</p>
@@ -24481,6 +26924,8 @@ <h3 id="sbi.inference.potentials.posterior_based_potential.posterior_estimator_b
 <p>The potential is the same as the log-probability of the <code>posterior_estimator</code>, but
 it is set to <span class="arithmatex">\(-\inf\)</span> outside of the prior bounds.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -24495,9 +26940,13 @@ <h3 id="sbi.inference.potentials.posterior_based_potential.posterior_estimator_b
         <tr>
           <td><code>posterior_estimator</code></td>
           <td>
-                <code><span title="torch.nn">nn</span>.<span title="torch.nn.Module">Module</span></code>
+                <code><span title="torch.nn.Module">Module</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>The neural network modelling the posterior.</p>
+            </div>
           </td>
-          <td><p>The neural network modelling the posterior.</p></td>
           <td>
               <em>required</em>
           </td>
@@ -24507,7 +26956,11 @@ <h3 id="sbi.inference.potentials.posterior_based_potential.posterior_estimator_b
           <td>
                 <code><span title="torch.distributions.Distribution">Distribution</span></code>
           </td>
-          <td><p>The prior distribution.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The prior distribution.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -24517,7 +26970,11 @@ <h3 id="sbi.inference.potentials.posterior_based_potential.posterior_estimator_b
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="torch.Tensor">Tensor</span>]</code>
           </td>
-          <td><p>The observed data at which to evaluate the posterior.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The observed data at which to evaluate the posterior.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -24527,8 +26984,12 @@ <h3 id="sbi.inference.potentials.posterior_based_potential.posterior_estimator_b
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to transform parameters to unconstrained space.
-When False, an identity transform will be returned for <code>theta_transform</code>.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to transform parameters to unconstrained space.
+When False, an identity transform will be returned for <code>theta_transform</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>True</code>
           </td>
@@ -24536,6 +26997,8 @@ <h3 id="sbi.inference.potentials.posterior_based_potential.posterior_estimator_b
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -24549,20 +27012,28 @@ <h3 id="sbi.inference.potentials.posterior_based_potential.posterior_estimator_b
           <td>
                 <code><span title="typing.Callable">Callable</span></code>
           </td>
-          <td><p>The potential function and a transformation that maps</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The potential function and a transformation that maps</p>
+            </div>
+          </td>
         </tr>
         <tr>
           <td>
                 <code><span title="sbi.types.TorchTransform">TorchTransform</span></code>
           </td>
-          <td><p>to unconstrained space.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>to unconstrained space.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/potentials/posterior_based_potential.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">18</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/potentials/posterior_based_potential.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">18</span>
 <span class="normal">19</span>
 <span class="normal">20</span>
 <span class="normal">21</span>
@@ -24636,27 +27107,25 @@ <h3 id="sbi.inference.potentials.posterior_based_potential.posterior_estimator_b
 
     <span class="k">return</span> <span class="n">potential_fn</span><span class="p">,</span> <span class="n">theta_transform</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
-<div class="doc doc-object doc-function">
-
-
 
-<h3 id="sbi.inference.potentials.likelihood_based_potential.likelihood_estimator_based_potential" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">sbi</span><span class="o">.</span><span class="n">inference</span><span class="o">.</span><span class="n">potentials</span><span class="o">.</span><span class="n">likelihood_based_potential</span><span class="o">.</span><span class="n">likelihood_estimator_based_potential</span><span class="p">(</span><span class="n">likelihood_estimator</span><span class="p">,</span> <span class="n">prior</span><span class="p">,</span> <span class="n">x_o</span><span class="p">,</span> <span class="n">enable_transform</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
+<div class="doc doc-object doc-function">
 
-<a href="#sbi.inference.potentials.likelihood_based_potential.likelihood_estimator_based_potential" class="headerlink" title="Permanent link">&para;</a></h3>
 
 
+<a id="sbi.inference.potentials.likelihood_based_potential.likelihood_estimator_based_potential"></a>
   <div class="doc doc-contents first">
   
       <p>Returns potential <span class="arithmatex">\(\log(p(x_o|\theta)p(\theta))\)</span> for likelihood-based methods.</p>
 <p>It also returns a transformation that can be used to transform the potential into
 unconstrained space.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -24671,9 +27140,13 @@ <h3 id="sbi.inference.potentials.likelihood_based_potential.likelihood_estimator
         <tr>
           <td><code>likelihood_estimator</code></td>
           <td>
-                <code><span title="torch.nn">nn</span>.<span title="torch.nn.Module">Module</span></code>
+                <code><span title="torch.nn.Module">Module</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>The neural network modelling the likelihood.</p>
+            </div>
           </td>
-          <td><p>The neural network modelling the likelihood.</p></td>
           <td>
               <em>required</em>
           </td>
@@ -24683,7 +27156,11 @@ <h3 id="sbi.inference.potentials.likelihood_based_potential.likelihood_estimator
           <td>
                 <code><span title="torch.distributions.Distribution">Distribution</span></code>
           </td>
-          <td><p>The prior distribution.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The prior distribution.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -24693,7 +27170,11 @@ <h3 id="sbi.inference.potentials.likelihood_based_potential.likelihood_estimator
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="torch.Tensor">Tensor</span>]</code>
           </td>
-          <td><p>The observed data at which to evaluate the likelihood.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The observed data at which to evaluate the likelihood.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -24703,8 +27184,12 @@ <h3 id="sbi.inference.potentials.likelihood_based_potential.likelihood_estimator
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to transform parameters to unconstrained space.
- When False, an identity transform will be returned for <code>theta_transform</code>.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to transform parameters to unconstrained space.
+ When False, an identity transform will be returned for <code>theta_transform</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>True</code>
           </td>
@@ -24712,6 +27197,8 @@ <h3 id="sbi.inference.potentials.likelihood_based_potential.likelihood_estimator
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -24725,20 +27212,28 @@ <h3 id="sbi.inference.potentials.likelihood_based_potential.likelihood_estimator
           <td>
                 <code><span title="typing.Callable">Callable</span></code>
           </td>
-          <td><p>The potential function <span class="arithmatex">\(p(x_o|\theta)p(\theta)\)</span> and a transformation that maps</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The potential function <span class="arithmatex">\(p(x_o|\theta)p(\theta)\)</span> and a transformation that maps</p>
+            </div>
+          </td>
         </tr>
         <tr>
           <td>
                 <code><span title="sbi.types.TorchTransform">TorchTransform</span></code>
           </td>
-          <td><p>to unconstrained space.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>to unconstrained space.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/potentials/likelihood_based_potential.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">18</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/potentials/likelihood_based_potential.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">18</span>
 <span class="normal">19</span>
 <span class="normal">20</span>
 <span class="normal">21</span>
@@ -24804,27 +27299,25 @@ <h3 id="sbi.inference.potentials.likelihood_based_potential.likelihood_estimator
 
     <span class="k">return</span> <span class="n">potential_fn</span><span class="p">,</span> <span class="n">theta_transform</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
-<div class="doc doc-object doc-function">
-
-
 
-<h3 id="sbi.inference.potentials.ratio_based_potential.ratio_estimator_based_potential" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">sbi</span><span class="o">.</span><span class="n">inference</span><span class="o">.</span><span class="n">potentials</span><span class="o">.</span><span class="n">ratio_based_potential</span><span class="o">.</span><span class="n">ratio_estimator_based_potential</span><span class="p">(</span><span class="n">ratio_estimator</span><span class="p">,</span> <span class="n">prior</span><span class="p">,</span> <span class="n">x_o</span><span class="p">,</span> <span class="n">enable_transform</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
+<div class="doc doc-object doc-function">
 
-<a href="#sbi.inference.potentials.ratio_based_potential.ratio_estimator_based_potential" class="headerlink" title="Permanent link">&para;</a></h3>
 
 
+<a id="sbi.inference.potentials.ratio_based_potential.ratio_estimator_based_potential"></a>
   <div class="doc doc-contents first">
   
       <p>Returns the potential for ratio-based methods.</p>
 <p>It also returns a transformation that can be used to transform the potential into
 unconstrained space.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -24839,9 +27332,13 @@ <h3 id="sbi.inference.potentials.ratio_based_potential.ratio_estimator_based_pot
         <tr>
           <td><code>ratio_estimator</code></td>
           <td>
-                <code><span title="torch.nn">nn</span>.<span title="torch.nn.Module">Module</span></code>
+                <code><span title="torch.nn.Module">Module</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>The neural network modelling likelihood-to-evidence ratio.</p>
+            </div>
           </td>
-          <td><p>The neural network modelling likelihood-to-evidence ratio.</p></td>
           <td>
               <em>required</em>
           </td>
@@ -24851,7 +27348,11 @@ <h3 id="sbi.inference.potentials.ratio_based_potential.ratio_estimator_based_pot
           <td>
                 <code><span title="torch.distributions.Distribution">Distribution</span></code>
           </td>
-          <td><p>The prior distribution.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The prior distribution.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -24861,7 +27362,11 @@ <h3 id="sbi.inference.potentials.ratio_based_potential.ratio_estimator_based_pot
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="torch.Tensor">Tensor</span>]</code>
           </td>
-          <td><p>The observed data at which to evaluate the likelihood-to-evidence ratio.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The observed data at which to evaluate the likelihood-to-evidence ratio.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -24871,8 +27376,12 @@ <h3 id="sbi.inference.potentials.ratio_based_potential.ratio_estimator_based_pot
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to transform parameters to unconstrained space.
-When False, an identity transform will be returned for <code>theta_transform</code>.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to transform parameters to unconstrained space.
+When False, an identity transform will be returned for <code>theta_transform</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>True</code>
           </td>
@@ -24880,6 +27389,8 @@ <h3 id="sbi.inference.potentials.ratio_based_potential.ratio_estimator_based_pot
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -24893,20 +27404,28 @@ <h3 id="sbi.inference.potentials.ratio_based_potential.ratio_estimator_based_pot
           <td>
                 <code><span title="typing.Callable">Callable</span></code>
           </td>
-          <td><p>The potential function and a transformation that maps</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>The potential function and a transformation that maps</p>
+            </div>
+          </td>
         </tr>
         <tr>
           <td>
                 <code><span title="sbi.types.TorchTransform">TorchTransform</span></code>
           </td>
-          <td><p>to unconstrained space.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>to unconstrained space.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/inference/potentials/ratio_based_potential.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">17</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/inference/potentials/ratio_based_potential.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">17</span>
 <span class="normal">18</span>
 <span class="normal">19</span>
 <span class="normal">20</span>
@@ -24968,22 +27487,18 @@ <h3 id="sbi.inference.potentials.ratio_based_potential.ratio_estimator_based_pot
 
     <span class="k">return</span> <span class="n">potential_fn</span><span class="p">,</span> <span class="n">theta_transform</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div><h2 id="analysis">Analysis<a class="headerlink" href="#analysis" title="Permanent link">&para;</a></h2>
 
 
-<div class="doc doc-object doc-function">
-
-
 
-<h3 id="sbi.analysis.plot.pairplot" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">sbi</span><span class="o">.</span><span class="n">analysis</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">pairplot</span><span class="p">(</span><span class="n">samples</span><span class="p">,</span> <span class="n">points</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">limits</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">subset</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">offdiag</span><span class="o">=</span><span class="s1">&#39;hist&#39;</span><span class="p">,</span> <span class="n">diag</span><span class="o">=</span><span class="s1">&#39;hist&#39;</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="n">labels</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ticks</span><span class="o">=</span><span class="p">[],</span> <span class="n">upper</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fig</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
+<div class="doc doc-object doc-function">
 
-<a href="#sbi.analysis.plot.pairplot" class="headerlink" title="Permanent link">&para;</a></h3>
 
 
+<a id="sbi.analysis.plot.pairplot"></a>
   <div class="doc doc-contents first">
   
       <p>Plot samples in a 2D grid showing marginals and pairwise marginals.</p>
@@ -24991,6 +27506,8 @@ <h3 id="sbi.analysis.plot.pairplot" class="doc doc-heading">
 that the samples were drawn from. Each upper-diagonal plot can be interpreted as a
 2D-marginal of the distribution.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -25005,9 +27522,13 @@ <h3 id="sbi.analysis.plot.pairplot" class="doc doc-heading">
         <tr>
           <td><code>samples</code></td>
           <td>
-                <code><span title="typing.Union">Union</span>[<span title="typing.List">List</span>[<span title="numpy">np</span>.<span title="numpy.ndarray">ndarray</span>], <span title="typing.List">List</span>[torch.<span title="torch.Tensor">Tensor</span>], <span title="numpy">np</span>.<span title="numpy.ndarray">ndarray</span>, torch.<span title="torch.Tensor">Tensor</span>]</code>
+                <code><span title="typing.Union">Union</span>[<span title="typing.List">List</span>[<span title="numpy.ndarray">ndarray</span>], <span title="typing.List">List</span>[<span title="torch.Tensor">Tensor</span>], <span title="numpy.ndarray">ndarray</span>, <span title="torch.Tensor">Tensor</span>]</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Samples used to build the histogram.</p>
+            </div>
           </td>
-          <td><p>Samples used to build the histogram.</p></td>
           <td>
               <em>required</em>
           </td>
@@ -25015,9 +27536,13 @@ <h3 id="sbi.analysis.plot.pairplot" class="doc doc-heading">
         <tr>
           <td><code>points</code></td>
           <td>
-                <code><span title="typing.Optional">Optional</span>[<span title="typing.Union">Union</span>[<span title="typing.List">List</span>[<span title="numpy">np</span>.<span title="numpy.ndarray">ndarray</span>], <span title="typing.List">List</span>[torch.<span title="torch.Tensor">Tensor</span>], <span title="numpy">np</span>.<span title="numpy.ndarray">ndarray</span>, torch.<span title="torch.Tensor">Tensor</span>]]</code>
+                <code><span title="typing.Optional">Optional</span>[<span title="typing.Union">Union</span>[<span title="typing.List">List</span>[<span title="numpy.ndarray">ndarray</span>], <span title="typing.List">List</span>[<span title="torch.Tensor">Tensor</span>], <span title="numpy.ndarray">ndarray</span>, <span title="torch.Tensor">Tensor</span>]]</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>List of additional points to scatter.</p>
+            </div>
           </td>
-          <td><p>List of additional points to scatter.</p></td>
           <td>
                 <code>None</code>
           </td>
@@ -25025,10 +27550,14 @@ <h3 id="sbi.analysis.plot.pairplot" class="doc doc-heading">
         <tr>
           <td><code>limits</code></td>
           <td>
-                <code><span title="typing.Optional">Optional</span>[<span title="typing.Union">Union</span>[<span title="typing.List">List</span>, torch.<span title="torch.Tensor">Tensor</span>]]</code>
+                <code><span title="typing.Optional">Optional</span>[<span title="typing.Union">Union</span>[<span title="typing.List">List</span>, <span title="torch.Tensor">Tensor</span>]]</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Array containing the plot xlim for each parameter dimension. If None,
+just use the min and max of the passed samples</p>
+            </div>
           </td>
-          <td><p>Array containing the plot xlim for each parameter dimension. If None,
-just use the min and max of the passed samples</p></td>
           <td>
                 <code>None</code>
           </td>
@@ -25038,9 +27567,13 @@ <h3 id="sbi.analysis.plot.pairplot" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="typing.List">List</span>[int]]</code>
           </td>
-          <td><p>List containing the dimensions to plot. E.g. subset=[1,3] will plot
+          <td>
+            <div class="doc-md-description">
+              <p>List containing the dimensions to plot. E.g. subset=[1,3] will plot
 plot only the 1<sup>st</sup> and 3<sup>rd</sup> dimension but will discard the 0<sup>th</sup> and 2<sup>nd</sup> (and,
-if they exist, the 4<sup>th</sup>, 5<sup>th</sup> and so on).</p></td>
+if they exist, the 4<sup>th</sup>, 5<sup>th</sup> and so on).</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -25050,8 +27583,12 @@ <h3 id="sbi.analysis.plot.pairplot" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="typing.Union">Union</span>[<span title="typing.List">List</span>[str], str]]</code>
           </td>
-          <td><p>Plotting style for upper diagonal, {hist, scatter, contour, cond,
-None}.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Plotting style for upper diagonal, {hist, scatter, contour, cond,
+None}.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;hist&#39;</code>
           </td>
@@ -25061,7 +27598,11 @@ <h3 id="sbi.analysis.plot.pairplot" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[str]</code>
           </td>
-          <td><p>deprecated, use offdiag instead.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>deprecated, use offdiag instead.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -25071,7 +27612,11 @@ <h3 id="sbi.analysis.plot.pairplot" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="typing.Union">Union</span>[<span title="typing.List">List</span>[str], str]]</code>
           </td>
-          <td><p>Plotting style for diagonal, {hist, cond, None}.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Plotting style for diagonal, {hist, cond, None}.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;hist&#39;</code>
           </td>
@@ -25081,7 +27626,11 @@ <h3 id="sbi.analysis.plot.pairplot" class="doc doc-heading">
           <td>
                 <code><span title="typing.Tuple">Tuple</span></code>
           </td>
-          <td><p>Size of the entire figure.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Size of the entire figure.</p>
+            </div>
+          </td>
           <td>
                 <code>(10, 10)</code>
           </td>
@@ -25091,7 +27640,11 @@ <h3 id="sbi.analysis.plot.pairplot" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="typing.List">List</span>[str]]</code>
           </td>
-          <td><p>List of strings specifying the names of the parameters.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>List of strings specifying the names of the parameters.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -25099,9 +27652,13 @@ <h3 id="sbi.analysis.plot.pairplot" class="doc doc-heading">
         <tr>
           <td><code>ticks</code></td>
           <td>
-                <code><span title="typing.Union">Union</span>[<span title="typing.List">List</span>, torch.<span title="torch.Tensor">Tensor</span>]</code>
+                <code><span title="typing.Union">Union</span>[<span title="typing.List">List</span>, <span title="torch.Tensor">Tensor</span>]</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Position of the ticks.</p>
+            </div>
           </td>
-          <td><p>Position of the ticks.</p></td>
           <td>
                 <code>[]</code>
           </td>
@@ -25110,7 +27667,11 @@ <h3 id="sbi.analysis.plot.pairplot" class="doc doc-heading">
           <td><code>fig</code></td>
           <td>
           </td>
-          <td><p>matplotlib figure to plot on.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>matplotlib figure to plot on.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -25119,7 +27680,11 @@ <h3 id="sbi.analysis.plot.pairplot" class="doc doc-heading">
           <td><code>axes</code></td>
           <td>
           </td>
-          <td><p>matplotlib axes corresponding to fig.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>matplotlib axes corresponding to fig.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -25128,19 +27693,24 @@ <h3 id="sbi.analysis.plot.pairplot" class="doc doc-heading">
           <td><code>**kwargs</code></td>
           <td>
           </td>
-          <td><p>Additional arguments to adjust the plot, e.g., <code>samples_colors</code>,
+          <td>
+            <div class="doc-md-description">
+              <p>Additional arguments to adjust the plot, e.g., <code>samples_colors</code>,
 <code>points_colors</code> and many more, see the source code in <code>_get_default_opts()</code>
-in <code>sbi.analysis.plot</code> for details.</p></td>
+in <code>sbi.analysis.plot</code> for details.</p>
+            </div>
+          </td>
           <td>
                 <code>{}</code>
           </td>
         </tr>
     </tbody>
   </table>
+      <p>Returns: figure and axis of posterior distribution plot</p>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/analysis/plot.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">280</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/analysis/plot.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">280</span>
 <span class="normal">281</span>
 <span class="normal">282</span>
 <span class="normal">283</span>
@@ -25508,27 +28078,25 @@ <h3 id="sbi.analysis.plot.pairplot" class="doc doc-heading">
         <span class="n">diag_func</span><span class="p">,</span> <span class="n">offdiag_func</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">limits</span><span class="p">,</span> <span class="n">points</span><span class="p">,</span> <span class="n">opts</span><span class="p">,</span> <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="n">axes</span>
     <span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
-<div class="doc doc-object doc-function">
-
 
+<div class="doc doc-object doc-function">
 
-<h3 id="sbi.analysis.plot.marginal_plot" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">sbi</span><span class="o">.</span><span class="n">analysis</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">marginal_plot</span><span class="p">(</span><span class="n">samples</span><span class="p">,</span> <span class="n">points</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">limits</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">subset</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">diag</span><span class="o">=</span><span class="s1">&#39;hist&#39;</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="n">labels</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ticks</span><span class="o">=</span><span class="p">[],</span> <span class="n">fig</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
-
-<a href="#sbi.analysis.plot.marginal_plot" class="headerlink" title="Permanent link">&para;</a></h3>
 
 
+<a id="sbi.analysis.plot.marginal_plot"></a>
   <div class="doc doc-contents first">
   
       <p>Plot samples in a row showing 1D marginals of selected dimensions.</p>
 <p>Each of the plots can be interpreted as a 1D-marginal of the distribution
 that the samples were drawn from.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -25543,9 +28111,13 @@ <h3 id="sbi.analysis.plot.marginal_plot" class="doc doc-heading">
         <tr>
           <td><code>samples</code></td>
           <td>
-                <code><span title="typing.Union">Union</span>[<span title="typing.List">List</span>[<span title="numpy">np</span>.<span title="numpy.ndarray">ndarray</span>], <span title="typing.List">List</span>[torch.<span title="torch.Tensor">Tensor</span>], <span title="numpy">np</span>.<span title="numpy.ndarray">ndarray</span>, torch.<span title="torch.Tensor">Tensor</span>]</code>
+                <code><span title="typing.Union">Union</span>[<span title="typing.List">List</span>[<span title="numpy.ndarray">ndarray</span>], <span title="typing.List">List</span>[<span title="torch.Tensor">Tensor</span>], <span title="numpy.ndarray">ndarray</span>, <span title="torch.Tensor">Tensor</span>]</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Samples used to build the histogram.</p>
+            </div>
           </td>
-          <td><p>Samples used to build the histogram.</p></td>
           <td>
               <em>required</em>
           </td>
@@ -25553,9 +28125,13 @@ <h3 id="sbi.analysis.plot.marginal_plot" class="doc doc-heading">
         <tr>
           <td><code>points</code></td>
           <td>
-                <code><span title="typing.Optional">Optional</span>[<span title="typing.Union">Union</span>[<span title="typing.List">List</span>[<span title="numpy">np</span>.<span title="numpy.ndarray">ndarray</span>], <span title="typing.List">List</span>[torch.<span title="torch.Tensor">Tensor</span>], <span title="numpy">np</span>.<span title="numpy.ndarray">ndarray</span>, torch.<span title="torch.Tensor">Tensor</span>]]</code>
+                <code><span title="typing.Optional">Optional</span>[<span title="typing.Union">Union</span>[<span title="typing.List">List</span>[<span title="numpy.ndarray">ndarray</span>], <span title="typing.List">List</span>[<span title="torch.Tensor">Tensor</span>], <span title="numpy.ndarray">ndarray</span>, <span title="torch.Tensor">Tensor</span>]]</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>List of additional points to scatter.</p>
+            </div>
           </td>
-          <td><p>List of additional points to scatter.</p></td>
           <td>
                 <code>None</code>
           </td>
@@ -25563,10 +28139,14 @@ <h3 id="sbi.analysis.plot.marginal_plot" class="doc doc-heading">
         <tr>
           <td><code>limits</code></td>
           <td>
-                <code><span title="typing.Optional">Optional</span>[<span title="typing.Union">Union</span>[<span title="typing.List">List</span>, torch.<span title="torch.Tensor">Tensor</span>]]</code>
+                <code><span title="typing.Optional">Optional</span>[<span title="typing.Union">Union</span>[<span title="typing.List">List</span>, <span title="torch.Tensor">Tensor</span>]]</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Array containing the plot xlim for each parameter dimension. If None,
+just use the min and max of the passed samples</p>
+            </div>
           </td>
-          <td><p>Array containing the plot xlim for each parameter dimension. If None,
-just use the min and max of the passed samples</p></td>
           <td>
                 <code>None</code>
           </td>
@@ -25576,9 +28156,13 @@ <h3 id="sbi.analysis.plot.marginal_plot" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="typing.List">List</span>[int]]</code>
           </td>
-          <td><p>List containing the dimensions to plot. E.g. subset=[1,3] will plot
+          <td>
+            <div class="doc-md-description">
+              <p>List containing the dimensions to plot. E.g. subset=[1,3] will plot
 plot only the 1<sup>st</sup> and 3<sup>rd</sup> dimension but will discard the 0<sup>th</sup> and 2<sup>nd</sup> (and,
-if they exist, the 4<sup>th</sup>, 5<sup>th</sup> and so on).</p></td>
+if they exist, the 4<sup>th</sup>, 5<sup>th</sup> and so on).</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -25588,7 +28172,11 @@ <h3 id="sbi.analysis.plot.marginal_plot" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[str]</code>
           </td>
-          <td><p>Plotting style for 1D marginals, {hist, kde cond, None}.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Plotting style for 1D marginals, {hist, kde cond, None}.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;hist&#39;</code>
           </td>
@@ -25598,7 +28186,11 @@ <h3 id="sbi.analysis.plot.marginal_plot" class="doc doc-heading">
           <td>
                 <code><span title="typing.Tuple">Tuple</span></code>
           </td>
-          <td><p>Size of the entire figure.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Size of the entire figure.</p>
+            </div>
+          </td>
           <td>
                 <code>(10, 10)</code>
           </td>
@@ -25608,7 +28200,11 @@ <h3 id="sbi.analysis.plot.marginal_plot" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="typing.List">List</span>[str]]</code>
           </td>
-          <td><p>List of strings specifying the names of the parameters.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>List of strings specifying the names of the parameters.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -25616,9 +28212,13 @@ <h3 id="sbi.analysis.plot.marginal_plot" class="doc doc-heading">
         <tr>
           <td><code>ticks</code></td>
           <td>
-                <code><span title="typing.Union">Union</span>[<span title="typing.List">List</span>, torch.<span title="torch.Tensor">Tensor</span>]</code>
+                <code><span title="typing.Union">Union</span>[<span title="typing.List">List</span>, <span title="torch.Tensor">Tensor</span>]</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Position of the ticks.</p>
+            </div>
           </td>
-          <td><p>Position of the ticks.</p></td>
           <td>
                 <code>[]</code>
           </td>
@@ -25627,7 +28227,11 @@ <h3 id="sbi.analysis.plot.marginal_plot" class="doc doc-heading">
           <td><code>points_colors</code></td>
           <td>
           </td>
-          <td><p>Colors of the <code>points</code>.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Colors of the <code>points</code>.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -25636,7 +28240,11 @@ <h3 id="sbi.analysis.plot.marginal_plot" class="doc doc-heading">
           <td><code>fig</code></td>
           <td>
           </td>
-          <td><p>matplotlib figure to plot on.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>matplotlib figure to plot on.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -25645,7 +28253,11 @@ <h3 id="sbi.analysis.plot.marginal_plot" class="doc doc-heading">
           <td><code>axes</code></td>
           <td>
           </td>
-          <td><p>matplotlib axes corresponding to fig.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>matplotlib axes corresponding to fig.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -25654,19 +28266,24 @@ <h3 id="sbi.analysis.plot.marginal_plot" class="doc doc-heading">
           <td><code>**kwargs</code></td>
           <td>
           </td>
-          <td><p>Additional arguments to adjust the plot, e.g., <code>samples_colors</code>,
+          <td>
+            <div class="doc-md-description">
+              <p>Additional arguments to adjust the plot, e.g., <code>samples_colors</code>,
 <code>points_colors</code> and many more, see the source code in <code>_get_default_opts()</code>
-in <code>sbi.analysis.plot</code> for details.</p></td>
+in <code>sbi.analysis.plot</code> for details.</p>
+            </div>
+          </td>
           <td>
                 <code>{}</code>
           </td>
         </tr>
     </tbody>
   </table>
+      <p>Returns: figure and axis of posterior distribution plot</p>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/analysis/plot.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">466</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/analysis/plot.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">466</span>
 <span class="normal">467</span>
 <span class="normal">468</span>
 <span class="normal">469</span>
@@ -25788,21 +28405,17 @@ <h3 id="sbi.analysis.plot.marginal_plot" class="doc doc-heading">
         <span class="n">diag_func</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">limits</span><span class="p">,</span> <span class="n">points</span><span class="p">,</span> <span class="n">opts</span><span class="p">,</span> <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="n">axes</span>
     <span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
-<div class="doc doc-object doc-function">
-
 
+<div class="doc doc-object doc-function">
 
-<h3 id="sbi.analysis.plot.conditional_pairplot" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">sbi</span><span class="o">.</span><span class="n">analysis</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">conditional_pairplot</span><span class="p">(</span><span class="n">density</span><span class="p">,</span> <span class="n">condition</span><span class="p">,</span> <span class="n">limits</span><span class="p">,</span> <span class="n">points</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">subset</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">resolution</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="n">labels</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ticks</span><span class="o">=</span><span class="p">[],</span> <span class="n">fig</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
-
-<a href="#sbi.analysis.plot.conditional_pairplot" class="headerlink" title="Permanent link">&para;</a></h3>
 
 
+<a id="sbi.analysis.plot.conditional_pairplot"></a>
   <div class="doc doc-contents first">
   
       <p>Plot conditional distribution given all other parameters.</p>
@@ -25817,6 +28430,8 @@ <h3 id="sbi.analysis.plot.conditional_pairplot" class="doc doc-heading">
 <span class="arithmatex">\(p(\theta_1, \theta_2 | \theta_0=c[0])\)</span>. All other diagonals and upper-diagonals
 are built in the corresponding way.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -25833,7 +28448,11 @@ <h3 id="sbi.analysis.plot.conditional_pairplot" class="doc doc-heading">
           <td>
                 <code><span title="typing.Any">Any</span></code>
           </td>
-          <td><p>Probability density with a <code>log_prob()</code> method.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Probability density with a <code>log_prob()</code> method.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -25841,11 +28460,15 @@ <h3 id="sbi.analysis.plot.conditional_pairplot" class="doc doc-heading">
         <tr>
           <td><code>condition</code></td>
           <td>
-                <code>torch.<span title="torch.Tensor">Tensor</span></code>
+                <code><span title="torch.Tensor">Tensor</span></code>
           </td>
-          <td><p>Condition that all but the one/two regarded parameters are fixed to.
+          <td>
+            <div class="doc-md-description">
+              <p>Condition that all but the one/two regarded parameters are fixed to.
 The condition should be of shape (1, dim_theta), i.e. it could e.g. be
-a sample from the posterior distribution.</p></td>
+a sample from the posterior distribution.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -25853,9 +28476,13 @@ <h3 id="sbi.analysis.plot.conditional_pairplot" class="doc doc-heading">
         <tr>
           <td><code>limits</code></td>
           <td>
-                <code><span title="typing.Union">Union</span>[<span title="typing.List">List</span>, torch.<span title="torch.Tensor">Tensor</span>]</code>
+                <code><span title="typing.Union">Union</span>[<span title="typing.List">List</span>, <span title="torch.Tensor">Tensor</span>]</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Limits in between which each parameter will be evaluated.</p>
+            </div>
           </td>
-          <td><p>Limits in between which each parameter will be evaluated.</p></td>
           <td>
               <em>required</em>
           </td>
@@ -25863,9 +28490,13 @@ <h3 id="sbi.analysis.plot.conditional_pairplot" class="doc doc-heading">
         <tr>
           <td><code>points</code></td>
           <td>
-                <code><span title="typing.Optional">Optional</span>[<span title="typing.Union">Union</span>[<span title="typing.List">List</span>[<span title="numpy">np</span>.<span title="numpy.ndarray">ndarray</span>], <span title="typing.List">List</span>[torch.<span title="torch.Tensor">Tensor</span>], <span title="numpy">np</span>.<span title="numpy.ndarray">ndarray</span>, torch.<span title="torch.Tensor">Tensor</span>]]</code>
+                <code><span title="typing.Optional">Optional</span>[<span title="typing.Union">Union</span>[<span title="typing.List">List</span>[<span title="numpy.ndarray">ndarray</span>], <span title="typing.List">List</span>[<span title="torch.Tensor">Tensor</span>], <span title="numpy.ndarray">ndarray</span>, <span title="torch.Tensor">Tensor</span>]]</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Additional points to scatter.</p>
+            </div>
           </td>
-          <td><p>Additional points to scatter.</p></td>
           <td>
                 <code>None</code>
           </td>
@@ -25875,9 +28506,13 @@ <h3 id="sbi.analysis.plot.conditional_pairplot" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="typing.List">List</span>[int]]</code>
           </td>
-          <td><p>List containing the dimensions to plot. E.g. subset=[1,3] will plot
+          <td>
+            <div class="doc-md-description">
+              <p>List containing the dimensions to plot. E.g. subset=[1,3] will plot
 plot only the 1<sup>st</sup> and 3<sup>rd</sup> dimension but will discard the 0<sup>th</sup> and 2<sup>nd</sup> (and,
-if they exist, the 4<sup>th</sup>, 5<sup>th</sup> and so on)</p></td>
+if they exist, the 4<sup>th</sup>, 5<sup>th</sup> and so on)</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -25887,7 +28522,11 @@ <h3 id="sbi.analysis.plot.conditional_pairplot" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Resolution of the grid at which we evaluate the <code>pdf</code>.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Resolution of the grid at which we evaluate the <code>pdf</code>.</p>
+            </div>
+          </td>
           <td>
                 <code>50</code>
           </td>
@@ -25897,7 +28536,11 @@ <h3 id="sbi.analysis.plot.conditional_pairplot" class="doc doc-heading">
           <td>
                 <code><span title="typing.Tuple">Tuple</span></code>
           </td>
-          <td><p>Size of the entire figure.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Size of the entire figure.</p>
+            </div>
+          </td>
           <td>
                 <code>(10, 10)</code>
           </td>
@@ -25907,7 +28550,11 @@ <h3 id="sbi.analysis.plot.conditional_pairplot" class="doc doc-heading">
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="typing.List">List</span>[str]]</code>
           </td>
-          <td><p>List of strings specifying the names of the parameters.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>List of strings specifying the names of the parameters.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -25915,9 +28562,13 @@ <h3 id="sbi.analysis.plot.conditional_pairplot" class="doc doc-heading">
         <tr>
           <td><code>ticks</code></td>
           <td>
-                <code><span title="typing.Union">Union</span>[<span title="typing.List">List</span>, torch.<span title="torch.Tensor">Tensor</span>]</code>
+                <code><span title="typing.Union">Union</span>[<span title="typing.List">List</span>, <span title="torch.Tensor">Tensor</span>]</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Position of the ticks.</p>
+            </div>
           </td>
-          <td><p>Position of the ticks.</p></td>
           <td>
                 <code>[]</code>
           </td>
@@ -25926,7 +28577,11 @@ <h3 id="sbi.analysis.plot.conditional_pairplot" class="doc doc-heading">
           <td><code>points_colors</code></td>
           <td>
           </td>
-          <td><p>Colors of the <code>points</code>.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Colors of the <code>points</code>.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -25935,7 +28590,11 @@ <h3 id="sbi.analysis.plot.conditional_pairplot" class="doc doc-heading">
           <td><code>fig</code></td>
           <td>
           </td>
-          <td><p>matplotlib figure to plot on.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>matplotlib figure to plot on.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -25944,7 +28603,11 @@ <h3 id="sbi.analysis.plot.conditional_pairplot" class="doc doc-heading">
           <td><code>axes</code></td>
           <td>
           </td>
-          <td><p>matplotlib axes corresponding to fig.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>matplotlib axes corresponding to fig.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -25953,19 +28616,24 @@ <h3 id="sbi.analysis.plot.conditional_pairplot" class="doc doc-heading">
           <td><code>**kwargs</code></td>
           <td>
           </td>
-          <td><p>Additional arguments to adjust the plot, e.g., <code>samples_colors</code>,
+          <td>
+            <div class="doc-md-description">
+              <p>Additional arguments to adjust the plot, e.g., <code>samples_colors</code>,
 <code>points_colors</code> and many more, see the source code in <code>_get_default_opts()</code>
-in <code>sbi.analysis.plot</code> for details.</p></td>
+in <code>sbi.analysis.plot</code> for details.</p>
+            </div>
+          </td>
           <td>
                 <code>{}</code>
           </td>
         </tr>
     </tbody>
   </table>
+      <p>Returns: figure and axis of posterior distribution plot</p>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/analysis/plot.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">602</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/analysis/plot.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">602</span>
 <span class="normal">603</span>
 <span class="normal">604</span>
 <span class="normal">605</span>
@@ -26169,21 +28837,17 @@ <h3 id="sbi.analysis.plot.conditional_pairplot" class="doc doc-heading">
         <span class="n">diag_func</span><span class="p">,</span> <span class="n">offdiag_func</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">limits</span><span class="p">,</span> <span class="n">points</span><span class="p">,</span> <span class="n">opts</span><span class="p">,</span> <span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="n">axes</span>
     <span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
-<div class="doc doc-object doc-function">
-
 
+<div class="doc doc-object doc-function">
 
-<h3 id="sbi.analysis.conditional_density.conditional_corrcoeff" class="doc doc-heading">
-<code class="codehilite language-python"><span class="n">sbi</span><span class="o">.</span><span class="n">analysis</span><span class="o">.</span><span class="n">conditional_density</span><span class="o">.</span><span class="n">conditional_corrcoeff</span><span class="p">(</span><span class="n">density</span><span class="p">,</span> <span class="n">limits</span><span class="p">,</span> <span class="n">condition</span><span class="p">,</span> <span class="n">subset</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">resolution</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span></code>
-
-<a href="#sbi.analysis.conditional_density.conditional_corrcoeff" class="headerlink" title="Permanent link">&para;</a></h3>
 
 
+<a id="sbi.analysis.conditional_density.conditional_corrcoeff"></a>
   <div class="doc doc-contents first">
   
       <p>Returns the conditional correlation matrix of a distribution.</p>
@@ -26196,6 +28860,8 @@ <h3 id="sbi.analysis.conditional_density.conditional_corrcoeff" class="doc doc-h
 <p>If <code>condition</code> is a batch of conditions, this function computes the conditional
 correlation matrix for each one of them and returns the mean.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -26212,7 +28878,11 @@ <h3 id="sbi.analysis.conditional_density.conditional_corrcoeff" class="doc doc-h
           <td>
                 <code><span title="typing.Any">Any</span></code>
           </td>
-          <td><p>Probability density function with <code>.log_prob()</code> function.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Probability density function with <code>.log_prob()</code> function.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -26222,7 +28892,11 @@ <h3 id="sbi.analysis.conditional_density.conditional_corrcoeff" class="doc doc-h
           <td>
                 <code><span title="torch.Tensor">Tensor</span></code>
           </td>
-          <td><p>Limits within which to evaluate the <code>density</code>.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Limits within which to evaluate the <code>density</code>.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -26232,9 +28906,13 @@ <h3 id="sbi.analysis.conditional_density.conditional_corrcoeff" class="doc doc-h
           <td>
                 <code><span title="torch.Tensor">Tensor</span></code>
           </td>
-          <td><p>Values to condition the <code>density</code> on. If a batch of conditions is
+          <td>
+            <div class="doc-md-description">
+              <p>Values to condition the <code>density</code> on. If a batch of conditions is
 passed, we compute the conditional correlation matrix for each of them and
-return the average conditional correlation matrix.</p></td>
+return the average conditional correlation matrix.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -26244,8 +28922,12 @@ <h3 id="sbi.analysis.conditional_density.conditional_corrcoeff" class="doc doc-h
           <td>
                 <code><span title="typing.Optional">Optional</span>[<span title="typing.List">List</span>[int]]</code>
           </td>
-          <td><p>Evaluate the conditional distribution only on a subset of dimensions.
-If <code>None</code> this function uses all dimensions.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Evaluate the conditional distribution only on a subset of dimensions.
+If <code>None</code> this function uses all dimensions.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -26255,20 +28937,25 @@ <h3 id="sbi.analysis.conditional_density.conditional_corrcoeff" class="doc doc-h
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of grid points on which the conditional distribution is
+          <td>
+            <div class="doc-md-description">
+              <p>Number of grid points on which the conditional distribution is
 evaluated. A higher value increases the accuracy of the estimated
-correlation but also increases the computational cost.</p></td>
+correlation but also increases the computational cost.</p>
+            </div>
+          </td>
           <td>
                 <code>50</code>
           </td>
         </tr>
     </tbody>
   </table>
-      <p>or <code>(len(subset), len(subset))</code> if <code>subset</code> was specified.</p>
+      <p>Returns: Average conditional correlation matrix of shape either <code>(num_dim, num_dim)</code>
+or <code>(len(subset), len(subset))</code> if <code>subset</code> was specified.</p>
 
-      <details class="quote">
-        <summary>Source code in <code>/home/michael/Documents/sbi/sbi/analysis/conditional_density.py</code></summary>
-        <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">106</span>
+          <details class="quote">
+            <summary>Source code in <code>sbi/analysis/conditional_density.py</code></summary>
+            <div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">106</span>
 <span class="normal">107</span>
 <span class="normal">108</span>
 <span class="normal">109</span>
@@ -26426,7 +29113,7 @@ <h3 id="sbi.analysis.conditional_density.conditional_corrcoeff" class="doc doc-h
     <span class="n">av_correlation_matrix</span><span class="o">.</span><span class="n">fill_diagonal_</span><span class="p">(</span><span class="mf">1.0</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">av_correlation_matrix</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
@@ -26468,7 +29155,7 @@ <h3 id="sbi.analysis.conditional_density.conditional_corrcoeff" class="doc doc-h
       
     
     <a href="https://github.com/sbi-dev/sbi" target="_blank" rel="noopener" title="github.com" class="md-social__link">
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
     </a>
   
 </div>
@@ -26482,10 +29169,11 @@ <h3 id="sbi.analysis.conditional_density.conditional_corrcoeff" class="doc doc-h
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.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": "../assets/javascripts/workers/search.f886a092.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.fac441b0.min.js"></script>
+      <script src="../assets/javascripts/bundle.81fa17fe.min.js"></script>
       
         <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML"></script>
       
diff --git a/search/search_index.json b/search/search_index.json
index fa26ccc1f..1cf35e486 100644
--- a/search/search_index.json
+++ b/search/search_index.json
@@ -1 +1 @@
-{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"<code>sbi</code>: simulation-based inference","text":"<p><code>sbi</code>: A Python toolbox for simulation-based inference.</p> <p></p> <p>Inference can be run in a single line of code</p> <pre><code>posterior = infer(simulator, prior, method='SNPE', num_simulations=1000)\n</code></pre> <p>or in a few lines for more flexibility:</p> <pre><code>inference = SNPE(prior=prior)\n_ = inference.append_simulations(theta, x).train()\nposterior = inference.build_posterior()\n</code></pre> <p><code>sbi</code> lets you choose from a variety of amortized and sequential SBI methods:</p> <p>Amortized methods return a posterior that can be applied to many different observations without retraining, whereas sequential methods focus the inference on one particular observation to be more simulation-efficient. For an overview of implemented methods see below, or checkout or GitHub page.</p>"},{"location":"#overview","title":"Overview","text":"<ul> <li> <p>To learn about the general motivation behind simulation-based inference, and the   inference methods included in <code>sbi</code>, read on below.</p> </li> <li> <p>For example applications to canonical problems in neuroscience, browse the recent   research article Training deep neural density estimators to identify mechanistic models of neural dynamics.</p> </li> <li> <p>If you want to get started using <code>sbi</code> on your own problem, jump to   installation and then check out the tutorial.</p> </li> </ul>"},{"location":"#motivation-and-approach","title":"Motivation and approach","text":"<p>Many areas of science and engineering make extensive use of complex, stochastic, numerical simulations to describe the structure and dynamics of the processes being investigated.</p> <p>A key challenge in simulation-based science is constraining these simulation models\u2019 parameters, which are intepretable quantities, with observational data. Bayesian inference provides a general and powerful framework to invert the simulators, i.e. describe the parameters which are consistent both with empirical data and prior knowledge.</p> <p>In the case of simulators, a key quantity required for statistical inference, the likelihood of observed data given parameters, \\(\\mathcal{L}(\\theta) = p(x_o|\\theta)\\), is typically intractable, rendering conventional statistical approaches inapplicable.</p> <p><code>sbi</code> implements powerful machine-learning methods that address this problem. Roughly, these algorithms can be categorized as:</p> <ul> <li>Neural Posterior Estimation (amortized <code>NPE</code> and sequential <code>SNPE</code>),</li> <li>Neural Likelihood Estimation (<code>(S)NLE</code>), and</li> <li>Neural Ratio Estimation (<code>(S)NRE</code>).</li> </ul> <p>Depending on the characteristics of the problem, e.g. the dimensionalities of the parameter space and the observation space, one of the methods will be more suitable.</p> <p></p> <p>Goal: Algorithmically identify mechanistic models which are consistent with data.</p> <p>Each of the methods above needs three inputs: A candidate mechanistic model, prior knowledge or constraints on model parameters, and observational data (or summary statistics thereof).</p> <p>The methods then proceed by</p> <ol> <li>sampling parameters from the prior followed by simulating synthetic data from    these parameters,</li> <li>learning the (probabilistic) association between data (or    data features) and underlying parameters, i.e. to learn statistical inference from    simulated data. The way in which this association is learned differs between the    above methods, but all use deep neural networks.</li> <li>This learned neural network is then applied to empirical data to derive the full    space of parameters consistent with the data and the prior, i.e. the posterior    distribution. High posterior probability is assigned to parameters which are    consistent with both the data and the prior, low probability to inconsistent    parameters. While SNPE directly learns the posterior distribution, SNLE and SNRE need    an extra MCMC sampling step to construct a posterior.</li> <li>If needed, an initial estimate of the posterior can be used to adaptively generate    additional informative simulations.</li> </ol>"},{"location":"#publications","title":"Publications","text":"<p>See Cranmer, Brehmer, Louppe (2020) for a recent review on simulation-based inference.</p> <p>The following papers offer additional details on the inference methods implemented in <code>sbi</code>. You can find a tutorial on how to run each of these methods here.</p>"},{"location":"#posterior-estimation-snpe","title":"Posterior estimation (<code>(S)NPE</code>)","text":"<ul> <li> <p>Fast \u03b5-free Inference of Simulation Models with Bayesian Conditional Density Estimation by Papamakarios &amp; Murray (NeurIPS 2016) [PDF] [BibTeX]</p> </li> <li> <p>Flexible statistical inference for mechanistic models of neural dynamics  by Lueckmann, Goncalves, Bassetto, \u00d6cal, Nonnenmacher &amp; Macke (NeurIPS 2017) [PDF] [BibTeX]</p> </li> <li> <p>Automatic posterior transformation for likelihood-free inferenceby Greenberg, Nonnenmacher &amp; Macke (ICML 2019) [PDF] [BibTeX]</p> </li> <li> <p>Truncated proposals for scalable and hassle-free simulation-based inference  by Deistler, Goncalves &amp; Macke (NeurIPS 2022) [Paper]</p> </li> </ul>"},{"location":"#likelihood-estimation-snle","title":"Likelihood-estimation (<code>(S)NLE</code>)","text":"<ul> <li> <p>Sequential neural likelihood: Fast likelihood-free inference with autoregressive flowsby Papamakarios, Sterratt &amp; Murray (AISTATS 2019) [PDF] [BibTeX]</p> </li> <li> <p>Variational methods for simulation-based inference  by Gl\u00f6ckler, Deistler, Macke (ICLR 2022) [Paper]</p> </li> <li> <p>Flexible and efficient simulation-based inference for models of decision-making  by Boelts, Lueckmann, Gao, Macke (Elife 2022) [Paper]</p> </li> </ul>"},{"location":"#likelihood-ratio-estimation-snre","title":"Likelihood-ratio-estimation (<code>(S)NRE</code>)","text":"<ul> <li> <p>Likelihood-free MCMC with Amortized Approximate Likelihood Ratiosby Hermans, Begy &amp; Louppe (ICML 2020) [PDF]</p> </li> <li> <p>On Contrastive Learning for Likelihood-free InferenceDurkan, Murray &amp; Papamakarios (ICML 2020) [PDF]</p> </li> <li> <p>Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimationby Delaunoy, Hermans, Rozet, Wehenkel &amp; Louppe (NeurIPS 2022) [PDF]</p> </li> <li> <p>Contrastive Neural Ratio EstimationBenjamin Kurt Miller, Christoph Weniger, Patrick Forr\u00e9 (NeurIPS 2022) [PDF]</p> </li> </ul>"},{"location":"#utilities","title":"Utilities","text":"<ul> <li> <p>Restriction estimatorby Deistler, Macke &amp; Goncalves (PNAS 2022) [Paper]</p> </li> <li> <p>Simulation-based calibrationby Talts, Betancourt, Simpson, Vehtari, Gelman (arxiv 2018) [Paper])</p> </li> <li> <p>Expected coverage (sample-based)as computed in Deistler, Goncalves, Macke [Paper] and in Rozet, Louppe [Paper]</p> </li> </ul>"},{"location":"citation/","title":"Citation","text":"<p>If you use <code>sbi</code> consider citing the sbi software paper, in addition to the original research articles describing the specific sbi-algorithm(s) you are using. </p> <pre><code>@article{tejero-cantero2020sbi,\n  doi = {10.21105/joss.02505},\n  url = {https://doi.org/10.21105/joss.02505},\n  year = {2020},\n  publisher = {The Open Journal},\n  volume = {5},\n  number = {52},\n  pages = {2505},\n  author = {Alvaro Tejero-Cantero and Jan Boelts and Michael Deistler and Jan-Matthis Lueckmann and Conor Durkan and Pedro J. Gon\u00e7alves and David S. Greenberg and Jakob H. Macke},\n  title = {sbi: A toolkit for simulation-based inference},\n  journal = {Journal of Open Source Software}\n}\n</code></pre> <p>The above citation refers to the original version of the <code>sbi</code> project and has a persistent DOI. Additionally, new releases of <code>sbi</code> are citable via Zenodo, where we create a new DOI for every release.</p>"},{"location":"code_of_conduct/","title":"Contributor Covenant Code of Conduct","text":""},{"location":"code_of_conduct/#our-pledge","title":"Our Pledge","text":"<p>We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, or sexual identity and orientation.</p> <p>We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community.</p>"},{"location":"code_of_conduct/#our-standards","title":"Our Standards","text":"<p>Examples of behavior that contributes to a positive environment for our community include:</p> <ul> <li>Demonstrating empathy and kindness toward other people</li> <li>Being respectful of differing opinions, viewpoints, and experiences</li> <li>Giving and gracefully accepting constructive feedback</li> <li>Accepting responsibility and apologizing to those affected by our mistakes,   and learning from the experience</li> <li>Focusing on what is best not just for us as individuals, but for the overall   community</li> </ul> <p>Examples of unacceptable behavior include:</p> <ul> <li>The use of sexualized language or imagery, and sexual attention or advances of   any kind</li> <li>Trolling, insulting or derogatory comments, and personal or political attacks</li> <li>Public or private harassment</li> <li>Publishing others\u2019 private information, such as a physical or email address,   without their explicit permission</li> <li>Other conduct which could reasonably be considered inappropriate in a   professional setting</li> </ul>"},{"location":"code_of_conduct/#enforcement-responsibilities","title":"Enforcement Responsibilities","text":"<p>Community leaders are responsible for clarifying and enforcing our standards of acceptable behavior and will take appropriate and fair corrective action in response to any behavior that they deem inappropriate, threatening, offensive, or harmful.</p> <p>Community leaders have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, and will communicate reasons for moderation decisions when appropriate.</p>"},{"location":"code_of_conduct/#scope","title":"Scope","text":"<p>This Code of Conduct applies within all community spaces, and also applies when an individual is officially representing the community in public spaces. Examples of representing our community include using an official e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event.</p>"},{"location":"code_of_conduct/#enforcement","title":"Enforcement","text":"<p>Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting <code>sbi</code> developer Jan Boelts via email (jan.boelts@uni-tuebingen.de). All complaints will be reviewed and investigated promptly and fairly.</p> <p>All community leaders are obligated to respect the privacy and security of the reporter of any incident.</p>"},{"location":"code_of_conduct/#enforcement-guidelines","title":"Enforcement Guidelines","text":"<p>Community leaders will follow these Community Impact Guidelines in determining the consequences for any action they deem in violation of this Code of Conduct:</p>"},{"location":"code_of_conduct/#1-correction","title":"1. Correction","text":"<p>Community Impact: Use of inappropriate language or other behavior deemed unprofessional or unwelcome in the community.</p> <p>Consequence: A private, written warning from community leaders, providing clarity around the nature of the violation and an explanation of why the behavior was inappropriate. A public apology may be requested.</p>"},{"location":"code_of_conduct/#2-warning","title":"2. Warning","text":"<p>Community Impact: A violation through a single incident or series of actions.</p> <p>Consequence: A warning with consequences for continued behavior. No interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, for a specified period of time. This includes avoiding interactions in community spaces as well as external channels like social media. Violating these terms may lead to a temporary or permanent ban.</p>"},{"location":"code_of_conduct/#3-temporary-ban","title":"3. Temporary Ban","text":"<p>Community Impact: A serious violation of community standards, including sustained inappropriate behavior.</p> <p>Consequence: A temporary ban from any sort of interaction or public communication with the community for a specified period of time. No public or private interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, is allowed during this period. Violating these terms may lead to a permanent ban.</p>"},{"location":"code_of_conduct/#4-permanent-ban","title":"4. Permanent Ban","text":"<p>Community Impact: Demonstrating a pattern of violation of community standards, including sustained inappropriate behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals.</p> <p>Consequence: A permanent ban from any sort of public interaction within the community.</p>"},{"location":"code_of_conduct/#attribution","title":"Attribution","text":"<p>This Code of Conduct is adapted from the Contributor Covenant, version 2.1, available at https://www.contributor-covenant.org/version/2/1/code_of_conduct.html.</p> <p>Community Impact Guidelines were inspired by Mozilla\u2019s code of conduct enforcement ladder.</p> <p>For answers to common questions about this code of conduct, see the FAQ at https://www.contributor-covenant.org/faq. Translations are available at https://www.contributor-covenant.org/translations.</p>"},{"location":"contribute/","title":"Guide","text":""},{"location":"contribute/#user-experiences-bugs-and-feature-requests","title":"User experiences, bugs, and feature requests","text":"<p>If you are using <code>sbi</code> to infer the parameters of a simulator, we would be delighted to know how it worked for you. If it didn\u2019t work according to plan, please open up an issue and tell us more about your use case: the dimensionality of the input parameters and of the output, as well as the setup you used to run inference (i.e. number of simulations, number of rounds,\u2026).</p> <p>To report bugs and suggest features (including better documentation), please equally head over to issues on GitHub.</p>"},{"location":"contribute/#code-contributions","title":"Code contributions","text":"<p>Contributions to the <code>sbi</code> package are welcome! In general, we use pull requests to make changes to <code>sbi</code>. So, if you are planning to make a contribution, please fork, create a feature branch and then make a PR from your feature branch to the upstream <code>sbi</code> (details). To give credits to contributors, we consider adding contributors who repeatedly and substantially contributed to <code>sbi</code> to the list of authors of the package at the end of every year. Additionally, we mention all contributors in the releases.</p>"},{"location":"contribute/#development-environment","title":"Development environment","text":"<p>Clone the repo and install all the dependencies using the <code>environment.yml</code> file to create a conda environment: <code>conda env create -f environment.yml</code>. If you already have an <code>sbi</code> environment and want to refresh dependencies, just run <code>conda env update -f environment.yml --prune</code>.</p> <p>Alternatively, you can install via <code>setup.py</code> using <code>pip install -e \".[dev]\"</code> (the dev flag installs development and testing dependencies).</p>"},{"location":"contribute/#style-conventions","title":"Style conventions","text":"<p>For docstrings and comments, we use Google Style.</p> <p>Code needs to pass through the following tools, which are installed alongside <code>sbi</code>:</p> <p>black: Automatic code formatting for Python. You can run black manually from the console using <code>black .</code> in the top directory of the repository, which will format all files.</p> <p>isort: Used to consistently order imports. You can run isort manually from the console using <code>isort</code> in the top directory.</p> <p>pyright: Used for static type checking.</p> <p><code>black</code> and <code>isort</code> and <code>pyright</code> are checked as part of our CI actions. If these checks fail please make sure you have installed the latest versions for each of them and run them locally.</p>"},{"location":"contribute/#online-documentation","title":"Online documentation","text":"<p>Most of the documentation is written in markdown (basic markdown guide).</p> <p>You can directly fix mistakes and suggest clearer formulations in markdown files simply by initiating a PR on through GitHub. Click on documentation file and look for the little pencil at top right.</p>"},{"location":"credits/","title":"Credits","text":""},{"location":"credits/#license","title":"License","text":"<p><code>sbi</code> is licensed under the Affero General Public License version 3 (AGPLv3) and</p> <p>Copyright (C) 2020 \u00c1lvaro Tejero-Cantero, Jakob H. Macke, Jan-Matthis L\u00fcckmann, Michael Deistler, Jan F. B\u00f6lts.</p> <p>Copyright (C) 2020 Conor M. Durkan.</p>"},{"location":"credits/#support","title":"Support","text":"<p><code>sbi</code> has been supported by the German Federal Ministry of Education and Research (BMBF) through the project ADIMEM, FKZ 01IS18052 A-D). ADIMEM is a collaborative project between the groups of Jakob Macke (Uni T\u00fcbingen), Philipp Berens (Uni T\u00fcbingen), Philipp Hennig (Uni T\u00fcbingen) and Marcel Oberlaender (caesar Bonn) which aims to develop inference methods for mechanistic models.</p> <p></p>"},{"location":"credits/#important-dependencies-and-prior-art","title":"Important dependencies and prior art","text":"<ul> <li> <p><code>sbi</code> is the successor to <code>delfi</code>, a Theano-based   toolbox for sequential neural posterior estimation developed at mackelab. If you were   using <code>delfi</code>, we strongly recommend to move your inference over to <code>sbi</code>. Please open   issues if you find unexpected behaviour or missing features. We will consider these   bugs and give them priority.</p> </li> <li> <p><code>sbi</code> as a PyTorch-based toolbox started as a fork of   conormdurkan/lfi, by Conor   M.Durkan.</p> </li> <li> <p><code>sbi</code> uses density estimators from bayesiains/nflows by Conor M.Durkan, George Papamakarios and Artur Bekasov. These are proxied through <code>pyknos</code>, a package focused on density estimation.</p> </li> <li> <p><code>sbi</code> uses <code>PyTorch</code> and tries to align with the interfaces (e.g. for probability   distributions) adopted by <code>PyTorch</code>.</p> </li> <li> <p>See README.md for a list of   publications describing the methods implemented in <code>sbi</code>.</p> </li> </ul>"},{"location":"faq/","title":"Frequently asked questions","text":"<p>Can the algorithms deal with invalid data, e.g. NaN or inf?</p> <p>What should I do when my \u2018posterior samples are outside of the prior support\u2019 in SNPE?</p> <p>When using multiple workers, I get a pickling error. Can I still use multiprocessing?</p> <p>Can I use the GPU for training the density estimator?</p> <p>How should I save and load objects in <code>sbi</code>?</p> <p>Can I stop neural network training and resume it later?</p> <p>How can I use a prior that is not defined in PyTorch?</p>"},{"location":"install/","title":"Installation","text":"<p><code>sbi</code> requires Python 3.6 or higher. We recommend to use a <code>conda</code> virtual environment (Miniconda installation instructions). If <code>conda</code> is installed on the system, an environment for installing <code>sbi</code> can be created as follows: <pre><code># Create an environment for sbi (indicate Python 3.6 or higher); activate it\n$ conda create -n sbi_env python=3.7 &amp;&amp; conda activate sbi_env\n</code></pre></p> <p>Independent of whether you are using <code>conda</code> or not, <code>sbi</code> can be installed using <code>pip</code>: <pre><code>$ pip install sbi\n</code></pre></p> <p>To test the installation, drop into a python prompt and run <pre><code>from sbi.examples.minimal import simple\nposterior = simple()\nprint(posterior)\n</code></pre></p>"},{"location":"reference/","title":"API Reference","text":""},{"location":"reference/#inference","title":"Inference","text":""},{"location":"reference/#sbi.inference.base.infer","title":"<code>sbi.inference.base.infer(simulator, prior, method, num_simulations, num_workers=1)</code>","text":"<p>Runs simulation-based inference and returns the posterior.</p> <p>This function provides a simple interface to run sbi. Inference is run for a single round and hence the returned posterior \\(p(\\theta|x)\\) can be sampled and evaluated for any \\(x\\) (i.e. it is amortized).</p> <p>The scope of this function is limited to the most essential features of sbi. For more flexibility (e.g. multi-round inference, different density estimators) please use the flexible interface described here: https://www.mackelab.org/sbi/tutorial/02_flexible_interface/</p> <p>Parameters:</p> Name Type Description Default <code>simulator</code> <code>Callable</code> <p>A function that takes parameters \\(\\theta\\) and maps them to simulations, or observations, <code>x</code>, \\(\\mathrm{sim}(\\theta)\\to x\\). Any regular Python callable (i.e. function or class with <code>__call__</code> method) can be used.</p> required <code>prior</code> <code>Distribution</code> <p>A probability distribution that expresses prior knowledge about the parameters, e.g. which ranges are meaningful for them. Any object with <code>.log_prob()</code>and <code>.sample()</code> (for example, a PyTorch distribution) can be used.</p> required <code>method</code> <code>str</code> <p>What inference method to use. Either of SNPE, SNLE or SNRE.</p> required <code>num_simulations</code> <code>int</code> <p>Number of simulation calls. More simulations means a longer runtime, but a better posterior estimate.</p> required <code>num_workers</code> <code>int</code> <p>Number of parallel workers to use for simulations.</p> <code>1</code> Source code in <code>/home/michael/Documents/sbi/sbi/inference/base.py</code> <pre><code>def infer(\n    simulator: Callable,\n    prior: Distribution,\n    method: str,\n    num_simulations: int,\n    num_workers: int = 1,\n) -&gt; NeuralPosterior:\nr\"\"\"Runs simulation-based inference and returns the posterior.\n\n    This function provides a simple interface to run sbi. Inference is run for a single\n    round and hence the returned posterior $p(\\theta|x)$ can be sampled and evaluated\n    for any $x$ (i.e. it is amortized).\n\n    The scope of this function is limited to the most essential features of sbi. For\n    more flexibility (e.g. multi-round inference, different density estimators) please\n    use the flexible interface described here:\n    https://www.mackelab.org/sbi/tutorial/02_flexible_interface/\n\n    Args:\n        simulator: A function that takes parameters $\\theta$ and maps them to\n            simulations, or observations, `x`, $\\mathrm{sim}(\\theta)\\to x$. Any\n            regular Python callable (i.e. function or class with `__call__` method)\n            can be used.\n        prior: A probability distribution that expresses prior knowledge about the\n            parameters, e.g. which ranges are meaningful for them. Any\n            object with `.log_prob()`and `.sample()` (for example, a PyTorch\n            distribution) can be used.\n        method: What inference method to use. Either of SNPE, SNLE or SNRE.\n        num_simulations: Number of simulation calls. More simulations means a longer\n            runtime, but a better posterior estimate.\n        num_workers: Number of parallel workers to use for simulations.\n\n    Returns: Posterior over parameters conditional on observations (amortized).\n    \"\"\"\n\n    try:\n        method_fun: Callable = getattr(sbi.inference, method.upper())\n    except AttributeError:\n        raise NameError(\n            \"Method not available. `method` must be one of 'SNPE', 'SNLE', 'SNRE'.\"\n        )\n\n    simulator, prior = prepare_for_sbi(simulator, prior)\n\n    inference = method_fun(prior=prior)\n    theta, x = simulate_for_sbi(\n        simulator=simulator,\n        proposal=prior,\n        num_simulations=num_simulations,\n        num_workers=num_workers,\n    )\n    _ = inference.append_simulations(theta, x).train()\n    posterior = inference.build_posterior()\n\n    return posterior\n</code></pre>"},{"location":"reference/#sbi.utils.user_input_checks.prepare_for_sbi","title":"<code>sbi.utils.user_input_checks.prepare_for_sbi(simulator, prior)</code>","text":"<p>Prepare simulator and prior for usage in sbi.</p> <p>NOTE: This is a wrapper around <code>process_prior</code> and <code>process_simulator</code> which can be used in isolation as well.</p> <p>Attempts to meet the following requirements by reshaping and type-casting:</p> <ul> <li>the simulator function receives as input and returns a Tensor.</li> <li>the simulator can simulate batches of parameters and return batches of data.</li> <li>the prior does not produce batches and samples and evaluates to Tensor.</li> <li>the output shape is a <code>torch.Size((1,N))</code> (i.e, has a leading batch dimension 1).</li> </ul> <p>If this is not possible, a suitable exception will be raised.</p> <p>Parameters:</p> Name Type Description Default <code>simulator</code> <code>Callable</code> <p>Simulator as provided by the user.</p> required <code>prior</code> <p>Prior as provided by the user.</p> required <p>Returns:</p> Type Description <code>Tuple[Callable, Distribution]</code> <p>Tuple (simulator, prior) checked and matching the requirements of sbi.</p> Source code in <code>/home/michael/Documents/sbi/sbi/utils/user_input_checks.py</code> <pre><code>def prepare_for_sbi(simulator: Callable, prior) -&gt; Tuple[Callable, Distribution]:\n\"\"\"Prepare simulator and prior for usage in sbi.\n\n    NOTE: This is a wrapper around `process_prior` and `process_simulator` which can be\n    used in isolation as well.\n\n    Attempts to meet the following requirements by reshaping and type-casting:\n\n    - the simulator function receives as input and returns a Tensor.&lt;br/&gt;\n    - the simulator can simulate batches of parameters and return batches of data.&lt;br/&gt;\n    - the prior does not produce batches and samples and evaluates to Tensor.&lt;br/&gt;\n    - the output shape is a `torch.Size((1,N))` (i.e, has a leading batch dimension 1).\n\n    If this is not possible, a suitable exception will be raised.\n\n    Args:\n        simulator: Simulator as provided by the user.\n        prior: Prior as provided by the user.\n\n    Returns:\n        Tuple (simulator, prior) checked and matching the requirements of sbi.\n    \"\"\"\n\n    # Check prior, return PyTorch prior.\n    prior, _, prior_returns_numpy = process_prior(prior)\n\n    # Check simulator, returns PyTorch simulator able to simulate batches.\n    simulator = process_simulator(simulator, prior, prior_returns_numpy)\n\n    # Consistency check after making ready for sbi.\n    check_sbi_inputs(simulator, prior)\n\n    return simulator, prior\n</code></pre>"},{"location":"reference/#sbi.inference.base.simulate_for_sbi","title":"<code>sbi.inference.base.simulate_for_sbi(simulator, proposal, num_simulations, num_workers=1, simulation_batch_size=1, seed=None, show_progress_bar=True)</code>","text":"<p>Returns (\\(\\theta, x\\)) pairs obtained from sampling the proposal and simulating.</p> <p>This function performs two steps:</p> <ul> <li>Sample parameters \\(\\theta\\) from the <code>proposal</code>.</li> <li>Simulate these parameters to obtain \\(x\\).</li> </ul> <p>Parameters:</p> Name Type Description Default <code>simulator</code> <code>Callable</code> <p>A function that takes parameters \\(\\theta\\) and maps them to simulations, or observations, <code>x</code>, \\(\\text{sim}(\\theta)\\to x\\). Any regular Python callable (i.e. function or class with <code>__call__</code> method) can be used.</p> required <code>proposal</code> <code>Any</code> <p>Probability distribution that the parameters \\(\\theta\\) are sampled from.</p> required <code>num_simulations</code> <code>int</code> <p>Number of simulations that are run.</p> required <code>num_workers</code> <code>int</code> <p>Number of parallel workers to use for simulations.</p> <code>1</code> <code>simulation_batch_size</code> <code>int</code> <p>Number of parameter sets that the simulator maps to data x at once. If None, we simulate all parameter sets at the same time. If &gt;= 1, the simulator has to process data of shape (simulation_batch_size, parameter_dimension).</p> <code>1</code> <code>seed</code> <code>Optional[int]</code> <p>Seed for reproducibility.</p> <code>None</code> <code>show_progress_bar</code> <code>bool</code> <p>Whether to show a progress bar for simulating. This will not affect whether there will be a progressbar while drawing samples from the proposal.</p> <code>True</code> Source code in <code>/home/michael/Documents/sbi/sbi/inference/base.py</code> <pre><code>def simulate_for_sbi(\n    simulator: Callable,\n    proposal: Any,\n    num_simulations: int,\n    num_workers: int = 1,\n    simulation_batch_size: int = 1,\n    seed: Optional[int] = None,\n    show_progress_bar: bool = True,\n) -&gt; Tuple[Tensor, Tensor]:\nr\"\"\"Returns ($\\theta, x$) pairs obtained from sampling the proposal and simulating.\n\n    This function performs two steps:\n\n    - Sample parameters $\\theta$ from the `proposal`.\n    - Simulate these parameters to obtain $x$.\n\n    Args:\n        simulator: A function that takes parameters $\\theta$ and maps them to\n            simulations, or observations, `x`, $\\text{sim}(\\theta)\\to x$. Any\n            regular Python callable (i.e. function or class with `__call__` method)\n            can be used.\n        proposal: Probability distribution that the parameters $\\theta$ are sampled\n            from.\n        num_simulations: Number of simulations that are run.\n        num_workers: Number of parallel workers to use for simulations.\n        simulation_batch_size: Number of parameter sets that the simulator\n            maps to data x at once. If None, we simulate all parameter sets at the\n            same time. If &gt;= 1, the simulator has to process data of shape\n            (simulation_batch_size, parameter_dimension).\n        seed: Seed for reproducibility.\n        show_progress_bar: Whether to show a progress bar for simulating. This will not\n            affect whether there will be a progressbar while drawing samples from the\n            proposal.\n\n    Returns: Sampled parameters $\\theta$ and simulation-outputs $x$.\n    \"\"\"\n\n    theta = proposal.sample((num_simulations,))\n\n    x = simulate_in_batches(\n        simulator=simulator,\n        theta=theta,\n        sim_batch_size=simulation_batch_size,\n        num_workers=num_workers,\n        seed=seed,\n        show_progress_bars=show_progress_bar,\n    )\n\n    return theta, x\n</code></pre>"},{"location":"reference/#sbi.inference.snpe.snpe_a.SNPE_A","title":"<code>sbi.inference.snpe.snpe_a.SNPE_A</code>","text":"<p>         Bases: <code>PosteriorEstimator</code></p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/snpe/snpe_a.py</code> <pre><code>class SNPE_A(PosteriorEstimator):\n    def __init__(\n        self,\n        prior: Optional[Distribution] = None,\n        density_estimator: Union[str, Callable] = \"mdn_snpe_a\",\n        num_components: int = 10,\n        device: str = \"cpu\",\n        logging_level: Union[int, str] = \"WARNING\",\n        summary_writer: Optional[TensorboardSummaryWriter] = None,\n        show_progress_bars: bool = True,\n    ):\nr\"\"\"SNPE-A [1].\n\n        [1] _Fast epsilon-free Inference of Simulation Models with Bayesian Conditional\n            Density Estimation_, Papamakarios et al., NeurIPS 2016,\n            https://arxiv.org/abs/1605.06376.\n\n        This class implements SNPE-A. SNPE-A trains across multiple rounds with a\n        maximum-likelihood-loss. This will make training converge to the proposal\n        posterior instead of the true posterior. To correct for this, SNPE-A applies a\n        post-hoc correction after training. This correction has to be performed\n        analytically. Thus, SNPE-A is limited to Gaussian distributions for all but the\n        last round. In the last round, SNPE-A can use a Mixture of Gaussians.\n\n        Args:\n            prior: A probability distribution that expresses prior knowledge about the\n                parameters, e.g. which ranges are meaningful for them. Any\n                object with `.log_prob()`and `.sample()` (for example, a PyTorch\n                distribution) can be used.\n            density_estimator: If it is a string (only \"mdn_snpe_a\" is valid), use a\n                pre-configured mixture of densities network. Alternatively, a function\n                that builds a custom neural network can be provided. The function will\n                be called with the first batch of simulations (theta, x), which can\n                thus be used for shape inference and potentially for z-scoring. It\n                needs to return a PyTorch `nn.Module` implementing the density\n                estimator. The density estimator needs to provide the methods\n                `.log_prob` and `.sample()`. Note that until the last round only a\n                single (multivariate) Gaussian component is used for training (see\n                Algorithm 1 in [1]). In the last round, this component is replicated\n                `num_components` times, its parameters are perturbed with a very small\n                noise, and then the last training round is done with the expanded\n                Gaussian mixture as estimator for the proposal posterior.\n            num_components: Number of components of the mixture of Gaussians in the\n                last round. This overrides the `num_components` value passed to\n                `posterior_nn()`.\n            device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n            logging_level: Minimum severity of messages to log. One of the strings\n                INFO, WARNING, DEBUG, ERROR and CRITICAL.\n            summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n                file location (default is `&lt;current working directory&gt;/logs`.)\n            show_progress_bars: Whether to show a progressbar during training.\n        \"\"\"\n\n        # Catch invalid inputs.\n        if not ((density_estimator == \"mdn_snpe_a\") or callable(density_estimator)):\n            raise TypeError(\n                \"The `density_estimator` passed to SNPE_A needs to be a \"\n                \"callable or the string 'mdn_snpe_a'!\"\n            )\n\n        # `num_components` will be used to replicate the Gaussian in the last round.\n        self._num_components = num_components\n        self._ran_final_round = False\n\n        # WARNING: sneaky trick ahead. We proxy the parent's `train` here,\n        # requiring the signature to have `num_atoms`, save it for use below, and\n        # continue. It's sneaky because we are using the object (self) as a namespace\n        # to pass arguments between functions, and that's implicit state management.\n        kwargs = utils.del_entries(\n            locals(),\n            entries=(\"self\", \"__class__\", \"num_components\"),\n        )\n        super().__init__(**kwargs)\n\n    def train(\n        self,\n        final_round: bool = False,\n        training_batch_size: int = 50,\n        learning_rate: float = 5e-4,\n        validation_fraction: float = 0.1,\n        stop_after_epochs: int = 20,\n        max_num_epochs: int = 2**31 - 1,\n        clip_max_norm: Optional[float] = 5.0,\n        calibration_kernel: Optional[Callable] = None,\n        resume_training: bool = False,\n        retrain_from_scratch: bool = False,\n        show_train_summary: bool = False,\n        dataloader_kwargs: Optional[Dict] = None,\n        component_perturbation: float = 5e-3,\n    ) -&gt; nn.Module:\nr\"\"\"Return density estimator that approximates the proposal posterior.\n\n        [1] _Fast epsilon-free Inference of Simulation Models with Bayesian Conditional\n            Density Estimation_, Papamakarios et al., NeurIPS 2016,\n            https://arxiv.org/abs/1605.06376.\n\n        Training is performed with maximum likelihood on samples from the latest round,\n        which leads the algorithm to converge to the proposal posterior.\n\n        Args:\n            final_round: Whether we are in the last round of training or not. For all\n                but the last round, Algorithm 1 from [1] is executed. In last the\n                round, Algorithm 2 from [1] is executed once.\n            training_batch_size: Training batch size.\n            learning_rate: Learning rate for Adam optimizer.\n            validation_fraction: The fraction of data to use for validation.\n            stop_after_epochs: The number of epochs to wait for improvement on the\n                validation set before terminating training.\n            max_num_epochs: Maximum number of epochs to run. If reached, we stop\n                training even when the validation loss is still decreasing. Otherwise,\n                we train until validation loss increases (see also `stop_after_epochs`).\n            clip_max_norm: Value at which to clip the total gradient norm in order to\n                prevent exploding gradients. Use None for no clipping.\n            calibration_kernel: A function to calibrate the loss with respect to the\n                simulations `x`. See Lueckmann, Gon\u00e7alves et al., NeurIPS 2017.\n            resume_training: Can be used in case training time is limited, e.g. on a\n                cluster. If `True`, the split between train and validation set, the\n                optimizer, the number of epochs, and the best validation log-prob will\n                be restored from the last time `.train()` was called.\n            force_first_round_loss: If `True`, train with maximum likelihood,\n                i.e., potentially ignoring the correction for using a proposal\n                distribution different from the prior.\n            retrain_from_scratch: Whether to retrain the conditional density\n                estimator for the posterior from scratch each round. Not supported for\n                SNPE-A.\n            show_train_summary: Whether to print the number of epochs and validation\n                loss and leakage after the training.\n            dataloader_kwargs: Additional or updated kwargs to be passed to the training\n                and validation dataloaders (like, e.g., a collate_fn)\n            component_perturbation: The standard deviation applied to all weights and\n                biases when, in the last round, the Mixture of Gaussians is build from\n                a single Gaussian. This value can be problem-specific and also depends\n                on the number of mixture components.\n\n        Returns:\n            Density estimator that approximates the distribution $p(\\theta|x)$.\n        \"\"\"\n\n        assert not retrain_from_scratch, \"\"\"Retraining from scratch is not supported in\n            SNPE-A yet. The reason for this is that, if we reininitialized the density\n            estimator, the z-scoring would change, which would break the posthoc\n            correction. This is a pure implementation issue.\"\"\"\n\n        kwargs = utils.del_entries(\n            locals(),\n            entries=(\n                \"self\",\n                \"__class__\",\n                \"final_round\",\n                \"component_perturbation\",\n            ),\n        )\n\n        # SNPE-A always discards the prior samples.\n        kwargs[\"discard_prior_samples\"] = True\n        kwargs[\"force_first_round_loss\"] = True\n\n        self._round = max(self._data_round_index)\n\n        if final_round:\n            # If there is (will be) only one round, train with Algorithm 2 from [1].\n            if self._round == 0:\n                self._build_neural_net = partial(\n                    self._build_neural_net, num_components=self._num_components\n                )\n            # Run Algorithm 2 from [1].\n            elif not self._ran_final_round:\n                # Now switch to the specified number of components. This method will\n                # only be used if `retrain_from_scratch=True`. Otherwise,\n                # the MDN will be built from replicating the single-component net for\n                # `num_component` times (via `_expand_mog()`).\n                self._build_neural_net = partial(\n                    self._build_neural_net, num_components=self._num_components\n                )\n\n                # Extend the MDN to the originally desired number of components.\n                self._expand_mog(eps=component_perturbation)\n            else:\n                warnings.warn(\n                    \"You have already run SNPE-A with `final_round=True`. Running it\"\n                    \"again with this setting will not allow computing the posthoc\"\n                    \"correction applied in SNPE-A. Thus, you will get an error when \"\n                    \"calling `.build_posterior()` after training.\",\n                    UserWarning,\n                )\n        else:\n            # Run Algorithm 1 from [1].\n            # Wrap the function that builds the MDN such that we can make\n            # sure that there is only one component when running.\n            self._build_neural_net = partial(self._build_neural_net, num_components=1)\n\n        if final_round:\n            self._ran_final_round = True\n\n        return super().train(**kwargs)\n\n    def correct_for_proposal(\n        self,\n        density_estimator: Optional[TorchModule] = None,\n    ) -&gt; \"SNPE_A_MDN\":\nr\"\"\"Build mixture of Gaussians that approximates the posterior.\n\n        Returns a `SNPE_A_MDN` object, which applies the posthoc-correction required in\n        SNPE-A.\n\n        Args:\n            density_estimator: The density estimator that the posterior is based on.\n                If `None`, use the latest neural density estimator that was trained.\n\n        Returns:\n            Posterior $p(\\theta|x)$  with `.sample()` and `.log_prob()` methods.\n        \"\"\"\n        if density_estimator is None:\n            density_estimator = deepcopy(\n                self._neural_net\n            )  # PosteriorEstimator.train() also returns a deepcopy, mimic this here\n            # If internal net is used device is defined.\n            device = self._device\n        else:\n            # Otherwise, infer it from the device of the net parameters.\n            device = str(next(density_estimator.parameters()).device)\n\n        # Set proposal of the density estimator.\n        # This also evokes the z-scoring correction if necessary.\n        if (\n            self._proposal_roundwise[-1] is self._prior\n            or self._proposal_roundwise[-1] is None\n        ):\n            proposal = self._prior\n            assert isinstance(\n                proposal, (MultivariateNormal, utils.BoxUniform)\n            ), \"\"\"Prior must be `torch.distributions.MultivariateNormal` or `sbi.utils.\n                BoxUniform`\"\"\"\n        else:\n            assert isinstance(\n                self._proposal_roundwise[-1], DirectPosterior\n            ), \"\"\"The proposal you passed to `append_simulations` is neither the prior\n                nor a `DirectPosterior`. SNPE-A currently only supports these scenarios.\n                \"\"\"\n            proposal = self._proposal_roundwise[-1]\n\n        # Create the SNPE_A_MDN\n        wrapped_density_estimator = SNPE_A_MDN(\n            flow=density_estimator,  # type: ignore\n            proposal=proposal,\n            prior=self._prior,\n            device=device,\n        )\n        return wrapped_density_estimator\n\n    def build_posterior(\n        self,\n        density_estimator: Optional[TorchModule] = None,\n        prior: Optional[Distribution] = None,\n    ) -&gt; \"DirectPosterior\":\nr\"\"\"Build posterior from the neural density estimator.\n\n        This method first corrects the estimated density with `correct_for_proposal`\n        and then returns a `DirectPosterior`.\n\n        Args:\n            density_estimator: The density estimator that the posterior is based on.\n                If `None`, use the latest neural density estimator that was trained.\n            prior: Prior distribution.\n\n        Returns:\n            Posterior $p(\\theta|x)$  with `.sample()` and `.log_prob()` methods.\n        \"\"\"\n        if prior is None:\n            assert (\n                self._prior is not None\n            ), \"\"\"You did not pass a prior. You have to pass the prior either at\n                initialization `inference = SNPE_A(prior)` or to `.build_posterior\n                (prior=prior)`.\"\"\"\n            prior = self._prior\n\n        wrapped_density_estimator = self.correct_for_proposal(\n            density_estimator=density_estimator\n        )\n        self._posterior = DirectPosterior(\n            posterior_estimator=wrapped_density_estimator,  # type: ignore\n            prior=prior,\n        )\n        return deepcopy(self._posterior)\n\n    def _log_prob_proposal_posterior(\n        self,\n        theta: Tensor,\n        x: Tensor,\n        masks: Tensor,\n        proposal: Optional[Any],\n    ) -&gt; Tensor:\n\"\"\"Return the log-probability of the proposal posterior.\n\n        For SNPE-A this is the same as `self._neural_net.log_prob(theta, x)` in\n        `_loss()` to be found in `snpe_base.py`.\n\n        Args:\n            theta: Batch of parameters \u03b8.\n            x: Batch of data.\n            masks: Mask that is True for prior samples in the batch in order to train\n                them with prior loss.\n            proposal: Proposal distribution.\n\n        Returns: Log-probability of the proposal posterior.\n        \"\"\"\n        return self._neural_net.log_prob(theta, x)\n\n    def _expand_mog(self, eps: float = 1e-5):\n\"\"\"\n        Replicate a singe Gaussian trained with Algorithm 1 before continuing\n        with Algorithm 2. The weights and biases of the associated MDN layers\n        are repeated `num_components` times, slightly perturbed to break the\n        symmetry such that the gradients in the subsequent training are not\n        all identical.\n\n        Args:\n            eps: Standard deviation for the random perturbation.\n        \"\"\"\n        assert isinstance(self._neural_net._distribution, MultivariateGaussianMDN)\n\n        # Increase the number of components\n        self._neural_net._distribution._num_components = self._num_components\n\n        # Expand the 1-dim Gaussian.\n        for name, param in self._neural_net.named_parameters():\n            if any(\n                key in name for key in [\"logits\", \"means\", \"unconstrained\", \"upper\"]\n            ):\n                if \"bias\" in name:\n                    param.data = param.data.repeat(self._num_components)\n                    param.data.add_(torch.randn_like(param.data) * eps)\n                    param.grad = None  # let autograd construct a new gradient\n                elif \"weight\" in name:\n                    param.data = param.data.repeat(self._num_components, 1)\n                    param.data.add_(torch.randn_like(param.data) * eps)\n                    param.grad = None  # let autograd construct a new gradient\n</code></pre>"},{"location":"reference/#sbi.inference.snpe.snpe_a.SNPE_A.__init__","title":"<code>__init__(prior=None, density_estimator='mdn_snpe_a', num_components=10, device='cpu', logging_level='WARNING', summary_writer=None, show_progress_bars=True)</code>","text":"<p>SNPE-A [1].</p> <p>[1] Fast epsilon-free Inference of Simulation Models with Bayesian Conditional     Density Estimation, Papamakarios et al., NeurIPS 2016,     https://arxiv.org/abs/1605.06376.</p> <p>This class implements SNPE-A. SNPE-A trains across multiple rounds with a maximum-likelihood-loss. This will make training converge to the proposal posterior instead of the true posterior. To correct for this, SNPE-A applies a post-hoc correction after training. This correction has to be performed analytically. Thus, SNPE-A is limited to Gaussian distributions for all but the last round. In the last round, SNPE-A can use a Mixture of Gaussians.</p> <p>Parameters:</p> Name Type Description Default <code>prior</code> <code>Optional[Distribution]</code> <p>A probability distribution that expresses prior knowledge about the parameters, e.g. which ranges are meaningful for them. Any object with <code>.log_prob()</code>and <code>.sample()</code> (for example, a PyTorch distribution) can be used.</p> <code>None</code> <code>density_estimator</code> <code>Union[str, Callable]</code> <p>If it is a string (only \u201cmdn_snpe_a\u201d is valid), use a pre-configured mixture of densities network. Alternatively, a function that builds a custom neural network can be provided. The function will be called with the first batch of simulations (theta, x), which can thus be used for shape inference and potentially for z-scoring. It needs to return a PyTorch <code>nn.Module</code> implementing the density estimator. The density estimator needs to provide the methods <code>.log_prob</code> and <code>.sample()</code>. Note that until the last round only a single (multivariate) Gaussian component is used for training (see Algorithm 1 in [1]). In the last round, this component is replicated <code>num_components</code> times, its parameters are perturbed with a very small noise, and then the last training round is done with the expanded Gaussian mixture as estimator for the proposal posterior.</p> <code>'mdn_snpe_a'</code> <code>num_components</code> <code>int</code> <p>Number of components of the mixture of Gaussians in the last round. This overrides the <code>num_components</code> value passed to <code>posterior_nn()</code>.</p> <code>10</code> <code>device</code> <code>str</code> <p>Training device, e.g., \u201ccpu\u201d, \u201ccuda\u201d or \u201ccuda:{0, 1, \u2026}\u201d.</p> <code>'cpu'</code> <code>logging_level</code> <code>Union[int, str]</code> <p>Minimum severity of messages to log. One of the strings INFO, WARNING, DEBUG, ERROR and CRITICAL.</p> <code>'WARNING'</code> <code>summary_writer</code> <code>Optional[TensorboardSummaryWriter]</code> <p>A tensorboard <code>SummaryWriter</code> to control, among others, log file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p> <code>None</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during training.</p> <code>True</code> Source code in <code>/home/michael/Documents/sbi/sbi/inference/snpe/snpe_a.py</code> <pre><code>def __init__(\n    self,\n    prior: Optional[Distribution] = None,\n    density_estimator: Union[str, Callable] = \"mdn_snpe_a\",\n    num_components: int = 10,\n    device: str = \"cpu\",\n    logging_level: Union[int, str] = \"WARNING\",\n    summary_writer: Optional[TensorboardSummaryWriter] = None,\n    show_progress_bars: bool = True,\n):\nr\"\"\"SNPE-A [1].\n\n    [1] _Fast epsilon-free Inference of Simulation Models with Bayesian Conditional\n        Density Estimation_, Papamakarios et al., NeurIPS 2016,\n        https://arxiv.org/abs/1605.06376.\n\n    This class implements SNPE-A. SNPE-A trains across multiple rounds with a\n    maximum-likelihood-loss. This will make training converge to the proposal\n    posterior instead of the true posterior. To correct for this, SNPE-A applies a\n    post-hoc correction after training. This correction has to be performed\n    analytically. Thus, SNPE-A is limited to Gaussian distributions for all but the\n    last round. In the last round, SNPE-A can use a Mixture of Gaussians.\n\n    Args:\n        prior: A probability distribution that expresses prior knowledge about the\n            parameters, e.g. which ranges are meaningful for them. Any\n            object with `.log_prob()`and `.sample()` (for example, a PyTorch\n            distribution) can be used.\n        density_estimator: If it is a string (only \"mdn_snpe_a\" is valid), use a\n            pre-configured mixture of densities network. Alternatively, a function\n            that builds a custom neural network can be provided. The function will\n            be called with the first batch of simulations (theta, x), which can\n            thus be used for shape inference and potentially for z-scoring. It\n            needs to return a PyTorch `nn.Module` implementing the density\n            estimator. The density estimator needs to provide the methods\n            `.log_prob` and `.sample()`. Note that until the last round only a\n            single (multivariate) Gaussian component is used for training (see\n            Algorithm 1 in [1]). In the last round, this component is replicated\n            `num_components` times, its parameters are perturbed with a very small\n            noise, and then the last training round is done with the expanded\n            Gaussian mixture as estimator for the proposal posterior.\n        num_components: Number of components of the mixture of Gaussians in the\n            last round. This overrides the `num_components` value passed to\n            `posterior_nn()`.\n        device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n        logging_level: Minimum severity of messages to log. One of the strings\n            INFO, WARNING, DEBUG, ERROR and CRITICAL.\n        summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n            file location (default is `&lt;current working directory&gt;/logs`.)\n        show_progress_bars: Whether to show a progressbar during training.\n    \"\"\"\n\n    # Catch invalid inputs.\n    if not ((density_estimator == \"mdn_snpe_a\") or callable(density_estimator)):\n        raise TypeError(\n            \"The `density_estimator` passed to SNPE_A needs to be a \"\n            \"callable or the string 'mdn_snpe_a'!\"\n        )\n\n    # `num_components` will be used to replicate the Gaussian in the last round.\n    self._num_components = num_components\n    self._ran_final_round = False\n\n    # WARNING: sneaky trick ahead. We proxy the parent's `train` here,\n    # requiring the signature to have `num_atoms`, save it for use below, and\n    # continue. It's sneaky because we are using the object (self) as a namespace\n    # to pass arguments between functions, and that's implicit state management.\n    kwargs = utils.del_entries(\n        locals(),\n        entries=(\"self\", \"__class__\", \"num_components\"),\n    )\n    super().__init__(**kwargs)\n</code></pre>"},{"location":"reference/#sbi.inference.snpe.snpe_a.SNPE_A.build_posterior","title":"<code>build_posterior(density_estimator=None, prior=None)</code>","text":"<p>Build posterior from the neural density estimator.</p> <p>This method first corrects the estimated density with <code>correct_for_proposal</code> and then returns a <code>DirectPosterior</code>.</p> <p>Parameters:</p> Name Type Description Default <code>density_estimator</code> <code>Optional[TorchModule]</code> <p>The density estimator that the posterior is based on. If <code>None</code>, use the latest neural density estimator that was trained.</p> <code>None</code> <code>prior</code> <code>Optional[Distribution]</code> <p>Prior distribution.</p> <code>None</code> <p>Returns:</p> Type Description <code>DirectPosterior</code> <p>Posterior \\(p(\\theta|x)\\)  with <code>.sample()</code> and <code>.log_prob()</code> methods.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/snpe/snpe_a.py</code> <pre><code>def build_posterior(\n    self,\n    density_estimator: Optional[TorchModule] = None,\n    prior: Optional[Distribution] = None,\n) -&gt; \"DirectPosterior\":\nr\"\"\"Build posterior from the neural density estimator.\n\n    This method first corrects the estimated density with `correct_for_proposal`\n    and then returns a `DirectPosterior`.\n\n    Args:\n        density_estimator: The density estimator that the posterior is based on.\n            If `None`, use the latest neural density estimator that was trained.\n        prior: Prior distribution.\n\n    Returns:\n        Posterior $p(\\theta|x)$  with `.sample()` and `.log_prob()` methods.\n    \"\"\"\n    if prior is None:\n        assert (\n            self._prior is not None\n        ), \"\"\"You did not pass a prior. You have to pass the prior either at\n            initialization `inference = SNPE_A(prior)` or to `.build_posterior\n            (prior=prior)`.\"\"\"\n        prior = self._prior\n\n    wrapped_density_estimator = self.correct_for_proposal(\n        density_estimator=density_estimator\n    )\n    self._posterior = DirectPosterior(\n        posterior_estimator=wrapped_density_estimator,  # type: ignore\n        prior=prior,\n    )\n    return deepcopy(self._posterior)\n</code></pre>"},{"location":"reference/#sbi.inference.snpe.snpe_a.SNPE_A.correct_for_proposal","title":"<code>correct_for_proposal(density_estimator=None)</code>","text":"<p>Build mixture of Gaussians that approximates the posterior.</p> <p>Returns a <code>SNPE_A_MDN</code> object, which applies the posthoc-correction required in SNPE-A.</p> <p>Parameters:</p> Name Type Description Default <code>density_estimator</code> <code>Optional[TorchModule]</code> <p>The density estimator that the posterior is based on. If <code>None</code>, use the latest neural density estimator that was trained.</p> <code>None</code> <p>Returns:</p> Type Description <code>SNPE_A_MDN</code> <p>Posterior \\(p(\\theta|x)\\)  with <code>.sample()</code> and <code>.log_prob()</code> methods.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/snpe/snpe_a.py</code> <pre><code>def correct_for_proposal(\n    self,\n    density_estimator: Optional[TorchModule] = None,\n) -&gt; \"SNPE_A_MDN\":\nr\"\"\"Build mixture of Gaussians that approximates the posterior.\n\n    Returns a `SNPE_A_MDN` object, which applies the posthoc-correction required in\n    SNPE-A.\n\n    Args:\n        density_estimator: The density estimator that the posterior is based on.\n            If `None`, use the latest neural density estimator that was trained.\n\n    Returns:\n        Posterior $p(\\theta|x)$  with `.sample()` and `.log_prob()` methods.\n    \"\"\"\n    if density_estimator is None:\n        density_estimator = deepcopy(\n            self._neural_net\n        )  # PosteriorEstimator.train() also returns a deepcopy, mimic this here\n        # If internal net is used device is defined.\n        device = self._device\n    else:\n        # Otherwise, infer it from the device of the net parameters.\n        device = str(next(density_estimator.parameters()).device)\n\n    # Set proposal of the density estimator.\n    # This also evokes the z-scoring correction if necessary.\n    if (\n        self._proposal_roundwise[-1] is self._prior\n        or self._proposal_roundwise[-1] is None\n    ):\n        proposal = self._prior\n        assert isinstance(\n            proposal, (MultivariateNormal, utils.BoxUniform)\n        ), \"\"\"Prior must be `torch.distributions.MultivariateNormal` or `sbi.utils.\n            BoxUniform`\"\"\"\n    else:\n        assert isinstance(\n            self._proposal_roundwise[-1], DirectPosterior\n        ), \"\"\"The proposal you passed to `append_simulations` is neither the prior\n            nor a `DirectPosterior`. SNPE-A currently only supports these scenarios.\n            \"\"\"\n        proposal = self._proposal_roundwise[-1]\n\n    # Create the SNPE_A_MDN\n    wrapped_density_estimator = SNPE_A_MDN(\n        flow=density_estimator,  # type: ignore\n        proposal=proposal,\n        prior=self._prior,\n        device=device,\n    )\n    return wrapped_density_estimator\n</code></pre>"},{"location":"reference/#sbi.inference.snpe.snpe_a.SNPE_A.train","title":"<code>train(final_round=False, training_batch_size=50, learning_rate=0.0005, validation_fraction=0.1, stop_after_epochs=20, max_num_epochs=2 ** 31 - 1, clip_max_norm=5.0, calibration_kernel=None, resume_training=False, retrain_from_scratch=False, show_train_summary=False, dataloader_kwargs=None, component_perturbation=0.005)</code>","text":"<p>Return density estimator that approximates the proposal posterior.</p> <p>[1] Fast epsilon-free Inference of Simulation Models with Bayesian Conditional     Density Estimation, Papamakarios et al., NeurIPS 2016,     https://arxiv.org/abs/1605.06376.</p> <p>Training is performed with maximum likelihood on samples from the latest round, which leads the algorithm to converge to the proposal posterior.</p> <p>Parameters:</p> Name Type Description Default <code>final_round</code> <code>bool</code> <p>Whether we are in the last round of training or not. For all but the last round, Algorithm 1 from [1] is executed. In last the round, Algorithm 2 from [1] is executed once.</p> <code>False</code> <code>training_batch_size</code> <code>int</code> <p>Training batch size.</p> <code>50</code> <code>learning_rate</code> <code>float</code> <p>Learning rate for Adam optimizer.</p> <code>0.0005</code> <code>validation_fraction</code> <code>float</code> <p>The fraction of data to use for validation.</p> <code>0.1</code> <code>stop_after_epochs</code> <code>int</code> <p>The number of epochs to wait for improvement on the validation set before terminating training.</p> <code>20</code> <code>max_num_epochs</code> <code>int</code> <p>Maximum number of epochs to run. If reached, we stop training even when the validation loss is still decreasing. Otherwise, we train until validation loss increases (see also <code>stop_after_epochs</code>).</p> <code>2 ** 31 - 1</code> <code>clip_max_norm</code> <code>Optional[float]</code> <p>Value at which to clip the total gradient norm in order to prevent exploding gradients. Use None for no clipping.</p> <code>5.0</code> <code>calibration_kernel</code> <code>Optional[Callable]</code> <p>A function to calibrate the loss with respect to the simulations <code>x</code>. See Lueckmann, Gon\u00e7alves et al., NeurIPS 2017.</p> <code>None</code> <code>resume_training</code> <code>bool</code> <p>Can be used in case training time is limited, e.g. on a cluster. If <code>True</code>, the split between train and validation set, the optimizer, the number of epochs, and the best validation log-prob will be restored from the last time <code>.train()</code> was called.</p> <code>False</code> <code>force_first_round_loss</code> <p>If <code>True</code>, train with maximum likelihood, i.e., potentially ignoring the correction for using a proposal distribution different from the prior.</p> required <code>retrain_from_scratch</code> <code>bool</code> <p>Whether to retrain the conditional density estimator for the posterior from scratch each round. Not supported for SNPE-A.</p> <code>False</code> <code>show_train_summary</code> <code>bool</code> <p>Whether to print the number of epochs and validation loss and leakage after the training.</p> <code>False</code> <code>dataloader_kwargs</code> <code>Optional[Dict]</code> <p>Additional or updated kwargs to be passed to the training and validation dataloaders (like, e.g., a collate_fn)</p> <code>None</code> <code>component_perturbation</code> <code>float</code> <p>The standard deviation applied to all weights and biases when, in the last round, the Mixture of Gaussians is build from a single Gaussian. This value can be problem-specific and also depends on the number of mixture components.</p> <code>0.005</code> <p>Returns:</p> Type Description <code>nn.Module</code> <p>Density estimator that approximates the distribution \\(p(\\theta|x)\\).</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/snpe/snpe_a.py</code> <pre><code>def train(\n    self,\n    final_round: bool = False,\n    training_batch_size: int = 50,\n    learning_rate: float = 5e-4,\n    validation_fraction: float = 0.1,\n    stop_after_epochs: int = 20,\n    max_num_epochs: int = 2**31 - 1,\n    clip_max_norm: Optional[float] = 5.0,\n    calibration_kernel: Optional[Callable] = None,\n    resume_training: bool = False,\n    retrain_from_scratch: bool = False,\n    show_train_summary: bool = False,\n    dataloader_kwargs: Optional[Dict] = None,\n    component_perturbation: float = 5e-3,\n) -&gt; nn.Module:\nr\"\"\"Return density estimator that approximates the proposal posterior.\n\n    [1] _Fast epsilon-free Inference of Simulation Models with Bayesian Conditional\n        Density Estimation_, Papamakarios et al., NeurIPS 2016,\n        https://arxiv.org/abs/1605.06376.\n\n    Training is performed with maximum likelihood on samples from the latest round,\n    which leads the algorithm to converge to the proposal posterior.\n\n    Args:\n        final_round: Whether we are in the last round of training or not. For all\n            but the last round, Algorithm 1 from [1] is executed. In last the\n            round, Algorithm 2 from [1] is executed once.\n        training_batch_size: Training batch size.\n        learning_rate: Learning rate for Adam optimizer.\n        validation_fraction: The fraction of data to use for validation.\n        stop_after_epochs: The number of epochs to wait for improvement on the\n            validation set before terminating training.\n        max_num_epochs: Maximum number of epochs to run. If reached, we stop\n            training even when the validation loss is still decreasing. Otherwise,\n            we train until validation loss increases (see also `stop_after_epochs`).\n        clip_max_norm: Value at which to clip the total gradient norm in order to\n            prevent exploding gradients. Use None for no clipping.\n        calibration_kernel: A function to calibrate the loss with respect to the\n            simulations `x`. See Lueckmann, Gon\u00e7alves et al., NeurIPS 2017.\n        resume_training: Can be used in case training time is limited, e.g. on a\n            cluster. If `True`, the split between train and validation set, the\n            optimizer, the number of epochs, and the best validation log-prob will\n            be restored from the last time `.train()` was called.\n        force_first_round_loss: If `True`, train with maximum likelihood,\n            i.e., potentially ignoring the correction for using a proposal\n            distribution different from the prior.\n        retrain_from_scratch: Whether to retrain the conditional density\n            estimator for the posterior from scratch each round. Not supported for\n            SNPE-A.\n        show_train_summary: Whether to print the number of epochs and validation\n            loss and leakage after the training.\n        dataloader_kwargs: Additional or updated kwargs to be passed to the training\n            and validation dataloaders (like, e.g., a collate_fn)\n        component_perturbation: The standard deviation applied to all weights and\n            biases when, in the last round, the Mixture of Gaussians is build from\n            a single Gaussian. This value can be problem-specific and also depends\n            on the number of mixture components.\n\n    Returns:\n        Density estimator that approximates the distribution $p(\\theta|x)$.\n    \"\"\"\n\n    assert not retrain_from_scratch, \"\"\"Retraining from scratch is not supported in\n        SNPE-A yet. The reason for this is that, if we reininitialized the density\n        estimator, the z-scoring would change, which would break the posthoc\n        correction. This is a pure implementation issue.\"\"\"\n\n    kwargs = utils.del_entries(\n        locals(),\n        entries=(\n            \"self\",\n            \"__class__\",\n            \"final_round\",\n            \"component_perturbation\",\n        ),\n    )\n\n    # SNPE-A always discards the prior samples.\n    kwargs[\"discard_prior_samples\"] = True\n    kwargs[\"force_first_round_loss\"] = True\n\n    self._round = max(self._data_round_index)\n\n    if final_round:\n        # If there is (will be) only one round, train with Algorithm 2 from [1].\n        if self._round == 0:\n            self._build_neural_net = partial(\n                self._build_neural_net, num_components=self._num_components\n            )\n        # Run Algorithm 2 from [1].\n        elif not self._ran_final_round:\n            # Now switch to the specified number of components. This method will\n            # only be used if `retrain_from_scratch=True`. Otherwise,\n            # the MDN will be built from replicating the single-component net for\n            # `num_component` times (via `_expand_mog()`).\n            self._build_neural_net = partial(\n                self._build_neural_net, num_components=self._num_components\n            )\n\n            # Extend the MDN to the originally desired number of components.\n            self._expand_mog(eps=component_perturbation)\n        else:\n            warnings.warn(\n                \"You have already run SNPE-A with `final_round=True`. Running it\"\n                \"again with this setting will not allow computing the posthoc\"\n                \"correction applied in SNPE-A. Thus, you will get an error when \"\n                \"calling `.build_posterior()` after training.\",\n                UserWarning,\n            )\n    else:\n        # Run Algorithm 1 from [1].\n        # Wrap the function that builds the MDN such that we can make\n        # sure that there is only one component when running.\n        self._build_neural_net = partial(self._build_neural_net, num_components=1)\n\n    if final_round:\n        self._ran_final_round = True\n\n    return super().train(**kwargs)\n</code></pre>"},{"location":"reference/#sbi.inference.snpe.snpe_c.SNPE_C","title":"<code>sbi.inference.snpe.snpe_c.SNPE_C</code>","text":"<p>         Bases: <code>PosteriorEstimator</code></p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/snpe/snpe_c.py</code> <pre><code>class SNPE_C(PosteriorEstimator):\n    def __init__(\n        self,\n        prior: Optional[Distribution] = None,\n        density_estimator: Union[str, Callable] = \"maf\",\n        device: str = \"cpu\",\n        logging_level: Union[int, str] = \"WARNING\",\n        summary_writer: Optional[TensorboardSummaryWriter] = None,\n        show_progress_bars: bool = True,\n    ):\nr\"\"\"SNPE-C / APT [1].\n\n        [1] _Automatic Posterior Transformation for Likelihood-free Inference_,\n            Greenberg et al., ICML 2019, https://arxiv.org/abs/1905.07488.\n\n        This class implements two loss variants of SNPE-C: the non-atomic and the atomic\n        version. The atomic loss of SNPE-C can be used for any density estimator,\n        i.e. also for normalizing flows. However, it suffers from leakage issues. On\n        the other hand, the non-atomic loss can only be used only if the proposal\n        distribution is a mixture of Gaussians, the density estimator is a mixture of\n        Gaussians, and the prior is either Gaussian or Uniform. It does not suffer from\n        leakage issues. At the beginning of each round, we print whether the non-atomic\n        or the atomic version is used.\n\n        In this codebase, we will automatically switch to the non-atomic loss if the\n        following criteria are fulfilled:&lt;br/&gt;\n        - proposal is a `DirectPosterior` with density_estimator `mdn`, as built\n            with `utils.sbi.posterior_nn()`.&lt;br/&gt;\n        - the density estimator is a `mdn`, as built with\n            `utils.sbi.posterior_nn()`.&lt;br/&gt;\n        - `isinstance(prior, MultivariateNormal)` (from `torch.distributions`) or\n            `isinstance(prior, sbi.utils.BoxUniform)`\n\n        Note that custom implementations of any of these densities (or estimators) will\n        not trigger the non-atomic loss, and the algorithm will fall back onto using\n        the atomic loss.\n\n        Args:\n            prior: A probability distribution that expresses prior knowledge about the\n                parameters, e.g. which ranges are meaningful for them.\n            density_estimator: If it is a string, use a pre-configured network of the\n                provided type (one of nsf, maf, mdn, made). Alternatively, a function\n                that builds a custom neural network can be provided. The function will\n                be called with the first batch of simulations (theta, x), which can\n                thus be used for shape inference and potentially for z-scoring. It\n                needs to return a PyTorch `nn.Module` implementing the density\n                estimator. The density estimator needs to provide the methods\n                `.log_prob` and `.sample()`.\n            device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n            logging_level: Minimum severity of messages to log. One of the strings\n                INFO, WARNING, DEBUG, ERROR and CRITICAL.\n            summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n                file location (default is `&lt;current working directory&gt;/logs`.)\n            show_progress_bars: Whether to show a progressbar during training.\n        \"\"\"\n\n        kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n        super().__init__(**kwargs)\n\n    def train(\n        self,\n        num_atoms: int = 10,\n        training_batch_size: int = 50,\n        learning_rate: float = 5e-4,\n        validation_fraction: float = 0.1,\n        stop_after_epochs: int = 20,\n        max_num_epochs: int = 2**31 - 1,\n        clip_max_norm: Optional[float] = 5.0,\n        calibration_kernel: Optional[Callable] = None,\n        resume_training: bool = False,\n        force_first_round_loss: bool = False,\n        discard_prior_samples: bool = False,\n        use_combined_loss: bool = False,\n        retrain_from_scratch: bool = False,\n        show_train_summary: bool = False,\n        dataloader_kwargs: Optional[Dict] = None,\n    ) -&gt; nn.Module:\nr\"\"\"Return density estimator that approximates the distribution $p(\\theta|x)$.\n\n        Args:\n            num_atoms: Number of atoms to use for classification.\n            training_batch_size: Training batch size.\n            learning_rate: Learning rate for Adam optimizer.\n            validation_fraction: The fraction of data to use for validation.\n            stop_after_epochs: The number of epochs to wait for improvement on the\n                validation set before terminating training.\n            max_num_epochs: Maximum number of epochs to run. If reached, we stop\n                training even when the validation loss is still decreasing. Otherwise,\n                we train until validation loss increases (see also `stop_after_epochs`).\n            clip_max_norm: Value at which to clip the total gradient norm in order to\n                prevent exploding gradients. Use None for no clipping.\n            calibration_kernel: A function to calibrate the loss with respect to the\n                simulations `x`. See Lueckmann, Gon\u00e7alves et al., NeurIPS 2017.\n            resume_training: Can be used in case training time is limited, e.g. on a\n                cluster. If `True`, the split between train and validation set, the\n                optimizer, the number of epochs, and the best validation log-prob will\n                be restored from the last time `.train()` was called.\n            force_first_round_loss: If `True`, train with maximum likelihood,\n                i.e., potentially ignoring the correction for using a proposal\n                distribution different from the prior.\n            discard_prior_samples: Whether to discard samples simulated in round 1, i.e.\n                from the prior. Training may be sped up by ignoring such less targeted\n                samples.\n            use_combined_loss: Whether to train the neural net also on prior samples\n                using maximum likelihood in addition to training it on all samples using\n                atomic loss. The extra MLE loss helps prevent density leaking with\n                bounded priors.\n            retrain_from_scratch: Whether to retrain the conditional density\n                estimator for the posterior from scratch each round.\n            show_train_summary: Whether to print the number of epochs and validation\n                loss and leakage after the training.\n            dataloader_kwargs: Additional or updated kwargs to be passed to the training\n                and validation dataloaders (like, e.g., a collate_fn)\n\n        Returns:\n            Density estimator that approximates the distribution $p(\\theta|x)$.\n        \"\"\"\n\n        # WARNING: sneaky trick ahead. We proxy the parent's `train` here,\n        # requiring the signature to have `num_atoms`, save it for use below, and\n        # continue. It's sneaky because we are using the object (self) as a namespace\n        # to pass arguments between functions, and that's implicit state management.\n        self._num_atoms = num_atoms\n        self._use_combined_loss = use_combined_loss\n        kwargs = del_entries(\n            locals(),\n            entries=(\"self\", \"__class__\", \"num_atoms\", \"use_combined_loss\"),\n        )\n\n        self._round = max(self._data_round_index)\n\n        if self._round &gt; 0:\n            # Set the proposal to the last proposal that was passed by the user. For\n            # atomic SNPE, it does not matter what the proposal is. For non-atomic\n            # SNPE, we only use the latest data that was passed, i.e. the one from the\n            # last proposal.\n            proposal = self._proposal_roundwise[-1]\n            self.use_non_atomic_loss = (\n                isinstance(proposal, DirectPosterior)\n                and isinstance(proposal.posterior_estimator._distribution, mdn)\n                and isinstance(self._neural_net._distribution, mdn)\n                and check_dist_class(\n                    self._prior, class_to_check=(Uniform, MultivariateNormal)\n                )[0]\n            )\n\n            algorithm = \"non-atomic\" if self.use_non_atomic_loss else \"atomic\"\n            print(f\"Using SNPE-C with {algorithm} loss\")\n\n            if self.use_non_atomic_loss:\n                # Take care of z-scoring, pre-compute and store prior terms.\n                self._set_state_for_mog_proposal()\n\n        return super().train(**kwargs)\n\n    def _set_state_for_mog_proposal(self) -&gt; None:\n\"\"\"Set state variables that are used at each training step of non-atomic SNPE-C.\n\n        Three things are computed:\n        1) Check if z-scoring was requested. To do so, we check if the `_transform`\n            argument of the net had been a `CompositeTransform`. See pyknos mdn.py.\n        2) Define a (potentially standardized) prior. It's standardized if z-scoring\n            had been requested.\n        3) Compute (Precision * mean) for the prior. This quantity is used at every\n            training step if the prior is Gaussian.\n        \"\"\"\n\n        self.z_score_theta = isinstance(self._neural_net._transform, CompositeTransform)\n\n        self._set_maybe_z_scored_prior()\n\n        if isinstance(self._maybe_z_scored_prior, MultivariateNormal):\n            self.prec_m_prod_prior = torch.mv(\n                self._maybe_z_scored_prior.precision_matrix,  # type: ignore\n                self._maybe_z_scored_prior.loc,  # type: ignore\n            )\n\n    def _set_maybe_z_scored_prior(self) -&gt; None:\nr\"\"\"Compute and store potentially standardized prior (if z-scoring was done).\n\n        The proposal posterior is:\n        $pp(\\theta|x) = 1/Z * q(\\theta|x) * prop(\\theta) / p(\\theta)$\n\n        Let's denote z-scored theta by `a`: a = (theta - mean) / std\n        Then pp'(a|x) = 1/Z_2 * q'(a|x) * prop'(a) / p'(a)$\n\n        The ' indicates that the evaluation occurs in standardized space. The constant\n        scaling factor has been absorbed into Z_2.\n        From the above equation, we see that we need to evaluate the prior **in\n        standardized space**. We build the standardized prior in this function.\n\n        The standardize transform that is applied to the samples theta does not use\n        the exact prior mean and std (due to implementation issues). Hence, the z-scored\n        prior will not be exactly have mean=0 and std=1.\n        \"\"\"\n\n        if self.z_score_theta:\n            scale = self._neural_net._transform._transforms[0]._scale\n            shift = self._neural_net._transform._transforms[0]._shift\n\n            # Following the definintion of the linear transform in\n            # `standardizing_transform` in `sbiutils.py`:\n            # shift=-mean / std\n            # scale=1 / std\n            # Solving these equations for mean and std:\n            estim_prior_std = 1 / scale\n            estim_prior_mean = -shift * estim_prior_std\n\n            # Compute the discrepancy of the true prior mean and std and the mean and\n            # std that was empirically estimated from samples.\n            # N(theta|m,s) = N((theta-m_e)/s_e|(m-m_e)/s_e, s/s_e)\n            # Above: m,s are true prior mean and std. m_e,s_e are estimated prior mean\n            # and std (estimated from samples and used to build standardize transform).\n            almost_zero_mean = (self._prior.mean - estim_prior_mean) / estim_prior_std\n            almost_one_std = torch.sqrt(self._prior.variance) / estim_prior_std\n\n            if isinstance(self._prior, MultivariateNormal):\n                self._maybe_z_scored_prior = MultivariateNormal(\n                    almost_zero_mean, torch.diag(almost_one_std)\n                )\n            else:\n                range_ = torch.sqrt(almost_one_std * 3.0)\n                self._maybe_z_scored_prior = utils.BoxUniform(\n                    almost_zero_mean - range_, almost_zero_mean + range_\n                )\n        else:\n            self._maybe_z_scored_prior = self._prior\n\n    def _log_prob_proposal_posterior(\n        self,\n        theta: Tensor,\n        x: Tensor,\n        masks: Tensor,\n        proposal: DirectPosterior,\n    ) -&gt; Tensor:\n\"\"\"Return the log-probability of the proposal posterior.\n\n        If the proposal is a MoG, the density estimator is a MoG, and the prior is\n        either Gaussian or uniform, we use non-atomic loss. Else, use atomic loss (which\n        suffers from leakage).\n\n        Args:\n            theta: Batch of parameters \u03b8.\n            x: Batch of data.\n            masks: Mask that is True for prior samples in the batch in order to train\n                them with prior loss.\n            proposal: Proposal distribution.\n\n        Returns: Log-probability of the proposal posterior.\n        \"\"\"\n\n        if self.use_non_atomic_loss:\n            return self._log_prob_proposal_posterior_mog(theta, x, proposal)\n        else:\n            return self._log_prob_proposal_posterior_atomic(theta, x, masks)\n\n    def _log_prob_proposal_posterior_atomic(\n        self, theta: Tensor, x: Tensor, masks: Tensor\n    ):\n\"\"\"Return log probability of the proposal posterior for atomic proposals.\n\n        We have two main options when evaluating the proposal posterior.\n            (1) Generate atoms from the proposal prior.\n            (2) Generate atoms from a more targeted distribution, such as the most\n                recent posterior.\n        If we choose the latter, it is likely beneficial not to do this in the first\n        round, since we would be sampling from a randomly-initialized neural density\n        estimator.\n\n        Args:\n            theta: Batch of parameters \u03b8.\n            x: Batch of data.\n            masks: Mask that is True for prior samples in the batch in order to train\n                them with prior loss.\n\n        Returns:\n            Log-probability of the proposal posterior.\n        \"\"\"\n\n        batch_size = theta.shape[0]\n\n        num_atoms = int(\n            clamp_and_warn(\"num_atoms\", self._num_atoms, min_val=2, max_val=batch_size)\n        )\n\n        # Each set of parameter atoms is evaluated using the same x,\n        # so we repeat rows of the data x, e.g. [1, 2] -&gt; [1, 1, 2, 2]\n        repeated_x = repeat_rows(x, num_atoms)\n\n        # To generate the full set of atoms for a given item in the batch,\n        # we sample without replacement num_atoms - 1 times from the rest\n        # of the theta in the batch.\n        probs = ones(batch_size, batch_size) * (1 - eye(batch_size)) / (batch_size - 1)\n\n        choices = torch.multinomial(probs, num_samples=num_atoms - 1, replacement=False)\n        contrasting_theta = theta[choices]\n\n        # We can now create our sets of atoms from the contrasting parameter sets\n        # we have generated.\n        atomic_theta = torch.cat((theta[:, None, :], contrasting_theta), dim=1).reshape(\n            batch_size * num_atoms, -1\n        )\n\n        # Evaluate large batch giving (batch_size * num_atoms) log prob posterior evals.\n        log_prob_posterior = self._neural_net.log_prob(atomic_theta, repeated_x)\n        utils.assert_all_finite(log_prob_posterior, \"posterior eval\")\n        log_prob_posterior = log_prob_posterior.reshape(batch_size, num_atoms)\n\n        # Get (batch_size * num_atoms) log prob prior evals.\n        log_prob_prior = self._prior.log_prob(atomic_theta)\n        log_prob_prior = log_prob_prior.reshape(batch_size, num_atoms)\n        utils.assert_all_finite(log_prob_prior, \"prior eval\")\n\n        # Compute unnormalized proposal posterior.\n        unnormalized_log_prob = log_prob_posterior - log_prob_prior\n\n        # Normalize proposal posterior across discrete set of atoms.\n        log_prob_proposal_posterior = unnormalized_log_prob[:, 0] - torch.logsumexp(\n            unnormalized_log_prob, dim=-1\n        )\n        utils.assert_all_finite(log_prob_proposal_posterior, \"proposal posterior eval\")\n\n        # XXX This evaluates the posterior on _all_ prior samples\n        if self._use_combined_loss:\n            log_prob_posterior_non_atomic = self._neural_net.log_prob(theta, x)\n            masks = masks.reshape(-1)\n            log_prob_proposal_posterior = (\n                masks * log_prob_posterior_non_atomic + log_prob_proposal_posterior\n            )\n\n        return log_prob_proposal_posterior\n\n    def _log_prob_proposal_posterior_mog(\n        self, theta: Tensor, x: Tensor, proposal: DirectPosterior\n    ) -&gt; Tensor:\n\"\"\"Return log-probability of the proposal posterior for MoG proposal.\n\n        For MoG proposals and MoG density estimators, this can be done in closed form\n        and does not require atomic loss (i.e. there will be no leakage issues).\n\n        Notation:\n\n        m are mean vectors.\n        prec are precision matrices.\n        cov are covariance matrices.\n\n        _p at the end indicates that it is the proposal.\n        _d indicates that it is the density estimator.\n        _pp indicates the proposal posterior.\n\n        All tensors will have shapes (batch_dim, num_components, ...)\n\n        Args:\n            theta: Batch of parameters \u03b8.\n            x: Batch of data.\n            proposal: Proposal distribution.\n\n        Returns:\n            Log-probability of the proposal posterior.\n        \"\"\"\n\n        # Evaluate the proposal. MDNs do not have functionality to run the embedding_net\n        # and then get the mixture_components (**without** calling log_prob()). Hence,\n        # we call them separately here.\n        encoded_x = proposal.posterior_estimator._embedding_net(proposal.default_x)\n        dist = (\n            proposal.posterior_estimator._distribution\n        )  # defined to avoid ugly black formatting.\n        logits_p, m_p, prec_p, _, _ = dist.get_mixture_components(encoded_x)\n        norm_logits_p = logits_p - torch.logsumexp(logits_p, dim=-1, keepdim=True)\n\n        # Evaluate the density estimator.\n        encoded_x = self._neural_net._embedding_net(x)\n        dist = self._neural_net._distribution  # defined to avoid black formatting.\n        logits_d, m_d, prec_d, _, _ = dist.get_mixture_components(encoded_x)\n        norm_logits_d = logits_d - torch.logsumexp(logits_d, dim=-1, keepdim=True)\n\n        # z-score theta if it z-scoring had been requested.\n        theta = self._maybe_z_score_theta(theta)\n\n        # Compute the MoG parameters of the proposal posterior.\n        (\n            logits_pp,\n            m_pp,\n            prec_pp,\n            cov_pp,\n        ) = self._automatic_posterior_transformation(\n            norm_logits_p, m_p, prec_p, norm_logits_d, m_d, prec_d\n        )\n\n        # Compute the log_prob of theta under the product.\n        log_prob_proposal_posterior = utils.mog_log_prob(\n            theta, logits_pp, m_pp, prec_pp\n        )\n        utils.assert_all_finite(\n            log_prob_proposal_posterior,\n\"\"\"the evaluation of the MoG proposal posterior. This is likely due to a\n            numerical instability in the training procedure. Please create an issue on\n            Github.\"\"\",\n        )\n\n        return log_prob_proposal_posterior\n\n    def _automatic_posterior_transformation(\n        self,\n        logits_p: Tensor,\n        means_p: Tensor,\n        precisions_p: Tensor,\n        logits_d: Tensor,\n        means_d: Tensor,\n        precisions_d: Tensor,\n    ):\nr\"\"\"Returns the MoG parameters of the proposal posterior.\n\n        The proposal posterior is:\n        $pp(\\theta|x) = 1/Z * q(\\theta|x) * prop(\\theta) / p(\\theta)$\n        In words: proposal posterior = posterior estimate * proposal / prior.\n\n        If the posterior estimate and the proposal are MoG and the prior is either\n        Gaussian or uniform, we can solve this in closed-form. The is implemented in\n        this function.\n\n        This function implements Appendix A1 from Greenberg et al. 2019.\n\n        We have to build L*K components. How do we do this?\n        Example: proposal has two components, density estimator has three components.\n        Let's call the two components of the proposal i,j and the three components\n        of the density estimator x,y,z. We have to multiply every component of the\n        proposal with every component of the density estimator. So, what we do is:\n        1) for the proposal, build: i,i,i,j,j,j. Done with torch.repeat_interleave()\n        2) for the density estimator, build: x,y,z,x,y,z. Done with torch.repeat()\n        3) Multiply them with simple matrix operations.\n\n        Args:\n            logits_p: Component weight of each Gaussian of the proposal.\n            means_p: Mean of each Gaussian of the proposal.\n            precisions_p: Precision matrix of each Gaussian of the proposal.\n            logits_d: Component weight for each Gaussian of the density estimator.\n            means_d: Mean of each Gaussian of the density estimator.\n            precisions_d: Precision matrix of each Gaussian of the density estimator.\n\n        Returns: (Component weight, mean, precision matrix, covariance matrix) of each\n            Gaussian of the proposal posterior. Has L*K terms (proposal has L terms,\n            density estimator has K terms).\n        \"\"\"\n\n        precisions_pp, covariances_pp = self._precisions_proposal_posterior(\n            precisions_p, precisions_d\n        )\n\n        means_pp = self._means_proposal_posterior(\n            covariances_pp, means_p, precisions_p, means_d, precisions_d\n        )\n\n        logits_pp = self._logits_proposal_posterior(\n            means_pp,\n            precisions_pp,\n            covariances_pp,\n            logits_p,\n            means_p,\n            precisions_p,\n            logits_d,\n            means_d,\n            precisions_d,\n        )\n\n        return logits_pp, means_pp, precisions_pp, covariances_pp\n\n    def _precisions_proposal_posterior(\n        self, precisions_p: Tensor, precisions_d: Tensor\n    ):\n\"\"\"Return the precisions and covariances of the proposal posterior.\n\n        Args:\n            precisions_p: Precision matrices of the proposal distribution.\n            precisions_d: Precision matrices of the density estimator.\n\n        Returns: (Precisions, Covariances) of the proposal posterior. L*K terms.\n        \"\"\"\n\n        num_comps_p = precisions_p.shape[1]\n        num_comps_d = precisions_d.shape[1]\n\n        precisions_p_rep = precisions_p.repeat_interleave(num_comps_d, dim=1)\n        precisions_d_rep = precisions_d.repeat(1, num_comps_p, 1, 1)\n\n        precisions_pp = precisions_p_rep + precisions_d_rep\n        if isinstance(self._maybe_z_scored_prior, MultivariateNormal):\n            precisions_pp -= self._maybe_z_scored_prior.precision_matrix\n\n        covariances_pp = torch.inverse(precisions_pp)\n\n        return precisions_pp, covariances_pp\n\n    def _means_proposal_posterior(\n        self,\n        covariances_pp: Tensor,\n        means_p: Tensor,\n        precisions_p: Tensor,\n        means_d: Tensor,\n        precisions_d: Tensor,\n    ):\n\"\"\"Return the means of the proposal posterior.\n\n        means_pp = C_ix * (P_i * m_i + P_x * m_x - P_o * m_o).\n\n        Args:\n            covariances_pp: Covariance matrices of the proposal posterior.\n            means_p: Means of the proposal distribution.\n            precisions_p: Precision matrices of the proposal distribution.\n            means_d: Means of the density estimator.\n            precisions_d: Precision matrices of the density estimator.\n\n        Returns: Means of the proposal posterior. L*K terms.\n        \"\"\"\n\n        num_comps_p = precisions_p.shape[1]\n        num_comps_d = precisions_d.shape[1]\n\n        # First, compute the product P_i * m_i and P_j * m_j\n        prec_m_prod_p = batched_mixture_mv(precisions_p, means_p)\n        prec_m_prod_d = batched_mixture_mv(precisions_d, means_d)\n\n        # Repeat them to allow for matrix operations: same trick as for the precisions.\n        prec_m_prod_p_rep = prec_m_prod_p.repeat_interleave(num_comps_d, dim=1)\n        prec_m_prod_d_rep = prec_m_prod_d.repeat(1, num_comps_p, 1)\n\n        # Means = C_ij * (P_i * m_i + P_x * m_x - P_o * m_o).\n        summed_cov_m_prod_rep = prec_m_prod_p_rep + prec_m_prod_d_rep\n        if isinstance(self._maybe_z_scored_prior, MultivariateNormal):\n            summed_cov_m_prod_rep -= self.prec_m_prod_prior\n\n        means_pp = batched_mixture_mv(covariances_pp, summed_cov_m_prod_rep)\n\n        return means_pp\n\n    @staticmethod\n    def _logits_proposal_posterior(\n        means_pp: Tensor,\n        precisions_pp: Tensor,\n        covariances_pp: Tensor,\n        logits_p: Tensor,\n        means_p: Tensor,\n        precisions_p: Tensor,\n        logits_d: Tensor,\n        means_d: Tensor,\n        precisions_d: Tensor,\n    ):\n\"\"\"Return the component weights (i.e. logits) of the proposal posterior.\n\n        Args:\n            means_pp: Means of the proposal posterior.\n            precisions_pp: Precision matrices of the proposal posterior.\n            covariances_pp: Covariance matrices of the proposal posterior.\n            logits_p: Component weights (i.e. logits) of the proposal distribution.\n            means_p: Means of the proposal distribution.\n            precisions_p: Precision matrices of the proposal distribution.\n            logits_d: Component weights (i.e. logits) of the density estimator.\n            means_d: Means of the density estimator.\n            precisions_d: Precision matrices of the density estimator.\n\n        Returns: Component weights of the proposal posterior. L*K terms.\n        \"\"\"\n\n        num_comps_p = precisions_p.shape[1]\n        num_comps_d = precisions_d.shape[1]\n\n        # Compute log(alpha_i * beta_j)\n        logits_p_rep = logits_p.repeat_interleave(num_comps_d, dim=1)\n        logits_d_rep = logits_d.repeat(1, num_comps_p)\n        logit_factors = logits_p_rep + logits_d_rep\n\n        # Compute sqrt(det()/(det()*det()))\n        logdet_covariances_pp = torch.logdet(covariances_pp)\n        logdet_covariances_p = -torch.logdet(precisions_p)\n        logdet_covariances_d = -torch.logdet(precisions_d)\n\n        # Repeat the proposal and density estimator terms such that there are LK terms.\n        # Same trick as has been used above.\n        logdet_covariances_p_rep = logdet_covariances_p.repeat_interleave(\n            num_comps_d, dim=1\n        )\n        logdet_covariances_d_rep = logdet_covariances_d.repeat(1, num_comps_p)\n\n        log_sqrt_det_ratio = 0.5 * (\n            logdet_covariances_pp\n            - (logdet_covariances_p_rep + logdet_covariances_d_rep)\n        )\n\n        # Compute for proposal, density estimator, and proposal posterior:\n        # mu_i.T * P_i * mu_i\n        exponent_p = batched_mixture_vmv(precisions_p, means_p)\n        exponent_d = batched_mixture_vmv(precisions_d, means_d)\n        exponent_pp = batched_mixture_vmv(precisions_pp, means_pp)\n\n        # Extend proposal and density estimator exponents to get LK terms.\n        exponent_p_rep = exponent_p.repeat_interleave(num_comps_d, dim=1)\n        exponent_d_rep = exponent_d.repeat(1, num_comps_p)\n        exponent = -0.5 * (exponent_p_rep + exponent_d_rep - exponent_pp)\n\n        logits_pp = logit_factors + log_sqrt_det_ratio + exponent\n\n        return logits_pp\n\n    def _maybe_z_score_theta(self, theta: Tensor) -&gt; Tensor:\n\"\"\"Return potentially standardized theta if z-scoring was requested.\"\"\"\n\n        if self.z_score_theta:\n            theta, _ = self._neural_net._transform(theta)\n\n        return theta\n</code></pre>"},{"location":"reference/#sbi.inference.snpe.snpe_c.SNPE_C.__init__","title":"<code>__init__(prior=None, density_estimator='maf', device='cpu', logging_level='WARNING', summary_writer=None, show_progress_bars=True)</code>","text":"<p>SNPE-C / APT [1].</p> <p>[1] Automatic Posterior Transformation for Likelihood-free Inference,     Greenberg et al., ICML 2019, https://arxiv.org/abs/1905.07488.</p> <p>This class implements two loss variants of SNPE-C: the non-atomic and the atomic version. The atomic loss of SNPE-C can be used for any density estimator, i.e. also for normalizing flows. However, it suffers from leakage issues. On the other hand, the non-atomic loss can only be used only if the proposal distribution is a mixture of Gaussians, the density estimator is a mixture of Gaussians, and the prior is either Gaussian or Uniform. It does not suffer from leakage issues. At the beginning of each round, we print whether the non-atomic or the atomic version is used.</p> <p>In this codebase, we will automatically switch to the non-atomic loss if the following criteria are fulfilled: - proposal is a <code>DirectPosterior</code> with density_estimator <code>mdn</code>, as built     with <code>utils.sbi.posterior_nn()</code>. - the density estimator is a <code>mdn</code>, as built with     <code>utils.sbi.posterior_nn()</code>. - <code>isinstance(prior, MultivariateNormal)</code> (from <code>torch.distributions</code>) or     <code>isinstance(prior, sbi.utils.BoxUniform)</code></p> <p>Note that custom implementations of any of these densities (or estimators) will not trigger the non-atomic loss, and the algorithm will fall back onto using the atomic loss.</p> <p>Parameters:</p> Name Type Description Default <code>prior</code> <code>Optional[Distribution]</code> <p>A probability distribution that expresses prior knowledge about the parameters, e.g. which ranges are meaningful for them.</p> <code>None</code> <code>density_estimator</code> <code>Union[str, Callable]</code> <p>If it is a string, use a pre-configured network of the provided type (one of nsf, maf, mdn, made). Alternatively, a function that builds a custom neural network can be provided. The function will be called with the first batch of simulations (theta, x), which can thus be used for shape inference and potentially for z-scoring. It needs to return a PyTorch <code>nn.Module</code> implementing the density estimator. The density estimator needs to provide the methods <code>.log_prob</code> and <code>.sample()</code>.</p> <code>'maf'</code> <code>device</code> <code>str</code> <p>Training device, e.g., \u201ccpu\u201d, \u201ccuda\u201d or \u201ccuda:{0, 1, \u2026}\u201d.</p> <code>'cpu'</code> <code>logging_level</code> <code>Union[int, str]</code> <p>Minimum severity of messages to log. One of the strings INFO, WARNING, DEBUG, ERROR and CRITICAL.</p> <code>'WARNING'</code> <code>summary_writer</code> <code>Optional[TensorboardSummaryWriter]</code> <p>A tensorboard <code>SummaryWriter</code> to control, among others, log file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p> <code>None</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during training.</p> <code>True</code> Source code in <code>/home/michael/Documents/sbi/sbi/inference/snpe/snpe_c.py</code> <pre><code>def __init__(\n    self,\n    prior: Optional[Distribution] = None,\n    density_estimator: Union[str, Callable] = \"maf\",\n    device: str = \"cpu\",\n    logging_level: Union[int, str] = \"WARNING\",\n    summary_writer: Optional[TensorboardSummaryWriter] = None,\n    show_progress_bars: bool = True,\n):\nr\"\"\"SNPE-C / APT [1].\n\n    [1] _Automatic Posterior Transformation for Likelihood-free Inference_,\n        Greenberg et al., ICML 2019, https://arxiv.org/abs/1905.07488.\n\n    This class implements two loss variants of SNPE-C: the non-atomic and the atomic\n    version. The atomic loss of SNPE-C can be used for any density estimator,\n    i.e. also for normalizing flows. However, it suffers from leakage issues. On\n    the other hand, the non-atomic loss can only be used only if the proposal\n    distribution is a mixture of Gaussians, the density estimator is a mixture of\n    Gaussians, and the prior is either Gaussian or Uniform. It does not suffer from\n    leakage issues. At the beginning of each round, we print whether the non-atomic\n    or the atomic version is used.\n\n    In this codebase, we will automatically switch to the non-atomic loss if the\n    following criteria are fulfilled:&lt;br/&gt;\n    - proposal is a `DirectPosterior` with density_estimator `mdn`, as built\n        with `utils.sbi.posterior_nn()`.&lt;br/&gt;\n    - the density estimator is a `mdn`, as built with\n        `utils.sbi.posterior_nn()`.&lt;br/&gt;\n    - `isinstance(prior, MultivariateNormal)` (from `torch.distributions`) or\n        `isinstance(prior, sbi.utils.BoxUniform)`\n\n    Note that custom implementations of any of these densities (or estimators) will\n    not trigger the non-atomic loss, and the algorithm will fall back onto using\n    the atomic loss.\n\n    Args:\n        prior: A probability distribution that expresses prior knowledge about the\n            parameters, e.g. which ranges are meaningful for them.\n        density_estimator: If it is a string, use a pre-configured network of the\n            provided type (one of nsf, maf, mdn, made). Alternatively, a function\n            that builds a custom neural network can be provided. The function will\n            be called with the first batch of simulations (theta, x), which can\n            thus be used for shape inference and potentially for z-scoring. It\n            needs to return a PyTorch `nn.Module` implementing the density\n            estimator. The density estimator needs to provide the methods\n            `.log_prob` and `.sample()`.\n        device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n        logging_level: Minimum severity of messages to log. One of the strings\n            INFO, WARNING, DEBUG, ERROR and CRITICAL.\n        summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n            file location (default is `&lt;current working directory&gt;/logs`.)\n        show_progress_bars: Whether to show a progressbar during training.\n    \"\"\"\n\n    kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n    super().__init__(**kwargs)\n</code></pre>"},{"location":"reference/#sbi.inference.snpe.snpe_c.SNPE_C.train","title":"<code>train(num_atoms=10, training_batch_size=50, learning_rate=0.0005, validation_fraction=0.1, stop_after_epochs=20, max_num_epochs=2 ** 31 - 1, clip_max_norm=5.0, calibration_kernel=None, resume_training=False, force_first_round_loss=False, discard_prior_samples=False, use_combined_loss=False, retrain_from_scratch=False, show_train_summary=False, dataloader_kwargs=None)</code>","text":"<p>Return density estimator that approximates the distribution \\(p(\\theta|x)\\).</p> <p>Parameters:</p> Name Type Description Default <code>num_atoms</code> <code>int</code> <p>Number of atoms to use for classification.</p> <code>10</code> <code>training_batch_size</code> <code>int</code> <p>Training batch size.</p> <code>50</code> <code>learning_rate</code> <code>float</code> <p>Learning rate for Adam optimizer.</p> <code>0.0005</code> <code>validation_fraction</code> <code>float</code> <p>The fraction of data to use for validation.</p> <code>0.1</code> <code>stop_after_epochs</code> <code>int</code> <p>The number of epochs to wait for improvement on the validation set before terminating training.</p> <code>20</code> <code>max_num_epochs</code> <code>int</code> <p>Maximum number of epochs to run. If reached, we stop training even when the validation loss is still decreasing. Otherwise, we train until validation loss increases (see also <code>stop_after_epochs</code>).</p> <code>2 ** 31 - 1</code> <code>clip_max_norm</code> <code>Optional[float]</code> <p>Value at which to clip the total gradient norm in order to prevent exploding gradients. Use None for no clipping.</p> <code>5.0</code> <code>calibration_kernel</code> <code>Optional[Callable]</code> <p>A function to calibrate the loss with respect to the simulations <code>x</code>. See Lueckmann, Gon\u00e7alves et al., NeurIPS 2017.</p> <code>None</code> <code>resume_training</code> <code>bool</code> <p>Can be used in case training time is limited, e.g. on a cluster. If <code>True</code>, the split between train and validation set, the optimizer, the number of epochs, and the best validation log-prob will be restored from the last time <code>.train()</code> was called.</p> <code>False</code> <code>force_first_round_loss</code> <code>bool</code> <p>If <code>True</code>, train with maximum likelihood, i.e., potentially ignoring the correction for using a proposal distribution different from the prior.</p> <code>False</code> <code>discard_prior_samples</code> <code>bool</code> <p>Whether to discard samples simulated in round 1, i.e. from the prior. Training may be sped up by ignoring such less targeted samples.</p> <code>False</code> <code>use_combined_loss</code> <code>bool</code> <p>Whether to train the neural net also on prior samples using maximum likelihood in addition to training it on all samples using atomic loss. The extra MLE loss helps prevent density leaking with bounded priors.</p> <code>False</code> <code>retrain_from_scratch</code> <code>bool</code> <p>Whether to retrain the conditional density estimator for the posterior from scratch each round.</p> <code>False</code> <code>show_train_summary</code> <code>bool</code> <p>Whether to print the number of epochs and validation loss and leakage after the training.</p> <code>False</code> <code>dataloader_kwargs</code> <code>Optional[Dict]</code> <p>Additional or updated kwargs to be passed to the training and validation dataloaders (like, e.g., a collate_fn)</p> <code>None</code> <p>Returns:</p> Type Description <code>nn.Module</code> <p>Density estimator that approximates the distribution \\(p(\\theta|x)\\).</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/snpe/snpe_c.py</code> <pre><code>def train(\n    self,\n    num_atoms: int = 10,\n    training_batch_size: int = 50,\n    learning_rate: float = 5e-4,\n    validation_fraction: float = 0.1,\n    stop_after_epochs: int = 20,\n    max_num_epochs: int = 2**31 - 1,\n    clip_max_norm: Optional[float] = 5.0,\n    calibration_kernel: Optional[Callable] = None,\n    resume_training: bool = False,\n    force_first_round_loss: bool = False,\n    discard_prior_samples: bool = False,\n    use_combined_loss: bool = False,\n    retrain_from_scratch: bool = False,\n    show_train_summary: bool = False,\n    dataloader_kwargs: Optional[Dict] = None,\n) -&gt; nn.Module:\nr\"\"\"Return density estimator that approximates the distribution $p(\\theta|x)$.\n\n    Args:\n        num_atoms: Number of atoms to use for classification.\n        training_batch_size: Training batch size.\n        learning_rate: Learning rate for Adam optimizer.\n        validation_fraction: The fraction of data to use for validation.\n        stop_after_epochs: The number of epochs to wait for improvement on the\n            validation set before terminating training.\n        max_num_epochs: Maximum number of epochs to run. If reached, we stop\n            training even when the validation loss is still decreasing. Otherwise,\n            we train until validation loss increases (see also `stop_after_epochs`).\n        clip_max_norm: Value at which to clip the total gradient norm in order to\n            prevent exploding gradients. Use None for no clipping.\n        calibration_kernel: A function to calibrate the loss with respect to the\n            simulations `x`. See Lueckmann, Gon\u00e7alves et al., NeurIPS 2017.\n        resume_training: Can be used in case training time is limited, e.g. on a\n            cluster. If `True`, the split between train and validation set, the\n            optimizer, the number of epochs, and the best validation log-prob will\n            be restored from the last time `.train()` was called.\n        force_first_round_loss: If `True`, train with maximum likelihood,\n            i.e., potentially ignoring the correction for using a proposal\n            distribution different from the prior.\n        discard_prior_samples: Whether to discard samples simulated in round 1, i.e.\n            from the prior. Training may be sped up by ignoring such less targeted\n            samples.\n        use_combined_loss: Whether to train the neural net also on prior samples\n            using maximum likelihood in addition to training it on all samples using\n            atomic loss. The extra MLE loss helps prevent density leaking with\n            bounded priors.\n        retrain_from_scratch: Whether to retrain the conditional density\n            estimator for the posterior from scratch each round.\n        show_train_summary: Whether to print the number of epochs and validation\n            loss and leakage after the training.\n        dataloader_kwargs: Additional or updated kwargs to be passed to the training\n            and validation dataloaders (like, e.g., a collate_fn)\n\n    Returns:\n        Density estimator that approximates the distribution $p(\\theta|x)$.\n    \"\"\"\n\n    # WARNING: sneaky trick ahead. We proxy the parent's `train` here,\n    # requiring the signature to have `num_atoms`, save it for use below, and\n    # continue. It's sneaky because we are using the object (self) as a namespace\n    # to pass arguments between functions, and that's implicit state management.\n    self._num_atoms = num_atoms\n    self._use_combined_loss = use_combined_loss\n    kwargs = del_entries(\n        locals(),\n        entries=(\"self\", \"__class__\", \"num_atoms\", \"use_combined_loss\"),\n    )\n\n    self._round = max(self._data_round_index)\n\n    if self._round &gt; 0:\n        # Set the proposal to the last proposal that was passed by the user. For\n        # atomic SNPE, it does not matter what the proposal is. For non-atomic\n        # SNPE, we only use the latest data that was passed, i.e. the one from the\n        # last proposal.\n        proposal = self._proposal_roundwise[-1]\n        self.use_non_atomic_loss = (\n            isinstance(proposal, DirectPosterior)\n            and isinstance(proposal.posterior_estimator._distribution, mdn)\n            and isinstance(self._neural_net._distribution, mdn)\n            and check_dist_class(\n                self._prior, class_to_check=(Uniform, MultivariateNormal)\n            )[0]\n        )\n\n        algorithm = \"non-atomic\" if self.use_non_atomic_loss else \"atomic\"\n        print(f\"Using SNPE-C with {algorithm} loss\")\n\n        if self.use_non_atomic_loss:\n            # Take care of z-scoring, pre-compute and store prior terms.\n            self._set_state_for_mog_proposal()\n\n    return super().train(**kwargs)\n</code></pre>"},{"location":"reference/#sbi.inference.snle.snle_a.SNLE_A","title":"<code>sbi.inference.snle.snle_a.SNLE_A</code>","text":"<p>         Bases: <code>LikelihoodEstimator</code></p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/snle/snle_a.py</code> <pre><code>class SNLE_A(LikelihoodEstimator):\n    def __init__(\n        self,\n        prior: Optional[Distribution] = None,\n        density_estimator: Union[str, Callable] = \"maf\",\n        device: str = \"cpu\",\n        logging_level: Union[int, str] = \"WARNING\",\n        summary_writer: Optional[TensorboardSummaryWriter] = None,\n        show_progress_bars: bool = True,\n    ):\nr\"\"\"Sequential Neural Likelihood [1].\n\n        [1] Sequential Neural Likelihood: Fast Likelihood-free Inference with\n        Autoregressive Flows_, Papamakarios et al., AISTATS 2019,\n        https://arxiv.org/abs/1805.07226\n\n        Args:\n            prior: A probability distribution that expresses prior knowledge about the\n                parameters, e.g. which ranges are meaningful for them. If `None`, the\n                prior must be passed to `.build_posterior()`.\n            density_estimator: If it is a string, use a pre-configured network of the\n                provided type (one of nsf, maf, mdn, made). Alternatively, a function\n                that builds a custom neural network can be provided. The function will\n                be called with the first batch of simulations (theta, x), which can\n                thus be used for shape inference and potentially for z-scoring. It\n                needs to return a PyTorch `nn.Module` implementing the density\n                estimator. The density estimator needs to provide the methods\n                `.log_prob` and `.sample()`.\n            device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n            logging_level: Minimum severity of messages to log. One of the strings\n                INFO, WARNING, DEBUG, ERROR and CRITICAL.\n            summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n                file location (default is `&lt;current working directory&gt;/logs`.)\n            show_progress_bars: Whether to show a progressbar during simulation and\n                sampling.\n        \"\"\"\n\n        kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n        super().__init__(**kwargs)\n</code></pre>"},{"location":"reference/#sbi.inference.snle.snle_a.SNLE_A.__init__","title":"<code>__init__(prior=None, density_estimator='maf', device='cpu', logging_level='WARNING', summary_writer=None, show_progress_bars=True)</code>","text":"<p>Sequential Neural Likelihood [1].</p> <p>[1] Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows_, Papamakarios et al., AISTATS 2019, https://arxiv.org/abs/1805.07226</p> <p>Parameters:</p> Name Type Description Default <code>prior</code> <code>Optional[Distribution]</code> <p>A probability distribution that expresses prior knowledge about the parameters, e.g. which ranges are meaningful for them. If <code>None</code>, the prior must be passed to <code>.build_posterior()</code>.</p> <code>None</code> <code>density_estimator</code> <code>Union[str, Callable]</code> <p>If it is a string, use a pre-configured network of the provided type (one of nsf, maf, mdn, made). Alternatively, a function that builds a custom neural network can be provided. The function will be called with the first batch of simulations (theta, x), which can thus be used for shape inference and potentially for z-scoring. It needs to return a PyTorch <code>nn.Module</code> implementing the density estimator. The density estimator needs to provide the methods <code>.log_prob</code> and <code>.sample()</code>.</p> <code>'maf'</code> <code>device</code> <code>str</code> <p>Training device, e.g., \u201ccpu\u201d, \u201ccuda\u201d or \u201ccuda:{0, 1, \u2026}\u201d.</p> <code>'cpu'</code> <code>logging_level</code> <code>Union[int, str]</code> <p>Minimum severity of messages to log. One of the strings INFO, WARNING, DEBUG, ERROR and CRITICAL.</p> <code>'WARNING'</code> <code>summary_writer</code> <code>Optional[TensorboardSummaryWriter]</code> <p>A tensorboard <code>SummaryWriter</code> to control, among others, log file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p> <code>None</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during simulation and sampling.</p> <code>True</code> Source code in <code>/home/michael/Documents/sbi/sbi/inference/snle/snle_a.py</code> <pre><code>def __init__(\n    self,\n    prior: Optional[Distribution] = None,\n    density_estimator: Union[str, Callable] = \"maf\",\n    device: str = \"cpu\",\n    logging_level: Union[int, str] = \"WARNING\",\n    summary_writer: Optional[TensorboardSummaryWriter] = None,\n    show_progress_bars: bool = True,\n):\nr\"\"\"Sequential Neural Likelihood [1].\n\n    [1] Sequential Neural Likelihood: Fast Likelihood-free Inference with\n    Autoregressive Flows_, Papamakarios et al., AISTATS 2019,\n    https://arxiv.org/abs/1805.07226\n\n    Args:\n        prior: A probability distribution that expresses prior knowledge about the\n            parameters, e.g. which ranges are meaningful for them. If `None`, the\n            prior must be passed to `.build_posterior()`.\n        density_estimator: If it is a string, use a pre-configured network of the\n            provided type (one of nsf, maf, mdn, made). Alternatively, a function\n            that builds a custom neural network can be provided. The function will\n            be called with the first batch of simulations (theta, x), which can\n            thus be used for shape inference and potentially for z-scoring. It\n            needs to return a PyTorch `nn.Module` implementing the density\n            estimator. The density estimator needs to provide the methods\n            `.log_prob` and `.sample()`.\n        device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n        logging_level: Minimum severity of messages to log. One of the strings\n            INFO, WARNING, DEBUG, ERROR and CRITICAL.\n        summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n            file location (default is `&lt;current working directory&gt;/logs`.)\n        show_progress_bars: Whether to show a progressbar during simulation and\n            sampling.\n    \"\"\"\n\n    kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n    super().__init__(**kwargs)\n</code></pre>"},{"location":"reference/#sbi.inference.snre.snre_a.SNRE_A","title":"<code>sbi.inference.snre.snre_a.SNRE_A</code>","text":"<p>         Bases: <code>RatioEstimator</code></p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/snre/snre_a.py</code> <pre><code>class SNRE_A(RatioEstimator):\n    def __init__(\n        self,\n        prior: Optional[Distribution] = None,\n        classifier: Union[str, Callable] = \"resnet\",\n        device: str = \"cpu\",\n        logging_level: Union[int, str] = \"warning\",\n        summary_writer: Optional[TensorboardSummaryWriter] = None,\n        show_progress_bars: bool = True,\n    ):\nr\"\"\"AALR[1], here known as SNRE_A.\n\n        [1] _Likelihood-free MCMC with Amortized Approximate Likelihood Ratios_, Hermans\n            et al., ICML 2020, https://arxiv.org/abs/1903.04057\n\n        Args:\n            prior: A probability distribution that expresses prior knowledge about the\n                parameters, e.g. which ranges are meaningful for them. If `None`, the\n                prior must be passed to `.build_posterior()`.\n            classifier: Classifier trained to approximate likelihood ratios. If it is\n                a string, use a pre-configured network of the provided type (one of\n                linear, mlp, resnet). Alternatively, a function that builds a custom\n                neural network can be provided. The function will be called with the\n                first batch of simulations (theta, x), which can thus be used for shape\n                inference and potentially for z-scoring. It needs to return a PyTorch\n                `nn.Module` implementing the classifier.\n            device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n            logging_level: Minimum severity of messages to log. One of the strings\n                INFO, WARNING, DEBUG, ERROR and CRITICAL.\n            summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n                file location (default is `&lt;current working directory&gt;/logs`.)\n            show_progress_bars: Whether to show a progressbar during simulation and\n                sampling.\n        \"\"\"\n\n        kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n        super().__init__(**kwargs)\n\n    def train(\n        self,\n        training_batch_size: int = 50,\n        learning_rate: float = 5e-4,\n        validation_fraction: float = 0.1,\n        stop_after_epochs: int = 20,\n        max_num_epochs: int = 2**31 - 1,\n        clip_max_norm: Optional[float] = 5.0,\n        resume_training: bool = False,\n        discard_prior_samples: bool = False,\n        retrain_from_scratch: bool = False,\n        show_train_summary: bool = False,\n        dataloader_kwargs: Optional[Dict] = None,\n        loss_kwargs: Dict[str, Any] = {},\n    ) -&gt; nn.Module:\nr\"\"\"Return classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n\n        Args:\n            training_batch_size: Training batch size.\n            learning_rate: Learning rate for Adam optimizer.\n            validation_fraction: The fraction of data to use for validation.\n            stop_after_epochs: The number of epochs to wait for improvement on the\n                validation set before terminating training.\n            max_num_epochs: Maximum number of epochs to run. If reached, we stop\n                training even when the validation loss is still decreasing. Otherwise,\n                we train until validation loss increases (see also `stop_after_epochs`).\n            clip_max_norm: Value at which to clip the total gradient norm in order to\n                prevent exploding gradients. Use None for no clipping.\n            resume_training: Can be used in case training time is limited, e.g. on a\n                cluster. If `True`, the split between train and validation set, the\n                optimizer, the number of epochs, and the best validation log-prob will\n                be restored from the last time `.train()` was called.\n            discard_prior_samples: Whether to discard samples simulated in round 1, i.e.\n                from the prior. Training may be sped up by ignoring such less targeted\n                samples.\n            retrain_from_scratch: Whether to retrain the conditional density\n                estimator for the posterior from scratch each round.\n            show_train_summary: Whether to print the number of epochs and validation\n                loss and leakage after the training.\n            dataloader_kwargs: Additional or updated kwargs to be passed to the training\n                and validation dataloaders (like, e.g., a collate_fn)\n            loss_kwargs: Additional or updated kwargs to be passed to the self._loss fn.\n\n        Returns:\n            Classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n        \"\"\"\n\n        # AALR is defined for `num_atoms=2`.\n        # Proxy to `super().__call__` to ensure right parameter.\n        kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n        return super().train(**kwargs, num_atoms=2)\n\n    def _loss(self, theta: Tensor, x: Tensor, num_atoms: int) -&gt; Tensor:\n\"\"\"Returns the binary cross-entropy loss for the trained classifier.\n\n        The classifier takes as input a $(\\theta,x)$ pair. It is trained to predict 1\n        if the pair was sampled from the joint $p(\\theta,x)$, and to predict 0 if the\n        pair was sampled from the marginals $p(\\theta)p(x)$.\n        \"\"\"\n\n        assert theta.shape[0] == x.shape[0], \"Batch sizes for theta and x must match.\"\n        batch_size = theta.shape[0]\n\n        logits = self._classifier_logits(theta, x, num_atoms)\n        likelihood = torch.sigmoid(logits).squeeze()\n\n        # Alternating pairs where there is one sampled from the joint and one\n        # sampled from the marginals. The first element is sampled from the\n        # joint p(theta, x) and is labelled 1. The second element is sampled\n        # from the marginals p(theta)p(x) and is labelled 0. And so on.\n        labels = ones(2 * batch_size, device=self._device)  # two atoms\n        labels[1::2] = 0.0\n\n        # Binary cross entropy to learn the likelihood (AALR-specific)\n        return nn.BCELoss()(likelihood, labels)\n</code></pre>"},{"location":"reference/#sbi.inference.snre.snre_a.SNRE_A.__init__","title":"<code>__init__(prior=None, classifier='resnet', device='cpu', logging_level='warning', summary_writer=None, show_progress_bars=True)</code>","text":"<p>AALR[1], here known as SNRE_A.</p> <p>[1] Likelihood-free MCMC with Amortized Approximate Likelihood Ratios, Hermans     et al., ICML 2020, https://arxiv.org/abs/1903.04057</p> <p>Parameters:</p> Name Type Description Default <code>prior</code> <code>Optional[Distribution]</code> <p>A probability distribution that expresses prior knowledge about the parameters, e.g. which ranges are meaningful for them. If <code>None</code>, the prior must be passed to <code>.build_posterior()</code>.</p> <code>None</code> <code>classifier</code> <code>Union[str, Callable]</code> <p>Classifier trained to approximate likelihood ratios. If it is a string, use a pre-configured network of the provided type (one of linear, mlp, resnet). Alternatively, a function that builds a custom neural network can be provided. The function will be called with the first batch of simulations (theta, x), which can thus be used for shape inference and potentially for z-scoring. It needs to return a PyTorch <code>nn.Module</code> implementing the classifier.</p> <code>'resnet'</code> <code>device</code> <code>str</code> <p>Training device, e.g., \u201ccpu\u201d, \u201ccuda\u201d or \u201ccuda:{0, 1, \u2026}\u201d.</p> <code>'cpu'</code> <code>logging_level</code> <code>Union[int, str]</code> <p>Minimum severity of messages to log. One of the strings INFO, WARNING, DEBUG, ERROR and CRITICAL.</p> <code>'warning'</code> <code>summary_writer</code> <code>Optional[TensorboardSummaryWriter]</code> <p>A tensorboard <code>SummaryWriter</code> to control, among others, log file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p> <code>None</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during simulation and sampling.</p> <code>True</code> Source code in <code>/home/michael/Documents/sbi/sbi/inference/snre/snre_a.py</code> <pre><code>def __init__(\n    self,\n    prior: Optional[Distribution] = None,\n    classifier: Union[str, Callable] = \"resnet\",\n    device: str = \"cpu\",\n    logging_level: Union[int, str] = \"warning\",\n    summary_writer: Optional[TensorboardSummaryWriter] = None,\n    show_progress_bars: bool = True,\n):\nr\"\"\"AALR[1], here known as SNRE_A.\n\n    [1] _Likelihood-free MCMC with Amortized Approximate Likelihood Ratios_, Hermans\n        et al., ICML 2020, https://arxiv.org/abs/1903.04057\n\n    Args:\n        prior: A probability distribution that expresses prior knowledge about the\n            parameters, e.g. which ranges are meaningful for them. If `None`, the\n            prior must be passed to `.build_posterior()`.\n        classifier: Classifier trained to approximate likelihood ratios. If it is\n            a string, use a pre-configured network of the provided type (one of\n            linear, mlp, resnet). Alternatively, a function that builds a custom\n            neural network can be provided. The function will be called with the\n            first batch of simulations (theta, x), which can thus be used for shape\n            inference and potentially for z-scoring. It needs to return a PyTorch\n            `nn.Module` implementing the classifier.\n        device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n        logging_level: Minimum severity of messages to log. One of the strings\n            INFO, WARNING, DEBUG, ERROR and CRITICAL.\n        summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n            file location (default is `&lt;current working directory&gt;/logs`.)\n        show_progress_bars: Whether to show a progressbar during simulation and\n            sampling.\n    \"\"\"\n\n    kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n    super().__init__(**kwargs)\n</code></pre>"},{"location":"reference/#sbi.inference.snre.snre_a.SNRE_A.train","title":"<code>train(training_batch_size=50, learning_rate=0.0005, validation_fraction=0.1, stop_after_epochs=20, max_num_epochs=2 ** 31 - 1, clip_max_norm=5.0, resume_training=False, discard_prior_samples=False, retrain_from_scratch=False, show_train_summary=False, dataloader_kwargs=None, loss_kwargs={})</code>","text":"<p>Return classifier that approximates the ratio \\(p(\\theta,x)/p(\\theta)p(x)\\).</p> <p>Parameters:</p> Name Type Description Default <code>training_batch_size</code> <code>int</code> <p>Training batch size.</p> <code>50</code> <code>learning_rate</code> <code>float</code> <p>Learning rate for Adam optimizer.</p> <code>0.0005</code> <code>validation_fraction</code> <code>float</code> <p>The fraction of data to use for validation.</p> <code>0.1</code> <code>stop_after_epochs</code> <code>int</code> <p>The number of epochs to wait for improvement on the validation set before terminating training.</p> <code>20</code> <code>max_num_epochs</code> <code>int</code> <p>Maximum number of epochs to run. If reached, we stop training even when the validation loss is still decreasing. Otherwise, we train until validation loss increases (see also <code>stop_after_epochs</code>).</p> <code>2 ** 31 - 1</code> <code>clip_max_norm</code> <code>Optional[float]</code> <p>Value at which to clip the total gradient norm in order to prevent exploding gradients. Use None for no clipping.</p> <code>5.0</code> <code>resume_training</code> <code>bool</code> <p>Can be used in case training time is limited, e.g. on a cluster. If <code>True</code>, the split between train and validation set, the optimizer, the number of epochs, and the best validation log-prob will be restored from the last time <code>.train()</code> was called.</p> <code>False</code> <code>discard_prior_samples</code> <code>bool</code> <p>Whether to discard samples simulated in round 1, i.e. from the prior. Training may be sped up by ignoring such less targeted samples.</p> <code>False</code> <code>retrain_from_scratch</code> <code>bool</code> <p>Whether to retrain the conditional density estimator for the posterior from scratch each round.</p> <code>False</code> <code>show_train_summary</code> <code>bool</code> <p>Whether to print the number of epochs and validation loss and leakage after the training.</p> <code>False</code> <code>dataloader_kwargs</code> <code>Optional[Dict]</code> <p>Additional or updated kwargs to be passed to the training and validation dataloaders (like, e.g., a collate_fn)</p> <code>None</code> <code>loss_kwargs</code> <code>Dict[str, Any]</code> <p>Additional or updated kwargs to be passed to the self._loss fn.</p> <code>{}</code> <p>Returns:</p> Type Description <code>nn.Module</code> <p>Classifier that approximates the ratio \\(p(\\theta,x)/p(\\theta)p(x)\\).</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/snre/snre_a.py</code> <pre><code>def train(\n    self,\n    training_batch_size: int = 50,\n    learning_rate: float = 5e-4,\n    validation_fraction: float = 0.1,\n    stop_after_epochs: int = 20,\n    max_num_epochs: int = 2**31 - 1,\n    clip_max_norm: Optional[float] = 5.0,\n    resume_training: bool = False,\n    discard_prior_samples: bool = False,\n    retrain_from_scratch: bool = False,\n    show_train_summary: bool = False,\n    dataloader_kwargs: Optional[Dict] = None,\n    loss_kwargs: Dict[str, Any] = {},\n) -&gt; nn.Module:\nr\"\"\"Return classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n\n    Args:\n        training_batch_size: Training batch size.\n        learning_rate: Learning rate for Adam optimizer.\n        validation_fraction: The fraction of data to use for validation.\n        stop_after_epochs: The number of epochs to wait for improvement on the\n            validation set before terminating training.\n        max_num_epochs: Maximum number of epochs to run. If reached, we stop\n            training even when the validation loss is still decreasing. Otherwise,\n            we train until validation loss increases (see also `stop_after_epochs`).\n        clip_max_norm: Value at which to clip the total gradient norm in order to\n            prevent exploding gradients. Use None for no clipping.\n        resume_training: Can be used in case training time is limited, e.g. on a\n            cluster. If `True`, the split between train and validation set, the\n            optimizer, the number of epochs, and the best validation log-prob will\n            be restored from the last time `.train()` was called.\n        discard_prior_samples: Whether to discard samples simulated in round 1, i.e.\n            from the prior. Training may be sped up by ignoring such less targeted\n            samples.\n        retrain_from_scratch: Whether to retrain the conditional density\n            estimator for the posterior from scratch each round.\n        show_train_summary: Whether to print the number of epochs and validation\n            loss and leakage after the training.\n        dataloader_kwargs: Additional or updated kwargs to be passed to the training\n            and validation dataloaders (like, e.g., a collate_fn)\n        loss_kwargs: Additional or updated kwargs to be passed to the self._loss fn.\n\n    Returns:\n        Classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n    \"\"\"\n\n    # AALR is defined for `num_atoms=2`.\n    # Proxy to `super().__call__` to ensure right parameter.\n    kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n    return super().train(**kwargs, num_atoms=2)\n</code></pre>"},{"location":"reference/#sbi.inference.snre.snre_b.SNRE_B","title":"<code>sbi.inference.snre.snre_b.SNRE_B</code>","text":"<p>         Bases: <code>RatioEstimator</code></p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/snre/snre_b.py</code> <pre><code>class SNRE_B(RatioEstimator):\n    def __init__(\n        self,\n        prior: Optional[Distribution] = None,\n        classifier: Union[str, Callable] = \"resnet\",\n        device: str = \"cpu\",\n        logging_level: Union[int, str] = \"warning\",\n        summary_writer: Optional[TensorboardSummaryWriter] = None,\n        show_progress_bars: bool = True,\n    ):\nr\"\"\"SRE[1], here known as SNRE_B.\n\n        [1] _On Contrastive Learning for Likelihood-free Inference_, Durkan et al.,\n            ICML 2020, https://arxiv.org/pdf/2002.03712\n\n        Args:\n            prior: A probability distribution that expresses prior knowledge about the\n                parameters, e.g. which ranges are meaningful for them. If `None`, the\n                prior must be passed to `.build_posterior()`.\n            classifier: Classifier trained to approximate likelihood ratios. If it is\n                a string, use a pre-configured network of the provided type (one of\n                linear, mlp, resnet). Alternatively, a function that builds a custom\n                neural network can be provided. The function will be called with the\n                first batch of simulations (theta, x), which can thus be used for shape\n                inference and potentially for z-scoring. It needs to return a PyTorch\n                `nn.Module` implementing the classifier.\n            device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n            logging_level: Minimum severity of messages to log. One of the strings\n                INFO, WARNING, DEBUG, ERROR and CRITICAL.\n            summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n                file location (default is `&lt;current working directory&gt;/logs`.)\n            show_progress_bars: Whether to show a progressbar during simulation and\n                sampling.\n        \"\"\"\n\n        kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n        super().__init__(**kwargs)\n\n    def train(\n        self,\n        num_atoms: int = 10,\n        training_batch_size: int = 50,\n        learning_rate: float = 5e-4,\n        validation_fraction: float = 0.1,\n        stop_after_epochs: int = 20,\n        max_num_epochs: int = 2**31 - 1,\n        clip_max_norm: Optional[float] = 5.0,\n        resume_training: bool = False,\n        discard_prior_samples: bool = False,\n        retrain_from_scratch: bool = False,\n        show_train_summary: bool = False,\n        dataloader_kwargs: Optional[Dict] = None,\n    ) -&gt; nn.Module:\nr\"\"\"Return classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n\n        Args:\n            num_atoms: Number of atoms to use for classification.\n            training_batch_size: Training batch size.\n            learning_rate: Learning rate for Adam optimizer.\n            validation_fraction: The fraction of data to use for validation.\n            stop_after_epochs: The number of epochs to wait for improvement on the\n                validation set before terminating training.\n            max_num_epochs: Maximum number of epochs to run. If reached, we stop\n                training even when the validation loss is still decreasing. Otherwise,\n                we train until validation loss increases (see also `stop_after_epochs`).\n            clip_max_norm: Value at which to clip the total gradient norm in order to\n                prevent exploding gradients. Use None for no clipping.\n            resume_training: Can be used in case training time is limited, e.g. on a\n                cluster. If `True`, the split between train and validation set, the\n                optimizer, the number of epochs, and the best validation log-prob will\n                be restored from the last time `.train()` was called.\n            discard_prior_samples: Whether to discard samples simulated in round 1, i.e.\n                from the prior. Training may be sped up by ignoring such less targeted\n                samples.\n            retrain_from_scratch: Whether to retrain the conditional density\n                estimator for the posterior from scratch each round.\n            show_train_summary: Whether to print the number of epochs and validation\n                loss and leakage after the training.\n            dataloader_kwargs: Additional or updated kwargs to be passed to the training\n                and validation dataloaders (like, e.g., a collate_fn)\n\n        Returns:\n            Classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n        \"\"\"\n        kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n        return super().train(**kwargs)\n\n    def _loss(self, theta: Tensor, x: Tensor, num_atoms: int) -&gt; Tensor:\nr\"\"\"Return cross-entropy (via softmax activation) loss for 1-out-of-`num_atoms`\n        classification.\n\n        The classifier takes as input `num_atoms` $(\\theta,x)$ pairs. Out of these\n        pairs, one pair was sampled from the joint $p(\\theta,x)$ and all others from the\n        marginals $p(\\theta)p(x)$. The classifier is trained to predict which of the\n        pairs was sampled from the joint $p(\\theta,x)$.\n        \"\"\"\n\n        assert theta.shape[0] == x.shape[0], \"Batch sizes for theta and x must match.\"\n        batch_size = theta.shape[0]\n        logits = self._classifier_logits(theta, x, num_atoms)\n\n        # For 1-out-of-`num_atoms` classification each datapoint consists\n        # of `num_atoms` points, with one of them being the correct one.\n        # We have a batch of `batch_size` such datapoints.\n        logits = logits.reshape(batch_size, num_atoms)\n\n        # Index 0 is the theta-x-pair sampled from the joint p(theta,x) and hence the\n        # \"correct\" one for the 1-out-of-N classification.\n        log_prob = logits[:, 0] - torch.logsumexp(logits, dim=-1)\n\n        return -torch.mean(log_prob)\n</code></pre>"},{"location":"reference/#sbi.inference.snre.snre_b.SNRE_B.__init__","title":"<code>__init__(prior=None, classifier='resnet', device='cpu', logging_level='warning', summary_writer=None, show_progress_bars=True)</code>","text":"<p>SRE[1], here known as SNRE_B.</p> <p>[1] On Contrastive Learning for Likelihood-free Inference, Durkan et al.,     ICML 2020, https://arxiv.org/pdf/2002.03712</p> <p>Parameters:</p> Name Type Description Default <code>prior</code> <code>Optional[Distribution]</code> <p>A probability distribution that expresses prior knowledge about the parameters, e.g. which ranges are meaningful for them. If <code>None</code>, the prior must be passed to <code>.build_posterior()</code>.</p> <code>None</code> <code>classifier</code> <code>Union[str, Callable]</code> <p>Classifier trained to approximate likelihood ratios. If it is a string, use a pre-configured network of the provided type (one of linear, mlp, resnet). Alternatively, a function that builds a custom neural network can be provided. The function will be called with the first batch of simulations (theta, x), which can thus be used for shape inference and potentially for z-scoring. It needs to return a PyTorch <code>nn.Module</code> implementing the classifier.</p> <code>'resnet'</code> <code>device</code> <code>str</code> <p>Training device, e.g., \u201ccpu\u201d, \u201ccuda\u201d or \u201ccuda:{0, 1, \u2026}\u201d.</p> <code>'cpu'</code> <code>logging_level</code> <code>Union[int, str]</code> <p>Minimum severity of messages to log. One of the strings INFO, WARNING, DEBUG, ERROR and CRITICAL.</p> <code>'warning'</code> <code>summary_writer</code> <code>Optional[TensorboardSummaryWriter]</code> <p>A tensorboard <code>SummaryWriter</code> to control, among others, log file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p> <code>None</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during simulation and sampling.</p> <code>True</code> Source code in <code>/home/michael/Documents/sbi/sbi/inference/snre/snre_b.py</code> <pre><code>def __init__(\n    self,\n    prior: Optional[Distribution] = None,\n    classifier: Union[str, Callable] = \"resnet\",\n    device: str = \"cpu\",\n    logging_level: Union[int, str] = \"warning\",\n    summary_writer: Optional[TensorboardSummaryWriter] = None,\n    show_progress_bars: bool = True,\n):\nr\"\"\"SRE[1], here known as SNRE_B.\n\n    [1] _On Contrastive Learning for Likelihood-free Inference_, Durkan et al.,\n        ICML 2020, https://arxiv.org/pdf/2002.03712\n\n    Args:\n        prior: A probability distribution that expresses prior knowledge about the\n            parameters, e.g. which ranges are meaningful for them. If `None`, the\n            prior must be passed to `.build_posterior()`.\n        classifier: Classifier trained to approximate likelihood ratios. If it is\n            a string, use a pre-configured network of the provided type (one of\n            linear, mlp, resnet). Alternatively, a function that builds a custom\n            neural network can be provided. The function will be called with the\n            first batch of simulations (theta, x), which can thus be used for shape\n            inference and potentially for z-scoring. It needs to return a PyTorch\n            `nn.Module` implementing the classifier.\n        device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n        logging_level: Minimum severity of messages to log. One of the strings\n            INFO, WARNING, DEBUG, ERROR and CRITICAL.\n        summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n            file location (default is `&lt;current working directory&gt;/logs`.)\n        show_progress_bars: Whether to show a progressbar during simulation and\n            sampling.\n    \"\"\"\n\n    kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n    super().__init__(**kwargs)\n</code></pre>"},{"location":"reference/#sbi.inference.snre.snre_b.SNRE_B.train","title":"<code>train(num_atoms=10, training_batch_size=50, learning_rate=0.0005, validation_fraction=0.1, stop_after_epochs=20, max_num_epochs=2 ** 31 - 1, clip_max_norm=5.0, resume_training=False, discard_prior_samples=False, retrain_from_scratch=False, show_train_summary=False, dataloader_kwargs=None)</code>","text":"<p>Return classifier that approximates the ratio \\(p(\\theta,x)/p(\\theta)p(x)\\).</p> <p>Parameters:</p> Name Type Description Default <code>num_atoms</code> <code>int</code> <p>Number of atoms to use for classification.</p> <code>10</code> <code>training_batch_size</code> <code>int</code> <p>Training batch size.</p> <code>50</code> <code>learning_rate</code> <code>float</code> <p>Learning rate for Adam optimizer.</p> <code>0.0005</code> <code>validation_fraction</code> <code>float</code> <p>The fraction of data to use for validation.</p> <code>0.1</code> <code>stop_after_epochs</code> <code>int</code> <p>The number of epochs to wait for improvement on the validation set before terminating training.</p> <code>20</code> <code>max_num_epochs</code> <code>int</code> <p>Maximum number of epochs to run. If reached, we stop training even when the validation loss is still decreasing. Otherwise, we train until validation loss increases (see also <code>stop_after_epochs</code>).</p> <code>2 ** 31 - 1</code> <code>clip_max_norm</code> <code>Optional[float]</code> <p>Value at which to clip the total gradient norm in order to prevent exploding gradients. Use None for no clipping.</p> <code>5.0</code> <code>resume_training</code> <code>bool</code> <p>Can be used in case training time is limited, e.g. on a cluster. If <code>True</code>, the split between train and validation set, the optimizer, the number of epochs, and the best validation log-prob will be restored from the last time <code>.train()</code> was called.</p> <code>False</code> <code>discard_prior_samples</code> <code>bool</code> <p>Whether to discard samples simulated in round 1, i.e. from the prior. Training may be sped up by ignoring such less targeted samples.</p> <code>False</code> <code>retrain_from_scratch</code> <code>bool</code> <p>Whether to retrain the conditional density estimator for the posterior from scratch each round.</p> <code>False</code> <code>show_train_summary</code> <code>bool</code> <p>Whether to print the number of epochs and validation loss and leakage after the training.</p> <code>False</code> <code>dataloader_kwargs</code> <code>Optional[Dict]</code> <p>Additional or updated kwargs to be passed to the training and validation dataloaders (like, e.g., a collate_fn)</p> <code>None</code> <p>Returns:</p> Type Description <code>nn.Module</code> <p>Classifier that approximates the ratio \\(p(\\theta,x)/p(\\theta)p(x)\\).</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/snre/snre_b.py</code> <pre><code>def train(\n    self,\n    num_atoms: int = 10,\n    training_batch_size: int = 50,\n    learning_rate: float = 5e-4,\n    validation_fraction: float = 0.1,\n    stop_after_epochs: int = 20,\n    max_num_epochs: int = 2**31 - 1,\n    clip_max_norm: Optional[float] = 5.0,\n    resume_training: bool = False,\n    discard_prior_samples: bool = False,\n    retrain_from_scratch: bool = False,\n    show_train_summary: bool = False,\n    dataloader_kwargs: Optional[Dict] = None,\n) -&gt; nn.Module:\nr\"\"\"Return classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n\n    Args:\n        num_atoms: Number of atoms to use for classification.\n        training_batch_size: Training batch size.\n        learning_rate: Learning rate for Adam optimizer.\n        validation_fraction: The fraction of data to use for validation.\n        stop_after_epochs: The number of epochs to wait for improvement on the\n            validation set before terminating training.\n        max_num_epochs: Maximum number of epochs to run. If reached, we stop\n            training even when the validation loss is still decreasing. Otherwise,\n            we train until validation loss increases (see also `stop_after_epochs`).\n        clip_max_norm: Value at which to clip the total gradient norm in order to\n            prevent exploding gradients. Use None for no clipping.\n        resume_training: Can be used in case training time is limited, e.g. on a\n            cluster. If `True`, the split between train and validation set, the\n            optimizer, the number of epochs, and the best validation log-prob will\n            be restored from the last time `.train()` was called.\n        discard_prior_samples: Whether to discard samples simulated in round 1, i.e.\n            from the prior. Training may be sped up by ignoring such less targeted\n            samples.\n        retrain_from_scratch: Whether to retrain the conditional density\n            estimator for the posterior from scratch each round.\n        show_train_summary: Whether to print the number of epochs and validation\n            loss and leakage after the training.\n        dataloader_kwargs: Additional or updated kwargs to be passed to the training\n            and validation dataloaders (like, e.g., a collate_fn)\n\n    Returns:\n        Classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n    \"\"\"\n    kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n    return super().train(**kwargs)\n</code></pre>"},{"location":"reference/#sbi.inference.snre.snre_c.SNRE_C","title":"<code>sbi.inference.snre.snre_c.SNRE_C</code>","text":"<p>         Bases: <code>RatioEstimator</code></p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/snre/snre_c.py</code> <pre><code>class SNRE_C(RatioEstimator):\n    def __init__(\n        self,\n        prior: Optional[Distribution] = None,\n        classifier: Union[str, Callable] = \"resnet\",\n        device: str = \"cpu\",\n        logging_level: Union[int, str] = \"warning\",\n        summary_writer: Optional[TensorboardSummaryWriter] = None,\n        show_progress_bars: bool = True,\n    ):\nr\"\"\"NRE-C[1] is a generalization of the non-sequential (amortized) versions of\n        SNRE_A and SNRE_B. We call the algorithm SNRE_C within `sbi`.\n\n        NRE-C:\n        (1) like SNRE_B, features a \"multiclass\" loss function where several marginally\n            drawn parameter-data pairs are contrasted against a jointly drawn pair.\n        (2) like AALR/NRE_A, i.e., the non-sequential version of SNRE_A, it encourages\n            the approximate ratio $p(\\theta,x)/p(\\theta)p(x)$, accessed through\n            `.potential()` within `sbi`, to be exact at optimum. This addresses the\n            issue that SNRE_B estimates this ratio only up to an arbitrary function\n            (normalizing constant) of the data $x$.\n\n        Just like for all ratio estimation algorithms, the sequential version of SNRE_C\n        will be estimated only up to a function (normalizing constant) of the data $x$\n        in rounds after the first.\n\n        [1] _Contrastive Neural Ratio Estimation_, Benajmin Kurt Miller, et. al.,\n            NeurIPS 2022, https://arxiv.org/abs/2210.06170\n\n        Args:\n            prior: A probability distribution that expresses prior knowledge about the\n                parameters, e.g. which ranges are meaningful for them. If `None`, the\n                prior must be passed to `.build_posterior()`.\n            classifier: Classifier trained to approximate likelihood ratios. If it is\n                a string, use a pre-configured network of the provided type (one of\n                linear, mlp, resnet). Alternatively, a function that builds a custom\n                neural network can be provided. The function will be called with the\n                first batch of simulations (theta, x), which can thus be used for shape\n                inference and potentially for z-scoring. It needs to return a PyTorch\n                `nn.Module` implementing the classifier.\n            device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n            logging_level: Minimum severity of messages to log. One of the strings\n                INFO, WARNING, DEBUG, ERROR and CRITICAL.\n            summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n                file location (default is `&lt;current working directory&gt;/logs`.)\n            show_progress_bars: Whether to show a progressbar during simulation and\n                sampling.\n        \"\"\"\n\n        kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n        super().__init__(**kwargs)\n\n    def train(\n        self,\n        num_classes: int = 5,\n        gamma: float = 1.0,\n        training_batch_size: int = 50,\n        learning_rate: float = 5e-4,\n        validation_fraction: float = 0.1,\n        stop_after_epochs: int = 20,\n        max_num_epochs: int = 2**31 - 1,\n        clip_max_norm: Optional[float] = 5.0,\n        resume_training: bool = False,\n        discard_prior_samples: bool = False,\n        retrain_from_scratch: bool = False,\n        show_train_summary: bool = False,\n        dataloader_kwargs: Optional[Dict] = None,\n    ) -&gt; nn.Module:\nr\"\"\"Return classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n\n        Args:\n            num_classes: Number of theta to classify against, corresponds to $K$ in\n                _Contrastive Neural Ratio Estimation_. Minimum value is 1. Similar to\n                `num_atoms` for SNRE_B except SNRE_C has an additional independently\n                drawn sample. The total number of alternative parameters `NRE-C` \"sees\"\n                is $2K-1$ or `2 * num_classes - 1` divided between two loss terms.\n            gamma: Determines the relative weight of the sum of all $K$ dependently\n                drawn classes against the marginally drawn one. Specifically,\n                $p(y=k) :=p_K$, $p(y=0) := p_0$, $p_0 = 1 - K p_K$, and finally\n                $\\gamma := K p_K / p_0$.\n            training_batch_size: Training batch size.\n            learning_rate: Learning rate for Adam optimizer.\n            validation_fraction: The fraction of data to use for validation.\n            stop_after_epochs: The number of epochs to wait for improvement on the\n                validation set before terminating training.\n            max_num_epochs: Maximum number of epochs to run. If reached, we stop\n                training even when the validation loss is still decreasing. Otherwise,\n                we train until validation loss increases (see also `stop_after_epochs`).\n            clip_max_norm: Value at which to clip the total gradient norm in order to\n                prevent exploding gradients. Use None for no clipping.\n            exclude_invalid_x: Whether to exclude simulation outputs `x=NaN` or `x=\u00b1\u221e`\n                during training. Expect errors, silent or explicit, when `False`.\n            resume_training: Can be used in case training time is limited, e.g. on a\n                cluster. If `True`, the split between train and validation set, the\n                optimizer, the number of epochs, and the best validation log-prob will\n                be restored from the last time `.train()` was called.\n            discard_prior_samples: Whether to discard samples simulated in round 1, i.e.\n                from the prior. Training may be sped up by ignoring such less targeted\n                samples.\n            retrain_from_scratch: Whether to retrain the conditional density\n                estimator for the posterior from scratch each round.\n            show_train_summary: Whether to print the number of epochs and validation\n                loss and leakage after the training.\n            dataloader_kwargs: Additional or updated kwargs to be passed to the training\n                and validation dataloaders (like, e.g., a collate_fn)\n\n        Returns:\n            Classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n        \"\"\"\n        kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n        kwargs[\"num_atoms\"] = kwargs.pop(\"num_classes\") + 1\n        kwargs[\"loss_kwargs\"] = {\"gamma\": kwargs.pop(\"gamma\")}\n        return super().train(**kwargs)\n\n    def _loss(\n        self, theta: Tensor, x: Tensor, num_atoms: int, gamma: float\n    ) -&gt; torch.Tensor:\nr\"\"\"Return cross-entropy loss (via ''multi-class sigmoid'' activation) for\n        1-out-of-`K + 1` classification.\n\n        At optimum, this loss function returns the exact likelihood-to-evidence ratio\n        in the first round.\n        Details of loss computation are described in Contrastive Neural Ratio\n        Estimation[1]. The paper does not discuss the sequential case.\n\n        [1] _Contrastive Neural Ratio Estimation_, Benajmin Kurt Miller, et. al.,\n            NeurIPS 2022, https://arxiv.org/abs/2210.06170\n        \"\"\"\n\n        # Reminder: K = num_classes\n        # The algorithm is written with K, so we convert back to K format rather than\n        # reasoning in num_atoms.\n        num_classes = num_atoms - 1\n        assert num_classes &gt;= 1, f\"num_classes = {num_classes} must be greater than 1.\"\n\n        assert theta.shape[0] == x.shape[0], \"Batch sizes for theta and x must match.\"\n        batch_size = theta.shape[0]\n\n        # We append an contrastive theta to the marginal case because we will remove\n        # the jointly drawn\n        # sample in the logits_marginal[:, 0] position. That makes the remaining sample\n        # marginally drawn.\n        # We have a batch of `batch_size` datapoints.\n        logits_marginal = self._classifier_logits(theta, x, num_classes + 1).reshape(\n            batch_size, num_classes + 1\n        )\n        logits_joint = self._classifier_logits(theta, x, num_classes).reshape(\n            batch_size, num_classes\n        )\n\n        dtype = logits_marginal.dtype\n        device = logits_marginal.device\n\n        # Index 0 is the theta-x-pair sampled from the joint p(theta,x) and hence\n        # we remove the jointly drawn sample from the logits_marginal\n        logits_marginal = logits_marginal[:, 1:]\n        # ... and retain it in the logits_joint. Now we have two arrays with K choices.\n\n        # To use logsumexp, we extend the denominator logits with loggamma\n        loggamma = torch.tensor(gamma, dtype=dtype, device=device).log()\n        logK = torch.tensor(num_classes, dtype=dtype, device=device).log()\n        denominator_marginal = torch.concat(\n            [loggamma + logits_marginal, logK.expand((batch_size, 1))],\n            dim=-1,\n        )\n        denominator_joint = torch.concat(\n            [loggamma + logits_joint, logK.expand((batch_size, 1))],\n            dim=-1,\n        )\n\n        # Compute the contributions to the loss from each term in the classification.\n        log_prob_marginal = logK - torch.logsumexp(denominator_marginal, dim=-1)\n        log_prob_joint = (\n            loggamma + logits_joint[:, 0] - torch.logsumexp(denominator_joint, dim=-1)\n        )\n\n        # relative weights. p_marginal := p_0, and p_joint := p_K * K from the notation.\n        p_marginal, p_joint = self._get_prior_probs_marginal_and_joint(gamma)\n        return -torch.mean(p_marginal * log_prob_marginal + p_joint * log_prob_joint)\n\n    @staticmethod\n    def _get_prior_probs_marginal_and_joint(gamma: float) -&gt; Tuple[float, float]:\nr\"\"\"Return a tuple (p_marginal, p_joint) where `p_marginal := `$p_0$,\n        `p_joint := `$p_K \\cdot K$.\n\n        We let the joint (dependently drawn) class to be equally likely across K\n        options. The marginal class is therefore restricted to get the remaining\n        probability.\n        \"\"\"\n        p_joint = gamma / (1 + gamma)\n        p_marginal = 1 / (1 + gamma)\n        return p_marginal, p_joint\n</code></pre>"},{"location":"reference/#sbi.inference.snre.snre_c.SNRE_C.__init__","title":"<code>__init__(prior=None, classifier='resnet', device='cpu', logging_level='warning', summary_writer=None, show_progress_bars=True)</code>","text":"<p>NRE-C[1] is a generalization of the non-sequential (amortized) versions of SNRE_A and SNRE_B. We call the algorithm SNRE_C within <code>sbi</code>.</p> <p>NRE-C: (1) like SNRE_B, features a \u201cmulticlass\u201d loss function where several marginally     drawn parameter-data pairs are contrasted against a jointly drawn pair. (2) like AALR/NRE_A, i.e., the non-sequential version of SNRE_A, it encourages     the approximate ratio \\(p(\\theta,x)/p(\\theta)p(x)\\), accessed through     <code>.potential()</code> within <code>sbi</code>, to be exact at optimum. This addresses the     issue that SNRE_B estimates this ratio only up to an arbitrary function     (normalizing constant) of the data \\(x\\).</p> <p>Just like for all ratio estimation algorithms, the sequential version of SNRE_C will be estimated only up to a function (normalizing constant) of the data \\(x\\) in rounds after the first.</p> <p>[1] Contrastive Neural Ratio Estimation, Benajmin Kurt Miller, et. al.,     NeurIPS 2022, https://arxiv.org/abs/2210.06170</p> <p>Parameters:</p> Name Type Description Default <code>prior</code> <code>Optional[Distribution]</code> <p>A probability distribution that expresses prior knowledge about the parameters, e.g. which ranges are meaningful for them. If <code>None</code>, the prior must be passed to <code>.build_posterior()</code>.</p> <code>None</code> <code>classifier</code> <code>Union[str, Callable]</code> <p>Classifier trained to approximate likelihood ratios. If it is a string, use a pre-configured network of the provided type (one of linear, mlp, resnet). Alternatively, a function that builds a custom neural network can be provided. The function will be called with the first batch of simulations (theta, x), which can thus be used for shape inference and potentially for z-scoring. It needs to return a PyTorch <code>nn.Module</code> implementing the classifier.</p> <code>'resnet'</code> <code>device</code> <code>str</code> <p>Training device, e.g., \u201ccpu\u201d, \u201ccuda\u201d or \u201ccuda:{0, 1, \u2026}\u201d.</p> <code>'cpu'</code> <code>logging_level</code> <code>Union[int, str]</code> <p>Minimum severity of messages to log. One of the strings INFO, WARNING, DEBUG, ERROR and CRITICAL.</p> <code>'warning'</code> <code>summary_writer</code> <code>Optional[TensorboardSummaryWriter]</code> <p>A tensorboard <code>SummaryWriter</code> to control, among others, log file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p> <code>None</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during simulation and sampling.</p> <code>True</code> Source code in <code>/home/michael/Documents/sbi/sbi/inference/snre/snre_c.py</code> <pre><code>def __init__(\n    self,\n    prior: Optional[Distribution] = None,\n    classifier: Union[str, Callable] = \"resnet\",\n    device: str = \"cpu\",\n    logging_level: Union[int, str] = \"warning\",\n    summary_writer: Optional[TensorboardSummaryWriter] = None,\n    show_progress_bars: bool = True,\n):\nr\"\"\"NRE-C[1] is a generalization of the non-sequential (amortized) versions of\n    SNRE_A and SNRE_B. We call the algorithm SNRE_C within `sbi`.\n\n    NRE-C:\n    (1) like SNRE_B, features a \"multiclass\" loss function where several marginally\n        drawn parameter-data pairs are contrasted against a jointly drawn pair.\n    (2) like AALR/NRE_A, i.e., the non-sequential version of SNRE_A, it encourages\n        the approximate ratio $p(\\theta,x)/p(\\theta)p(x)$, accessed through\n        `.potential()` within `sbi`, to be exact at optimum. This addresses the\n        issue that SNRE_B estimates this ratio only up to an arbitrary function\n        (normalizing constant) of the data $x$.\n\n    Just like for all ratio estimation algorithms, the sequential version of SNRE_C\n    will be estimated only up to a function (normalizing constant) of the data $x$\n    in rounds after the first.\n\n    [1] _Contrastive Neural Ratio Estimation_, Benajmin Kurt Miller, et. al.,\n        NeurIPS 2022, https://arxiv.org/abs/2210.06170\n\n    Args:\n        prior: A probability distribution that expresses prior knowledge about the\n            parameters, e.g. which ranges are meaningful for them. If `None`, the\n            prior must be passed to `.build_posterior()`.\n        classifier: Classifier trained to approximate likelihood ratios. If it is\n            a string, use a pre-configured network of the provided type (one of\n            linear, mlp, resnet). Alternatively, a function that builds a custom\n            neural network can be provided. The function will be called with the\n            first batch of simulations (theta, x), which can thus be used for shape\n            inference and potentially for z-scoring. It needs to return a PyTorch\n            `nn.Module` implementing the classifier.\n        device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n        logging_level: Minimum severity of messages to log. One of the strings\n            INFO, WARNING, DEBUG, ERROR and CRITICAL.\n        summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n            file location (default is `&lt;current working directory&gt;/logs`.)\n        show_progress_bars: Whether to show a progressbar during simulation and\n            sampling.\n    \"\"\"\n\n    kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n    super().__init__(**kwargs)\n</code></pre>"},{"location":"reference/#sbi.inference.snre.snre_c.SNRE_C.train","title":"<code>train(num_classes=5, gamma=1.0, training_batch_size=50, learning_rate=0.0005, validation_fraction=0.1, stop_after_epochs=20, max_num_epochs=2 ** 31 - 1, clip_max_norm=5.0, resume_training=False, discard_prior_samples=False, retrain_from_scratch=False, show_train_summary=False, dataloader_kwargs=None)</code>","text":"<p>Return classifier that approximates the ratio \\(p(\\theta,x)/p(\\theta)p(x)\\).</p> <p>Parameters:</p> Name Type Description Default <code>num_classes</code> <code>int</code> <p>Number of theta to classify against, corresponds to \\(K\\) in Contrastive Neural Ratio Estimation. Minimum value is 1. Similar to <code>num_atoms</code> for SNRE_B except SNRE_C has an additional independently drawn sample. The total number of alternative parameters <code>NRE-C</code> \u201csees\u201d is \\(2K-1\\) or <code>2 * num_classes - 1</code> divided between two loss terms.</p> <code>5</code> <code>gamma</code> <code>float</code> <p>Determines the relative weight of the sum of all \\(K\\) dependently drawn classes against the marginally drawn one. Specifically, \\(p(y=k) :=p_K\\), \\(p(y=0) := p_0\\), \\(p_0 = 1 - K p_K\\), and finally \\(\\gamma := K p_K / p_0\\).</p> <code>1.0</code> <code>training_batch_size</code> <code>int</code> <p>Training batch size.</p> <code>50</code> <code>learning_rate</code> <code>float</code> <p>Learning rate for Adam optimizer.</p> <code>0.0005</code> <code>validation_fraction</code> <code>float</code> <p>The fraction of data to use for validation.</p> <code>0.1</code> <code>stop_after_epochs</code> <code>int</code> <p>The number of epochs to wait for improvement on the validation set before terminating training.</p> <code>20</code> <code>max_num_epochs</code> <code>int</code> <p>Maximum number of epochs to run. If reached, we stop training even when the validation loss is still decreasing. Otherwise, we train until validation loss increases (see also <code>stop_after_epochs</code>).</p> <code>2 ** 31 - 1</code> <code>clip_max_norm</code> <code>Optional[float]</code> <p>Value at which to clip the total gradient norm in order to prevent exploding gradients. Use None for no clipping.</p> <code>5.0</code> <code>exclude_invalid_x</code> <p>Whether to exclude simulation outputs <code>x=NaN</code> or <code>x=\u00b1\u221e</code> during training. Expect errors, silent or explicit, when <code>False</code>.</p> required <code>resume_training</code> <code>bool</code> <p>Can be used in case training time is limited, e.g. on a cluster. If <code>True</code>, the split between train and validation set, the optimizer, the number of epochs, and the best validation log-prob will be restored from the last time <code>.train()</code> was called.</p> <code>False</code> <code>discard_prior_samples</code> <code>bool</code> <p>Whether to discard samples simulated in round 1, i.e. from the prior. Training may be sped up by ignoring such less targeted samples.</p> <code>False</code> <code>retrain_from_scratch</code> <code>bool</code> <p>Whether to retrain the conditional density estimator for the posterior from scratch each round.</p> <code>False</code> <code>show_train_summary</code> <code>bool</code> <p>Whether to print the number of epochs and validation loss and leakage after the training.</p> <code>False</code> <code>dataloader_kwargs</code> <code>Optional[Dict]</code> <p>Additional or updated kwargs to be passed to the training and validation dataloaders (like, e.g., a collate_fn)</p> <code>None</code> <p>Returns:</p> Type Description <code>nn.Module</code> <p>Classifier that approximates the ratio \\(p(\\theta,x)/p(\\theta)p(x)\\).</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/snre/snre_c.py</code> <pre><code>def train(\n    self,\n    num_classes: int = 5,\n    gamma: float = 1.0,\n    training_batch_size: int = 50,\n    learning_rate: float = 5e-4,\n    validation_fraction: float = 0.1,\n    stop_after_epochs: int = 20,\n    max_num_epochs: int = 2**31 - 1,\n    clip_max_norm: Optional[float] = 5.0,\n    resume_training: bool = False,\n    discard_prior_samples: bool = False,\n    retrain_from_scratch: bool = False,\n    show_train_summary: bool = False,\n    dataloader_kwargs: Optional[Dict] = None,\n) -&gt; nn.Module:\nr\"\"\"Return classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n\n    Args:\n        num_classes: Number of theta to classify against, corresponds to $K$ in\n            _Contrastive Neural Ratio Estimation_. Minimum value is 1. Similar to\n            `num_atoms` for SNRE_B except SNRE_C has an additional independently\n            drawn sample. The total number of alternative parameters `NRE-C` \"sees\"\n            is $2K-1$ or `2 * num_classes - 1` divided between two loss terms.\n        gamma: Determines the relative weight of the sum of all $K$ dependently\n            drawn classes against the marginally drawn one. Specifically,\n            $p(y=k) :=p_K$, $p(y=0) := p_0$, $p_0 = 1 - K p_K$, and finally\n            $\\gamma := K p_K / p_0$.\n        training_batch_size: Training batch size.\n        learning_rate: Learning rate for Adam optimizer.\n        validation_fraction: The fraction of data to use for validation.\n        stop_after_epochs: The number of epochs to wait for improvement on the\n            validation set before terminating training.\n        max_num_epochs: Maximum number of epochs to run. If reached, we stop\n            training even when the validation loss is still decreasing. Otherwise,\n            we train until validation loss increases (see also `stop_after_epochs`).\n        clip_max_norm: Value at which to clip the total gradient norm in order to\n            prevent exploding gradients. Use None for no clipping.\n        exclude_invalid_x: Whether to exclude simulation outputs `x=NaN` or `x=\u00b1\u221e`\n            during training. Expect errors, silent or explicit, when `False`.\n        resume_training: Can be used in case training time is limited, e.g. on a\n            cluster. If `True`, the split between train and validation set, the\n            optimizer, the number of epochs, and the best validation log-prob will\n            be restored from the last time `.train()` was called.\n        discard_prior_samples: Whether to discard samples simulated in round 1, i.e.\n            from the prior. Training may be sped up by ignoring such less targeted\n            samples.\n        retrain_from_scratch: Whether to retrain the conditional density\n            estimator for the posterior from scratch each round.\n        show_train_summary: Whether to print the number of epochs and validation\n            loss and leakage after the training.\n        dataloader_kwargs: Additional or updated kwargs to be passed to the training\n            and validation dataloaders (like, e.g., a collate_fn)\n\n    Returns:\n        Classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n    \"\"\"\n    kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n    kwargs[\"num_atoms\"] = kwargs.pop(\"num_classes\") + 1\n    kwargs[\"loss_kwargs\"] = {\"gamma\": kwargs.pop(\"gamma\")}\n    return super().train(**kwargs)\n</code></pre>"},{"location":"reference/#sbi.inference.snre.bnre.BNRE","title":"<code>sbi.inference.snre.bnre.BNRE</code>","text":"<p>         Bases: <code>SNRE_A</code></p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/snre/bnre.py</code> <pre><code>class BNRE(SNRE_A):\n    def __init__(\n        self,\n        prior: Optional[Distribution] = None,\n        classifier: Union[str, Callable] = \"resnet\",\n        device: str = \"cpu\",\n        logging_level: Union[int, str] = \"warning\",\n        summary_writer: Optional[TensorboardSummaryWriter] = None,\n        show_progress_bars: bool = True,\n    ):\nr\"\"\"Balanced neural ratio estimation (BNRE)[1]. BNRE is a variation of NRE\n        aiming to produce more conservative posterior approximations\n\n        [1] Delaunoy, A., Hermans, J., Rozet, F., Wehenkel, A., &amp; Louppe, G..\n        Towards Reliable Simulation-Based Inference with Balanced Neural Ratio\n        Estimation.\n        NeurIPS 2022. https://arxiv.org/abs/2208.13624\n\n        Args:\n            prior: A probability distribution that expresses prior knowledge about the\n                parameters, e.g. which ranges are meaningful for them. If `None`, the\n                prior must be passed to `.build_posterior()`.\n            classifier: Classifier trained to approximate likelihood ratios. If it is\n                a string, use a pre-configured network of the provided type (one of\n                linear, mlp, resnet). Alternatively, a function that builds a custom\n                neural network can be provided. The function will be called with the\n                first batch of simulations $(\\theta, x)$, which can thus be used for\n                shape inference and potentially for z-scoring. It needs to return a\n                PyTorch `nn.Module` implementing the classifier.\n            device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n            logging_level: Minimum severity of messages to log. One of the strings\n                INFO, WARNING, DEBUG, ERROR and CRITICAL.\n            summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n                file location (default is `&lt;current working directory&gt;/logs`.)\n            show_progress_bars: Whether to show a progressbar during simulation and\n                sampling.\n        \"\"\"\n\n        kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n        super().__init__(**kwargs)\n\n    def train(\n        self,\n        regularization_strength: float = 100.0,\n        training_batch_size: int = 50,\n        learning_rate: float = 5e-4,\n        validation_fraction: float = 0.1,\n        stop_after_epochs: int = 20,\n        max_num_epochs: int = 2**31 - 1,\n        clip_max_norm: Optional[float] = 5.0,\n        resume_training: bool = False,\n        discard_prior_samples: bool = False,\n        retrain_from_scratch: bool = False,\n        show_train_summary: bool = False,\n        dataloader_kwargs: Optional[Dict] = None,\n    ) -&gt; nn.Module:\nr\"\"\"Return classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n        Args:\n\n            regularization_strength: The multiplicative coefficient applied to the\n                balancing regularizer ($\\lambda$).\n            training_batch_size: Training batch size.\n            learning_rate: Learning rate for Adam optimizer.\n            validation_fraction: The fraction of data to use for validation.\n            stop_after_epochs: The number of epochs to wait for improvement on the\n                validation set before terminating training.\n            max_num_epochs: Maximum number of epochs to run. If reached, we stop\n                training even when the validation loss is still decreasing. Otherwise,\n                we train until validation loss increases (see also `stop_after_epochs`).\n            clip_max_norm: Value at which to clip the total gradient norm in order to\n                prevent exploding gradients. Use None for no clipping.\n            exclude_invalid_x: Whether to exclude simulation outputs `x=NaN` or `x=\u00b1\u221e`\n                during training. Expect errors, silent or explicit, when `False`.\n            resume_training: Can be used in case training time is limited, e.g. on a\n                cluster. If `True`, the split between train and validation set, the\n                optimizer, the number of epochs, and the best validation log-prob will\n                be restored from the last time `.train()` was called.\n            discard_prior_samples: Whether to discard samples simulated in round 1, i.e.\n                from the prior. Training may be sped up by ignoring such less targeted\n                samples.\n            retrain_from_scratch: Whether to retrain the conditional density\n                estimator for the posterior from scratch each round.\n            show_train_summary: Whether to print the number of epochs and validation\n                loss and leakage after the training.\n            dataloader_kwargs: Additional or updated kwargs to be passed to the training\n                and validation dataloaders (like, e.g., a collate_fn)\n        Returns:\n            Classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n        \"\"\"\n        kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n        kwargs[\"loss_kwargs\"] = {\n            \"regularization_strength\": kwargs.pop(\"regularization_strength\")\n        }\n        return super().train(**kwargs)\n\n    def _loss(\n        self, theta: Tensor, x: Tensor, num_atoms: int, regularization_strength: float\n    ) -&gt; Tensor:\n\"\"\"Returns the binary cross-entropy loss for the trained classifier.\n\n        The classifier takes as input a $(\\theta,x)$ pair. It is trained to predict 1\n        if the pair was sampled from the joint $p(\\theta,x)$, and to predict 0 if the\n        pair was sampled from the marginals $p(\\theta)p(x)$.\n        \"\"\"\n\n        assert theta.shape[0] == x.shape[0], \"Batch sizes for theta and x must match.\"\n        batch_size = theta.shape[0]\n\n        logits = self._classifier_logits(theta, x, num_atoms)\n        likelihood = torch.sigmoid(logits).squeeze()\n\n        # Alternating pairs where there is one sampled from the joint and one\n        # sampled from the marginals. The first element is sampled from the\n        # joint p(theta, x) and is labelled 1. The second element is sampled\n        # from the marginals p(theta)p(x) and is labelled 0. And so on.\n        labels = ones(2 * batch_size, device=self._device)  # two atoms\n        labels[1::2] = 0.0\n\n        # Binary cross entropy to learn the likelihood (AALR-specific)\n        bce = nn.BCELoss()(likelihood, labels)\n\n        # Balancing regularizer\n        regularizer = (\n            (torch.sigmoid(logits[0::2]) + torch.sigmoid(logits[1::2]) - 1)\n            .mean()\n            .square()\n        )\n\n        return bce + regularization_strength * regularizer\n</code></pre>"},{"location":"reference/#sbi.inference.snre.bnre.BNRE.__init__","title":"<code>__init__(prior=None, classifier='resnet', device='cpu', logging_level='warning', summary_writer=None, show_progress_bars=True)</code>","text":"<p>Balanced neural ratio estimation (BNRE)[1]. BNRE is a variation of NRE aiming to produce more conservative posterior approximations</p> <p>[1] Delaunoy, A., Hermans, J., Rozet, F., Wehenkel, A., &amp; Louppe, G.. Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation. NeurIPS 2022. https://arxiv.org/abs/2208.13624</p> <p>Parameters:</p> Name Type Description Default <code>prior</code> <code>Optional[Distribution]</code> <p>A probability distribution that expresses prior knowledge about the parameters, e.g. which ranges are meaningful for them. If <code>None</code>, the prior must be passed to <code>.build_posterior()</code>.</p> <code>None</code> <code>classifier</code> <code>Union[str, Callable]</code> <p>Classifier trained to approximate likelihood ratios. If it is a string, use a pre-configured network of the provided type (one of linear, mlp, resnet). Alternatively, a function that builds a custom neural network can be provided. The function will be called with the first batch of simulations \\((\\theta, x)\\), which can thus be used for shape inference and potentially for z-scoring. It needs to return a PyTorch <code>nn.Module</code> implementing the classifier.</p> <code>'resnet'</code> <code>device</code> <code>str</code> <p>Training device, e.g., \u201ccpu\u201d, \u201ccuda\u201d or \u201ccuda:{0, 1, \u2026}\u201d.</p> <code>'cpu'</code> <code>logging_level</code> <code>Union[int, str]</code> <p>Minimum severity of messages to log. One of the strings INFO, WARNING, DEBUG, ERROR and CRITICAL.</p> <code>'warning'</code> <code>summary_writer</code> <code>Optional[TensorboardSummaryWriter]</code> <p>A tensorboard <code>SummaryWriter</code> to control, among others, log file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p> <code>None</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during simulation and sampling.</p> <code>True</code> Source code in <code>/home/michael/Documents/sbi/sbi/inference/snre/bnre.py</code> <pre><code>def __init__(\n    self,\n    prior: Optional[Distribution] = None,\n    classifier: Union[str, Callable] = \"resnet\",\n    device: str = \"cpu\",\n    logging_level: Union[int, str] = \"warning\",\n    summary_writer: Optional[TensorboardSummaryWriter] = None,\n    show_progress_bars: bool = True,\n):\nr\"\"\"Balanced neural ratio estimation (BNRE)[1]. BNRE is a variation of NRE\n    aiming to produce more conservative posterior approximations\n\n    [1] Delaunoy, A., Hermans, J., Rozet, F., Wehenkel, A., &amp; Louppe, G..\n    Towards Reliable Simulation-Based Inference with Balanced Neural Ratio\n    Estimation.\n    NeurIPS 2022. https://arxiv.org/abs/2208.13624\n\n    Args:\n        prior: A probability distribution that expresses prior knowledge about the\n            parameters, e.g. which ranges are meaningful for them. If `None`, the\n            prior must be passed to `.build_posterior()`.\n        classifier: Classifier trained to approximate likelihood ratios. If it is\n            a string, use a pre-configured network of the provided type (one of\n            linear, mlp, resnet). Alternatively, a function that builds a custom\n            neural network can be provided. The function will be called with the\n            first batch of simulations $(\\theta, x)$, which can thus be used for\n            shape inference and potentially for z-scoring. It needs to return a\n            PyTorch `nn.Module` implementing the classifier.\n        device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n        logging_level: Minimum severity of messages to log. One of the strings\n            INFO, WARNING, DEBUG, ERROR and CRITICAL.\n        summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n            file location (default is `&lt;current working directory&gt;/logs`.)\n        show_progress_bars: Whether to show a progressbar during simulation and\n            sampling.\n    \"\"\"\n\n    kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n    super().__init__(**kwargs)\n</code></pre>"},{"location":"reference/#sbi.inference.snre.bnre.BNRE.train","title":"<code>train(regularization_strength=100.0, training_batch_size=50, learning_rate=0.0005, validation_fraction=0.1, stop_after_epochs=20, max_num_epochs=2 ** 31 - 1, clip_max_norm=5.0, resume_training=False, discard_prior_samples=False, retrain_from_scratch=False, show_train_summary=False, dataloader_kwargs=None)</code>","text":"<p>Return classifier that approximates the ratio \\(p(\\theta,x)/p(\\theta)p(x)\\).</p> <p>Parameters:</p> Name Type Description Default <code>regularization_strength</code> <code>float</code> <p>The multiplicative coefficient applied to the balancing regularizer (\\(\\lambda\\)).</p> <code>100.0</code> <code>training_batch_size</code> <code>int</code> <p>Training batch size.</p> <code>50</code> <code>learning_rate</code> <code>float</code> <p>Learning rate for Adam optimizer.</p> <code>0.0005</code> <code>validation_fraction</code> <code>float</code> <p>The fraction of data to use for validation.</p> <code>0.1</code> <code>stop_after_epochs</code> <code>int</code> <p>The number of epochs to wait for improvement on the validation set before terminating training.</p> <code>20</code> <code>max_num_epochs</code> <code>int</code> <p>Maximum number of epochs to run. If reached, we stop training even when the validation loss is still decreasing. Otherwise, we train until validation loss increases (see also <code>stop_after_epochs</code>).</p> <code>2 ** 31 - 1</code> <code>clip_max_norm</code> <code>Optional[float]</code> <p>Value at which to clip the total gradient norm in order to prevent exploding gradients. Use None for no clipping.</p> <code>5.0</code> <code>exclude_invalid_x</code> <p>Whether to exclude simulation outputs <code>x=NaN</code> or <code>x=\u00b1\u221e</code> during training. Expect errors, silent or explicit, when <code>False</code>.</p> required <code>resume_training</code> <code>bool</code> <p>Can be used in case training time is limited, e.g. on a cluster. If <code>True</code>, the split between train and validation set, the optimizer, the number of epochs, and the best validation log-prob will be restored from the last time <code>.train()</code> was called.</p> <code>False</code> <code>discard_prior_samples</code> <code>bool</code> <p>Whether to discard samples simulated in round 1, i.e. from the prior. Training may be sped up by ignoring such less targeted samples.</p> <code>False</code> <code>retrain_from_scratch</code> <code>bool</code> <p>Whether to retrain the conditional density estimator for the posterior from scratch each round.</p> <code>False</code> <code>show_train_summary</code> <code>bool</code> <p>Whether to print the number of epochs and validation loss and leakage after the training.</p> <code>False</code> <code>dataloader_kwargs</code> <code>Optional[Dict]</code> <p>Additional or updated kwargs to be passed to the training and validation dataloaders (like, e.g., a collate_fn)</p> <code>None</code> <p>Returns:</p> Type Description <code>nn.Module</code> <p>Classifier that approximates the ratio \\(p(\\theta,x)/p(\\theta)p(x)\\).</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/snre/bnre.py</code> <pre><code>def train(\n    self,\n    regularization_strength: float = 100.0,\n    training_batch_size: int = 50,\n    learning_rate: float = 5e-4,\n    validation_fraction: float = 0.1,\n    stop_after_epochs: int = 20,\n    max_num_epochs: int = 2**31 - 1,\n    clip_max_norm: Optional[float] = 5.0,\n    resume_training: bool = False,\n    discard_prior_samples: bool = False,\n    retrain_from_scratch: bool = False,\n    show_train_summary: bool = False,\n    dataloader_kwargs: Optional[Dict] = None,\n) -&gt; nn.Module:\nr\"\"\"Return classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n    Args:\n\n        regularization_strength: The multiplicative coefficient applied to the\n            balancing regularizer ($\\lambda$).\n        training_batch_size: Training batch size.\n        learning_rate: Learning rate for Adam optimizer.\n        validation_fraction: The fraction of data to use for validation.\n        stop_after_epochs: The number of epochs to wait for improvement on the\n            validation set before terminating training.\n        max_num_epochs: Maximum number of epochs to run. If reached, we stop\n            training even when the validation loss is still decreasing. Otherwise,\n            we train until validation loss increases (see also `stop_after_epochs`).\n        clip_max_norm: Value at which to clip the total gradient norm in order to\n            prevent exploding gradients. Use None for no clipping.\n        exclude_invalid_x: Whether to exclude simulation outputs `x=NaN` or `x=\u00b1\u221e`\n            during training. Expect errors, silent or explicit, when `False`.\n        resume_training: Can be used in case training time is limited, e.g. on a\n            cluster. If `True`, the split between train and validation set, the\n            optimizer, the number of epochs, and the best validation log-prob will\n            be restored from the last time `.train()` was called.\n        discard_prior_samples: Whether to discard samples simulated in round 1, i.e.\n            from the prior. Training may be sped up by ignoring such less targeted\n            samples.\n        retrain_from_scratch: Whether to retrain the conditional density\n            estimator for the posterior from scratch each round.\n        show_train_summary: Whether to print the number of epochs and validation\n            loss and leakage after the training.\n        dataloader_kwargs: Additional or updated kwargs to be passed to the training\n            and validation dataloaders (like, e.g., a collate_fn)\n    Returns:\n        Classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n    \"\"\"\n    kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n    kwargs[\"loss_kwargs\"] = {\n        \"regularization_strength\": kwargs.pop(\"regularization_strength\")\n    }\n    return super().train(**kwargs)\n</code></pre>"},{"location":"reference/#sbi.inference.abc.mcabc.MCABC","title":"<code>sbi.inference.abc.mcabc.MCABC</code>","text":"<p>         Bases: <code>ABCBASE</code></p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/abc/mcabc.py</code> <pre><code>class MCABC(ABCBASE):\n    def __init__(\n        self,\n        simulator: Callable,\n        prior,\n        distance: Union[str, Callable] = \"l2\",\n        num_workers: int = 1,\n        simulation_batch_size: int = 1,\n        show_progress_bars: bool = True,\n    ):\nr\"\"\"Monte-Carlo Approximate Bayesian Computation (Rejection ABC) [1].\n\n        [1] Pritchard, J. K., Seielstad, M. T., Perez-Lezaun, A., &amp; Feldman, M. W.\n        (1999). Population growth of human Y chromosomes: a study of Y chromosome\n        microsatellites. Molecular biology and evolution, 16(12), 1791-1798.\n\n        Args:\n            simulator: A function that takes parameters $\\theta$ and maps them to\n                simulations, or observations, `x`, $\\mathrm{sim}(\\theta)\\to x$. Any\n                regular Python callable (i.e. function or class with `__call__` method)\n                can be used.\n            prior: A probability distribution that expresses prior knowledge about the\n                parameters, e.g. which ranges are meaningful for them. Any\n                object with `.log_prob()`and `.sample()` (for example, a PyTorch\n                distribution) can be used.\n            distance: Distance function to compare observed and simulated data. Can be\n                a custom function or one of `l1`, `l2`, `mse`.\n            num_workers: Number of parallel workers to use for simulations.\n            simulation_batch_size: Number of parameter sets that the simulator\n                maps to data x at once. If None, we simulate all parameter sets at the\n                same time. If &gt;= 1, the simulator has to process data of shape\n                (simulation_batch_size, parameter_dimension).\n            show_progress_bars: Whether to show a progressbar during simulation and\n                sampling.\n        \"\"\"\n\n        super().__init__(\n            simulator=simulator,\n            prior=prior,\n            distance=distance,\n            num_workers=num_workers,\n            simulation_batch_size=simulation_batch_size,\n            show_progress_bars=show_progress_bars,\n        )\n\n    def __call__(\n        self,\n        x_o: Union[Tensor, ndarray],\n        num_simulations: int,\n        eps: Optional[float] = None,\n        quantile: Optional[float] = None,\n        lra: bool = False,\n        sass: bool = False,\n        sass_fraction: float = 0.25,\n        sass_expansion_degree: int = 1,\n        kde: bool = False,\n        kde_kwargs: Dict[str, Any] = {},\n        return_summary: bool = False,\n    ) -&gt; Union[Tuple[Tensor, dict], Tuple[KDEWrapper, dict], Tensor, KDEWrapper]:\nr\"\"\"Run MCABC and return accepted parameters or KDE object fitted on them.\n\n        Args:\n            x_o: Observed data.\n            num_simulations: Number of simulations to run.\n            eps: Acceptance threshold $\\epsilon$ for distance between observed and\n                simulated data.\n            quantile: Upper quantile of smallest distances for which the corresponding\n                parameters are returned, e.g, q=0.01 will return the top 1%. Exactly\n                one of quantile or `eps` have to be passed.\n            lra: Whether to run linear regression adjustment as in Beaumont et al. 2002\n            sass: Whether to determine semi-automatic summary statistics as in\n                Fearnhead &amp; Prangle 2012.\n            sass_fraction: Fraction of simulation budget used for the initial sass run.\n            sass_expansion_degree: Degree of the polynomial feature expansion for the\n                sass regression, default 1 - no expansion.\n            kde: Whether to run KDE on the accepted parameters to return a KDE\n                object from which one can sample.\n            kde_kwargs: kwargs for performing KDE:\n                'bandwidth='; either a float, or a string naming a bandwidth\n                heuristics, e.g., 'cv' (cross validation), 'silvermann' or 'scott',\n                default 'cv'.\n                'transform': transform applied to the parameters before doing KDE.\n                'sample_weights': weights associated with samples. See 'get_kde' for\n                more details\n            return_summary: Whether to return the distances and data corresponding to\n                the accepted parameters.\n\n        Returns:\n            theta (if kde False): accepted parameters\n            kde (if kde True): KDE object based on accepted parameters from which one\n                can .sample() and .log_prob().\n            summary (if summary True): dictionary containing the accepted paramters (if\n                kde True), distances and simulated data x.\n        \"\"\"\n\n        # Exactly one of eps or quantile need to be passed.\n        assert (eps is not None) ^ (\n            quantile is not None\n        ), \"Eps or quantile must be passed, but not both.\"\n\n        # Run SASS and change the simulator and x_o accordingly.\n        if sass:\n            num_pilot_simulations = int(sass_fraction * num_simulations)\n            self.logger.info(\n                f\"Running SASS with {num_pilot_simulations} pilot samples.\"\n            )\n            num_simulations -= num_pilot_simulations\n\n            pilot_theta = self.prior.sample((num_pilot_simulations,))\n            pilot_x = self._batched_simulator(pilot_theta)\n\n            sass_transform = self.get_sass_transform(\n                pilot_theta, pilot_x, sass_expansion_degree\n            )\n\n            simulator = lambda theta: sass_transform(self._batched_simulator(theta))\n            x_o = sass_transform(x_o)\n        else:\n            simulator = self._batched_simulator\n\n        # Simulate and calculate distances.\n        theta = self.prior.sample((num_simulations,))\n        x = simulator(theta)\n\n        # Infer shape of x to test and set x_o.\n        self.x_shape = x[0].unsqueeze(0).shape\n        self.x_o = process_x(x_o, self.x_shape)\n\n        distances = self.distance(self.x_o, x)\n\n        # Select based on acceptance threshold epsilon.\n        if eps is not None:\n            is_accepted = distances &lt; eps\n            num_accepted = is_accepted.sum().item()\n            assert num_accepted &gt; 0, f\"No parameters accepted, eps={eps} too small\"\n\n            theta_accepted = theta[is_accepted]\n            distances_accepted = distances[is_accepted]\n            x_accepted = x[is_accepted]\n\n        # Select based on quantile on sorted distances.\n        elif quantile is not None:\n            num_top_samples = int(num_simulations * quantile)\n            sort_idx = torch.argsort(distances)\n            theta_accepted = theta[sort_idx][:num_top_samples]\n            distances_accepted = distances[sort_idx][:num_top_samples]\n            x_accepted = x[sort_idx][:num_top_samples]\n\n        else:\n            raise ValueError(\"One of epsilon or quantile has to be passed.\")\n\n        # Maybe adjust theta with LRA.\n        if lra:\n            self.logger.info(\"Running Linear regression adjustment.\")\n            final_theta = self.run_lra(theta_accepted, x_accepted, observation=self.x_o)\n        else:\n            final_theta = theta_accepted\n\n        if kde:\n            self.logger.info(\n                f\"\"\"KDE on {final_theta.shape[0]} samples with bandwidth option\n{kde_kwargs[\"bandwidth\"] if \"bandwidth\" in kde_kwargs else \"cv\"}.\n                Beware that KDE can give unreliable results when used with too few\n                samples and in high dimensions.\"\"\"\n            )\n\n            kde_dist = get_kde(final_theta, **kde_kwargs)\n\n            if return_summary:\n                return (\n                    kde_dist,\n                    dict(theta=final_theta, distances=distances_accepted, x=x_accepted),\n                )\n            else:\n                return kde_dist\n        elif return_summary:\n            return final_theta, dict(distances=distances_accepted, x=x_accepted)\n        else:\n            return final_theta\n</code></pre>"},{"location":"reference/#sbi.inference.abc.mcabc.MCABC.__call__","title":"<code>__call__(x_o, num_simulations, eps=None, quantile=None, lra=False, sass=False, sass_fraction=0.25, sass_expansion_degree=1, kde=False, kde_kwargs={}, return_summary=False)</code>","text":"<p>Run MCABC and return accepted parameters or KDE object fitted on them.</p> <p>Parameters:</p> Name Type Description Default <code>x_o</code> <code>Union[Tensor, ndarray]</code> <p>Observed data.</p> required <code>num_simulations</code> <code>int</code> <p>Number of simulations to run.</p> required <code>eps</code> <code>Optional[float]</code> <p>Acceptance threshold \\(\\epsilon\\) for distance between observed and simulated data.</p> <code>None</code> <code>quantile</code> <code>Optional[float]</code> <p>Upper quantile of smallest distances for which the corresponding parameters are returned, e.g, q=0.01 will return the top 1%. Exactly one of quantile or <code>eps</code> have to be passed.</p> <code>None</code> <code>lra</code> <code>bool</code> <p>Whether to run linear regression adjustment as in Beaumont et al. 2002</p> <code>False</code> <code>sass</code> <code>bool</code> <p>Whether to determine semi-automatic summary statistics as in Fearnhead &amp; Prangle 2012.</p> <code>False</code> <code>sass_fraction</code> <code>float</code> <p>Fraction of simulation budget used for the initial sass run.</p> <code>0.25</code> <code>sass_expansion_degree</code> <code>int</code> <p>Degree of the polynomial feature expansion for the sass regression, default 1 - no expansion.</p> <code>1</code> <code>kde</code> <code>bool</code> <p>Whether to run KDE on the accepted parameters to return a KDE object from which one can sample.</p> <code>False</code> <code>kde_kwargs</code> <code>Dict[str, Any]</code> <p>kwargs for performing KDE: \u2018bandwidth=\u2019; either a float, or a string naming a bandwidth heuristics, e.g., \u2018cv\u2019 (cross validation), \u2018silvermann\u2019 or \u2018scott\u2019, default \u2018cv\u2019. \u2018transform\u2019: transform applied to the parameters before doing KDE. \u2018sample_weights\u2019: weights associated with samples. See \u2018get_kde\u2019 for more details</p> <code>{}</code> <code>return_summary</code> <code>bool</code> <p>Whether to return the distances and data corresponding to the accepted parameters.</p> <code>False</code> <p>Returns:</p> Name Type Description <code>theta</code> <code>if kde False</code> <p>accepted parameters</p> <code>kde</code> <code>if kde True</code> <p>KDE object based on accepted parameters from which one can .sample() and .log_prob().</p> <code>summary</code> <code>if summary True</code> <p>dictionary containing the accepted paramters (if kde True), distances and simulated data x.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/abc/mcabc.py</code> <pre><code>def __call__(\n    self,\n    x_o: Union[Tensor, ndarray],\n    num_simulations: int,\n    eps: Optional[float] = None,\n    quantile: Optional[float] = None,\n    lra: bool = False,\n    sass: bool = False,\n    sass_fraction: float = 0.25,\n    sass_expansion_degree: int = 1,\n    kde: bool = False,\n    kde_kwargs: Dict[str, Any] = {},\n    return_summary: bool = False,\n) -&gt; Union[Tuple[Tensor, dict], Tuple[KDEWrapper, dict], Tensor, KDEWrapper]:\nr\"\"\"Run MCABC and return accepted parameters or KDE object fitted on them.\n\n    Args:\n        x_o: Observed data.\n        num_simulations: Number of simulations to run.\n        eps: Acceptance threshold $\\epsilon$ for distance between observed and\n            simulated data.\n        quantile: Upper quantile of smallest distances for which the corresponding\n            parameters are returned, e.g, q=0.01 will return the top 1%. Exactly\n            one of quantile or `eps` have to be passed.\n        lra: Whether to run linear regression adjustment as in Beaumont et al. 2002\n        sass: Whether to determine semi-automatic summary statistics as in\n            Fearnhead &amp; Prangle 2012.\n        sass_fraction: Fraction of simulation budget used for the initial sass run.\n        sass_expansion_degree: Degree of the polynomial feature expansion for the\n            sass regression, default 1 - no expansion.\n        kde: Whether to run KDE on the accepted parameters to return a KDE\n            object from which one can sample.\n        kde_kwargs: kwargs for performing KDE:\n            'bandwidth='; either a float, or a string naming a bandwidth\n            heuristics, e.g., 'cv' (cross validation), 'silvermann' or 'scott',\n            default 'cv'.\n            'transform': transform applied to the parameters before doing KDE.\n            'sample_weights': weights associated with samples. See 'get_kde' for\n            more details\n        return_summary: Whether to return the distances and data corresponding to\n            the accepted parameters.\n\n    Returns:\n        theta (if kde False): accepted parameters\n        kde (if kde True): KDE object based on accepted parameters from which one\n            can .sample() and .log_prob().\n        summary (if summary True): dictionary containing the accepted paramters (if\n            kde True), distances and simulated data x.\n    \"\"\"\n\n    # Exactly one of eps or quantile need to be passed.\n    assert (eps is not None) ^ (\n        quantile is not None\n    ), \"Eps or quantile must be passed, but not both.\"\n\n    # Run SASS and change the simulator and x_o accordingly.\n    if sass:\n        num_pilot_simulations = int(sass_fraction * num_simulations)\n        self.logger.info(\n            f\"Running SASS with {num_pilot_simulations} pilot samples.\"\n        )\n        num_simulations -= num_pilot_simulations\n\n        pilot_theta = self.prior.sample((num_pilot_simulations,))\n        pilot_x = self._batched_simulator(pilot_theta)\n\n        sass_transform = self.get_sass_transform(\n            pilot_theta, pilot_x, sass_expansion_degree\n        )\n\n        simulator = lambda theta: sass_transform(self._batched_simulator(theta))\n        x_o = sass_transform(x_o)\n    else:\n        simulator = self._batched_simulator\n\n    # Simulate and calculate distances.\n    theta = self.prior.sample((num_simulations,))\n    x = simulator(theta)\n\n    # Infer shape of x to test and set x_o.\n    self.x_shape = x[0].unsqueeze(0).shape\n    self.x_o = process_x(x_o, self.x_shape)\n\n    distances = self.distance(self.x_o, x)\n\n    # Select based on acceptance threshold epsilon.\n    if eps is not None:\n        is_accepted = distances &lt; eps\n        num_accepted = is_accepted.sum().item()\n        assert num_accepted &gt; 0, f\"No parameters accepted, eps={eps} too small\"\n\n        theta_accepted = theta[is_accepted]\n        distances_accepted = distances[is_accepted]\n        x_accepted = x[is_accepted]\n\n    # Select based on quantile on sorted distances.\n    elif quantile is not None:\n        num_top_samples = int(num_simulations * quantile)\n        sort_idx = torch.argsort(distances)\n        theta_accepted = theta[sort_idx][:num_top_samples]\n        distances_accepted = distances[sort_idx][:num_top_samples]\n        x_accepted = x[sort_idx][:num_top_samples]\n\n    else:\n        raise ValueError(\"One of epsilon or quantile has to be passed.\")\n\n    # Maybe adjust theta with LRA.\n    if lra:\n        self.logger.info(\"Running Linear regression adjustment.\")\n        final_theta = self.run_lra(theta_accepted, x_accepted, observation=self.x_o)\n    else:\n        final_theta = theta_accepted\n\n    if kde:\n        self.logger.info(\n            f\"\"\"KDE on {final_theta.shape[0]} samples with bandwidth option\n{kde_kwargs[\"bandwidth\"] if \"bandwidth\" in kde_kwargs else \"cv\"}.\n            Beware that KDE can give unreliable results when used with too few\n            samples and in high dimensions.\"\"\"\n        )\n\n        kde_dist = get_kde(final_theta, **kde_kwargs)\n\n        if return_summary:\n            return (\n                kde_dist,\n                dict(theta=final_theta, distances=distances_accepted, x=x_accepted),\n            )\n        else:\n            return kde_dist\n    elif return_summary:\n        return final_theta, dict(distances=distances_accepted, x=x_accepted)\n    else:\n        return final_theta\n</code></pre>"},{"location":"reference/#sbi.inference.abc.mcabc.MCABC.__init__","title":"<code>__init__(simulator, prior, distance='l2', num_workers=1, simulation_batch_size=1, show_progress_bars=True)</code>","text":"<p>Monte-Carlo Approximate Bayesian Computation (Rejection ABC) [1].</p> <p>[1] Pritchard, J. K., Seielstad, M. T., Perez-Lezaun, A., &amp; Feldman, M. W. (1999). Population growth of human Y chromosomes: a study of Y chromosome microsatellites. Molecular biology and evolution, 16(12), 1791-1798.</p> <p>Parameters:</p> Name Type Description Default <code>simulator</code> <code>Callable</code> <p>A function that takes parameters \\(\\theta\\) and maps them to simulations, or observations, <code>x</code>, \\(\\mathrm{sim}(\\theta)\\to x\\). Any regular Python callable (i.e. function or class with <code>__call__</code> method) can be used.</p> required <code>prior</code> <p>A probability distribution that expresses prior knowledge about the parameters, e.g. which ranges are meaningful for them. Any object with <code>.log_prob()</code>and <code>.sample()</code> (for example, a PyTorch distribution) can be used.</p> required <code>distance</code> <code>Union[str, Callable]</code> <p>Distance function to compare observed and simulated data. Can be a custom function or one of <code>l1</code>, <code>l2</code>, <code>mse</code>.</p> <code>'l2'</code> <code>num_workers</code> <code>int</code> <p>Number of parallel workers to use for simulations.</p> <code>1</code> <code>simulation_batch_size</code> <code>int</code> <p>Number of parameter sets that the simulator maps to data x at once. If None, we simulate all parameter sets at the same time. If &gt;= 1, the simulator has to process data of shape (simulation_batch_size, parameter_dimension).</p> <code>1</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during simulation and sampling.</p> <code>True</code> Source code in <code>/home/michael/Documents/sbi/sbi/inference/abc/mcabc.py</code> <pre><code>def __init__(\n    self,\n    simulator: Callable,\n    prior,\n    distance: Union[str, Callable] = \"l2\",\n    num_workers: int = 1,\n    simulation_batch_size: int = 1,\n    show_progress_bars: bool = True,\n):\nr\"\"\"Monte-Carlo Approximate Bayesian Computation (Rejection ABC) [1].\n\n    [1] Pritchard, J. K., Seielstad, M. T., Perez-Lezaun, A., &amp; Feldman, M. W.\n    (1999). Population growth of human Y chromosomes: a study of Y chromosome\n    microsatellites. Molecular biology and evolution, 16(12), 1791-1798.\n\n    Args:\n        simulator: A function that takes parameters $\\theta$ and maps them to\n            simulations, or observations, `x`, $\\mathrm{sim}(\\theta)\\to x$. Any\n            regular Python callable (i.e. function or class with `__call__` method)\n            can be used.\n        prior: A probability distribution that expresses prior knowledge about the\n            parameters, e.g. which ranges are meaningful for them. Any\n            object with `.log_prob()`and `.sample()` (for example, a PyTorch\n            distribution) can be used.\n        distance: Distance function to compare observed and simulated data. Can be\n            a custom function or one of `l1`, `l2`, `mse`.\n        num_workers: Number of parallel workers to use for simulations.\n        simulation_batch_size: Number of parameter sets that the simulator\n            maps to data x at once. If None, we simulate all parameter sets at the\n            same time. If &gt;= 1, the simulator has to process data of shape\n            (simulation_batch_size, parameter_dimension).\n        show_progress_bars: Whether to show a progressbar during simulation and\n            sampling.\n    \"\"\"\n\n    super().__init__(\n        simulator=simulator,\n        prior=prior,\n        distance=distance,\n        num_workers=num_workers,\n        simulation_batch_size=simulation_batch_size,\n        show_progress_bars=show_progress_bars,\n    )\n</code></pre>"},{"location":"reference/#sbi.inference.abc.smcabc.SMCABC","title":"<code>sbi.inference.abc.smcabc.SMCABC</code>","text":"<p>         Bases: <code>ABCBASE</code></p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/abc/smcabc.py</code> <pre><code>class SMCABC(ABCBASE):\n    def __init__(\n        self,\n        simulator: Callable,\n        prior: Distribution,\n        distance: Union[str, Callable] = \"l2\",\n        num_workers: int = 1,\n        simulation_batch_size: int = 1,\n        show_progress_bars: bool = True,\n        kernel: Optional[str] = \"gaussian\",\n        algorithm_variant: str = \"C\",\n    ):\nr\"\"\"Sequential Monte Carlo Approximate Bayesian Computation.\n\n        We distinguish between three different SMC methods here:\n            - A: Toni et al. 2010 (Phd Thesis)\n            - B: Sisson et al. 2007 (with correction from 2009)\n            - C: Beaumont et al. 2009\n\n        In Toni et al. 2010 we find an overview of the differences on page 34:\n            - B: same as A except for resampling of weights if the effective sampling\n                size is too small.\n            - C: same as A except for calculation of the covariance of the perturbation\n                kernel: the kernel covariance is a scaled version of the covariance of\n                the previous population.\n\n        Args:\n            simulator: A function that takes parameters $\\theta$ and maps them to\n                simulations, or observations, `x`, $\\mathrm{sim}(\\theta)\\to x$. Any\n                regular Python callable (i.e. function or class with `__call__` method)\n                can be used.\n            prior: A probability distribution that expresses prior knowledge about the\n                parameters, e.g. which ranges are meaningful for them. Any\n                object with `.log_prob()`and `.sample()` (for example, a PyTorch\n                distribution) can be used.\n            distance: Distance function to compare observed and simulated data. Can be\n                a custom function or one of `l1`, `l2`, `mse`.\n            num_workers: Number of parallel workers to use for simulations.\n            simulation_batch_size: Number of parameter sets that the simulator\n                maps to data x at once. If None, we simulate all parameter sets at the\n                same time. If &gt;= 1, the simulator has to process data of shape\n                (simulation_batch_size, parameter_dimension).\n            show_progress_bars: Whether to show a progressbar during simulation and\n                sampling.\n            kernel: Perturbation kernel.\n            algorithm_variant: Indicating the choice of algorithm variant, A, B, or C.\n\n        \"\"\"\n\n        super().__init__(\n            simulator=simulator,\n            prior=prior,\n            distance=distance,\n            num_workers=num_workers,\n            simulation_batch_size=simulation_batch_size,\n            show_progress_bars=show_progress_bars,\n        )\n\n        kernels = (\"gaussian\", \"uniform\")\n        assert (\n            kernel in kernels\n        ), f\"Kernel '{kernel}' not supported. Choose one from {kernels}.\"\n        self.kernel = kernel\n\n        algorithm_variants = (\"A\", \"B\", \"C\")\n        assert algorithm_variant in algorithm_variants, (\n            f\"SMCABC variant '{algorithm_variant}' not supported, choose one from\"\n            \" {algorithm_variants}.\"\n        )\n        self.algorithm_variant = algorithm_variant\n        self.distance_to_x0 = None\n        self.simulation_counter = 0\n        self.num_simulations = 0\n\n        # Define simulator that keeps track of budget.\n        def simulate_with_budget(theta):\n            self.simulation_counter += theta.shape[0]\n            return self._batched_simulator(theta)\n\n        self._simulate_with_budget = simulate_with_budget\n\n    def __call__(\n        self,\n        x_o: Union[Tensor, ndarray],\n        num_particles: int,\n        num_initial_pop: int,\n        num_simulations: int,\n        epsilon_decay: float,\n        distance_based_decay: bool = False,\n        ess_min: Optional[float] = None,\n        kernel_variance_scale: float = 1.0,\n        use_last_pop_samples: bool = True,\n        return_summary: bool = False,\n        kde: bool = False,\n        kde_kwargs: Dict[str, Any] = {},\n        kde_sample_weights: bool = False,\n        lra: bool = False,\n        lra_with_weights: bool = False,\n        sass: bool = False,\n        sass_fraction: float = 0.25,\n        sass_expansion_degree: int = 1,\n    ) -&gt; Union[Tensor, KDEWrapper, Tuple[Tensor, dict], Tuple[KDEWrapper, dict]]:\nr\"\"\"Run SMCABC and return accepted parameters or KDE object fitted on them.\n\n        Args:\n            x_o: Observed data.\n            num_particles: Number of particles in each population.\n            num_initial_pop: Number of simulations used for initial population.\n            num_simulations: Total number of possible simulations.\n            epsilon_decay: Factor with which the acceptance threshold $\\epsilon$ decays.\n            distance_based_decay: Whether the $\\epsilon$ decay is constant over\n                populations or calculated from the previous populations distribution of\n                distances.\n            ess_min: Threshold of effective sampling size for resampling weights. Not\n                used when None (default).\n            kernel_variance_scale: Factor for scaling the perturbation kernel variance.\n            use_last_pop_samples: Whether to fill up the current population with\n                samples from the previous population when the budget is used up. If\n                False, the current population is discarded and the previous population\n                is returned.\n            lra: Whether to run linear regression adjustment as in Beaumont et al. 2002\n            lra_with_weights: Whether to run lra as weighted linear regression with SMC\n                weights\n            sass: Whether to determine semi-automatic summary statistics as in\n                Fearnhead &amp; Prangle 2012.\n            sass_fraction: Fraction of simulation budget used for the initial sass run.\n            sass_expansion_degree: Degree of the polynomial feature expansion for the\n                sass regression, default 1 - no expansion.\n            kde: Whether to run KDE on the accepted parameters to return a KDE\n                object from which one can sample.\n            kde_kwargs: kwargs for performing KDE:\n                'bandwidth='; either a float, or a string naming a bandwidth\n                heuristics, e.g., 'cv' (cross validation), 'silvermann' or 'scott',\n                default 'cv'.\n                'transform': transform applied to the parameters before doing KDE.\n                'sample_weights': weights associated with samples. See 'get_kde' for\n                more details\n            kde_sample_weights: Whether perform weighted KDE with SMC weights or on raw\n                particles.\n            return_summary: Whether to return a dictionary with all accepted particles,\n                weights, etc. at the end.\n\n        Returns:\n            theta (if kde False): accepted parameters of the last population.\n            kde (if kde True): KDE object fitted on accepted parameters, from which one\n                can .sample() and .log_prob().\n            summary (if return_summary True): dictionary containing the accepted\n                paramters (if kde True), distances and simulated data x of all\n                populations.\n        \"\"\"\n\n        pop_idx = 0\n        self.num_simulations = num_simulations\n\n        # Pilot run for SASS.\n        if sass:\n            num_pilot_simulations = int(sass_fraction * num_simulations)\n            self.logger.info(\n                f\"Running SASS with {num_pilot_simulations} pilot samples.\"\n            )\n            sass_transform = self.run_sass_set_xo(\n                num_particles, num_pilot_simulations, x_o, lra, sass_expansion_degree\n            )\n            # Udpate simulator and xo\n            x_o = sass_transform(self.x_o)\n\n            def sass_simulator(theta):\n                self.simulation_counter += theta.shape[0]\n                return sass_transform(self._batched_simulator(theta))\n\n            self._simulate_with_budget = sass_simulator\n\n        # run initial population\n        particles, epsilon, distances, x = self._set_xo_and_sample_initial_population(\n            x_o, num_particles, num_initial_pop\n        )\n        log_weights = torch.log(1 / num_particles * ones(num_particles))\n\n        self.logger.info(\n            (\n                f\"population={pop_idx}, eps={epsilon}, ess={1.0}, \"\n                f\"num_sims={num_initial_pop}\"\n            )\n        )\n\n        all_particles = [particles]\n        all_log_weights = [log_weights]\n        all_distances = [distances]\n        all_epsilons = [epsilon]\n        all_x = [x]\n\n        while self.simulation_counter &lt; self.num_simulations:\n            pop_idx += 1\n            # Decay based on quantile of distances from previous pop.\n            if distance_based_decay:\n                epsilon = self._get_next_epsilon(\n                    all_distances[pop_idx - 1], epsilon_decay\n                )\n            # Constant decay.\n            else:\n                epsilon *= epsilon_decay\n\n            # Get kernel variance from previous pop.\n            self.kernel_variance = self.get_kernel_variance(\n                all_particles[pop_idx - 1],\n                torch.exp(all_log_weights[pop_idx - 1]),\n                samples_per_dim=500,\n                kernel_variance_scale=kernel_variance_scale,\n            )\n            particles, log_weights, distances, x = self._sample_next_population(\n                particles=all_particles[pop_idx - 1],\n                log_weights=all_log_weights[pop_idx - 1],\n                distances=all_distances[pop_idx - 1],\n                epsilon=epsilon,\n                x=all_x[pop_idx - 1],\n                use_last_pop_samples=use_last_pop_samples,\n            )\n\n            # Resample population if effective sampling size is too small.\n            if ess_min is not None:\n                particles, log_weights = self.resample_if_ess_too_small(\n                    particles, log_weights, ess_min, pop_idx\n                )\n\n            self.logger.info(\n                (\n                    f\"population={pop_idx} done: eps={epsilon:.6f},\"\n                    f\" num_sims={self.simulation_counter}.\"\n                )\n            )\n\n            # collect results\n            all_particles.append(particles)\n            all_log_weights.append(log_weights)\n            all_distances.append(distances)\n            all_epsilons.append(epsilon)\n            all_x.append(x)\n\n        # Maybe run LRA and adjust weights.\n        if lra:\n            self.logger.info(\"Running Linear regression adjustment.\")\n            adjusted_particles, adjusted_weights = self.run_lra_update_weights(\n                particles=all_particles[-1],\n                xs=all_x[-1],\n                observation=process_x(x_o),\n                log_weights=all_log_weights[-1],\n                lra_with_weights=lra_with_weights,\n            )\n            final_particles = adjusted_particles\n        else:\n            final_particles = all_particles[-1]\n\n        if kde:\n            self.logger.info(\n                f\"\"\"KDE on {final_particles.shape[0]} samples with bandwidth option\n{kde_kwargs[\"bandwidth\"] if \"bandwidth\" in kde_kwargs else \"cv\"}.\n                Beware that KDE can give unreliable results when used with too few\n                samples and in high dimensions.\"\"\"\n            )\n            # Maybe get particles weights from last population for weighted KDE.\n            if kde_sample_weights:\n                kde_kwargs[\"sample_weights\"] = all_log_weights[-1].exp()\n\n            kde_dist = get_kde(final_particles, **kde_kwargs)\n\n            if return_summary:\n                return (\n                    kde_dist,\n                    dict(\n                        particles=all_particles,\n                        weights=all_log_weights,\n                        epsilons=all_epsilons,\n                        distances=all_distances,\n                        xs=all_x,\n                    ),\n                )\n            else:\n                return kde_dist\n\n        if return_summary:\n            return (\n                final_particles,\n                dict(\n                    particles=all_particles,\n                    weights=all_log_weights,\n                    epsilons=all_epsilons,\n                    distances=all_distances,\n                    xs=all_x,\n                ),\n            )\n        else:\n            return final_particles\n\n    def _set_xo_and_sample_initial_population(\n        self,\n        x_o: Array,\n        num_particles: int,\n        num_initial_pop: int,\n    ) -&gt; Tuple[Tensor, float, Tensor, Tensor]:\n\"\"\"Return particles, epsilon and distances of initial population.\"\"\"\n\n        assert (\n            num_particles &lt;= num_initial_pop\n        ), \"number of initial round simulations must be greater than population size\"\n\n        theta = self.prior.sample((num_initial_pop,))\n        x = self._simulate_with_budget(theta)\n\n        # Infer x shape to test and set x_o.\n        self.x_shape = x[0].unsqueeze(0).shape\n        self.x_o = process_x(x_o, self.x_shape)\n\n        distances = self.distance(self.x_o, x)\n        sortidx = torch.argsort(distances)\n        particles = theta[sortidx][:num_particles]\n        # Take last accepted distance as epsilon.\n        initial_epsilon = distances[sortidx][num_particles - 1]\n\n        if not torch.isfinite(initial_epsilon):\n            initial_epsilon = 1e8\n\n        return (\n            particles,\n            initial_epsilon,\n            distances[sortidx][:num_particles],\n            x[sortidx][:num_particles],\n        )\n\n    def _sample_next_population(\n        self,\n        particles: Tensor,\n        log_weights: Tensor,\n        distances: Tensor,\n        epsilon: float,\n        x: Tensor,\n        use_last_pop_samples: bool = True,\n    ) -&gt; Tuple[Tensor, Tensor, Tensor, Tensor]:\n\"\"\"Return particles, weights and distances of new population.\"\"\"\n\n        new_particles = []\n        new_log_weights = []\n        new_distances = []\n        new_x = []\n\n        num_accepted_particles = 0\n        num_particles = particles.shape[0]\n\n        while num_accepted_particles &lt; num_particles:\n            # Upperbound for batch size to not exceed simulation budget.\n            num_batch = min(\n                num_particles - num_accepted_particles,\n                self.num_simulations - self.simulation_counter,\n            )\n\n            # Sample from previous population and perturb.\n            particle_candidates = self._sample_and_perturb(\n                particles, torch.exp(log_weights), num_samples=num_batch\n            )\n            # Simulate and select based on distance.\n            x_candidates = self._simulate_with_budget(particle_candidates)\n            dists = self.distance(self.x_o, x_candidates)\n            is_accepted = dists &lt;= epsilon\n            num_accepted_batch = is_accepted.sum().item()\n\n            if num_accepted_batch &gt; 0:\n                new_particles.append(particle_candidates[is_accepted])\n                new_log_weights.append(\n                    self._calculate_new_log_weights(\n                        particle_candidates[is_accepted],\n                        particles,\n                        log_weights,\n                    )\n                )\n                new_distances.append(dists[is_accepted])\n                new_x.append(x_candidates[is_accepted])\n                num_accepted_particles += num_accepted_batch\n\n            # If simulation budget was exceeded and we still need particles, take\n            # previous population or fill up with previous population.\n            if (\n                self.simulation_counter &gt;= self.num_simulations\n                and num_accepted_particles &lt; num_particles\n            ):\n                if use_last_pop_samples:\n                    num_remaining = num_particles - num_accepted_particles\n                    self.logger.info(\n                        f\"\"\"Simulation Budget exceeded, filling up with {num_remaining}\n                        samples from last population.\"\"\"\n                    )\n                    # Some new particles have been accepted already, therefore\n                    # fill up the remaining once with old particles and weights.\n                    new_particles.append(particles[:num_remaining, :])\n                    # Recalculate weights with new particles.\n                    new_log_weights = [\n                        self._calculate_new_log_weights(\n                            torch.cat(new_particles),\n                            particles,\n                            log_weights,\n                        )\n                    ]\n                    new_distances.append(distances[:num_remaining])\n                    new_x.append(x[:num_remaining])\n                else:\n                    self.logger.info(\n                        \"Simulation Budget exceeded, returning previous population.\"\n                    )\n                    new_particles = [particles]\n                    new_log_weights = [log_weights]\n                    new_distances = [distances]\n                    new_x = [x]\n\n                break\n\n        # collect lists of tensors into tensors\n        new_particles = torch.cat(new_particles)\n        new_log_weights = torch.cat(new_log_weights)\n        new_distances = torch.cat(new_distances)\n        new_x = torch.cat(new_x)\n\n        # normalize the new weights\n        new_log_weights -= torch.logsumexp(new_log_weights, dim=0)\n\n        # Return sorted wrt distances.\n        sort_idx = torch.argsort(new_distances)\n\n        return (\n            new_particles[sort_idx],\n            new_log_weights[sort_idx],\n            new_distances[sort_idx],\n            new_x[sort_idx],\n        )\n\n    def _get_next_epsilon(self, distances: Tensor, quantile: float) -&gt; float:\n\"\"\"Return epsilon for next round based on quantile of this round's distances.\n\n        Note: distances are made unique to avoid repeated distances from simulations\n        that result in the same observation.\n\n        Args:\n            distances: The distances accepted in this round.\n            quantile: Quantile in the distance distribution to determine new epsilon.\n\n        Returns:\n            epsilon: Epsilon for the next population.\n        \"\"\"\n        # Take unique distances to skip same distances simulations (return is sorted).\n        distances = torch.unique(distances)\n        # Cumsum as cdf proxy.\n        distances_cdf = torch.cumsum(distances, dim=0) / distances.sum()\n        # Take the q quantile of distances.\n        try:\n            qidx = torch.where(distances_cdf &gt;= quantile)[0][0]\n        except IndexError:\n            self.logger.warning(\n                (\n                    f\"Accepted unique distances={distances} don't match \"\n                    f\"quantile={quantile:.2f}. Selecting last distance.\"\n                )\n            )\n            qidx = -1\n\n        # The new epsilon is given by that distance.\n        return distances[qidx].item()\n\n    def _calculate_new_log_weights(\n        self,\n        new_particles: Tensor,\n        old_particles: Tensor,\n        old_log_weights: Tensor,\n    ) -&gt; Tensor:\n\"\"\"Return new log weights following formulas in publications A,B anc C.\"\"\"\n\n        # Prior can be batched across new particles.\n        prior_log_probs = self.prior.log_prob(new_particles)\n\n        # Contstruct function to get kernel log prob for given old particle.\n        # The kernel is centered on each old particle as in all three variants (A,B,C).\n        def kernel_log_prob(new_particle):\n            return self.get_new_kernel(old_particles).log_prob(new_particle)\n\n        # We still have to loop over particles here because\n        # the kernel log probs are already batched across old particles.\n        log_weighted_sum = tensor(\n            [\n                torch.logsumexp(old_log_weights + kernel_log_prob(new_particle), dim=0)\n                for new_particle in new_particles\n            ],\n            dtype=torch.float32,\n        )\n        # new weights are prior probs over weighted sum:\n        return prior_log_probs - log_weighted_sum\n\n    @staticmethod\n    def sample_from_population_with_weights(\n        particles: Tensor, weights: Tensor, num_samples: int = 1\n    ) -&gt; Tensor:\n\"\"\"Return samples from particles sampled with weights.\"\"\"\n\n        # define multinomial with weights as probs\n        multi = Multinomial(probs=weights)\n        # sample num samples, with replacement\n        samples = multi.sample(sample_shape=torch.Size((num_samples,)))\n        # get indices of success trials\n        indices = torch.where(samples)[1]\n        # return those indices from trace\n        return particles[indices]\n\n    def _sample_and_perturb(\n        self, particles: Tensor, weights: Tensor, num_samples: int = 1\n    ) -&gt; Tensor:\n\"\"\"Sample and perturb batch of new parameters from trace.\n\n        Reject sampled and perturbed parameters outside of prior.\n        \"\"\"\n\n        num_accepted = 0\n        parameters = []\n        while num_accepted &lt; num_samples:\n            parms = self.sample_from_population_with_weights(\n                particles, weights, num_samples=num_samples - num_accepted\n            )\n\n            # Create kernel on params and perturb.\n            parms_perturbed = self.get_new_kernel(parms).sample()\n\n            is_within_prior = within_support(self.prior, parms_perturbed)\n            num_accepted += int(is_within_prior.sum().item())\n\n            if num_accepted &gt; 0:\n                parameters.append(parms_perturbed[is_within_prior])\n\n        return torch.cat(parameters)\n\n    def get_kernel_variance(\n        self,\n        particles: Tensor,\n        weights: Tensor,\n        samples_per_dim: int = 100,\n        kernel_variance_scale: float = 1.0,\n    ) -&gt; Tensor:\n        if self.kernel == \"gaussian\":\n            # For variant C, Beaumont et al. 2009, the kernel variance comes from the\n            # previous population.\n            if self.algorithm_variant == \"C\":\n                # Calculate weighted covariance of particles.\n                population_cov = torch.tensor(\n                    np.atleast_2d(np.cov(particles, rowvar=False, aweights=weights)),\n                    dtype=torch.float32,\n                )\n                # Make sure variance is nonsingular.\n                try:\n                    torch.cholesky(kernel_variance_scale * population_cov)\n                except RuntimeError:\n                    self.logger.warning(\n\"\"\"\"Singular particle covariance, using unit covariance.\"\"\"\n                    )\n                    population_cov = torch.eye(particles.shape[1])\n                return kernel_variance_scale * population_cov\n            # While for Toni et al. and Sisson et al. it comes from the parameter\n            # ranges.\n            elif self.algorithm_variant in (\"A\", \"B\"):\n                particle_ranges = self.get_particle_ranges(\n                    particles, weights, samples_per_dim=samples_per_dim\n                )\n                return kernel_variance_scale * torch.diag(particle_ranges)\n            else:\n                raise ValueError(f\"Variant, '{self.algorithm_variant}' not supported.\")\n        elif self.kernel == \"uniform\":\n            # Variance spans the range of parameters for every dimension.\n            return kernel_variance_scale * self.get_particle_ranges(\n                particles, weights, samples_per_dim=samples_per_dim\n            )\n        else:\n            raise ValueError(f\"Kernel, '{self.kernel}' not supported.\")\n\n    def get_new_kernel(self, thetas: Tensor) -&gt; Distribution:\n\"\"\"Return new kernel distribution for a given set of paramters.\"\"\"\n\n        if self.kernel == \"gaussian\":\n            assert self.kernel_variance.ndim == 2\n            return MultivariateNormal(\n                loc=thetas, covariance_matrix=self.kernel_variance\n            )\n\n        elif self.kernel == \"uniform\":\n            low = thetas - self.kernel_variance\n            high = thetas + self.kernel_variance\n            # Move batch shape to event shape to get Uniform that is multivariate in\n            # parameter dimension.\n            return Uniform(low=low, high=high).to_event(1)\n        else:\n            raise ValueError(f\"Kernel, '{self.kernel}' not supported.\")\n\n    def resample_if_ess_too_small(\n        self,\n        particles: Tensor,\n        log_weights: Tensor,\n        ess_min: float,\n        pop_idx: int,\n    ) -&gt; Tuple[Tensor, Tensor]:\n\"\"\"Return resampled particles and uniform weights if effectice sampling size is\n        too small.\n        \"\"\"\n\n        num_particles = particles.shape[0]\n        ess = (1 / torch.sum(torch.exp(2.0 * log_weights), dim=0)) / num_particles\n        # Resampling of weights for low ESS only for Sisson et al. 2007.\n        if ess &lt; ess_min:\n            self.logger.info(f\"ESS={ess:.2f} too low, resampling pop {pop_idx}...\")\n            # First resample, then set to uniform weights as in Sisson et al. 2007.\n            particles = self.sample_from_population_with_weights(\n                particles, torch.exp(log_weights), num_samples=num_particles\n            )\n            log_weights = torch.log(1 / num_particles * ones(num_particles))\n\n        return particles, log_weights\n\n    def run_lra_update_weights(\n        self,\n        particles: Tensor,\n        xs: Tensor,\n        observation: Tensor,\n        log_weights: Tensor,\n        lra_with_weights: bool,\n    ) -&gt; Tuple[Tensor, Tensor]:\n\"\"\"Return particles and weights adjusted with LRA.\n\n        Runs (weighted) linear regression from xs onto particles to adjust the\n        particles.\n\n        Updates the SMC weights according to the new particles.\n        \"\"\"\n\n        adjusted_particels = self.run_lra(\n            theta=particles,\n            x=xs,\n            observation=observation,\n            sample_weight=log_weights.exp() if lra_with_weights else None,\n        )\n\n        # Update SMC weights with LRA adjusted weights\n        adjusted_log_weights = self._calculate_new_log_weights(\n            new_particles=adjusted_particels,\n            old_particles=particles,\n            old_log_weights=log_weights,\n        )\n\n        return adjusted_particels, adjusted_log_weights\n\n    def run_sass_set_xo(\n        self,\n        num_particles: int,\n        num_pilot_simulations: int,\n        x_o,\n        lra: bool = False,\n        sass_expansion_degree: int = 1,\n    ) -&gt; Callable:\n\"\"\"Return transform for semi-automatic summary statistics.\n\n        Runs an single round of rejection abc with fixed budget and accepts\n        num_particles simulations to run the regression for sass.\n\n        Sets self.x_o once the x_shape can be derived from simulations.\n        \"\"\"\n        (\n            pilot_particles,\n            _,\n            _,\n            pilot_xs,\n        ) = self._set_xo_and_sample_initial_population(\n            x_o, num_particles, num_pilot_simulations\n        )\n        # Adjust with LRA.\n        if lra:\n            pilot_particles = self.run_lra(pilot_particles, pilot_xs, self.x_o)\n        sass_transform = self.get_sass_transform(\n            pilot_particles,\n            pilot_xs,\n            expansion_degree=sass_expansion_degree,\n            sample_weight=None,\n        )\n        return sass_transform\n\n    def get_particle_ranges(\n        self, particles: Tensor, weights: Tensor, samples_per_dim: int = 100\n    ) -&gt; Tensor:\n\"\"\"Return range of particles in each parameter dimension.\"\"\"\n\n        # get weighted samples\n        samples = self.sample_from_population_with_weights(\n            particles,\n            weights,\n            num_samples=samples_per_dim * particles.shape[1],\n        )\n\n        # Variance spans the range of particles for every dimension.\n        particle_ranges = samples.max(0).values - samples.min(0).values\n        assert particle_ranges.ndim &lt; 2\n        return particle_ranges\n</code></pre>"},{"location":"reference/#sbi.inference.abc.smcabc.SMCABC.__call__","title":"<code>__call__(x_o, num_particles, num_initial_pop, num_simulations, epsilon_decay, distance_based_decay=False, ess_min=None, kernel_variance_scale=1.0, use_last_pop_samples=True, return_summary=False, kde=False, kde_kwargs={}, kde_sample_weights=False, lra=False, lra_with_weights=False, sass=False, sass_fraction=0.25, sass_expansion_degree=1)</code>","text":"<p>Run SMCABC and return accepted parameters or KDE object fitted on them.</p> <p>Parameters:</p> Name Type Description Default <code>x_o</code> <code>Union[Tensor, ndarray]</code> <p>Observed data.</p> required <code>num_particles</code> <code>int</code> <p>Number of particles in each population.</p> required <code>num_initial_pop</code> <code>int</code> <p>Number of simulations used for initial population.</p> required <code>num_simulations</code> <code>int</code> <p>Total number of possible simulations.</p> required <code>epsilon_decay</code> <code>float</code> <p>Factor with which the acceptance threshold \\(\\epsilon\\) decays.</p> required <code>distance_based_decay</code> <code>bool</code> <p>Whether the \\(\\epsilon\\) decay is constant over populations or calculated from the previous populations distribution of distances.</p> <code>False</code> <code>ess_min</code> <code>Optional[float]</code> <p>Threshold of effective sampling size for resampling weights. Not used when None (default).</p> <code>None</code> <code>kernel_variance_scale</code> <code>float</code> <p>Factor for scaling the perturbation kernel variance.</p> <code>1.0</code> <code>use_last_pop_samples</code> <code>bool</code> <p>Whether to fill up the current population with samples from the previous population when the budget is used up. If False, the current population is discarded and the previous population is returned.</p> <code>True</code> <code>lra</code> <code>bool</code> <p>Whether to run linear regression adjustment as in Beaumont et al. 2002</p> <code>False</code> <code>lra_with_weights</code> <code>bool</code> <p>Whether to run lra as weighted linear regression with SMC weights</p> <code>False</code> <code>sass</code> <code>bool</code> <p>Whether to determine semi-automatic summary statistics as in Fearnhead &amp; Prangle 2012.</p> <code>False</code> <code>sass_fraction</code> <code>float</code> <p>Fraction of simulation budget used for the initial sass run.</p> <code>0.25</code> <code>sass_expansion_degree</code> <code>int</code> <p>Degree of the polynomial feature expansion for the sass regression, default 1 - no expansion.</p> <code>1</code> <code>kde</code> <code>bool</code> <p>Whether to run KDE on the accepted parameters to return a KDE object from which one can sample.</p> <code>False</code> <code>kde_kwargs</code> <code>Dict[str, Any]</code> <p>kwargs for performing KDE: \u2018bandwidth=\u2019; either a float, or a string naming a bandwidth heuristics, e.g., \u2018cv\u2019 (cross validation), \u2018silvermann\u2019 or \u2018scott\u2019, default \u2018cv\u2019. \u2018transform\u2019: transform applied to the parameters before doing KDE. \u2018sample_weights\u2019: weights associated with samples. See \u2018get_kde\u2019 for more details</p> <code>{}</code> <code>kde_sample_weights</code> <code>bool</code> <p>Whether perform weighted KDE with SMC weights or on raw particles.</p> <code>False</code> <code>return_summary</code> <code>bool</code> <p>Whether to return a dictionary with all accepted particles, weights, etc. at the end.</p> <code>False</code> <p>Returns:</p> Name Type Description <code>theta</code> <code>if kde False</code> <p>accepted parameters of the last population.</p> <code>kde</code> <code>if kde True</code> <p>KDE object fitted on accepted parameters, from which one can .sample() and .log_prob().</p> <code>summary</code> <code>if return_summary True</code> <p>dictionary containing the accepted paramters (if kde True), distances and simulated data x of all populations.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/abc/smcabc.py</code> <pre><code>def __call__(\n    self,\n    x_o: Union[Tensor, ndarray],\n    num_particles: int,\n    num_initial_pop: int,\n    num_simulations: int,\n    epsilon_decay: float,\n    distance_based_decay: bool = False,\n    ess_min: Optional[float] = None,\n    kernel_variance_scale: float = 1.0,\n    use_last_pop_samples: bool = True,\n    return_summary: bool = False,\n    kde: bool = False,\n    kde_kwargs: Dict[str, Any] = {},\n    kde_sample_weights: bool = False,\n    lra: bool = False,\n    lra_with_weights: bool = False,\n    sass: bool = False,\n    sass_fraction: float = 0.25,\n    sass_expansion_degree: int = 1,\n) -&gt; Union[Tensor, KDEWrapper, Tuple[Tensor, dict], Tuple[KDEWrapper, dict]]:\nr\"\"\"Run SMCABC and return accepted parameters or KDE object fitted on them.\n\n    Args:\n        x_o: Observed data.\n        num_particles: Number of particles in each population.\n        num_initial_pop: Number of simulations used for initial population.\n        num_simulations: Total number of possible simulations.\n        epsilon_decay: Factor with which the acceptance threshold $\\epsilon$ decays.\n        distance_based_decay: Whether the $\\epsilon$ decay is constant over\n            populations or calculated from the previous populations distribution of\n            distances.\n        ess_min: Threshold of effective sampling size for resampling weights. Not\n            used when None (default).\n        kernel_variance_scale: Factor for scaling the perturbation kernel variance.\n        use_last_pop_samples: Whether to fill up the current population with\n            samples from the previous population when the budget is used up. If\n            False, the current population is discarded and the previous population\n            is returned.\n        lra: Whether to run linear regression adjustment as in Beaumont et al. 2002\n        lra_with_weights: Whether to run lra as weighted linear regression with SMC\n            weights\n        sass: Whether to determine semi-automatic summary statistics as in\n            Fearnhead &amp; Prangle 2012.\n        sass_fraction: Fraction of simulation budget used for the initial sass run.\n        sass_expansion_degree: Degree of the polynomial feature expansion for the\n            sass regression, default 1 - no expansion.\n        kde: Whether to run KDE on the accepted parameters to return a KDE\n            object from which one can sample.\n        kde_kwargs: kwargs for performing KDE:\n            'bandwidth='; either a float, or a string naming a bandwidth\n            heuristics, e.g., 'cv' (cross validation), 'silvermann' or 'scott',\n            default 'cv'.\n            'transform': transform applied to the parameters before doing KDE.\n            'sample_weights': weights associated with samples. See 'get_kde' for\n            more details\n        kde_sample_weights: Whether perform weighted KDE with SMC weights or on raw\n            particles.\n        return_summary: Whether to return a dictionary with all accepted particles,\n            weights, etc. at the end.\n\n    Returns:\n        theta (if kde False): accepted parameters of the last population.\n        kde (if kde True): KDE object fitted on accepted parameters, from which one\n            can .sample() and .log_prob().\n        summary (if return_summary True): dictionary containing the accepted\n            paramters (if kde True), distances and simulated data x of all\n            populations.\n    \"\"\"\n\n    pop_idx = 0\n    self.num_simulations = num_simulations\n\n    # Pilot run for SASS.\n    if sass:\n        num_pilot_simulations = int(sass_fraction * num_simulations)\n        self.logger.info(\n            f\"Running SASS with {num_pilot_simulations} pilot samples.\"\n        )\n        sass_transform = self.run_sass_set_xo(\n            num_particles, num_pilot_simulations, x_o, lra, sass_expansion_degree\n        )\n        # Udpate simulator and xo\n        x_o = sass_transform(self.x_o)\n\n        def sass_simulator(theta):\n            self.simulation_counter += theta.shape[0]\n            return sass_transform(self._batched_simulator(theta))\n\n        self._simulate_with_budget = sass_simulator\n\n    # run initial population\n    particles, epsilon, distances, x = self._set_xo_and_sample_initial_population(\n        x_o, num_particles, num_initial_pop\n    )\n    log_weights = torch.log(1 / num_particles * ones(num_particles))\n\n    self.logger.info(\n        (\n            f\"population={pop_idx}, eps={epsilon}, ess={1.0}, \"\n            f\"num_sims={num_initial_pop}\"\n        )\n    )\n\n    all_particles = [particles]\n    all_log_weights = [log_weights]\n    all_distances = [distances]\n    all_epsilons = [epsilon]\n    all_x = [x]\n\n    while self.simulation_counter &lt; self.num_simulations:\n        pop_idx += 1\n        # Decay based on quantile of distances from previous pop.\n        if distance_based_decay:\n            epsilon = self._get_next_epsilon(\n                all_distances[pop_idx - 1], epsilon_decay\n            )\n        # Constant decay.\n        else:\n            epsilon *= epsilon_decay\n\n        # Get kernel variance from previous pop.\n        self.kernel_variance = self.get_kernel_variance(\n            all_particles[pop_idx - 1],\n            torch.exp(all_log_weights[pop_idx - 1]),\n            samples_per_dim=500,\n            kernel_variance_scale=kernel_variance_scale,\n        )\n        particles, log_weights, distances, x = self._sample_next_population(\n            particles=all_particles[pop_idx - 1],\n            log_weights=all_log_weights[pop_idx - 1],\n            distances=all_distances[pop_idx - 1],\n            epsilon=epsilon,\n            x=all_x[pop_idx - 1],\n            use_last_pop_samples=use_last_pop_samples,\n        )\n\n        # Resample population if effective sampling size is too small.\n        if ess_min is not None:\n            particles, log_weights = self.resample_if_ess_too_small(\n                particles, log_weights, ess_min, pop_idx\n            )\n\n        self.logger.info(\n            (\n                f\"population={pop_idx} done: eps={epsilon:.6f},\"\n                f\" num_sims={self.simulation_counter}.\"\n            )\n        )\n\n        # collect results\n        all_particles.append(particles)\n        all_log_weights.append(log_weights)\n        all_distances.append(distances)\n        all_epsilons.append(epsilon)\n        all_x.append(x)\n\n    # Maybe run LRA and adjust weights.\n    if lra:\n        self.logger.info(\"Running Linear regression adjustment.\")\n        adjusted_particles, adjusted_weights = self.run_lra_update_weights(\n            particles=all_particles[-1],\n            xs=all_x[-1],\n            observation=process_x(x_o),\n            log_weights=all_log_weights[-1],\n            lra_with_weights=lra_with_weights,\n        )\n        final_particles = adjusted_particles\n    else:\n        final_particles = all_particles[-1]\n\n    if kde:\n        self.logger.info(\n            f\"\"\"KDE on {final_particles.shape[0]} samples with bandwidth option\n{kde_kwargs[\"bandwidth\"] if \"bandwidth\" in kde_kwargs else \"cv\"}.\n            Beware that KDE can give unreliable results when used with too few\n            samples and in high dimensions.\"\"\"\n        )\n        # Maybe get particles weights from last population for weighted KDE.\n        if kde_sample_weights:\n            kde_kwargs[\"sample_weights\"] = all_log_weights[-1].exp()\n\n        kde_dist = get_kde(final_particles, **kde_kwargs)\n\n        if return_summary:\n            return (\n                kde_dist,\n                dict(\n                    particles=all_particles,\n                    weights=all_log_weights,\n                    epsilons=all_epsilons,\n                    distances=all_distances,\n                    xs=all_x,\n                ),\n            )\n        else:\n            return kde_dist\n\n    if return_summary:\n        return (\n            final_particles,\n            dict(\n                particles=all_particles,\n                weights=all_log_weights,\n                epsilons=all_epsilons,\n                distances=all_distances,\n                xs=all_x,\n            ),\n        )\n    else:\n        return final_particles\n</code></pre>"},{"location":"reference/#sbi.inference.abc.smcabc.SMCABC.__init__","title":"<code>__init__(simulator, prior, distance='l2', num_workers=1, simulation_batch_size=1, show_progress_bars=True, kernel='gaussian', algorithm_variant='C')</code>","text":"<p>Sequential Monte Carlo Approximate Bayesian Computation.</p> We distinguish between three different SMC methods here <ul> <li>A: Toni et al. 2010 (Phd Thesis)</li> <li>B: Sisson et al. 2007 (with correction from 2009)</li> <li>C: Beaumont et al. 2009</li> </ul> <p>In Toni et al. 2010 we find an overview of the differences on page 34:     - B: same as A except for resampling of weights if the effective sampling         size is too small.     - C: same as A except for calculation of the covariance of the perturbation         kernel: the kernel covariance is a scaled version of the covariance of         the previous population.</p> <p>Parameters:</p> Name Type Description Default <code>simulator</code> <code>Callable</code> <p>A function that takes parameters \\(\\theta\\) and maps them to simulations, or observations, <code>x</code>, \\(\\mathrm{sim}(\\theta)\\to x\\). Any regular Python callable (i.e. function or class with <code>__call__</code> method) can be used.</p> required <code>prior</code> <code>Distribution</code> <p>A probability distribution that expresses prior knowledge about the parameters, e.g. which ranges are meaningful for them. Any object with <code>.log_prob()</code>and <code>.sample()</code> (for example, a PyTorch distribution) can be used.</p> required <code>distance</code> <code>Union[str, Callable]</code> <p>Distance function to compare observed and simulated data. Can be a custom function or one of <code>l1</code>, <code>l2</code>, <code>mse</code>.</p> <code>'l2'</code> <code>num_workers</code> <code>int</code> <p>Number of parallel workers to use for simulations.</p> <code>1</code> <code>simulation_batch_size</code> <code>int</code> <p>Number of parameter sets that the simulator maps to data x at once. If None, we simulate all parameter sets at the same time. If &gt;= 1, the simulator has to process data of shape (simulation_batch_size, parameter_dimension).</p> <code>1</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during simulation and sampling.</p> <code>True</code> <code>kernel</code> <code>Optional[str]</code> <p>Perturbation kernel.</p> <code>'gaussian'</code> <code>algorithm_variant</code> <code>str</code> <p>Indicating the choice of algorithm variant, A, B, or C.</p> <code>'C'</code> Source code in <code>/home/michael/Documents/sbi/sbi/inference/abc/smcabc.py</code> <pre><code>def __init__(\n    self,\n    simulator: Callable,\n    prior: Distribution,\n    distance: Union[str, Callable] = \"l2\",\n    num_workers: int = 1,\n    simulation_batch_size: int = 1,\n    show_progress_bars: bool = True,\n    kernel: Optional[str] = \"gaussian\",\n    algorithm_variant: str = \"C\",\n):\nr\"\"\"Sequential Monte Carlo Approximate Bayesian Computation.\n\n    We distinguish between three different SMC methods here:\n        - A: Toni et al. 2010 (Phd Thesis)\n        - B: Sisson et al. 2007 (with correction from 2009)\n        - C: Beaumont et al. 2009\n\n    In Toni et al. 2010 we find an overview of the differences on page 34:\n        - B: same as A except for resampling of weights if the effective sampling\n            size is too small.\n        - C: same as A except for calculation of the covariance of the perturbation\n            kernel: the kernel covariance is a scaled version of the covariance of\n            the previous population.\n\n    Args:\n        simulator: A function that takes parameters $\\theta$ and maps them to\n            simulations, or observations, `x`, $\\mathrm{sim}(\\theta)\\to x$. Any\n            regular Python callable (i.e. function or class with `__call__` method)\n            can be used.\n        prior: A probability distribution that expresses prior knowledge about the\n            parameters, e.g. which ranges are meaningful for them. Any\n            object with `.log_prob()`and `.sample()` (for example, a PyTorch\n            distribution) can be used.\n        distance: Distance function to compare observed and simulated data. Can be\n            a custom function or one of `l1`, `l2`, `mse`.\n        num_workers: Number of parallel workers to use for simulations.\n        simulation_batch_size: Number of parameter sets that the simulator\n            maps to data x at once. If None, we simulate all parameter sets at the\n            same time. If &gt;= 1, the simulator has to process data of shape\n            (simulation_batch_size, parameter_dimension).\n        show_progress_bars: Whether to show a progressbar during simulation and\n            sampling.\n        kernel: Perturbation kernel.\n        algorithm_variant: Indicating the choice of algorithm variant, A, B, or C.\n\n    \"\"\"\n\n    super().__init__(\n        simulator=simulator,\n        prior=prior,\n        distance=distance,\n        num_workers=num_workers,\n        simulation_batch_size=simulation_batch_size,\n        show_progress_bars=show_progress_bars,\n    )\n\n    kernels = (\"gaussian\", \"uniform\")\n    assert (\n        kernel in kernels\n    ), f\"Kernel '{kernel}' not supported. Choose one from {kernels}.\"\n    self.kernel = kernel\n\n    algorithm_variants = (\"A\", \"B\", \"C\")\n    assert algorithm_variant in algorithm_variants, (\n        f\"SMCABC variant '{algorithm_variant}' not supported, choose one from\"\n        \" {algorithm_variants}.\"\n    )\n    self.algorithm_variant = algorithm_variant\n    self.distance_to_x0 = None\n    self.simulation_counter = 0\n    self.num_simulations = 0\n\n    # Define simulator that keeps track of budget.\n    def simulate_with_budget(theta):\n        self.simulation_counter += theta.shape[0]\n        return self._batched_simulator(theta)\n\n    self._simulate_with_budget = simulate_with_budget\n</code></pre>"},{"location":"reference/#sbi.inference.abc.smcabc.SMCABC.get_new_kernel","title":"<code>get_new_kernel(thetas)</code>","text":"<p>Return new kernel distribution for a given set of paramters.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/abc/smcabc.py</code> <pre><code>def get_new_kernel(self, thetas: Tensor) -&gt; Distribution:\n\"\"\"Return new kernel distribution for a given set of paramters.\"\"\"\n\n    if self.kernel == \"gaussian\":\n        assert self.kernel_variance.ndim == 2\n        return MultivariateNormal(\n            loc=thetas, covariance_matrix=self.kernel_variance\n        )\n\n    elif self.kernel == \"uniform\":\n        low = thetas - self.kernel_variance\n        high = thetas + self.kernel_variance\n        # Move batch shape to event shape to get Uniform that is multivariate in\n        # parameter dimension.\n        return Uniform(low=low, high=high).to_event(1)\n    else:\n        raise ValueError(f\"Kernel, '{self.kernel}' not supported.\")\n</code></pre>"},{"location":"reference/#sbi.inference.abc.smcabc.SMCABC.get_particle_ranges","title":"<code>get_particle_ranges(particles, weights, samples_per_dim=100)</code>","text":"<p>Return range of particles in each parameter dimension.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/abc/smcabc.py</code> <pre><code>def get_particle_ranges(\n    self, particles: Tensor, weights: Tensor, samples_per_dim: int = 100\n) -&gt; Tensor:\n\"\"\"Return range of particles in each parameter dimension.\"\"\"\n\n    # get weighted samples\n    samples = self.sample_from_population_with_weights(\n        particles,\n        weights,\n        num_samples=samples_per_dim * particles.shape[1],\n    )\n\n    # Variance spans the range of particles for every dimension.\n    particle_ranges = samples.max(0).values - samples.min(0).values\n    assert particle_ranges.ndim &lt; 2\n    return particle_ranges\n</code></pre>"},{"location":"reference/#sbi.inference.abc.smcabc.SMCABC.resample_if_ess_too_small","title":"<code>resample_if_ess_too_small(particles, log_weights, ess_min, pop_idx)</code>","text":"<p>Return resampled particles and uniform weights if effectice sampling size is too small.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/abc/smcabc.py</code> <pre><code>def resample_if_ess_too_small(\n    self,\n    particles: Tensor,\n    log_weights: Tensor,\n    ess_min: float,\n    pop_idx: int,\n) -&gt; Tuple[Tensor, Tensor]:\n\"\"\"Return resampled particles and uniform weights if effectice sampling size is\n    too small.\n    \"\"\"\n\n    num_particles = particles.shape[0]\n    ess = (1 / torch.sum(torch.exp(2.0 * log_weights), dim=0)) / num_particles\n    # Resampling of weights for low ESS only for Sisson et al. 2007.\n    if ess &lt; ess_min:\n        self.logger.info(f\"ESS={ess:.2f} too low, resampling pop {pop_idx}...\")\n        # First resample, then set to uniform weights as in Sisson et al. 2007.\n        particles = self.sample_from_population_with_weights(\n            particles, torch.exp(log_weights), num_samples=num_particles\n        )\n        log_weights = torch.log(1 / num_particles * ones(num_particles))\n\n    return particles, log_weights\n</code></pre>"},{"location":"reference/#sbi.inference.abc.smcabc.SMCABC.run_lra_update_weights","title":"<code>run_lra_update_weights(particles, xs, observation, log_weights, lra_with_weights)</code>","text":"<p>Return particles and weights adjusted with LRA.</p> <p>Runs (weighted) linear regression from xs onto particles to adjust the particles.</p> <p>Updates the SMC weights according to the new particles.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/abc/smcabc.py</code> <pre><code>def run_lra_update_weights(\n    self,\n    particles: Tensor,\n    xs: Tensor,\n    observation: Tensor,\n    log_weights: Tensor,\n    lra_with_weights: bool,\n) -&gt; Tuple[Tensor, Tensor]:\n\"\"\"Return particles and weights adjusted with LRA.\n\n    Runs (weighted) linear regression from xs onto particles to adjust the\n    particles.\n\n    Updates the SMC weights according to the new particles.\n    \"\"\"\n\n    adjusted_particels = self.run_lra(\n        theta=particles,\n        x=xs,\n        observation=observation,\n        sample_weight=log_weights.exp() if lra_with_weights else None,\n    )\n\n    # Update SMC weights with LRA adjusted weights\n    adjusted_log_weights = self._calculate_new_log_weights(\n        new_particles=adjusted_particels,\n        old_particles=particles,\n        old_log_weights=log_weights,\n    )\n\n    return adjusted_particels, adjusted_log_weights\n</code></pre>"},{"location":"reference/#sbi.inference.abc.smcabc.SMCABC.run_sass_set_xo","title":"<code>run_sass_set_xo(num_particles, num_pilot_simulations, x_o, lra=False, sass_expansion_degree=1)</code>","text":"<p>Return transform for semi-automatic summary statistics.</p> <p>Runs an single round of rejection abc with fixed budget and accepts num_particles simulations to run the regression for sass.</p> <p>Sets self.x_o once the x_shape can be derived from simulations.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/abc/smcabc.py</code> <pre><code>def run_sass_set_xo(\n    self,\n    num_particles: int,\n    num_pilot_simulations: int,\n    x_o,\n    lra: bool = False,\n    sass_expansion_degree: int = 1,\n) -&gt; Callable:\n\"\"\"Return transform for semi-automatic summary statistics.\n\n    Runs an single round of rejection abc with fixed budget and accepts\n    num_particles simulations to run the regression for sass.\n\n    Sets self.x_o once the x_shape can be derived from simulations.\n    \"\"\"\n    (\n        pilot_particles,\n        _,\n        _,\n        pilot_xs,\n    ) = self._set_xo_and_sample_initial_population(\n        x_o, num_particles, num_pilot_simulations\n    )\n    # Adjust with LRA.\n    if lra:\n        pilot_particles = self.run_lra(pilot_particles, pilot_xs, self.x_o)\n    sass_transform = self.get_sass_transform(\n        pilot_particles,\n        pilot_xs,\n        expansion_degree=sass_expansion_degree,\n        sample_weight=None,\n    )\n    return sass_transform\n</code></pre>"},{"location":"reference/#sbi.inference.abc.smcabc.SMCABC.sample_from_population_with_weights","title":"<code>sample_from_population_with_weights(particles, weights, num_samples=1)</code>  <code>staticmethod</code>","text":"<p>Return samples from particles sampled with weights.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/abc/smcabc.py</code> <pre><code>@staticmethod\ndef sample_from_population_with_weights(\n    particles: Tensor, weights: Tensor, num_samples: int = 1\n) -&gt; Tensor:\n\"\"\"Return samples from particles sampled with weights.\"\"\"\n\n    # define multinomial with weights as probs\n    multi = Multinomial(probs=weights)\n    # sample num samples, with replacement\n    samples = multi.sample(sample_shape=torch.Size((num_samples,)))\n    # get indices of success trials\n    indices = torch.where(samples)[1]\n    # return those indices from trace\n    return particles[indices]\n</code></pre>"},{"location":"reference/#posteriors","title":"Posteriors","text":""},{"location":"reference/#sbi.inference.posteriors.direct_posterior.DirectPosterior","title":"<code>sbi.inference.posteriors.direct_posterior.DirectPosterior</code>","text":"<p>         Bases: <code>NeuralPosterior</code></p> <p>Posterior \\(p(\\theta|x_o)\\) with <code>log_prob()</code> and <code>sample()</code> methods, only applicable to SNPE. SNPE trains a neural network to directly approximate the posterior distribution. However, for bounded priors, the neural network can have leakage: it puts non-zero mass in regions where the prior is zero. The <code>DirectPosterior</code> class wraps the trained network to deal with these cases. Specifically, this class offers the following functionality: - correct the calculation of the log probability such that it compensates for the   leakage. - reject samples that lie outside of the prior bounds. This class can not be used in combination with SNLE or SNRE.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/direct_posterior.py</code> <pre><code>class DirectPosterior(NeuralPosterior):\nr\"\"\"Posterior $p(\\theta|x_o)$ with `log_prob()` and `sample()` methods, only\n    applicable to SNPE.&lt;br/&gt;&lt;br/&gt;\n    SNPE trains a neural network to directly approximate the posterior distribution.\n    However, for bounded priors, the neural network can have leakage: it puts non-zero\n    mass in regions where the prior is zero. The `DirectPosterior` class wraps the\n    trained network to deal with these cases.&lt;br/&gt;&lt;br/&gt;\n    Specifically, this class offers the following functionality:&lt;br/&gt;\n    - correct the calculation of the log probability such that it compensates for the\n      leakage.&lt;br/&gt;\n    - reject samples that lie outside of the prior bounds.&lt;br/&gt;&lt;br/&gt;\n    This class can not be used in combination with SNLE or SNRE.\n    \"\"\"\n\n    def __init__(\n        self,\n        posterior_estimator: flows.Flow,\n        prior: Distribution,\n        max_sampling_batch_size: int = 10_000,\n        device: Optional[str] = None,\n        x_shape: Optional[torch.Size] = None,\n        enable_transform: bool = True,\n    ):\n\"\"\"\n        Args:\n            prior: Prior distribution with `.log_prob()` and `.sample()`.\n            posterior_estimator: The trained neural posterior.\n            max_sampling_batch_size: Batchsize of samples being drawn from\n                the proposal at every iteration.\n            device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:0\". If None,\n                `potential_fn.device` is used.\n            x_shape: Shape of a single simulator output. If passed, it is used to check\n                the shape of the observed data and give a descriptive error.\n            enable_transform: Whether to transform parameters to unconstrained space\n                during MAP optimization. When False, an identity transform will be\n                returned for `theta_transform`.\n        \"\"\"\n        # Because `DirectPosterior` does not take the `potential_fn` as input, it\n        # builds it itself. The `potential_fn` and `theta_transform` are used only for\n        # obtaining the MAP.\n        check_prior(prior)\n        potential_fn, theta_transform = posterior_estimator_based_potential(\n            posterior_estimator,\n            prior,\n            x_o=None,\n            enable_transform=enable_transform,\n        )\n\n        super().__init__(\n            potential_fn=potential_fn,\n            theta_transform=theta_transform,\n            device=device,\n            x_shape=x_shape,\n        )\n\n        self.prior = prior\n        self.posterior_estimator = posterior_estimator\n\n        self.max_sampling_batch_size = max_sampling_batch_size\n        self._leakage_density_correction_factor = None\n\n        self._purpose = \"\"\"It samples the posterior network and rejects samples that\n            lie outside of the prior bounds.\"\"\"\n\n    def sample(\n        self,\n        sample_shape: Shape = torch.Size(),\n        x: Optional[Tensor] = None,\n        max_sampling_batch_size: int = 10_000,\n        sample_with: Optional[str] = None,\n        show_progress_bars: bool = True,\n    ) -&gt; Tensor:\nr\"\"\"Return samples from posterior distribution $p(\\theta|x)$.\n\n        Args:\n            sample_shape: Desired shape of samples that are drawn from posterior. If\n                sample_shape is multidimensional we simply draw `sample_shape.numel()`\n                samples and then reshape into the desired shape.\n            sample_with: This argument only exists to keep backward-compatibility with\n                `sbi` v0.17.2 or older. If it is set, we instantly raise an error.\n            show_progress_bars: Whether to show sampling progress monitor.\n        \"\"\"\n\n        num_samples = torch.Size(sample_shape).numel()\n        x = self._x_else_default_x(x)\n        max_sampling_batch_size = (\n            self.max_sampling_batch_size\n            if max_sampling_batch_size is None\n            else max_sampling_batch_size\n        )\n\n        if sample_with is not None:\n            raise ValueError(\n                f\"You set `sample_with={sample_with}`. As of sbi v0.18.0, setting \"\n                f\"`sample_with` is no longer supported. You have to rerun \"\n                f\"`.build_posterior(sample_with={sample_with}).`\"\n            )\n\n        samples = accept_reject_sample(\n            proposal=self.posterior_estimator,\n            accept_reject_fn=lambda theta: within_support(self.prior, theta),\n            num_samples=num_samples,\n            show_progress_bars=show_progress_bars,\n            max_sampling_batch_size=max_sampling_batch_size,\n            proposal_sampling_kwargs={\"context\": x},\n            alternative_method=\"build_posterior(..., sample_with='mcmc')\",\n        )[0]\n        return samples\n\n    def log_prob(\n        self,\n        theta: Tensor,\n        x: Optional[Tensor] = None,\n        norm_posterior: bool = True,\n        track_gradients: bool = False,\n        leakage_correction_params: Optional[dict] = None,\n    ) -&gt; Tensor:\nr\"\"\"Returns the log-probability of the posterior $p(\\theta|x)$.\n\n        Args:\n            theta: Parameters $\\theta$.\n            norm_posterior: Whether to enforce a normalized posterior density.\n                Renormalization of the posterior is useful when some\n                probability falls out or leaks out of the prescribed prior support.\n                The normalizing factor is calculated via rejection sampling, so if you\n                need speedier but unnormalized log posterior estimates set here\n                `norm_posterior=False`. The returned log posterior is set to\n                -\u221e outside of the prior support regardless of this setting.\n            track_gradients: Whether the returned tensor supports tracking gradients.\n                This can be helpful for e.g. sensitivity analysis, but increases memory\n                consumption.\n            leakage_correction_params: A `dict` of keyword arguments to override the\n                default values of `leakage_correction()`. Possible options are:\n                `num_rejection_samples`, `force_update`, `show_progress_bars`, and\n                `rejection_sampling_batch_size`.\n                These parameters only have an effect if `norm_posterior=True`.\n\n        Returns:\n            `(len(\u03b8),)`-shaped log posterior probability $\\log p(\\theta|x)$ for \u03b8 in the\n            support of the prior, -\u221e (corresponding to 0 probability) outside.\n        \"\"\"\n        x = self._x_else_default_x(x)\n\n        # TODO Train exited here, entered after sampling?\n        self.posterior_estimator.eval()\n\n        theta = ensure_theta_batched(torch.as_tensor(theta))\n        theta_repeated, x_repeated = match_theta_and_x_batch_shapes(theta, x)\n\n        with torch.set_grad_enabled(track_gradients):\n            # Evaluate on device, move back to cpu for comparison with prior.\n            unnorm_log_prob = self.posterior_estimator.log_prob(\n                theta_repeated, context=x_repeated\n            )\n\n            # Force probability to be zero outside prior support.\n            in_prior_support = within_support(self.prior, theta_repeated)\n\n            masked_log_prob = torch.where(\n                in_prior_support,\n                unnorm_log_prob,\n                torch.tensor(float(\"-inf\"), dtype=torch.float32, device=self._device),\n            )\n\n            if leakage_correction_params is None:\n                leakage_correction_params = dict()  # use defaults\n            log_factor = (\n                log(self.leakage_correction(x=x, **leakage_correction_params))\n                if norm_posterior\n                else 0\n            )\n\n            return masked_log_prob - log_factor\n\n    @torch.no_grad()\n    def leakage_correction(\n        self,\n        x: Tensor,\n        num_rejection_samples: int = 10_000,\n        force_update: bool = False,\n        show_progress_bars: bool = False,\n        rejection_sampling_batch_size: int = 10_000,\n    ) -&gt; Tensor:\nr\"\"\"Return leakage correction factor for a leaky posterior density estimate.\n\n        The factor is estimated from the acceptance probability during rejection\n        sampling from the posterior.\n\n        This is to avoid re-estimating the acceptance probability from scratch\n        whenever `log_prob` is called and `norm_posterior=True`. Here, it\n        is estimated only once for `self.default_x` and saved for later. We\n        re-evaluate only whenever a new `x` is passed.\n\n        Arguments:\n            num_rejection_samples: Number of samples used to estimate correction factor.\n            show_progress_bars: Whether to show a progress bar during sampling.\n            rejection_sampling_batch_size: Batch size for rejection sampling.\n\n        Returns:\n            Saved or newly-estimated correction factor (as a scalar `Tensor`).\n        \"\"\"\n\n        def acceptance_at(x: Tensor) -&gt; Tensor:\n            return accept_reject_sample(\n                proposal=self.posterior_estimator,\n                accept_reject_fn=lambda theta: within_support(self.prior, theta),\n                num_samples=num_rejection_samples,\n                show_progress_bars=show_progress_bars,\n                sample_for_correction_factor=True,\n                max_sampling_batch_size=rejection_sampling_batch_size,\n                proposal_sampling_kwargs={\"context\": x},\n            )[1]\n\n        # Check if the provided x matches the default x (short-circuit on identity).\n        is_new_x = self.default_x is None or (\n            x is not self.default_x and (x != self.default_x).any()\n        )\n\n        not_saved_at_default_x = self._leakage_density_correction_factor is None\n\n        if is_new_x:  # Calculate at x; don't save.\n            return acceptance_at(x)\n        elif not_saved_at_default_x or force_update:  # Calculate at default_x; save.\n            assert self.default_x is not None\n            self._leakage_density_correction_factor = acceptance_at(self.default_x)\n\n        return self._leakage_density_correction_factor  # type: ignore\n\n    def map(\n        self,\n        x: Optional[Tensor] = None,\n        num_iter: int = 1_000,\n        num_to_optimize: int = 100,\n        learning_rate: float = 0.01,\n        init_method: Union[str, Tensor] = \"posterior\",\n        num_init_samples: int = 1_000,\n        save_best_every: int = 10,\n        show_progress_bars: bool = False,\n        force_update: bool = False,\n    ) -&gt; Tensor:\nr\"\"\"Returns the maximum-a-posteriori estimate (MAP).\n\n        The method can be interrupted (Ctrl-C) when the user sees that the\n        log-probability converges. The best estimate will be saved in `self._map` and\n        can be accessed with `self.map()`. The MAP is obtained by running gradient\n        ascent from a given number of starting positions (samples from the posterior\n        with the highest log-probability). After the optimization is done, we select the\n        parameter set that has the highest log-probability after the optimization.\n\n        Warning: The default values used by this function are not well-tested. They\n        might require hand-tuning for the problem at hand.\n\n        For developers: if the prior is a `BoxUniform`, we carry out the optimization\n        in unbounded space and transform the result back into bounded space.\n\n        Args:\n            x: Deprecated - use `.set_default_x()` prior to `.map()`.\n            num_iter: Number of optimization steps that the algorithm takes\n                to find the MAP.\n            learning_rate: Learning rate of the optimizer.\n            init_method: How to select the starting parameters for the optimization. If\n                it is a string, it can be either [`posterior`, `prior`], which samples\n                the respective distribution `num_init_samples` times. If it is a\n                tensor, the tensor will be used as init locations.\n            num_init_samples: Draw this number of samples from the posterior and\n                evaluate the log-probability of all of them.\n            num_to_optimize: From the drawn `num_init_samples`, use the\n                `num_to_optimize` with highest log-probability as the initial points\n                for the optimization.\n            save_best_every: The best log-probability is computed, saved in the\n                `map`-attribute, and printed every `save_best_every`-th iteration.\n                Computing the best log-probability creates a significant overhead\n                (thus, the default is `10`.)\n            show_progress_bars: Whether to show a progressbar during sampling from the\n                posterior.\n            force_update: Whether to re-calculate the MAP when x is unchanged and\n                have a cached value.\n            log_prob_kwargs: Will be empty for SNLE and SNRE. Will contain\n                {'norm_posterior': True} for SNPE.\n\n        Returns:\n            The MAP estimate.\n        \"\"\"\n        return super().map(\n            x=x,\n            num_iter=num_iter,\n            num_to_optimize=num_to_optimize,\n            learning_rate=learning_rate,\n            init_method=init_method,\n            num_init_samples=num_init_samples,\n            save_best_every=save_best_every,\n            show_progress_bars=show_progress_bars,\n            force_update=force_update,\n        )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.direct_posterior.DirectPosterior.__init__","title":"<code>__init__(posterior_estimator, prior, max_sampling_batch_size=10000, device=None, x_shape=None, enable_transform=True)</code>","text":"<p>Parameters:</p> Name Type Description Default <code>prior</code> <code>Distribution</code> <p>Prior distribution with <code>.log_prob()</code> and <code>.sample()</code>.</p> required <code>posterior_estimator</code> <code>flows.Flow</code> <p>The trained neural posterior.</p> required <code>max_sampling_batch_size</code> <code>int</code> <p>Batchsize of samples being drawn from the proposal at every iteration.</p> <code>10000</code> <code>device</code> <code>Optional[str]</code> <p>Training device, e.g., \u201ccpu\u201d, \u201ccuda\u201d or \u201ccuda:0\u201d. If None, <code>potential_fn.device</code> is used.</p> <code>None</code> <code>x_shape</code> <code>Optional[torch.Size]</code> <p>Shape of a single simulator output. If passed, it is used to check the shape of the observed data and give a descriptive error.</p> <code>None</code> <code>enable_transform</code> <code>bool</code> <p>Whether to transform parameters to unconstrained space during MAP optimization. When False, an identity transform will be returned for <code>theta_transform</code>.</p> <code>True</code> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/direct_posterior.py</code> <pre><code>def __init__(\n    self,\n    posterior_estimator: flows.Flow,\n    prior: Distribution,\n    max_sampling_batch_size: int = 10_000,\n    device: Optional[str] = None,\n    x_shape: Optional[torch.Size] = None,\n    enable_transform: bool = True,\n):\n\"\"\"\n    Args:\n        prior: Prior distribution with `.log_prob()` and `.sample()`.\n        posterior_estimator: The trained neural posterior.\n        max_sampling_batch_size: Batchsize of samples being drawn from\n            the proposal at every iteration.\n        device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:0\". If None,\n            `potential_fn.device` is used.\n        x_shape: Shape of a single simulator output. If passed, it is used to check\n            the shape of the observed data and give a descriptive error.\n        enable_transform: Whether to transform parameters to unconstrained space\n            during MAP optimization. When False, an identity transform will be\n            returned for `theta_transform`.\n    \"\"\"\n    # Because `DirectPosterior` does not take the `potential_fn` as input, it\n    # builds it itself. The `potential_fn` and `theta_transform` are used only for\n    # obtaining the MAP.\n    check_prior(prior)\n    potential_fn, theta_transform = posterior_estimator_based_potential(\n        posterior_estimator,\n        prior,\n        x_o=None,\n        enable_transform=enable_transform,\n    )\n\n    super().__init__(\n        potential_fn=potential_fn,\n        theta_transform=theta_transform,\n        device=device,\n        x_shape=x_shape,\n    )\n\n    self.prior = prior\n    self.posterior_estimator = posterior_estimator\n\n    self.max_sampling_batch_size = max_sampling_batch_size\n    self._leakage_density_correction_factor = None\n\n    self._purpose = \"\"\"It samples the posterior network and rejects samples that\n        lie outside of the prior bounds.\"\"\"\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.direct_posterior.DirectPosterior.leakage_correction","title":"<code>leakage_correction(x, num_rejection_samples=10000, force_update=False, show_progress_bars=False, rejection_sampling_batch_size=10000)</code>","text":"<p>Return leakage correction factor for a leaky posterior density estimate.</p> <p>The factor is estimated from the acceptance probability during rejection sampling from the posterior.</p> <p>This is to avoid re-estimating the acceptance probability from scratch whenever <code>log_prob</code> is called and <code>norm_posterior=True</code>. Here, it is estimated only once for <code>self.default_x</code> and saved for later. We re-evaluate only whenever a new <code>x</code> is passed.</p> <p>Parameters:</p> Name Type Description Default <code>num_rejection_samples</code> <code>int</code> <p>Number of samples used to estimate correction factor.</p> <code>10000</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progress bar during sampling.</p> <code>False</code> <code>rejection_sampling_batch_size</code> <code>int</code> <p>Batch size for rejection sampling.</p> <code>10000</code> <p>Returns:</p> Type Description <code>Tensor</code> <p>Saved or newly-estimated correction factor (as a scalar <code>Tensor</code>).</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/direct_posterior.py</code> <pre><code>@torch.no_grad()\ndef leakage_correction(\n    self,\n    x: Tensor,\n    num_rejection_samples: int = 10_000,\n    force_update: bool = False,\n    show_progress_bars: bool = False,\n    rejection_sampling_batch_size: int = 10_000,\n) -&gt; Tensor:\nr\"\"\"Return leakage correction factor for a leaky posterior density estimate.\n\n    The factor is estimated from the acceptance probability during rejection\n    sampling from the posterior.\n\n    This is to avoid re-estimating the acceptance probability from scratch\n    whenever `log_prob` is called and `norm_posterior=True`. Here, it\n    is estimated only once for `self.default_x` and saved for later. We\n    re-evaluate only whenever a new `x` is passed.\n\n    Arguments:\n        num_rejection_samples: Number of samples used to estimate correction factor.\n        show_progress_bars: Whether to show a progress bar during sampling.\n        rejection_sampling_batch_size: Batch size for rejection sampling.\n\n    Returns:\n        Saved or newly-estimated correction factor (as a scalar `Tensor`).\n    \"\"\"\n\n    def acceptance_at(x: Tensor) -&gt; Tensor:\n        return accept_reject_sample(\n            proposal=self.posterior_estimator,\n            accept_reject_fn=lambda theta: within_support(self.prior, theta),\n            num_samples=num_rejection_samples,\n            show_progress_bars=show_progress_bars,\n            sample_for_correction_factor=True,\n            max_sampling_batch_size=rejection_sampling_batch_size,\n            proposal_sampling_kwargs={\"context\": x},\n        )[1]\n\n    # Check if the provided x matches the default x (short-circuit on identity).\n    is_new_x = self.default_x is None or (\n        x is not self.default_x and (x != self.default_x).any()\n    )\n\n    not_saved_at_default_x = self._leakage_density_correction_factor is None\n\n    if is_new_x:  # Calculate at x; don't save.\n        return acceptance_at(x)\n    elif not_saved_at_default_x or force_update:  # Calculate at default_x; save.\n        assert self.default_x is not None\n        self._leakage_density_correction_factor = acceptance_at(self.default_x)\n\n    return self._leakage_density_correction_factor  # type: ignore\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.direct_posterior.DirectPosterior.log_prob","title":"<code>log_prob(theta, x=None, norm_posterior=True, track_gradients=False, leakage_correction_params=None)</code>","text":"<p>Returns the log-probability of the posterior \\(p(\\theta|x)\\).</p> <p>Parameters:</p> Name Type Description Default <code>theta</code> <code>Tensor</code> <p>Parameters \\(\\theta\\).</p> required <code>norm_posterior</code> <code>bool</code> <p>Whether to enforce a normalized posterior density. Renormalization of the posterior is useful when some probability falls out or leaks out of the prescribed prior support. The normalizing factor is calculated via rejection sampling, so if you need speedier but unnormalized log posterior estimates set here <code>norm_posterior=False</code>. The returned log posterior is set to -\u221e outside of the prior support regardless of this setting.</p> <code>True</code> <code>track_gradients</code> <code>bool</code> <p>Whether the returned tensor supports tracking gradients. This can be helpful for e.g. sensitivity analysis, but increases memory consumption.</p> <code>False</code> <code>leakage_correction_params</code> <code>Optional[dict]</code> <p>A <code>dict</code> of keyword arguments to override the default values of <code>leakage_correction()</code>. Possible options are: <code>num_rejection_samples</code>, <code>force_update</code>, <code>show_progress_bars</code>, and <code>rejection_sampling_batch_size</code>. These parameters only have an effect if <code>norm_posterior=True</code>.</p> <code>None</code> <p>Returns:</p> Type Description <code>Tensor</code> <p><code>(len(\u03b8),)</code>-shaped log posterior probability \\(\\log p(\\theta|x)\\) for \u03b8 in the</p> <code>Tensor</code> <p>support of the prior, -\u221e (corresponding to 0 probability) outside.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/direct_posterior.py</code> <pre><code>def log_prob(\n    self,\n    theta: Tensor,\n    x: Optional[Tensor] = None,\n    norm_posterior: bool = True,\n    track_gradients: bool = False,\n    leakage_correction_params: Optional[dict] = None,\n) -&gt; Tensor:\nr\"\"\"Returns the log-probability of the posterior $p(\\theta|x)$.\n\n    Args:\n        theta: Parameters $\\theta$.\n        norm_posterior: Whether to enforce a normalized posterior density.\n            Renormalization of the posterior is useful when some\n            probability falls out or leaks out of the prescribed prior support.\n            The normalizing factor is calculated via rejection sampling, so if you\n            need speedier but unnormalized log posterior estimates set here\n            `norm_posterior=False`. The returned log posterior is set to\n            -\u221e outside of the prior support regardless of this setting.\n        track_gradients: Whether the returned tensor supports tracking gradients.\n            This can be helpful for e.g. sensitivity analysis, but increases memory\n            consumption.\n        leakage_correction_params: A `dict` of keyword arguments to override the\n            default values of `leakage_correction()`. Possible options are:\n            `num_rejection_samples`, `force_update`, `show_progress_bars`, and\n            `rejection_sampling_batch_size`.\n            These parameters only have an effect if `norm_posterior=True`.\n\n    Returns:\n        `(len(\u03b8),)`-shaped log posterior probability $\\log p(\\theta|x)$ for \u03b8 in the\n        support of the prior, -\u221e (corresponding to 0 probability) outside.\n    \"\"\"\n    x = self._x_else_default_x(x)\n\n    # TODO Train exited here, entered after sampling?\n    self.posterior_estimator.eval()\n\n    theta = ensure_theta_batched(torch.as_tensor(theta))\n    theta_repeated, x_repeated = match_theta_and_x_batch_shapes(theta, x)\n\n    with torch.set_grad_enabled(track_gradients):\n        # Evaluate on device, move back to cpu for comparison with prior.\n        unnorm_log_prob = self.posterior_estimator.log_prob(\n            theta_repeated, context=x_repeated\n        )\n\n        # Force probability to be zero outside prior support.\n        in_prior_support = within_support(self.prior, theta_repeated)\n\n        masked_log_prob = torch.where(\n            in_prior_support,\n            unnorm_log_prob,\n            torch.tensor(float(\"-inf\"), dtype=torch.float32, device=self._device),\n        )\n\n        if leakage_correction_params is None:\n            leakage_correction_params = dict()  # use defaults\n        log_factor = (\n            log(self.leakage_correction(x=x, **leakage_correction_params))\n            if norm_posterior\n            else 0\n        )\n\n        return masked_log_prob - log_factor\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.direct_posterior.DirectPosterior.map","title":"<code>map(x=None, num_iter=1000, num_to_optimize=100, learning_rate=0.01, init_method='posterior', num_init_samples=1000, save_best_every=10, show_progress_bars=False, force_update=False)</code>","text":"<p>Returns the maximum-a-posteriori estimate (MAP).</p> <p>The method can be interrupted (Ctrl-C) when the user sees that the log-probability converges. The best estimate will be saved in <code>self._map</code> and can be accessed with <code>self.map()</code>. The MAP is obtained by running gradient ascent from a given number of starting positions (samples from the posterior with the highest log-probability). After the optimization is done, we select the parameter set that has the highest log-probability after the optimization.</p> <p>Warning: The default values used by this function are not well-tested. They might require hand-tuning for the problem at hand.</p> <p>For developers: if the prior is a <code>BoxUniform</code>, we carry out the optimization in unbounded space and transform the result back into bounded space.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>Optional[Tensor]</code> <p>Deprecated - use <code>.set_default_x()</code> prior to <code>.map()</code>.</p> <code>None</code> <code>num_iter</code> <code>int</code> <p>Number of optimization steps that the algorithm takes to find the MAP.</p> <code>1000</code> <code>learning_rate</code> <code>float</code> <p>Learning rate of the optimizer.</p> <code>0.01</code> <code>init_method</code> <code>Union[str, Tensor]</code> <p>How to select the starting parameters for the optimization. If it is a string, it can be either [<code>posterior</code>, <code>prior</code>], which samples the respective distribution <code>num_init_samples</code> times. If it is a tensor, the tensor will be used as init locations.</p> <code>'posterior'</code> <code>num_init_samples</code> <code>int</code> <p>Draw this number of samples from the posterior and evaluate the log-probability of all of them.</p> <code>1000</code> <code>num_to_optimize</code> <code>int</code> <p>From the drawn <code>num_init_samples</code>, use the <code>num_to_optimize</code> with highest log-probability as the initial points for the optimization.</p> <code>100</code> <code>save_best_every</code> <code>int</code> <p>The best log-probability is computed, saved in the <code>map</code>-attribute, and printed every <code>save_best_every</code>-th iteration. Computing the best log-probability creates a significant overhead (thus, the default is <code>10</code>.)</p> <code>10</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during sampling from the posterior.</p> <code>False</code> <code>force_update</code> <code>bool</code> <p>Whether to re-calculate the MAP when x is unchanged and have a cached value.</p> <code>False</code> <code>log_prob_kwargs</code> <p>Will be empty for SNLE and SNRE. Will contain {\u2018norm_posterior\u2019: True} for SNPE.</p> required <p>Returns:</p> Type Description <code>Tensor</code> <p>The MAP estimate.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/direct_posterior.py</code> <pre><code>def map(\n    self,\n    x: Optional[Tensor] = None,\n    num_iter: int = 1_000,\n    num_to_optimize: int = 100,\n    learning_rate: float = 0.01,\n    init_method: Union[str, Tensor] = \"posterior\",\n    num_init_samples: int = 1_000,\n    save_best_every: int = 10,\n    show_progress_bars: bool = False,\n    force_update: bool = False,\n) -&gt; Tensor:\nr\"\"\"Returns the maximum-a-posteriori estimate (MAP).\n\n    The method can be interrupted (Ctrl-C) when the user sees that the\n    log-probability converges. The best estimate will be saved in `self._map` and\n    can be accessed with `self.map()`. The MAP is obtained by running gradient\n    ascent from a given number of starting positions (samples from the posterior\n    with the highest log-probability). After the optimization is done, we select the\n    parameter set that has the highest log-probability after the optimization.\n\n    Warning: The default values used by this function are not well-tested. They\n    might require hand-tuning for the problem at hand.\n\n    For developers: if the prior is a `BoxUniform`, we carry out the optimization\n    in unbounded space and transform the result back into bounded space.\n\n    Args:\n        x: Deprecated - use `.set_default_x()` prior to `.map()`.\n        num_iter: Number of optimization steps that the algorithm takes\n            to find the MAP.\n        learning_rate: Learning rate of the optimizer.\n        init_method: How to select the starting parameters for the optimization. If\n            it is a string, it can be either [`posterior`, `prior`], which samples\n            the respective distribution `num_init_samples` times. If it is a\n            tensor, the tensor will be used as init locations.\n        num_init_samples: Draw this number of samples from the posterior and\n            evaluate the log-probability of all of them.\n        num_to_optimize: From the drawn `num_init_samples`, use the\n            `num_to_optimize` with highest log-probability as the initial points\n            for the optimization.\n        save_best_every: The best log-probability is computed, saved in the\n            `map`-attribute, and printed every `save_best_every`-th iteration.\n            Computing the best log-probability creates a significant overhead\n            (thus, the default is `10`.)\n        show_progress_bars: Whether to show a progressbar during sampling from the\n            posterior.\n        force_update: Whether to re-calculate the MAP when x is unchanged and\n            have a cached value.\n        log_prob_kwargs: Will be empty for SNLE and SNRE. Will contain\n            {'norm_posterior': True} for SNPE.\n\n    Returns:\n        The MAP estimate.\n    \"\"\"\n    return super().map(\n        x=x,\n        num_iter=num_iter,\n        num_to_optimize=num_to_optimize,\n        learning_rate=learning_rate,\n        init_method=init_method,\n        num_init_samples=num_init_samples,\n        save_best_every=save_best_every,\n        show_progress_bars=show_progress_bars,\n        force_update=force_update,\n    )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.direct_posterior.DirectPosterior.sample","title":"<code>sample(sample_shape=torch.Size(), x=None, max_sampling_batch_size=10000, sample_with=None, show_progress_bars=True)</code>","text":"<p>Return samples from posterior distribution \\(p(\\theta|x)\\).</p> <p>Parameters:</p> Name Type Description Default <code>sample_shape</code> <code>Shape</code> <p>Desired shape of samples that are drawn from posterior. If sample_shape is multidimensional we simply draw <code>sample_shape.numel()</code> samples and then reshape into the desired shape.</p> <code>torch.Size()</code> <code>sample_with</code> <code>Optional[str]</code> <p>This argument only exists to keep backward-compatibility with <code>sbi</code> v0.17.2 or older. If it is set, we instantly raise an error.</p> <code>None</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show sampling progress monitor.</p> <code>True</code> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/direct_posterior.py</code> <pre><code>def sample(\n    self,\n    sample_shape: Shape = torch.Size(),\n    x: Optional[Tensor] = None,\n    max_sampling_batch_size: int = 10_000,\n    sample_with: Optional[str] = None,\n    show_progress_bars: bool = True,\n) -&gt; Tensor:\nr\"\"\"Return samples from posterior distribution $p(\\theta|x)$.\n\n    Args:\n        sample_shape: Desired shape of samples that are drawn from posterior. If\n            sample_shape is multidimensional we simply draw `sample_shape.numel()`\n            samples and then reshape into the desired shape.\n        sample_with: This argument only exists to keep backward-compatibility with\n            `sbi` v0.17.2 or older. If it is set, we instantly raise an error.\n        show_progress_bars: Whether to show sampling progress monitor.\n    \"\"\"\n\n    num_samples = torch.Size(sample_shape).numel()\n    x = self._x_else_default_x(x)\n    max_sampling_batch_size = (\n        self.max_sampling_batch_size\n        if max_sampling_batch_size is None\n        else max_sampling_batch_size\n    )\n\n    if sample_with is not None:\n        raise ValueError(\n            f\"You set `sample_with={sample_with}`. As of sbi v0.18.0, setting \"\n            f\"`sample_with` is no longer supported. You have to rerun \"\n            f\"`.build_posterior(sample_with={sample_with}).`\"\n        )\n\n    samples = accept_reject_sample(\n        proposal=self.posterior_estimator,\n        accept_reject_fn=lambda theta: within_support(self.prior, theta),\n        num_samples=num_samples,\n        show_progress_bars=show_progress_bars,\n        max_sampling_batch_size=max_sampling_batch_size,\n        proposal_sampling_kwargs={\"context\": x},\n        alternative_method=\"build_posterior(..., sample_with='mcmc')\",\n    )[0]\n    return samples\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior","title":"<code>sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior</code>","text":"<p>         Bases: <code>NeuralPosterior</code></p> <p>Provides importance sampling to sample from the posterior. SNLE or SNRE train neural networks to approximate the likelihood(-ratios). <code>ImportanceSamplingPosterior</code> allows to estimate the posterior log-probability by estimating the normlalization constant with importance sampling. It also allows to perform importance sampling (with <code>.sample()</code>) and to draw approximate samples with sampling-importance-resampling (SIR) (with <code>.sir_sample()</code>)</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/importance_posterior.py</code> <pre><code>class ImportanceSamplingPosterior(NeuralPosterior):\nr\"\"\"Provides importance sampling to sample from the posterior.&lt;br/&gt;&lt;br/&gt;\n    SNLE or SNRE train neural networks to approximate the likelihood(-ratios).\n    `ImportanceSamplingPosterior` allows to estimate the posterior log-probability by\n    estimating the normlalization constant with importance sampling. It also allows to\n    perform importance sampling (with `.sample()`) and to draw approximate samples with\n    sampling-importance-resampling (SIR) (with `.sir_sample()`)\n    \"\"\"\n\n    def __init__(\n        self,\n        potential_fn: Callable,\n        proposal: Any,\n        theta_transform: Optional[TorchTransform] = None,\n        method: str = \"sir\",\n        oversampling_factor: int = 32,\n        max_sampling_batch_size: int = 10_000,\n        device: Optional[str] = None,\n        x_shape: Optional[torch.Size] = None,\n    ):\n\"\"\"\n        Args:\n            potential_fn: The potential function from which to draw samples.\n            proposal: The proposal distribution.\n            theta_transform: Transformation that is applied to parameters. Is not used\n                during but only when calling `.map()`.\n            method: Either of [`sir`|`importance`]. This sets the behavior of the\n                `.sample()` method. With `sir`, approximate posterior samples are\n                generated with sampling importance resampling (SIR). With\n                `importance`, the `.sample()` method returns a tuple of samples and\n                corresponding importance weights.\n            oversampling_factor: Number of proposed samples from which only one is\n                selected based on its importance weight.\n            max_sampling_batch_size: The batch size of samples being drawn from the\n                proposal at every iteration.\n            device: Device on which to sample, e.g., \"cpu\", \"cuda\" or \"cuda:0\". If\n                None, `potential_fn.device` is used.\n            x_shape: Shape of a single simulator output. If passed, it is used to check\n                the shape of the observed data and give a descriptive error.\n        \"\"\"\n        super().__init__(\n            potential_fn,\n            theta_transform=theta_transform,\n            device=device,\n            x_shape=x_shape,\n        )\n\n        self.proposal = proposal\n        self._normalization_constant = None\n        self.method = method\n\n        self.oversampling_factor = oversampling_factor\n        self.max_sampling_batch_size = max_sampling_batch_size\n\n        self._purpose = (\n            \"It provides sampling-importance resampling (SIR) to .sample() from the \"\n            \"posterior and can evaluate the _unnormalized_ posterior density with \"\n            \".log_prob().\"\n        )\n\n    def log_prob(\n        self,\n        theta: Tensor,\n        x: Optional[Tensor] = None,\n        track_gradients: bool = False,\n        normalization_constant_params: Optional[dict] = None,\n    ) -&gt; Tensor:\nr\"\"\"Returns the log-probability of theta under the posterior.\n\n        The normalization constant is estimated with importance sampling.\n\n        Args:\n            theta: Parameters $\\theta$.\n            track_gradients: Whether the returned tensor supports tracking gradients.\n                This can be helpful for e.g. sensitivity analysis, but increases memory\n                consumption.\n            normalization_constant_params: Parameters passed on to\n                `estimate_normalization_constant()`.\n\n        Returns:\n            `len($\\theta$)`-shaped log-probability.\n        \"\"\"\n        x = self._x_else_default_x(x)\n        self.potential_fn.set_x(x)\n\n        theta = ensure_theta_batched(torch.as_tensor(theta))\n\n        with torch.set_grad_enabled(track_gradients):\n            potential_values = self.potential_fn(\n                theta.to(self._device), track_gradients=track_gradients\n            )\n\n            if normalization_constant_params is None:\n                normalization_constant_params = dict()  # use defaults\n            normalization_constant = self.estimate_normalization_constant(\n                x, **normalization_constant_params\n            )\n\n            return (potential_values - torch.log(normalization_constant)).to(\n                self._device\n            )\n\n    @torch.no_grad()\n    def estimate_normalization_constant(\n        self, x: Tensor, num_samples: int = 10_000, force_update: bool = False\n    ) -&gt; Tensor:\n\"\"\"Returns the normalization constant via importance sampling.\n\n        Args:\n            num_samples: Number of importance samples used for the estimate.\n            force_update: Whether to re-calculate the normlization constant when x is\n                unchanged and have a cached value.\n        \"\"\"\n        # Check if the provided x matches the default x (short-circuit on identity).\n        is_new_x = self.default_x is None or (\n            x is not self.default_x and (x != self.default_x).any()\n        )\n\n        not_saved_at_default_x = self._normalization_constant is None\n\n        if is_new_x:  # Calculate at x; don't save.\n            _, log_importance_weights = importance_sample(\n                self.potential_fn,\n                proposal=self.proposal,\n                num_samples=num_samples,\n            )\n            return torch.mean(torch.exp(log_importance_weights))\n        elif not_saved_at_default_x or force_update:  # Calculate at default_x; save.\n            assert self.default_x is not None\n            _, log_importance_weights = importance_sample(\n                self.potential_fn,\n                proposal=self.proposal,\n                num_samples=num_samples,\n            )\n            self._normalization_constant = torch.mean(torch.exp(log_importance_weights))\n\n        return self._normalization_constant.to(self._device)  # type: ignore\n\n    def sample(\n        self,\n        sample_shape: Shape = torch.Size(),\n        x: Optional[Tensor] = None,\n        oversampling_factor: int = 32,\n        max_sampling_batch_size: int = 10_000,\n        sample_with: Optional[str] = None,\n    ) -&gt; Union[Tensor, Tuple[Tensor, Tensor]]:\n\"\"\"Return samples from the approximate posterior distribution.\n\n        Args:\n            sample_shape: _description_\n            x: _description_\n        \"\"\"\n        if sample_with is not None:\n            raise ValueError(\n                f\"You set `sample_with={sample_with}`. As of sbi v0.18.0, setting \"\n                f\"`sample_with` is no longer supported. You have to rerun \"\n                f\"`.build_posterior(sample_with={sample_with}).`\"\n            )\n\n        self.potential_fn.set_x(self._x_else_default_x(x))\n\n        if self.method == \"sir\":\n            return self._sir_sample(\n                sample_shape,\n                oversampling_factor=oversampling_factor,\n                max_sampling_batch_size=max_sampling_batch_size,\n            )\n        elif self.method == \"importance\":\n            return self._importance_sample(sample_shape)\n        else:\n            raise NameError\n\n    def _importance_sample(\n        self,\n        sample_shape: Shape = torch.Size(),\n    ) -&gt; Tuple[Tensor, Tensor]:\n\"\"\"Returns samples from the proposal and log of their importance weights.\n\n        Args:\n            sample_shape: Desired shape of samples that are drawn from posterior.\n            sample_with: This argument only exists to keep backward-compatibility with\n                `sbi` v0.17.2 or older. If it is set, we instantly raise an error.\n\n        Returns:\n            Samples and logarithm of corresponding importance weights.\n        \"\"\"\n        num_samples = torch.Size(sample_shape).numel()\n        samples, log_importance_weights = importance_sample(\n            self.potential_fn,\n            proposal=self.proposal,\n            num_samples=num_samples,\n        )\n\n        samples = samples.reshape((*sample_shape, -1)).to(self._device)\n        return samples, log_importance_weights.to(self._device)\n\n    def _sir_sample(\n        self,\n        sample_shape: Shape = torch.Size(),\n        oversampling_factor: int = 32,\n        max_sampling_batch_size: int = 10_000,\n        show_progress_bars: bool = True,\n    ):\nr\"\"\"Returns approximate samples from posterior $p(\\theta|x)$ via SIR.\n\n        Args:\n            sample_shape: Desired shape of samples that are drawn from posterior. If\n                sample_shape is multidimensional we simply draw `sample_shape.numel()`\n                samples and then reshape into the desired shape.\n            x: Observed data.\n            sample_with: This argument only exists to keep backward-compatibility with\n                `sbi` v0.17.2 or older. If it is set, we instantly raise an error.\n            oversampling_factor: Number of proposed samples form which only one is\n                selected based on its importance weight.\n            max_sampling_batch_size: The batchsize of samples being drawn from\n                the proposal at every iteration. Used only in `sir_sample()`.\n            show_progress_bars: Whether to show sampling progress monitor.\n\n        Returns:\n            Samples from posterior.\n        \"\"\"\n        # Replace arguments that were not passed with their default.\n        oversampling_factor = (\n            self.oversampling_factor\n            if oversampling_factor is None\n            else oversampling_factor\n        )\n        max_sampling_batch_size = (\n            self.max_sampling_batch_size\n            if max_sampling_batch_size is None\n            else max_sampling_batch_size\n        )\n\n        num_samples = torch.Size(sample_shape).numel()\n        samples = sampling_importance_resampling(\n            self.potential_fn,\n            proposal=self.proposal,\n            num_samples=num_samples,\n            oversampling_factor=oversampling_factor,\n            show_progress_bars=show_progress_bars,\n            max_sampling_batch_size=max_sampling_batch_size,\n            device=self._device,\n        )\n\n        return samples.reshape((*sample_shape, -1)).to(self._device)\n\n    def map(\n        self,\n        x: Optional[Tensor] = None,\n        num_iter: int = 1_000,\n        num_to_optimize: int = 100,\n        learning_rate: float = 0.01,\n        init_method: Union[str, Tensor] = \"proposal\",\n        num_init_samples: int = 1_000,\n        save_best_every: int = 10,\n        show_progress_bars: bool = False,\n        force_update: bool = False,\n    ) -&gt; Tensor:\nr\"\"\"Returns the maximum-a-posteriori estimate (MAP).\n\n        The method can be interrupted (Ctrl-C) when the user sees that the\n        log-probability converges. The best estimate will be saved in `self._map` and\n        can be accessed with `self.map()`. The MAP is obtained by running gradient\n        ascent from a given number of starting positions (samples from the posterior\n        with the highest log-probability). After the optimization is done, we select the\n        parameter set that has the highest log-probability after the optimization.\n\n        Warning: The default values used by this function are not well-tested. They\n        might require hand-tuning for the problem at hand.\n\n        For developers: if the prior is a `BoxUniform`, we carry out the optimization\n        in unbounded space and transform the result back into bounded space.\n\n        Args:\n            x: Deprecated - use `.set_default_x()` prior to `.map()`.\n            num_iter: Number of optimization steps that the algorithm takes\n                to find the MAP.\n            learning_rate: Learning rate of the optimizer.\n            init_method: How to select the starting parameters for the optimization. If\n                it is a string, it can be either [`posterior`, `prior`], which samples\n                the respective distribution `num_init_samples` times. If it is a\n                tensor, the tensor will be used as init locations.\n            num_init_samples: Draw this number of samples from the posterior and\n                evaluate the log-probability of all of them.\n            num_to_optimize: From the drawn `num_init_samples`, use the\n                `num_to_optimize` with highest log-probability as the initial points\n                for the optimization.\n            save_best_every: The best log-probability is computed, saved in the\n                `map`-attribute, and printed every `save_best_every`-th iteration.\n                Computing the best log-probability creates a significant overhead\n                (thus, the default is `10`.)\n            show_progress_bars: Whether to show a progressbar during sampling from the\n                posterior.\n            force_update: Whether to re-calculate the MAP when x is unchanged and\n                have a cached value.\n            log_prob_kwargs: Will be empty for SNLE and SNRE. Will contain\n                {'norm_posterior': True} for SNPE.\n\n        Returns:\n            The MAP estimate.\n        \"\"\"\n        return super().map(\n            x=x,\n            num_iter=num_iter,\n            num_to_optimize=num_to_optimize,\n            learning_rate=learning_rate,\n            init_method=init_method,\n            num_init_samples=num_init_samples,\n            save_best_every=save_best_every,\n            show_progress_bars=show_progress_bars,\n            force_update=force_update,\n        )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.__init__","title":"<code>__init__(potential_fn, proposal, theta_transform=None, method='sir', oversampling_factor=32, max_sampling_batch_size=10000, device=None, x_shape=None)</code>","text":"<p>Parameters:</p> Name Type Description Default <code>potential_fn</code> <code>Callable</code> <p>The potential function from which to draw samples.</p> required <code>proposal</code> <code>Any</code> <p>The proposal distribution.</p> required <code>theta_transform</code> <code>Optional[TorchTransform]</code> <p>Transformation that is applied to parameters. Is not used during but only when calling <code>.map()</code>.</p> <code>None</code> <code>method</code> <code>str</code> <p>Either of [<code>sir</code>|<code>importance</code>]. This sets the behavior of the <code>.sample()</code> method. With <code>sir</code>, approximate posterior samples are generated with sampling importance resampling (SIR). With <code>importance</code>, the <code>.sample()</code> method returns a tuple of samples and corresponding importance weights.</p> <code>'sir'</code> <code>oversampling_factor</code> <code>int</code> <p>Number of proposed samples from which only one is selected based on its importance weight.</p> <code>32</code> <code>max_sampling_batch_size</code> <code>int</code> <p>The batch size of samples being drawn from the proposal at every iteration.</p> <code>10000</code> <code>device</code> <code>Optional[str]</code> <p>Device on which to sample, e.g., \u201ccpu\u201d, \u201ccuda\u201d or \u201ccuda:0\u201d. If None, <code>potential_fn.device</code> is used.</p> <code>None</code> <code>x_shape</code> <code>Optional[torch.Size]</code> <p>Shape of a single simulator output. If passed, it is used to check the shape of the observed data and give a descriptive error.</p> <code>None</code> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/importance_posterior.py</code> <pre><code>def __init__(\n    self,\n    potential_fn: Callable,\n    proposal: Any,\n    theta_transform: Optional[TorchTransform] = None,\n    method: str = \"sir\",\n    oversampling_factor: int = 32,\n    max_sampling_batch_size: int = 10_000,\n    device: Optional[str] = None,\n    x_shape: Optional[torch.Size] = None,\n):\n\"\"\"\n    Args:\n        potential_fn: The potential function from which to draw samples.\n        proposal: The proposal distribution.\n        theta_transform: Transformation that is applied to parameters. Is not used\n            during but only when calling `.map()`.\n        method: Either of [`sir`|`importance`]. This sets the behavior of the\n            `.sample()` method. With `sir`, approximate posterior samples are\n            generated with sampling importance resampling (SIR). With\n            `importance`, the `.sample()` method returns a tuple of samples and\n            corresponding importance weights.\n        oversampling_factor: Number of proposed samples from which only one is\n            selected based on its importance weight.\n        max_sampling_batch_size: The batch size of samples being drawn from the\n            proposal at every iteration.\n        device: Device on which to sample, e.g., \"cpu\", \"cuda\" or \"cuda:0\". If\n            None, `potential_fn.device` is used.\n        x_shape: Shape of a single simulator output. If passed, it is used to check\n            the shape of the observed data and give a descriptive error.\n    \"\"\"\n    super().__init__(\n        potential_fn,\n        theta_transform=theta_transform,\n        device=device,\n        x_shape=x_shape,\n    )\n\n    self.proposal = proposal\n    self._normalization_constant = None\n    self.method = method\n\n    self.oversampling_factor = oversampling_factor\n    self.max_sampling_batch_size = max_sampling_batch_size\n\n    self._purpose = (\n        \"It provides sampling-importance resampling (SIR) to .sample() from the \"\n        \"posterior and can evaluate the _unnormalized_ posterior density with \"\n        \".log_prob().\"\n    )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.estimate_normalization_constant","title":"<code>estimate_normalization_constant(x, num_samples=10000, force_update=False)</code>","text":"<p>Returns the normalization constant via importance sampling.</p> <p>Parameters:</p> Name Type Description Default <code>num_samples</code> <code>int</code> <p>Number of importance samples used for the estimate.</p> <code>10000</code> <code>force_update</code> <code>bool</code> <p>Whether to re-calculate the normlization constant when x is unchanged and have a cached value.</p> <code>False</code> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/importance_posterior.py</code> <pre><code>@torch.no_grad()\ndef estimate_normalization_constant(\n    self, x: Tensor, num_samples: int = 10_000, force_update: bool = False\n) -&gt; Tensor:\n\"\"\"Returns the normalization constant via importance sampling.\n\n    Args:\n        num_samples: Number of importance samples used for the estimate.\n        force_update: Whether to re-calculate the normlization constant when x is\n            unchanged and have a cached value.\n    \"\"\"\n    # Check if the provided x matches the default x (short-circuit on identity).\n    is_new_x = self.default_x is None or (\n        x is not self.default_x and (x != self.default_x).any()\n    )\n\n    not_saved_at_default_x = self._normalization_constant is None\n\n    if is_new_x:  # Calculate at x; don't save.\n        _, log_importance_weights = importance_sample(\n            self.potential_fn,\n            proposal=self.proposal,\n            num_samples=num_samples,\n        )\n        return torch.mean(torch.exp(log_importance_weights))\n    elif not_saved_at_default_x or force_update:  # Calculate at default_x; save.\n        assert self.default_x is not None\n        _, log_importance_weights = importance_sample(\n            self.potential_fn,\n            proposal=self.proposal,\n            num_samples=num_samples,\n        )\n        self._normalization_constant = torch.mean(torch.exp(log_importance_weights))\n\n    return self._normalization_constant.to(self._device)  # type: ignore\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.log_prob","title":"<code>log_prob(theta, x=None, track_gradients=False, normalization_constant_params=None)</code>","text":"<p>Returns the log-probability of theta under the posterior.</p> <p>The normalization constant is estimated with importance sampling.</p> <p>Parameters:</p> Name Type Description Default <code>theta</code> <code>Tensor</code> <p>Parameters \\(\\theta\\).</p> required <code>track_gradients</code> <code>bool</code> <p>Whether the returned tensor supports tracking gradients. This can be helpful for e.g. sensitivity analysis, but increases memory consumption.</p> <code>False</code> <code>normalization_constant_params</code> <code>Optional[dict]</code> <p>Parameters passed on to <code>estimate_normalization_constant()</code>.</p> <code>None</code> <p>Returns:</p> Type Description <code>Tensor</code> <p><code>len($\\theta$)</code>-shaped log-probability.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/importance_posterior.py</code> <pre><code>def log_prob(\n    self,\n    theta: Tensor,\n    x: Optional[Tensor] = None,\n    track_gradients: bool = False,\n    normalization_constant_params: Optional[dict] = None,\n) -&gt; Tensor:\nr\"\"\"Returns the log-probability of theta under the posterior.\n\n    The normalization constant is estimated with importance sampling.\n\n    Args:\n        theta: Parameters $\\theta$.\n        track_gradients: Whether the returned tensor supports tracking gradients.\n            This can be helpful for e.g. sensitivity analysis, but increases memory\n            consumption.\n        normalization_constant_params: Parameters passed on to\n            `estimate_normalization_constant()`.\n\n    Returns:\n        `len($\\theta$)`-shaped log-probability.\n    \"\"\"\n    x = self._x_else_default_x(x)\n    self.potential_fn.set_x(x)\n\n    theta = ensure_theta_batched(torch.as_tensor(theta))\n\n    with torch.set_grad_enabled(track_gradients):\n        potential_values = self.potential_fn(\n            theta.to(self._device), track_gradients=track_gradients\n        )\n\n        if normalization_constant_params is None:\n            normalization_constant_params = dict()  # use defaults\n        normalization_constant = self.estimate_normalization_constant(\n            x, **normalization_constant_params\n        )\n\n        return (potential_values - torch.log(normalization_constant)).to(\n            self._device\n        )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.map","title":"<code>map(x=None, num_iter=1000, num_to_optimize=100, learning_rate=0.01, init_method='proposal', num_init_samples=1000, save_best_every=10, show_progress_bars=False, force_update=False)</code>","text":"<p>Returns the maximum-a-posteriori estimate (MAP).</p> <p>The method can be interrupted (Ctrl-C) when the user sees that the log-probability converges. The best estimate will be saved in <code>self._map</code> and can be accessed with <code>self.map()</code>. The MAP is obtained by running gradient ascent from a given number of starting positions (samples from the posterior with the highest log-probability). After the optimization is done, we select the parameter set that has the highest log-probability after the optimization.</p> <p>Warning: The default values used by this function are not well-tested. They might require hand-tuning for the problem at hand.</p> <p>For developers: if the prior is a <code>BoxUniform</code>, we carry out the optimization in unbounded space and transform the result back into bounded space.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>Optional[Tensor]</code> <p>Deprecated - use <code>.set_default_x()</code> prior to <code>.map()</code>.</p> <code>None</code> <code>num_iter</code> <code>int</code> <p>Number of optimization steps that the algorithm takes to find the MAP.</p> <code>1000</code> <code>learning_rate</code> <code>float</code> <p>Learning rate of the optimizer.</p> <code>0.01</code> <code>init_method</code> <code>Union[str, Tensor]</code> <p>How to select the starting parameters for the optimization. If it is a string, it can be either [<code>posterior</code>, <code>prior</code>], which samples the respective distribution <code>num_init_samples</code> times. If it is a tensor, the tensor will be used as init locations.</p> <code>'proposal'</code> <code>num_init_samples</code> <code>int</code> <p>Draw this number of samples from the posterior and evaluate the log-probability of all of them.</p> <code>1000</code> <code>num_to_optimize</code> <code>int</code> <p>From the drawn <code>num_init_samples</code>, use the <code>num_to_optimize</code> with highest log-probability as the initial points for the optimization.</p> <code>100</code> <code>save_best_every</code> <code>int</code> <p>The best log-probability is computed, saved in the <code>map</code>-attribute, and printed every <code>save_best_every</code>-th iteration. Computing the best log-probability creates a significant overhead (thus, the default is <code>10</code>.)</p> <code>10</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during sampling from the posterior.</p> <code>False</code> <code>force_update</code> <code>bool</code> <p>Whether to re-calculate the MAP when x is unchanged and have a cached value.</p> <code>False</code> <code>log_prob_kwargs</code> <p>Will be empty for SNLE and SNRE. Will contain {\u2018norm_posterior\u2019: True} for SNPE.</p> required <p>Returns:</p> Type Description <code>Tensor</code> <p>The MAP estimate.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/importance_posterior.py</code> <pre><code>def map(\n    self,\n    x: Optional[Tensor] = None,\n    num_iter: int = 1_000,\n    num_to_optimize: int = 100,\n    learning_rate: float = 0.01,\n    init_method: Union[str, Tensor] = \"proposal\",\n    num_init_samples: int = 1_000,\n    save_best_every: int = 10,\n    show_progress_bars: bool = False,\n    force_update: bool = False,\n) -&gt; Tensor:\nr\"\"\"Returns the maximum-a-posteriori estimate (MAP).\n\n    The method can be interrupted (Ctrl-C) when the user sees that the\n    log-probability converges. The best estimate will be saved in `self._map` and\n    can be accessed with `self.map()`. The MAP is obtained by running gradient\n    ascent from a given number of starting positions (samples from the posterior\n    with the highest log-probability). After the optimization is done, we select the\n    parameter set that has the highest log-probability after the optimization.\n\n    Warning: The default values used by this function are not well-tested. They\n    might require hand-tuning for the problem at hand.\n\n    For developers: if the prior is a `BoxUniform`, we carry out the optimization\n    in unbounded space and transform the result back into bounded space.\n\n    Args:\n        x: Deprecated - use `.set_default_x()` prior to `.map()`.\n        num_iter: Number of optimization steps that the algorithm takes\n            to find the MAP.\n        learning_rate: Learning rate of the optimizer.\n        init_method: How to select the starting parameters for the optimization. If\n            it is a string, it can be either [`posterior`, `prior`], which samples\n            the respective distribution `num_init_samples` times. If it is a\n            tensor, the tensor will be used as init locations.\n        num_init_samples: Draw this number of samples from the posterior and\n            evaluate the log-probability of all of them.\n        num_to_optimize: From the drawn `num_init_samples`, use the\n            `num_to_optimize` with highest log-probability as the initial points\n            for the optimization.\n        save_best_every: The best log-probability is computed, saved in the\n            `map`-attribute, and printed every `save_best_every`-th iteration.\n            Computing the best log-probability creates a significant overhead\n            (thus, the default is `10`.)\n        show_progress_bars: Whether to show a progressbar during sampling from the\n            posterior.\n        force_update: Whether to re-calculate the MAP when x is unchanged and\n            have a cached value.\n        log_prob_kwargs: Will be empty for SNLE and SNRE. Will contain\n            {'norm_posterior': True} for SNPE.\n\n    Returns:\n        The MAP estimate.\n    \"\"\"\n    return super().map(\n        x=x,\n        num_iter=num_iter,\n        num_to_optimize=num_to_optimize,\n        learning_rate=learning_rate,\n        init_method=init_method,\n        num_init_samples=num_init_samples,\n        save_best_every=save_best_every,\n        show_progress_bars=show_progress_bars,\n        force_update=force_update,\n    )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.sample","title":"<code>sample(sample_shape=torch.Size(), x=None, oversampling_factor=32, max_sampling_batch_size=10000, sample_with=None)</code>","text":"<p>Return samples from the approximate posterior distribution.</p> <p>Parameters:</p> Name Type Description Default <code>sample_shape</code> <code>Shape</code> <p>description</p> <code>torch.Size()</code> <code>x</code> <code>Optional[Tensor]</code> <p>description</p> <code>None</code> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/importance_posterior.py</code> <pre><code>def sample(\n    self,\n    sample_shape: Shape = torch.Size(),\n    x: Optional[Tensor] = None,\n    oversampling_factor: int = 32,\n    max_sampling_batch_size: int = 10_000,\n    sample_with: Optional[str] = None,\n) -&gt; Union[Tensor, Tuple[Tensor, Tensor]]:\n\"\"\"Return samples from the approximate posterior distribution.\n\n    Args:\n        sample_shape: _description_\n        x: _description_\n    \"\"\"\n    if sample_with is not None:\n        raise ValueError(\n            f\"You set `sample_with={sample_with}`. As of sbi v0.18.0, setting \"\n            f\"`sample_with` is no longer supported. You have to rerun \"\n            f\"`.build_posterior(sample_with={sample_with}).`\"\n        )\n\n    self.potential_fn.set_x(self._x_else_default_x(x))\n\n    if self.method == \"sir\":\n        return self._sir_sample(\n            sample_shape,\n            oversampling_factor=oversampling_factor,\n            max_sampling_batch_size=max_sampling_batch_size,\n        )\n    elif self.method == \"importance\":\n        return self._importance_sample(sample_shape)\n    else:\n        raise NameError\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior","title":"<code>sbi.inference.posteriors.mcmc_posterior.MCMCPosterior</code>","text":"<p>         Bases: <code>NeuralPosterior</code></p> <p>Provides MCMC to sample from the posterior. SNLE or SNRE train neural networks to approximate the likelihood(-ratios). <code>MCMCPosterior</code> allows to sample from the posterior with MCMC.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/mcmc_posterior.py</code> <pre><code>class MCMCPosterior(NeuralPosterior):\nr\"\"\"Provides MCMC to sample from the posterior.&lt;br/&gt;&lt;br/&gt;\n    SNLE or SNRE train neural networks to approximate the likelihood(-ratios).\n    `MCMCPosterior` allows to sample from the posterior with MCMC.\n    \"\"\"\n\n    def __init__(\n        self,\n        potential_fn: Callable,\n        proposal: Any,\n        theta_transform: Optional[TorchTransform] = None,\n        method: str = \"slice_np\",\n        thin: int = 10,\n        warmup_steps: int = 10,\n        num_chains: int = 1,\n        init_strategy: str = \"resample\",\n        init_strategy_parameters: Dict[str, Any] = {},\n        init_strategy_num_candidates: Optional[int] = None,\n        num_workers: int = 1,\n        device: Optional[str] = None,\n        x_shape: Optional[torch.Size] = None,\n    ):\n\"\"\"\n        Args:\n            potential_fn: The potential function from which to draw samples.\n            proposal: Proposal distribution that is used to initialize the MCMC chain.\n            theta_transform: Transformation that will be applied during sampling.\n                Allows to perform MCMC in unconstrained space.\n            method: Method used for MCMC sampling, one of `slice_np`,\n                `slice_np_vectorized`, `slice`, `hmc`, `nuts`. `slice_np` is a custom\n                numpy implementation of slice sampling. `slice_np_vectorized` is\n                identical to `slice_np`, but if `num_chains&gt;1`, the chains are\n                vectorized for `slice_np_vectorized` whereas they are run sequentially\n                for `slice_np`. The samplers `hmc`, `nuts` or `slice` sample with Pyro.\n            thin: The thinning factor for the chain.\n            warmup_steps: The initial number of samples to discard.\n            num_chains: The number of chains.\n            init_strategy: The initialisation strategy for chains; `proposal` will draw\n                init locations from `proposal`, whereas `sir` will use Sequential-\n                Importance-Resampling (SIR). SIR initially samples\n                `init_strategy_num_candidates` from the `proposal`, evaluates all of\n                them under the `potential_fn` and `proposal`, and then resamples the\n                initial locations with weights proportional to `exp(potential_fn -\n                proposal.log_prob`. `resample` is the same as `sir` but\n                uses `exp(potential_fn)` as weights.\n            init_strategy_parameters: Dictionary of keyword arguments passed to the\n                init strategy, e.g., for `init_strategy=sir` this could be\n                `num_candidate_samples`, i.e., the number of candidates to to find init\n                locations (internal default is `1000`), or `device`.\n            init_strategy_num_candidates: Number of candidates to to find init\n                 locations in `init_strategy=sir` (deprecated, use\n                 init_strategy_parameters instead).\n            num_workers: number of cpu cores used to parallelize mcmc\n            device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:0\". If None,\n                `potential_fn.device` is used.\n            x_shape: Shape of a single simulator output. If passed, it is used to check\n                the shape of the observed data and give a descriptive error.\n        \"\"\"\n\n        super().__init__(\n            potential_fn,\n            theta_transform=theta_transform,\n            device=device,\n            x_shape=x_shape,\n        )\n\n        self.proposal = proposal\n        self.method = method\n        self.thin = thin\n        self.warmup_steps = warmup_steps\n        self.num_chains = num_chains\n        self.init_strategy = init_strategy\n        self.init_strategy_parameters = init_strategy_parameters\n        self.num_workers = num_workers\n        self._posterior_sampler = None\n        # Hardcode parameter name to reduce clutter kwargs.\n        self.param_name = \"theta\"\n\n        if init_strategy_num_candidates is not None:\n            warn(\n\"\"\"Passing `init_strategy_num_candidates` is deprecated as of sbi\n                v0.19.0. Instead, use e.g.,\n                `init_strategy_parameters={\"num_candidate_samples\": 1000}`\"\"\"\n            )\n            self.init_strategy_parameters[\n                \"num_candidate_samples\"\n            ] = init_strategy_num_candidates\n\n        self.potential_ = self._prepare_potential(method)\n\n        self._purpose = (\n            \"It provides MCMC to .sample() from the posterior and \"\n            \"can evaluate the _unnormalized_ posterior density with .log_prob().\"\n        )\n\n    @property\n    def mcmc_method(self) -&gt; str:\n\"\"\"Returns MCMC method.\"\"\"\n        return self._mcmc_method\n\n    @mcmc_method.setter\n    def mcmc_method(self, method: str) -&gt; None:\n\"\"\"See `set_mcmc_method`.\"\"\"\n        self.set_mcmc_method(method)\n\n    @property\n    def posterior_sampler(self):\n\"\"\"Returns sampler created by `sample`.\"\"\"\n        return self._posterior_sampler\n\n    def set_mcmc_method(self, method: str) -&gt; \"NeuralPosterior\":\n\"\"\"Sets sampling method to for MCMC and returns `NeuralPosterior`.\n\n        Args:\n            method: Method to use.\n\n        Returns:\n            `NeuralPosterior` for chainable calls.\n        \"\"\"\n        self._mcmc_method = method\n        return self\n\n    def log_prob(\n        self, theta: Tensor, x: Optional[Tensor] = None, track_gradients: bool = False\n    ) -&gt; Tensor:\nr\"\"\"Returns the log-probability of theta under the posterior.\n\n        Args:\n            theta: Parameters $\\theta$.\n            track_gradients: Whether the returned tensor supports tracking gradients.\n                This can be helpful for e.g. sensitivity analysis, but increases memory\n                consumption.\n\n        Returns:\n            `len($\\theta$)`-shaped log-probability.\n        \"\"\"\n        warn(\n\"\"\"`.log_prob()` is deprecated for methods that can only evaluate the\n            log-probability up to a normalizing constant. Use `.potential()` instead.\"\"\"\n        )\n        warn(\"The log-probability is unnormalized!\")\n\n        self.potential_fn.set_x(self._x_else_default_x(x))\n\n        theta = ensure_theta_batched(torch.as_tensor(theta))\n        return self.potential_fn(\n            theta.to(self._device), track_gradients=track_gradients\n        )\n\n    def sample(\n        self,\n        sample_shape: Shape = torch.Size(),\n        x: Optional[Tensor] = None,\n        method: Optional[str] = None,\n        thin: Optional[int] = None,\n        warmup_steps: Optional[int] = None,\n        num_chains: Optional[int] = None,\n        init_strategy: Optional[str] = None,\n        init_strategy_parameters: Optional[Dict[str, Any]] = None,\n        init_strategy_num_candidates: Optional[int] = None,\n        mcmc_parameters: Dict = {},\n        mcmc_method: Optional[str] = None,\n        sample_with: Optional[str] = None,\n        num_workers: Optional[int] = None,\n        show_progress_bars: bool = True,\n    ) -&gt; Tensor:\nr\"\"\"Return samples from posterior distribution $p(\\theta|x)$ with MCMC.\n\n        Check the `__init__()` method for a description of all arguments as well as\n        their default values.\n\n        Args:\n            sample_shape: Desired shape of samples that are drawn from posterior. If\n                sample_shape is multidimensional we simply draw `sample_shape.numel()`\n                samples and then reshape into the desired shape.\n            mcmc_parameters: Dictionary that is passed only to support the API of\n                `sbi` v0.17.2 or older.\n            mcmc_method: This argument only exists to keep backward-compatibility with\n                `sbi` v0.17.2 or older. Please use `method` instead.\n            sample_with: This argument only exists to keep backward-compatibility with\n                `sbi` v0.17.2 or older. If it is set, we instantly raise an error.\n            show_progress_bars: Whether to show sampling progress monitor.\n\n        Returns:\n            Samples from posterior.\n        \"\"\"\n        self.potential_fn.set_x(self._x_else_default_x(x))\n\n        # Replace arguments that were not passed with their default.\n        method = self.method if method is None else method\n        thin = self.thin if thin is None else thin\n        warmup_steps = self.warmup_steps if warmup_steps is None else warmup_steps\n        num_chains = self.num_chains if num_chains is None else num_chains\n        init_strategy = self.init_strategy if init_strategy is None else init_strategy\n        num_workers = self.num_workers if num_workers is None else num_workers\n        init_strategy_parameters = (\n            self.init_strategy_parameters\n            if init_strategy_parameters is None\n            else init_strategy_parameters\n        )\n        if init_strategy_num_candidates is not None:\n            warn(\n\"\"\"Passing `init_strategy_num_candidates` is deprecated as of sbi\n                v0.19.0. Instead, use e.g.,\n                `init_strategy_parameters={\"num_candidate_samples\": 1000}`\"\"\"\n            )\n            self.init_strategy_parameters[\n                \"num_candidate_samples\"\n            ] = init_strategy_num_candidates\n        if sample_with is not None:\n            raise ValueError(\n                f\"You set `sample_with={sample_with}`. As of sbi v0.18.0, setting \"\n                f\"`sample_with` is no longer supported. You have to rerun \"\n                f\"`.build_posterior(sample_with={sample_with}).`\"\n            )\n        if mcmc_method is not None:\n            warn(\n                \"You passed `mcmc_method` to `.sample()`. As of sbi v0.18.0, this \"\n                \"is deprecated and will be removed in a future release. Use `method` \"\n                \"instead of `mcmc_method`.\"\n            )\n            method = mcmc_method\n        if mcmc_parameters:\n            warn(\n                \"You passed `mcmc_parameters` to `.sample()`. As of sbi v0.18.0, this \"\n                \"is deprecated and will be removed in a future release. Instead, pass \"\n                \"the variable to `.sample()` directly, e.g. \"\n                \"`posterior.sample((1,), num_chains=5)`.\"\n            )\n        # The following lines are only for backwards compatibility with sbi v0.17.2 or\n        # older.\n        m_p = mcmc_parameters  # define to shorten the variable name\n        method = _maybe_use_dict_entry(method, \"mcmc_method\", m_p)\n        thin = _maybe_use_dict_entry(thin, \"thin\", m_p)\n        warmup_steps = _maybe_use_dict_entry(warmup_steps, \"warmup_steps\", m_p)\n        num_chains = _maybe_use_dict_entry(num_chains, \"num_chains\", m_p)\n        init_strategy = _maybe_use_dict_entry(init_strategy, \"init_strategy\", m_p)\n        self.potential_ = self._prepare_potential(method)  # type: ignore\n\n        initial_params = self._get_initial_params(\n            init_strategy,  # type: ignore\n            num_chains,  # type: ignore\n            num_workers,\n            show_progress_bars,\n            **init_strategy_parameters,\n        )\n        num_samples = torch.Size(sample_shape).numel()\n\n        track_gradients = method in (\"hmc\", \"nuts\")\n        with torch.set_grad_enabled(track_gradients):\n            if method in (\"slice_np\", \"slice_np_vectorized\"):\n                transformed_samples = self._slice_np_mcmc(\n                    num_samples=num_samples,\n                    potential_function=self.potential_,\n                    initial_params=initial_params,\n                    thin=thin,  # type: ignore\n                    warmup_steps=warmup_steps,  # type: ignore\n                    vectorized=(method == \"slice_np_vectorized\"),\n                    num_workers=num_workers,\n                    show_progress_bars=show_progress_bars,\n                )\n            elif method in (\"hmc\", \"nuts\", \"slice\"):\n                transformed_samples = self._pyro_mcmc(\n                    num_samples=num_samples,\n                    potential_function=self.potential_,\n                    initial_params=initial_params,\n                    mcmc_method=method,  # type: ignore\n                    thin=thin,  # type: ignore\n                    warmup_steps=warmup_steps,  # type: ignore\n                    num_chains=num_chains,\n                    show_progress_bars=show_progress_bars,\n                )\n            else:\n                raise NameError\n\n        samples = self.theta_transform.inv(transformed_samples)\n\n        return samples.reshape((*sample_shape, -1))  # type: ignore\n\n    def _build_mcmc_init_fn(\n        self,\n        proposal: Any,\n        potential_fn: Callable,\n        transform: torch_tf.Transform,\n        init_strategy: str,\n        **kwargs,\n    ) -&gt; Callable:\n\"\"\"Return function that, when called, creates an initial parameter set for MCMC.\n\n        Args:\n            proposal: Proposal distribution.\n            potential_fn: Potential function that the candidate samples are weighted\n                with.\n            init_strategy: Specifies the initialization method. Either of\n                [`proposal`|`sir`|`resample`|`latest_sample`].\n            kwargs: Passed on to init function. This way, init specific keywords can\n                be set through `mcmc_parameters`. Unused arguments will be absorbed by\n                the intitialization method.\n\n        Returns: Initialization function.\n        \"\"\"\n        if init_strategy == \"proposal\" or init_strategy == \"prior\":\n            if init_strategy == \"prior\":\n                warn(\n                    \"You set `init_strategy=prior`. As of sbi v0.18.0, this is \"\n                    \"deprecated and it will be removed in a future release. Use \"\n                    \"`init_strategy=proposal` instead.\"\n                )\n            return lambda: proposal_init(proposal, transform=transform, **kwargs)\n        elif init_strategy == \"sir\":\n            warn(\n                \"As of sbi v0.19.0, the behavior of the SIR initialization for MCMC \"\n                \"has changed. If you wish to restore the behavior of sbi v0.18.0, set \"\n                \"`init_strategy='resample'.`\"\n            )\n            return lambda: sir_init(\n                proposal, potential_fn, transform=transform, **kwargs\n            )\n        elif init_strategy == \"resample\":\n            return lambda: resample_given_potential_fn(\n                proposal, potential_fn, transform=transform, **kwargs\n            )\n        elif init_strategy == \"latest_sample\":\n            latest_sample = IterateParameters(self._mcmc_init_params, **kwargs)\n            return latest_sample\n        else:\n            raise NotImplementedError\n\n    def _get_initial_params(\n        self,\n        init_strategy: str,\n        num_chains: int,\n        num_workers: int,\n        show_progress_bars: bool,\n        **kwargs,\n    ) -&gt; Tensor:\n\"\"\"Return initial parameters for MCMC obtained with given init strategy.\n\n        Parallelizes across CPU cores only for SIR.\n\n        Args:\n            init_strategy: Specifies the initialization method. Either of\n                [`proposal`|`sir`|`resample`|`latest_sample`].\n            num_chains: number of MCMC chains, generates initial params for each\n            num_workers: number of CPU cores for parallization\n            show_progress_bars: whether to show progress bars for SIR init\n            kwargs: Passed on to `_build_mcmc_init_fn`.\n\n        Returns:\n            Tensor: initial parameters, one for each chain\n        \"\"\"\n        # Build init function\n        init_fn = self._build_mcmc_init_fn(\n            self.proposal,\n            self.potential_fn,\n            transform=self.theta_transform,\n            init_strategy=init_strategy,  # type: ignore\n            **kwargs,\n        )\n\n        # Parallelize inits for resampling only.\n        if num_workers &gt; 1 and (init_strategy == \"resample\" or init_strategy == \"sir\"):\n\n            def seeded_init_fn(seed):\n                torch.manual_seed(seed)\n                return init_fn()\n\n            seeds = torch.randint(high=2**31, size=(num_chains,))\n\n            # Generate initial params parallelized over num_workers.\n            with tqdm_joblib(\n                tqdm(\n                    range(num_chains),  # type: ignore\n                    disable=not show_progress_bars,\n                    desc=f\"\"\"Generating {num_chains} MCMC inits with {num_workers}\n                         workers.\"\"\",\n                    total=num_chains,\n                )\n            ):\n                initial_params = torch.cat(\n                    Parallel(n_jobs=num_workers)(\n                        delayed(seeded_init_fn)(seed) for seed in seeds\n                    )\n                )\n        else:\n            initial_params = torch.cat(\n                [init_fn() for _ in range(num_chains)]  # type: ignore\n            )\n\n        return initial_params\n\n    def _slice_np_mcmc(\n        self,\n        num_samples: int,\n        potential_function: Callable,\n        initial_params: Tensor,\n        thin: int,\n        warmup_steps: int,\n        vectorized: bool = False,\n        num_workers: int = 1,\n        init_width: Union[float, ndarray] = 0.01,\n        show_progress_bars: bool = True,\n    ) -&gt; Tensor:\n\"\"\"Custom implementation of slice sampling using Numpy.\n\n        Args:\n            num_samples: Desired number of samples.\n            potential_function: A callable **class**.\n            initial_params: Initial parameters for MCMC chain.\n            thin: Thinning (subsampling) factor.\n            warmup_steps: Initial number of samples to discard.\n            vectorized: Whether to use a vectorized implementation of the Slice sampler.\n            num_workers: Number of CPU cores to use.\n            init_width: Inital width of brackets.\n            show_progress_bars: Whether to show a progressbar during sampling;\n                can only be turned off for vectorized sampler.\n\n        Returns:\n            Tensor of shape (num_samples, shape_of_single_theta).\n        \"\"\"\n\n        num_chains, dim_samples = initial_params.shape\n\n        if not vectorized:\n            SliceSamplerMultiChain = SliceSamplerSerial\n        else:\n            SliceSamplerMultiChain = SliceSamplerVectorized\n\n        posterior_sampler = SliceSamplerMultiChain(\n            init_params=tensor2numpy(initial_params),\n            log_prob_fn=potential_function,\n            num_chains=num_chains,\n            thin=thin,\n            verbose=show_progress_bars,\n            num_workers=num_workers,\n            init_width=init_width,\n        )\n        warmup_ = warmup_steps * thin\n        num_samples_ = ceil((num_samples * thin) / num_chains)\n        # Run mcmc including warmup\n        samples = posterior_sampler.run(warmup_ + num_samples_)\n        samples = samples[:, warmup_steps:, :]  # discard warmup steps\n        samples = torch.from_numpy(samples)  # chains x samples x dim\n\n        # Save posterior sampler.\n        self._posterior_sampler = posterior_sampler\n\n        # Save sample as potential next init (if init_strategy == 'latest_sample').\n        self._mcmc_init_params = samples[:, -1, :].reshape(num_chains, dim_samples)\n\n        # Collect samples from all chains.\n        samples = samples.reshape(-1, dim_samples)[:num_samples, :]\n        assert samples.shape[0] == num_samples\n\n        return samples.type(torch.float32).to(self._device)\n\n    def _pyro_mcmc(\n        self,\n        num_samples: int,\n        potential_function: Callable,\n        initial_params: Tensor,\n        mcmc_method: str = \"slice\",\n        thin: int = 10,\n        warmup_steps: int = 200,\n        num_chains: Optional[int] = 1,\n        show_progress_bars: bool = True,\n    ) -&gt; Tensor:\nr\"\"\"Return samples obtained using Pyro HMC, NUTS for slice kernels.\n\n        Args:\n            num_samples: Desired number of samples.\n            potential_function: A callable **class**. A class, but not a function,\n                is picklable for Pyro MCMC to use it across chains in parallel,\n                even when the potential function requires evaluating a neural network.\n            mcmc_method: One of `hmc`, `nuts` or `slice`.\n            thin: Thinning (subsampling) factor.\n            warmup_steps: Initial number of samples to discard.\n            num_chains: Whether to sample in parallel. If None, use all but one CPU.\n            show_progress_bars: Whether to show a progressbar during sampling.\n\n        Returns:\n            Tensor of shape (num_samples, shape_of_single_theta).\n        \"\"\"\n        num_chains = mp.cpu_count() - 1 if num_chains is None else num_chains\n\n        kernels = dict(slice=Slice, hmc=HMC, nuts=NUTS)\n\n        sampler = MCMC(\n            kernel=kernels[mcmc_method](potential_fn=potential_function),\n            num_samples=(thin * num_samples) // num_chains + num_chains,\n            warmup_steps=warmup_steps,\n            initial_params={self.param_name: initial_params},\n            num_chains=num_chains,\n            mp_context=\"spawn\",\n            disable_progbar=not show_progress_bars,\n            transforms={},\n        )\n        sampler.run()\n        samples = next(iter(sampler.get_samples().values())).reshape(\n            -1, initial_params.shape[1]  # .shape[1] = dim of theta\n        )\n\n        # Save posterior sampler.\n        self._posterior_sampler = sampler\n\n        samples = samples[::thin][:num_samples]\n        assert samples.shape[0] == num_samples\n\n        return samples.detach()\n\n    def _prepare_potential(self, method: str) -&gt; Callable:\n\"\"\"Combines potential and transform and takes care of gradients and pyro.\n\n        Args:\n            method: Which MCMC method to use.\n\n        Returns:\n            A potential function that is ready to be used in MCMC.\n        \"\"\"\n        if method == \"slice\":\n            track_gradients = False\n            pyro = True\n        elif method in (\"hmc\", \"nuts\"):\n            track_gradients = True\n            pyro = True\n        elif \"slice_np\" in method:\n            track_gradients = False\n            pyro = False\n        else:\n            raise NotImplementedError\n\n        prepared_potential = partial(\n            transformed_potential,\n            potential_fn=self.potential_fn,\n            theta_transform=self.theta_transform,\n            device=self._device,\n            track_gradients=track_gradients,\n        )\n        if pyro:\n            prepared_potential = partial(\n                pyro_potential_wrapper, potential=prepared_potential\n            )\n\n        return prepared_potential\n\n    def map(\n        self,\n        x: Optional[Tensor] = None,\n        num_iter: int = 1_000,\n        num_to_optimize: int = 100,\n        learning_rate: float = 0.01,\n        init_method: Union[str, Tensor] = \"proposal\",\n        num_init_samples: int = 1_000,\n        save_best_every: int = 10,\n        show_progress_bars: bool = False,\n        force_update: bool = False,\n    ) -&gt; Tensor:\nr\"\"\"Returns the maximum-a-posteriori estimate (MAP).\n\n        The method can be interrupted (Ctrl-C) when the user sees that the\n        log-probability converges. The best estimate will be saved in `self._map` and\n        can be accessed with `self.map()`. The MAP is obtained by running gradient\n        ascent from a given number of starting positions (samples from the posterior\n        with the highest log-probability). After the optimization is done, we select the\n        parameter set that has the highest log-probability after the optimization.\n\n        Warning: The default values used by this function are not well-tested. They\n        might require hand-tuning for the problem at hand.\n\n        For developers: if the prior is a `BoxUniform`, we carry out the optimization\n        in unbounded space and transform the result back into bounded space.\n\n        Args:\n            x: Deprecated - use `.set_default_x()` prior to `.map()`.\n            num_iter: Number of optimization steps that the algorithm takes\n                to find the MAP.\n            learning_rate: Learning rate of the optimizer.\n            init_method: How to select the starting parameters for the optimization. If\n                it is a string, it can be either [`posterior`, `prior`], which samples\n                the respective distribution `num_init_samples` times. If it is a\n                tensor, the tensor will be used as init locations.\n            num_init_samples: Draw this number of samples from the posterior and\n                evaluate the log-probability of all of them.\n            num_to_optimize: From the drawn `num_init_samples`, use the\n                `num_to_optimize` with highest log-probability as the initial points\n                for the optimization.\n            save_best_every: The best log-probability is computed, saved in the\n                `map`-attribute, and printed every `save_best_every`-th iteration.\n                Computing the best log-probability creates a significant overhead\n                (thus, the default is `10`.)\n            show_progress_bars: Whether to show a progressbar during sampling from\n                the posterior.\n            force_update: Whether to re-calculate the MAP when x is unchanged and\n                have a cached value.\n            log_prob_kwargs: Will be empty for SNLE and SNRE. Will contain\n                {'norm_posterior': True} for SNPE.\n\n        Returns:\n            The MAP estimate.\n        \"\"\"\n        return super().map(\n            x=x,\n            num_iter=num_iter,\n            num_to_optimize=num_to_optimize,\n            learning_rate=learning_rate,\n            init_method=init_method,\n            num_init_samples=num_init_samples,\n            save_best_every=save_best_every,\n            show_progress_bars=show_progress_bars,\n            force_update=force_update,\n        )\n\n    def get_arviz_inference_data(self) -&gt; InferenceData:\n\"\"\"Returns arviz InferenceData object constructed most recent samples.\n\n        Note: the InferenceData is constructed using the posterior samples generated in\n        most recent call to `.sample(...)`.\n\n        For Pyro HMC and NUTS kernels InferenceData will contain diagnostics, for Pyro\n        Slice or sbi slice sampling samples, only the samples are added.\n\n        Returns:\n            inference_data: Arviz InferenceData object.\n        \"\"\"\n        assert (\n            self._posterior_sampler is not None\n        ), \"\"\"No samples have been generated, call .sample() first.\"\"\"\n\n        sampler: Union[\n            MCMC, SliceSamplerSerial, SliceSamplerVectorized\n        ] = self._posterior_sampler\n\n        # If Pyro sampler and samples not transformed, use arviz' from_pyro.\n        # Exclude 'slice' kernel as it lacks the 'divergence' diagnostics key.\n        if isinstance(self._posterior_sampler, (HMC, NUTS)) and isinstance(\n            self.theta_transform, torch_tf.IndependentTransform\n        ):\n            inference_data = az.from_pyro(sampler)\n\n        # otherwise get samples from sampler and transform to original space.\n        else:\n            transformed_samples = sampler.get_samples(group_by_chain=True)\n            # Pyro samplers returns dicts, get values.\n            if isinstance(transformed_samples, Dict):\n                # popitem gets last items, [1] get the values as tensor.\n                transformed_samples = transformed_samples.popitem()[1]\n            # Our slice samplers return numpy arrays.\n            elif isinstance(transformed_samples, ndarray):\n                transformed_samples = torch.from_numpy(transformed_samples).type(\n                    torch.float32\n                )\n            # For MultipleIndependent priors transforms first dim must be batch dim.\n            # thus, reshape back and forth to have batch dim in front.\n            samples_shape = transformed_samples.shape\n            samples = self.theta_transform.inv(  # type: ignore\n                transformed_samples.reshape(-1, samples_shape[-1])\n            ).reshape(  # type: ignore\n                *samples_shape\n            )\n\n            inference_data = az.convert_to_inference_data(\n                {f\"{self.param_name}\": samples}\n            )\n\n        return inference_data\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.mcmc_method","title":"<code>mcmc_method: str</code>  <code>property</code> <code>writable</code>","text":"<p>Returns MCMC method.</p>"},{"location":"reference/#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.posterior_sampler","title":"<code>posterior_sampler</code>  <code>property</code>","text":"<p>Returns sampler created by <code>sample</code>.</p>"},{"location":"reference/#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.__init__","title":"<code>__init__(potential_fn, proposal, theta_transform=None, method='slice_np', thin=10, warmup_steps=10, num_chains=1, init_strategy='resample', init_strategy_parameters={}, init_strategy_num_candidates=None, num_workers=1, device=None, x_shape=None)</code>","text":"<p>Parameters:</p> Name Type Description Default <code>potential_fn</code> <code>Callable</code> <p>The potential function from which to draw samples.</p> required <code>proposal</code> <code>Any</code> <p>Proposal distribution that is used to initialize the MCMC chain.</p> required <code>theta_transform</code> <code>Optional[TorchTransform]</code> <p>Transformation that will be applied during sampling. Allows to perform MCMC in unconstrained space.</p> <code>None</code> <code>method</code> <code>str</code> <p>Method used for MCMC sampling, one of <code>slice_np</code>, <code>slice_np_vectorized</code>, <code>slice</code>, <code>hmc</code>, <code>nuts</code>. <code>slice_np</code> is a custom numpy implementation of slice sampling. <code>slice_np_vectorized</code> is identical to <code>slice_np</code>, but if <code>num_chains&gt;1</code>, the chains are vectorized for <code>slice_np_vectorized</code> whereas they are run sequentially for <code>slice_np</code>. The samplers <code>hmc</code>, <code>nuts</code> or <code>slice</code> sample with Pyro.</p> <code>'slice_np'</code> <code>thin</code> <code>int</code> <p>The thinning factor for the chain.</p> <code>10</code> <code>warmup_steps</code> <code>int</code> <p>The initial number of samples to discard.</p> <code>10</code> <code>num_chains</code> <code>int</code> <p>The number of chains.</p> <code>1</code> <code>init_strategy</code> <code>str</code> <p>The initialisation strategy for chains; <code>proposal</code> will draw init locations from <code>proposal</code>, whereas <code>sir</code> will use Sequential- Importance-Resampling (SIR). SIR initially samples <code>init_strategy_num_candidates</code> from the <code>proposal</code>, evaluates all of them under the <code>potential_fn</code> and <code>proposal</code>, and then resamples the initial locations with weights proportional to <code>exp(potential_fn - proposal.log_prob</code>. <code>resample</code> is the same as <code>sir</code> but uses <code>exp(potential_fn)</code> as weights.</p> <code>'resample'</code> <code>init_strategy_parameters</code> <code>Dict[str, Any]</code> <p>Dictionary of keyword arguments passed to the init strategy, e.g., for <code>init_strategy=sir</code> this could be <code>num_candidate_samples</code>, i.e., the number of candidates to to find init locations (internal default is <code>1000</code>), or <code>device</code>.</p> <code>{}</code> <code>init_strategy_num_candidates</code> <code>Optional[int]</code> <p>Number of candidates to to find init  locations in <code>init_strategy=sir</code> (deprecated, use  init_strategy_parameters instead).</p> <code>None</code> <code>num_workers</code> <code>int</code> <p>number of cpu cores used to parallelize mcmc</p> <code>1</code> <code>device</code> <code>Optional[str]</code> <p>Training device, e.g., \u201ccpu\u201d, \u201ccuda\u201d or \u201ccuda:0\u201d. If None, <code>potential_fn.device</code> is used.</p> <code>None</code> <code>x_shape</code> <code>Optional[torch.Size]</code> <p>Shape of a single simulator output. If passed, it is used to check the shape of the observed data and give a descriptive error.</p> <code>None</code> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/mcmc_posterior.py</code> <pre><code>def __init__(\n    self,\n    potential_fn: Callable,\n    proposal: Any,\n    theta_transform: Optional[TorchTransform] = None,\n    method: str = \"slice_np\",\n    thin: int = 10,\n    warmup_steps: int = 10,\n    num_chains: int = 1,\n    init_strategy: str = \"resample\",\n    init_strategy_parameters: Dict[str, Any] = {},\n    init_strategy_num_candidates: Optional[int] = None,\n    num_workers: int = 1,\n    device: Optional[str] = None,\n    x_shape: Optional[torch.Size] = None,\n):\n\"\"\"\n    Args:\n        potential_fn: The potential function from which to draw samples.\n        proposal: Proposal distribution that is used to initialize the MCMC chain.\n        theta_transform: Transformation that will be applied during sampling.\n            Allows to perform MCMC in unconstrained space.\n        method: Method used for MCMC sampling, one of `slice_np`,\n            `slice_np_vectorized`, `slice`, `hmc`, `nuts`. `slice_np` is a custom\n            numpy implementation of slice sampling. `slice_np_vectorized` is\n            identical to `slice_np`, but if `num_chains&gt;1`, the chains are\n            vectorized for `slice_np_vectorized` whereas they are run sequentially\n            for `slice_np`. The samplers `hmc`, `nuts` or `slice` sample with Pyro.\n        thin: The thinning factor for the chain.\n        warmup_steps: The initial number of samples to discard.\n        num_chains: The number of chains.\n        init_strategy: The initialisation strategy for chains; `proposal` will draw\n            init locations from `proposal`, whereas `sir` will use Sequential-\n            Importance-Resampling (SIR). SIR initially samples\n            `init_strategy_num_candidates` from the `proposal`, evaluates all of\n            them under the `potential_fn` and `proposal`, and then resamples the\n            initial locations with weights proportional to `exp(potential_fn -\n            proposal.log_prob`. `resample` is the same as `sir` but\n            uses `exp(potential_fn)` as weights.\n        init_strategy_parameters: Dictionary of keyword arguments passed to the\n            init strategy, e.g., for `init_strategy=sir` this could be\n            `num_candidate_samples`, i.e., the number of candidates to to find init\n            locations (internal default is `1000`), or `device`.\n        init_strategy_num_candidates: Number of candidates to to find init\n             locations in `init_strategy=sir` (deprecated, use\n             init_strategy_parameters instead).\n        num_workers: number of cpu cores used to parallelize mcmc\n        device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:0\". If None,\n            `potential_fn.device` is used.\n        x_shape: Shape of a single simulator output. If passed, it is used to check\n            the shape of the observed data and give a descriptive error.\n    \"\"\"\n\n    super().__init__(\n        potential_fn,\n        theta_transform=theta_transform,\n        device=device,\n        x_shape=x_shape,\n    )\n\n    self.proposal = proposal\n    self.method = method\n    self.thin = thin\n    self.warmup_steps = warmup_steps\n    self.num_chains = num_chains\n    self.init_strategy = init_strategy\n    self.init_strategy_parameters = init_strategy_parameters\n    self.num_workers = num_workers\n    self._posterior_sampler = None\n    # Hardcode parameter name to reduce clutter kwargs.\n    self.param_name = \"theta\"\n\n    if init_strategy_num_candidates is not None:\n        warn(\n\"\"\"Passing `init_strategy_num_candidates` is deprecated as of sbi\n            v0.19.0. Instead, use e.g.,\n            `init_strategy_parameters={\"num_candidate_samples\": 1000}`\"\"\"\n        )\n        self.init_strategy_parameters[\n            \"num_candidate_samples\"\n        ] = init_strategy_num_candidates\n\n    self.potential_ = self._prepare_potential(method)\n\n    self._purpose = (\n        \"It provides MCMC to .sample() from the posterior and \"\n        \"can evaluate the _unnormalized_ posterior density with .log_prob().\"\n    )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.get_arviz_inference_data","title":"<code>get_arviz_inference_data()</code>","text":"<p>Returns arviz InferenceData object constructed most recent samples.</p> <p>Note: the InferenceData is constructed using the posterior samples generated in most recent call to <code>.sample(...)</code>.</p> <p>For Pyro HMC and NUTS kernels InferenceData will contain diagnostics, for Pyro Slice or sbi slice sampling samples, only the samples are added.</p> <p>Returns:</p> Name Type Description <code>inference_data</code> <code>InferenceData</code> <p>Arviz InferenceData object.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/mcmc_posterior.py</code> <pre><code>def get_arviz_inference_data(self) -&gt; InferenceData:\n\"\"\"Returns arviz InferenceData object constructed most recent samples.\n\n    Note: the InferenceData is constructed using the posterior samples generated in\n    most recent call to `.sample(...)`.\n\n    For Pyro HMC and NUTS kernels InferenceData will contain diagnostics, for Pyro\n    Slice or sbi slice sampling samples, only the samples are added.\n\n    Returns:\n        inference_data: Arviz InferenceData object.\n    \"\"\"\n    assert (\n        self._posterior_sampler is not None\n    ), \"\"\"No samples have been generated, call .sample() first.\"\"\"\n\n    sampler: Union[\n        MCMC, SliceSamplerSerial, SliceSamplerVectorized\n    ] = self._posterior_sampler\n\n    # If Pyro sampler and samples not transformed, use arviz' from_pyro.\n    # Exclude 'slice' kernel as it lacks the 'divergence' diagnostics key.\n    if isinstance(self._posterior_sampler, (HMC, NUTS)) and isinstance(\n        self.theta_transform, torch_tf.IndependentTransform\n    ):\n        inference_data = az.from_pyro(sampler)\n\n    # otherwise get samples from sampler and transform to original space.\n    else:\n        transformed_samples = sampler.get_samples(group_by_chain=True)\n        # Pyro samplers returns dicts, get values.\n        if isinstance(transformed_samples, Dict):\n            # popitem gets last items, [1] get the values as tensor.\n            transformed_samples = transformed_samples.popitem()[1]\n        # Our slice samplers return numpy arrays.\n        elif isinstance(transformed_samples, ndarray):\n            transformed_samples = torch.from_numpy(transformed_samples).type(\n                torch.float32\n            )\n        # For MultipleIndependent priors transforms first dim must be batch dim.\n        # thus, reshape back and forth to have batch dim in front.\n        samples_shape = transformed_samples.shape\n        samples = self.theta_transform.inv(  # type: ignore\n            transformed_samples.reshape(-1, samples_shape[-1])\n        ).reshape(  # type: ignore\n            *samples_shape\n        )\n\n        inference_data = az.convert_to_inference_data(\n            {f\"{self.param_name}\": samples}\n        )\n\n    return inference_data\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.log_prob","title":"<code>log_prob(theta, x=None, track_gradients=False)</code>","text":"<p>Returns the log-probability of theta under the posterior.</p> <p>Parameters:</p> Name Type Description Default <code>theta</code> <code>Tensor</code> <p>Parameters \\(\\theta\\).</p> required <code>track_gradients</code> <code>bool</code> <p>Whether the returned tensor supports tracking gradients. This can be helpful for e.g. sensitivity analysis, but increases memory consumption.</p> <code>False</code> <p>Returns:</p> Type Description <code>Tensor</code> <p><code>len($\\theta$)</code>-shaped log-probability.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/mcmc_posterior.py</code> <pre><code>def log_prob(\n    self, theta: Tensor, x: Optional[Tensor] = None, track_gradients: bool = False\n) -&gt; Tensor:\nr\"\"\"Returns the log-probability of theta under the posterior.\n\n    Args:\n        theta: Parameters $\\theta$.\n        track_gradients: Whether the returned tensor supports tracking gradients.\n            This can be helpful for e.g. sensitivity analysis, but increases memory\n            consumption.\n\n    Returns:\n        `len($\\theta$)`-shaped log-probability.\n    \"\"\"\n    warn(\n\"\"\"`.log_prob()` is deprecated for methods that can only evaluate the\n        log-probability up to a normalizing constant. Use `.potential()` instead.\"\"\"\n    )\n    warn(\"The log-probability is unnormalized!\")\n\n    self.potential_fn.set_x(self._x_else_default_x(x))\n\n    theta = ensure_theta_batched(torch.as_tensor(theta))\n    return self.potential_fn(\n        theta.to(self._device), track_gradients=track_gradients\n    )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.map","title":"<code>map(x=None, num_iter=1000, num_to_optimize=100, learning_rate=0.01, init_method='proposal', num_init_samples=1000, save_best_every=10, show_progress_bars=False, force_update=False)</code>","text":"<p>Returns the maximum-a-posteriori estimate (MAP).</p> <p>The method can be interrupted (Ctrl-C) when the user sees that the log-probability converges. The best estimate will be saved in <code>self._map</code> and can be accessed with <code>self.map()</code>. The MAP is obtained by running gradient ascent from a given number of starting positions (samples from the posterior with the highest log-probability). After the optimization is done, we select the parameter set that has the highest log-probability after the optimization.</p> <p>Warning: The default values used by this function are not well-tested. They might require hand-tuning for the problem at hand.</p> <p>For developers: if the prior is a <code>BoxUniform</code>, we carry out the optimization in unbounded space and transform the result back into bounded space.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>Optional[Tensor]</code> <p>Deprecated - use <code>.set_default_x()</code> prior to <code>.map()</code>.</p> <code>None</code> <code>num_iter</code> <code>int</code> <p>Number of optimization steps that the algorithm takes to find the MAP.</p> <code>1000</code> <code>learning_rate</code> <code>float</code> <p>Learning rate of the optimizer.</p> <code>0.01</code> <code>init_method</code> <code>Union[str, Tensor]</code> <p>How to select the starting parameters for the optimization. If it is a string, it can be either [<code>posterior</code>, <code>prior</code>], which samples the respective distribution <code>num_init_samples</code> times. If it is a tensor, the tensor will be used as init locations.</p> <code>'proposal'</code> <code>num_init_samples</code> <code>int</code> <p>Draw this number of samples from the posterior and evaluate the log-probability of all of them.</p> <code>1000</code> <code>num_to_optimize</code> <code>int</code> <p>From the drawn <code>num_init_samples</code>, use the <code>num_to_optimize</code> with highest log-probability as the initial points for the optimization.</p> <code>100</code> <code>save_best_every</code> <code>int</code> <p>The best log-probability is computed, saved in the <code>map</code>-attribute, and printed every <code>save_best_every</code>-th iteration. Computing the best log-probability creates a significant overhead (thus, the default is <code>10</code>.)</p> <code>10</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during sampling from the posterior.</p> <code>False</code> <code>force_update</code> <code>bool</code> <p>Whether to re-calculate the MAP when x is unchanged and have a cached value.</p> <code>False</code> <code>log_prob_kwargs</code> <p>Will be empty for SNLE and SNRE. Will contain {\u2018norm_posterior\u2019: True} for SNPE.</p> required <p>Returns:</p> Type Description <code>Tensor</code> <p>The MAP estimate.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/mcmc_posterior.py</code> <pre><code>def map(\n    self,\n    x: Optional[Tensor] = None,\n    num_iter: int = 1_000,\n    num_to_optimize: int = 100,\n    learning_rate: float = 0.01,\n    init_method: Union[str, Tensor] = \"proposal\",\n    num_init_samples: int = 1_000,\n    save_best_every: int = 10,\n    show_progress_bars: bool = False,\n    force_update: bool = False,\n) -&gt; Tensor:\nr\"\"\"Returns the maximum-a-posteriori estimate (MAP).\n\n    The method can be interrupted (Ctrl-C) when the user sees that the\n    log-probability converges. The best estimate will be saved in `self._map` and\n    can be accessed with `self.map()`. The MAP is obtained by running gradient\n    ascent from a given number of starting positions (samples from the posterior\n    with the highest log-probability). After the optimization is done, we select the\n    parameter set that has the highest log-probability after the optimization.\n\n    Warning: The default values used by this function are not well-tested. They\n    might require hand-tuning for the problem at hand.\n\n    For developers: if the prior is a `BoxUniform`, we carry out the optimization\n    in unbounded space and transform the result back into bounded space.\n\n    Args:\n        x: Deprecated - use `.set_default_x()` prior to `.map()`.\n        num_iter: Number of optimization steps that the algorithm takes\n            to find the MAP.\n        learning_rate: Learning rate of the optimizer.\n        init_method: How to select the starting parameters for the optimization. If\n            it is a string, it can be either [`posterior`, `prior`], which samples\n            the respective distribution `num_init_samples` times. If it is a\n            tensor, the tensor will be used as init locations.\n        num_init_samples: Draw this number of samples from the posterior and\n            evaluate the log-probability of all of them.\n        num_to_optimize: From the drawn `num_init_samples`, use the\n            `num_to_optimize` with highest log-probability as the initial points\n            for the optimization.\n        save_best_every: The best log-probability is computed, saved in the\n            `map`-attribute, and printed every `save_best_every`-th iteration.\n            Computing the best log-probability creates a significant overhead\n            (thus, the default is `10`.)\n        show_progress_bars: Whether to show a progressbar during sampling from\n            the posterior.\n        force_update: Whether to re-calculate the MAP when x is unchanged and\n            have a cached value.\n        log_prob_kwargs: Will be empty for SNLE and SNRE. Will contain\n            {'norm_posterior': True} for SNPE.\n\n    Returns:\n        The MAP estimate.\n    \"\"\"\n    return super().map(\n        x=x,\n        num_iter=num_iter,\n        num_to_optimize=num_to_optimize,\n        learning_rate=learning_rate,\n        init_method=init_method,\n        num_init_samples=num_init_samples,\n        save_best_every=save_best_every,\n        show_progress_bars=show_progress_bars,\n        force_update=force_update,\n    )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.sample","title":"<code>sample(sample_shape=torch.Size(), x=None, method=None, thin=None, warmup_steps=None, num_chains=None, init_strategy=None, init_strategy_parameters=None, init_strategy_num_candidates=None, mcmc_parameters={}, mcmc_method=None, sample_with=None, num_workers=None, show_progress_bars=True)</code>","text":"<p>Return samples from posterior distribution \\(p(\\theta|x)\\) with MCMC.</p> <p>Check the <code>__init__()</code> method for a description of all arguments as well as their default values.</p> <p>Parameters:</p> Name Type Description Default <code>sample_shape</code> <code>Shape</code> <p>Desired shape of samples that are drawn from posterior. If sample_shape is multidimensional we simply draw <code>sample_shape.numel()</code> samples and then reshape into the desired shape.</p> <code>torch.Size()</code> <code>mcmc_parameters</code> <code>Dict</code> <p>Dictionary that is passed only to support the API of <code>sbi</code> v0.17.2 or older.</p> <code>{}</code> <code>mcmc_method</code> <code>Optional[str]</code> <p>This argument only exists to keep backward-compatibility with <code>sbi</code> v0.17.2 or older. Please use <code>method</code> instead.</p> <code>None</code> <code>sample_with</code> <code>Optional[str]</code> <p>This argument only exists to keep backward-compatibility with <code>sbi</code> v0.17.2 or older. If it is set, we instantly raise an error.</p> <code>None</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show sampling progress monitor.</p> <code>True</code> <p>Returns:</p> Type Description <code>Tensor</code> <p>Samples from posterior.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/mcmc_posterior.py</code> <pre><code>def sample(\n    self,\n    sample_shape: Shape = torch.Size(),\n    x: Optional[Tensor] = None,\n    method: Optional[str] = None,\n    thin: Optional[int] = None,\n    warmup_steps: Optional[int] = None,\n    num_chains: Optional[int] = None,\n    init_strategy: Optional[str] = None,\n    init_strategy_parameters: Optional[Dict[str, Any]] = None,\n    init_strategy_num_candidates: Optional[int] = None,\n    mcmc_parameters: Dict = {},\n    mcmc_method: Optional[str] = None,\n    sample_with: Optional[str] = None,\n    num_workers: Optional[int] = None,\n    show_progress_bars: bool = True,\n) -&gt; Tensor:\nr\"\"\"Return samples from posterior distribution $p(\\theta|x)$ with MCMC.\n\n    Check the `__init__()` method for a description of all arguments as well as\n    their default values.\n\n    Args:\n        sample_shape: Desired shape of samples that are drawn from posterior. If\n            sample_shape is multidimensional we simply draw `sample_shape.numel()`\n            samples and then reshape into the desired shape.\n        mcmc_parameters: Dictionary that is passed only to support the API of\n            `sbi` v0.17.2 or older.\n        mcmc_method: This argument only exists to keep backward-compatibility with\n            `sbi` v0.17.2 or older. Please use `method` instead.\n        sample_with: This argument only exists to keep backward-compatibility with\n            `sbi` v0.17.2 or older. If it is set, we instantly raise an error.\n        show_progress_bars: Whether to show sampling progress monitor.\n\n    Returns:\n        Samples from posterior.\n    \"\"\"\n    self.potential_fn.set_x(self._x_else_default_x(x))\n\n    # Replace arguments that were not passed with their default.\n    method = self.method if method is None else method\n    thin = self.thin if thin is None else thin\n    warmup_steps = self.warmup_steps if warmup_steps is None else warmup_steps\n    num_chains = self.num_chains if num_chains is None else num_chains\n    init_strategy = self.init_strategy if init_strategy is None else init_strategy\n    num_workers = self.num_workers if num_workers is None else num_workers\n    init_strategy_parameters = (\n        self.init_strategy_parameters\n        if init_strategy_parameters is None\n        else init_strategy_parameters\n    )\n    if init_strategy_num_candidates is not None:\n        warn(\n\"\"\"Passing `init_strategy_num_candidates` is deprecated as of sbi\n            v0.19.0. Instead, use e.g.,\n            `init_strategy_parameters={\"num_candidate_samples\": 1000}`\"\"\"\n        )\n        self.init_strategy_parameters[\n            \"num_candidate_samples\"\n        ] = init_strategy_num_candidates\n    if sample_with is not None:\n        raise ValueError(\n            f\"You set `sample_with={sample_with}`. As of sbi v0.18.0, setting \"\n            f\"`sample_with` is no longer supported. You have to rerun \"\n            f\"`.build_posterior(sample_with={sample_with}).`\"\n        )\n    if mcmc_method is not None:\n        warn(\n            \"You passed `mcmc_method` to `.sample()`. As of sbi v0.18.0, this \"\n            \"is deprecated and will be removed in a future release. Use `method` \"\n            \"instead of `mcmc_method`.\"\n        )\n        method = mcmc_method\n    if mcmc_parameters:\n        warn(\n            \"You passed `mcmc_parameters` to `.sample()`. As of sbi v0.18.0, this \"\n            \"is deprecated and will be removed in a future release. Instead, pass \"\n            \"the variable to `.sample()` directly, e.g. \"\n            \"`posterior.sample((1,), num_chains=5)`.\"\n        )\n    # The following lines are only for backwards compatibility with sbi v0.17.2 or\n    # older.\n    m_p = mcmc_parameters  # define to shorten the variable name\n    method = _maybe_use_dict_entry(method, \"mcmc_method\", m_p)\n    thin = _maybe_use_dict_entry(thin, \"thin\", m_p)\n    warmup_steps = _maybe_use_dict_entry(warmup_steps, \"warmup_steps\", m_p)\n    num_chains = _maybe_use_dict_entry(num_chains, \"num_chains\", m_p)\n    init_strategy = _maybe_use_dict_entry(init_strategy, \"init_strategy\", m_p)\n    self.potential_ = self._prepare_potential(method)  # type: ignore\n\n    initial_params = self._get_initial_params(\n        init_strategy,  # type: ignore\n        num_chains,  # type: ignore\n        num_workers,\n        show_progress_bars,\n        **init_strategy_parameters,\n    )\n    num_samples = torch.Size(sample_shape).numel()\n\n    track_gradients = method in (\"hmc\", \"nuts\")\n    with torch.set_grad_enabled(track_gradients):\n        if method in (\"slice_np\", \"slice_np_vectorized\"):\n            transformed_samples = self._slice_np_mcmc(\n                num_samples=num_samples,\n                potential_function=self.potential_,\n                initial_params=initial_params,\n                thin=thin,  # type: ignore\n                warmup_steps=warmup_steps,  # type: ignore\n                vectorized=(method == \"slice_np_vectorized\"),\n                num_workers=num_workers,\n                show_progress_bars=show_progress_bars,\n            )\n        elif method in (\"hmc\", \"nuts\", \"slice\"):\n            transformed_samples = self._pyro_mcmc(\n                num_samples=num_samples,\n                potential_function=self.potential_,\n                initial_params=initial_params,\n                mcmc_method=method,  # type: ignore\n                thin=thin,  # type: ignore\n                warmup_steps=warmup_steps,  # type: ignore\n                num_chains=num_chains,\n                show_progress_bars=show_progress_bars,\n            )\n        else:\n            raise NameError\n\n    samples = self.theta_transform.inv(transformed_samples)\n\n    return samples.reshape((*sample_shape, -1))  # type: ignore\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.set_mcmc_method","title":"<code>set_mcmc_method(method)</code>","text":"<p>Sets sampling method to for MCMC and returns <code>NeuralPosterior</code>.</p> <p>Parameters:</p> Name Type Description Default <code>method</code> <code>str</code> <p>Method to use.</p> required <p>Returns:</p> Type Description <code>NeuralPosterior</code> <p><code>NeuralPosterior</code> for chainable calls.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/mcmc_posterior.py</code> <pre><code>def set_mcmc_method(self, method: str) -&gt; \"NeuralPosterior\":\n\"\"\"Sets sampling method to for MCMC and returns `NeuralPosterior`.\n\n    Args:\n        method: Method to use.\n\n    Returns:\n        `NeuralPosterior` for chainable calls.\n    \"\"\"\n    self._mcmc_method = method\n    return self\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.rejection_posterior.RejectionPosterior","title":"<code>sbi.inference.posteriors.rejection_posterior.RejectionPosterior</code>","text":"<p>         Bases: <code>NeuralPosterior</code></p> <p>Provides rejection sampling to sample from the posterior. SNLE or SNRE train neural networks to approximate the likelihood(-ratios). <code>RejectionPosterior</code> allows to sample from the posterior with rejection sampling.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/rejection_posterior.py</code> <pre><code>class RejectionPosterior(NeuralPosterior):\nr\"\"\"Provides rejection sampling to sample from the posterior.&lt;br/&gt;&lt;br/&gt;\n    SNLE or SNRE train neural networks to approximate the likelihood(-ratios).\n    `RejectionPosterior` allows to sample from the posterior with rejection sampling.\n    \"\"\"\n\n    def __init__(\n        self,\n        potential_fn: Callable,\n        proposal: Any,\n        theta_transform: Optional[TorchTransform] = None,\n        max_sampling_batch_size: int = 10_000,\n        num_samples_to_find_max: int = 10_000,\n        num_iter_to_find_max: int = 100,\n        m: float = 1.2,\n        device: Optional[str] = None,\n        x_shape: Optional[torch.Size] = None,\n    ):\n\"\"\"\n        Args:\n            potential_fn: The potential function from which to draw samples.\n            proposal: The proposal distribution.\n            theta_transform: Transformation that is applied to parameters. Is not used\n                during but only when calling `.map()`.\n            max_sampling_batch_size: The batchsize of samples being drawn from\n                the proposal at every iteration.\n            num_samples_to_find_max: The number of samples that are used to find the\n                maximum of the `potential_fn / proposal` ratio.\n            num_iter_to_find_max: The number of gradient ascent iterations to find the\n                maximum of the `potential_fn / proposal` ratio.\n            m: Multiplier to the `potential_fn / proposal` ratio.\n            device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:0\". If None,\n                `potential_fn.device` is used.\n            x_shape: Shape of a single simulator output. If passed, it is used to check\n                the shape of the observed data and give a descriptive error.\n        \"\"\"\n        super().__init__(\n            potential_fn,\n            theta_transform=theta_transform,\n            device=device,\n            x_shape=x_shape,\n        )\n\n        self.proposal = proposal\n        self.max_sampling_batch_size = max_sampling_batch_size\n        self.num_samples_to_find_max = num_samples_to_find_max\n        self.num_iter_to_find_max = num_iter_to_find_max\n        self.m = m\n\n        self._purpose = (\n            \"It provides rejection sampling to .sample() from the posterior and \"\n            \"can evaluate the _unnormalized_ posterior density with .log_prob().\"\n        )\n\n    def log_prob(\n        self, theta: Tensor, x: Optional[Tensor] = None, track_gradients: bool = False\n    ) -&gt; Tensor:\nr\"\"\"Returns the log-probability of theta under the posterior.\n\n        Args:\n            theta: Parameters $\\theta$.\n            track_gradients: Whether the returned tensor supports tracking gradients.\n                This can be helpful for e.g. sensitivity analysis, but increases memory\n                consumption.\n\n        Returns:\n            `len($\\theta$)`-shaped log-probability.\n        \"\"\"\n        warn(\n            \"`.log_prob()` is deprecated for methods that can only evaluate the log-probability up to a normalizing constant. Use `.potential()` instead.\"\n        )\n        warn(\"The log-probability is unnormalized!\")\n\n        self.potential_fn.set_x(self._x_else_default_x(x))\n\n        theta = ensure_theta_batched(torch.as_tensor(theta))\n        return self.potential_fn(\n            theta.to(self._device), track_gradients=track_gradients\n        )\n\n    def sample(\n        self,\n        sample_shape: Shape = torch.Size(),\n        x: Optional[Tensor] = None,\n        max_sampling_batch_size: Optional[int] = None,\n        num_samples_to_find_max: Optional[int] = None,\n        num_iter_to_find_max: Optional[int] = None,\n        m: Optional[float] = None,\n        sample_with: Optional[str] = None,\n        show_progress_bars: bool = True,\n    ):\nr\"\"\"Return samples from posterior $p(\\theta|x)$ via rejection sampling.\n\n        Args:\n            sample_shape: Desired shape of samples that are drawn from posterior. If\n                sample_shape is multidimensional we simply draw `sample_shape.numel()`\n                samples and then reshape into the desired shape.\n            sample_with: This argument only exists to keep backward-compatibility with\n                `sbi` v0.17.2 or older. If it is set, we instantly raise an error.\n            show_progress_bars: Whether to show sampling progress monitor.\n\n        Returns:\n            Samples from posterior.\n        \"\"\"\n        num_samples = torch.Size(sample_shape).numel()\n        self.potential_fn.set_x(self._x_else_default_x(x))\n\n        potential = partial(self.potential_fn, track_gradients=True)\n\n        if sample_with is not None:\n            raise ValueError(\n                f\"You set `sample_with={sample_with}`. As of sbi v0.18.0, setting \"\n                f\"`sample_with` is no longer supported. You have to rerun \"\n                f\"`.build_posterior(sample_with={sample_with}).`\"\n            )\n        # Replace arguments that were not passed with their default.\n        max_sampling_batch_size = (\n            self.max_sampling_batch_size\n            if max_sampling_batch_size is None\n            else max_sampling_batch_size\n        )\n        num_samples_to_find_max = (\n            self.num_samples_to_find_max\n            if num_samples_to_find_max is None\n            else num_samples_to_find_max\n        )\n        num_iter_to_find_max = (\n            self.num_iter_to_find_max\n            if num_iter_to_find_max is None\n            else num_iter_to_find_max\n        )\n        m = self.m if m is None else m\n\n        samples, _ = rejection_sample(\n            potential,\n            proposal=self.proposal,\n            num_samples=num_samples,\n            show_progress_bars=show_progress_bars,\n            warn_acceptance=0.01,\n            max_sampling_batch_size=max_sampling_batch_size,\n            num_samples_to_find_max=num_samples_to_find_max,\n            num_iter_to_find_max=num_iter_to_find_max,\n            m=m,\n            device=self._device,\n        )\n\n        return samples.reshape((*sample_shape, -1))\n\n    def map(\n        self,\n        x: Optional[Tensor] = None,\n        num_iter: int = 1_000,\n        num_to_optimize: int = 100,\n        learning_rate: float = 0.01,\n        init_method: Union[str, Tensor] = \"proposal\",\n        num_init_samples: int = 1_000,\n        save_best_every: int = 10,\n        show_progress_bars: bool = False,\n        force_update: bool = False,\n    ) -&gt; Tensor:\nr\"\"\"Returns the maximum-a-posteriori estimate (MAP).\n\n        The method can be interrupted (Ctrl-C) when the user sees that the\n        log-probability converges. The best estimate will be saved in `self._map` and\n        can be accessed with `self.map()`. The MAP is obtained by running gradient\n        ascent from a given number of starting positions (samples from the posterior\n        with the highest log-probability). After the optimization is done, we select the\n        parameter set that has the highest log-probability after the optimization.\n\n        Warning: The default values used by this function are not well-tested. They\n        might require hand-tuning for the problem at hand.\n\n        For developers: if the prior is a `BoxUniform`, we carry out the optimization\n        in unbounded space and transform the result back into bounded space.\n\n        Args:\n            x: Deprecated - use `.set_default_x()` prior to `.map()`.\n            num_iter: Number of optimization steps that the algorithm takes\n                to find the MAP.\n            learning_rate: Learning rate of the optimizer.\n            init_method: How to select the starting parameters for the optimization. If\n                it is a string, it can be either [`posterior`, `prior`], which samples\n                the respective distribution `num_init_samples` times. If it is a\n                tensor, the tensor will be used as init locations.\n            num_init_samples: Draw this number of samples from the posterior and\n                evaluate the log-probability of all of them.\n            num_to_optimize: From the drawn `num_init_samples`, use the\n                `num_to_optimize` with highest log-probability as the initial points\n                for the optimization.\n            save_best_every: The best log-probability is computed, saved in the\n                `map`-attribute, and printed every `save_best_every`-th iteration.\n                Computing the best log-probability creates a significant overhead\n                (thus, the default is `10`.)\n            show_progress_bars: Whether to show a progressbar during sampling from\n                the posterior.\n            force_update: Whether to re-calculate the MAP when x is unchanged and\n                have a cached value.\n            log_prob_kwargs: Will be empty for SNLE and SNRE. Will contain\n                {'norm_posterior': True} for SNPE.\n\n        Returns:\n            The MAP estimate.\n        \"\"\"\n        return super().map(\n            x=x,\n            num_iter=num_iter,\n            num_to_optimize=num_to_optimize,\n            learning_rate=learning_rate,\n            init_method=init_method,\n            num_init_samples=num_init_samples,\n            save_best_every=save_best_every,\n            show_progress_bars=show_progress_bars,\n            force_update=force_update,\n        )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.rejection_posterior.RejectionPosterior.__init__","title":"<code>__init__(potential_fn, proposal, theta_transform=None, max_sampling_batch_size=10000, num_samples_to_find_max=10000, num_iter_to_find_max=100, m=1.2, device=None, x_shape=None)</code>","text":"<p>Parameters:</p> Name Type Description Default <code>potential_fn</code> <code>Callable</code> <p>The potential function from which to draw samples.</p> required <code>proposal</code> <code>Any</code> <p>The proposal distribution.</p> required <code>theta_transform</code> <code>Optional[TorchTransform]</code> <p>Transformation that is applied to parameters. Is not used during but only when calling <code>.map()</code>.</p> <code>None</code> <code>max_sampling_batch_size</code> <code>int</code> <p>The batchsize of samples being drawn from the proposal at every iteration.</p> <code>10000</code> <code>num_samples_to_find_max</code> <code>int</code> <p>The number of samples that are used to find the maximum of the <code>potential_fn / proposal</code> ratio.</p> <code>10000</code> <code>num_iter_to_find_max</code> <code>int</code> <p>The number of gradient ascent iterations to find the maximum of the <code>potential_fn / proposal</code> ratio.</p> <code>100</code> <code>m</code> <code>float</code> <p>Multiplier to the <code>potential_fn / proposal</code> ratio.</p> <code>1.2</code> <code>device</code> <code>Optional[str]</code> <p>Training device, e.g., \u201ccpu\u201d, \u201ccuda\u201d or \u201ccuda:0\u201d. If None, <code>potential_fn.device</code> is used.</p> <code>None</code> <code>x_shape</code> <code>Optional[torch.Size]</code> <p>Shape of a single simulator output. If passed, it is used to check the shape of the observed data and give a descriptive error.</p> <code>None</code> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/rejection_posterior.py</code> <pre><code>def __init__(\n    self,\n    potential_fn: Callable,\n    proposal: Any,\n    theta_transform: Optional[TorchTransform] = None,\n    max_sampling_batch_size: int = 10_000,\n    num_samples_to_find_max: int = 10_000,\n    num_iter_to_find_max: int = 100,\n    m: float = 1.2,\n    device: Optional[str] = None,\n    x_shape: Optional[torch.Size] = None,\n):\n\"\"\"\n    Args:\n        potential_fn: The potential function from which to draw samples.\n        proposal: The proposal distribution.\n        theta_transform: Transformation that is applied to parameters. Is not used\n            during but only when calling `.map()`.\n        max_sampling_batch_size: The batchsize of samples being drawn from\n            the proposal at every iteration.\n        num_samples_to_find_max: The number of samples that are used to find the\n            maximum of the `potential_fn / proposal` ratio.\n        num_iter_to_find_max: The number of gradient ascent iterations to find the\n            maximum of the `potential_fn / proposal` ratio.\n        m: Multiplier to the `potential_fn / proposal` ratio.\n        device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:0\". If None,\n            `potential_fn.device` is used.\n        x_shape: Shape of a single simulator output. If passed, it is used to check\n            the shape of the observed data and give a descriptive error.\n    \"\"\"\n    super().__init__(\n        potential_fn,\n        theta_transform=theta_transform,\n        device=device,\n        x_shape=x_shape,\n    )\n\n    self.proposal = proposal\n    self.max_sampling_batch_size = max_sampling_batch_size\n    self.num_samples_to_find_max = num_samples_to_find_max\n    self.num_iter_to_find_max = num_iter_to_find_max\n    self.m = m\n\n    self._purpose = (\n        \"It provides rejection sampling to .sample() from the posterior and \"\n        \"can evaluate the _unnormalized_ posterior density with .log_prob().\"\n    )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.rejection_posterior.RejectionPosterior.log_prob","title":"<code>log_prob(theta, x=None, track_gradients=False)</code>","text":"<p>Returns the log-probability of theta under the posterior.</p> <p>Parameters:</p> Name Type Description Default <code>theta</code> <code>Tensor</code> <p>Parameters \\(\\theta\\).</p> required <code>track_gradients</code> <code>bool</code> <p>Whether the returned tensor supports tracking gradients. This can be helpful for e.g. sensitivity analysis, but increases memory consumption.</p> <code>False</code> <p>Returns:</p> Type Description <code>Tensor</code> <p><code>len($\\theta$)</code>-shaped log-probability.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/rejection_posterior.py</code> <pre><code>def log_prob(\n    self, theta: Tensor, x: Optional[Tensor] = None, track_gradients: bool = False\n) -&gt; Tensor:\nr\"\"\"Returns the log-probability of theta under the posterior.\n\n    Args:\n        theta: Parameters $\\theta$.\n        track_gradients: Whether the returned tensor supports tracking gradients.\n            This can be helpful for e.g. sensitivity analysis, but increases memory\n            consumption.\n\n    Returns:\n        `len($\\theta$)`-shaped log-probability.\n    \"\"\"\n    warn(\n        \"`.log_prob()` is deprecated for methods that can only evaluate the log-probability up to a normalizing constant. Use `.potential()` instead.\"\n    )\n    warn(\"The log-probability is unnormalized!\")\n\n    self.potential_fn.set_x(self._x_else_default_x(x))\n\n    theta = ensure_theta_batched(torch.as_tensor(theta))\n    return self.potential_fn(\n        theta.to(self._device), track_gradients=track_gradients\n    )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.rejection_posterior.RejectionPosterior.map","title":"<code>map(x=None, num_iter=1000, num_to_optimize=100, learning_rate=0.01, init_method='proposal', num_init_samples=1000, save_best_every=10, show_progress_bars=False, force_update=False)</code>","text":"<p>Returns the maximum-a-posteriori estimate (MAP).</p> <p>The method can be interrupted (Ctrl-C) when the user sees that the log-probability converges. The best estimate will be saved in <code>self._map</code> and can be accessed with <code>self.map()</code>. The MAP is obtained by running gradient ascent from a given number of starting positions (samples from the posterior with the highest log-probability). After the optimization is done, we select the parameter set that has the highest log-probability after the optimization.</p> <p>Warning: The default values used by this function are not well-tested. They might require hand-tuning for the problem at hand.</p> <p>For developers: if the prior is a <code>BoxUniform</code>, we carry out the optimization in unbounded space and transform the result back into bounded space.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>Optional[Tensor]</code> <p>Deprecated - use <code>.set_default_x()</code> prior to <code>.map()</code>.</p> <code>None</code> <code>num_iter</code> <code>int</code> <p>Number of optimization steps that the algorithm takes to find the MAP.</p> <code>1000</code> <code>learning_rate</code> <code>float</code> <p>Learning rate of the optimizer.</p> <code>0.01</code> <code>init_method</code> <code>Union[str, Tensor]</code> <p>How to select the starting parameters for the optimization. If it is a string, it can be either [<code>posterior</code>, <code>prior</code>], which samples the respective distribution <code>num_init_samples</code> times. If it is a tensor, the tensor will be used as init locations.</p> <code>'proposal'</code> <code>num_init_samples</code> <code>int</code> <p>Draw this number of samples from the posterior and evaluate the log-probability of all of them.</p> <code>1000</code> <code>num_to_optimize</code> <code>int</code> <p>From the drawn <code>num_init_samples</code>, use the <code>num_to_optimize</code> with highest log-probability as the initial points for the optimization.</p> <code>100</code> <code>save_best_every</code> <code>int</code> <p>The best log-probability is computed, saved in the <code>map</code>-attribute, and printed every <code>save_best_every</code>-th iteration. Computing the best log-probability creates a significant overhead (thus, the default is <code>10</code>.)</p> <code>10</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during sampling from the posterior.</p> <code>False</code> <code>force_update</code> <code>bool</code> <p>Whether to re-calculate the MAP when x is unchanged and have a cached value.</p> <code>False</code> <code>log_prob_kwargs</code> <p>Will be empty for SNLE and SNRE. Will contain {\u2018norm_posterior\u2019: True} for SNPE.</p> required <p>Returns:</p> Type Description <code>Tensor</code> <p>The MAP estimate.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/rejection_posterior.py</code> <pre><code>def map(\n    self,\n    x: Optional[Tensor] = None,\n    num_iter: int = 1_000,\n    num_to_optimize: int = 100,\n    learning_rate: float = 0.01,\n    init_method: Union[str, Tensor] = \"proposal\",\n    num_init_samples: int = 1_000,\n    save_best_every: int = 10,\n    show_progress_bars: bool = False,\n    force_update: bool = False,\n) -&gt; Tensor:\nr\"\"\"Returns the maximum-a-posteriori estimate (MAP).\n\n    The method can be interrupted (Ctrl-C) when the user sees that the\n    log-probability converges. The best estimate will be saved in `self._map` and\n    can be accessed with `self.map()`. The MAP is obtained by running gradient\n    ascent from a given number of starting positions (samples from the posterior\n    with the highest log-probability). After the optimization is done, we select the\n    parameter set that has the highest log-probability after the optimization.\n\n    Warning: The default values used by this function are not well-tested. They\n    might require hand-tuning for the problem at hand.\n\n    For developers: if the prior is a `BoxUniform`, we carry out the optimization\n    in unbounded space and transform the result back into bounded space.\n\n    Args:\n        x: Deprecated - use `.set_default_x()` prior to `.map()`.\n        num_iter: Number of optimization steps that the algorithm takes\n            to find the MAP.\n        learning_rate: Learning rate of the optimizer.\n        init_method: How to select the starting parameters for the optimization. If\n            it is a string, it can be either [`posterior`, `prior`], which samples\n            the respective distribution `num_init_samples` times. If it is a\n            tensor, the tensor will be used as init locations.\n        num_init_samples: Draw this number of samples from the posterior and\n            evaluate the log-probability of all of them.\n        num_to_optimize: From the drawn `num_init_samples`, use the\n            `num_to_optimize` with highest log-probability as the initial points\n            for the optimization.\n        save_best_every: The best log-probability is computed, saved in the\n            `map`-attribute, and printed every `save_best_every`-th iteration.\n            Computing the best log-probability creates a significant overhead\n            (thus, the default is `10`.)\n        show_progress_bars: Whether to show a progressbar during sampling from\n            the posterior.\n        force_update: Whether to re-calculate the MAP when x is unchanged and\n            have a cached value.\n        log_prob_kwargs: Will be empty for SNLE and SNRE. Will contain\n            {'norm_posterior': True} for SNPE.\n\n    Returns:\n        The MAP estimate.\n    \"\"\"\n    return super().map(\n        x=x,\n        num_iter=num_iter,\n        num_to_optimize=num_to_optimize,\n        learning_rate=learning_rate,\n        init_method=init_method,\n        num_init_samples=num_init_samples,\n        save_best_every=save_best_every,\n        show_progress_bars=show_progress_bars,\n        force_update=force_update,\n    )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.rejection_posterior.RejectionPosterior.sample","title":"<code>sample(sample_shape=torch.Size(), x=None, max_sampling_batch_size=None, num_samples_to_find_max=None, num_iter_to_find_max=None, m=None, sample_with=None, show_progress_bars=True)</code>","text":"<p>Return samples from posterior \\(p(\\theta|x)\\) via rejection sampling.</p> <p>Parameters:</p> Name Type Description Default <code>sample_shape</code> <code>Shape</code> <p>Desired shape of samples that are drawn from posterior. If sample_shape is multidimensional we simply draw <code>sample_shape.numel()</code> samples and then reshape into the desired shape.</p> <code>torch.Size()</code> <code>sample_with</code> <code>Optional[str]</code> <p>This argument only exists to keep backward-compatibility with <code>sbi</code> v0.17.2 or older. If it is set, we instantly raise an error.</p> <code>None</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show sampling progress monitor.</p> <code>True</code> <p>Returns:</p> Type Description <p>Samples from posterior.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/rejection_posterior.py</code> <pre><code>def sample(\n    self,\n    sample_shape: Shape = torch.Size(),\n    x: Optional[Tensor] = None,\n    max_sampling_batch_size: Optional[int] = None,\n    num_samples_to_find_max: Optional[int] = None,\n    num_iter_to_find_max: Optional[int] = None,\n    m: Optional[float] = None,\n    sample_with: Optional[str] = None,\n    show_progress_bars: bool = True,\n):\nr\"\"\"Return samples from posterior $p(\\theta|x)$ via rejection sampling.\n\n    Args:\n        sample_shape: Desired shape of samples that are drawn from posterior. If\n            sample_shape is multidimensional we simply draw `sample_shape.numel()`\n            samples and then reshape into the desired shape.\n        sample_with: This argument only exists to keep backward-compatibility with\n            `sbi` v0.17.2 or older. If it is set, we instantly raise an error.\n        show_progress_bars: Whether to show sampling progress monitor.\n\n    Returns:\n        Samples from posterior.\n    \"\"\"\n    num_samples = torch.Size(sample_shape).numel()\n    self.potential_fn.set_x(self._x_else_default_x(x))\n\n    potential = partial(self.potential_fn, track_gradients=True)\n\n    if sample_with is not None:\n        raise ValueError(\n            f\"You set `sample_with={sample_with}`. As of sbi v0.18.0, setting \"\n            f\"`sample_with` is no longer supported. You have to rerun \"\n            f\"`.build_posterior(sample_with={sample_with}).`\"\n        )\n    # Replace arguments that were not passed with their default.\n    max_sampling_batch_size = (\n        self.max_sampling_batch_size\n        if max_sampling_batch_size is None\n        else max_sampling_batch_size\n    )\n    num_samples_to_find_max = (\n        self.num_samples_to_find_max\n        if num_samples_to_find_max is None\n        else num_samples_to_find_max\n    )\n    num_iter_to_find_max = (\n        self.num_iter_to_find_max\n        if num_iter_to_find_max is None\n        else num_iter_to_find_max\n    )\n    m = self.m if m is None else m\n\n    samples, _ = rejection_sample(\n        potential,\n        proposal=self.proposal,\n        num_samples=num_samples,\n        show_progress_bars=show_progress_bars,\n        warn_acceptance=0.01,\n        max_sampling_batch_size=max_sampling_batch_size,\n        num_samples_to_find_max=num_samples_to_find_max,\n        num_iter_to_find_max=num_iter_to_find_max,\n        m=m,\n        device=self._device,\n    )\n\n    return samples.reshape((*sample_shape, -1))\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.vi_posterior.VIPosterior","title":"<code>sbi.inference.posteriors.vi_posterior.VIPosterior</code>","text":"<p>         Bases: <code>NeuralPosterior</code></p> <p>Provides VI (Variational Inference) to sample from the posterior. SNLE or SNRE train neural networks to approximate the likelihood(-ratios). <code>VIPosterior</code> allows to learn a tractable variational posterior \\(q(\\theta)\\) which approximates the true posterior \\(p(\\theta|x_o)\\). After this second training stage, we can produce approximate posterior samples, by just sampling from q with no additional cost. For additional information see [1] and [2]. References: [1] Variational methods for simulation-based inference, Manuel Gl\u00f6ckler, Michael Deistler, Jakob Macke, 2022, https://openreview.net/forum?id=kZ0UYdhqkNY [2] Sequential Neural Posterior and Likelihood Approximation, Samuel Wiqvist, Jes Frellsen, Umberto Picchini, 2021, https://arxiv.org/abs/2102.06522</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/vi_posterior.py</code> <pre><code>class VIPosterior(NeuralPosterior):\nr\"\"\"Provides VI (Variational Inference) to sample from the posterior.&lt;br/&gt;&lt;br/&gt;\n    SNLE or SNRE train neural networks to approximate the likelihood(-ratios).\n    `VIPosterior` allows to learn a tractable variational posterior $q(\\theta)$ which\n    approximates the true posterior $p(\\theta|x_o)$. After this second training stage,\n    we can produce approximate posterior samples, by just sampling from q with no\n    additional cost. For additional information see [1] and [2].&lt;br/&gt;&lt;br/&gt;\n    References:&lt;br/&gt;\n    [1] Variational methods for simulation-based inference, Manuel Gl\u00f6ckler, Michael\n    Deistler, Jakob Macke, 2022, https://openreview.net/forum?id=kZ0UYdhqkNY&lt;br/&gt;\n    [2] Sequential Neural Posterior and Likelihood Approximation, Samuel Wiqvist, Jes\n    Frellsen, Umberto Picchini, 2021, https://arxiv.org/abs/2102.06522\n    \"\"\"\n\n    def __init__(\n        self,\n        potential_fn: Callable,\n        prior: Optional[TorchDistribution] = None,\n        q: Union[str, PyroTransformedDistribution, \"VIPosterior\", Callable] = \"maf\",\n        theta_transform: Optional[TorchTransform] = None,\n        vi_method: str = \"rKL\",\n        device: str = \"cpu\",\n        x_shape: Optional[torch.Size] = None,\n        parameters: Iterable = [],\n        modules: Iterable = [],\n    ):\n\"\"\"\n        Args:\n            potential_fn: The potential function from which to draw samples.\n            prior: This is the prior distribution. Note that this is only\n                used to check/construct the variational distribution or within some\n                quality metrics. Please make sure that this matches with the prior\n                within the potential_fn. If `None` is given, we will try to infer it\n                from potential_fn or q, if this fails we raise an Error.\n            q: Variational distribution, either string, `TransformedDistribution`, or a\n                `VIPosterior` object. This specifies a parametric class of distribution\n                over which the best possible posterior approximation is searched. For\n                string input, we currently support [nsf, scf, maf, mcf, gaussian,\n                gaussian_diag]. You can also specify your own variational family by\n                passing a pyro `TransformedDistribution`.\n                Additionally, we allow a `Callable`, which allows you the pass a\n                `builder` function, which if called returns a distribution. This may be\n                useful for setting the hyperparameters e.g. `num_transfroms` within the\n                `get_flow_builder` method specifying the number of transformations\n                within a normalizing flow. If q is already a `VIPosterior`, then the\n                arguments will be copied from it (relevant for multi-round training).\n            theta_transform: Maps form prior support to unconstrained space. The\n                inverse is used here to ensure that the posterior support is equal to\n                that of the prior.\n            vi_method: This specifies the variational methods which are used to fit q to\n                the posterior. We currently support [rKL, fKL, IW, alpha]. Note that\n                some of the divergences are `mode seeking` i.e. they underestimate\n                variance and collapse on multimodal targets (`rKL`, `alpha` for alpha &gt;\n                1) and some are `mass covering` i.e. they overestimate variance but\n                typically cover all modes (`fKL`, `IW`, `alpha` for alpha &lt; 1).\n            device: Training device, e.g., `cpu`, `cuda` or `cuda:0`. We will ensure\n                that all other objects are also on this device.\n            x_shape: Shape of a single simulator output. If passed, it is used to check\n                the shape of the observed data and give a descriptive error.\n            parameters: List of parameters of the variational posterior. This is only\n                required for user-defined q i.e. if q does not have a `parameters`\n                attribute.\n            modules: List of modules of the variational posterior. This is only\n                required for user-defined q i.e. if q does not have a `modules`\n                attribute.\n        \"\"\"\n        super().__init__(potential_fn, theta_transform, device, x_shape=x_shape)\n\n        # Especially the prior may be on another device -&gt; move it...\n        self._device = device\n        self.potential_fn.device = device\n        move_all_tensor_to_device(self.potential_fn, device)\n\n        # Get prior and previous builds\n        if prior is not None:\n            self._prior = prior\n        elif hasattr(self.potential_fn, \"prior\") and isinstance(\n            self.potential_fn.prior, Distribution\n        ):\n            self._prior = self.potential_fn.prior\n        elif isinstance(q, VIPosterior) and isinstance(q._prior, Distribution):\n            self._prior = q._prior\n        else:\n            raise ValueError(\n                \"We could not find a suitable prior distribution within `potential_fn`\"\n                \"or `q` (if a VIPosterior is given). Please explicitly specify a prior.\"\n            )\n        move_all_tensor_to_device(self._prior, device)\n        self._optimizer = None\n\n        # In contrast to MCMC we want to project into constrained space.\n        if theta_transform is None:\n            self.link_transform = mcmc_transform(self._prior).inv\n        else:\n            self.link_transform = theta_transform.inv\n\n        # This will set the variational distribution and VI method\n        self.set_q(q, parameters=parameters, modules=modules)\n        self.set_vi_method(vi_method)\n\n        self._purpose = (\n            \"It provides Variational inference to .sample() from the posterior and \"\n            \"can evaluate the _normalized_ posterior density with .log_prob().\"\n        )\n\n    @property\n    def q(self) -&gt; Distribution:\n\"\"\"Returns the variational posterior.\"\"\"\n        return self._q\n\n    @q.setter\n    def q(\n        self,\n        q: Union[str, Distribution, \"VIPosterior\", Callable],\n    ) -&gt; None:\n\"\"\"Sets the variational distribution. If the distribution does not admit access\n        through `parameters` and `modules` function, please use `set_q` if you want to\n        explicitly specify the parameters and modules.\n\n\n        Args:\n            q: Variational distribution, either string, distribution, or a VIPosterior\n                object. This specifies a parametric class of distribution over which\n                the best possible posterior approximation is searched. For string input,\n                we currently support [nsf, scf, maf, mcf, gaussian, gaussian_diag]. Of\n                course, you can also specify your own variational family by passing a\n                `parameterized` distribution object i.e. a torch.distributions\n                Distribution with methods `parameters` returning an iterable of all\n                parameters (you can pass them within the paramters/modules attribute).\n                Additionally, we allow a `Callable`, which allows you the pass a\n                `builder` function, which if called returns an distribution. This may be\n                useful for setting the hyperparameters e.g. `num_transfroms:int` by\n                using the `get_flow_builder` method specifying the hyperparameters. If q\n                is already a `VIPosterior`, then the arguments will be copied from it\n                (relevant for multi-round training).\n\n\n        \"\"\"\n        self.set_q(q)\n\n    def set_q(\n        self,\n        q: Union[str, PyroTransformedDistribution, \"VIPosterior\", Callable],\n        parameters: Iterable = [],\n        modules: Iterable = [],\n    ) -&gt; None:\n\"\"\"Defines the variational family.\n\n        You can specify over which parameters/modules we optimize. This is required for\n        custom distributions which e.g. do not inherit nn.Modules or has the function\n        `parameters` or `modules` to give direct access to trainable parameters.\n        Further, you can pass a function, which constructs a variational distribution\n        if called.\n\n        Args:\n            q: Variational distribution, either string, distribution, or a VIPosterior\n                object. This specifies a parametric class of distribution over which\n                the best possible posterior approximation is searched. For string input,\n                we currently support [nsf, scf, maf, mcf, gaussian, gaussian_diag]. Of\n                course, you can also specify your own variational family by passing a\n                `parameterized` distribution object i.e. a torch.distributions\n                Distribution with methods `parameters` returning an iterable of all\n                parameters (you can pass them within the paramters/modules attribute).\n                Additionally, we allow a `Callable`, which allows you the pass a\n                `builder` function, which if called returns an distribution. This may be\n                useful for setting the hyperparameters e.g. `num_transfroms:int` by\n                using the `get_flow_builder` method specifying the hyperparameters. If q\n                is already a `VIPosterior`, then the arguments will be copied from it\n                (relevant for multi-round training).\n            parameters: List of parameters associated with the distribution object.\n            modules: List of modules associated with the distribution object.\n\n        \"\"\"\n        self._q_arg = q\n        if isinstance(q, Distribution):\n            q = adapt_variational_distribution(\n                q,\n                self._prior,\n                self.link_transform,\n                parameters=parameters,\n                modules=modules,\n            )\n            make_object_deepcopy_compatible(q)\n            self_custom_q_init_cache = deepcopy(q)\n            self._q_build_fn = lambda *args, **kwargs: self_custom_q_init_cache\n            self._trained_on = None\n        elif isinstance(q, str) or isinstance(q, Callable):\n            if isinstance(q, str):\n                self._q_build_fn = get_flow_builder(q)\n            else:\n                self._q_build_fn = q\n\n            q = self._q_build_fn(\n                self._prior.event_shape,\n                self.link_transform,\n                device=self._device,\n            )\n            make_object_deepcopy_compatible(q)\n            self._trained_on = None\n        elif isinstance(q, VIPosterior):\n            self._q_build_fn = q._q_build_fn\n            self._trained_on = q._trained_on\n            self.vi_method = q.vi_method  # type: ignore\n            self._device = q._device\n            self._prior = q._prior\n            self._x = q._x\n            self._q_arg = q._q_arg\n            make_object_deepcopy_compatible(q.q)\n            q = deepcopy(q.q)\n        move_all_tensor_to_device(q, self._device)\n        assert isinstance(\n            q, Distribution\n        ), \"\"\"Something went wrong when initializing the variational distribution.\n            Please create an issue on github https://github.com/mackelab/sbi/issues\"\"\"\n        check_variational_distribution(q, self._prior)\n        self._q = q\n\n    @property\n    def vi_method(self) -&gt; str:\n\"\"\"Variational inference method e.g. one of [rKL, fKL, IW, alpha].\"\"\"\n        return self._vi_method\n\n    @vi_method.setter\n    def vi_method(self, method: str) -&gt; None:\n\"\"\"See `set_vi_method`.\"\"\"\n        self.set_vi_method(method)\n\n    def set_vi_method(self, method: str) -&gt; \"VIPosterior\":\n\"\"\"Sets variational inference method.\n\n        Args:\n            method: One of [rKL, fKL, IW, alpha].\n\n        Returns:\n            `VIPosterior` for chainable calls.\n        \"\"\"\n        self._vi_method = method\n        self._optimizer_builder = get_VI_method(method)\n        return self\n\n    def sample(\n        self,\n        sample_shape: Shape = torch.Size(),\n        x: Optional[Tensor] = None,\n        **kwargs,\n    ) -&gt; Tensor:\n\"\"\"Samples from the variational posterior distribution.\n\n        Args:\n            sample_shape: Shape of samples\n\n        Returns:\n            Samples from posterior.\n        \"\"\"\n        x = self._x_else_default_x(x)\n        if self._trained_on is None or (x != self._trained_on).all():\n            raise AttributeError(\n                f\"The variational posterior was not fit on the specified `default_x` \"\n                f\"{x}. Please train using `posterior.train()`.\"\n            )\n        samples = self.q.sample(torch.Size(sample_shape))\n        return samples.reshape((*sample_shape, samples.shape[-1]))\n\n    def log_prob(\n        self,\n        theta: Tensor,\n        x: Optional[Tensor] = None,\n        track_gradients: bool = False,\n    ) -&gt; Tensor:\nr\"\"\"Returns the log-probability of theta under the variational posterior.\n\n        Args:\n            theta: Parameters\n            track_gradients: Whether the returned tensor supports tracking gradients.\n                This can be helpful for e.g. sensitivity analysis but increases memory\n                consumption.\n\n        Returns:\n            `len($\\theta$)`-shaped log-probability.\n        \"\"\"\n        x = self._x_else_default_x(x)\n        if self._trained_on is None or (x != self._trained_on).all():\n            raise AttributeError(\n                f\"The variational posterior was not fit using observation {x}.\\\n                     Please train.\"\n            )\n        with torch.set_grad_enabled(track_gradients):\n            theta = ensure_theta_batched(torch.as_tensor(theta))\n            return self.q.log_prob(theta)\n\n    def train(\n        self,\n        x: Optional[TorchTensor] = None,\n        n_particles: int = 256,\n        learning_rate: float = 1e-3,\n        gamma: float = 0.999,\n        max_num_iters: int = 2000,\n        min_num_iters: int = 10,\n        clip_value: float = 10.0,\n        warm_up_rounds: int = 100,\n        retrain_from_scratch: bool = False,\n        reset_optimizer: bool = False,\n        show_progress_bar: bool = True,\n        check_for_convergence: bool = True,\n        quality_control: bool = True,\n        quality_control_metric: str = \"psis\",\n        **kwargs,\n    ) -&gt; \"VIPosterior\":\n\"\"\"This method trains the variational posterior.\n\n        Args:\n            x: The observation.\n            n_particles: Number of samples to approximate expectations within the\n                variational bounds. The larger the more accurate are gradient\n                estimates, but the computational cost per iteration increases.\n            learning_rate: Learning rate of the optimizer.\n            gamma: Learning rate decay per iteration. We use an exponential decay\n                scheduler.\n            max_num_iters: Maximum number of iterations.\n            min_num_iters: Minimum number of iterations.\n            clip_value: Gradient clipping value, decreasing may help if you see invalid\n                values.\n            warm_up_rounds: Initialize the posterior as the prior.\n            retrain_from_scratch: Retrain the variational distributions from scratch.\n            reset_optimizer: Reset the divergence optimizer\n            show_progress_bar: If any progress report should be displayed.\n            quality_control: If False quality control is skipped.\n            quality_control_metric: Which metric to use for evaluating the quality.\n            kwargs: Hyperparameters check corresponding `DivergenceOptimizer` for detail\n                eps: Determines sensitivity of convergence check.\n                retain_graph: Boolean which decides whether to retain the computation\n                    graph. This may be required for some `exotic` user-specified q's.\n                optimizer: A PyTorch Optimizer class e.g. Adam or SGD. See\n                    `DivergenceOptimizer` for details.\n                scheduler: A PyTorch learning rate scheduler. See\n                    `DivergenceOptimizer` for details.\n                alpha: Only used if vi_method=`alpha`. Determines the alpha divergence.\n                K: Only used if vi_method=`IW`. Determines the number of importance\n                    weighted particles.\n                stick_the_landing: If one should use the STL estimator (only for rKL,\n                    IW, alpha).\n                dreg: If one should use the DREG estimator (only for rKL, IW, alpha).\n                weight_transform: Callable applied to importance weights (only for fKL)\n        Returns:\n            VIPosterior: `VIPosterior` (can be used to chain calls).\n        \"\"\"\n        # Update optimizer with current arguments.\n        if self._optimizer is not None:\n            self._optimizer.update({**locals(), **kwargs})\n\n        # Init q and the optimizer if necessary\n        if retrain_from_scratch:\n            self.q = self._q_build_fn()  # type: ignore\n            self._optimizer = self._optimizer_builder(\n                self.potential_fn,\n                self.q,\n                lr=learning_rate,\n                clip_value=clip_value,\n                gamma=gamma,\n                n_particles=n_particles,\n                prior=self._prior,\n                **kwargs,\n            )\n\n        if (\n            reset_optimizer\n            or self._optimizer is None\n            or not isinstance(self._optimizer, self._optimizer_builder)\n        ):\n            self._optimizer = self._optimizer_builder(\n                self.potential_fn,\n                self.q,\n                lr=learning_rate,\n                clip_value=clip_value,\n                gamma=gamma,\n                n_particles=n_particles,\n                prior=self._prior,\n                **kwargs,\n            )\n\n        # Check context\n        x = atleast_2d_float32_tensor(self._x_else_default_x(x)).to(  # type: ignore\n            self._device\n        )\n\n        already_trained = self._trained_on is not None and (x == self._trained_on).all()\n\n        # Optimize\n        optimizer = self._optimizer\n        optimizer.to(self._device)\n        optimizer.reset_loss_stats()\n\n        if show_progress_bar:\n            iters = tqdm(range(max_num_iters))\n        else:\n            iters = range(max_num_iters)\n\n        # Warmup before training\n        if reset_optimizer or (not optimizer.warm_up_was_done and not already_trained):\n            if show_progress_bar:\n                iters.set_description(  # type: ignore\n                    \"Warmup phase, this may take a few seconds...\"\n                )\n            optimizer.warm_up(warm_up_rounds)\n\n        for i in iters:\n            optimizer.step(x)\n            mean_loss, std_loss = optimizer.get_loss_stats()\n            # Update progress bar\n            if show_progress_bar:\n                assert isinstance(iters, tqdm)\n                iters.set_description(  # type: ignore\n                    f\"Loss: {np.round(float(mean_loss), 2)}\"\n                    f\"Std: {np.round(float(std_loss), 2)}\"\n                )\n            # Check for convergence\n            if check_for_convergence and i &gt; min_num_iters:\n                if optimizer.converged():\n                    if show_progress_bar:\n                        print(f\"\\nConverged with loss: {np.round(float(mean_loss), 2)}\")\n                    break\n        # Training finished:\n        self._trained_on = x\n\n        # Evaluate quality\n        if quality_control:\n            try:\n                self.evaluate(quality_control_metric=quality_control_metric)\n            except Exception as e:\n                print(\n                    f\"Quality control did not work, we reset the variational \\\n                        posterior,please check your setting. \\\n\\n Following error occured {e}\"\n                )\n                self.train(\n                    learning_rate=learning_rate * 0.1,\n                    retrain_from_scratch=True,\n                    reset_optimizer=True,\n                )\n\n        return self\n\n    def evaluate(self, quality_control_metric: str = \"psis\", N: int = int(5e4)) -&gt; None:\n\"\"\"This function will evaluate the quality of the variational posterior\n        distribution. We currently support two different metrics of type `psis`, which\n        checks the quality based on the tails of importance weights (there should not be\n        much with a large one), or `prop` which checks the proportionality between q\n        and potential_fn.\n\n        NOTE: In our experience `prop` is sensitive to distinguish ``good`` from ``ok``\n        whereas `psis` is more sensitive in distinguishing `very bad` from `ok`.\n\n        Args:\n            quality_control_metric: The metric of choice, we currently support [psis,\n                prop, prop_prior].\n            N: Number of samples which is used to evaluate the metric.\n        \"\"\"\n        quality_control_fn, quality_control_msg = get_quality_metric(\n            quality_control_metric\n        )\n        metric = round(float(quality_control_fn(self, N=N)), 3)\n        print(f\"Quality Score: {metric} \" + quality_control_msg)\n\n    def map(\n        self,\n        x: Optional[TorchTensor] = None,\n        num_iter: int = 1_000,\n        num_to_optimize: int = 100,\n        learning_rate: float = 0.01,\n        init_method: Union[str, TorchTensor] = \"proposal\",\n        num_init_samples: int = 10_000,\n        save_best_every: int = 10,\n        show_progress_bars: bool = False,\n        force_update: bool = False,\n    ) -&gt; Tensor:\nr\"\"\"Returns the maximum-a-posteriori estimate (MAP).\n\n        The method can be interrupted (Ctrl-C) when the user sees that the\n        log-probability converges. The best estimate will be saved in `self._map` and\n        can be accessed with `self.map()`. The MAP is obtained by running gradient\n        ascent from a given number of starting positions (samples from the posterior\n        with the highest log-probability). After the optimization is done, we select the\n        parameter set that has the highest log-probability after the optimization.\n\n        Warning: The default values used by this function are not well-tested. They\n        might require hand-tuning for the problem at hand.\n\n        For developers: if the prior is a `BoxUniform`, we carry out the optimization\n        in unbounded space and transform the result back into bounded space.\n\n        Args:\n            x: Deprecated - use `.set_default_x()` prior to `.map()`.\n            num_iter: Number of optimization steps that the algorithm takes\n                to find the MAP.\n            learning_rate: Learning rate of the optimizer.\n            init_method: How to select the starting parameters for the optimization. If\n                it is a string, it can be either [`posterior`, `prior`], which samples\n                the respective distribution `num_init_samples` times. If it is a\n                tensor, the tensor will be used as init locations.\n            num_init_samples: Draw this number of samples from the posterior and\n                evaluate the log-probability of all of them.\n            num_to_optimize: From the drawn `num_init_samples`, use the\n                `num_to_optimize` with highest log-probability as the initial points\n                for the optimization.\n            save_best_every: The best log-probability is computed, saved in the\n                `map`-attribute, and printed every `save_best_every`-th iteration.\n                Computing the best log-probability creates a significant overhead\n                (thus, the default is `10`.)\n            show_progress_bars: Whether to show a progressbar during sampling from\n                the posterior.\n            force_update: Whether to re-calculate the MAP when x is unchanged and\n                have a cached value.\n            log_prob_kwargs: Will be empty for SNLE and SNRE. Will contain\n                {'norm_posterior': True} for SNPE.\n\n        Returns:\n            The MAP estimate.\n        \"\"\"\n        self.proposal = self.q\n        return super().map(\n            x=x,\n            num_iter=num_iter,\n            num_to_optimize=num_to_optimize,\n            learning_rate=learning_rate,\n            init_method=init_method,\n            num_init_samples=num_init_samples,\n            save_best_every=save_best_every,\n            show_progress_bars=show_progress_bars,\n            force_update=force_update,\n        )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.vi_posterior.VIPosterior.q","title":"<code>q: Distribution</code>  <code>property</code> <code>writable</code>","text":"<p>Returns the variational posterior.</p>"},{"location":"reference/#sbi.inference.posteriors.vi_posterior.VIPosterior.vi_method","title":"<code>vi_method: str</code>  <code>property</code> <code>writable</code>","text":"<p>Variational inference method e.g. one of [rKL, fKL, IW, alpha].</p>"},{"location":"reference/#sbi.inference.posteriors.vi_posterior.VIPosterior.__init__","title":"<code>__init__(potential_fn, prior=None, q='maf', theta_transform=None, vi_method='rKL', device='cpu', x_shape=None, parameters=[], modules=[])</code>","text":"<p>Parameters:</p> Name Type Description Default <code>potential_fn</code> <code>Callable</code> <p>The potential function from which to draw samples.</p> required <code>prior</code> <code>Optional[TorchDistribution]</code> <p>This is the prior distribution. Note that this is only used to check/construct the variational distribution or within some quality metrics. Please make sure that this matches with the prior within the potential_fn. If <code>None</code> is given, we will try to infer it from potential_fn or q, if this fails we raise an Error.</p> <code>None</code> <code>q</code> <code>Union[str, PyroTransformedDistribution, VIPosterior, Callable]</code> <p>Variational distribution, either string, <code>TransformedDistribution</code>, or a <code>VIPosterior</code> object. This specifies a parametric class of distribution over which the best possible posterior approximation is searched. For string input, we currently support [nsf, scf, maf, mcf, gaussian, gaussian_diag]. You can also specify your own variational family by passing a pyro <code>TransformedDistribution</code>. Additionally, we allow a <code>Callable</code>, which allows you the pass a <code>builder</code> function, which if called returns a distribution. This may be useful for setting the hyperparameters e.g. <code>num_transfroms</code> within the <code>get_flow_builder</code> method specifying the number of transformations within a normalizing flow. If q is already a <code>VIPosterior</code>, then the arguments will be copied from it (relevant for multi-round training).</p> <code>'maf'</code> <code>theta_transform</code> <code>Optional[TorchTransform]</code> <p>Maps form prior support to unconstrained space. The inverse is used here to ensure that the posterior support is equal to that of the prior.</p> <code>None</code> <code>vi_method</code> <code>str</code> <p>This specifies the variational methods which are used to fit q to the posterior. We currently support [rKL, fKL, IW, alpha]. Note that some of the divergences are <code>mode seeking</code> i.e. they underestimate variance and collapse on multimodal targets (<code>rKL</code>, <code>alpha</code> for alpha &gt; 1) and some are <code>mass covering</code> i.e. they overestimate variance but typically cover all modes (<code>fKL</code>, <code>IW</code>, <code>alpha</code> for alpha &lt; 1).</p> <code>'rKL'</code> <code>device</code> <code>str</code> <p>Training device, e.g., <code>cpu</code>, <code>cuda</code> or <code>cuda:0</code>. We will ensure that all other objects are also on this device.</p> <code>'cpu'</code> <code>x_shape</code> <code>Optional[torch.Size]</code> <p>Shape of a single simulator output. If passed, it is used to check the shape of the observed data and give a descriptive error.</p> <code>None</code> <code>parameters</code> <code>Iterable</code> <p>List of parameters of the variational posterior. This is only required for user-defined q i.e. if q does not have a <code>parameters</code> attribute.</p> <code>[]</code> <code>modules</code> <code>Iterable</code> <p>List of modules of the variational posterior. This is only required for user-defined q i.e. if q does not have a <code>modules</code> attribute.</p> <code>[]</code> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/vi_posterior.py</code> <pre><code>def __init__(\n    self,\n    potential_fn: Callable,\n    prior: Optional[TorchDistribution] = None,\n    q: Union[str, PyroTransformedDistribution, \"VIPosterior\", Callable] = \"maf\",\n    theta_transform: Optional[TorchTransform] = None,\n    vi_method: str = \"rKL\",\n    device: str = \"cpu\",\n    x_shape: Optional[torch.Size] = None,\n    parameters: Iterable = [],\n    modules: Iterable = [],\n):\n\"\"\"\n    Args:\n        potential_fn: The potential function from which to draw samples.\n        prior: This is the prior distribution. Note that this is only\n            used to check/construct the variational distribution or within some\n            quality metrics. Please make sure that this matches with the prior\n            within the potential_fn. If `None` is given, we will try to infer it\n            from potential_fn or q, if this fails we raise an Error.\n        q: Variational distribution, either string, `TransformedDistribution`, or a\n            `VIPosterior` object. This specifies a parametric class of distribution\n            over which the best possible posterior approximation is searched. For\n            string input, we currently support [nsf, scf, maf, mcf, gaussian,\n            gaussian_diag]. You can also specify your own variational family by\n            passing a pyro `TransformedDistribution`.\n            Additionally, we allow a `Callable`, which allows you the pass a\n            `builder` function, which if called returns a distribution. This may be\n            useful for setting the hyperparameters e.g. `num_transfroms` within the\n            `get_flow_builder` method specifying the number of transformations\n            within a normalizing flow. If q is already a `VIPosterior`, then the\n            arguments will be copied from it (relevant for multi-round training).\n        theta_transform: Maps form prior support to unconstrained space. The\n            inverse is used here to ensure that the posterior support is equal to\n            that of the prior.\n        vi_method: This specifies the variational methods which are used to fit q to\n            the posterior. We currently support [rKL, fKL, IW, alpha]. Note that\n            some of the divergences are `mode seeking` i.e. they underestimate\n            variance and collapse on multimodal targets (`rKL`, `alpha` for alpha &gt;\n            1) and some are `mass covering` i.e. they overestimate variance but\n            typically cover all modes (`fKL`, `IW`, `alpha` for alpha &lt; 1).\n        device: Training device, e.g., `cpu`, `cuda` or `cuda:0`. We will ensure\n            that all other objects are also on this device.\n        x_shape: Shape of a single simulator output. If passed, it is used to check\n            the shape of the observed data and give a descriptive error.\n        parameters: List of parameters of the variational posterior. This is only\n            required for user-defined q i.e. if q does not have a `parameters`\n            attribute.\n        modules: List of modules of the variational posterior. This is only\n            required for user-defined q i.e. if q does not have a `modules`\n            attribute.\n    \"\"\"\n    super().__init__(potential_fn, theta_transform, device, x_shape=x_shape)\n\n    # Especially the prior may be on another device -&gt; move it...\n    self._device = device\n    self.potential_fn.device = device\n    move_all_tensor_to_device(self.potential_fn, device)\n\n    # Get prior and previous builds\n    if prior is not None:\n        self._prior = prior\n    elif hasattr(self.potential_fn, \"prior\") and isinstance(\n        self.potential_fn.prior, Distribution\n    ):\n        self._prior = self.potential_fn.prior\n    elif isinstance(q, VIPosterior) and isinstance(q._prior, Distribution):\n        self._prior = q._prior\n    else:\n        raise ValueError(\n            \"We could not find a suitable prior distribution within `potential_fn`\"\n            \"or `q` (if a VIPosterior is given). Please explicitly specify a prior.\"\n        )\n    move_all_tensor_to_device(self._prior, device)\n    self._optimizer = None\n\n    # In contrast to MCMC we want to project into constrained space.\n    if theta_transform is None:\n        self.link_transform = mcmc_transform(self._prior).inv\n    else:\n        self.link_transform = theta_transform.inv\n\n    # This will set the variational distribution and VI method\n    self.set_q(q, parameters=parameters, modules=modules)\n    self.set_vi_method(vi_method)\n\n    self._purpose = (\n        \"It provides Variational inference to .sample() from the posterior and \"\n        \"can evaluate the _normalized_ posterior density with .log_prob().\"\n    )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.vi_posterior.VIPosterior.evaluate","title":"<code>evaluate(quality_control_metric='psis', N=int(50000.0))</code>","text":"<p>This function will evaluate the quality of the variational posterior distribution. We currently support two different metrics of type <code>psis</code>, which checks the quality based on the tails of importance weights (there should not be much with a large one), or <code>prop</code> which checks the proportionality between q and potential_fn.</p> <p>NOTE: In our experience <code>prop</code> is sensitive to distinguish <code>good</code> from <code>ok</code> whereas <code>psis</code> is more sensitive in distinguishing <code>very bad</code> from <code>ok</code>.</p> <p>Parameters:</p> Name Type Description Default <code>quality_control_metric</code> <code>str</code> <p>The metric of choice, we currently support [psis, prop, prop_prior].</p> <code>'psis'</code> <code>N</code> <code>int</code> <p>Number of samples which is used to evaluate the metric.</p> <code>int(50000.0)</code> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/vi_posterior.py</code> <pre><code>def evaluate(self, quality_control_metric: str = \"psis\", N: int = int(5e4)) -&gt; None:\n\"\"\"This function will evaluate the quality of the variational posterior\n    distribution. We currently support two different metrics of type `psis`, which\n    checks the quality based on the tails of importance weights (there should not be\n    much with a large one), or `prop` which checks the proportionality between q\n    and potential_fn.\n\n    NOTE: In our experience `prop` is sensitive to distinguish ``good`` from ``ok``\n    whereas `psis` is more sensitive in distinguishing `very bad` from `ok`.\n\n    Args:\n        quality_control_metric: The metric of choice, we currently support [psis,\n            prop, prop_prior].\n        N: Number of samples which is used to evaluate the metric.\n    \"\"\"\n    quality_control_fn, quality_control_msg = get_quality_metric(\n        quality_control_metric\n    )\n    metric = round(float(quality_control_fn(self, N=N)), 3)\n    print(f\"Quality Score: {metric} \" + quality_control_msg)\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.vi_posterior.VIPosterior.log_prob","title":"<code>log_prob(theta, x=None, track_gradients=False)</code>","text":"<p>Returns the log-probability of theta under the variational posterior.</p> <p>Parameters:</p> Name Type Description Default <code>theta</code> <code>Tensor</code> <p>Parameters</p> required <code>track_gradients</code> <code>bool</code> <p>Whether the returned tensor supports tracking gradients. This can be helpful for e.g. sensitivity analysis but increases memory consumption.</p> <code>False</code> <p>Returns:</p> Type Description <code>Tensor</code> <p><code>len($\\theta$)</code>-shaped log-probability.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/vi_posterior.py</code> <pre><code>def log_prob(\n    self,\n    theta: Tensor,\n    x: Optional[Tensor] = None,\n    track_gradients: bool = False,\n) -&gt; Tensor:\nr\"\"\"Returns the log-probability of theta under the variational posterior.\n\n    Args:\n        theta: Parameters\n        track_gradients: Whether the returned tensor supports tracking gradients.\n            This can be helpful for e.g. sensitivity analysis but increases memory\n            consumption.\n\n    Returns:\n        `len($\\theta$)`-shaped log-probability.\n    \"\"\"\n    x = self._x_else_default_x(x)\n    if self._trained_on is None or (x != self._trained_on).all():\n        raise AttributeError(\n            f\"The variational posterior was not fit using observation {x}.\\\n                 Please train.\"\n        )\n    with torch.set_grad_enabled(track_gradients):\n        theta = ensure_theta_batched(torch.as_tensor(theta))\n        return self.q.log_prob(theta)\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.vi_posterior.VIPosterior.map","title":"<code>map(x=None, num_iter=1000, num_to_optimize=100, learning_rate=0.01, init_method='proposal', num_init_samples=10000, save_best_every=10, show_progress_bars=False, force_update=False)</code>","text":"<p>Returns the maximum-a-posteriori estimate (MAP).</p> <p>The method can be interrupted (Ctrl-C) when the user sees that the log-probability converges. The best estimate will be saved in <code>self._map</code> and can be accessed with <code>self.map()</code>. The MAP is obtained by running gradient ascent from a given number of starting positions (samples from the posterior with the highest log-probability). After the optimization is done, we select the parameter set that has the highest log-probability after the optimization.</p> <p>Warning: The default values used by this function are not well-tested. They might require hand-tuning for the problem at hand.</p> <p>For developers: if the prior is a <code>BoxUniform</code>, we carry out the optimization in unbounded space and transform the result back into bounded space.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>Optional[TorchTensor]</code> <p>Deprecated - use <code>.set_default_x()</code> prior to <code>.map()</code>.</p> <code>None</code> <code>num_iter</code> <code>int</code> <p>Number of optimization steps that the algorithm takes to find the MAP.</p> <code>1000</code> <code>learning_rate</code> <code>float</code> <p>Learning rate of the optimizer.</p> <code>0.01</code> <code>init_method</code> <code>Union[str, TorchTensor]</code> <p>How to select the starting parameters for the optimization. If it is a string, it can be either [<code>posterior</code>, <code>prior</code>], which samples the respective distribution <code>num_init_samples</code> times. If it is a tensor, the tensor will be used as init locations.</p> <code>'proposal'</code> <code>num_init_samples</code> <code>int</code> <p>Draw this number of samples from the posterior and evaluate the log-probability of all of them.</p> <code>10000</code> <code>num_to_optimize</code> <code>int</code> <p>From the drawn <code>num_init_samples</code>, use the <code>num_to_optimize</code> with highest log-probability as the initial points for the optimization.</p> <code>100</code> <code>save_best_every</code> <code>int</code> <p>The best log-probability is computed, saved in the <code>map</code>-attribute, and printed every <code>save_best_every</code>-th iteration. Computing the best log-probability creates a significant overhead (thus, the default is <code>10</code>.)</p> <code>10</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during sampling from the posterior.</p> <code>False</code> <code>force_update</code> <code>bool</code> <p>Whether to re-calculate the MAP when x is unchanged and have a cached value.</p> <code>False</code> <code>log_prob_kwargs</code> <p>Will be empty for SNLE and SNRE. Will contain {\u2018norm_posterior\u2019: True} for SNPE.</p> required <p>Returns:</p> Type Description <code>Tensor</code> <p>The MAP estimate.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/vi_posterior.py</code> <pre><code>def map(\n    self,\n    x: Optional[TorchTensor] = None,\n    num_iter: int = 1_000,\n    num_to_optimize: int = 100,\n    learning_rate: float = 0.01,\n    init_method: Union[str, TorchTensor] = \"proposal\",\n    num_init_samples: int = 10_000,\n    save_best_every: int = 10,\n    show_progress_bars: bool = False,\n    force_update: bool = False,\n) -&gt; Tensor:\nr\"\"\"Returns the maximum-a-posteriori estimate (MAP).\n\n    The method can be interrupted (Ctrl-C) when the user sees that the\n    log-probability converges. The best estimate will be saved in `self._map` and\n    can be accessed with `self.map()`. The MAP is obtained by running gradient\n    ascent from a given number of starting positions (samples from the posterior\n    with the highest log-probability). After the optimization is done, we select the\n    parameter set that has the highest log-probability after the optimization.\n\n    Warning: The default values used by this function are not well-tested. They\n    might require hand-tuning for the problem at hand.\n\n    For developers: if the prior is a `BoxUniform`, we carry out the optimization\n    in unbounded space and transform the result back into bounded space.\n\n    Args:\n        x: Deprecated - use `.set_default_x()` prior to `.map()`.\n        num_iter: Number of optimization steps that the algorithm takes\n            to find the MAP.\n        learning_rate: Learning rate of the optimizer.\n        init_method: How to select the starting parameters for the optimization. If\n            it is a string, it can be either [`posterior`, `prior`], which samples\n            the respective distribution `num_init_samples` times. If it is a\n            tensor, the tensor will be used as init locations.\n        num_init_samples: Draw this number of samples from the posterior and\n            evaluate the log-probability of all of them.\n        num_to_optimize: From the drawn `num_init_samples`, use the\n            `num_to_optimize` with highest log-probability as the initial points\n            for the optimization.\n        save_best_every: The best log-probability is computed, saved in the\n            `map`-attribute, and printed every `save_best_every`-th iteration.\n            Computing the best log-probability creates a significant overhead\n            (thus, the default is `10`.)\n        show_progress_bars: Whether to show a progressbar during sampling from\n            the posterior.\n        force_update: Whether to re-calculate the MAP when x is unchanged and\n            have a cached value.\n        log_prob_kwargs: Will be empty for SNLE and SNRE. Will contain\n            {'norm_posterior': True} for SNPE.\n\n    Returns:\n        The MAP estimate.\n    \"\"\"\n    self.proposal = self.q\n    return super().map(\n        x=x,\n        num_iter=num_iter,\n        num_to_optimize=num_to_optimize,\n        learning_rate=learning_rate,\n        init_method=init_method,\n        num_init_samples=num_init_samples,\n        save_best_every=save_best_every,\n        show_progress_bars=show_progress_bars,\n        force_update=force_update,\n    )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.vi_posterior.VIPosterior.sample","title":"<code>sample(sample_shape=torch.Size(), x=None, **kwargs)</code>","text":"<p>Samples from the variational posterior distribution.</p> <p>Parameters:</p> Name Type Description Default <code>sample_shape</code> <code>Shape</code> <p>Shape of samples</p> <code>torch.Size()</code> <p>Returns:</p> Type Description <code>Tensor</code> <p>Samples from posterior.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/vi_posterior.py</code> <pre><code>def sample(\n    self,\n    sample_shape: Shape = torch.Size(),\n    x: Optional[Tensor] = None,\n    **kwargs,\n) -&gt; Tensor:\n\"\"\"Samples from the variational posterior distribution.\n\n    Args:\n        sample_shape: Shape of samples\n\n    Returns:\n        Samples from posterior.\n    \"\"\"\n    x = self._x_else_default_x(x)\n    if self._trained_on is None or (x != self._trained_on).all():\n        raise AttributeError(\n            f\"The variational posterior was not fit on the specified `default_x` \"\n            f\"{x}. Please train using `posterior.train()`.\"\n        )\n    samples = self.q.sample(torch.Size(sample_shape))\n    return samples.reshape((*sample_shape, samples.shape[-1]))\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.vi_posterior.VIPosterior.set_q","title":"<code>set_q(q, parameters=[], modules=[])</code>","text":"<p>Defines the variational family.</p> <p>You can specify over which parameters/modules we optimize. This is required for custom distributions which e.g. do not inherit nn.Modules or has the function <code>parameters</code> or <code>modules</code> to give direct access to trainable parameters. Further, you can pass a function, which constructs a variational distribution if called.</p> <p>Parameters:</p> Name Type Description Default <code>q</code> <code>Union[str, PyroTransformedDistribution, VIPosterior, Callable]</code> <p>Variational distribution, either string, distribution, or a VIPosterior object. This specifies a parametric class of distribution over which the best possible posterior approximation is searched. For string input, we currently support [nsf, scf, maf, mcf, gaussian, gaussian_diag]. Of course, you can also specify your own variational family by passing a <code>parameterized</code> distribution object i.e. a torch.distributions Distribution with methods <code>parameters</code> returning an iterable of all parameters (you can pass them within the paramters/modules attribute). Additionally, we allow a <code>Callable</code>, which allows you the pass a <code>builder</code> function, which if called returns an distribution. This may be useful for setting the hyperparameters e.g. <code>num_transfroms:int</code> by using the <code>get_flow_builder</code> method specifying the hyperparameters. If q is already a <code>VIPosterior</code>, then the arguments will be copied from it (relevant for multi-round training).</p> required <code>parameters</code> <code>Iterable</code> <p>List of parameters associated with the distribution object.</p> <code>[]</code> <code>modules</code> <code>Iterable</code> <p>List of modules associated with the distribution object.</p> <code>[]</code> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/vi_posterior.py</code> <pre><code>def set_q(\n    self,\n    q: Union[str, PyroTransformedDistribution, \"VIPosterior\", Callable],\n    parameters: Iterable = [],\n    modules: Iterable = [],\n) -&gt; None:\n\"\"\"Defines the variational family.\n\n    You can specify over which parameters/modules we optimize. This is required for\n    custom distributions which e.g. do not inherit nn.Modules or has the function\n    `parameters` or `modules` to give direct access to trainable parameters.\n    Further, you can pass a function, which constructs a variational distribution\n    if called.\n\n    Args:\n        q: Variational distribution, either string, distribution, or a VIPosterior\n            object. This specifies a parametric class of distribution over which\n            the best possible posterior approximation is searched. For string input,\n            we currently support [nsf, scf, maf, mcf, gaussian, gaussian_diag]. Of\n            course, you can also specify your own variational family by passing a\n            `parameterized` distribution object i.e. a torch.distributions\n            Distribution with methods `parameters` returning an iterable of all\n            parameters (you can pass them within the paramters/modules attribute).\n            Additionally, we allow a `Callable`, which allows you the pass a\n            `builder` function, which if called returns an distribution. This may be\n            useful for setting the hyperparameters e.g. `num_transfroms:int` by\n            using the `get_flow_builder` method specifying the hyperparameters. If q\n            is already a `VIPosterior`, then the arguments will be copied from it\n            (relevant for multi-round training).\n        parameters: List of parameters associated with the distribution object.\n        modules: List of modules associated with the distribution object.\n\n    \"\"\"\n    self._q_arg = q\n    if isinstance(q, Distribution):\n        q = adapt_variational_distribution(\n            q,\n            self._prior,\n            self.link_transform,\n            parameters=parameters,\n            modules=modules,\n        )\n        make_object_deepcopy_compatible(q)\n        self_custom_q_init_cache = deepcopy(q)\n        self._q_build_fn = lambda *args, **kwargs: self_custom_q_init_cache\n        self._trained_on = None\n    elif isinstance(q, str) or isinstance(q, Callable):\n        if isinstance(q, str):\n            self._q_build_fn = get_flow_builder(q)\n        else:\n            self._q_build_fn = q\n\n        q = self._q_build_fn(\n            self._prior.event_shape,\n            self.link_transform,\n            device=self._device,\n        )\n        make_object_deepcopy_compatible(q)\n        self._trained_on = None\n    elif isinstance(q, VIPosterior):\n        self._q_build_fn = q._q_build_fn\n        self._trained_on = q._trained_on\n        self.vi_method = q.vi_method  # type: ignore\n        self._device = q._device\n        self._prior = q._prior\n        self._x = q._x\n        self._q_arg = q._q_arg\n        make_object_deepcopy_compatible(q.q)\n        q = deepcopy(q.q)\n    move_all_tensor_to_device(q, self._device)\n    assert isinstance(\n        q, Distribution\n    ), \"\"\"Something went wrong when initializing the variational distribution.\n        Please create an issue on github https://github.com/mackelab/sbi/issues\"\"\"\n    check_variational_distribution(q, self._prior)\n    self._q = q\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.vi_posterior.VIPosterior.set_vi_method","title":"<code>set_vi_method(method)</code>","text":"<p>Sets variational inference method.</p> <p>Parameters:</p> Name Type Description Default <code>method</code> <code>str</code> <p>One of [rKL, fKL, IW, alpha].</p> required <p>Returns:</p> Type Description <code>VIPosterior</code> <p><code>VIPosterior</code> for chainable calls.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/vi_posterior.py</code> <pre><code>def set_vi_method(self, method: str) -&gt; \"VIPosterior\":\n\"\"\"Sets variational inference method.\n\n    Args:\n        method: One of [rKL, fKL, IW, alpha].\n\n    Returns:\n        `VIPosterior` for chainable calls.\n    \"\"\"\n    self._vi_method = method\n    self._optimizer_builder = get_VI_method(method)\n    return self\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.vi_posterior.VIPosterior.train","title":"<code>train(x=None, n_particles=256, learning_rate=0.001, gamma=0.999, max_num_iters=2000, min_num_iters=10, clip_value=10.0, warm_up_rounds=100, retrain_from_scratch=False, reset_optimizer=False, show_progress_bar=True, check_for_convergence=True, quality_control=True, quality_control_metric='psis', **kwargs)</code>","text":"<p>This method trains the variational posterior.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>Optional[TorchTensor]</code> <p>The observation.</p> <code>None</code> <code>n_particles</code> <code>int</code> <p>Number of samples to approximate expectations within the variational bounds. The larger the more accurate are gradient estimates, but the computational cost per iteration increases.</p> <code>256</code> <code>learning_rate</code> <code>float</code> <p>Learning rate of the optimizer.</p> <code>0.001</code> <code>gamma</code> <code>float</code> <p>Learning rate decay per iteration. We use an exponential decay scheduler.</p> <code>0.999</code> <code>max_num_iters</code> <code>int</code> <p>Maximum number of iterations.</p> <code>2000</code> <code>min_num_iters</code> <code>int</code> <p>Minimum number of iterations.</p> <code>10</code> <code>clip_value</code> <code>float</code> <p>Gradient clipping value, decreasing may help if you see invalid values.</p> <code>10.0</code> <code>warm_up_rounds</code> <code>int</code> <p>Initialize the posterior as the prior.</p> <code>100</code> <code>retrain_from_scratch</code> <code>bool</code> <p>Retrain the variational distributions from scratch.</p> <code>False</code> <code>reset_optimizer</code> <code>bool</code> <p>Reset the divergence optimizer</p> <code>False</code> <code>show_progress_bar</code> <code>bool</code> <p>If any progress report should be displayed.</p> <code>True</code> <code>quality_control</code> <code>bool</code> <p>If False quality control is skipped.</p> <code>True</code> <code>quality_control_metric</code> <code>str</code> <p>Which metric to use for evaluating the quality.</p> <code>'psis'</code> <code>kwargs</code> <p>Hyperparameters check corresponding <code>DivergenceOptimizer</code> for detail eps: Determines sensitivity of convergence check. retain_graph: Boolean which decides whether to retain the computation     graph. This may be required for some <code>exotic</code> user-specified q\u2019s. optimizer: A PyTorch Optimizer class e.g. Adam or SGD. See     <code>DivergenceOptimizer</code> for details. scheduler: A PyTorch learning rate scheduler. See     <code>DivergenceOptimizer</code> for details. alpha: Only used if vi_method=<code>alpha</code>. Determines the alpha divergence. K: Only used if vi_method=<code>IW</code>. Determines the number of importance     weighted particles. stick_the_landing: If one should use the STL estimator (only for rKL,     IW, alpha). dreg: If one should use the DREG estimator (only for rKL, IW, alpha). weight_transform: Callable applied to importance weights (only for fKL)</p> <code>{}</code> <p>Returns:</p> Name Type Description <code>VIPosterior</code> <code>VIPosterior</code> <p><code>VIPosterior</code> (can be used to chain calls).</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/posteriors/vi_posterior.py</code> <pre><code>def train(\n    self,\n    x: Optional[TorchTensor] = None,\n    n_particles: int = 256,\n    learning_rate: float = 1e-3,\n    gamma: float = 0.999,\n    max_num_iters: int = 2000,\n    min_num_iters: int = 10,\n    clip_value: float = 10.0,\n    warm_up_rounds: int = 100,\n    retrain_from_scratch: bool = False,\n    reset_optimizer: bool = False,\n    show_progress_bar: bool = True,\n    check_for_convergence: bool = True,\n    quality_control: bool = True,\n    quality_control_metric: str = \"psis\",\n    **kwargs,\n) -&gt; \"VIPosterior\":\n\"\"\"This method trains the variational posterior.\n\n    Args:\n        x: The observation.\n        n_particles: Number of samples to approximate expectations within the\n            variational bounds. The larger the more accurate are gradient\n            estimates, but the computational cost per iteration increases.\n        learning_rate: Learning rate of the optimizer.\n        gamma: Learning rate decay per iteration. We use an exponential decay\n            scheduler.\n        max_num_iters: Maximum number of iterations.\n        min_num_iters: Minimum number of iterations.\n        clip_value: Gradient clipping value, decreasing may help if you see invalid\n            values.\n        warm_up_rounds: Initialize the posterior as the prior.\n        retrain_from_scratch: Retrain the variational distributions from scratch.\n        reset_optimizer: Reset the divergence optimizer\n        show_progress_bar: If any progress report should be displayed.\n        quality_control: If False quality control is skipped.\n        quality_control_metric: Which metric to use for evaluating the quality.\n        kwargs: Hyperparameters check corresponding `DivergenceOptimizer` for detail\n            eps: Determines sensitivity of convergence check.\n            retain_graph: Boolean which decides whether to retain the computation\n                graph. This may be required for some `exotic` user-specified q's.\n            optimizer: A PyTorch Optimizer class e.g. Adam or SGD. See\n                `DivergenceOptimizer` for details.\n            scheduler: A PyTorch learning rate scheduler. See\n                `DivergenceOptimizer` for details.\n            alpha: Only used if vi_method=`alpha`. Determines the alpha divergence.\n            K: Only used if vi_method=`IW`. Determines the number of importance\n                weighted particles.\n            stick_the_landing: If one should use the STL estimator (only for rKL,\n                IW, alpha).\n            dreg: If one should use the DREG estimator (only for rKL, IW, alpha).\n            weight_transform: Callable applied to importance weights (only for fKL)\n    Returns:\n        VIPosterior: `VIPosterior` (can be used to chain calls).\n    \"\"\"\n    # Update optimizer with current arguments.\n    if self._optimizer is not None:\n        self._optimizer.update({**locals(), **kwargs})\n\n    # Init q and the optimizer if necessary\n    if retrain_from_scratch:\n        self.q = self._q_build_fn()  # type: ignore\n        self._optimizer = self._optimizer_builder(\n            self.potential_fn,\n            self.q,\n            lr=learning_rate,\n            clip_value=clip_value,\n            gamma=gamma,\n            n_particles=n_particles,\n            prior=self._prior,\n            **kwargs,\n        )\n\n    if (\n        reset_optimizer\n        or self._optimizer is None\n        or not isinstance(self._optimizer, self._optimizer_builder)\n    ):\n        self._optimizer = self._optimizer_builder(\n            self.potential_fn,\n            self.q,\n            lr=learning_rate,\n            clip_value=clip_value,\n            gamma=gamma,\n            n_particles=n_particles,\n            prior=self._prior,\n            **kwargs,\n        )\n\n    # Check context\n    x = atleast_2d_float32_tensor(self._x_else_default_x(x)).to(  # type: ignore\n        self._device\n    )\n\n    already_trained = self._trained_on is not None and (x == self._trained_on).all()\n\n    # Optimize\n    optimizer = self._optimizer\n    optimizer.to(self._device)\n    optimizer.reset_loss_stats()\n\n    if show_progress_bar:\n        iters = tqdm(range(max_num_iters))\n    else:\n        iters = range(max_num_iters)\n\n    # Warmup before training\n    if reset_optimizer or (not optimizer.warm_up_was_done and not already_trained):\n        if show_progress_bar:\n            iters.set_description(  # type: ignore\n                \"Warmup phase, this may take a few seconds...\"\n            )\n        optimizer.warm_up(warm_up_rounds)\n\n    for i in iters:\n        optimizer.step(x)\n        mean_loss, std_loss = optimizer.get_loss_stats()\n        # Update progress bar\n        if show_progress_bar:\n            assert isinstance(iters, tqdm)\n            iters.set_description(  # type: ignore\n                f\"Loss: {np.round(float(mean_loss), 2)}\"\n                f\"Std: {np.round(float(std_loss), 2)}\"\n            )\n        # Check for convergence\n        if check_for_convergence and i &gt; min_num_iters:\n            if optimizer.converged():\n                if show_progress_bar:\n                    print(f\"\\nConverged with loss: {np.round(float(mean_loss), 2)}\")\n                break\n    # Training finished:\n    self._trained_on = x\n\n    # Evaluate quality\n    if quality_control:\n        try:\n            self.evaluate(quality_control_metric=quality_control_metric)\n        except Exception as e:\n            print(\n                f\"Quality control did not work, we reset the variational \\\n                    posterior,please check your setting. \\\n\\n Following error occured {e}\"\n            )\n            self.train(\n                learning_rate=learning_rate * 0.1,\n                retrain_from_scratch=True,\n                reset_optimizer=True,\n            )\n\n    return self\n</code></pre>"},{"location":"reference/#models","title":"Models","text":""},{"location":"reference/#sbi.utils.get_nn_models.posterior_nn","title":"<code>sbi.utils.get_nn_models.posterior_nn(model, z_score_theta='independent', z_score_x='independent', hidden_features=50, num_transforms=5, num_bins=10, embedding_net=nn.Identity(), num_components=10, **kwargs)</code>","text":"<p>Returns a function that builds a density estimator for learning the posterior.</p> <p>This function will usually be used for SNPE. The returned function is to be passed to the inference class when using the flexible interface.</p> <p>Parameters:</p> Name Type Description Default <code>model</code> <code>str</code> <p>The type of density estimator that will be created. One of [<code>mdn</code>, <code>made</code>, <code>maf</code>, <code>maf_rqs</code>, <code>nsf</code>].</p> required <code>z_score_theta</code> <code>Optional[str]</code> <p>Whether to z-score parameters \\(\\theta\\) before passing them into the network, can take one of the following: - <code>none</code>, or None: do not z-score. - <code>independent</code>: z-score each dimension independently. - <code>structured</code>: treat dimensions as related, therefore compute mean and std over the entire batch, instead of per-dimension. Should be used when each sample is, for example, a time series or an image.</p> <code>'independent'</code> <code>z_score_x</code> <code>Optional[str]</code> <p>Whether to z-score simulation outputs \\(x\\) before passing them into the network, same options as z_score_theta.</p> <code>'independent'</code> <code>hidden_features</code> <code>int</code> <p>Number of hidden features.</p> <code>50</code> <code>num_transforms</code> <code>int</code> <p>Number of transforms when a flow is used. Only relevant if density estimator is a normalizing flow (i.e. currently either a <code>maf</code> or a <code>nsf</code>). Ignored if density estimator is a <code>mdn</code> or <code>made</code>.</p> <code>5</code> <code>num_bins</code> <code>int</code> <p>Number of bins used for the splines in <code>nsf</code>. Ignored if density estimator not <code>nsf</code>.</p> <code>10</code> <code>embedding_net</code> <code>nn.Module</code> <p>Optional embedding network for simulation outputs \\(x\\). This embedding net allows to learn features from potentially high-dimensional simulation outputs.</p> <code>nn.Identity()</code> <code>num_components</code> <code>int</code> <p>Number of mixture components for a mixture of Gaussians. Ignored if density estimator is not an mdn.</p> <code>10</code> <code>kwargs</code> <p>additional custom arguments passed to downstream build functions.</p> <code>{}</code> Source code in <code>/home/michael/Documents/sbi/sbi/utils/get_nn_models.py</code> <pre><code>def posterior_nn(\n    model: str,\n    z_score_theta: Optional[str] = \"independent\",\n    z_score_x: Optional[str] = \"independent\",\n    hidden_features: int = 50,\n    num_transforms: int = 5,\n    num_bins: int = 10,\n    embedding_net: nn.Module = nn.Identity(),\n    num_components: int = 10,\n    **kwargs,\n) -&gt; Callable:\nr\"\"\"\n    Returns a function that builds a density estimator for learning the posterior.\n\n    This function will usually be used for SNPE. The returned function is to be passed\n    to the inference class when using the flexible interface.\n\n    Args:\n        model: The type of density estimator that will be created. One of [`mdn`,\n            `made`, `maf`, `maf_rqs`, `nsf`].\n        z_score_theta: Whether to z-score parameters $\\theta$ before passing them into\n            the network, can take one of the following:\n            - `none`, or None: do not z-score.\n            - `independent`: z-score each dimension independently.\n            - `structured`: treat dimensions as related, therefore compute mean and std\n            over the entire batch, instead of per-dimension. Should be used when each\n            sample is, for example, a time series or an image.\n        z_score_x: Whether to z-score simulation outputs $x$ before passing them into\n            the network, same options as z_score_theta.\n        hidden_features: Number of hidden features.\n        num_transforms: Number of transforms when a flow is used. Only relevant if\n            density estimator is a normalizing flow (i.e. currently either a `maf` or a\n            `nsf`). Ignored if density estimator is a `mdn` or `made`.\n        num_bins: Number of bins used for the splines in `nsf`. Ignored if density\n            estimator not `nsf`.\n        embedding_net: Optional embedding network for simulation outputs $x$. This\n            embedding net allows to learn features from potentially high-dimensional\n            simulation outputs.\n        num_components: Number of mixture components for a mixture of Gaussians.\n            Ignored if density estimator is not an mdn.\n        kwargs: additional custom arguments passed to downstream build functions.\n    \"\"\"\n\n    kwargs = dict(\n        zip(\n            (\n                \"z_score_x\",\n                \"z_score_y\",\n                \"hidden_features\",\n                \"num_transforms\",\n                \"num_bins\",\n                \"embedding_net\",\n                \"num_components\",\n            ),\n            (\n                z_score_theta,\n                z_score_x,\n                hidden_features,\n                num_transforms,\n                num_bins,\n                embedding_net,\n                num_components,\n            ),\n        ),\n        **kwargs,\n    )\n\n    def build_fn_snpe_a(batch_theta, batch_x, num_components):\n\"\"\"Build function for SNPE-A\n\n        Extract the number of components from the kwargs, such that they are exposed as\n        a kwargs, offering the possibility to later override this kwarg with\n        `functools.partial`. This is necessary in order to make sure that the MDN in\n        SNPE-A only has one component when running the Algorithm 1 part.\n        \"\"\"\n        return build_mdn(\n            batch_x=batch_theta,\n            batch_y=batch_x,\n            num_components=num_components,\n            **kwargs,\n        )\n\n    def build_fn(batch_theta, batch_x):\n        if model == \"mdn\":\n            return build_mdn(batch_x=batch_theta, batch_y=batch_x, **kwargs)\n        elif model == \"made\":\n            return build_made(batch_x=batch_theta, batch_y=batch_x, **kwargs)\n        elif model == \"maf\":\n            return build_maf(batch_x=batch_theta, batch_y=batch_x, **kwargs)\n        elif model == \"maf_rqs\":\n            return build_maf_rqs(batch_x=batch_theta, batch_y=batch_x, **kwargs)\n        elif model == \"nsf\":\n            return build_nsf(batch_x=batch_theta, batch_y=batch_x, **kwargs)\n        else:\n            raise NotImplementedError\n\n    if model == \"mdn_snpe_a\":\n        if num_components != 10:\n            raise ValueError(\n                \"You set `num_components`. For SNPE-A, this has to be done at \"\n                \"instantiation of the inference object, i.e. \"\n                \"`inference = SNPE_A(..., num_components=20)`\"\n            )\n        kwargs.pop(\"num_components\")\n\n    return build_fn_snpe_a if model == \"mdn_snpe_a\" else build_fn\n</code></pre>"},{"location":"reference/#sbi.utils.get_nn_models.likelihood_nn","title":"<code>sbi.utils.get_nn_models.likelihood_nn(model, z_score_theta='independent', z_score_x='independent', hidden_features=50, num_transforms=5, num_bins=10, embedding_net=nn.Identity(), num_components=10, **kwargs)</code>","text":"<p>Returns a function that builds a density estimator for learning the likelihood.</p> <p>This function will usually be used for SNLE. The returned function is to be passed to the inference class when using the flexible interface.</p> <p>Parameters:</p> Name Type Description Default <code>model</code> <code>str</code> <p>The type of density estimator that will be created. One of [<code>mdn</code>, <code>made</code>, <code>maf</code>, <code>maf_rqs</code>, <code>nsf</code>].</p> required <code>z_score_theta</code> <code>Optional[str]</code> <p>Whether to z-score parameters \\(\\theta\\) before passing them into the network, can take one of the following: - <code>none</code>, or None: do not z-score. - <code>independent</code>: z-score each dimension independently. - <code>structured</code>: treat dimensions as related, therefore compute mean and std over the entire batch, instead of per-dimension. Should be used when each sample is, for example, a time series or an image.</p> <code>'independent'</code> <code>z_score_x</code> <code>Optional[str]</code> <p>Whether to z-score simulation outputs \\(x\\) before passing them into the network, same options as z_score_theta.</p> <code>'independent'</code> <code>hidden_features</code> <code>int</code> <p>Number of hidden features.</p> <code>50</code> <code>num_transforms</code> <code>int</code> <p>Number of transforms when a flow is used. Only relevant if density estimator is a normalizing flow (i.e. currently either a <code>maf</code> or a <code>nsf</code>). Ignored if density estimator is a <code>mdn</code> or <code>made</code>.</p> <code>5</code> <code>num_bins</code> <code>int</code> <p>Number of bins used for the splines in <code>nsf</code>. Ignored if density estimator not <code>nsf</code>.</p> <code>10</code> <code>embedding_net</code> <code>nn.Module</code> <p>Optional embedding network for parameters \\(\\theta\\).</p> <code>nn.Identity()</code> <code>num_components</code> <code>int</code> <p>Number of mixture components for a mixture of Gaussians. Ignored if density estimator is not an mdn.</p> <code>10</code> <code>kwargs</code> <p>additional custom arguments passed to downstream build functions.</p> <code>{}</code> Source code in <code>/home/michael/Documents/sbi/sbi/utils/get_nn_models.py</code> <pre><code>def likelihood_nn(\n    model: str,\n    z_score_theta: Optional[str] = \"independent\",\n    z_score_x: Optional[str] = \"independent\",\n    hidden_features: int = 50,\n    num_transforms: int = 5,\n    num_bins: int = 10,\n    embedding_net: nn.Module = nn.Identity(),\n    num_components: int = 10,\n    **kwargs,\n) -&gt; Callable:\nr\"\"\"\n    Returns a function that builds a density estimator for learning the likelihood.\n\n    This function will usually be used for SNLE. The returned function is to be passed\n    to the inference class when using the flexible interface.\n\n    Args:\n        model: The type of density estimator that will be created. One of [`mdn`,\n            `made`, `maf`, `maf_rqs`, `nsf`].\n        z_score_theta: Whether to z-score parameters $\\theta$ before passing them into\n            the network, can take one of the following:\n            - `none`, or None: do not z-score.\n            - `independent`: z-score each dimension independently.\n            - `structured`: treat dimensions as related, therefore compute mean and std\n            over the entire batch, instead of per-dimension. Should be used when each\n            sample is, for example, a time series or an image.\n        z_score_x: Whether to z-score simulation outputs $x$ before passing them into\n            the network, same options as z_score_theta.\n        hidden_features: Number of hidden features.\n        num_transforms: Number of transforms when a flow is used. Only relevant if\n            density estimator is a normalizing flow (i.e. currently either a `maf` or a\n            `nsf`). Ignored if density estimator is a `mdn` or `made`.\n        num_bins: Number of bins used for the splines in `nsf`. Ignored if density\n            estimator not `nsf`.\n        embedding_net: Optional embedding network for parameters $\\theta$.\n        num_components: Number of mixture components for a mixture of Gaussians.\n            Ignored if density estimator is not an mdn.\n        kwargs: additional custom arguments passed to downstream build functions.\n    \"\"\"\n\n    kwargs = dict(\n        zip(\n            (\n                \"z_score_x\",\n                \"z_score_y\",\n                \"hidden_features\",\n                \"num_transforms\",\n                \"num_bins\",\n                \"embedding_net\",\n                \"num_components\",\n            ),\n            (\n                z_score_x,\n                z_score_theta,\n                hidden_features,\n                num_transforms,\n                num_bins,\n                embedding_net,\n                num_components,\n            ),\n        ),\n        **kwargs,\n    )\n\n    def build_fn(batch_theta, batch_x):\n        if model == \"mdn\":\n            return build_mdn(batch_x=batch_x, batch_y=batch_theta, **kwargs)\n        elif model == \"made\":\n            return build_made(batch_x=batch_x, batch_y=batch_theta, **kwargs)\n        elif model == \"maf\":\n            return build_maf(batch_x=batch_x, batch_y=batch_theta, **kwargs)\n        elif model == \"maf_rqs\":\n            return build_maf_rqs(batch_x=batch_x, batch_y=batch_theta, **kwargs)\n        elif model == \"nsf\":\n            return build_nsf(batch_x=batch_x, batch_y=batch_theta, **kwargs)\n        elif model == \"mnle\":\n            return build_mnle(batch_x=batch_x, batch_y=batch_theta, **kwargs)\n        else:\n            raise NotImplementedError\n\n    return build_fn\n</code></pre>"},{"location":"reference/#sbi.utils.get_nn_models.classifier_nn","title":"<code>sbi.utils.get_nn_models.classifier_nn(model, z_score_theta='independent', z_score_x='independent', hidden_features=50, embedding_net_theta=nn.Identity(), embedding_net_x=nn.Identity(), **kwargs)</code>","text":"<p>Returns a function that builds a classifier for learning density ratios.</p> <p>This function will usually be used for SNRE. The returned function is to be passed to the inference class when using the flexible interface.</p> <p>Note that in the view of the SNRE classifier we build below, x=theta and y=x.</p> <p>Parameters:</p> Name Type Description Default <code>model</code> <code>str</code> <p>The type of classifier that will be created. One of [<code>linear</code>, <code>mlp</code>, <code>resnet</code>].</p> required <code>z_score_theta</code> <code>Optional[str]</code> <p>Whether to z-score parameters \\(\\theta\\) before passing them into the network, can take one of the following: - <code>none</code>, or None: do not z-score. - <code>independent</code>: z-score each dimension independently. - <code>structured</code>: treat dimensions as related, therefore compute mean and std over the entire batch, instead of per-dimension. Should be used when each sample is, for example, a time series or an image.</p> <code>'independent'</code> <code>z_score_x</code> <code>Optional[str]</code> <p>Whether to z-score simulation outputs \\(x\\) before passing them into the network, same options as z_score_theta.</p> <code>'independent'</code> <code>hidden_features</code> <code>int</code> <p>Number of hidden features.</p> <code>50</code> <code>embedding_net_theta</code> <code>nn.Module</code> <p>Optional embedding network for parameters \\(\\theta\\).</p> <code>nn.Identity()</code> <code>embedding_net_x</code> <code>nn.Module</code> <p>Optional embedding network for simulation outputs \\(x\\). This embedding net allows to learn features from potentially high-dimensional simulation outputs.</p> <code>nn.Identity()</code> <code>kwargs</code> <p>additional custom arguments passed to downstream build functions.</p> <code>{}</code> Source code in <code>/home/michael/Documents/sbi/sbi/utils/get_nn_models.py</code> <pre><code>def classifier_nn(\n    model: str,\n    z_score_theta: Optional[str] = \"independent\",\n    z_score_x: Optional[str] = \"independent\",\n    hidden_features: int = 50,\n    embedding_net_theta: nn.Module = nn.Identity(),\n    embedding_net_x: nn.Module = nn.Identity(),\n    **kwargs,\n) -&gt; Callable:\nr\"\"\"\n    Returns a function that builds a classifier for learning density ratios.\n\n    This function will usually be used for SNRE. The returned function is to be passed\n    to the inference class when using the flexible interface.\n\n    Note that in the view of the SNRE classifier we build below, x=theta and y=x.\n\n    Args:\n        model: The type of classifier that will be created. One of [`linear`, `mlp`,\n            `resnet`].\n        z_score_theta: Whether to z-score parameters $\\theta$ before passing them into\n            the network, can take one of the following:\n            - `none`, or None: do not z-score.\n            - `independent`: z-score each dimension independently.\n            - `structured`: treat dimensions as related, therefore compute mean and std\n            over the entire batch, instead of per-dimension. Should be used when each\n            sample is, for example, a time series or an image.\n        z_score_x: Whether to z-score simulation outputs $x$ before passing them into\n            the network, same options as z_score_theta.\n        hidden_features: Number of hidden features.\n        embedding_net_theta:  Optional embedding network for parameters $\\theta$.\n        embedding_net_x:  Optional embedding network for simulation outputs $x$. This\n            embedding net allows to learn features from potentially high-dimensional\n            simulation outputs.\n        kwargs: additional custom arguments passed to downstream build functions.\n    \"\"\"\n\n    kwargs = dict(\n        zip(\n            (\n                \"z_score_x\",\n                \"z_score_y\",\n                \"hidden_features\",\n                \"embedding_net_x\",\n                \"embedding_net_y\",\n            ),\n            (\n                z_score_theta,\n                z_score_x,\n                hidden_features,\n                embedding_net_theta,\n                embedding_net_x,\n            ),\n        ),\n        **kwargs,\n    )\n\n    def build_fn(batch_theta, batch_x):\n        if model == \"linear\":\n            return build_linear_classifier(\n                batch_x=batch_theta, batch_y=batch_x, **kwargs\n            )\n        if model == \"mlp\":\n            return build_mlp_classifier(batch_x=batch_theta, batch_y=batch_x, **kwargs)\n        if model == \"resnet\":\n            return build_resnet_classifier(\n                batch_x=batch_theta, batch_y=batch_x, **kwargs\n            )\n        else:\n            raise NotImplementedError\n\n    return build_fn\n</code></pre>"},{"location":"reference/#potentials","title":"Potentials","text":""},{"location":"reference/#sbi.inference.potentials.posterior_based_potential.posterior_estimator_based_potential","title":"<code>sbi.inference.potentials.posterior_based_potential.posterior_estimator_based_potential(posterior_estimator, prior, x_o, enable_transform=True)</code>","text":"<p>Returns the potential for posterior-based methods.</p> <p>It also returns a transformation that can be used to transform the potential into unconstrained space.</p> <p>The potential is the same as the log-probability of the <code>posterior_estimator</code>, but it is set to \\(-\\inf\\) outside of the prior bounds.</p> <p>Parameters:</p> Name Type Description Default <code>posterior_estimator</code> <code>nn.Module</code> <p>The neural network modelling the posterior.</p> required <code>prior</code> <code>Distribution</code> <p>The prior distribution.</p> required <code>x_o</code> <code>Optional[Tensor]</code> <p>The observed data at which to evaluate the posterior.</p> required <code>enable_transform</code> <code>bool</code> <p>Whether to transform parameters to unconstrained space. When False, an identity transform will be returned for <code>theta_transform</code>.</p> <code>True</code> <p>Returns:</p> Type Description <code>Callable</code> <p>The potential function and a transformation that maps</p> <code>TorchTransform</code> <p>to unconstrained space.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/potentials/posterior_based_potential.py</code> <pre><code>def posterior_estimator_based_potential(\n    posterior_estimator: nn.Module,\n    prior: Distribution,\n    x_o: Optional[Tensor],\n    enable_transform: bool = True,\n) -&gt; Tuple[Callable, TorchTransform]:\nr\"\"\"Returns the potential for posterior-based methods.\n\n    It also returns a transformation that can be used to transform the potential into\n    unconstrained space.\n\n    The potential is the same as the log-probability of the `posterior_estimator`, but\n    it is set to $-\\inf$ outside of the prior bounds.\n\n    Args:\n        posterior_estimator: The neural network modelling the posterior.\n        prior: The prior distribution.\n        x_o: The observed data at which to evaluate the posterior.\n        enable_transform: Whether to transform parameters to unconstrained space.\n            When False, an identity transform will be returned for `theta_transform`.\n\n    Returns:\n        The potential function and a transformation that maps\n        to unconstrained space.\n    \"\"\"\n\n    device = str(next(posterior_estimator.parameters()).device)\n\n    potential_fn = PosteriorBasedPotential(\n        posterior_estimator, prior, x_o, device=device\n    )\n\n    theta_transform = mcmc_transform(\n        prior, device=device, enable_transform=enable_transform\n    )\n\n    return potential_fn, theta_transform\n</code></pre>"},{"location":"reference/#sbi.inference.potentials.likelihood_based_potential.likelihood_estimator_based_potential","title":"<code>sbi.inference.potentials.likelihood_based_potential.likelihood_estimator_based_potential(likelihood_estimator, prior, x_o, enable_transform=True)</code>","text":"<p>Returns potential \\(\\log(p(x_o|\\theta)p(\\theta))\\) for likelihood-based methods.</p> <p>It also returns a transformation that can be used to transform the potential into unconstrained space.</p> <p>Parameters:</p> Name Type Description Default <code>likelihood_estimator</code> <code>nn.Module</code> <p>The neural network modelling the likelihood.</p> required <code>prior</code> <code>Distribution</code> <p>The prior distribution.</p> required <code>x_o</code> <code>Optional[Tensor]</code> <p>The observed data at which to evaluate the likelihood.</p> required <code>enable_transform</code> <code>bool</code> <p>Whether to transform parameters to unconstrained space.  When False, an identity transform will be returned for <code>theta_transform</code>.</p> <code>True</code> <p>Returns:</p> Type Description <code>Callable</code> <p>The potential function \\(p(x_o|\\theta)p(\\theta)\\) and a transformation that maps</p> <code>TorchTransform</code> <p>to unconstrained space.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/potentials/likelihood_based_potential.py</code> <pre><code>def likelihood_estimator_based_potential(\n    likelihood_estimator: nn.Module,\n    prior: Distribution,\n    x_o: Optional[Tensor],\n    enable_transform: bool = True,\n) -&gt; Tuple[Callable, TorchTransform]:\nr\"\"\"Returns potential $\\log(p(x_o|\\theta)p(\\theta))$ for likelihood-based methods.\n\n    It also returns a transformation that can be used to transform the potential into\n    unconstrained space.\n\n    Args:\n        likelihood_estimator: The neural network modelling the likelihood.\n        prior: The prior distribution.\n        x_o: The observed data at which to evaluate the likelihood.\n        enable_transform: Whether to transform parameters to unconstrained space.\n             When False, an identity transform will be returned for `theta_transform`.\n\n    Returns:\n        The potential function $p(x_o|\\theta)p(\\theta)$ and a transformation that maps\n        to unconstrained space.\n    \"\"\"\n\n    device = str(next(likelihood_estimator.parameters()).device)\n\n    potential_fn = LikelihoodBasedPotential(\n        likelihood_estimator, prior, x_o, device=device\n    )\n    theta_transform = mcmc_transform(\n        prior, device=device, enable_transform=enable_transform\n    )\n\n    return potential_fn, theta_transform\n</code></pre>"},{"location":"reference/#sbi.inference.potentials.ratio_based_potential.ratio_estimator_based_potential","title":"<code>sbi.inference.potentials.ratio_based_potential.ratio_estimator_based_potential(ratio_estimator, prior, x_o, enable_transform=True)</code>","text":"<p>Returns the potential for ratio-based methods.</p> <p>It also returns a transformation that can be used to transform the potential into unconstrained space.</p> <p>Parameters:</p> Name Type Description Default <code>ratio_estimator</code> <code>nn.Module</code> <p>The neural network modelling likelihood-to-evidence ratio.</p> required <code>prior</code> <code>Distribution</code> <p>The prior distribution.</p> required <code>x_o</code> <code>Optional[Tensor]</code> <p>The observed data at which to evaluate the likelihood-to-evidence ratio.</p> required <code>enable_transform</code> <code>bool</code> <p>Whether to transform parameters to unconstrained space. When False, an identity transform will be returned for <code>theta_transform</code>.</p> <code>True</code> <p>Returns:</p> Type Description <code>Callable</code> <p>The potential function and a transformation that maps</p> <code>TorchTransform</code> <p>to unconstrained space.</p> Source code in <code>/home/michael/Documents/sbi/sbi/inference/potentials/ratio_based_potential.py</code> <pre><code>def ratio_estimator_based_potential(\n    ratio_estimator: nn.Module,\n    prior: Distribution,\n    x_o: Optional[Tensor],\n    enable_transform: bool = True,\n) -&gt; Tuple[Callable, TorchTransform]:\nr\"\"\"Returns the potential for ratio-based methods.\n\n    It also returns a transformation that can be used to transform the potential into\n    unconstrained space.\n\n    Args:\n        ratio_estimator: The neural network modelling likelihood-to-evidence ratio.\n        prior: The prior distribution.\n        x_o: The observed data at which to evaluate the likelihood-to-evidence ratio.\n        enable_transform: Whether to transform parameters to unconstrained space.\n            When False, an identity transform will be returned for `theta_transform`.\n\n    Returns:\n        The potential function and a transformation that maps\n        to unconstrained space.\n    \"\"\"\n\n    device = str(next(ratio_estimator.parameters()).device)\n\n    potential_fn = RatioBasedPotential(ratio_estimator, prior, x_o, device=device)\n    theta_transform = mcmc_transform(\n        prior, device=device, enable_transform=enable_transform\n    )\n\n    return potential_fn, theta_transform\n</code></pre>"},{"location":"reference/#analysis","title":"Analysis","text":""},{"location":"reference/#sbi.analysis.plot.pairplot","title":"<code>sbi.analysis.plot.pairplot(samples, points=None, limits=None, subset=None, offdiag='hist', diag='hist', figsize=(10, 10), labels=None, ticks=[], upper=None, fig=None, axes=None, **kwargs)</code>","text":"<p>Plot samples in a 2D grid showing marginals and pairwise marginals.</p> <p>Each of the diagonal plots can be interpreted as a 1D-marginal of the distribution that the samples were drawn from. Each upper-diagonal plot can be interpreted as a 2D-marginal of the distribution.</p> <p>Parameters:</p> Name Type Description Default <code>samples</code> <code>Union[List[np.ndarray], List[torch.Tensor], np.ndarray, torch.Tensor]</code> <p>Samples used to build the histogram.</p> required <code>points</code> <code>Optional[Union[List[np.ndarray], List[torch.Tensor], np.ndarray, torch.Tensor]]</code> <p>List of additional points to scatter.</p> <code>None</code> <code>limits</code> <code>Optional[Union[List, torch.Tensor]]</code> <p>Array containing the plot xlim for each parameter dimension. If None, just use the min and max of the passed samples</p> <code>None</code> <code>subset</code> <code>Optional[List[int]]</code> <p>List containing the dimensions to plot. E.g. subset=[1,3] will plot plot only the 1st and 3rd dimension but will discard the 0th and 2nd (and, if they exist, the 4th, 5th and so on).</p> <code>None</code> <code>offdiag</code> <code>Optional[Union[List[str], str]]</code> <p>Plotting style for upper diagonal, {hist, scatter, contour, cond, None}.</p> <code>'hist'</code> <code>upper</code> <code>Optional[str]</code> <p>deprecated, use offdiag instead.</p> <code>None</code> <code>diag</code> <code>Optional[Union[List[str], str]]</code> <p>Plotting style for diagonal, {hist, cond, None}.</p> <code>'hist'</code> <code>figsize</code> <code>Tuple</code> <p>Size of the entire figure.</p> <code>(10, 10)</code> <code>labels</code> <code>Optional[List[str]]</code> <p>List of strings specifying the names of the parameters.</p> <code>None</code> <code>ticks</code> <code>Union[List, torch.Tensor]</code> <p>Position of the ticks.</p> <code>[]</code> <code>fig</code> <p>matplotlib figure to plot on.</p> <code>None</code> <code>axes</code> <p>matplotlib axes corresponding to fig.</p> <code>None</code> <code>**kwargs</code> <p>Additional arguments to adjust the plot, e.g., <code>samples_colors</code>, <code>points_colors</code> and many more, see the source code in <code>_get_default_opts()</code> in <code>sbi.analysis.plot</code> for details.</p> <code>{}</code> Source code in <code>/home/michael/Documents/sbi/sbi/analysis/plot.py</code> <pre><code>def pairplot(\n    samples: Union[List[np.ndarray], List[torch.Tensor], np.ndarray, torch.Tensor],\n    points: Optional[\n        Union[List[np.ndarray], List[torch.Tensor], np.ndarray, torch.Tensor]\n    ] = None,\n    limits: Optional[Union[List, torch.Tensor]] = None,\n    subset: Optional[List[int]] = None,\n    offdiag: Optional[Union[List[str], str]] = \"hist\",\n    diag: Optional[Union[List[str], str]] = \"hist\",\n    figsize: Tuple = (10, 10),\n    labels: Optional[List[str]] = None,\n    ticks: Union[List, torch.Tensor] = [],\n    upper: Optional[str] = None,\n    fig=None,\n    axes=None,\n    **kwargs,\n):\n\"\"\"\n    Plot samples in a 2D grid showing marginals and pairwise marginals.\n\n    Each of the diagonal plots can be interpreted as a 1D-marginal of the distribution\n    that the samples were drawn from. Each upper-diagonal plot can be interpreted as a\n    2D-marginal of the distribution.\n\n    Args:\n        samples: Samples used to build the histogram.\n        points: List of additional points to scatter.\n        limits: Array containing the plot xlim for each parameter dimension. If None,\n            just use the min and max of the passed samples\n        subset: List containing the dimensions to plot. E.g. subset=[1,3] will plot\n            plot only the 1st and 3rd dimension but will discard the 0th and 2nd (and,\n            if they exist, the 4th, 5th and so on).\n        offdiag: Plotting style for upper diagonal, {hist, scatter, contour, cond,\n            None}.\n        upper: deprecated, use offdiag instead.\n        diag: Plotting style for diagonal, {hist, cond, None}.\n        figsize: Size of the entire figure.\n        labels: List of strings specifying the names of the parameters.\n        ticks: Position of the ticks.\n        fig: matplotlib figure to plot on.\n        axes: matplotlib axes corresponding to fig.\n        **kwargs: Additional arguments to adjust the plot, e.g., `samples_colors`,\n            `points_colors` and many more, see the source code in `_get_default_opts()`\n            in `sbi.analysis.plot` for details.\n\n    Returns: figure and axis of posterior distribution plot\n    \"\"\"\n\n    # TODO: add color map support\n    # TODO: automatically determine good bin sizes for histograms\n    # TODO: add legend (if legend is True)\n\n    opts = _get_default_opts()\n    # update the defaults dictionary by the current values of the variables (passed by\n    # the user)\n\n    opts = _update(opts, locals())\n    opts = _update(opts, kwargs)\n\n    samples, dim, limits = prepare_for_plot(samples, limits)\n\n    # checks.\n    if opts[\"legend\"]:\n        assert len(opts[\"samples_labels\"]) &gt;= len(\n            samples\n        ), \"Provide at least as many labels as samples.\"\n    if opts[\"upper\"] is not None:\n        warn(\"upper is deprecated, use offdiag instead.\")\n        opts[\"offdiag\"] = opts[\"upper\"]\n\n    # Prepare diag/upper/lower\n    if type(opts[\"diag\"]) is not list:\n        opts[\"diag\"] = [opts[\"diag\"] for _ in range(len(samples))]\n    if type(opts[\"offdiag\"]) is not list:\n        opts[\"offdiag\"] = [opts[\"offdiag\"] for _ in range(len(samples))]\n    # if type(opts['lower']) is not list:\n    #    opts['lower'] = [opts['lower'] for _ in range(len(samples))]\n    opts[\"lower\"] = None\n\n    diag_func = get_diag_func(samples, limits, opts, **kwargs)\n\n    def offdiag_func(row, col, limits, **kwargs):\n        if len(samples) &gt; 0:\n            for n, v in enumerate(samples):\n                if opts[\"offdiag\"][n] == \"hist\" or opts[\"offdiag\"][n] == \"hist2d\":\n                    hist, xedges, yedges = np.histogram2d(\n                        v[:, col],\n                        v[:, row],\n                        range=[\n                            [limits[col][0], limits[col][1]],\n                            [limits[row][0], limits[row][1]],\n                        ],\n                        **opts[\"hist_offdiag\"],\n                    )\n                    plt.imshow(\n                        hist.T,\n                        origin=\"lower\",\n                        extent=(\n                            xedges[0],\n                            xedges[-1],\n                            yedges[0],\n                            yedges[-1],\n                        ),\n                        aspect=\"auto\",\n                    )\n\n                elif opts[\"offdiag\"][n] in [\n                    \"kde\",\n                    \"kde2d\",\n                    \"contour\",\n                    \"contourf\",\n                ]:\n                    density = gaussian_kde(\n                        v[:, [col, row]].T,\n                        bw_method=opts[\"kde_offdiag\"][\"bw_method\"],\n                    )\n                    X, Y = np.meshgrid(\n                        np.linspace(\n                            limits[col][0],\n                            limits[col][1],\n                            opts[\"kde_offdiag\"][\"bins\"],\n                        ),\n                        np.linspace(\n                            limits[row][0],\n                            limits[row][1],\n                            opts[\"kde_offdiag\"][\"bins\"],\n                        ),\n                    )\n                    positions = np.vstack([X.ravel(), Y.ravel()])\n                    Z = np.reshape(density(positions).T, X.shape)\n\n                    if opts[\"offdiag\"][n] == \"kde\" or opts[\"offdiag\"][n] == \"kde2d\":\n                        plt.imshow(\n                            Z,\n                            extent=(\n                                limits[col][0],\n                                limits[col][1],\n                                limits[row][0],\n                                limits[row][1],\n                            ),\n                            origin=\"lower\",\n                            aspect=\"auto\",\n                        )\n                    elif opts[\"offdiag\"][n] == \"contour\":\n                        if opts[\"contour_offdiag\"][\"percentile\"]:\n                            Z = probs2contours(Z, opts[\"contour_offdiag\"][\"levels\"])\n                        else:\n                            Z = (Z - Z.min()) / (Z.max() - Z.min())\n                        plt.contour(\n                            X,\n                            Y,\n                            Z,\n                            origin=\"lower\",\n                            extent=[\n                                limits[col][0],\n                                limits[col][1],\n                                limits[row][0],\n                                limits[row][1],\n                            ],\n                            colors=opts[\"samples_colors\"][n],\n                            levels=opts[\"contour_offdiag\"][\"levels\"],\n                        )\n                    else:\n                        pass\n                elif opts[\"offdiag\"][n] == \"scatter\":\n                    plt.scatter(\n                        v[:, col],\n                        v[:, row],\n                        color=opts[\"samples_colors\"][n],\n                        **opts[\"scatter_offdiag\"],\n                    )\n                elif opts[\"offdiag\"][n] == \"plot\":\n                    plt.plot(\n                        v[:, col],\n                        v[:, row],\n                        color=opts[\"samples_colors\"][n],\n                        **opts[\"plot_offdiag\"],\n                    )\n                else:\n                    pass\n\n    return _arrange_plots(\n        diag_func, offdiag_func, dim, limits, points, opts, fig=fig, axes=axes\n    )\n</code></pre>"},{"location":"reference/#sbi.analysis.plot.marginal_plot","title":"<code>sbi.analysis.plot.marginal_plot(samples, points=None, limits=None, subset=None, diag='hist', figsize=(10, 10), labels=None, ticks=[], fig=None, axes=None, **kwargs)</code>","text":"<p>Plot samples in a row showing 1D marginals of selected dimensions.</p> <p>Each of the plots can be interpreted as a 1D-marginal of the distribution that the samples were drawn from.</p> <p>Parameters:</p> Name Type Description Default <code>samples</code> <code>Union[List[np.ndarray], List[torch.Tensor], np.ndarray, torch.Tensor]</code> <p>Samples used to build the histogram.</p> required <code>points</code> <code>Optional[Union[List[np.ndarray], List[torch.Tensor], np.ndarray, torch.Tensor]]</code> <p>List of additional points to scatter.</p> <code>None</code> <code>limits</code> <code>Optional[Union[List, torch.Tensor]]</code> <p>Array containing the plot xlim for each parameter dimension. If None, just use the min and max of the passed samples</p> <code>None</code> <code>subset</code> <code>Optional[List[int]]</code> <p>List containing the dimensions to plot. E.g. subset=[1,3] will plot plot only the 1st and 3rd dimension but will discard the 0th and 2nd (and, if they exist, the 4th, 5th and so on).</p> <code>None</code> <code>diag</code> <code>Optional[str]</code> <p>Plotting style for 1D marginals, {hist, kde cond, None}.</p> <code>'hist'</code> <code>figsize</code> <code>Tuple</code> <p>Size of the entire figure.</p> <code>(10, 10)</code> <code>labels</code> <code>Optional[List[str]]</code> <p>List of strings specifying the names of the parameters.</p> <code>None</code> <code>ticks</code> <code>Union[List, torch.Tensor]</code> <p>Position of the ticks.</p> <code>[]</code> <code>points_colors</code> <p>Colors of the <code>points</code>.</p> required <code>fig</code> <p>matplotlib figure to plot on.</p> <code>None</code> <code>axes</code> <p>matplotlib axes corresponding to fig.</p> <code>None</code> <code>**kwargs</code> <p>Additional arguments to adjust the plot, e.g., <code>samples_colors</code>, <code>points_colors</code> and many more, see the source code in <code>_get_default_opts()</code> in <code>sbi.analysis.plot</code> for details.</p> <code>{}</code> Source code in <code>/home/michael/Documents/sbi/sbi/analysis/plot.py</code> <pre><code>def marginal_plot(\n    samples: Union[List[np.ndarray], List[torch.Tensor], np.ndarray, torch.Tensor],\n    points: Optional[\n        Union[List[np.ndarray], List[torch.Tensor], np.ndarray, torch.Tensor]\n    ] = None,\n    limits: Optional[Union[List, torch.Tensor]] = None,\n    subset: Optional[List[int]] = None,\n    diag: Optional[str] = \"hist\",\n    figsize: Tuple = (10, 10),\n    labels: Optional[List[str]] = None,\n    ticks: Union[List, torch.Tensor] = [],\n    fig=None,\n    axes=None,\n    **kwargs,\n):\n\"\"\"\n    Plot samples in a row showing 1D marginals of selected dimensions.\n\n    Each of the plots can be interpreted as a 1D-marginal of the distribution\n    that the samples were drawn from.\n\n    Args:\n        samples: Samples used to build the histogram.\n        points: List of additional points to scatter.\n        limits: Array containing the plot xlim for each parameter dimension. If None,\n            just use the min and max of the passed samples\n        subset: List containing the dimensions to plot. E.g. subset=[1,3] will plot\n            plot only the 1st and 3rd dimension but will discard the 0th and 2nd (and,\n            if they exist, the 4th, 5th and so on).\n        diag: Plotting style for 1D marginals, {hist, kde cond, None}.\n        figsize: Size of the entire figure.\n        labels: List of strings specifying the names of the parameters.\n        ticks: Position of the ticks.\n        points_colors: Colors of the `points`.\n        fig: matplotlib figure to plot on.\n        axes: matplotlib axes corresponding to fig.\n        **kwargs: Additional arguments to adjust the plot, e.g., `samples_colors`,\n            `points_colors` and many more, see the source code in `_get_default_opts()`\n            in `sbi.analysis.plot` for details.\n\n    Returns: figure and axis of posterior distribution plot\n    \"\"\"\n\n    opts = _get_default_opts()\n    # update the defaults dictionary by the current values of the variables (passed by\n    # the user)\n\n    opts = _update(opts, locals())\n    opts = _update(opts, kwargs)\n\n    samples, dim, limits = prepare_for_plot(samples, limits)\n\n    # Prepare diag/upper/lower\n    if type(opts[\"diag\"]) is not list:\n        opts[\"diag\"] = [opts[\"diag\"] for _ in range(len(samples))]\n\n    diag_func = get_diag_func(samples, limits, opts, **kwargs)\n\n    return _arrange_plots(\n        diag_func, None, dim, limits, points, opts, fig=fig, axes=axes\n    )\n</code></pre>"},{"location":"reference/#sbi.analysis.plot.conditional_pairplot","title":"<code>sbi.analysis.plot.conditional_pairplot(density, condition, limits, points=None, subset=None, resolution=50, figsize=(10, 10), labels=None, ticks=[], fig=None, axes=None, **kwargs)</code>","text":"<p>Plot conditional distribution given all other parameters.</p> <p>The conditionals can be interpreted as slices through the <code>density</code> at a location given by <code>condition</code>.</p> <p>For example: Say we have a 3D density with parameters \\(\\theta_0\\), \\(\\theta_1\\), \\(\\theta_2\\) and a condition \\(c\\) passed by the user in the <code>condition</code> argument. For the plot of \\(\\theta_0\\) on the diagonal, this will plot the conditional \\(p(\\theta_0 | \\theta_1=c[1], \\theta_2=c[2])\\). For the upper diagonal of \\(\\theta_1\\) and \\(\\theta_2\\), it will plot \\(p(\\theta_1, \\theta_2 | \\theta_0=c[0])\\). All other diagonals and upper-diagonals are built in the corresponding way.</p> <p>Parameters:</p> Name Type Description Default <code>density</code> <code>Any</code> <p>Probability density with a <code>log_prob()</code> method.</p> required <code>condition</code> <code>torch.Tensor</code> <p>Condition that all but the one/two regarded parameters are fixed to. The condition should be of shape (1, dim_theta), i.e. it could e.g. be a sample from the posterior distribution.</p> required <code>limits</code> <code>Union[List, torch.Tensor]</code> <p>Limits in between which each parameter will be evaluated.</p> required <code>points</code> <code>Optional[Union[List[np.ndarray], List[torch.Tensor], np.ndarray, torch.Tensor]]</code> <p>Additional points to scatter.</p> <code>None</code> <code>subset</code> <code>Optional[List[int]]</code> <p>List containing the dimensions to plot. E.g. subset=[1,3] will plot plot only the 1st and 3rd dimension but will discard the 0th and 2nd (and, if they exist, the 4th, 5th and so on)</p> <code>None</code> <code>resolution</code> <code>int</code> <p>Resolution of the grid at which we evaluate the <code>pdf</code>.</p> <code>50</code> <code>figsize</code> <code>Tuple</code> <p>Size of the entire figure.</p> <code>(10, 10)</code> <code>labels</code> <code>Optional[List[str]]</code> <p>List of strings specifying the names of the parameters.</p> <code>None</code> <code>ticks</code> <code>Union[List, torch.Tensor]</code> <p>Position of the ticks.</p> <code>[]</code> <code>points_colors</code> <p>Colors of the <code>points</code>.</p> required <code>fig</code> <p>matplotlib figure to plot on.</p> <code>None</code> <code>axes</code> <p>matplotlib axes corresponding to fig.</p> <code>None</code> <code>**kwargs</code> <p>Additional arguments to adjust the plot, e.g., <code>samples_colors</code>, <code>points_colors</code> and many more, see the source code in <code>_get_default_opts()</code> in <code>sbi.analysis.plot</code> for details.</p> <code>{}</code> Source code in <code>/home/michael/Documents/sbi/sbi/analysis/plot.py</code> <pre><code>def conditional_pairplot(\n    density: Any,\n    condition: torch.Tensor,\n    limits: Union[List, torch.Tensor],\n    points: Optional[\n        Union[List[np.ndarray], List[torch.Tensor], np.ndarray, torch.Tensor]\n    ] = None,\n    subset: Optional[List[int]] = None,\n    resolution: int = 50,\n    figsize: Tuple = (10, 10),\n    labels: Optional[List[str]] = None,\n    ticks: Union[List, torch.Tensor] = [],\n    fig=None,\n    axes=None,\n    **kwargs,\n):\nr\"\"\"\n    Plot conditional distribution given all other parameters.\n\n    The conditionals can be interpreted as slices through the `density` at a location\n    given by `condition`.\n\n    For example:\n    Say we have a 3D density with parameters $\\theta_0$, $\\theta_1$, $\\theta_2$ and\n    a condition $c$ passed by the user in the `condition` argument.\n    For the plot of $\\theta_0$ on the diagonal, this will plot the conditional\n    $p(\\theta_0 | \\theta_1=c[1], \\theta_2=c[2])$. For the upper\n    diagonal of $\\theta_1$ and $\\theta_2$, it will plot\n    $p(\\theta_1, \\theta_2 | \\theta_0=c[0])$. All other diagonals and upper-diagonals\n    are built in the corresponding way.\n\n    Args:\n        density: Probability density with a `log_prob()` method.\n        condition: Condition that all but the one/two regarded parameters are fixed to.\n            The condition should be of shape (1, dim_theta), i.e. it could e.g. be\n            a sample from the posterior distribution.\n        limits: Limits in between which each parameter will be evaluated.\n        points: Additional points to scatter.\n        subset: List containing the dimensions to plot. E.g. subset=[1,3] will plot\n            plot only the 1st and 3rd dimension but will discard the 0th and 2nd (and,\n            if they exist, the 4th, 5th and so on)\n        resolution: Resolution of the grid at which we evaluate the `pdf`.\n        figsize: Size of the entire figure.\n        labels: List of strings specifying the names of the parameters.\n        ticks: Position of the ticks.\n        points_colors: Colors of the `points`.\n\n        fig: matplotlib figure to plot on.\n        axes: matplotlib axes corresponding to fig.\n        **kwargs: Additional arguments to adjust the plot, e.g., `samples_colors`,\n            `points_colors` and many more, see the source code in `_get_default_opts()`\n            in `sbi.analysis.plot` for details.\n\n    Returns: figure and axis of posterior distribution plot\n    \"\"\"\n    device = density._device if hasattr(density, \"_device\") else \"cpu\"\n\n    # Setting these is required because _pairplot_scaffold will check if opts['diag'] is\n    # `None`. This would break if opts has no key 'diag'. Same for 'upper'.\n    diag = \"cond\"\n    offdiag = \"cond\"\n\n    opts = _get_default_opts()\n    # update the defaults dictionary by the current values of the variables (passed by\n    # the user)\n    opts = _update(opts, locals())\n    opts = _update(opts, kwargs)\n    opts[\"lower\"] = None\n\n    dim, limits, eps_margins = prepare_for_conditional_plot(condition, opts)\n    diag_func = get_conditional_diag_func(opts, limits, eps_margins, resolution)\n\n    def offdiag_func(row, col, **kwargs):\n        p_image = (\n            eval_conditional_density(\n                opts[\"density\"],\n                opts[\"condition\"].to(device),\n                limits.to(device),\n                row,\n                col,\n                resolution=resolution,\n                eps_margins1=eps_margins[row],\n                eps_margins2=eps_margins[col],\n            )\n            .to(\"cpu\")\n            .numpy()\n        )\n        plt.imshow(\n            p_image.T,\n            origin=\"lower\",\n            extent=(\n                limits[col, 0].item(),\n                limits[col, 1].item(),\n                limits[row, 0].item(),\n                limits[row, 1].item(),\n            ),\n            aspect=\"auto\",\n        )\n\n    return _arrange_plots(\n        diag_func, offdiag_func, dim, limits, points, opts, fig=fig, axes=axes\n    )\n</code></pre>"},{"location":"reference/#sbi.analysis.conditional_density.conditional_corrcoeff","title":"<code>sbi.analysis.conditional_density.conditional_corrcoeff(density, limits, condition, subset=None, resolution=50)</code>","text":"<p>Returns the conditional correlation matrix of a distribution.</p> <p>To compute the conditional distribution, we condition all but two parameters to values from <code>condition</code>, and then compute the Pearson correlation coefficient \\(\\rho\\) between the remaining two parameters under the distribution <code>density</code>. We do so for any pair of parameters specified in <code>subset</code>, thus creating a matrix containing conditional correlations between any pair of parameters.</p> <p>If <code>condition</code> is a batch of conditions, this function computes the conditional correlation matrix for each one of them and returns the mean.</p> <p>Parameters:</p> Name Type Description Default <code>density</code> <code>Any</code> <p>Probability density function with <code>.log_prob()</code> function.</p> required <code>limits</code> <code>Tensor</code> <p>Limits within which to evaluate the <code>density</code>.</p> required <code>condition</code> <code>Tensor</code> <p>Values to condition the <code>density</code> on. If a batch of conditions is passed, we compute the conditional correlation matrix for each of them and return the average conditional correlation matrix.</p> required <code>subset</code> <code>Optional[List[int]]</code> <p>Evaluate the conditional distribution only on a subset of dimensions. If <code>None</code> this function uses all dimensions.</p> <code>None</code> <code>resolution</code> <code>int</code> <p>Number of grid points on which the conditional distribution is evaluated. A higher value increases the accuracy of the estimated correlation but also increases the computational cost.</p> <code>50</code> <p>or <code>(len(subset), len(subset))</code> if <code>subset</code> was specified.</p> Source code in <code>/home/michael/Documents/sbi/sbi/analysis/conditional_density.py</code> <pre><code>def conditional_corrcoeff(\n    density: Any,\n    limits: Tensor,\n    condition: Tensor,\n    subset: Optional[List[int]] = None,\n    resolution: int = 50,\n) -&gt; Tensor:\nr\"\"\"Returns the conditional correlation matrix of a distribution.\n\n    To compute the conditional distribution, we condition all but two parameters to\n    values from `condition`, and then compute the Pearson correlation\n    coefficient $\\rho$ between the remaining two parameters under the distribution\n    `density`. We do so for any pair of parameters specified in `subset`, thus\n    creating a matrix containing conditional correlations between any pair of\n    parameters.\n\n    If `condition` is a batch of conditions, this function computes the conditional\n    correlation matrix for each one of them and returns the mean.\n\n    Args:\n        density: Probability density function with `.log_prob()` function.\n        limits: Limits within which to evaluate the `density`.\n        condition: Values to condition the `density` on. If a batch of conditions is\n            passed, we compute the conditional correlation matrix for each of them and\n            return the average conditional correlation matrix.\n        subset: Evaluate the conditional distribution only on a subset of dimensions.\n            If `None` this function uses all dimensions.\n        resolution: Number of grid points on which the conditional distribution is\n            evaluated. A higher value increases the accuracy of the estimated\n            correlation but also increases the computational cost.\n\n    Returns: Average conditional correlation matrix of shape either `(num_dim, num_dim)`\n    or `(len(subset), len(subset))` if `subset` was specified.\n    \"\"\"\n\n    device = density._device if hasattr(density, \"_device\") else \"cpu\"\n\n    subset_ = subset if subset is not None else range(condition.shape[1])\n\n    correlation_matrices = []\n    for cond in condition:\n        correlation_matrices.append(\n            torch.stack(\n                [\n                    compute_corrcoeff(\n                        eval_conditional_density(\n                            density,\n                            cond.to(device),\n                            limits.to(device),\n                            dim1=dim1,\n                            dim2=dim2,\n                            resolution=resolution,\n                        ),\n                        limits[[dim1, dim2]].to(device),\n                    )\n                    for dim1 in subset_\n                    for dim2 in subset_\n                    if dim1 &lt; dim2\n                ]\n            )\n        )\n\n    average_correlations = torch.mean(torch.stack(correlation_matrices), dim=0)\n\n    # `average_correlations` is still a vector containing the upper triangular entries.\n    # Below, assemble them into a matrix:\n    av_correlation_matrix = torch.zeros((len(subset_), len(subset_)), device=device)\n    triu_indices = torch.triu_indices(\n        row=len(subset_), col=len(subset_), offset=1, device=device\n    )\n    av_correlation_matrix[triu_indices[0], triu_indices[1]] = average_correlations\n\n    # Make the matrix symmetric by copying upper diagonal to lower diagonal.\n    av_correlation_matrix = torch.triu(av_correlation_matrix) + torch.tril(\n        av_correlation_matrix.T\n    )\n\n    av_correlation_matrix.fill_diagonal_(1.0)\n    return av_correlation_matrix\n</code></pre>"},{"location":"examples/00_HH_simulator/","title":"Inference on Hodgkin-Huxley model: tutorial","text":"<p>In this tutorial, we use <code>sbi</code> to do inference on a Hodgkin-Huxley model from neuroscience (Hodgkin and Huxley, 1952). We will learn two parameters (\\(\\bar g_{Na}\\),\\(\\bar g_K\\)) based on a current-clamp recording, that we generate synthetically (in practice, this would be an experimental observation).</p> <p>Note, you find the original version of this notebook at https://github.com/sbi-dev/sbi/blob/main/examples/00_HH_simulator.ipynb in the <code>sbi</code> repository.</p> <p>First we are going to import basic packages.</p> <pre><code>import numpy as np\nimport torch\n\n# visualization\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\n\n# sbi\nfrom sbi import utils as utils\nfrom sbi import analysis as analysis\nfrom sbi.inference.base import infer\n</code></pre> <pre><code># remove top and right axis from plots\nmpl.rcParams[\"axes.spines.right\"] = False\nmpl.rcParams[\"axes.spines.top\"] = False\n</code></pre>"},{"location":"examples/00_HH_simulator/#different-required-components","title":"Different required components","text":"<p>Before running inference, let us define the different required components:</p> <ol> <li>observed data</li> <li>prior over model parameters</li> <li>simulator</li> </ol>"},{"location":"examples/00_HH_simulator/#1-observed-data","title":"1. Observed data","text":"<p>Let us assume we current-clamped a neuron and recorded the following voltage trace:</p> <p> </p> <p>In fact, this voltage trace was not measured experimentally but synthetically generated by simulating a Hodgkin-Huxley model with particular parameters (\\(\\bar g_{Na}\\),\\(\\bar g_K\\)). We will come back to this point later in the tutorial.</p>"},{"location":"examples/00_HH_simulator/#2-simulator","title":"2. Simulator","text":"<p>We would like to infer the posterior over the two parameters (\\(\\bar g_{Na}\\),\\(\\bar g_K\\)) of a Hodgkin-Huxley model, given the observed electrophysiological recording above. The model has channel kinetics as in Pospischil et al. 2008, and is defined by the following set of differential equations (parameters of interest highlighted in orange):</p> \\[ \\scriptsize \\begin{align} C_m\\frac{dV}{dt}&amp;=g_1\\left(E_1-V\\right)+                     \\color{orange}{\\bar{g}_{Na}}m^3h\\left(E_{Na}-V\\right)+                     \\color{orange}{\\bar{g}_{K}}n^4\\left(E_K-V\\right)+                     \\bar{g}_Mp\\left(E_K-V\\right)+                     I_{inj}+                     \\sigma\\eta\\left(t\\right)\\\\ \\frac{dq}{dt}&amp;=\\frac{q_\\infty\\left(V\\right)-q}{\\tau_q\\left(V\\right)},\\;q\\in\\{m,h,n,p\\} \\end{align} \\] <p>Above, \\(V\\) represents the membrane potential, \\(C_m\\) is the membrane capacitance, \\(g_{\\text{l}}\\) is the leak conductance, \\(E_{\\text{l}}\\) is the membrane reversal potential, \\(\\bar{g}_c\\) is the density of channels of type \\(c\\) (\\(\\text{Na}^+\\), \\(\\text{K}^+\\), M), \\(E_c\\) is the reversal potential of \\(c\\), (\\(m\\), \\(h\\), \\(n\\), \\(p\\)) are the respective channel gating kinetic variables, and \\(\\sigma \\eta(t)\\) is the intrinsic neural noise. The right hand side of the voltage dynamics is composed of a leak current, a voltage-dependent \\(\\text{Na}^+\\) current, a delayed-rectifier \\(\\text{K}^+\\) current, a slow voltage-dependent \\(\\text{K}^+\\) current responsible for spike-frequency adaptation, and an injected current \\(I_{\\text{inj}}\\). Channel gating variables \\(q\\) have dynamics fully characterized by the neuron membrane potential \\(V\\), given the respective steady-state \\(q_{\\infty}(V)\\) and time constant \\(\\tau_{q}(V)\\) (details in Pospischil et al. 2008).</p> <p>The input current \\(I_{\\text{inj}}\\) is defined as</p> <pre><code>from HH_helper_functions import syn_current\n\nI, t_on, t_off, dt, t, A_soma = syn_current()\n</code></pre> <p>The Hodgkin-Huxley simulator is given by:</p> <pre><code>from HH_helper_functions import HHsimulator\n</code></pre> <p>Putting the input current and the simulator together:</p> <pre><code>def run_HH_model(params):\n\n    params = np.asarray(params)\n\n    # input current, time step\n    I, t_on, t_off, dt, t, A_soma = syn_current()\n\n    t = np.arange(0, len(I), 1) * dt\n\n    # initial voltage\n    V0 = -70\n\n    states = HHsimulator(V0, params.reshape(1, -1), dt, t, I)\n\n    return dict(data=states.reshape(-1), time=t, dt=dt, I=I.reshape(-1))\n</code></pre> <p>To get an idea of the output of the Hodgkin-Huxley model, let us generate some voltage traces for different parameters (\\(\\bar g_{Na}\\),\\(\\bar g_K\\)), given the input current \\(I_{\\text{inj}}\\):</p> <pre><code># three sets of (g_Na, g_K)\nparams = np.array([[50.0, 1.0], [4.0, 1.5], [20.0, 15.0]])\n\nnum_samples = len(params[:, 0])\nsim_samples = np.zeros((num_samples, len(I)))\nfor i in range(num_samples):\n    sim_samples[i, :] = run_HH_model(params=params[i, :])[\"data\"]\n</code></pre> <pre><code># colors for traces\ncol_min = 2\nnum_colors = num_samples + col_min\ncm1 = mpl.cm.Blues\ncol1 = [cm1(1.0 * i / num_colors) for i in range(col_min, num_colors)]\n\nfig = plt.figure(figsize=(7, 5))\ngs = mpl.gridspec.GridSpec(2, 1, height_ratios=[4, 1])\nax = plt.subplot(gs[0])\nfor i in range(num_samples):\n    plt.plot(t, sim_samples[i, :], color=col1[i], lw=2)\nplt.ylabel(\"voltage (mV)\")\nax.set_xticks([])\nax.set_yticks([-80, -20, 40])\n\nax = plt.subplot(gs[1])\nplt.plot(t, I * A_soma * 1e3, \"k\", lw=2)\nplt.xlabel(\"time (ms)\")\nplt.ylabel(\"input (nA)\")\n\nax.set_xticks([0, max(t) / 2, max(t)])\nax.set_yticks([0, 1.1 * np.max(I * A_soma * 1e3)])\nax.yaxis.set_major_formatter(mpl.ticker.FormatStrFormatter(\"%.2f\"))\nplt.show()\n</code></pre> <p></p> <p>As can be seen, the voltage traces can be quite diverse for different parameter values.</p> <p>Often, we are not interested in matching the exact trace, but only in matching certain features thereof. In this example of the Hodgkin-Huxley model, the summary features are the number of spikes, the mean resting potential, the standard deviation of the resting potential, and the first four voltage moments: mean, standard deviation, skewness and kurtosis. Using the function <code>calculate_summary_statistics()</code> imported below, we obtain these statistics from the output of the Hodgkin Huxley simulator. </p> <pre><code>from HH_helper_functions import calculate_summary_statistics\n</code></pre> <p>Lastly, we define a function that performs all of the above steps at once. The function <code>simulation_wrapper</code> takes in conductance values, runs the Hodgkin Huxley model and then returns the summary statistics.</p> <pre><code>def simulation_wrapper(params):\n\"\"\"\n    Returns summary statistics from conductance values in `params`.\n\n    Summarizes the output of the HH simulator and converts it to `torch.Tensor`.\n    \"\"\"\n    obs = run_HH_model(params)\n    summstats = torch.as_tensor(calculate_summary_statistics(obs))\n    return summstats\n</code></pre> <p><code>sbi</code> takes any function as simulator. Thus, <code>sbi</code> also has the flexibility to use simulators that utilize external packages, e.g., Brian (http://briansimulator.org/), nest (https://www.nest-simulator.org/), or NEURON (https://neuron.yale.edu/neuron/). External simulators do not even need to be Python-based as long as they store simulation outputs in a format that can be read from Python. All that is necessary is to wrap your external simulator of choice into a Python callable that takes a parameter set and outputs a set of summary statistics we want to fit the parameters to.</p>"},{"location":"examples/00_HH_simulator/#3-prior-over-model-parameters","title":"3. Prior over model parameters","text":"<p>Now that we have the simulator, we need to define a function with the prior over the model parameters (\\(\\bar g_{Na}\\),\\(\\bar g_K\\)), which in this case is chosen to be a Uniform distribution:</p> <pre><code>prior_min = [0.5, 1e-4]\nprior_max = [80.0, 15.0]\nprior = utils.torchutils.BoxUniform(\n    low=torch.as_tensor(prior_min), high=torch.as_tensor(prior_max)\n)\n</code></pre>"},{"location":"examples/00_HH_simulator/#inference","title":"Inference","text":"<p>Now that we have all the required components, we can run inference with SNPE to identify parameters whose activity matches this trace.</p> <pre><code>posterior = infer(\n    simulation_wrapper, prior, method=\"SNPE\", num_simulations=300, num_workers=4\n)\n</code></pre> <pre><code>HBox(children=(FloatProgress(value=0.0, description='Running 300 simulations in 300 batches.', max=300.0, styl\u2026\n\n\n\nNeural network successfully converged after 233 epochs.\n</code></pre> <p>Note <code>sbi</code> can parallelize your simulator. If you experience problems with parallelization, try setting <code>num_workers=1</code> and please give us an error report as a GitHub issue.</p>"},{"location":"examples/00_HH_simulator/#coming-back-to-the-observed-data","title":"Coming back to the observed data","text":"<p>As mentioned at the beginning of the tutorial, the observed data are generated by the Hodgkin-Huxley model with a set of known parameters (\\(\\bar g_{Na}\\),\\(\\bar g_K\\)). To illustrate how to compute the summary statistics of the observed data, let us regenerate the observed data:</p> <pre><code># true parameters and respective labels\ntrue_params = np.array([50.0, 5.0])\nlabels_params = [r\"$g_{Na}$\", r\"$g_{K}$\"]\n</code></pre> <pre><code>observation_trace = run_HH_model(true_params)\nobservation_summary_statistics = calculate_summary_statistics(observation_trace)\n</code></pre> <p>As we already shown above, the observed voltage traces look as follows:</p> <pre><code>fig = plt.figure(figsize=(7, 5))\ngs = mpl.gridspec.GridSpec(2, 1, height_ratios=[4, 1])\nax = plt.subplot(gs[0])\nplt.plot(observation_trace[\"time\"], observation_trace[\"data\"])\nplt.ylabel(\"voltage (mV)\")\nplt.title(\"observed data\")\nplt.setp(ax, xticks=[], yticks=[-80, -20, 40])\n\nax = plt.subplot(gs[1])\nplt.plot(observation_trace[\"time\"], I * A_soma * 1e3, \"k\", lw=2)\nplt.xlabel(\"time (ms)\")\nplt.ylabel(\"input (nA)\")\n\nax.set_xticks([0, max(observation_trace[\"time\"]) / 2, max(observation_trace[\"time\"])])\nax.set_yticks([0, 1.1 * np.max(I * A_soma * 1e3)])\nax.yaxis.set_major_formatter(mpl.ticker.FormatStrFormatter(\"%.2f\"))\n</code></pre> <p></p>"},{"location":"examples/00_HH_simulator/#analysis-of-the-posterior-given-the-observed-data","title":"Analysis of the posterior given the observed data","text":"<p>After running the inference algorithm, let us inspect the inferred posterior distribution over the parameters (\\(\\bar g_{Na}\\),\\(\\bar g_K\\)), given the observed trace. To do so, we first draw samples (i.e. consistent parameter sets) from the posterior:</p> <pre><code>samples = posterior.sample((10000,), x=observation_summary_statistics)\n</code></pre> <pre><code>HBox(children=(FloatProgress(value=0.0, description='Drawing 10000 posterior samples', max=10000.0, style=Prog\u2026\n</code></pre> <pre><code>fig, axes = analysis.pairplot(\n    samples,\n    limits=[[0.5, 80], [1e-4, 15.0]],\n    ticks=[[0.5, 80], [1e-4, 15.0]],\n    figsize=(5, 5),\n    points=true_params,\n    points_offdiag={\"markersize\": 6},\n    points_colors=\"r\",\n);\n</code></pre> <p></p> <p>As can be seen, the inferred posterior contains the ground-truth parameters (red) in a high-probability region. Now, let us sample parameters from the posterior distribution, simulate the Hodgkin-Huxley model for this parameter set and compare the simulations with the observed data:</p> <pre><code># Draw a sample from the posterior and convert to numpy for plotting.\nposterior_sample = posterior.sample((1,), x=observation_summary_statistics).numpy()\n</code></pre> <pre><code>HBox(children=(FloatProgress(value=0.0, description='Drawing 1 posterior samples', max=1.0, style=ProgressStyl\u2026\n</code></pre> <pre><code>fig = plt.figure(figsize=(7, 5))\n\n# plot observation\nt = observation_trace[\"time\"]\ny_obs = observation_trace[\"data\"]\nplt.plot(t, y_obs, lw=2, label=\"observation\")\n\n# simulate and plot samples\nx = run_HH_model(posterior_sample)\nplt.plot(t, x[\"data\"], \"--\", lw=2, label=\"posterior sample\")\n\nplt.xlabel(\"time (ms)\")\nplt.ylabel(\"voltage (mV)\")\n\nax = plt.gca()\nhandles, labels = ax.get_legend_handles_labels()\nax.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.3, 1), loc=\"upper right\")\n\nax.set_xticks([0, 60, 120])\nax.set_yticks([-80, -20, 40]);\n</code></pre> <p></p> <p>As can be seen, the sample from the inferred posterior leads to simulations that closely resemble the observed data, confirming that <code>SNPE</code> did a good job at capturing the observed data in this simple case.</p>"},{"location":"examples/00_HH_simulator/#references","title":"References","text":"<p>A. L. Hodgkin and A. F. Huxley. A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 117(4):500\u2013544, 1952.</p> <p>M. Pospischil, M. Toledo-Rodriguez, C. Monier, Z. Piwkowska, T. Bal, Y. Fr\u00e9gnac, H. Markram, and A. Destexhe. Minimal Hodgkin-Huxley type models for different classes of cortical and thalamic neurons. Biological Cybernetics, 99(4-5), 2008.</p>"},{"location":"examples/01_decision_making_model/","title":"SBI for decision-making models","text":"<p>In a previous tutorial, we showed how to use SBI with trial-based iid data. Such scenarios can arise, for example, in models of perceptual decision making. In addition to trial-based iid data points, these models often come with mixed data types and varying experimental conditions. Here, we show how <code>sbi</code> can be used to perform inference in such models with the <code>MNLE</code> method.</p>"},{"location":"examples/01_decision_making_model/#trial-based-sbi-with-mixed-data-types","title":"Trial-based SBI with mixed data types","text":"<p>In some cases, models with trial-based data additionally return data with mixed data types, e.g., continous and discrete data. For example, most computational models of decision-making have continuous reaction times and discrete choices as output. </p> <p>This can induce a problem when performing trial-based SBI that relies on learning a neural likelihood: It is challenging for most density estimators to handle both, continuous and discrete data at the same time.  However, there is a recent SBI method for solving this problem, it\u2019s called Mixed Neural Likelihood Estimation (MNLE). It works just like NLE, but with mixed data types. The trick is that it learns two separate density estimators, one for the discrete part of the data, and one for the continuous part, and combines the two to obtain the final neural likelihood. Crucially, the continuous density estimator is trained conditioned on the output of the discrete one, such that statistical dependencies between the discrete and continuous data (e.g., between choices and reaction times) are modeled as well. The interested reader is referred to the original paper available here.</p> <p>MNLE was recently added to <code>sbi</code> (see this PR and also issue) and follows the same API as <code>SNLE</code>.</p> <p>In this tutorial we will show how to apply <code>MNLE</code> to mixed data, and how to deal with varying experimental conditions. </p>"},{"location":"examples/01_decision_making_model/#toy-problem-for-mnle","title":"Toy problem for <code>MNLE</code>","text":"<p>To illustrate <code>MNLE</code> we set up a toy simulator that outputs mixed data and for which we know the likelihood such we can obtain reference posterior samples via MCMC.</p> <p>Simulator: To simulate mixed data we do the following</p> <ul> <li>Sample reaction time from <code>inverse Gamma</code></li> <li>Sample choices from <code>Binomial</code></li> <li>Return reaction time \\(rt \\in (0, \\infty)\\) and choice index \\(c \\in \\{0, 1\\}\\)</li> </ul> \\[ c \\sim \\text{Binomial}(\\rho) \\\\ rt \\sim \\text{InverseGamma}(\\alpha=2, \\beta) \\\\ \\] <p>Prior: The priors of the two parameters \\(\\rho\\) and \\(\\beta\\) are independent. We define a <code>Beta</code> prior over the probabilty parameter of the <code>Binomial</code> used in the simulator and a <code>Gamma</code> prior over the shape-parameter of the <code>inverse Gamma</code> used in the simulator:</p> \\[ p(\\beta, \\rho) = p(\\beta) \\; p(\\rho) ; \\\\ p(\\beta) = \\text{Gamma}(1, 0.5) \\\\ p(\\text{probs}) = \\text{Beta}(2, 2)  \\] <p>Because the <code>InverseGamma</code> and the <code>Binomial</code> likelihoods are well-defined we can perform MCMC on this problem and obtain reference-posterior samples.</p> <pre><code>import matplotlib.pyplot as plt\nimport torch\nfrom torch import Tensor\n\n\nfrom sbi.inference import MNLE\nfrom pyro.distributions import InverseGamma\nfrom torch.distributions import Beta, Binomial, Categorical, Gamma\nfrom sbi.utils import MultipleIndependent\nfrom sbi.utils.metrics import c2st\n\nfrom sbi.analysis import pairplot\nfrom sbi.inference import MCMCPosterior\nfrom sbi.utils.torchutils import atleast_2d\n\nfrom sbi.inference.potentials.likelihood_based_potential import (\n    MixedLikelihoodBasedPotential,\n)\nfrom sbi.utils.conditional_density_utils import ConditionedPotential\n\nfrom sbi.utils import mcmc_transform\nfrom sbi.inference.potentials.base_potential import BasePotential\n</code></pre> <pre><code># Toy simulator for mixed data\ndef mixed_simulator(theta: Tensor, concentration_scaling: float = 1.0):\n\"\"\"Returns a sample from a mixed distribution given parameters theta.\n\n    Args:\n        theta: batch of parameters, shape (batch_size, 2)\n        concentration_scaling: scaling factor for the concentration parameter of the InverseGamma distribution, mimics an experimental condition.\n\n    \"\"\"\n    beta, ps = theta[:, :1], theta[:, 1:]\n\n    choices = Binomial(probs=ps).sample()\n    rts = InverseGamma(\n        concentration=concentration_scaling * torch.ones_like(beta), rate=beta\n    ).sample()\n\n    return torch.cat((rts, choices), dim=1)\n\n\n# The potential function defines the ground truth likelihood and allows us to obtain reference posterior samples via MCMC.\nclass PotentialFunctionProvider(BasePotential):\n    allow_iid_x = True  # type: ignore\n\n    def __init__(self, prior, x_o, concentration_scaling=1.0, device=\"cpu\"):\n        super().__init__(prior, x_o, device)\n        self.concentration_scaling = concentration_scaling\n\n    def __call__(self, theta, track_gradients: bool = True):\n        theta = atleast_2d(theta)\n\n        with torch.set_grad_enabled(track_gradients):\n            iid_ll = self.iid_likelihood(theta)\n\n        return iid_ll + self.prior.log_prob(theta)\n\n    def iid_likelihood(self, theta):\n        lp_choices = torch.stack(\n            [\n                Binomial(probs=th.reshape(1, -1)).log_prob(self.x_o[:, 1:])\n                for th in theta[:, 1:]\n            ],\n            dim=1,\n        )\n\n        lp_rts = torch.stack(\n            [\n                InverseGamma(\n                    concentration=self.concentration_scaling * torch.ones_like(beta_i),\n                    rate=beta_i,\n                ).log_prob(self.x_o[:, :1])\n                for beta_i in theta[:, :1]\n            ],\n            dim=1,\n        )\n\n        joint_likelihood = (lp_choices + lp_rts).squeeze()\n\n        assert joint_likelihood.shape == torch.Size([self.x_o.shape[0], theta.shape[0]])\n        return joint_likelihood.sum(0)\n</code></pre> <pre><code># Define independent prior.\nprior = MultipleIndependent(\n    [\n        Gamma(torch.tensor([1.0]), torch.tensor([0.5])),\n        Beta(torch.tensor([2.0]), torch.tensor([2.0])),\n    ],\n    validate_args=False,\n)\n</code></pre>"},{"location":"examples/01_decision_making_model/#obtain-reference-posterior-samples-via-analytical-likelihood-and-mcmc","title":"Obtain reference-posterior samples via analytical likelihood and MCMC","text":"<pre><code>torch.manual_seed(42)\nnum_trials = 10\nnum_samples = 1000\ntheta_o = prior.sample((1,))\nx_o = mixed_simulator(theta_o.repeat(num_trials, 1))\n</code></pre> <pre><code>mcmc_kwargs = dict(\n    num_chains=20,\n    warmup_steps=50,\n    method=\"slice_np_vectorized\",\n    init_strategy=\"proposal\",\n)\n\ntrue_posterior = MCMCPosterior(\n    potential_fn=PotentialFunctionProvider(prior, x_o),\n    proposal=prior,\n    theta_transform=mcmc_transform(prior, enable_transform=True),\n    **mcmc_kwargs,\n)\ntrue_samples = true_posterior.sample((num_samples,))\n</code></pre> <pre><code>/Users/janbolts/qode/sbi/sbi/utils/sbiutils.py:342: UserWarning: An x with a batch size of 10 was passed. It will be interpreted as a batch of independent and identically\n            distributed data X={x_1, ..., x_n}, i.e., data generated based on the\n            same underlying (unknown) parameter. The resulting posterior will be with\n            respect to entire batch, i.e,. p(theta | X).\n  warnings.warn(\n\n\n\nRunning vectorized MCMC with 20 chains:   0%|          | 0/20000 [00:00&lt;?, ?it/s]\n</code></pre>"},{"location":"examples/01_decision_making_model/#train-mnle-and-generate-samples-via-mcmc","title":"Train MNLE and generate samples via MCMC","text":"<pre><code># Training data\nnum_simulations = 20000\n# For training the MNLE emulator we need to define a proposal distribution, the prior is\n# a good choice.\nproposal = prior\ntheta = proposal.sample((num_simulations,))\nx = mixed_simulator(theta)\n\n# Train MNLE and obtain MCMC-based posterior.\ntrainer = MNLE()\nestimator = trainer.append_simulations(theta, x).train(training_batch_size=1000)\n</code></pre> <pre><code>/Users/janbolts/qode/sbi/sbi/neural_nets/mnle.py:60: UserWarning: The mixed neural likelihood estimator assumes that x contains\n        continuous data in the first n-1 columns (e.g., reaction times) and\n        categorical data in the last column (e.g., corresponding choices). If\n        this is not the case for the passed `x` do not use this function.\n  warnings.warn(\n\n\n Neural network successfully converged after 73 epochs.\n</code></pre> <pre><code># Build posterior from the trained estimator and prior.\nmnle_posterior = trainer.build_posterior(prior=prior)\n\nmnle_samples = mnle_posterior.sample((num_samples,), x=x_o, **mcmc_kwargs)\n</code></pre> <pre><code>Running vectorized MCMC with 20 chains:   0%|          | 0/20000 [00:00&lt;?, ?it/s]\n</code></pre>"},{"location":"examples/01_decision_making_model/#compare-mnle-and-reference-posterior","title":"Compare MNLE and reference posterior","text":"<pre><code># Plot them in one pairplot as contours (obtained via KDE on the samples).\nfig, ax = pairplot(\n    [\n        prior.sample((1000,)),\n        true_samples,\n        mnle_samples,\n    ],\n    points=theta_o,\n    diag=\"kde\",\n    upper=\"contour\",\n    kde_offdiag=dict(bins=50),\n    kde_diag=dict(bins=100),\n    contour_offdiag=dict(levels=[0.95]),\n    points_colors=[\"k\"],\n    points_offdiag=dict(marker=\"*\", markersize=10),\n    labels=[r\"$\\beta$\", r\"$\\rho$\"],\n)\n\nplt.sca(ax[1, 1])\nplt.legend(\n    [\"Prior\", \"Reference\", \"MNLE\", r\"$\\theta_o$\"],\n    frameon=False,\n    fontsize=12,\n);\n</code></pre> <p>We see that the inferred <code>MNLE</code> posterior nicely matches the reference posterior, and how both inferred a posterior that is quite different from the prior.</p> <p>Because MNLE training is amortized we can obtain another posterior given a different observation with potentially a different number of trials, just by running MCMC again (without re-training <code>MNLE</code>):</p>"},{"location":"examples/01_decision_making_model/#repeat-inference-with-different-x_o-that-contains-more-trials","title":"Repeat inference with different <code>x_o</code> that contains more trials","text":"<pre><code>num_trials = 50\nx_o = mixed_simulator(theta_o.repeat(num_trials, 1))\ntrue_samples = true_posterior.sample((num_samples,), x=x_o, **mcmc_kwargs)\nmnle_samples = mnle_posterior.sample((num_samples,), x=x_o, **mcmc_kwargs)\n</code></pre> <pre><code>/Users/janbolts/qode/sbi/sbi/utils/sbiutils.py:342: UserWarning: An x with a batch size of 50 was passed. It will be interpreted as a batch of independent and identically\n            distributed data X={x_1, ..., x_n}, i.e., data generated based on the\n            same underlying (unknown) parameter. The resulting posterior will be with\n            respect to entire batch, i.e,. p(theta | X).\n  warnings.warn(\n\n\n\nRunning vectorized MCMC with 20 chains:   0%|          | 0/20000 [00:00&lt;?, ?it/s]\n\n\n\nRunning vectorized MCMC with 20 chains:   0%|          | 0/20000 [00:00&lt;?, ?it/s]\n</code></pre> <pre><code># Plot them in one pairplot as contours (obtained via KDE on the samples).\nfig, ax = pairplot(\n    [\n        prior.sample((1000,)),\n        true_samples,\n        mnle_samples,\n    ],\n    points=theta_o,\n    diag=\"kde\",\n    upper=\"contour\",\n    kde_offdiag=dict(bins=50),\n    kde_diag=dict(bins=100),\n    contour_offdiag=dict(levels=[0.95]),\n    points_colors=[\"k\"],\n    points_offdiag=dict(marker=\"*\", markersize=10),\n    labels=[r\"$\\beta$\", r\"$\\rho$\"],\n)\n\nplt.sca(ax[1, 1])\nplt.legend(\n    [\"Prior\", \"Reference\", \"MNLE\", r\"$\\theta_o$\"],\n    frameon=False,\n    fontsize=12,\n);\n</code></pre> <pre><code>print(c2st(true_samples, mnle_samples)[0])\n</code></pre> <pre><code>tensor(0.5565)\n</code></pre> <p>Again we can see that the posteriors match nicely. In addition, we observe that the posterior\u2019s (epistemic) uncertainty reduces as we increase the number of trials. </p> <p>Note: <code>MNLE</code> is trained on single-trial data. Theoretically, density estimation is perfectly accurate only in the limit of infinite training data. Thus, training with a finite amount of training data naturally induces a small bias in the density estimator.  As we observed above, this bias is so small that we don\u2019t really notice it, e.g., the <code>c2st</code> scores were close to 0.5.  However, when we increase the number of trials in <code>x_o</code> dramatically (on the order of 1000s) the small bias can accumulate over the trials and inference with <code>MNLE</code> can become less accurate.</p>"},{"location":"examples/01_decision_making_model/#mnle-with-experimental-conditions","title":"MNLE with experimental conditions","text":"<p>In the perceptual decision-making research it is common to design experiments with varying experimental decisions, e.g., to vary the difficulty of the task.  During parameter inference, it can be beneficial to incorporate the experimental conditions.  In MNLE, we are learning an emulator that should be able to generate synthetic experimental data including reaction times and choices given different experimental conditions.  Thus, to make MNLE work with experimental conditions, we need to include them in the training process, i.e., treat them like auxiliary parameters of the simulator: </p> <pre><code># define a simulator wrapper in which the experimental condition are contained in theta and passed to the simulator.\ndef sim_wrapper(theta):\n    # simulate with experiment conditions\n    return mixed_simulator(\n        theta=theta[:, :2],\n        concentration_scaling=theta[:, 2:]\n        + 1,  # add 1 to deal with 0 values from Categorical distribution\n    )\n</code></pre> <pre><code># Define a proposal that contains both, priors for the parameters and a discrte prior over experimental conditions.\nproposal = MultipleIndependent(\n    [\n        Gamma(torch.tensor([1.0]), torch.tensor([0.5])),\n        Beta(torch.tensor([2.0]), torch.tensor([2.0])),\n        Categorical(probs=torch.ones(1, 3)),\n    ],\n    validate_args=False,\n)\n\n# Simulated data\nnum_simulations = 10000\nnum_samples = 1000\ntheta = proposal.sample((num_simulations,))\nx = sim_wrapper(theta)\nassert x.shape == (num_simulations, 2)\n\n# simulate observed data and define ground truth parameters\nnum_trials = 10\ntheta_o = proposal.sample((1,))\ntheta_o[0, 2] = 2.0  # set condition to 2 as in original simulator.\nx_o = sim_wrapper(theta_o.repeat(num_trials, 1))\n</code></pre>"},{"location":"examples/01_decision_making_model/#obtain-ground-truth-posterior-via-mcmc","title":"Obtain ground truth posterior via MCMC","text":"<p>We obtain a ground-truth posterior via MCMC by using the PotentialFunctionProvider.</p> <p>For that, we first the define the actual prior, i.e., the distribution over the parameter we want to infer (not the proposal).</p> <p>Thus, we leave out the discrete prior over experimental conditions.</p> <pre><code>prior = MultipleIndependent(\n    [\n        Gamma(torch.tensor([1.0]), torch.tensor([0.5])),\n        Beta(torch.tensor([2.0]), torch.tensor([2.0])),\n    ],\n    validate_args=False,\n)\nprior_transform = mcmc_transform(prior)\n\n# We can now use the PotentialFunctionProvider to obtain a ground-truth posterior via MCMC.\ntrue_posterior_samples = MCMCPosterior(\n    PotentialFunctionProvider(\n        prior,\n        x_o,\n        concentration_scaling=float(theta_o[0, 2])\n        + 1.0,  # add one because the sim_wrapper adds one (see above)\n    ),\n    theta_transform=prior_transform,\n    proposal=prior,\n    **mcmc_kwargs,\n).sample((num_samples,), show_progress_bars=True)\n</code></pre> <pre><code>/Users/janbolts/qode/sbi/sbi/utils/sbiutils.py:342: UserWarning: An x with a batch size of 10 was passed. It will be interpreted as a batch of independent and identically\n            distributed data X={x_1, ..., x_n}, i.e., data generated based on the\n            same underlying (unknown) parameter. The resulting posterior will be with\n            respect to entire batch, i.e,. p(theta | X).\n  warnings.warn(\n\n\n\nRunning vectorized MCMC with 20 chains:   0%|          | 0/20000 [00:00&lt;?, ?it/s]\n</code></pre>"},{"location":"examples/01_decision_making_model/#train-mnle-including-experimental-conditions","title":"Train MNLE including experimental conditions","text":"<pre><code>trainer = MNLE(proposal)\nestimator = trainer.append_simulations(theta, x).train(training_batch_size=100)\n</code></pre> <pre><code>/Users/janbolts/qode/sbi/sbi/neural_nets/mnle.py:60: UserWarning: The mixed neural likelihood estimator assumes that x contains\n        continuous data in the first n-1 columns (e.g., reaction times) and\n        categorical data in the last column (e.g., corresponding choices). If\n        this is not the case for the passed `x` do not use this function.\n  warnings.warn(\n\n\n Neural network successfully converged after 73 epochs.\n</code></pre>"},{"location":"examples/01_decision_making_model/#construct-conditional-potential-function","title":"Construct conditional potential function","text":"<p>To obtain posterior samples conditioned on a particular experimental condition (and on x_o), we need to construct a corresponding potential function.</p> <pre><code># First, we define the potential function for the complete, unconditional MNLE-likelihood.\npotential_fn = MixedLikelihoodBasedPotential(estimator, proposal, x_o)\n\n# Then we use the potential to construct the conditional potential function.\n# Here, we tell the constructor to condition on the last dimension (index 2) by passing dims_to_sample=[0, 1].\nconditioned_potential_fn = ConditionedPotential(\n    potential_fn,\n    condition=theta_o,\n    dims_to_sample=[0, 1],\n    allow_iid_x=True,  # we also need to explicitly tell that MNLE allows iid_x\n)\n\n# Using this potential function, we can now obtain conditional samples.\nmnle_posterior = MCMCPosterior(\n    potential_fn=conditioned_potential_fn,\n    theta_transform=prior_transform,\n    proposal=prior,\n    **mcmc_kwargs\n)\nconditional_samples = mnle_posterior.sample((num_samples,), x=x_o)\n</code></pre> <pre><code>Running vectorized MCMC with 20 chains:   0%|          | 0/20000 [00:00&lt;?, ?it/s]\n</code></pre> <pre><code># Finally, we can compare the ground truth conditional posterior with the MNLE-conditional posterior.\nfig, ax = pairplot(\n    [\n        prior.sample((1000,)),\n        true_posterior_samples,\n        conditional_samples,\n    ],\n    points=theta_o,\n    diag=\"kde\",\n    upper=\"contour\",\n    kde_offdiag=dict(bins=50),\n    kde_diag=dict(bins=100),\n    contour_offdiag=dict(levels=[0.95]),\n    points_colors=[\"k\"],\n    points_offdiag=dict(marker=\"*\", markersize=10),\n    labels=[r\"$\\beta$\", r\"$\\rho$\"],\n)\n\nplt.sca(ax[1, 1])\nplt.legend(\n    [\"Prior\", \"Reference\", \"MNLE\", r\"$\\theta_o$\"],\n    frameon=False,\n    fontsize=12,\n);\n</code></pre> <p></p> <p>They match accurately, showing that we can indeed post-hoc condition the trained MNLE likelihood on different experimental conditions.</p> <pre><code>\n</code></pre>"},{"location":"faq/question_01/","title":"What should I do when my \u2018posterior samples are outside of the prior support\u2019 in SNPE?","text":"<p>When working with multi-round SNPE, you might have experienced the following warning:  <pre><code>Only x% posterior samples are within the prior support. It may take a long time to collect the remaining 10000 samples. Consider interrupting (Ctrl-C) and switching to 'sample_with_mcmc=True'.\n</code></pre></p> <p>This reason for this issue is described in more detail  here and here.  The following fixes are possible:  </p> <ul> <li> <p>sample with MCMC: <code>samples = posterior((num_samples,), x=x_o, sample_with_mcmc=True)</code>. This will make sampling slower, but samples will not \u2018leak\u2019.  </p> </li> <li> <p>resort to single-round SNPE and (if necessary) increase your simulation budget.  </p> </li> <li> <p>if your prior is either Gaussian (torch.distributions.multivariateNormal) or Uniform  (sbi.utils.BoxUniform), you can avoid leakage by using a mixture density network as  density estimator. I.e., using the  flexible interface, set  <code>density_estimator='mdn'</code>. When running inference, there should be a print statement  \u201cUsing SNPE-C with non-atomic loss\u201d</p> </li> <li> <p>use a different algorithm, e.g. SNRE and SNLE. Note, however, that these algorithms can have different issues and potential pitfalls.  </p> </li> </ul>"},{"location":"faq/question_02/","title":"Can the algorithms deal with invalid data, e.g. NaN or inf?","text":"<p>Yes. By default, whenever a simulation returns at least one <code>NaN</code> or <code>inf</code>, it is  completely excluded from the training data. In other words, the simulation is simply  discarded.</p> <p>In cases where a very large fraction of simulations return <code>NaN</code> or <code>inf</code>, discarding many simulations can be wasteful. There are two options to deal with this: Either, you use the <code>RestrictionEstimator</code> to learn regions in parameter space that do not produce <code>NaN</code> or <code>inf</code>, see here. Alternatively, you can manually substitute the \u2018invalid\u2019 values with a reasonable replacement.  I.e., at the end of your simulation code, you search for invalid entries and replace  them with a floating point number. Importantly, in order for neural network training  work well, the floating point number should still be in a reasonable range, i.e. maybe a  few standard deviations outside of \u2018good\u2019 values.</p> <p>If you are running multi-round SNPE, however, things can go fully wrong if invalid  data are encountered. In that case, you will get the following warning <pre><code>When invalid simulations are excluded, multi-round SNPE-C can leak into the regions where parameters led to invalid simulations. This can lead to poor results.\n</code></pre> Hence, if you are running multi-round SNPE and a significant fraction of simulations returns at least one invalid number, we strongly recommend to manually replace the value  in your simulation code as described above (or resort to single-round SNPE or use a   different method).</p>"},{"location":"faq/question_03/","title":"When using multiple workers, I get a pickling error. Can I still use multiprocessing?","text":"<p>Yes, but you will have to make a few adjustments to your code. </p> <p>Some background: when using <code>num_workers &gt; 1</code>, you might experience an error that a  certain object from your simulator could not be pickled (an example can be found here).</p> <p>This can be fixed by forcing <code>sbi</code> to pickle with <code>dill</code> instead of the default  <code>cloudpickle</code>. To do so, adjust your code as  follows:</p> <ul> <li>Install <code>dill</code>: <pre><code>pip install dill\n</code></pre></li> <li>At the very beginning of your python script, set the pickler to <code>dill</code>: <pre><code>from joblib.externals.loky import set_loky_pickler\nset_loky_pickler(\"dill\")\n</code></pre></li> <li>Move all imports required by your simulator into the simulator: <pre><code># Imports specified outside of the simulator will break dill:\nimport torch\ndef my_simulator(parameters):\n    return torch.ones(1,10)\n\n# Therefore, move the imports into the simulator:\ndef my_simulator(parameters):\n    import torch\n    return torch.ones(1,10)\n</code></pre></li> </ul>"},{"location":"faq/question_03/#alternative-parallelize-yourself","title":"Alternative: parallelize yourself","text":"<p>You can also write your own code to parallelize simulations with whatever  multiprocessing framework you prefer. You can then simulate your data outside of <code>sbi</code> and pass the simulated data as shown in the  flexible interface: </p>"},{"location":"faq/question_03/#some-more-background","title":"Some more background","text":"<p><code>sbi</code> uses <code>joblib</code> to parallelize simulations, which in turn uses <code>pickle</code> or  <code>cloudpickle</code> to serialize the simulator. Almost all simulators will be picklable with  <code>cloudpickle</code>, but we have experienced issues e.g. with <code>neuron</code> simulators, see here.</p>"},{"location":"faq/question_04/","title":"Can I use the GPU for training the density estimator?","text":"<p>TLDR; Yes, by passing <code>device=\"cuda\"</code> and by passing a prior that lives on the device name your passed. But no speed-ups for default density estimators.</p> <p>Yes. When creating the inference object in the flexible interface, you can pass the <code>device</code> as an argument, e.g.,</p> <pre><code>inference = SNPE(prior, device=\"cuda\", density_estimator=\"maf\")\n</code></pre> <p>The device is set to <code>\"cpu\"</code> by default, and it can be set to anything, as long as it maps to an existing PyTorch CUDA device. <code>sbi</code> will take care of copying the <code>net</code> and the training data to and from the <code>device</code>.  Note that the prior must be on the training device already, e.g., when passing <code>device=\"cuda:0\"</code>, make sure to pass a prior object that was created on that device, e.g.,  <code>prior = torch.distributions.MultivariateNormal(loc=torch.zeros(2, device=\"cuda:0\"),                                                  covariance_matrix=torch.eye(2, device=\"cuda:0\"))</code>.</p>"},{"location":"faq/question_04/#performance","title":"Performance","text":"<p>Whether or not you reduce your training time when training on a GPU depends on the problem at hand. We provide a couple of default density estimators for <code>SNPE</code>, <code>SNLE</code> and <code>SNRE</code>, e.g., a mixture density network (<code>density_estimator=\"mdn\"</code>) or a Masked Autoregressive Flow (<code>density_estimator=\"maf\"</code>). For those default density estimators we do not expect a speed up. This is because the underlying neural networks are quite shallow and not tall, e.g., they do not have many parameters or matrix operations that profit a lot from being executed on the GPU. </p> <p>A speed up through training on the GPU will most likely become visible when you are using convolutional modules in your neural networks. E.g., when passing an embedding net for image processing like in this example: https://github.com/sbi-dev/sbi/blob/main/tutorials/05_embedding_net.ipynb. </p>"},{"location":"faq/question_05/","title":"How should I save and load objects in <code>sbi</code>?","text":"<p><code>NeuralPosterior</code> objects are picklable. <pre><code>import pickle\n\n# ... run inference\nposterior = inference.build_posterior()\n\nwith open(\"/path/to/my_posterior.pkl\", \"wb\") as handle:\n    pickle.dump(posterior, handle)\n</code></pre></p> <p>Note: posterior objects that were saved under <code>sbi v0.17.2</code> or older can not be loaded under <code>sbi v0.18.0</code> or newer.</p> <p>Note: if you try to load a posterior that was saved under <code>sbi v0.14.x</code> or earlier under <code>sbi v0.15.x</code> until <code>sbi v0.17.x</code>, you have to add: <pre><code>import sys\nfrom sbi.utils import user_input_checks_utils\n\nsys.modules[\"sbi.user_input.user_input_checks_utils\"] = user_input_checks_utils\n</code></pre> to your script before loading the posterior.</p> <p>As of <code>sbi v0.18.0</code>, <code>NeuralInference</code> objects are also picklable. <pre><code>import pickle\n\n# ... run inference\nposterior = inference.build_posterior()\n\nwith open(\"/path/to/my_inference.pkl\", \"wb\") as handle:\n    pickle.dump(inference, handle)\n</code></pre> However, saving and loading the <code>inference</code> object will slightly modify the object (in order to make it serializable). These modifications lead to the following two changes in behaviour: 1) Retraining from scratch is not supported, i.e. <code>.train(..., retrain_from_scratch=True)</code> does not work. 2) When the loaded object calls the <code>.train()</code> method, it generates a new tensorboard summary writer (instead of appending to the current one).</p>"},{"location":"faq/question_06/","title":"Can I stop neural network training and resume it later?","text":"<p>Many clusters have a time limit and <code>sbi</code> might exceed this limit. You can circumvent this problem by using the flexible interface. After simulations are finished, <code>sbi</code> trains a neural network. If this process takes too long, you can stop training and resume it later. The syntax is:</p> <pre><code>inference = SNPE(prior=prior)\ninference = inference.append_simulations(theta, x)\ninference.train(max_num_epochs=300)  # Pick `max_num_epochs` such that it does not exceed the runtime.\n\nwith open(\"path/to/my/inference.pkl\", \"wb\") as handle:\n    pickle.dump(inference, handle)\n\n# To resume training:\nwith open(\"path/to/my/inference.pkl\", \"rb\") as handle:\n    inference_from_disk = pickle.load(handle)\ninference_from_disk.train(resume_training=True, max_num_epochs=600)  # Run epochs 301 until 600 (or stop early).\nposterior = inference_from_disk.build_posterior()\n</code></pre>"},{"location":"faq/question_07/","title":"Can I use a custom prior with sbi?","text":"<p><code>sbi</code> works with torch distributions only so we recommend to use those whenever possible. For example, when you are used to using <code>scipy.stats</code> distributions as priors then we recommend using the corresponding <code>torch.distributions</code>, most common distributions are implemented there. </p> <p>In case you want to use a custom prior that is not in the set of common distributions that\u2019s possible as well: You need to write a prior class that mimicks the behaviour of a <code>torch.distributions.Distribution</code> class. Then <code>sbi</code> will wrap this class to make it a fully functional torch <code>Distribution</code>.</p> <p>Essentially, the class needs two methods:</p> <ul> <li><code>.sample(sample_shape)</code>, where sample_shape is a shape tuple, e.g., <code>(n,)</code>, and returns a batch of n samples, e.g., of shape (n, 2)` for a two dimenional prior.</li> <li><code>.log_prob(value)</code> method that returns the \u201clog probs\u201d of parameters under the prior, e.g., for a batches of n parameters with shape <code>(n, ndims)</code> it should return a log probs array of shape <code>(n,)</code>.</li> </ul> <p>For sbi &gt; 0.17.2 this could look like the following:</p> <pre><code>class CustomUniformPrior:\n\"\"\"User defined numpy uniform prior.\n\n    Custom prior with user-defined valid .sample and .log_prob methods.\n    \"\"\"\n\n    def __init__(self, lower: Tensor, upper: Tensor, return_numpy: bool = False):\n        self.lower = lower\n        self.upper = upper\n        self.dist = BoxUniform(lower, upper)\n        self.return_numpy = return_numpy\n\n    def sample(self, sample_shape=torch.Size([])):\n        samples = self.dist.sample(sample_shape)\n        return samples.numpy() if self.return_numpy else samples\n\n    def log_prob(self, values):\n        if self.return_numpy:\n            values = torch.as_tensor(values)\n        log_probs = self.dist.log_prob(values)\n        return log_probs.numpy() if self.return_numpy else log_probs\n</code></pre> <p>Once you have such a class you can wrap into a <code>Distribution</code> using the <code>process_prior</code> function <code>sbi</code> provides:</p> <pre><code>from sbi.utils import process_prior\n\ncustom_prior = CustomUniformPrior(torch.zeros(2), torch.ones(2))\nprior, *_ = process_prior(custom_prior)  # Keeping only the first return.\n# use this wrapped prior in sbi...\n</code></pre> <p>In <code>sbi</code> it is sometimes required to check the support of the prior, e.g., when the prior support is bounded and one wants to reject samples from the posterior density estimator that lie outside the prior support. In torch <code>Distributions</code> this is handled automatically, however, when using a custom prior it is not. Thus,  if your prior has bounded support (like the one above) it makes sense to pass the bounds to the wrapper function such that <code>sbi</code> can pass them to torch <code>Distributions</code>:</p> <pre><code>from sbi.utils import process_prior\n\ncustom_prior = CustomUniformPrior(torch.zeros(2), torch.ones(2))\nprior = process_prior(custom_prior, \n                      custom_prior_wrapper_kwargs=dict(lower_bound=torch.zeros(2), \n                                                       upper_bound=torch.ones(2)))\n# use this wrapped prior in sbi...\n</code></pre> <p>Note that in <code>custom_prior_wrapper_kwargs</code> you can pass additinal arguments for the wrapper, e.g., <code>validate_args</code> or <code>arg_constraints</code> see the <code>Distribution</code> documentation for more details. </p> <p>If you are running sbi &lt; 0.17.2 and use <code>SNLE</code> the code above will produce a <code>NotImplementedError</code> (see #581). In this case you need to update to a newer version of <code>sbi</code> or use <code>SNPE</code> instead. </p>"},{"location":"tutorial/00_getting_started/","title":"Getting started with <code>sbi</code>","text":"<p>Note, you can find the original version of this notebook at https://github.com/sbi-dev/sbi/blob/main/tutorials/00_getting_started.ipynb in the <code>sbi</code> repository.</p> <pre><code>import torch\nfrom sbi import utils as utils\nfrom sbi import analysis as analysis\nfrom sbi.inference.base import infer\n</code></pre>"},{"location":"tutorial/00_getting_started/#running-the-inference-procedure","title":"Running the inference procedure","text":"<p><code>sbi</code> provides a simple interface to run state-of-the-art algorithms for simulation-based inference.</p> <p>For inference, you need to provide two ingredients:</p> <p>1) a prior distribution that allows to sample parameter sets. 2) a simulator that takes parameter sets and produces simulation outputs.</p> <p>For example, we can have a 3-dimensional parameter space with a uniform prior between [-1,1] and a simple simulator that for the sake of example adds 1.0 and some Gaussian noise to the parameter set:</p> <pre><code>num_dim = 3\nprior = utils.BoxUniform(low=-2 * torch.ones(num_dim), high=2 * torch.ones(num_dim))\n\ndef simulator(parameter_set):\n    return 1.0 + parameter_set + torch.randn(parameter_set.shape) * 0.1\n</code></pre> <p><code>sbi</code> can then run inference:</p> <pre><code># Other methods are \"SNLE\" or \"SNRE\".\nposterior = infer(simulator, prior, method=\"SNPE\", num_simulations=1000)\n</code></pre> <pre><code>Running 1000 simulations.:   0%|          | 0/1000 [00:00&lt;?, ?it/s]\n\n\n Neural network successfully converged after 119 epochs.\n</code></pre> <p>Let\u2019s say we have made some observation \\(x\\):</p> <pre><code>observation = torch.zeros(3)\n</code></pre> <p>Given this observation, we can then sample from the posterior \\(p(\\theta|x)\\), evaluate its log-probability, or plot it.</p> <pre><code>samples = posterior.sample((10000,), x=observation)\nlog_probability = posterior.log_prob(samples, x=observation)\n_ = analysis.pairplot(samples, limits=[[-2, 2], [-2, 2], [-2, 2]], figsize=(6, 6))\n</code></pre> <pre><code>Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00&lt;?, ?it/s]\n</code></pre> <p></p>"},{"location":"tutorial/00_getting_started/#next-steps","title":"Next steps","text":"<p>The single-line interface described above provides an easy entry for using <code>sbi</code>. However, on almost any real-world problem that goes beyond a simple demonstration, we strongly recommend using the flexible interface.</p>"},{"location":"tutorial/01_gaussian_amortized/","title":"Amortized posterior inference on Gaussian example","text":"<p>Note, you can find the original version of this notebook at https://github.com/sbi-dev/sbi/blob/main/tutorials/01_gaussian_amortized.ipynb in the <code>sbi</code> repository.</p> <p>In this tutorial, we will demonstrate how <code>sbi</code> can infer an amortized posterior for a simple toy model with a uniform prior and Gaussian likelihood.</p> <pre><code>import torch\nimport numpy as np\n\nfrom sbi import utils as utils\nfrom sbi import analysis as analysis\nfrom sbi.inference.base import infer\n</code></pre>"},{"location":"tutorial/01_gaussian_amortized/#defining-prior-simulator-and-running-inference","title":"Defining prior, simulator, and running inference","text":"<p>Say we have a 3-dimensional parameter space, and the prior is uniformly distributed between <code>-2</code> and <code>2</code> in each dimension, i.e. \\(\\theta \\in [-2,2], y\\in [-2,2], z \\in [-2,2]\\).</p> <pre><code>num_dim = 3\nprior = utils.BoxUniform(low=-2 * torch.ones(num_dim), high=2 * torch.ones(num_dim))\n</code></pre> <p>Our simulator takes the input parameters, adds <code>1.0</code> in each dimension, and then adds some Gaussian noise:</p> <pre><code>def linear_gaussian(theta):\n    return theta + 1.0 + torch.randn_like(theta) * 0.1\n</code></pre> <p>We can then run inference (either with the simple interface of with the flexible interface):</p> <pre><code>posterior = infer(linear_gaussian, prior, \"SNPE\", num_simulations=1000)\n</code></pre>"},{"location":"tutorial/01_gaussian_amortized/#amortized-inference","title":"Amortized inference","text":"<p>Note that we have not yet provided an observation to the inference procedure. In fact, we can evaluate the posterior for different observations without having to re-run inference. This is called amortization. An amortized posterior is one that is not focused on any particular observation. Naturally, if the diversity of observations is large, any of the inference methods will need to run a sufficient number of simulations for the resulting posterior to perform well across these diverse observations.</p> <p>Let\u2019s say we have two observations <code>x_o_1 = [0,0,0]</code> and <code>x_o_2 = [2,2,2]</code>:</p> <pre><code>x_o_1 = torch.zeros(3,)\nx_o_2 = 2.0 * torch.ones(3,)\n</code></pre> <p>We can draw samples from the posterior given <code>x_o_1</code> and then plot them:</p> <pre><code>posterior_samples_1 = posterior.sample((10000,), x=x_o_1)\n\n# plot posterior samples\n_ = analysis.pairplot(\n    posterior_samples_1, limits=[[-2, 2], [-2, 2], [-2, 2]], figsize=(5, 5)\n)\n</code></pre> <pre><code>Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00&lt;?, ?it/s]\n</code></pre> <p></p> <p>As it can be seen, the posterior samples are centered around <code>[-1,-1,-1]</code> in each dimension.  This makes sense because the simulator always adds <code>1.0</code> in each dimension and we have observed <code>x_o_1 = [0,0,0]</code>.</p> <p>Since the learned posterior is amortized, we can also draw samples from the posterior given the second observation without having to re-run inference:</p> <pre><code>posterior_samples_2 = posterior.sample((10000,), x=x_o_2)\n\n# plot posterior samples\n_ = analysis.pairplot(\n    posterior_samples_2, limits=[[-2, 2], [-2, 2], [-2, 2]], figsize=(5, 5)\n)\n</code></pre> <pre><code>Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00&lt;?, ?it/s]\n</code></pre> <p></p> <p>So, if we observed <code>x_o_2 = [2,2,2]</code>, the posterior is centered around <code>[1,1,1]</code> \u2013 again, this makes sense because the simulator adds <code>1.0</code> in each dimension.</p>"},{"location":"tutorial/02_flexible_interface/","title":"The flexible interface","text":"<p>In the previous tutorial, we have demonstrated how <code>sbi</code> can be used to run simulation-based inference with just a single line of code.</p> <p>In addition to this simple interface, <code>sbi</code> also provides a flexible interface which provides several additional features implemented in <code>sbi</code>.</p> <p>Note, you can find the original version of this notebook at https://github.com/sbi-dev/sbi/blob/main/tutorials/02_flexible_interface.ipynb in the <code>sbi</code> repository.</p>"},{"location":"tutorial/02_flexible_interface/#features","title":"Features","text":"<p>The flexible interface offers the following features (and many more):</p> <ul> <li>performing sequential posterior estimation by focusing on a particular observation over multiple rounds. This can decrease the number of simulations one has to run, but the inference procedure is no longer amortized (tutorial).    </li> <li>specify your own density estimator, or change hyperparameters of existing ones (e.g. number of hidden units for NSF) (tutorial).    </li> <li>use an <code>embedding_net</code> to learn summary features from high-dimensional simulation outputs (tutorial).  </li> <li>provide presimulated data  </li> <li>choose between different methods to sample from the posterior.  </li> <li>use calibration kernels as proposed by Lueckmann, Goncalves et al. 2017.</li> </ul>"},{"location":"tutorial/02_flexible_interface/#main-syntax","title":"Main syntax","text":"<pre><code>from sbi.inference import SNPE, prepare_for_sbi, simulate_for_sbi\n\nsimulator, prior = prepare_for_sbi(simulator, prior)\ninference = SNPE(prior)\n\ntheta, x = simulate_for_sbi(simulator, proposal=prior, num_simulations=1000)\ndensity_estimator = inference.append_simulations(theta, x).train()\nposterior = inference.build_posterior(density_estimator)\n</code></pre>"},{"location":"tutorial/02_flexible_interface/#linear-gaussian-example","title":"Linear Gaussian example","text":"<p>We will show an example of how we can use the flexible interface to infer the posterior for an example with a Gaussian likelihood (same example as before). First, we import the inference method we want to use (<code>SNPE</code>, <code>SNLE</code>, or <code>SNRE</code>) and other helper functions.</p> <pre><code>import torch\n\nfrom sbi.inference import SNPE, prepare_for_sbi, simulate_for_sbi\nfrom sbi.utils.get_nn_models import posterior_nn\nfrom sbi import utils as utils\nfrom sbi import analysis as analysis\n</code></pre> <p>Next, we define the prior and simulator:</p> <pre><code>num_dim = 3\nprior = utils.BoxUniform(low=-2 * torch.ones(num_dim), high=2 * torch.ones(num_dim))\n</code></pre> <pre><code>def linear_gaussian(theta):\n    return theta + 1.0 + torch.randn_like(theta) * 0.1\n</code></pre> <p>In the flexible interface, you have to ensure that your simulator and prior adhere the requirements of <code>sbi</code>. You can do so with the <code>prepare_for_sbi()</code> function.</p> <pre><code>simulator, prior = prepare_for_sbi(linear_gaussian, prior)\n</code></pre> <p>Then, we instantiate the inference object:</p> <pre><code>inference = SNPE(prior=prior)\n</code></pre> <p>Next, we run simulations. You can do so either by yourself by sampling from the prior and running the simulator (e.g. on a compute cluster), or you can use a helper function provided by <code>sbi</code> called <code>simulate_for_sbi</code>. This function allows to parallelize your code with <code>joblib</code>.</p> <pre><code>theta, x = simulate_for_sbi(simulator, proposal=prior, num_simulations=2000)\n</code></pre> <pre><code>Running 2000 simulations.:   0%|          | 0/2000 [00:00&lt;?, ?it/s]\n</code></pre> <p>We then pass the simulated data to the inference object. <code>theta</code> and <code>x</code> should both be a <code>torch.Tensor</code> of type <code>float32</code>.</p> <pre><code>inference = inference.append_simulations(theta, x)\n</code></pre> <p>Next, we train the neural density estimator.</p> <pre><code>density_estimator = inference.train()\n</code></pre> <pre><code> Neural network successfully converged after 73 epochs.\n</code></pre> <p>Lastly, we can use this density estimator to build the posterior:</p> <pre><code>posterior = inference.build_posterior(density_estimator)\n</code></pre> <p>Once we have obtained the posterior, we can <code>.sample()</code>, <code>.log_prob()</code>, or <code>.pairplot()</code> in the same way as for the simple interface.</p> <pre><code>x_o = torch.zeros(3,)\n</code></pre> <pre><code>posterior_samples = posterior.sample((10000,), x=x_o)\n\n# plot posterior samples\n_ = analysis.pairplot(\n    posterior_samples, limits=[[-2, 2], [-2, 2], [-2, 2]], figsize=(5, 5)\n)\n</code></pre> <pre><code>Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00&lt;?, ?it/s]\n</code></pre> <p></p> <p>We can always print the posterior to know how it was trained:</p> <pre><code>print(posterior)\n</code></pre> <pre><code>Posterior conditional density p(\u03b8|x) of type DirectPosterior. It samples the posterior network and rejects samples that\n            lie outside of the prior bounds.\n</code></pre>"},{"location":"tutorial/03_multiround_inference/","title":"Multi-round inference","text":"<p>In the previous tutorials, we have inferred the posterior using single-round inference. In single-round inference, we draw parameters from the prior, simulate the corresponding data, and then train a neural network to obtain the posterior. However,  if one is interested in only one particular observation <code>x_o</code> sampling from the prior can be inefficient in the number of simulations because one is effectively learning a posterior estimate for all observations in the prior space. In this tutorial, we show how one can alleviate this issue by performing multi-round inference with <code>sbi</code>.  </p> <p>Multi-round inference also starts by drawing parameters from the prior, simulating them, and training a neural network to estimate the posterior distribution. Afterwards, however, it continues inference in multiple rounds, focusing on a particular observation <code>x_o</code>. In each new round of inference, it draws samples from the obtained posterior distribution conditioned at <code>x_o</code> (instead of from the prior), simulates these, and trains the network again. This process can be repeated arbitrarily often to get increasingly good approximations to the true posterior distribution at <code>x_o</code>.</p> <p>Running multi-round inference can be more efficient in the number of simulations, but it will lead to the posterior no longer being amortized (i.e. it will be accurate only for a specific observation <code>x_o</code>, not for any <code>x</code>).</p> <p>Note, you can find the original version of this notebook at https://github.com/sbi-dev/sbi/blob/main/tutorials/03_multiround_inference.ipynb in the <code>sbi</code> repository.</p>"},{"location":"tutorial/03_multiround_inference/#main-syntax","title":"Main syntax","text":"<pre><code># 2 rounds: first round simulates from the prior, second round simulates parameter set\n# that were sampled from the obtained posterior.\nnum_rounds = 2\n# The specific observation we want to focus the inference on.\nx_o = torch.zeros(\n    3,\n)\n\nposteriors = []\nproposal = prior\n\nfor _ in range(num_rounds):\n    theta, x = simulate_for_sbi(simulator, proposal, num_simulations=500)\n\n    # In `SNLE` and `SNRE`, you should not pass the `proposal` to `.append_simulations()`\n    density_estimator = inference.append_simulations(\n        theta, x, proposal=proposal\n    ).train()\n    posterior = inference.build_posterior(density_estimator)\n    posteriors.append(posterior)\n    proposal = posterior.set_default_x(x_o)\n</code></pre>"},{"location":"tutorial/03_multiround_inference/#linear-gaussian-example","title":"Linear Gaussian example","text":"<p>Below, we give a full example of inferring the posterior distribution over multiple rounds.</p> <pre><code>import torch\n\nfrom sbi.inference import SNPE, prepare_for_sbi, simulate_for_sbi\nfrom sbi.utils.get_nn_models import posterior_nn\nfrom sbi import utils as utils\nfrom sbi import analysis as analysis\n\n_ = torch.manual_seed(0)\n</code></pre> <p>First, we define a simple prior and simulator and ensure that they comply with <code>sbi</code> by using <code>prepare_for_sbi</code>:</p> <pre><code>num_dim = 3\nprior = utils.BoxUniform(low=-2 * torch.ones(num_dim), high=2 * torch.ones(num_dim))\n</code></pre> <pre><code>def linear_gaussian(theta):\n    return theta + 1.0 + torch.randn_like(theta) * 0.1\n</code></pre> <pre><code>simulator, prior = prepare_for_sbi(linear_gaussian, prior)\n</code></pre> <p>Then, we instantiate the inference object:</p> <pre><code>inference = SNPE(prior=prior)\n</code></pre> <p>And we can run inference. In this example, we will run inference over <code>2</code> rounds, potentially leading to a more focused posterior around the observation <code>x_o</code>.</p> <pre><code>num_rounds = 2\nx_o = torch.zeros(\n    3,\n)\n\nposteriors = []\nproposal = prior\n\nfor _ in range(num_rounds):\n    theta, x = simulate_for_sbi(simulator, proposal, num_simulations=500)\n    density_estimator = inference.append_simulations(\n        theta, x, proposal=proposal\n    ).train()\n    posterior = inference.build_posterior(density_estimator)\n    posteriors.append(posterior)\n    proposal = posterior.set_default_x(x_o)\n</code></pre> <pre><code>Running 500 simulations.:   0%|          | 0/500 [00:00&lt;?, ?it/s]\n\n\n Neural network successfully converged after 157 epochs.\n\n\nDrawing 500 posterior samples:   0%|          | 0/500 [00:00&lt;?, ?it/s]\n\n\n\nRunning 500 simulations.:   0%|          | 0/500 [00:00&lt;?, ?it/s]\n\n\nUsing SNPE-C with atomic loss\n Neural network successfully converged after 58 epochs.\n</code></pre> <p>Note that, for <code>num_rounds&gt;1</code>, the posterior is no longer amortized: it will give good results when sampled around <code>x=observation</code>, but possibly bad results for other <code>x</code>.</p> <p>Once we have obtained the posterior, we can <code>.sample()</code>, <code>.log_prob()</code>, or <code>.pairplot()</code> in the same way as for the simple interface.</p> <pre><code>posterior_samples = posterior.sample((10000,), x=x_o)\n\n# plot posterior samples\n_ = analysis.pairplot(\n    posterior_samples, limits=[[-2, 2], [-2, 2], [-2, 2]], figsize=(5, 5)\n)\n</code></pre> <pre><code>Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00&lt;?, ?it/s]\n</code></pre> <p></p>"},{"location":"tutorial/04_density_estimators/","title":"Customizing the density estimator","text":"<p><code>sbi</code> allows to specify a custom density estimator for each of the implemented methods. For all options, check the API reference here.</p>"},{"location":"tutorial/04_density_estimators/#changing-the-type-of-density-estimator","title":"Changing the type of density estimator","text":"<p>One option is to use one of set of preconfigured density estimators by passing a string in the <code>density_estimator</code> keyword argument to the inference object (<code>SNPE</code> or <code>SNLE</code>), e.g., \u201cmaf\u201d to use a Masked Autoregressive Flow, of \u201cnsf\u201d to use a Neural Spline Flow with default hyperparameters.</p> <pre><code>inference = SNPE(prior=prior, density_estimator=\"maf\")\n</code></pre> <p>In the case of <code>SNRE</code>, the argument is called <code>classifier</code>:</p> <pre><code>inference = SNRE(prior=prior, classifier=\"resnet\")\n</code></pre>"},{"location":"tutorial/04_density_estimators/#changing-hyperparameters-of-density-estimators","title":"Changing hyperparameters of density estimators","text":"<p>Alternatively, you can use a set of utils functions to configure a density estimator yourself, e.g., use a MAF with hyperparameters chosen for your problem at hand.</p> <p>Here, because we want to use SN*P*E, we specifiy a neural network targeting the posterior (using the utils function <code>posterior_nn</code>). In this example, we will create a neural spline flow (<code>'nsf'</code>) with <code>60</code> hidden units and <code>3</code> transform layers:</p> <pre><code>from sbi.utils.get_nn_models import (\n    posterior_nn,\n)  # For SNLE: likelihood_nn(). For SNRE: classifier_nn()\n\ndensity_estimator_build_fun = posterior_nn(\n    model=\"nsf\", hidden_features=60, num_transforms=3\n)\ninference = SNPE(prior=prior, density_estimator=density_estimator_build_fun)\n</code></pre> <p>It is also possible to pass an <code>embedding_net</code> to <code>posterior_nn()</code> which learn summary statistics from high-dimensional simulation outputs. You can find a more detailed tutorial on this here.</p>"},{"location":"tutorial/04_density_estimators/#building-new-density-estimators-from-scratch","title":"Building new density estimators from scratch","text":"<p>Finally, it is also possible to implement your own density estimator from scratch, e.g., including embedding nets to preprocess data, or to a density estimator architecture of your choice. </p> <p>For this, the <code>density_estimator</code> argument needs to be a function that takes <code>theta</code> and <code>x</code> batches as arguments to then construct the density estimator after the first set of simulations was generated. Our utils functions in <code>sbi/utils/get_nn_models.py</code> return such a function. </p>"},{"location":"tutorial/05_embedding_net/","title":"Learning summary statistics with a neural net","text":"<p>When doing simulation-based inference, it is very important to use well-chosen summary statistics for describing the data generated by the simulator. Usually, these statistics take into account domain knowledge. For instance, in the example of the Hodgkin-Huxley model, the summary statistics are defined by a function which takes a 120 ms recording as input (a 12000-dimensional input vector) and outputs a 7-dimensional feature vector containing different statistical descriptors of the recording (e.g., number of spikes, average value, etc.). </p> <p>However, in other cases, it might be of interest to actually learn from the data which summary statistics to use, e.g., because the raw data is highly complex and domain knowledge is not available or not applicable. </p> <p><code>sbi</code> offers functionality to learn summary statistics from (potentially high-dimensional) simulation outputs with a neural network. In <code>sbi</code>, this neural network is referred to as <code>embedding_net</code>. If an <code>embedding_net</code> is specified, the simulation outputs are passed through the <code>embedding_net</code>, whose outputs are then passed to the neural density estimator. The parameters of the <code>embedding_net</code> are learned together with the parameters of the neural density estimator.</p> <p>NB: only <code>SNPE</code> and <code>SNRE</code> methods can use an <code>embedding_net</code> to learn summary statistics from simulation outputs. <code>SNLE</code> does not offer this functionality since the simulation outputs \\(x\\) are the outputs of the neural density estimator.</p> <p>In the example that follows, we illustrate a situation where the data points generated by the simulator model are high-dimensional (32 by 32 images) and we use a convolutional neural network as summary statistics extractor.</p> <p>Note, you can find the original version of this notebook at https://github.com/sbi-dev/sbi/blob/main/tutorials/05_embedding_net.ipynb in the <code>sbi</code> repository.</p> <p>First of all, we import all the packages required for running the tutorial</p> <pre><code>import matplotlib.pyplot as plt\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom sbi import utils\nfrom sbi import analysis\nfrom sbi import inference\nfrom sbi.inference import SNPE, simulate_for_sbi, prepare_for_sbi\n\nseed = 0\ntorch.manual_seed(seed)\n</code></pre> <pre><code>&lt;torch._C.Generator at 0x7f124d5d04b0&gt;\n</code></pre>"},{"location":"tutorial/05_embedding_net/#the-simulator-model","title":"The simulator model","text":"<p>The simulator model that we consider has two parameters: \\(r\\) and \\(\\theta\\). On each run, it generates 100 two-dimensional points centered around \\((r \\cos(\\theta), r \\sin(\\theta))\\) and perturbed by a Gaussian noise with variance 0.01. Instead of simply outputting the \\((x,y)\\) coordinates of each data point, the model generates a grayscale image of the scattered points with dimensions 32 by 32. This image is further perturbed by an uniform noise with values betweeen 0 and 0.2. The code below defines such model.</p> <pre><code>def simulator_model(parameter, return_points=False):\n\"\"\"Simulator model with two-dimensional input parameter and 1024-dimensional output\n\n    This simulator serves as a basic example for using a neural net for learning summary features.\n    It has only two input parameters but generates high-dimensional output vectors.\n    The data is generated as follows:\n        (-) Input:  parameter = [r, theta]\n        (1) Generate 100 two-dimensional points centered around (r cos(theta),r sin(theta))\n            and perturbed by a Gaussian noise with variance 0.01\n        (2) Create a grayscale image I of the scattered points with dimensions 32 by 32\n        (3) Perturb I with an uniform noise with values betweeen 0 and 0.2\n        (-) Output: I\n\n    Parameters\n    ----------\n    parameter : array-like, shape (2)\n        The two input parameters of the model, ordered as [r, theta]\n    return_points : bool (default: False)\n        Whether the simulator should return the coordinates of the simulated data points as well\n\n    Returns\n    -------\n    I: torch tensor, shape (1, 1024)\n        Output flattened image\n    (optional) points: array-like, shape (100, 2)\n        Coordinates of the 2D simulated data points\n\n    \"\"\"\n    r = parameter[0]\n    theta = parameter[1]\n\n    sigma_points = 0.10\n    npoints = 100\n    points = []\n    for _ in range(npoints):\n        x = r * torch.cos(theta) + sigma_points * torch.randn(1)\n        y = r * torch.sin(theta) + sigma_points * torch.randn(1)\n        points.append([x, y])\n    points = torch.as_tensor(points)\n\n    nx = 32\n    ny = 32\n    sigma_image = 0.20\n    I = torch.zeros(nx, ny)\n    for point in points:\n        pi = int((point[0] - (-1)) / ((+1) - (-1)) * nx)\n        pj = int((point[1] - (-1)) / ((+1) - (-1)) * ny)\n        if (pi &lt; nx) and (pj &lt; ny):\n            I[pi, pj] = 1\n    I = I + sigma_image * torch.rand(nx, ny)\n    I = I.T\n    I = I.reshape(1, -1)\n\n    if return_points:\n        return I, points\n    else:\n        return I\n</code></pre> <p>The figure below shows an example of the output of the simulator when \\(r = 0.70\\) and \\(\\theta = \\pi/4\\)</p> <pre><code># simulate samples\ntrue_parameter = torch.tensor([0.70, torch.pi / 4])\nx_observed, x_points = simulator_model(true_parameter, return_points=True)\n\n# plot the observation\nfig, ax = plt.subplots(\n    facecolor=\"white\", figsize=(11.15, 5.61), ncols=2, constrained_layout=True\n)\ncircle = plt.Circle((0, 0), 1.0, color=\"k\", ls=\"--\", lw=0.8, fill=False)\nax[0].add_artist(circle)\nax[0].scatter(x_points[:, 0], x_points[:, 1], s=20)\nax[0].set_xlabel(\"x\")\nax[0].set_ylabel(\"y\")\nax[0].set_xlim(-1, +1)\nax[0].set_xticks([-1, 0.0, +1.0])\nax[0].set_ylim(-1, +1)\nax[0].set_yticks([-1, 0.0, +1.0])\nax[0].set_title(r\"original simulated points with $r = 0.70$ and $\\theta = \\pi/4$\")\nax[1].imshow(x_observed.view(32, 32), origin=\"lower\", cmap=\"gray\")\nax[1].set_xticks([])\nax[1].set_yticks([])\nax[1].set_title(\"noisy observed data (gray image with 32 x 32 pixels)\")\n</code></pre> <pre><code>Text(0.5, 1.0, 'noisy observed data (gray image with 32 x 32 pixels)')\n</code></pre> <p></p>"},{"location":"tutorial/05_embedding_net/#defining-an-embedding_net","title":"Defining an <code>embedding_net</code>","text":"<p>An inference procedure applied to the output data from this simulator model determines the posterior distribution of \\(r\\) and \\(\\theta\\) given an observation of \\(x\\), which lives in a 1024 dimensional space (32 x 32 = 1024). To avoid working directly on these high-dimensional vectors, one can use a convolutional neural network (CNN) that takes the 32x32 images as input and encodes them into 8-dimensional feature vectors. This CNN is trained along with the neural density estimator of the inference procedure and serves as an automatic summary statistics extractor. </p> <p>We define and instantiate the CNN as follows:</p> <pre><code>class SummaryNet(nn.Module):\n    def __init__(self):\n        super().__init__()\n        # 2D convolutional layer\n        self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, padding=2)\n        # Maxpool layer that reduces 32x32 image to 4x4\n        self.pool = nn.MaxPool2d(kernel_size=8, stride=8)\n        # Fully connected layer taking as input the 6 flattened output arrays from the maxpooling layer\n        self.fc = nn.Linear(in_features=6 * 4 * 4, out_features=8)\n\n    def forward(self, x):\n        x = x.view(-1, 1, 32, 32)\n        x = self.pool(F.relu(self.conv1(x)))\n        x = x.view(-1, 6 * 4 * 4)\n        x = F.relu(self.fc(x))\n        return x\n\n\nembedding_net = SummaryNet()\n</code></pre>"},{"location":"tutorial/05_embedding_net/#the-inference-procedure","title":"The inference procedure","text":"<p>With the <code>embedding_net</code> defined and instantiated, we can follow the usual workflow of an inference procedure in <code>sbi</code>. The <code>embedding_net</code> object appears as an input argument when instantiating the neural density estimator with <code>utils.posterior_nn</code>.</p> <pre><code># set prior distribution for the parameters\nprior = utils.BoxUniform(\n    low=torch.tensor([0.0, 0.0]), high=torch.tensor([1.0, 2 * torch.pi])\n)\n\n# make a SBI-wrapper on the simulator object for compatibility\nsimulator_wrapper, prior = prepare_for_sbi(simulator_model, prior)\n</code></pre> <pre><code># instantiate the neural density estimator\nneural_posterior = utils.posterior_nn(\n    model=\"maf\", embedding_net=embedding_net, hidden_features=10, num_transforms=2\n)\n\n# setup the inference procedure with the SNPE-C procedure\ninference = SNPE(prior=prior, density_estimator=neural_posterior)\n</code></pre> <pre><code># run the inference procedure on one round and 10000 simulated data points\ntheta, x = simulate_for_sbi(simulator_wrapper, prior, num_simulations=10000)\n</code></pre> <pre><code>Running 10000 simulations.:   0%|          | 0/10000 [00:00&lt;?, ?it/s]\n</code></pre> <pre><code>density_estimator = inference.append_simulations(theta, x).train()\nposterior = inference.build_posterior(density_estimator)\n</code></pre> <pre><code> Neural network successfully converged after 76 epochs.\n</code></pre>"},{"location":"tutorial/05_embedding_net/#visualizing-the-results","title":"Visualizing the results","text":"<p>We now generate 50000 samples of the posterior distribution of \\(r\\) and \\(\\theta\\) when observing an input data point \\(x\\) generated from the <code>simulator model</code> with \\(r = 0.70\\) and \\(\\theta = \\pi/4\\). </p> <pre><code># generate posterior samples\ntrue_parameter = torch.tensor([0.70, torch.pi / 4])\nx_observed = simulator_model(true_parameter)\nsamples = posterior.set_default_x(x_observed).sample((50000,))\n</code></pre> <pre><code>Drawing 50000 posterior samples:   0%|          | 0/50000 [00:00&lt;?, ?it/s]\n</code></pre> <p>The figure below shows the statistics of the generated samples.</p> <pre><code># create the figure\nfig, ax = analysis.pairplot(\n    samples,\n    points=true_parameter,\n    labels=[\"r\", r\"$\\theta$\"],\n    limits=[[0, 1], [0, 2 * torch.pi]],\n    points_colors=\"r\",\n    points_offdiag={\"markersize\": 6},\n    figsize=(5, 5),\n)\n</code></pre> <p></p> <pre><code>\n</code></pre>"},{"location":"tutorial/07_conditional_distributions/","title":"Analysing variability and compensation mechansims with conditional distributions","text":"<p>A central advantage of <code>sbi</code> over parameter search methods such as genetic algorithms is that the posterior captures all models that can reproduce experimental data. This allows us to analyse whether parameters can be variable or have to be narrowly tuned, and to analyse compensation mechanisms between different parameters. See also Marder and Taylor, 2011 for further motivation to identify all models that capture experimental data.  </p> <p>In this tutorial, we will show how one can use the posterior distribution to identify whether parameters can be variable or have to be finely tuned, and how we can use the posterior to find potential compensation mechanisms between model parameters. To investigate this, we will extract conditional distributions from the posterior inferred with <code>sbi</code>.</p> <p>Note, you can find the original version of this notebook at https://github.com/sbi-dev/sbi/blob/main/tutorials/07_conditional_distributions.ipynb in the <code>sbi</code> repository.</p>"},{"location":"tutorial/07_conditional_distributions/#main-syntax","title":"Main syntax","text":"<pre><code>from sbi.analysis import conditional_pairplot, conditional_corrcoeff\n\n# Plot slices through posterior, i.e. conditionals.\n_ = conditional_pairplot(\n    density=posterior,\n    condition=posterior.sample((1,)),\n    limits=torch.tensor([[-2.0, 2.0], [-2.0, 2.0]]),\n)\n\n# Compute the matrix of correlation coefficients of the slices.\ncond_coeff_mat = conditional_corrcoeff(\n    density=posterior,\n    condition=posterior.sample((1,)),\n    limits=torch.tensor([[-2.0, 2.0], [-2.0, 2.0]]),\n)\nplt.imshow(cond_coeff_mat, clim=[-1, 1])\n</code></pre>"},{"location":"tutorial/07_conditional_distributions/#analysing-variability-and-compensation-mechanisms-in-a-toy-example","title":"Analysing variability and compensation mechanisms in a toy example","text":"<p>Below, we use a simple toy example to demonstrate the above described features. For an application of these features to a neuroscience problem, see figure 6 in Gon\u00e7alves, Lueckmann, Deistler et al., 2019.</p> <pre><code>from sbi import utils as utils\nfrom sbi.analysis import pairplot, conditional_pairplot, conditional_corrcoeff\nimport torch\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib import animation, rc\nfrom IPython.display import HTML, Image\n\n_ = torch.manual_seed(0)\n</code></pre> <p>Let\u2019s say we have used SNPE to obtain a posterior distribution over three parameters. In this tutorial, we just load the posterior from a file:</p> <pre><code>from toy_posterior_for_07_cc import ExamplePosterior\n\nposterior = ExamplePosterior()\n</code></pre> <p>First, we specify the experimental observation \\(x_o\\) at which we want to evaluate and sample the posterior \\(p(\\theta|x_o)\\):</p> <pre><code>x_o = torch.ones(1, 20)  # simulator output was 20-dimensional\nposterior.set_default_x(x_o)\n</code></pre> <p>As always, we can inspect the posterior marginals with the <code>pairplot()</code> function:</p> <pre><code>posterior_samples = posterior.sample((5000,))\n\nfig, ax = pairplot(\n    samples=posterior_samples,\n    limits=torch.tensor([[-2.0, 2.0]] * 3),\n    offdiag=[\"kde\"],\n    diag=[\"kde\"],\n    figsize=(5, 5),\n)\n</code></pre> <p></p> <p>The 1D and 2D marginals of the posterior fill almost the entire parameter space! Also, the Pearson correlation coefficient matrix of the marginal shows rather weak interactions (low correlations):</p> <pre><code>corr_matrix_marginal = np.corrcoef(posterior_samples.T)\nfig, ax = plt.subplots(1, 1, figsize=(4, 4))\nim = plt.imshow(corr_matrix_marginal, clim=[-1, 1], cmap=\"PiYG\")\n_ = fig.colorbar(im)\n</code></pre> <p></p> <p>It might be tempting to conclude that the experimental data barely constrains our parameters and that almost all parameter combinations can reproduce the experimental data. As we will show below, this is not the case.</p> <p>Because our toy posterior has only three parameters, we can plot posterior samples in a 3D plot:</p> <pre><code>rc(\"animation\", html=\"html5\")\n\n# First set up the figure, the axis, and the plot element we want to animate\nfig = plt.figure(figsize=(6, 6))\nax = fig.add_subplot(111, projection=\"3d\")\n\nax.set_xlim((-2, 2))\nax.set_ylim((-2, 2))\n\n\ndef init():\n    (line,) = ax.plot([], [], lw=2)\n    line.set_data([], [])\n    return (line,)\n\n\ndef animate(angle):\n    num_samples_vis = 1000\n    line = ax.scatter(\n        posterior_samples[:num_samples_vis, 0],\n        posterior_samples[:num_samples_vis, 1],\n        posterior_samples[:num_samples_vis, 2],\n        zdir=\"z\",\n        s=15,\n        c=\"#2171b5\",\n        depthshade=False,\n    )\n    ax.view_init(20, angle)\n    return (line,)\n\n\nanim = animation.FuncAnimation(\n    fig, animate, init_func=init, frames=range(0, 360, 5), interval=150, blit=True\n)\n\nplt.close()\n</code></pre> <pre><code>HTML(anim.to_html5_video())\n</code></pre>    Your browser does not support the video tag.  <p>Clearly, the range of admissible parameters is constrained to a narrow region in parameter space, which had not been evident from the marginals.</p> <p>If the posterior has more than three dimensions, inspecting all dimensions at once will not be possible anymore. One way to still reveal structures in high-dimensional posteriors is to inspect 2D-slices through the posterior. In <code>sbi</code>, this can be done with the <code>conditional_pairplot()</code> function, which computes the conditional distributions within the posterior. We can slice (i.e. condition) the posterior at any location, given by the <code>condition</code>. In the plot below, for all upper diagonal plots, we keep all but two parameters constant at values sampled from the posterior, and inspect what combinations of the remaining two parameters can reproduce experimental data. For the plots on the diagonal (the 1D conditionals), we keep all but one parameter constant.</p> <pre><code>condition = posterior.sample((1,))\n\n_ = conditional_pairplot(\n    density=posterior,\n    condition=condition,\n    limits=torch.tensor([[-2.0, 2.0]] * 3),\n    figsize=(5, 5),\n)\n</code></pre> <p></p> <p>This plot looks completely different from the marginals obtained with <code>pairplot()</code>. As it can be seen on the diagonal plots, if all parameters but one are kept constant, the remaining parameter has to be tuned to a narrow region in parameter space. In addition, the upper diagonal plots show strong correlations: deviations in one parameter can be compensated through changes in another parameter.</p> <p>We can summarize these correlations in a conditional correlation matrix, which computes the Pearson correlation coefficient of each of these pairwise plots. This matrix (below) shows strong correlations between many parameters, which can be interpreted as potential compensation mechansims:</p> <pre><code>cond_coeff_mat = conditional_corrcoeff(\n    density=posterior,\n    condition=condition,\n    limits=torch.tensor([[-2.0, 2.0]] * 3),\n)\nfig, ax = plt.subplots(1, 1, figsize=(4, 4))\nim = plt.imshow(cond_coeff_mat, clim=[-1, 1], cmap=\"PiYG\")\n_ = fig.colorbar(im)\n</code></pre> <p></p> <p>So far, we have investigated the conditional distribution only at a specific <code>condition</code> sampled from the posterior. In many applications, it makes sense to repeat the above analyses with a different <code>condition</code> (another sample from the posterior), which can be interpreted as slicing the posterior at a different location. Note that <code>conditional_corrcoeff()</code> can directly compute the matrix for several <code>conditions</code> and then outputs the average over them. This can be done by passing a batch of \\(N\\) conditions as the <code>condition</code> argument.</p>"},{"location":"tutorial/07_conditional_distributions/#sampling-conditional-distributions","title":"Sampling conditional distributions","text":"<p>So far, we have demonstrated how one can plot 2D conditional distributions with <code>conditional_pairplot()</code> and how one can compute the pairwise conditional correlation coefficient with <code>conditional_corrcoeff()</code>. In some cases, it can be useful to keep a subset of parameters fixed and to vary more than two parameters. This can be done by sampling the conditonal posterior \\(p(\\theta_i | \\theta_{j \\neq i}, x_o)\\). As of <code>sbi</code> <code>v0.18.0</code>, this functionality requires using the sampler interface. In this tutorial, we demonstrate this functionality on a linear gaussian simulator with four parameters. We would like to fix the forth parameter to \\(\\theta_4=0.2\\) and sample the first three parameters given that value, i.e. we want to sample \\(p(\\theta_1, \\theta_2, \\theta_3 | \\theta_4 = 0.2, x_o)\\). For an application in neuroscience, see Deistler, Gon\u00e7alves, Macke, 2021.</p> <p>In this tutorial, we will use SNPE, but the same also works for SNLE and SNRE. First, we define the prior and the simulator and train the deep neural density estimator:</p> <pre><code>from sbi.inference import SNPE, prepare_for_sbi, simulate_for_sbi\nfrom sbi.inference import posterior_estimator_based_potential, MCMCPosterior\nfrom sbi.utils import BoxUniform\nimport torch\n\nnum_dim = 4\nprior = BoxUniform(low=-2 * torch.ones(num_dim), high=2 * torch.ones(num_dim))\n\n\ndef linear_gaussian(theta):\n    return theta + 1.0 + torch.randn_like(theta) * 0.1\n\n\nsimulator, prior = prepare_for_sbi(linear_gaussian, prior)\n\ninference = SNPE()\ntheta, x = simulate_for_sbi(simulator, prior, 1000)\nposterior_estimator = inference.append_simulations(theta, x).train()\n</code></pre> <pre><code>Running 1000 simulations.:   0%|          | 0/1000 [00:00&lt;?, ?it/s]\n\n\n Neural network successfully converged after 147 epochs.\n</code></pre> <p>Next, we follow the sampler interface and create a potential_function. The observation in this example is <code>x_o=[1, 1, 1, 1]</code>.</p> <pre><code>potential_fn, theta_transform = posterior_estimator_based_potential(\n    posterior_estimator, prior=prior, x_o=torch.ones(4)\n)\n</code></pre> <p>Now we want to build the conditional potential (please read throught the sampler interface tutorial for an explanation of potential functions). For this, we have to pass a <code>condition</code>. In our case, we want to condition the forth parameter on \\(\\theta_4=0.2\\). Regardless of how many parameters one wants to condition on, in <code>sbi</code>, one has to pass a <code>condition</code> value for all parameters. The first three values will simply be ignored. We can tell the algorithm which parameters should be kept fixed and which ones should be sampled with the argument <code>dims_to_sample</code>.</p> <pre><code>from sbi.analysis import conditional_potential\n\nconditioned_potential_fn, restricted_tf, restricted_prior = conditional_potential(\n    potential_fn=potential_fn,\n    theta_transform=theta_transform,\n    prior=prior,\n    condition=torch.as_tensor(\n        [0.0, 0.0, 0.0, 0.2]\n    ),  # the first three values are arbitrary and are ignored internally\n    dims_to_sample=[0, 1, 2],\n)\n</code></pre> <p>Finally, we have to build a sampler for the <code>conditioned_potential_fn</code>. E.g., we can sample the conditional posterior with MCMC:</p> <pre><code>mcmc_posterior = MCMCPosterior(\n    potential_fn=conditioned_potential_fn,\n    theta_transform=restricted_tf,\n    proposal=restricted_prior,\n)\ncond_samples = mcmc_posterior.sample((100,))\n</code></pre> <pre><code>  0%|          | 0/50 [00:00&lt;?, ?it/s]\n\n\n\n  0%|          | 0/10 [00:00&lt;?, ?it/s]\n\n\n\n  0%|          | 0/100 [00:00&lt;?, ?it/s]\n</code></pre> <p>The resulting samples are 3-dimensional, corresponding to \\([\\theta_1, \\theta_2, \\theta_3]\\), sampled from \\(p(\\theta_1, \\theta_2, \\theta_3 | \\theta_4=0.2, x_o)\\).</p> <pre><code>print(cond_samples.shape)\n</code></pre> <pre><code>torch.Size([100, 3])\n</code></pre> <p>We can also plot them with <code>pairplot</code>:</p> <pre><code>from sbi.analysis import pairplot\n\n_ = pairplot(cond_samples, limits=[[-2, 2], [-2, 2], [-2, 2], [-2, 2]], figsize=(4, 4))\n</code></pre> <p></p>"},{"location":"tutorial/08_restriction_estimator/","title":"Efficient handling of invalid simulation outputs","text":"<p>For many simulators, the output of the simulator can be ill-defined or it can have non-sensical values. For example, in neuroscience models, if a specific parameter set does not produce a spike, features such as the spike shape can not be computed. When using <code>sbi</code>, such simulations that have <code>NaN</code> or <code>inf</code> in their output are discarded during neural network training. This can lead to inefficetive use of simulation budget: we carry out many simulations, but a potentially large fraction of them is discarded. </p> <p>In this tutorial, we show how we can use <code>sbi</code> to learn regions in parameter space that produce <code>valid</code> simulation outputs, and thereby improve the sampling efficiency. The key idea of the method is to use a classifier to distinguish parameters that lead to <code>valid</code> simulations from regions that lead to <code>invalid</code> simulations. After we have obtained the region in parameter space that produes <code>valid</code> simulation outputs, we train the deep neural density estimator used in <code>SNPE</code>. The method was originally proposed in Lueckmann, Goncalves et al. 2017 and later used in Deistler et al. 2021.</p>"},{"location":"tutorial/08_restriction_estimator/#main-syntax","title":"Main syntax","text":"<pre><code>from sbi.inference import SNPE\nfrom sbi.utils import RestrictionEstimator\n\nrestriction_estimator = RestrictionEstimator(prior=prior)\nproposals = [prior]\n\nfor r in range(num_rounds):\n    theta, x = simulate_for_sbi(simulator, proposals[-1], 1000)\n    restriction_estimator.append_simulations(theta, x)\n    if (\n        r &lt; num_rounds - 1\n    ):  # training not needed in last round because classifier will not be used anymore.\n        classifier = restriction_estimator.train()\n    proposals.append(restriction_estimator.restrict_prior())\n\nall_theta, all_x, _ = restriction_estimator.get_simulations()\n\ninference = SNPE(prior=prior)\ndensity_estimator = inference.append_simulations(all_theta, all_x).train()\nposterior = inference.build_posterior()\n</code></pre>"},{"location":"tutorial/08_restriction_estimator/#further-explanation-in-a-toy-example","title":"Further explanation in a toy example","text":"<pre><code>from sbi.inference import SNPE, simulate_for_sbi\nfrom sbi.utils import RestrictionEstimator, BoxUniform\nfrom sbi.analysis import pairplot\nimport torch\n\n_ = torch.manual_seed(2)\n</code></pre> <p>We will define a simulator with two parameters and two simulation outputs. The simulator produces <code>NaN</code> whenever the first parameter is below <code>0.0</code>. If it is above <code>0.0</code> the simulator simply perturbs the parameter set with Gaussian noise:</p> <pre><code>def simulator(theta):\n    perturbed_theta = theta + 0.5 * torch.randn(2)\n    perturbed_theta[theta[:, 0] &lt; 0.0] = torch.as_tensor([float(\"nan\"), float(\"nan\")])\n    return perturbed_theta\n</code></pre> <p>The prior is a uniform distribution in [-2, 2]:</p> <pre><code>prior = BoxUniform(-2 * torch.ones(2), 2 * torch.ones(2))\n</code></pre> <p>We then begin by drawing samples from the prior and simulating them. Looking at the simulation outputs, half of them contain <code>NaN</code>:</p> <pre><code>theta, x = simulate_for_sbi(simulator, prior, 1000)\nprint(\"Simulation outputs: \", x)\n</code></pre> <pre><code>Running 1000 simulations.:   0%|          | 0/1000 [00:00&lt;?, ?it/s]\n\n\nSimulation outputs:  tensor([[ 0.0411, -0.5656],\n        [ 0.0096, -1.0841],\n        [ 1.2937,  0.9448],\n        ...,\n        [    nan,     nan],\n        [    nan,     nan],\n        [ 2.7940,  0.6461]])\n</code></pre> <p>The simulations that contain <code>NaN</code> are wasted, and we want to learn to \u201crestrict\u201d the prior such that it produces only <code>valid</code> simulation outputs. To do so, we set up the <code>RestrictionEstimator</code>:</p> <pre><code>restriction_estimator = RestrictionEstimator(prior=prior)\n</code></pre> <p>The <code>RestrictionEstimator</code> trains a classifier to distinguish parameters that lead to <code>valid</code> simulation outputs from parameters that lead to <code>invalid</code> simulation outputs </p> <pre><code>restriction_estimator.append_simulations(theta, x)\nclassifier = restriction_estimator.train()\n</code></pre> <pre><code>Training neural network. Epochs trained:  35\n</code></pre> <p>We can inspect the <code>restricted_prior</code>, i.e. the parameters that the classifier believes will lead to <code>valid</code> simulation outputs, with:</p> <pre><code>restricted_prior = restriction_estimator.restrict_prior()\nsamples = restricted_prior.sample((10_000,))\n_ = pairplot(samples, limits=[[-2, 2], [-2, 2]], fig_size=(4, 4))\n</code></pre> <pre><code>The classifier rejected 51.6% of all samples. You will get a speed-up of 106.5%.\n</code></pre> <p></p> <p>Indeed, parameter sets sampled from the <code>restricted_prior</code> always have a first parameter larger than <code>0.0</code>. These are the ones that produce <code>valid</code> simulation outputs (see our definition of the simulator above). We can then use the <code>restricted_prior</code> to generate more simulations. Almost all of them will have <code>valid</code> simulation outputs:</p> <pre><code>new_theta, new_x = simulate_for_sbi(simulator, restricted_prior, 1000)\nprint(\"Simulation outputs: \", new_x)\n</code></pre> <pre><code>The classifier rejected 50.9% of all samples. You will get a speed-up of 103.6%.\n\n\n\nRunning 1000 simulations.:   0%|          | 0/1000 [00:00&lt;?, ?it/s]\n\n\nSimulation outputs:  tensor([[ 0.6834, -0.2415],\n        [ 1.3459,  1.5373],\n        [ 2.1092,  1.9180],\n        ...,\n        [ 0.8845,  0.4036],\n        [ 1.9111,  1.2526],\n        [ 0.8320,  2.3755]])\n</code></pre> <p>We can now use all simulations and run <code>SNPE</code> as always:</p> <pre><code>restriction_estimator.append_simulations(\n    new_theta, new_x\n)  # Gather the new simulations in the `restriction_estimator`.\n(\n    all_theta,\n    all_x,\n    _,\n) = restriction_estimator.get_simulations()  # Get all simulations run so far.\n\ninference = SNPE(prior=prior)\ndensity_estimator = inference.append_simulations(all_theta, all_x).train()\nposterior = inference.build_posterior()\n\nposterior_samples = posterior.sample((10_000,), x=torch.ones(2))\n_ = pairplot(posterior_samples, limits=[[-2, 2], [-2, 2]], fig_size=(3, 3))\n</code></pre> <pre><code>WARNING:root:Found 523 NaN simulations and 0 Inf simulations. They will be excluded from training.\n\n\n Neural network successfully converged after 118 epochs.\n\n\nDrawing 10000 posterior samples:   0%|          | 0/10000 [00:00&lt;?, ?it/s]\n</code></pre> <p></p>"},{"location":"tutorial/08_restriction_estimator/#further-options-for-tuning-the-algorithm","title":"Further options for tuning the algorithm","text":"<ul> <li>the whole procedure can be repeated many times (see the loop shown in \u201cMain syntax\u201d in this tutorial)  </li> <li>the classifier is trained to be relatively conservative, i.e. it will try to be very sure that a specific parameter set can indeed not produce <code>valid</code> simulation outputs. If you are ok with the restricted prior potentially ignoring a small fraction of parameter sets that might have produced <code>valid</code> data, you can use <code>restriction_estimator.restrict_prior(allowed_false_negatives=...)</code>. The argument <code>allowed_false_negatives</code> sets the fraction of potentially ignored parameter sets. A higher value will lead to more <code>valid</code> simulations.  </li> <li>By default, the algorithm considers simulations that have at least one <code>NaN</code> of <code>inf</code> as <code>invalid</code>. You can specify custom criterions with <code>RestrictionEstimator(decision_criterion=...)</code></li> </ul>"},{"location":"tutorial/09_sensitivity_analysis/","title":"Active subspaces for sensitivity analysis","text":"<p>A standard method to analyse dynamical systems such as models of neural dynamics is to use a sensitivity analysis. We can use the posterior obtained with <code>sbi</code>, to perform such analyses.</p>"},{"location":"tutorial/09_sensitivity_analysis/#main-syntax","title":"Main syntax","text":"<pre><code>from sbi.analysis import ActiveSubspace\n\nsensitivity = ActiveSubspace(posterior.set_default_x(x_o))\ne_vals, e_vecs = sensitivity.find_directions(posterior_log_prob_as_property=True)\nprojected_data = sensitivity.project(theta_project, num_dimensions=1)\n</code></pre>"},{"location":"tutorial/09_sensitivity_analysis/#example-and-further-explanation","title":"Example and further explanation","text":"<pre><code>import torch\nfrom torch.distributions import MultivariateNormal\n\nfrom sbi.analysis import ActiveSubspace, pairplot\nfrom sbi.simulators import linear_gaussian\nfrom sbi.inference import simulate_for_sbi, infer\n\n_ = torch.manual_seed(0)\n</code></pre> <p>Let\u2019s define a simple Gaussian toy example:</p> <pre><code>prior = MultivariateNormal(0.0 * torch.ones(2), 2 * torch.eye(2))\n\n\ndef simulator(theta):\n    return linear_gaussian(\n        theta, -0.8 * torch.ones(2), torch.tensor([[1.0, 0.98], [0.98, 1.0]])\n    )\n\n\nposterior = infer(simulator, prior, num_simulations=2000, method=\"SNPE\").set_default_x(\n    torch.zeros(2)\n)\n</code></pre> <pre><code>Running 2000 simulations.:   0%|          | 0/2000 [00:00&lt;?, ?it/s]\n\n\n Neural network successfully converged after 117 epochs.\n</code></pre> <pre><code>posterior_samples = posterior.sample((2000,))\n</code></pre> <pre><code>Drawing 2000 posterior samples:   0%|          | 0/2000 [00:00&lt;?, ?it/s]\n</code></pre> <pre><code>_ = pairplot(posterior_samples, limits=[[-3, 3], [-3, 3]], figsize=(4, 4))\n</code></pre> <p></p> <p>When performing a sensitivity analysis on this model, we would expect that there is one direction that is less sensitive (from bottom left to top right, along the vector [1, 1]) and one direction that is more sensitive (from top left to bottom right, along [1, -1]). We can recover these directions with the <code>ActiveSubspace</code> module in <code>sbi</code>.</p> <pre><code>sensitivity = ActiveSubspace(posterior)\ne_vals, e_vecs = sensitivity.find_directions(posterior_log_prob_as_property=True)\n</code></pre> <pre><code>Drawing 1000 posterior samples:   0%|          | 0/1000 [00:00&lt;?, ?it/s]\n</code></pre> <p>The method <code>.find_active()</code> returns eigenvalues and the corresponding eigenvectors. It does so by computing the matrix:</p> <p>\\(M = \\mathbb{E}_{p(\\theta|x_o)}[\\nabla_{\\theta}p(\\theta|x_o)^T \\nabla_{\\theta}p(\\theta|x_o)\\)]  </p> <p>It then does an eigendecomposition: \\(M = Q \\Lambda Q^{-1}\\) </p> <p>A strong eigenvalue indicates that the gradient of the posterior density is large, i.e. the system output is sensitive to changes along the direction of the corresponding eigenvector (or <code>active</code>). The eigenvalue corresponding to the vector <code>[0.68, -0.73]</code> is much larger than the eigenvalue of <code>[0.73, 0.67]</code>. This matches the intuition we developed above.</p> <pre><code>print(\"Eigenvalues: \\n\", e_vals, \"\\n\")\nprint(\"Eigenvectors: \\n\", e_vecs)\n</code></pre> <pre><code>Eigenvalues: \n tensor([2.3552e-06, 7.0754e-05])\n\nEigenvectors: \n tensor([[-0.7066, -0.7076],\n        [-0.7076,  0.7066]])\n</code></pre> <p>Lastly, we can project the data into the active dimensions. In this case, we will just use one active dimension:</p> <pre><code>projected_data = sensitivity.project(posterior_samples, num_dimensions=1)\n</code></pre>"},{"location":"tutorial/09_sensitivity_analysis/#some-technical-details","title":"Some technical details","text":"<ul> <li>The gradients and thus the eigenvectors are computed in z-scored space. The mean and standard deviation are computed w.r.t. the prior distribution. Thus, the gradients (and thus the eigenvales) reflect changes on the scale of the prior.</li> <li>The expected value used to compute the matrix \\(M\\) is estimated using <code>1000</code> posterior samples. This value can be set with the <code>.find_active(num_monte_carlo_samples=...)</code> variable.</li> <li>How does this relate to Principal Component Analysis (PCA)? In the example above, the results of PCA would be very similar. However, there are two main differences to PCA: First, PCA ignores local changes in the posterior, whereas the active subspace can change a lot (since it computes the gradient, which is a local quantity). Second, active subspaces can be used characterize the sensitivity of any other quantity w.r.t. circuit parameters. This is outlined below:</li> </ul>"},{"location":"tutorial/09_sensitivity_analysis/#computing-the-sensitivity-of-a-specific-summary-statistic","title":"Computing the sensitivity of a specific summary statistic","text":"<p>Above, we have shown how to identify directions along which the posterior probability changes rapidly. Notably, the posterior probability reflects how consistent a specific parameter set is with all summary statistics, i.e. the entire \\(x_o\\). Sometimes, we might be interested in investigating how a specific features is influenced by the parameters. This feature could be one of the values of \\(x_o\\), but it could also be a different property.</p> <p>As a neuroscience example, in Deistler et al. 2021, we obtained the posterior distribution given burst durations and delays between bursts. After having obtained the posterior, we then wanted to analyse the sensitivity of metabolic cost w.r.t. circuit parameters. The framework we presented above can easily be extended to study such questions.</p> <pre><code>prior = MultivariateNormal(0.0 * torch.ones(2), 2 * torch.eye(2))\n\n\ndef simulator(theta):\n    return linear_gaussian(theta, -0.8 * torch.ones(2), torch.eye(2))\n\n\nposterior = infer(simulator, prior, num_simulations=2000, method=\"SNPE\").set_default_x(\n    torch.zeros(2)\n)\n</code></pre> <pre><code>Running 2000 simulations.:   0%|          | 0/2000 [00:00&lt;?, ?it/s]\n\n\n Neural network successfully converged after 139 epochs.\n</code></pre> <pre><code>_ = pairplot(posterior.sample((10_000,)), limits=[[-3, 3], [-3, 3]], figsize=(4, 4))\n</code></pre> <pre><code>Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00&lt;?, ?it/s]\n</code></pre> <p></p> <pre><code>sensitivity = ActiveSubspace(posterior)\n</code></pre> <p>This time, we begin by drawing samples from the posterior and then computing the desired property for each of the samples (i.e. you will probably have to run simulations for each theta and extract the property from the simulation output). As an example, we assume that the property is just the cube of the first dimension of the simulation output:</p> <pre><code>theta, x = simulate_for_sbi(simulator, posterior, 5000)\nproperty_ = x[:, :1] ** 3  # E.g. metabolic cost.\n</code></pre> <pre><code>Drawing 5000 posterior samples:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n\n\n\nRunning 5000 simulations.:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n</code></pre> <p>To investigate the sensitivity of a given parameter, we train a neural network to predict the <code>property_</code> from the parameters and then analyse the neural network as above:</p> <p>\\(M = \\mathbb{E}_{p(\\theta|x_o)}[\\nabla_{\\theta}f(\\theta)^T \\nabla_{\\theta}f(\\theta)\\)]  </p> <p>where \\(f(\\cdot)\\) is the trained neural network.</p> <pre><code>_ = sensitivity.add_property(theta, property_).train()\ne_vals, e_vecs = sensitivity.find_directions()\n</code></pre> <pre><code> Training neural network. Epochs trained:  24\n\n\nDrawing 1000 posterior samples:   0%|          | 0/1000 [00:00&lt;?, ?it/s]\n</code></pre> <pre><code>print(\"Eigenvalues: \\n\", e_vals, \"\\n\")\nprint(\"Eigenvectors: \\n\", e_vecs)\n</code></pre> <pre><code>Eigenvalues: \n tensor([2.8801e-06, 6.1131e-05])\n\nEigenvectors: \n tensor([[ 0.0362,  0.9993],\n        [ 0.9993, -0.0362]])\n</code></pre> <p>As we can see, one of the eigenvalues is much smaller than the other one. The larger eigenvalue represents (approximately) the vector <code>[1.0, 0.0]</code>. This makes sense, because only the <code>property_</code> is influenced only by the first output which, in turn, is influenced only by the first parameter.</p>"},{"location":"tutorial/10_crafting_summary_statistics/","title":"Crafting summary statistics","text":"<p>Many simulators produce outputs that are high-dimesional. For example, a simulator might generate a time series or an image. In a previous tutorial, we discussed how a neural networks can be used to learn summary statistics from such data. In this notebook, we will instead focus on hand-crafting summary statistics. We demonstrate that the choice of summary statistics can be crucial for the performance of the inference algorithm.</p> <pre><code>import numpy as np\nimport torch\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\n\n# sbi\nimport sbi.utils as utils\nfrom sbi.inference.base import infer\nfrom sbi.inference import SNPE, prepare_for_sbi, simulate_for_sbi\nfrom sbi.utils.get_nn_models import posterior_nn\nfrom sbi.analysis import pairplot\n</code></pre> <pre><code># remove top and right axis from plots\nmpl.rcParams[\"axes.spines.right\"] = False\nmpl.rcParams[\"axes.spines.top\"] = False\n</code></pre> <p>This notebook is not intended to provide a one-fits-all approach. In fact it argues against this: it argues for the user to carefully construct their summary statistics to (i) further help the user understand his observed data, (ii) help them understand exactly what they want the model to recover from the observation and (iii) help the inference framework itself.</p>"},{"location":"tutorial/10_crafting_summary_statistics/#example-1-the-quadratic-function","title":"Example 1: The quadratic function","text":"<p>Assume we have a simulator that is given by a quadratic function:</p> <p>\\(x(t) = a\\cdot t^2 + b\\cdot t + c + \\epsilon\\),</p> <p>where \\(\\epsilon\\) is Gaussian observation noise and \\(\\theta = \\{a, b, c\\}\\) are the parameters. Given an observed quadratic function \\(x_o\\), we would like to recover the posterior over parameters \\(a_o\\), \\(b_o\\) and \\(c_o\\).</p>"},{"location":"tutorial/10_crafting_summary_statistics/#11-prior-over-parameters","title":"1.1 Prior over parameters","text":"<p>First we define a prior distribution over parameters \\(a\\), \\(b\\) and \\(c\\). Here, we use a uniform prior for \\(a\\), \\(b\\) and \\(c\\) to go from \\(-1\\) to \\(1\\).</p> <pre><code>prior_min = [-1, -1, -1]\nprior_max = [1, 1, 1]\nprior = utils.torchutils.BoxUniform(\n    low=torch.as_tensor(prior_min), high=torch.as_tensor(prior_max)\n)\n</code></pre>"},{"location":"tutorial/10_crafting_summary_statistics/#12-simulator","title":"1.2 Simulator","text":"<p>Defining some helper functions first:</p> <pre><code>def create_t_x(theta, seed=None):\n\"\"\"Return an t, x array for plotting based on params\"\"\"\n    if theta.ndim == 1:\n        theta = theta[np.newaxis, :]\n\n    if seed is not None:\n        rng = np.random.RandomState(seed)\n    else:\n        rng = np.random.RandomState()\n\n    t = np.linspace(-1, 1, 200)\n    ts = np.repeat(t[:, np.newaxis], theta.shape[0], axis=1)\n    x = (\n        theta[:, 0] * ts**2\n        + theta[:, 1] * ts\n        + theta[:, 2]\n        + 0.01 * rng.randn(ts.shape[0], theta.shape[0])\n    )\n    return t, x\n\n\ndef eval(theta, t, seed=None):\n\"\"\"Evaluate the quadratic function at `t`\"\"\"\n\n    if theta.ndim == 1:\n        theta = theta[np.newaxis, :]\n\n    if seed is not None:\n        rng = np.random.RandomState(seed)\n    else:\n        rng = np.random.RandomState()\n\n    return theta[:, 0] * t**2 + theta[:, 1] * t + theta[:, 2] + 0.01 * rng.randn(1)\n</code></pre> <p>In this example, we generate the observation \\(x_o\\) from parameters \\(\\theta_o=(a_o, b_o, c_o)=(0.3, -0.2, -0.1)\\). The observation as follows.</p> <pre><code>theta_o = np.array([0.3, -0.2, -0.1])\nt, x = create_t_x(theta_o)\nplt.plot(t, x, \"k\")\n</code></pre> <pre><code>[&lt;matplotlib.lines.Line2D at 0x7f828b191d60&gt;]\n</code></pre> <p></p>"},{"location":"tutorial/10_crafting_summary_statistics/#13-summary-statistics","title":"1.3 Summary statistics","text":"<p>We will compare two methods for defining summary statistics. One method uses three summary statistics which are function evaluations at three points in time. The other method uses a single summary statistic: the mean squared error between the observed and the simulated trace. In the second case, one then tries to obtain the posterior \\(p(\\theta | 0)\\), i.e. the error being zero. These two methods are implemented below:  \\(\\textbf{get_3_values()}\\) returns 3 function evaluations at \\(x=-0.5, x=0\\) and \\(x=0.75\\).  \\(\\textbf{get_MSE()}\\) returns the mean squared error between true and a quadratic function corresponding to a prior distributions sample.</p> <pre><code>def get_3_values(theta, seed=None):\n\"\"\"\n    Return 3 'y' values corresponding to x=-0.5,0,0.75 as summary statistic vector\n    \"\"\"\n    return np.array(\n        [\n            eval(theta, -0.5, seed=seed),\n            eval(theta, 0, seed=seed),\n            eval(theta, 0.75, seed=seed),\n        ]\n    ).T\n</code></pre> <pre><code>def get_MSE(theta, theta_o, seed=None):\n\"\"\"\n    Return the mean-squared error (MSE) i.e. Euclidean distance from the observation function\n    \"\"\"\n    _, y = create_t_x(theta_o, seed=seed)  # truth\n    _, y_ = create_t_x(theta, seed=seed)  # simulations\n    return np.mean(np.square(y_ - y), axis=0, keepdims=True).T  # MSE\n</code></pre> <p>Let\u2019s try a couple of samples from our prior and see their summary statistics. Notice that these indeed change in small amounts every time you rerun it due to the noise, except if you set the seed.</p>"},{"location":"tutorial/10_crafting_summary_statistics/#14-simulating-data","title":"1.4 Simulating data","text":"<p>Let us see various plots of prior samples and their summary statistics versus the truth, i.e. our artificial observation.</p> <pre><code>t, x_truth = create_t_x(theta_o)\nplt.plot(t, x_truth, \"k\", zorder=1, label=\"truth\")\nn_samples = 100\ntheta = prior.sample((n_samples,))\nt, x = create_t_x(theta.numpy())\nplt.plot(t, x, \"grey\", zorder=0)\nplt.legend()\n</code></pre> <pre><code>&lt;matplotlib.legend.Legend at 0x7f8289154eb0&gt;\n</code></pre> <p></p> <p>In summary, we defined reasonable summary statistics and, a priori, there might be an appararent reason why one method would be better than another. When we do inference, we\u2019d like our posterior to focus around parameter samples that have their simulated MSE very close to 0 (i.e. the truth MSE summary statistic) or their 3 extracted \\((t, x)\\) coordinates to be the truthful ones.</p>"},{"location":"tutorial/10_crafting_summary_statistics/#15-inference","title":"1.5 Inference","text":""},{"location":"tutorial/10_crafting_summary_statistics/#151-using-the-mse","title":"1.5.1 Using the MSE","text":"<p>Let\u2019s see if we can use the MSE to recover the true observation parameters \\(\\theta_o=(a_0,b_0,c_0)\\).</p> <pre><code>theta = prior.sample((1000,))\nx = get_MSE(theta.numpy(), theta_o)\n\ntheta = torch.as_tensor(theta, dtype=torch.float32)\nx = torch.as_tensor(x, dtype=torch.float32)\n</code></pre> <pre><code>inference = SNPE(prior)\n_ = inference.append_simulations(theta, x).train()\nposterior = inference.build_posterior()\n</code></pre> <pre><code> Neural network successfully converged after 181 epochs.\n</code></pre> <p>Now that we\u2019ve build the posterior as such, we can see how likely it finds certain parameters given that we tell it that we\u2019ve observed a certain summary statistic (in this case the MSE). We can then sample from it.</p> <pre><code>x_o = torch.as_tensor(\n    [\n        [\n            0.0,\n        ]\n    ]\n)\ntheta_p = posterior.sample((10000,), x=x_o)\n</code></pre> <pre><code>Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00&lt;?, ?it/s]\n</code></pre> <pre><code>fig, axes = pairplot(\n    theta_p,\n    limits=list(zip(prior_min, prior_max)),\n    ticks=list(zip(prior_min, prior_max)),\n    figsize=(7, 7),\n    labels=[\"a\", \"b\", \"c\"],\n    points_offdiag={\"markersize\": 6},\n    points_colors=\"r\",\n    points=theta_o,\n);\n</code></pre> <p></p> <p>The posterior seems to pretty broad: i.e. it is not so certain about the \u2018true\u2019 parameters (here showcased in red).</p> <pre><code>x_o_t, x_o_x = create_t_x(theta_o)\nplt.plot(x_o_t, x_o_x, \"k\", zorder=1, label=\"truth\")\n\ntheta_p = posterior.sample((10,), x=x_o)\nx_t, x_x = create_t_x(theta_p.numpy())\nplt.plot(x_t, x_x, \"grey\", zorder=0)\nplt.legend()\n</code></pre> <pre><code>Drawing 10 posterior samples:   0%|          | 0/10 [00:00&lt;?, ?it/s]\n\n\n\n\n\n&lt;matplotlib.legend.Legend at 0x7f82882cd670&gt;\n</code></pre> <p></p> <p>The functions are a bit closer to the observation than prior samples, but many posterior samples generate activity that is very far off from the observation. We would expect <code>sbi</code> do better on such a simple example. So what\u2019s going on? Do we need more simulations? Feel free to try, but below we will show that one can use the same number of simulation samples with different summary statistics and do much better.</p>"},{"location":"tutorial/10_crafting_summary_statistics/#152-using-3-coordinates-as-summary-statistics","title":"1.5.2 Using 3 coordinates as summary statistics","text":"<pre><code>x = get_3_values(theta.numpy())\nx = torch.as_tensor(x, dtype=torch.float32)\n</code></pre> <pre><code>inference = SNPE(prior)\n\n_ = inference.append_simulations(theta, x).train()\nposterior = inference.build_posterior()\n</code></pre> <pre><code> Neural network successfully converged after 127 epochs.\n</code></pre> <p>The observation is now given by the values of the observed trace at three different coordinates:</p> <pre><code>x_o = torch.as_tensor(get_3_values(theta_o), dtype=float)\n</code></pre> <pre><code>theta_p = posterior.sample((10000,), x=x_o)\n\nfig, axes = pairplot(\n    theta_p,\n    limits=list(zip(prior_min, prior_max)),\n    ticks=list(zip(prior_min, prior_max)),\n    figsize=(7, 7),\n    labels=[\"a\", \"b\", \"c\"],\n    points_offdiag={\"markersize\": 6},\n    points_colors=\"r\",\n    points=theta_o,\n);\n</code></pre> <pre><code>Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00&lt;?, ?it/s]\n</code></pre> <p></p> <pre><code>x_o_x, x_o_y = create_t_x(theta_o)\nplt.plot(x_o_x, x_o_y, \"k\", zorder=1, label=\"truth\")\ntheta_p = posterior.sample((100,), x=x_o)\nind_10_highest = np.argsort(np.array(posterior.log_prob(theta=theta_p, x=x_o)))[-10:]\ntheta_p_considered = theta_p[ind_10_highest, :]\nx_x, x_y = create_t_x(theta_p_considered.numpy())\nplt.plot(x_x, x_y, \"grey\", zorder=0)\nplt.legend()\n</code></pre> <pre><code>Drawing 100 posterior samples:   0%|          | 0/100 [00:00&lt;?, ?it/s]\n\n\n\n\n\n&lt;matplotlib.legend.Legend at 0x7f82885b4af0&gt;\n</code></pre> <p></p> <p>Ok this definitely seems to work! The posterior correctly focuses on the true parameters with greater confidence. You can experiment yourself how this improves further with more training samples or you could try to see how many you\u2019d exactly need to keep having a satisfyingly looking posterior and high posterior sample simulations.</p> <p>So, what\u2019s up with the MSE? Why does it not seem so informative to constrain the posterior? In 1.6, we\u2019ll see both the power and pitfalls of summary statistics.</p>"},{"location":"tutorial/10_crafting_summary_statistics/#16-prior-simulations-summary-statistics-vs-observed-summary-statistics","title":"1.6 Prior simulations\u2019 summary statistics vs observed summary statistics","text":"<p>Let\u2019s try to understand this\u2026Let\u2019s look at a histogram of the four summary statistics we\u2019ve experimented with, and see how they compare to our observed truth summary statistic vector:</p> <pre><code>stats = np.concatenate(\n    (get_3_values(theta.numpy()), get_MSE(theta.numpy(), theta_o)), axis=1\n)\nx_o = np.concatenate((get_3_values(theta_o), np.asarray([[0.0]])), axis=1)\n\nfeatures = [\"y @ x=-0.5\", \"y @ x=0\", \"y @ x=0.7\", \"MSE\"]\nfig, axes = plt.subplots(1, 4, figsize=(10, 3))\nxlabelfontsize = 10\nfor i, ax in enumerate(axes.reshape(-1)):\n    ax.hist(\n        stats[:, i],\n        color=[\"grey\"],\n        alpha=0.5,\n        bins=30,\n        density=True,\n        histtype=\"stepfilled\",\n        label=[\"simulations\"],\n    )\n    ax.axvline(x_o[:, i], label=\"observation\")\n    ax.set_xlabel(features[i], fontsize=xlabelfontsize)\n    if i == 3:\n        ax.legend()\nplt.tight_layout()\n</code></pre> <p></p> <p>We see that for the coordinates (three plots on the left), simulations cover the observation. That is: it covers it from the left and right side in each case. For the MSE, simulations never truly reach the observation \\(0.0\\).</p> <p>For the trained neural network, it is strongly preferable if the simulations cover the observation. In that case, the neural network can interpolate between simulated data. Contrary to that, for the MSE, the neural network has to extrapolate: it never observes a simulation that is to the left of the observation and has to extrapolate to the region of MSE=\\(0.0\\). This seems like a technical point but, as we saw above, it makes a huge difference in performance.</p>"},{"location":"tutorial/10_crafting_summary_statistics/#17-explicit-recommendations","title":"1.7 Explicit recommendations","text":"<p>We give some explicit recommendation when using summary statistics</p> <ul> <li> <p>Visualize the histogram of each summary statistic and plot the value of the observation. If, for some summary statistics, the observation is not covered (or is at the very border, e.g. the MSE above), the trained neural network will struggle.</p> </li> <li> <p>Do not use an \u201cerror\u201d as summary statistic. This is common in optimization (e.g. genetic algorithms), but it often leads to trouble in <code>sbi</code> due to the reason above.</p> </li> <li> <p>Only use summary statistics that are necessary. The less summary statistics you use, the less can go wrong with them. Of course, you have to ensure that the summary statistics describe the raw data sufficiently well.</p> </li> </ul>"},{"location":"tutorial/11_sampler_interface/","title":"Sampling algorithms in <code>sbi</code>","text":"<p>Note: this tutorial requires that the user is already familiar with the flexible interface.</p> <p><code>sbi</code> implements three methods: SNPE, SNLE, and SNRE. When using SNPE, the trained neural network directly approximates the posterior. Thus, sampling from the posterior can be done by sampling from the trained neural network. The neural networks trained in SNLE and SNRE approximate the likelihood(-ratio). Thus, in order to draw samples from the posterior, one has to perform additional sampling steps, e.g. Markov-chain Monte-Carlo (MCMC). In <code>sbi</code>, the implemented samplers are:</p> <ul> <li> <p>Markov-chain Monte-Carlo (MCMC)</p> </li> <li> <p>Rejection sampling  </p> </li> <li> <p>Variational inference (VI)</p> </li> </ul> <p>Below, we will demonstrate how these samplers can be used in <code>sbi</code>. First, we train the neural network as always:</p> <pre><code>import torch\nfrom sbi.inference import SNLE\n\n# dummy Gaussian simulator for demonstration\nnum_dim = 2\nprior = torch.distributions.MultivariateNormal(torch.zeros(num_dim), torch.eye(num_dim))\ntheta = prior.sample((1000,))\nx = theta + torch.randn((1000, num_dim))\nx_o = torch.randn((1, num_dim))\n\ninference = SNLE(prior=prior, show_progress_bars=False)\nlikelihood_estimator = inference.append_simulations(theta, x).train()\n</code></pre> <p>And then we pass the options for which sampling method to use to the <code>build_posterior()</code> method:</p> <pre><code># Sampling with MCMC\nsampling_algorithm = \"mcmc\"\nmcmc_method = \"slice_np\"  # or nuts, or hmc\nposterior = inference.build_posterior(sample_with=sampling_algorithm, mcmc_method=mcmc_method)\n\n# Sampling with variational inference\nsampling_algorithm = \"vi\"\nvi_method = \"rKL\"  # or fKL\nposterior = inference.build_posterior(sample_with=sampling_algorithm, vi_method=vi_method)\n# Unlike other methods, vi needs a training step for every observation.\nposterior = posterior.set_default_x(x_o).train()\n\n# Sampling with rejection sampling\nsampling_algorithm = \"rejection\"\nposterior = inference.build_posterior(sample_with=sampling_algorithm)\n</code></pre>"},{"location":"tutorial/11_sampler_interface/#more-flexibility-in-adjusting-the-sampler","title":"More flexibility in adjusting the sampler","text":"<p>With the above syntax, you can easily try out different sampling algorithms. However, in many cases, you might want to customize your sampler. Below, we demonstrate how you can change hyperparameters of the samplers (e.g. number of warm-up steps of MCMC) or how you can write your own sampler from scratch.</p>"},{"location":"tutorial/11_sampler_interface/#main-syntax-for-snle-and-snre","title":"Main syntax (for SNLE and SNRE)","text":"<p>As above, we begin by training the neural network as always:</p> <pre><code>import torch\n\nfrom sbi.inference import SNLE\n\n# dummy Gaussian simulator for demonstration\nnum_dim = 2\nprior = torch.distributions.MultivariateNormal(torch.zeros(num_dim), torch.eye(num_dim))\ntheta = prior.sample((1000,))\nx = theta + torch.randn((1000, num_dim))\nx_o = torch.randn((1, num_dim))\n\ninference = SNLE(show_progress_bars=False)\nlikelihood_estimator = inference.append_simulations(theta, x).train()\n</code></pre> <pre><code> Neural network successfully converged after 52 epochs.\n</code></pre> <p>Then, for full flexibility on using the sampler, we do not use the <code>.build_posterior()</code> method, but instead we explicitly define the potential function and the sampling algorithm (see below for explanation):</p> <pre><code>from sbi.inference import likelihood_estimator_based_potential, MCMCPosterior\n\npotential_fn, parameter_transform = likelihood_estimator_based_potential(\n    likelihood_estimator, prior, x_o\n)\nposterior = MCMCPosterior(\n    potential_fn, proposal=prior, theta_transform=parameter_transform, warmup_steps=10\n)\n</code></pre> <p>If you want to use variational inference or rejection sampling, you have to replace the last line with <code>VIPosterior</code> or <code>RejectionPosterior</code>:</p> <pre><code># For VI, we have to train.\nposterior = VIPosterior(\n    potential_fn, proposal=prior, theta_transform=parameter_transform\n).train()\n\nposterior = RejectionPosterior(\n    potential_fn, proposal=prior, theta_transform=parameter_transform\n)\n</code></pre> <p>At this point, you could also plug the <code>potential_fn</code> into any sampler of your choice and not rely on any of the in-built <code>sbi</code>-samplers.</p>"},{"location":"tutorial/11_sampler_interface/#further-explanation","title":"Further explanation","text":"<p>The first lines are the same as for the flexible interface:</p> <pre><code>inference = SNLE()\nlikelihood_estimator = inference.append_simulations(theta, x).train()\n</code></pre> <pre><code> Neural network successfully converged after 33 epochs.\n</code></pre> <p>Next, we obtain the potential function. A potential function is a function of the parameter \\(f(\\theta)\\). The posterior is proportional to the product of likelihood and prior: \\(p(\\theta | x_o) \\propto p(x_o | \\theta)p(\\theta)\\). The potential function is the logarithm of the right-hand side of this equation: \\(f(\\theta) = \\log(p(x_o | \\theta)p(\\theta))\\)</p> <pre><code>potential_fn, parameter_transform = likelihood_estimator_based_potential(\n    likelihood_estimator, prior, x_o\n)\n</code></pre> <p>By calling the <code>potential_fn</code>, you can evaluate the potential:</p> <pre><code># Assuming that your parameters are 1D.\npotential = potential_fn(\n    torch.zeros(1, num_dim)\n)  # -&gt; returns f(0) = log( p(x_o|0) p(0) )\n</code></pre> <p>The other object that is returned by <code>likelihood_estimator_based_potential</code> is a <code>parameter_transform</code>. The <code>parameter_transform</code> is a pytorch transform. The <code>parameter_transform</code> is a fixed transform that is can be applied to parameter <code>theta</code>. It transforms the parameters into unconstrained space (if the prior is bounded, e.g. <code>BoxUniform</code>), and standardizes the parameters (i.e. zero mean, one std). Using <code>parameter_transform</code> during sampling is optional, but it usually improves the performance of MCMC.</p> <pre><code>theta_tf = parameter_transform(torch.zeros(1, num_dim))\ntheta_original = parameter_transform.inv(theta_tf)\nprint(theta_original)  # -&gt; tensor([[0.0]])\n</code></pre> <pre><code>tensor([[0., 0.]])\n</code></pre> <p>After having obtained the <code>potential_fn</code>, we can sample from the posterior with MCMC or rejection sampling:</p> <pre><code>from sbi.inference import MCMCPosterior, RejectionPosterior\n\nposterior = MCMCPosterior(\n    potential_fn, proposal=prior, theta_transform=parameter_transform\n)\nposterior = RejectionPosterior(potential_fn, proposal=prior)\n</code></pre>"},{"location":"tutorial/11_sampler_interface/#main-syntax-for-snpe","title":"Main syntax for SNPE","text":"<p>SNPE usually does not require MCMC or rejection sampling (if you still need it, you can use the same syntax as above with the <code>posterior_estimator_based_potential</code> function). Instead, SNPE samples from the neural network. If the support of the prior is bounded, some samples can lie outside of the support of the prior. The <code>DirectPosterior</code> class automatically rejects these samples:</p> <pre><code>from sbi.inference import SNPE\nfrom sbi.inference import DirectPosterior\n\ninference = SNPE()\nposterior_estimator = inference.append_simulations(theta, x).train()\n\nposterior = DirectPosterior(posterior_estimator, prior=prior)\n</code></pre> <pre><code> Neural network successfully converged after 57 epochs.\n</code></pre> <pre><code>\n</code></pre>"},{"location":"tutorial/12_diagnostics_posterior_predictive_check/","title":"Posterior Predictive Checks (PPC) in SBI","text":"<p>A common safety check performed as part of inference are Posterior Predictive Checks (PPC). A PPC compares data \\(x_{\\text{pp}}\\) generated using the parameters \\(\\theta_{\\text{posterior}}\\) sampled from the posterior with the observed data \\(x_o\\). The general concept is that -if the inference is correct- the generated data \\(x_{\\text{pp}}\\) should \u201clook similar\u201d the oberved data \\(x_0\\). Said differently, \\(x_o\\) should be within the support of \\(x_{\\text{pp}}\\).</p> <p>A PPC usually shouldn\u2019t be used as a validation metric. Nonetheless a PPC is a good start for an inference diagnosis and can provide with an intuition about any bias introduced in inference: does \\(x_{\\text{pp}}\\) systematically differ from \\(x_o\\)?</p>"},{"location":"tutorial/12_diagnostics_posterior_predictive_check/#main-syntax","title":"Main syntax","text":"<pre><code>from sbi.analysis import pairplot\n\n# A PPC is performed after we trained or neural posterior\nposterior.set_default_x(x_o)\n\n# We draw theta samples from the posterior. This part is not in the scope of SBI\nposterior_samples = posterior.sample((5_000,))\n\n# We use posterior theta samples to generate x data\nx_pp = simulator(posterior_samples)\n\n# We verify if the observed data falls within the support of the generated data\n_ = pairplot(\n    samples=x_pp,\n    points=x_o\n)\n</code></pre>"},{"location":"tutorial/12_diagnostics_posterior_predictive_check/#performing-a-ppc-over-a-toy-example","title":"Performing a PPC over a toy example","text":"<p>Below we provide an example Posterior Predictive Check (PPC) over some toy example:</p> <pre><code>from sbi.analysis import pairplot\nimport torch\n\n_ = torch.manual_seed(0)\n</code></pre> <p>We work on an inference problem over three parameters using any of the techniques implemented in <code>sbi</code>. In this tutorial, we load the dummy posterior:</p> <pre><code>from toy_posterior_for_07_cc import ExamplePosterior\n\nposterior = ExamplePosterior()\n</code></pre> <p>Let us say that we are observing the data point \\(x_o\\):</p> <pre><code>D = 5  # simulator output was 5-dimensional\nx_o = torch.ones(1, D)\nposterior.set_default_x(x_o)\n</code></pre> <p>The posterior can be used to draw \\(\\theta_{\\text{posterior}}\\) samples:</p> <pre><code>posterior_samples = posterior.sample((5_000,))\n\nfig, ax = pairplot(\n    samples=posterior_samples,\n    limits=torch.tensor([[-2.5, 2.5]] * 3),\n    offdiag=[\"kde\"],\n    diag=[\"kde\"],\n    figsize=(5, 5),\n    labels=[rf\"$\\theta_{d}$\" for d in range(3)],\n)\n</code></pre> <p></p> <p>Now we can use our simulator to generate some data \\(x_{\\text{PP}}\\), using as input parameters the poterior samples \\(\\theta_{\\text{posterior}}\\). Note that the simulation part is not in the <code>sbi</code> scope, so any simulator -including a non-Python one- can be used at this stage. In our case we\u2019ll use a dummy simulator:</p> <pre><code>def dummy_simulator(posterior_samples: torch.Tensor, *args, **kwargs) -&gt; torch.Tensor:\n    sample_size = posterior_samples.shape[0]\n    scale = 1.0\n\n    shift = torch.distributions.Gumbel(loc=torch.zeros(D), scale=scale / 2).sample()\n    return torch.distributions.Gumbel(loc=x_o[0] + shift, scale=scale).sample(\n        (sample_size,)\n    )\n\n\nx_pp = dummy_simulator(posterior_samples)\n</code></pre> <p>Plotting \\(x_o\\) against the \\(x_{\\text{pp}}\\), we perform a PPC that plays the role of a sanity check. In this case, the check indicates that \\(x_o\\) falls right within the support of \\(x_{\\text{pp}}\\), which should make the experimenter rather confident about the estimated <code>posterior</code>:</p> <pre><code>_ = pairplot(\n    samples=x_pp,\n    points=x_o[0],\n    limits=torch.tensor([[-2.0, 5.0]] * 5),\n    points_colors=\"red\",\n    figsize=(8, 8),\n    offdiag=\"scatter\",\n    scatter_offdiag=dict(marker=\".\", s=5),\n    points_offdiag=dict(marker=\"+\", markersize=20),\n    labels=[rf\"$x_{d}$\" for d in range(D)],\n)\n</code></pre> <p></p> <p>In contrast, \\(x_o\\) falling well outside the support of \\(x_{\\text{pp}}\\) is indicative of a failure to estimate the correct posterior. Here we simulate such a failure mode:</p> <pre><code>error_shift = -2.0 * torch.ones(1, 5)\n\n_ = pairplot(\n    samples=x_pp,\n    points=x_o[0] + error_shift,\n    limits=torch.tensor([[-2.0, 5.0]] * 5),\n    points_colors=\"red\",\n    figsize=(8, 8),\n    offdiag=\"scatter\",\n    scatter_offdiag=dict(marker=\".\", s=5),\n    points_offdiag=dict(marker=\"+\", markersize=20),\n    labels=[rf\"$x_{d}$\" for d in range(D)],\n)\n</code></pre> <p></p> <p>A typical way to investigate this issue would be to run a prior* predictive check, applying the same plotting strategy, but drawing \\(\\theta\\) from the prior instead of the posterior. **The support for \\(x_{\\text{pp}}\\) should be larger and should contain \\(x_o\\)*. If this check is successful, the \u201cblame\u201d can then be shifted to the inference (method used, convergence of density estimators, number of sequential rounds, etc\u2026).</p>"},{"location":"tutorial/13_diagnostics_simulation_based_calibration/","title":"Simulation-based Calibration in SBI","text":"<p>After a density estimator has been trained with simulated data to obtain a posterior, the estimator should be made subject to several diagnostic tests, before being used for inference given the actual observed data. Posterior Predictive Checks (see tutorial 12) provide one way to \u201ccritique\u201d a trained estimator via its predictive performance. Another important approach to such diagnostics is simulation-based calibration as reported by Talts et al, 2018.</p> <p>Simulation-based calibration (SBC) provides a (qualitative) view and a quantitive measure to check, whether the uncertainties of the posterior are balanced, i.e., neither over-confident nor under-confident. As such, SBC can be viewed as a necessary condition (but not sufficient) for a valid inference algorithm: If SBC checks fail, this tells you that your inference is invalid. If SBC checks pass, this is no guarantee that the posterior estimation is working.</p>"},{"location":"tutorial/13_diagnostics_simulation_based_calibration/#in-a-nutshell","title":"In a nutshell","text":"<p>To run SBC,  </p> <ol> <li>we sample <code>theta_o_i</code> values from the prior of the problem at hand</li> <li>we simulate \u201cobservations\u201d from these parameters: <code>x_o_i = simulator(theta_o_i)</code> </li> <li>we perform inference given each observation <code>x_o_i</code>.</li> </ol> <p>This produces a separate posterior \\(p_i(\\theta | x_{o,i})\\) for each of <code>x_o_i</code>. The key step for SBC is to generate a set of posterior samples \\(\\{\\theta\\}_i\\) from each posterior (let\u2019s call this <code>theta_i_s</code>, referring to <code>s</code> samples from posterior \\(p_i(\\theta | x_{o,i})\\)), and to rank the corresponding <code>theta_o_i</code> under this set of samples. A rank is computed by counting how many samples <code>theta_i_s</code> fall below their corresponding <code>theta_o_i</code> (see section 4.1 in Talts et al.). These ranks are then used to perform the SBC check.</p>"},{"location":"tutorial/13_diagnostics_simulation_based_calibration/#key-ideas-behind-sbc","title":"Key ideas behind SBC","text":"<p>The core idea behind SBC is two fold: </p> <ul> <li> <p>SBC ranks of ground truth parameters under the inferred posterior samples follow a uniform distribution. (If the SBC ranks are not uniformly distributed, the posterior is not well calibrated.)</p> </li> <li> <p>samples from the data averaged posterior (ensemble of randomly chosen posterior samples given multiple distinct observations <code>x_o</code>) are distributed according to the prior</p> </li> </ul>"},{"location":"tutorial/13_diagnostics_simulation_based_calibration/#what-can-sbc-diagnose","title":"What can SBC diagnose?","text":"<p>SBC can inform us whether we are not wrong. However, it cannot tell us whether we are right, i.e., SBC checks a necessary condition. For example, imagine you run SBC using the prior as a posterior. The ranks would be perfectly uniform. But the inference would be wrong.</p> <p>The Posterior Predictive Checks (see tutorial 12) can be seen as the complementary sufficient check for the posterior (only as a methaphor, no theoretical guarantees here). Using the prior as a posterior and then doing predictive checks would clearly show that inference failed. </p> <p>To summarize SBC can:</p> <ul> <li>tell us whether the SBI method applied to the problem at hand produces posteriors that have well-calibrated uncertainties,</li> <li>and if not, what kind of systematic bias it has: negative or positive bias (shift in the mean of the predictions) or over- or underdispersion (too large or too small variance)</li> </ul>"},{"location":"tutorial/13_diagnostics_simulation_based_calibration/#a-healthy-posterior","title":"A healthy posterior","text":"<p>Let\u2019s take the gaussian linear simulator from the previous tutorials and run inference with NPE on it. </p> <p>Note: SBC requires running inference several times. Using SBC with amortized methods like NPE is hence a justified endavour: repeated inference is cheap and SBC can be performed with little runtime penalty. This does not hold for sequential methods or anything relying on MCMC or VI (here, parallelization is your friend, <code>num_workers&gt;1</code>).</p> <pre><code>import torch\n\nfrom torch import eye, ones, zeros\nfrom torch.distributions import MultivariateNormal\n\nfrom sbi.analysis import check_sbc, run_sbc, get_nltp, sbc_rank_plot\nfrom sbi.inference import SNPE, SNPE_C, prepare_for_sbi, simulate_for_sbi\nfrom sbi.simulators import linear_gaussian, diagonal_linear_gaussian\n</code></pre> <pre><code>num_dim = 2\nnum_simulations = 5_000\n\nprior_mean = ones(num_dim)\nprior_cov = 2 * eye(num_dim)\nprior = MultivariateNormal(\n    loc=prior_mean, covariance_matrix=prior_cov, validate_args=False\n)\n</code></pre>"},{"location":"tutorial/13_diagnostics_simulation_based_calibration/#an-ideal-case","title":"An ideal case","text":"<p>To explore SBC, we make our life easy and assume that we deal with a problem where the likelihood is modelled by an identity mapping and a bit of smear. But to start, we only use an almost vanishing smear of <code>0.01</code>.</p> <pre><code>default_likelihood_loc = 0.0  # let's start with 0 shift\ndefault_likelihood_scale = 0.01  # let's smear theta only by a little bit\n\n\ndef simulator(theta, loc=default_likelihood_loc, scale=default_likelihood_scale):\n\"\"\"linear gaussian inspired by sbibm\n    https://github.com/sbi-benchmark/sbibm/blob/15f068a08a938383116ffd92b92de50c580810a3/sbibm/tasks/gaussian_linear/task.py#L74\n    \"\"\"\n    num_dim = theta.shape[-1]\n    cov_ = scale * eye(num_dim)  # always positively semi-definite\n\n    # using validate_args=False disables sanity checks on `covariance_matrix`\n    # for the sake of speed\n    value = MultivariateNormal(\n        loc=(theta + loc), covariance_matrix=cov_, validate_args=False\n    ).sample()\n    return value\n\n\n_ = torch.manual_seed(3)\ntheta, x = simulate_for_sbi(simulator, prior, num_simulations)\n</code></pre> <pre><code>Running 5000 simulations.:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n</code></pre> <pre><code>_ = torch.manual_seed(1)\n\n# let's obtain an observation\ntheta_o = prior.sample((1,))\nx_o = simulator(theta_o)\nprint(\"theta:\", theta_o.numpy())\nprint(\"x    :\", x_o.numpy())\n</code></pre> <pre><code>theta: [[1.9352932 1.3774877]]\nx    : [[1.941461  1.4396194]]\n</code></pre> <pre><code>_ = torch.manual_seed(2)\n\n# we use a mdn model to have a fast turnaround with training.\ninferer = SNPE(prior, density_estimator=\"mdn\")\n# append simulations and run training.\ninferer.append_simulations(theta, x).train();\n</code></pre> <pre><code> Neural network successfully converged after 65 epochs.\n</code></pre> <pre><code>posterior = inferer.build_posterior()\nposterior_samples = posterior.sample((15_000,), x=x_o)\n# Generate predictive samples by simulating from posterior samples.\nposterior_predictive_samples = simulator(posterior_samples)\n</code></pre> <pre><code>Drawing 15000 posterior samples:   0%|          | 0/15000 [00:00&lt;?, ?it/s]\n</code></pre> <pre><code># let's do some posterior predictive checks to see if the\n# posterior predictive samples cluster aournd the observation `x_o`.\nfrom sbi.analysis import pairplot\n\nfig, ax = pairplot(\n    samples=posterior_predictive_samples,\n    points=x_o,\n    limits=list(zip(x_o.flatten() - 1.0, x_o.flatten() + 1.0)),\n    offdiag=\"kde\",\n    diag=\"kde\",\n    figsize=(5, 5),\n    labels=[rf\"$x_{d}$\" for d in range(3)],\n)\n</code></pre> <p></p> <p>The observation <code>x_o</code> falls into the support of the predicted posterior samples, i.e. it is within <code>simulator(posterior_samples)</code>. Given the simulator, this is indicative that our posterior estimates the data well.</p>"},{"location":"tutorial/13_diagnostics_simulation_based_calibration/#running-sbc","title":"Running SBC","text":"<p>We have a working and trained posterior at this point! Hurray! Let\u2019s look at the SBC metrics now.</p> <pre><code>num_sbc_runs = 1_000  # choose a number of sbc runs, should be ~100s or ideally 1000\n# generate ground truth parameters and corresponding simulated observations for SBC.\nthetas = prior.sample((num_sbc_runs,))\nxs = simulator(thetas)\n</code></pre> <p>SBC is implemented in <code>sbi</code> for your use on any <code>sbi</code> posterior. To run it, we only need to call <code>run_sbc</code> with appropriate parameters. </p> <p>Note: For amortized neural posteriors (like in this tutorial), execution of <code>sbc</code> is expected to be fast. For posteriors that conduct inference with MCMC and hence are slow, <code>run_sbc</code> exposes the use of multiple internal parallel workers to the user. To use this feature, add <code>num_workers = 2</code> to the parameters for use of two workers. See the API documentation for details.</p> <pre><code># run SBC: for each inference we draw 1000 posterior samples.\nnum_posterior_samples = 1_000\nranks, dap_samples = run_sbc(\n    thetas, xs, posterior, num_posterior_samples=num_posterior_samples\n)\n</code></pre> <pre><code>Running 1000 sbc samples.:   0%|          | 0/1000 [00:00&lt;?, ?it/s]\n</code></pre> <p><code>sbi</code> establishes two methods to do simulation-based calibration:</p> <ul> <li>metrics to compare the sbc ranks with a uniform distribution </li> <li>control plots for visual inspections like fig. 1 or 2 in Talts et al, 2018</li> </ul> <p>The <code>ranks</code> count is performed per dimension of <code>theta</code>, i.e. on the 1-D marginal posterior estimates. According to theory, the distribution of these ranks (per dimension of <code>theta</code>) should turn out to be uniformly distributed. </p> <p>The data average posterior <code>dap</code> (see equation 1 of Talts et al, 2018) is yet another metric of interest. It is built from singular random samples of the estimated posterior samples for each <code>xs</code> above. The <code>dap</code> is expected to match the prior distribution used (see equation 1 in Talts et al, 2018 too). </p> <pre><code>check_stats = check_sbc(\n    ranks, thetas, dap_samples, num_posterior_samples=num_posterior_samples\n)\n</code></pre> <p>The <code>check_stats</code> variable created contains a dictionary with 3 metrics that help to judge our posterior. The \u201cfirst\u201d two compare the ranks to a uniform distribution.</p>"},{"location":"tutorial/13_diagnostics_simulation_based_calibration/#ranks-versus-uniform-distribution","title":"Ranks versus Uniform distribution","text":"<pre><code>print(\n    f\"kolmogorov-smirnov p-values \\ncheck_stats['ks_pvals'] = {check_stats['ks_pvals'].numpy()}\"\n)\n</code></pre> <pre><code>kolmogorov-smirnov p-values \ncheck_stats['ks_pvals'] = [0.1261853  0.07933955]\n</code></pre> <p>The Kolmogorov-Smirnov (KS test, see also here) as used by <code>check_sbc</code> provides p-values <code>pvals</code> on the null hypothesis that the samples from <code>ranks</code> are drawn from a uniform distribution (in other words <code>H_0: PDF(ranks) == PDF(uniform)</code>). We are provided two values as our problem is two-dimensional - one p-value for each dimension. </p> <p>The null hypothesis (of both distributions being equal) is rejected if the p-values fall below a significance threshold (usually <code>&lt; 0.05</code>). Therefor, vanishing p-values (<code>ks_pvals=0</code>) are interpreted to indicate a vanishing false positive rate to (mistakenly) consider both distrubtions being \u201cequal\u201d. Both values are above <code>0.05</code> and we, therefore, cannot claim that inference clearly went wrong. Nonetheless, we can add additional checks:</p> <pre><code>print(\n    f\"c2st accuracies \\ncheck_stats['c2st_ranks'] = {check_stats['c2st_ranks'].numpy()}\"\n)\n</code></pre> <pre><code>c2st accuracies \ncheck_stats['c2st_ranks'] = [0.604  0.5935]\n</code></pre> <p>The second tier of metrics comparing <code>ranks</code> with a uniform distributions is a <code>c2st</code> test (see here for details). This is a nonparametric two sample test based on training a classifier to differented one of the ensembles (<code>ranks</code> versus samples from a uniform distribution) by being trained on the other. The values reported are the accuracies from cross-validation. If you see values around <code>0.5</code>, the classifier was unable to differentiate both ensembles, i.e. <code>ranks</code> are very uniform. If the values are high towards <code>1</code>, this matches the case where <code>ranks</code> is very unlike a uniform distribution.</p>"},{"location":"tutorial/13_diagnostics_simulation_based_calibration/#data-averaged-posterior-dap-versus-prior","title":"Data averaged posterior (DAP) versus prior","text":"<pre><code>print(f\"- c2st accuracies check_stats['c2st_dap'] = {check_stats['c2st_dap'].numpy()}\")\n</code></pre> <pre><code>- c2st accuracies check_stats['c2st_dap'] = [0.5195 0.4785]\n</code></pre> <p>The last metric reported is again based on <code>c2st</code> computed per dimension of <code>theta</code>. If you see values around <code>0.5</code>, the <code>c2st</code> classifier was unable to differentiate both ensembles for each dimension of <code>theta</code>, i.e. <code>dap</code> are much like (if not identical to) the prior. If the values are very high towards <code>1</code>, this represents the case where <code>dap</code> is very unlike the prior distribution.</p>"},{"location":"tutorial/13_diagnostics_simulation_based_calibration/#visual-inspection","title":"Visual Inspection","text":"<pre><code>from sbi.analysis import sbc_rank_plot\n\nf, ax = sbc_rank_plot(\n    ranks=ranks,\n    num_posterior_samples=num_posterior_samples,\n    plot_type=\"hist\",\n    num_bins=None,  # by passing None we use a heuristic for the number of bins.\n)\n</code></pre> <p>The two plots visualize the distribution of <code>ranks</code> (here depicted in red) in each dimension. Highlighted in grey you see the 99% confidence interval of a uniform distribution given the number of samples provided. In plain english: for a uniform distribution, we would expect 1 out of 100 (red) bars to lie outside the grey area.</p> <p>We also observe, that the entries fluctuate to some degree. This can be considered a hint that <code>sbc</code> should be conducted with a lot more samples than 1000. A good rule of thumb is that given the number of bins <code>B</code> and the number of SBC samples <code>N</code> (chosed to be <code>1_000</code> here) should amount to <code>N / B ~ 20</code>.</p> <pre><code>f, ax = sbc_rank_plot(ranks, 1_000, plot_type=\"cdf\")\n</code></pre> <p></p> <p>The above provides a visual representation of the cumulative density function (CDF) of <code>ranks</code> (blue and orange for each dimension of <code>theta</code>) with respect to the 95% confidence interval of a uniform distribution (grey).</p>"},{"location":"tutorial/13_diagnostics_simulation_based_calibration/#when-things-go-haywire","title":"When things go haywire","text":"<p>Next, we would like to explore some pathologies visible in sbc plots which can hint at our estimated posterior being somewhat wrong or completely off.</p>"},{"location":"tutorial/13_diagnostics_simulation_based_calibration/#a-shifted-posterior-mean","title":"A shifted posterior mean","text":"<p>In this scenario we emulate the situation that our posterior estimates incorrectly with a constant shift. We reuse our trained NPE posterior from above and wrap it so that all samples returned expose a constant shift by <code>+0.1</code>.</p> <pre><code>from utils_13_diagnosis_sbc import BiasedPosterior\n\n# this posterior shifts the expected value of the prior by .1\nposterior_ = BiasedPosterior(posterior, shift=0.1)\n</code></pre> <pre><code>ranks, dap_samples = run_sbc(thetas, xs, posterior_)\ncheck_stats = check_sbc(ranks, thetas, dap_samples, 1_000)\nprint(check_stats)\n</code></pre> <pre><code>Running 1000 sbc samples.:   0%|          | 0/1000 [00:00&lt;?, ?it/s]\n\n\n{'ks_pvals': tensor([0., 0.]), 'c2st_ranks': tensor([0.6815, 0.6730]), 'c2st_dap': tensor([0.5025, 0.4935])}\n</code></pre> <p>We can see that the Kolmogorv-Smirnov p-values vanish (<code>'ks_pvals': tensor([0., 0.])</code>). Thus, we can reject the hypothesis that the <code>ranks</code> PDF is the uniform PDF. The <code>c2st</code> accuracies show values higher than <code>0.5</code>. This is indicative that the <code>ranks</code> distribution is not a uniform PDF as well.</p> <pre><code>f, ax = sbc_rank_plot(ranks, 1_000, plot_type=\"hist\", num_bins=30)\n</code></pre> <p></p> <p>Inspecting the histograms for both dimenions, the rank distribution is clearly tilted to low rank values for both dimensions. Because we have shifted the expected value of the posterior to higher values (by <code>0.1</code>), we see more entries at low rank values.</p> <p>Let\u2019s try to shift all posterior samples in the opposite direction. We shift the expectation value by <code>-0.1</code>:</p> <pre><code>posterior_ = BiasedPosterior(posterior, shift=-0.1)\n</code></pre> <pre><code>ranks, dap_samples = run_sbc(thetas, xs, posterior_)\ncheck_stats = check_sbc(ranks, thetas, dap_samples, 1_000)\nprint(check_stats)\nf, ax = sbc_rank_plot(ranks, 1_000, plot_type=\"hist\", num_bins=30)\n</code></pre> <pre><code>Running 1000 sbc samples.:   0%|          | 0/1000 [00:00&lt;?, ?it/s]\n\n\n{'ks_pvals': tensor([0., 0.]), 'c2st_ranks': tensor([0.6795, 0.6955]), 'c2st_dap': tensor([0.4910, 0.4955])}\n</code></pre> <p></p> <p>A similar behavior is observed, but this time we see an upshot of ranks to higher values. Because we have shifted the expected value of the posterior to smaller values, we see an upshot in high rank counts.</p> <p>It is interesting to see that the historgams obtained provide very convincing evidence that this is not a uniform distribution. </p> <p>To conlude at this point, the rank distribution is capable of identifying pathologies of the estimated posterior:</p> <ul> <li>a left-skewed rank distribution shows a systematic underestimation of the posterior mean (we shifted the posterior by <code>0.1</code>)</li> <li>a rank-skewed rank distribution shows a systematic overestimation of the posterior mean (we shifted the posterior by <code>-0.1</code>)</li> </ul>"},{"location":"tutorial/13_diagnostics_simulation_based_calibration/#a-dispersed-posterior","title":"A dispersed posterior","text":"<p>In this scenario we emulate the situation if our posterior estimates incorrectly with a dispersion, i.e. the posterior is too wide or too thin. We reuse our trained NPE posterior from above and wrap it so that all samples return a dispersion by 100% more wide (<code>2</code>), i.e. the variance is overestimated by a factor of 2.</p> <pre><code>from utils_13_diagnosis_sbc import DispersedPosterior\n\n# this posterior which disperses the expected posterior value of the prior by 2.\nposterior_ = DispersedPosterior(posterior, dispersion=2.0)\n</code></pre> <pre><code>ranks, dap_samples = run_sbc(thetas, xs, posterior_)\ncheck_stats = check_sbc(ranks, thetas, dap_samples, 1_000)\nprint(check_stats)\nf, ax = sbc_rank_plot(ranks, 1_000, plot_type=\"hist\", num_bins=30)\n</code></pre> <pre><code>Running 1000 sbc samples.:   0%|          | 0/1000 [00:00&lt;?, ?it/s]\n\n\n{'ks_pvals': tensor([8.2151e-09, 6.9635e-07]), 'c2st_ranks': tensor([0.6150, 0.6160]), 'c2st_dap': tensor([0.5050, 0.4905])}\n</code></pre> <p></p> <p>The rank histograms now look more like a very wide gaussian distribution centered in the middle. The KS p-values again vanish unsurprisingly (we must reject the hypothesis that both distributions are from the same uniform PDF) and the c2st_ranks indicate that the rank histogram is not uniform too. As our posterior samples are distributed too broad now, we obtain more \u201cmedium\u201d range ranks and hence produce the peak of ranks in the center of the histogram.</p> <p>We can repeat this exercise by making our posterior too thin, i.e. the variance of the posterior is too small. Let\u2019s cut it by half.</p> <pre><code>posterior_ = DispersedPosterior(posterior, dispersion=0.5)\n</code></pre> <pre><code>ranks, dap_samples = run_sbc(thetas, xs, posterior_)\ncheck_stats = check_sbc(ranks, thetas, dap_samples, 1_000)\nprint(check_stats)\nf, ax = sbc_rank_plot(ranks, 1_000, plot_type=\"hist\", num_bins=30)\n</code></pre> <pre><code>Running 1000 sbc samples.:   0%|          | 0/1000 [00:00&lt;?, ?it/s]\n\n\n{'ks_pvals': tensor([1.1753e-07, 1.7929e-08]), 'c2st_ranks': tensor([0.5755, 0.6125]), 'c2st_dap': tensor([0.4980, 0.5075])}\n</code></pre> <p></p> <p>The histogram of ranks now shoots above the allowed (greyed) area for a uniform distributed around the extrema. We made the posterior samples too thin, so we received more extreme counts of ranks. The KS p-values vanish again and the <code>c2st</code> metric of the ranks is also larger than <code>.5</code> which underlines that our rank distribution is not uniformly distributed.</p> <p>We again see, the rank distribution is capable of identifying pathologies of the estimated posterior:</p> <ul> <li>a centrally peaked rank distribution shows a systematic over-estimation of the posterior variance (we dispersed the variance of the posterior by a factor of <code>2</code>)</li> <li>a U shaped rank distribution shows a systematic under-estimation of the posterior variance (we dispersed the variance of the posterior by a factor of <code>.5</code>)</li> </ul> <p>Simulation-based calibration offers a direct handle on which pathology an estimated posterior examines. Outside of this tutorial, you may very well encounter situations with mixtures of effects (a shifted mean and over-estimated variance). Moreover, uncovering a malignant posterior is only the first step to fix your analysis. </p> <pre><code>\n</code></pre>"},{"location":"tutorial/14_iid_data_and_permutation_invariant_embeddings/","title":"SBI with iid data and permutation-invariant embeddings","text":"<p>There are scenarios in which we observe multiple data points per experiment and we can assume that they are independent and identically distributed (iid, i.e., they are assumed to have the same underlying model parameters).  For example, in decision-making experiments, the experiment is often repeated in trials with the same experimental settings and conditions. The corresponding set of trials is then assumed to be \u201ciid\u201d given a single parameter set.  In such a scenario, we may want to obtain the posterior given a set of observation \\(p(\\theta | X=\\{x_i\\}_i^N)\\). </p>"},{"location":"tutorial/14_iid_data_and_permutation_invariant_embeddings/#amortization-of-neural-network-training-iid-inference-with-nle-nre","title":"Amortization of neural network training: iid-inference with NLE / NRE","text":"<p>For some SBI variants the iid assumption can be exploited: when using a likelihood-based SBI method (<code>SNLE</code>, <code>SNRE</code>) one can train the density or ratio estimator on single-trial data, and then perform inference with <code>MCMC</code> or variational inference (<code>VI</code>). Crucially, because the data is iid and the estimator is trained on single-trial data, one can repeat the inference with a different <code>x_o</code> (a different set of trials, or different number of trials) without having to retrain the density estimator. One can interpet this as amortization of the SBI training: we can obtain a neural likelihood, or likelihood-ratio estimate for new <code>x_o</code>s without retraining, but we still have to run <code>MCMC</code> or <code>VI</code> to do inference. </p> <p>In addition, one cannot only change the number of trials of a new <code>x_o</code>, but also the entire inference setting.  For example, one can apply hierarchical inference with changing hierarchical denpendencies between the model parameters\u2013all without having to retrain the density estimator because it estimates single-trail likelihoods.</p>"},{"location":"tutorial/14_iid_data_and_permutation_invariant_embeddings/#full-amortization-iid-inference-with-npe-and-permutation-invariant-embedding-nets","title":"Full amortization: iid-inference with NPE and permutation-invariant embedding nets","text":"<p>When performing neural posterior estimation (<code>SNPE</code>) we cannot exploit the iid assumption directly.  Thus, the underlying neural network takes <code>x</code> as input and predicts the parameters of the density estimator.  As a consequence, if <code>x</code> is a set of iid observations \\(X=\\{x_i\\}_i^N\\) then the neural network has to be invariant to permutations of this set, i.e., it has to be permutation invariant. In addition, the neural network has to be able to consume a varying number of iid datapoints in order to be amortized over the number of trials. Therefore, in order to use <code>SNPE</code> for inference on iid data, we need to provide a corresponding embedding network that handles the iid-data.  This will likely require some hyperparameter tuning and more training data for inference to work accurately. But once we have this, inference is fully amortized, i.e., we can get new posterior samples almost instantly without retraining and without running <code>MCMC</code> or <code>VI</code>. </p>"},{"location":"tutorial/14_iid_data_and_permutation_invariant_embeddings/#sbi-with-trial-based-data","title":"SBI with trial-based data","text":"<p>For illustration, we use a simple linear Gaussian simulator, as in previous tutorials. The simulator takes a single parameter (vector) which is the mean of a Gaussian. The simulator then adds noise with a fixed variance (set to one).  We define a Gaussian prior over the mean and perform inference. </p> <p>The observed data is also sampled from a Gaussian with some fixed \u201cground-truth\u201d parameter \\(\\theta_o\\).  Crucially, the observed data <code>x_o</code> can consist of multiple samples given the same ground-truth parameters and these samples are iid given \\(\\theta\\): </p> \\[  \\theta \\sim \\mathcal{N}(\\mu_0,\\; \\Sigma_0) \\\\ x | \\theta \\sim \\mathcal{N}(\\theta,\\; \\Sigma=I) \\\\ \\mathbf{x_o} = \\{x_o^i\\}_{i=1}^N \\sim  \\mathcal{N}(\\theta_o,\\; \\Sigma=I) \\] <p>For this toy problem, the ground-truth posterior is well defined, it is again a Gaussian, centered on the mean of \\(\\mathbf{x_o}\\) and with variance scaled by the number of trials \\(N\\), i.e., the more trials we observe, the more information about the underlying \\(\\theta_o\\) we have and the more concentrated the posteriors becomes.</p> <p>We will illustrate this below:</p> <pre><code>import torch\nimport matplotlib.pyplot as plt\n\nfrom torch import zeros, ones, eye\nfrom torch.distributions import MultivariateNormal\nfrom sbi.inference import SNLE, SNPE, prepare_for_sbi, simulate_for_sbi\nfrom sbi.analysis import pairplot\nfrom sbi.utils.metrics import c2st\n\nfrom sbi.simulators.linear_gaussian import (\n    linear_gaussian,\n    true_posterior_linear_gaussian_mvn_prior,\n)\n\n# Seeding\ntorch.manual_seed(1);\n</code></pre> <pre><code># Gaussian simulator\ntheta_dim = 2\nx_dim = theta_dim\n\n# likelihood_mean will be likelihood_shift+theta\nlikelihood_shift = -1.0 * zeros(x_dim)\nlikelihood_cov = 0.3 * eye(x_dim)\n\nprior_mean = zeros(theta_dim)\nprior_cov = eye(theta_dim)\nprior = MultivariateNormal(loc=prior_mean, covariance_matrix=prior_cov)\n\n# Define Gaussian simulator\nsimulator, prior = prepare_for_sbi(\n    lambda theta: linear_gaussian(theta, likelihood_shift, likelihood_cov), prior\n)\n\n\n# Use built-in function to obtain ground-truth posterior given x_o\ndef get_true_posterior_samples(x_o, num_samples=1):\n    return true_posterior_linear_gaussian_mvn_prior(\n        x_o, likelihood_shift, likelihood_cov, prior_mean, prior_cov\n    ).sample((num_samples,))\n</code></pre>"},{"location":"tutorial/14_iid_data_and_permutation_invariant_embeddings/#the-analytical-posterior-concentrates-around-true-parameters-with-increasing-number-of-iid-trials","title":"The analytical posterior concentrates around true parameters with increasing number of IID trials","text":"<pre><code>num_trials = [1, 5, 15, 20]\ntheta_o = zeros(1, theta_dim)\n\n# Generate multiple x_os with increasing number of trials.\nxos = [theta_o.repeat(nt, 1) for nt in num_trials]\n\n# Obtain analytical posterior samples for each of them.\ntrue_samples = [get_true_posterior_samples(xo, 5000) for xo in xos]\n</code></pre> <pre><code># Plot them in one pairplot as contours (obtained via KDE on the samples).\nfig, ax = pairplot(\n    true_samples,\n    points=theta_o,\n    diag=\"kde\",\n    upper=\"contour\",\n    kde_offdiag=dict(bins=50),\n    kde_diag=dict(bins=100),\n    contour_offdiag=dict(levels=[0.95]),\n    points_colors=[\"k\"],\n    points_offdiag=dict(marker=\"*\", markersize=10),\n)\nplt.sca(ax[1, 1])\nplt.legend(\n    [f\"{nt} trials\" if nt &gt; 1 else f\"{nt} trial\" for nt in num_trials]\n    + [r\"$\\theta_o$\"],\n    frameon=False,\n    fontsize=12,\n);\n</code></pre> <p>Indeed, with increasing number of trials the posterior density concentrates around the true underlying parameter.</p>"},{"location":"tutorial/14_iid_data_and_permutation_invariant_embeddings/#iid-inference-with-nle","title":"IID inference with NLE","text":"<p>(S)NLE and (S)NRE can perform inference given multiple IID obserations by using only single-trial training data (i.e., for training, we run the simulator only once per parameter set). Once the likelihood is learned on single trials (i.e., a neural network that predicts the likelihood of a single observation given a parameter set), one can sample the posterior for any number of trials. This works because, given a single-trial neural likelihood from (S)NLE or (S)NRE, we can calculate the joint likelihoods of all trials by multiplying them together (or adding them in log-space). The joint likelihood can then be plugged into <code>MCMC</code> or <code>VI</code>. <code>sbi</code> takes care of all of these steps, so you do not have to implement anything yourself:</p> <pre><code># Train SNLE.\ninferer = SNLE(prior, show_progress_bars=True, density_estimator=\"mdn\")\ntheta, x = simulate_for_sbi(simulator, prior, 10000, simulation_batch_size=1000)\ninferer.append_simulations(theta, x).train(training_batch_size=1000);\n</code></pre> <pre><code>Running 10000 simulations.:   0%|          | 0/10000 [00:00&lt;?, ?it/s]\n\n\n Neural network successfully converged after 43 epochs.\n</code></pre> <pre><code># Obtain posterior samples for different number of iid xos.\nnle_samples = []\nnum_samples = 5000\n\nmcmc_parameters = dict(\n    num_chains=50,\n    thin=10,\n    warmup_steps=50,\n    init_strategy=\"proposal\",\n)\nmcmc_method = \"slice_np_vectorized\"\n\nposterior = inferer.build_posterior(\n    mcmc_method=mcmc_method,\n    mcmc_parameters=mcmc_parameters,\n)\n\n# Generate samples with MCMC given the same set of x_os as above.\nfor xo in xos:\n    nle_samples.append(posterior.sample(sample_shape=(num_samples,), x=xo))\n</code></pre> <pre><code>Running vectorized MCMC with 50 chains:   0%|          | 0/75000 [00:00&lt;?, ?it/s]\n\n\n/Users/janbolts/qode/sbi/sbi/utils/sbiutils.py:342: UserWarning: An x with a batch size of 5 was passed. It will be interpreted as a batch of independent and identically\n            distributed data X={x_1, ..., x_n}, i.e., data generated based on the\n            same underlying (unknown) parameter. The resulting posterior will be with\n            respect to entire batch, i.e,. p(theta | X).\n  warnings.warn(\n\n\n\nRunning vectorized MCMC with 50 chains:   0%|          | 0/75000 [00:00&lt;?, ?it/s]\n\n\n/Users/janbolts/qode/sbi/sbi/utils/sbiutils.py:342: UserWarning: An x with a batch size of 15 was passed. It will be interpreted as a batch of independent and identically\n            distributed data X={x_1, ..., x_n}, i.e., data generated based on the\n            same underlying (unknown) parameter. The resulting posterior will be with\n            respect to entire batch, i.e,. p(theta | X).\n  warnings.warn(\n\n\n\nRunning vectorized MCMC with 50 chains:   0%|          | 0/75000 [00:00&lt;?, ?it/s]\n\n\n/Users/janbolts/qode/sbi/sbi/utils/sbiutils.py:342: UserWarning: An x with a batch size of 20 was passed. It will be interpreted as a batch of independent and identically\n            distributed data X={x_1, ..., x_n}, i.e., data generated based on the\n            same underlying (unknown) parameter. The resulting posterior will be with\n            respect to entire batch, i.e,. p(theta | X).\n  warnings.warn(\n\n\n\nRunning vectorized MCMC with 50 chains:   0%|          | 0/75000 [00:00&lt;?, ?it/s]\n</code></pre> <p>Note that <code>sbi</code> warns about <code>iid-x</code> with increasing number of trial here. We ignore the warning because that\u2019s exactly what we want to do.</p> <pre><code># Plot them in one pairplot as contours (obtained via KDE on the samples).\nfig, ax = pairplot(\n    nle_samples,\n    points=theta_o,\n    diag=\"kde\",\n    upper=\"contour\",\n    kde_offdiag=dict(bins=50),\n    kde_diag=dict(bins=100),\n    contour_offdiag=dict(levels=[0.95]),\n    points_colors=[\"k\"],\n    points_offdiag=dict(marker=\"*\", markersize=10),\n)\nplt.sca(ax[1, 1])\nplt.legend(\n    [f\"{nt} trials\" if nt &gt; 1 else f\"{nt} trial\" for nt in num_trials]\n    + [r\"$\\theta_o$\"],\n    frameon=False,\n    fontsize=12,\n);\n</code></pre> <p></p> <p>The pairplot above already indicates that (S)NLE is well able to obtain accurate posterior samples also for increasing number of trials (note that we trained the single-round version of SNLE so that we did not have to re-train it for new \\(x_o\\)). </p> <p>Quantitatively we can measure the accuracy of SNLE by calculating the <code>c2st</code> score between SNLE and the true posterior samples, where the best accuracy is perfect for <code>0.5</code>:</p> <pre><code>cs = [\n    c2st(torch.from_numpy(s1), torch.from_numpy(s2))\n    for s1, s2 in zip(true_samples, nle_samples)\n]\n\nfor _ in range(len(num_trials)):\n    print(f\"c2st score for num_trials={num_trials[_]}: {cs[_].item():.2f}\")\n</code></pre> <pre><code>c2st score for num_trials=1: 0.50\nc2st score for num_trials=5: 0.51\nc2st score for num_trials=15: 0.51\nc2st score for num_trials=20: 0.51\n</code></pre>"},{"location":"tutorial/14_iid_data_and_permutation_invariant_embeddings/#iid-inference-with-npe-using-permutation-invariant-embedding-nets","title":"IID inference with NPE using permutation-invariant embedding nets","text":"<p>For NPE we need to define an embedding net that handles the set-like structure of iid-data, i.e., that it permutation invariant and can handle different number of trials.  </p> <p>We implemented several embedding net classes that allow to construct such a permutation- and number-of-trials invariant embedding net. </p> <p>To become permutation invariant, the neural net first learns embeddings for single trials and then performs a permutation invariant operation on those embeddings, e.g., by taking the sum or the mean (Chen et al. 2018, Radev et al. 2021).</p> <p>To become invariant w.r.t. the number-of-trials, we train the net with varying number of trials for each parameter setting. This means that, unlike for (S)NLE and (S)NRE, (S)NPE requires to run the simulator multiple times for individual parameter sets to generate the training data.</p> <p>In order to implement this in <code>sbi</code>, \u201cunobserved\u201d trials in the training dataset have to be masked by NaNs (and ignore the resulting SBI warning about NaNs in the training data).</p>"},{"location":"tutorial/14_iid_data_and_permutation_invariant_embeddings/#construct-training-data-set","title":"Construct training data set.","text":"<pre><code># we need to fix the maximum number of trials.\nmax_num_trials = 20\n\n# construct training data set: we want to cover the full range of possible number of\n# trials\nnum_training_samples = 5000\ntheta = prior.sample((num_training_samples,))\n\n# there are certainly smarter ways to construct the training data set, but we go with a\n# for loop here for illustration purposes.\nx = torch.ones(num_training_samples * max_num_trials, max_num_trials, x_dim) * float(\n    \"nan\"\n)\nfor i in range(num_training_samples):\n    xi = simulator(theta[i].repeat(max_num_trials, 1))\n    for j in range(max_num_trials):\n        x[i * max_num_trials + j, : j + 1, :] = xi[: j + 1, :]\n\ntheta = theta.repeat_interleave(max_num_trials, dim=0)\n</code></pre>"},{"location":"tutorial/14_iid_data_and_permutation_invariant_embeddings/#build-embedding-net","title":"Build embedding net","text":"<pre><code>from sbi.neural_nets.embedding_nets import (\n    FCEmbedding,\n    PermutationInvariantEmbedding,\n)\nfrom sbi.utils import posterior_nn\n\n# embedding\nlatent_dim = 10\nsingle_trial_net = FCEmbedding(\n    input_dim=theta_dim,\n    num_hiddens=40,\n    num_layers=2,\n    output_dim=latent_dim,\n)\nembedding_net = PermutationInvariantEmbedding(\n    single_trial_net,\n    trial_net_output_dim=latent_dim,\n    # NOTE: post-embedding is not needed really.\n    num_layers=1,\n    num_hiddens=10,\n    output_dim=10,\n)\n\n# we choose a simple MDN as the density estimator.\n# NOTE: we turn off z-scoring of the data, as we used NaNs for the missing trials.\ndensity_estimator = posterior_nn(\"mdn\", embedding_net=embedding_net, z_score_x=\"none\")\n</code></pre>"},{"location":"tutorial/14_iid_data_and_permutation_invariant_embeddings/#run-training","title":"Run training","text":"<pre><code>inference = SNPE(prior, density_estimator=density_estimator)\n# NOTE: we don't exclude invalid x because we used NaNs for the missing trials.\ninference.append_simulations(\n    theta,\n    x,\n    exclude_invalid_x=False,\n).train(training_batch_size=1000)\nposterior = inference.build_posterior()\n</code></pre> <pre><code>WARNING:root:Found 95000 NaN simulations and 0 Inf simulations. They are not excluded from training due to `exclude_invalid_x=False`.Training will likely fail, we strongly recommend `exclude_invalid_x=True` for Single-round NPE.\n\n\n Neural network successfully converged after 168 epochs.\n</code></pre>"},{"location":"tutorial/14_iid_data_and_permutation_invariant_embeddings/#amortized-inference","title":"Amortized inference","text":"<p>Comparing runtimes, we see that the NPE training takes a bit longer than the training on single trials for <code>NLE</code> above. </p> <p>However, we trained the density estimator such that it can handle multiple and changing number of iid trials (up to 20). </p> <p>Thus, we can obtain posterior samples for different <code>x_o</code> with just a single forward pass instead of having to run <code>MCMC</code> for each new observation.</p> <p>As you can see below, the c2st score for increasing number of observed trials remains close to the ideal <code>0.5</code>. </p> <pre><code>npe_samples = []\nfor xo in xos:\n    # we need to pad the x_os with NaNs to match the shape of the training data.\n    xoi = torch.ones(1, max_num_trials, x_dim) * float(\"nan\")\n    xoi[0, : len(xo), :] = xo\n    npe_samples.append(posterior.sample(sample_shape=(num_samples,), x=xoi))\n\ncs = [c2st(torch.from_numpy(s1), s2) for s1, s2 in zip(true_samples, npe_samples)]\n\nfor _ in range(len(num_trials)):\n    print(f\"c2st score for num_trials={num_trials[_]}: {cs[_].item():.2f}\")\n</code></pre> <pre><code>Drawing 5000 posterior samples:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n\n\n\nDrawing 5000 posterior samples:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n\n\n\nDrawing 5000 posterior samples:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n\n\n\nDrawing 5000 posterior samples:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n\n\nc2st score for num_trials=1: 0.50\nc2st score for num_trials=5: 0.50\nc2st score for num_trials=15: 0.52\nc2st score for num_trials=20: 0.55\n</code></pre> <pre><code>num_trials = [1, 5, 15, 20]\nxos = [theta_o.repeat(nt, 1) for nt in num_trials]\n\nnpe_samples = []\nfor xo in xos:\n    # we need to pad the x_os with NaNs to match the shape of the training data.\n    xoi = torch.ones(1, max_num_trials, x_dim) * float(\"nan\")\n    xoi[0, : len(xo), :] = xo\n    npe_samples.append(posterior.sample(sample_shape=(num_samples,), x=xoi))\n\n\n# Plot them in one pairplot as contours (obtained via KDE on the samples).\nfig, ax = pairplot(\n    npe_samples,\n    points=theta_o,\n    diag=\"kde\",\n    upper=\"contour\",\n    kde_offdiag=dict(bins=50),\n    kde_diag=dict(bins=100),\n    contour_offdiag=dict(levels=[0.95]),\n    points_colors=[\"k\"],\n    points_offdiag=dict(marker=\"*\", markersize=10),\n)\nplt.sca(ax[1, 1])\nplt.legend(\n    [f\"{nt} trials\" if nt &gt; 1 else f\"{nt} trial\" for nt in num_trials]\n    + [r\"$\\theta_o$\"],\n    frameon=False,\n    fontsize=12,\n);\n</code></pre> <pre><code>Drawing 5000 posterior samples:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n\n\n\nDrawing 5000 posterior samples:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n\n\n\nDrawing 5000 posterior samples:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n\n\n\nDrawing 5000 posterior samples:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n</code></pre> <p></p> <pre><code># We can easily obtain posteriors for many different x_os, instantly, because NPE is fully amortized:\nnum_trials = [2, 4, 6, 8, 12, 14, 18]\nnpe_samples = []\nfor xo in xos:\n    # we need to pad the x_os with NaNs to match the shape of the training data.\n    xoi = torch.ones(1, max_num_trials, x_dim) * float(\"nan\")\n    xoi[0, : len(xo), :] = xo\n    npe_samples.append(posterior.sample(sample_shape=(num_samples,), x=xoi))\n\n\n# Plot them in one pairplot as contours (obtained via KDE on the samples).\nfig, ax = pairplot(\n    npe_samples,\n    points=theta_o,\n    diag=\"kde\",\n    upper=\"contour\",\n    kde_offdiag=dict(bins=50),\n    kde_diag=dict(bins=100),\n    contour_offdiag=dict(levels=[0.95]),\n    points_colors=[\"k\"],\n    points_offdiag=dict(marker=\"*\", markersize=10),\n)\nplt.sca(ax[1, 1])\nplt.legend(\n    [f\"{nt} trials\" if nt &gt; 1 else f\"{nt} trial\" for nt in num_trials]\n    + [r\"$\\theta_o$\"],\n    frameon=False,\n    fontsize=12,\n);\n</code></pre> <pre><code>Drawing 5000 posterior samples:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n\n\n\nDrawing 5000 posterior samples:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n\n\n\nDrawing 5000 posterior samples:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n\n\n\nDrawing 5000 posterior samples:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n</code></pre> <p></p> <pre><code>\n</code></pre>"},{"location":"tutorial/14_multi-trial-data-and-mixed-data-types/","title":"SBI with trial-based data and models of mixed data types","text":"<p>Trial-based data often has the property that the individual trials can be assumed to be independent and identically distributed (iid), i.e., they are assumed to have the same underlying model parameters. For example, in a decision-making experiments, the experiment is often repeated in trials with the same experimental settings and conditions. The corresponding set of trials is then assumed to be \u201ciid\u201d. </p>"},{"location":"tutorial/14_multi-trial-data-and-mixed-data-types/#amortization-of-neural-network-training-with-likelihood-based-sbi","title":"Amortization of neural network training with likelihood-based SBI","text":"<p>For some SBI variants the iid assumption can be exploited: when using a likelihood-based SBI method (<code>SNLE</code>, <code>SNRE</code>) one can train the density or ratio estimator on single-trial data, and then perform inference with <code>MCMC</code>. Crucially, because the data is iid and the estimator is trained on single-trial data, one can repeat the inference with a different <code>x_o</code> (a different set of trials, or different number of trials) without having to retrain the density estimator. One can interpet this as amortization of the SBI training: we can obtain a neural likelihood, or likelihood-ratio estimate for new <code>x_o</code>s without retraining, but we still have to run <code>MCMC</code> or <code>VI</code> to do inference. </p> <p>In addition, one can not only change the number of trials of a new <code>x_o</code>, but also the entire inference setting. For example, one can apply hierarchical inference scenarios with changing hierarchical denpendencies between the model parameters\u2013all without having to retrain the density estimator because that is based on estimating single-trail likelihoods.</p> <p>Let us first have a look how trial-based inference works in <code>SBI</code> before we discuss models with \u201cmixed data types\u201d.</p>"},{"location":"tutorial/14_multi-trial-data-and-mixed-data-types/#sbi-with-trial-based-data","title":"SBI with trial-based data","text":"<p>For illustration we use a simple linear Gaussian simulator, as in previous tutorials. The simulator takes a single parameter (vector), the mean of the Gaussian and its variance is set to one. We define a Gaussian prior over the mean and perform inference. The observed data is again a from a Gaussian with some fixed \u201cground-truth\u201d parameter \\(\\theta_o\\). Crucially, the observed data <code>x_o</code> can consist of multiple samples given the same ground-truth parameters and these samples are then iid: </p> \\[  \\theta \\sim \\mathcal{N}(\\mu_0,\\; \\Sigma_0) \\\\ x | \\theta \\sim \\mathcal{N}(\\theta,\\; \\Sigma=I) \\\\ \\mathbf{x_o} = \\{x_o^i\\}_{i=1}^N \\sim  \\mathcal{N}(\\theta_o,\\; \\Sigma=I) \\] <p>For this toy problem the ground-truth posterior is well defined, it is again a Gaussian, centered on the mean of \\(\\mathbf{x_o}\\) and with variance scaled by the number of trials \\(N\\), i.e., the more trials we observe, the more information about the underlying \\(\\theta_o\\) we have and the more concentrated the posteriors becomes.</p> <p>We will illustrate this below:</p> <pre><code>import torch\nimport matplotlib.pyplot as plt\n\nfrom torch import zeros, ones, eye\nfrom torch.distributions import MultivariateNormal\nfrom sbi.inference import SNLE, prepare_for_sbi, simulate_for_sbi\nfrom sbi.analysis import pairplot\nfrom sbi.utils.metrics import c2st\n\nfrom sbi.simulators.linear_gaussian import (\n    linear_gaussian,\n    true_posterior_linear_gaussian_mvn_prior,\n)\n\n# Seeding\ntorch.manual_seed(1);\n</code></pre> <pre><code># Gaussian simulator\ntheta_dim = 2\nx_dim = theta_dim\n\n# likelihood_mean will be likelihood_shift+theta\nlikelihood_shift = -1.0 * zeros(x_dim)\nlikelihood_cov = 0.3 * eye(x_dim)\n\nprior_mean = zeros(theta_dim)\nprior_cov = eye(theta_dim)\nprior = MultivariateNormal(loc=prior_mean, covariance_matrix=prior_cov)\n\n# Define Gaussian simulator\nsimulator, prior = prepare_for_sbi(\n    lambda theta: linear_gaussian(theta, likelihood_shift, likelihood_cov), prior\n)\n\n# Use built-in function to obtain ground-truth posterior given x_o\ndef get_true_posterior_samples(x_o, num_samples=1):\n    return true_posterior_linear_gaussian_mvn_prior(\n        x_o, likelihood_shift, likelihood_cov, prior_mean, prior_cov\n    ).sample((num_samples,))\n</code></pre>"},{"location":"tutorial/14_multi-trial-data-and-mixed-data-types/#the-analytical-posterior-concentrates-around-true-parameters-with-increasing-number-of-iid-trials","title":"The analytical posterior concentrates around true parameters with increasing number of IID trials","text":"<pre><code>num_trials = [1, 5, 15, 20]\ntheta_o = zeros(1, theta_dim)\n\n# Generate multiple x_os with increasing number of trials.\nxos = [theta_o.repeat(nt, 1) for nt in num_trials]\n\n# Obtain analytical posterior samples for each of them.\nss = [get_true_posterior_samples(xo, 5000) for xo in xos]\n</code></pre> <pre><code># Plot them in one pairplot as contours (obtained via KDE on the samples).\nfig, ax = pairplot(\n    ss,\n    points=theta_o,\n    diag=\"kde\",\n    upper=\"contour\",\n    kde_offdiag=dict(bins=50),\n    kde_diag=dict(bins=100),\n    contour_offdiag=dict(levels=[0.95]),\n    points_colors=[\"k\"],\n    points_offdiag=dict(marker=\"*\", markersize=10),\n)\nplt.sca(ax[1, 1])\nplt.legend(\n    [f\"{nt} trials\" if nt &gt; 1 else f\"{nt} trial\" for nt in num_trials]\n    + [r\"$\\theta_o$\"],\n    frameon=False,\n    fontsize=12,\n);\n</code></pre> <pre><code>/home/janfb/qode/sbi/sbi/analysis/plot.py:425: UserWarning: No contour levels were found within the data range.\n  levels=opts[\"contour_offdiag\"][\"levels\"],\n</code></pre> <p>Indeed, with increasing number of trials the posterior density concentrates around the true underlying parameter.</p>"},{"location":"tutorial/14_multi-trial-data-and-mixed-data-types/#trial-based-inference-with-nle","title":"Trial-based inference with NLE","text":"<p>(S)NLE can easily perform inference given multiple IID x because it is based on learning the likelihood. Once the likelihood is learned on single trials, i.e., a neural network that given a single observation and a parameter predicts the likelihood of that observation given the parameter, one can perform MCMC to obtain posterior samples. </p> <p>MCMC relies on evaluating ratios of likelihoods of candidate parameters to either accept or reject them to be posterior samples. When inferring the posterior given multiple IID observation, these likelihoods are just the joint likelihoods of each IID observation given the current parameter candidate. Thus, given a neural likelihood from SNLE, we can calculate these joint likelihoods and perform MCMC given IID data, we just have to multiply together (or add in log-space) the individual trial-likelihoods (<code>sbi</code> takes care of that).</p> <pre><code># Train SNLE.\ninferer = SNLE(prior, show_progress_bars=True, density_estimator=\"mdn\")\ntheta, x = simulate_for_sbi(simulator, prior, 10000, simulation_batch_size=1000)\ninferer.append_simulations(theta, x).train(training_batch_size=100);\n</code></pre> <pre><code>Running 10000 simulations.:   0%|          | 0/10000 [00:00&lt;?, ?it/s]\n\n\n Neural network successfully converged after 40 epochs.\n</code></pre> <pre><code># Obtain posterior samples for different number of iid xos.\nsamples = []\nnum_samples = 5000\n\nmcmc_parameters = dict(\n    num_chains=50,\n    thin=10,\n    warmup_steps=50,\n    init_strategy=\"proposal\",\n)\nmcmc_method = \"slice_np_vectorized\"\n\nposterior = inferer.build_posterior(\n    mcmc_method=mcmc_method,\n    mcmc_parameters=mcmc_parameters,\n)\n\n# Generate samples with MCMC given the same set of x_os as above.\nfor xo in xos:\n    samples.append(posterior.sample(sample_shape=(num_samples,), x=xo))\n</code></pre> <pre><code>MCMC init with proposal: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00&lt;00:00, 13448.45it/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 75000/75000 [00:34&lt;00:00, 2173.97it/s]\n/home/janfb/qode/sbi/sbi/utils/sbiutils.py:282: UserWarning: An x with a batch size of 5 was passed. It will be interpreted as a batch of independent and identically\n            distributed data X={x_1, ..., x_n}, i.e., data generated based on the\n            same underlying (unknown) parameter. The resulting posterior will be with\n            respect to entire batch, i.e,. p(theta | X).\n  respect to entire batch, i.e,. p(theta | X).\"\"\"\nMCMC init with proposal: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00&lt;00:00, 16636.14it/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 75000/75000 [00:40&lt;00:00, 1871.97it/s]\n/home/janfb/qode/sbi/sbi/utils/sbiutils.py:282: UserWarning: An x with a batch size of 15 was passed. It will be interpreted as a batch of independent and identically\n            distributed data X={x_1, ..., x_n}, i.e., data generated based on the\n            same underlying (unknown) parameter. The resulting posterior will be with\n            respect to entire batch, i.e,. p(theta | X).\n  respect to entire batch, i.e,. p(theta | X).\"\"\"\nMCMC init with proposal: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00&lt;00:00, 16856.78it/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 75000/75000 [01:00&lt;00:00, 1234.76it/s]\n/home/janfb/qode/sbi/sbi/utils/sbiutils.py:282: UserWarning: An x with a batch size of 20 was passed. It will be interpreted as a batch of independent and identically\n            distributed data X={x_1, ..., x_n}, i.e., data generated based on the\n            same underlying (unknown) parameter. The resulting posterior will be with\n            respect to entire batch, i.e,. p(theta | X).\n  respect to entire batch, i.e,. p(theta | X).\"\"\"\nMCMC init with proposal: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00&lt;00:00, 16580.90it/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 75000/75000 [01:13&lt;00:00, 1020.25it/s]\n</code></pre> <p>Note that <code>sbi</code> warns about <code>iid-x</code> with increasing number of trial here. We ignore the warning because that\u2019s exactly what we want to do.</p> <pre><code># Plot them in one pairplot as contours (obtained via KDE on the samples).\nfig, ax = pairplot(\n    samples,\n    points=theta_o,\n    diag=\"kde\",\n    upper=\"contour\",\n    kde_offdiag=dict(bins=50),\n    kde_diag=dict(bins=100),\n    contour_offdiag=dict(levels=[0.95]),\n    points_colors=[\"k\"],\n    points_offdiag=dict(marker=\"*\", markersize=10),\n)\nplt.sca(ax[1, 1])\nplt.legend(\n    [f\"{nt} trials\" if nt &gt; 1 else f\"{nt} trial\" for nt in num_trials]\n    + [r\"$\\theta_o$\"],\n    frameon=False,\n    fontsize=12,\n);\n</code></pre> <pre><code>/home/janfb/qode/sbi/sbi/analysis/plot.py:425: UserWarning: No contour levels were found within the data range.\n  levels=opts[\"contour_offdiag\"][\"levels\"],\n</code></pre> <p></p> <p>The pairplot above already indicates that (S)NLE is well able to obtain accurate posterior samples also for increasing number of trials (note that we trained the single-round version of SNLE so that we did not have to re-train it for new \\(x_o\\)). </p> <p>Quantitatively we can measure the accuracy of SNLE by calculating the <code>c2st</code> score between SNLE and the true posterior samples, where the best accuracy is perfect for <code>0.5</code>:</p> <pre><code>cs = [c2st(torch.from_numpy(s1), torch.from_numpy(s2)) for s1, s2 in zip(ss, samples)]\n\nfor _ in range(len(num_trials)):\n    print(f\"c2st score for num_trials={num_trials[_]}: {cs[_].item():.2f}\")\n</code></pre> <pre><code>c2st score for num_trials=1: 0.51\nc2st score for num_trials=5: 0.50\nc2st score for num_trials=15: 0.53\nc2st score for num_trials=20: 0.55\n</code></pre> <p>This inference procedure would work similarly when using <code>SNRE</code>. However, note that it does not work for <code>SNPE</code> because in <code>SNPE</code> we are learning the posterior directly so that whenever <code>x_o</code> changes (in terms of the number of trials or the parameter dependencies) the posterior changes and <code>SNPE</code> needs to be trained again. </p>"},{"location":"tutorial/14_multi-trial-data-and-mixed-data-types/#trial-based-sbi-with-mixed-data-types","title":"Trial-based SBI with mixed data types","text":"<p>In some cases, models with trial-based data additionally return data with mixed data types, e.g., continous and discrete data. For example, most computational models of decision-making have continuous reaction times and discrete choices as output. </p> <p>This can induce a problem when performing trial-based SBI that relies on learning a neural likelihood. The problem is that it is challenging for most density estimators to handle both, continous and discrete data at the same time. There has been developed a method for solving this problem, it\u2019s called Mixed Neural Likelihood Estimation (MNLE). It works just like NLE, but with mixed data types. The trick is that it learns two separate density estimators, one for the discrete part of the data, and one for the continuous part, and combines the two to obtain the final neural likelihood. Crucially, the continuous density estimator is trained conditioned on the output of the discrete one, such that statistical dependencies between the discrete and continous data (e.g., between choices and reaction times) are modeled as well. The interested reader is referred to the original paper available here.</p> <p>MNLE was recently added to <code>sbi</code> (see PR) and follow the same API as <code>SNLE</code>.</p>"},{"location":"tutorial/14_multi-trial-data-and-mixed-data-types/#toy-problem-for-mnle","title":"Toy problem for <code>MNLE</code>","text":"<p>To illustrate <code>MNLE</code> we set up a toy simulator that outputs mixed data and for which we know the likelihood such we can obtain reference posterior samples via MCMC.</p> <p>Simulator: To simulate mixed data we do the following</p> <ul> <li>Sample reaction time from <code>inverse Gamma</code></li> <li>Sample choices from <code>Binomial</code></li> <li>Return reaction time \\(rt \\in (0, \\infty\\) and choice index \\(c \\in \\{0, 1\\}\\)</li> </ul> \\[ c \\sim \\text{Binomial}(\\rho) \\\\ rt \\sim \\text{InverseGamma}(\\alpha=2, \\beta) \\\\ \\] <p>Prior: The priors of the two parameters \\(\\rho\\) and \\(\\beta\\) are independent. We define a <code>Beta</code> prior over the probabilty parameter of the <code>Binomial</code> used in the simulator and a <code>Gamma</code> prior over the shape-parameter of the <code>inverse Gamma</code> used in the simulator:</p> \\[ p(\\beta, \\rho) = p(\\beta) \\; p(\\rho) ; \\\\ p(\\beta) = \\text{Gamma}(1, 0.5) \\\\ p(\\text{probs}) = \\text{Beta}(2, 2)  \\] <p>Because the <code>InverseGamma</code> and the <code>Binomial</code> likelihoods are well-defined we can perform MCMC on this problem and obtain reference-posterior samples.</p> <pre><code>from sbi.inference import MNLE\nfrom pyro.distributions import InverseGamma\nfrom torch.distributions import Beta, Binomial, Gamma\nfrom sbi.utils import MultipleIndependent\n\nfrom sbi.inference import MCMCPosterior, VIPosterior, RejectionPosterior\nfrom sbi.utils.torchutils import atleast_2d\n\nfrom sbi.utils import mcmc_transform\nfrom sbi.inference.potentials.base_potential import BasePotential\n</code></pre> <pre><code># Toy simulator for mixed data\ndef mixed_simulator(theta):\n    beta, ps = theta[:, :1], theta[:, 1:]\n\n    choices = Binomial(probs=ps).sample()\n    rts = InverseGamma(concentration=2 * torch.ones_like(beta), rate=beta).sample()\n\n    return torch.cat((rts, choices), dim=1)\n\n\n# Potential function to perform MCMC to obtain the reference posterior samples.\nclass PotentialFunctionProvider(BasePotential):\n    allow_iid_x = True  # type: ignore\n\n    def __init__(self, prior, x_o, device=\"cpu\"):\n        super().__init__(prior, x_o, device)\n\n    def __call__(self, theta, track_gradients: bool = True):\n\n        theta = atleast_2d(theta)\n\n        with torch.set_grad_enabled(track_gradients):\n            iid_ll = self.iid_likelihood(theta)\n\n        return iid_ll + self.prior.log_prob(theta)\n\n    def iid_likelihood(self, theta):\n\n        lp_choices = torch.stack(\n            [\n                Binomial(probs=th.reshape(1, -1)).log_prob(self.x_o[:, 1:])\n                for th in theta[:, 1:]\n            ],\n            dim=1,\n        )\n\n        lp_rts = torch.stack(\n            [\n                InverseGamma(\n                    concentration=2 * torch.ones_like(beta_i), rate=beta_i\n                ).log_prob(self.x_o[:, :1])\n                for beta_i in theta[:, :1]\n            ],\n            dim=1,\n        )\n\n        joint_likelihood = (lp_choices + lp_rts).squeeze()\n\n        assert joint_likelihood.shape == torch.Size([x_o.shape[0], theta.shape[0]])\n        return joint_likelihood.sum(0)\n</code></pre> <pre><code># Define independent prior.\nprior = MultipleIndependent(\n    [\n        Gamma(torch.tensor([1.0]), torch.tensor([0.5])),\n        Beta(torch.tensor([2.0]), torch.tensor([2.0])),\n    ],\n    validate_args=False,\n)\n</code></pre>"},{"location":"tutorial/14_multi-trial-data-and-mixed-data-types/#obtain-reference-posterior-samples-via-analytical-likelihood-and-mcmc","title":"Obtain reference-posterior samples via analytical likelihood and MCMC","text":"<pre><code>torch.manual_seed(42)\nnum_trials = 10\nnum_samples = 1000\ntheta_o = prior.sample((1,))\nx_o = mixed_simulator(theta_o.repeat(num_trials, 1))\n</code></pre> <pre><code>true_posterior = MCMCPosterior(\n    potential_fn=PotentialFunctionProvider(prior, x_o),\n    proposal=prior,\n    method=\"slice_np_vectorized\",\n    theta_transform=mcmc_transform(prior, enable_transform=True),\n    **mcmc_parameters,\n)\ntrue_samples = true_posterior.sample((num_samples,))\n</code></pre> <pre><code>/home/janfb/qode/sbi/sbi/utils/sbiutils.py:282: UserWarning: An x with a batch size of 10 was passed. It will be interpreted as a batch of independent and identically\n            distributed data X={x_1, ..., x_n}, i.e., data generated based on the\n            same underlying (unknown) parameter. The resulting posterior will be with\n            respect to entire batch, i.e,. p(theta | X).\n  respect to entire batch, i.e,. p(theta | X).\"\"\"\nMCMC init with proposal: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00&lt;00:00, 4365.61it/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 35000/35000 [02:39&lt;00:00, 219.06it/s]\n</code></pre>"},{"location":"tutorial/14_multi-trial-data-and-mixed-data-types/#train-mnle-and-generate-samples-via-mcmc","title":"Train MNLE and generate samples via MCMC","text":"<pre><code># Training data\nnum_simulations = 5000\ntheta = prior.sample((num_simulations,))\nx = mixed_simulator(theta)\n\n# Train MNLE and obtain MCMC-based posterior.\ntrainer = MNLE()\nestimator = trainer.append_simulations(theta, x).train()\n</code></pre> <pre><code> Neural network successfully converged after 84 epochs.\n</code></pre> <pre><code>posterior = trainer.build_posterior(estimator, prior)\n</code></pre> <pre><code># Training data\nnum_simulations = 5000\ntheta = prior.sample((num_simulations,))\nx = mixed_simulator(theta)\n\n# Train MNLE and obtain MCMC-based posterior.\ntrainer = MNLE(prior)\nestimator = trainer.append_simulations(theta, x).train()\nmnle_posterior = trainer.build_posterior(\n    mcmc_method=\"slice_np_vectorized\", mcmc_parameters=mcmc_parameters\n)\nmnle_samples = mnle_posterior.sample((num_samples,), x=x_o)\n</code></pre> <pre><code>/home/janfb/qode/sbi/sbi/neural_nets/mnle.py:64: UserWarning: The mixed neural likelihood estimator assumes that x contains\n        continuous data in the first n-1 columns (e.g., reaction times) and\n        categorical data in the last column (e.g., corresponding choices). If\n        this is not the case for the passed `x` do not use this function.\n  this is not the case for the passed `x` do not use this function.\"\"\"\n\n\n Neural network successfully converged after 35 epochs.\n\nMCMC init with proposal: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00&lt;00:00, 6125.93it/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 35000/35000 [01:26&lt;00:00, 404.04it/s]\n</code></pre>"},{"location":"tutorial/14_multi-trial-data-and-mixed-data-types/#compare-mnle-and-reference-posterior","title":"Compare MNLE and reference posterior","text":"<pre><code># Plot them in one pairplot as contours (obtained via KDE on the samples).\nfig, ax = pairplot(\n    [\n        prior.sample((1000,)),\n        true_samples,\n        mnle_samples,\n    ],\n    points=theta_o,\n    diag=\"kde\",\n    upper=\"contour\",\n    kde_offdiag=dict(bins=50),\n    kde_diag=dict(bins=100),\n    contour_offdiag=dict(levels=[0.95]),\n    points_colors=[\"k\"],\n    points_offdiag=dict(marker=\"*\", markersize=10),\n    labels=[r\"$\\beta$\", r\"$\\rho$\"],\n)\n\nplt.sca(ax[1, 1])\nplt.legend(\n    [\"Prior\", \"Reference\", \"MNLE\", r\"$\\theta_o$\"],\n    frameon=False,\n    fontsize=12,\n);\n</code></pre> <pre><code>/home/janfb/qode/sbi/sbi/analysis/plot.py:425: UserWarning: No contour levels were found within the data range.\n  levels=opts[\"contour_offdiag\"][\"levels\"],\n</code></pre> <p>We see that the inferred <code>MNLE</code> posterior nicely matches the reference posterior, and how both inferred a posterior that is quite different from the prior.</p> <p>Because MNLE training is amortized we can obtain another posterior given a different observation with potentially a different number of trials, just by running MCMC again (without re-training <code>MNLE</code>):</p>"},{"location":"tutorial/14_multi-trial-data-and-mixed-data-types/#repeat-inference-with-different-x_o-that-has-more-trials","title":"Repeat inference with different <code>x_o</code> that has more trials","text":"<pre><code>num_trials = 100\nx_o = mixed_simulator(theta_o.repeat(num_trials, 1))\ntrue_samples = true_posterior.sample((num_samples,), x=x_o)\nmnle_samples = mnle_posterior.sample((num_samples,), x=x_o)\n</code></pre> <pre><code>/home/janfb/qode/sbi/sbi/utils/sbiutils.py:282: UserWarning: An x with a batch size of 100 was passed. It will be interpreted as a batch of independent and identically\n            distributed data X={x_1, ..., x_n}, i.e., data generated based on the\n            same underlying (unknown) parameter. The resulting posterior will be with\n            respect to entire batch, i.e,. p(theta | X).\n  respect to entire batch, i.e,. p(theta | X).\"\"\"\nMCMC init with proposal: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00&lt;00:00, 4685.01it/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 35000/35000 [02:47&lt;00:00, 209.25it/s]\nMCMC init with proposal: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00&lt;00:00, 6136.69it/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 35000/35000 [08:23&lt;00:00, 69.57it/s]\n</code></pre> <pre><code># Plot them in one pairplot as contours (obtained via KDE on the samples).\nfig, ax = pairplot(\n    [\n        prior.sample((1000,)),\n        true_samples,\n        mnle_samples,\n    ],\n    points=theta_o,\n    diag=\"kde\",\n    upper=\"contour\",\n    kde_offdiag=dict(bins=50),\n    kde_diag=dict(bins=100),\n    contour_offdiag=dict(levels=[0.95]),\n    points_colors=[\"k\"],\n    points_offdiag=dict(marker=\"*\", markersize=10),\n    labels=[r\"$\\beta$\", r\"$\\rho$\"],\n)\n\nplt.sca(ax[1, 1])\nplt.legend(\n    [\"Prior\", \"Reference\", \"MNLE\", r\"$\\theta_o$\"],\n    frameon=False,\n    fontsize=12,\n);\n</code></pre> <pre><code>/home/janfb/qode/sbi/sbi/analysis/plot.py:425: UserWarning: No contour levels were found within the data range.\n  levels=opts[\"contour_offdiag\"][\"levels\"],\n</code></pre> <p>Again we can see that the posteriors match nicely. In addition, we observe that the posterior variance reduces as we increase the number of trials, similar to the illustration with the Gaussian example at the beginning of the tutorial. </p> <p>A final note: <code>MNLE</code> is trained on single-trial data. Theoretically, density estimation is perfectly accurate only in the limit of infinite training data. Thus, training with a finite amount of training data naturally induces a small bias in the density estimator. As we observed above, this bias is so small that we don\u2019t really notice it, e.g., the <code>c2st</code> scores were close to 0.5. However, when we increase the number of trials in <code>x_o</code> dramatically (on the order of 1000s) the small bias can accumulate over the trials and inference with <code>MNLE</code> can become less accurate.</p> <pre><code>\n</code></pre>"},{"location":"tutorial/15_mcmc_diagnostics_with_arviz/","title":"MCMC diagnostics with Arviz","text":"<p>This tutorial shows how to evaluate the quality of MCMC samples generated via <code>sbi</code> using the <code>arviz</code> package. </p> <p>Outline:</p> <p>1) Train MNLE to approximate the likelihood underlying the simulator 2) Run MCMC using <code>pyro</code> MCMC samplers via <code>sbi</code> interface 3) Use <code>arviz</code> to visualize the posterior, predictive distributions and MCMC diagnostics. </p> <pre><code>import arviz as az\nimport torch\n\nfrom sbi.inference import MNLE, likelihood_estimator_based_potential\nfrom pyro.distributions import InverseGamma\nfrom torch.distributions import Beta, Binomial, Gamma\nfrom sbi.utils import MultipleIndependent\n\nfrom sbi.inference import MCMCPosterior\n\n# Seeding\ntorch.manual_seed(1);\n</code></pre> <pre><code># Toy simulator for mixed data\ndef mixed_simulator(theta):\n    beta, ps = theta[:, :1], theta[:, 1:]\n\n    choices = Binomial(probs=ps).sample()\n    rts = InverseGamma(concentration=2 * torch.ones_like(beta), rate=beta).sample()\n\n    return torch.cat((rts, choices), dim=1)\n\n\n# Define independent priors for each dimension.\nprior = MultipleIndependent(\n    [\n        Gamma(torch.tensor([1.0]), torch.tensor([0.5])),\n        Beta(torch.tensor([2.0]), torch.tensor([2.0])),\n    ],\n    validate_args=False,\n)\n</code></pre>"},{"location":"tutorial/15_mcmc_diagnostics_with_arviz/#train-mnle-to-approximate-the-likelihood","title":"Train MNLE to approximate the likelihood","text":"<p>For this tutorial, we will use a simple simulator with two parameters. For details see the example on the decision making model. </p> <p>Here, we pass <code>mcmc_method=\"nuts\"</code> in order to use the underlying <code>pyro</code> No-U-turn sampler, but it would work as well with other samplers (e.g. \u201cslice_np_vectorized\u201d, \u201chmc\u201d). </p> <p>Additionally, when calling <code>posterior.sample(...)</code> we pass <code>return_arviz=True</code> so that the <code>Arviz InferenceData</code> object is returned. This object gives us access to the wealth of MCMC diagnostics tool provided by <code>arviz</code>.</p> <pre><code># Generate training data and train MNLE.\nnum_simulations = 10000\ntheta = prior.sample((num_simulations,))\nx = mixed_simulator(theta)\n\ntrainer = MNLE(prior)\nlikelihood_estimator = trainer.append_simulations(theta, x).train()\n</code></pre> <pre><code>/Users/janbolts/qode/sbi/sbi/neural_nets/mnle.py:60: UserWarning: The mixed neural likelihood estimator assumes that x contains\n        continuous data in the first n-1 columns (e.g., reaction times) and\n        categorical data in the last column (e.g., corresponding choices). If\n        this is not the case for the passed `x` do not use this function.\n  warnings.warn(\n\n\n Neural network successfully converged after 65 epochs.\n</code></pre>"},{"location":"tutorial/15_mcmc_diagnostics_with_arviz/#run-pyro-nuts-mcmc-and-obtain-arviz-inferencedata-object","title":"Run Pyro NUTS MCMC and obtain <code>arviz InferenceData</code> object","text":"<pre><code># Simulate \"observed\" data x_o\ntorch.manual_seed(42)\nnum_trials = 100\ntheta_o = prior.sample((1,))\nx_o = mixed_simulator(theta_o.repeat(num_trials, 1))\n\n# Set MCMC parameters and run Pyro NUTS.\nmcmc_parameters = dict(\n    num_chains=4,\n    thin=5,\n    warmup_steps=50,\n    init_strategy=\"proposal\",\n    method=\"nuts\",\n)\nnum_samples = 1000\n\n# get the potential function and parameter transform for constructing the posterior\npotential_fn, parameter_transform = likelihood_estimator_based_potential(\n    likelihood_estimator, prior, x_o\n)\nmnle_posterior = MCMCPosterior(\n    potential_fn, proposal=prior, theta_transform=parameter_transform, **mcmc_parameters\n)\n\nmnle_samples = mnle_posterior.sample(\n    (num_samples,), x=x_o, show_progress_bars=False\n)\n# get arviz InferenceData object from posterior\ninference_data = mnle_posterior.get_arviz_inference_data()\n</code></pre> <pre><code>/Users/janbolts/qode/sbi/sbi/utils/sbiutils.py:280: UserWarning: An x with a batch size of 100 was passed. It will be interpreted as a batch of independent and identically\n            distributed data X={x_1, ..., x_n}, i.e., data generated based on the\n            same underlying (unknown) parameter. The resulting posterior will be with\n            respect to entire batch, i.e,. p(theta | X).\n  warnings.warn(\n</code></pre>"},{"location":"tutorial/15_mcmc_diagnostics_with_arviz/#generate-arviz-plots","title":"Generate <code>arviz</code> plots","text":"<p>The resulting <code>InferenceData</code> object can be passed to most <code>arviz</code> plotting functions, and there are plenty see here for an overview.</p> <p>To get a better understanding of the <code>InferenceData</code> object see here. </p> <p>Below and overview of common MCMC diagnostics plot, see the corresponding <code>arviz</code> documentation for interpretation of the plots. </p> <p>We will a full use-case using the SBI-MCMC-arviz workflow soon.</p> <pre><code>print(inference_data.posterior)\n</code></pre> <pre><code>&lt;xarray.Dataset&gt;\nDimensions:      (chain: 4, draw: 1254, theta_dim_0: 2)\nCoordinates:\n  * chain        (chain) int64 0 1 2 3\n  * draw         (draw) int64 0 1 2 3 4 5 6 ... 1248 1249 1250 1251 1252 1253\n  * theta_dim_0  (theta_dim_0) int64 0 1\nData variables:\n    theta        (chain, draw, theta_dim_0) float32 2.125 0.8092 ... 0.8088\nAttributes:\n    created_at:     2022-08-10T14:02:41.300799\n    arviz_version:  0.11.2\n</code></pre>"},{"location":"tutorial/15_mcmc_diagnostics_with_arviz/#diagnostic-plots","title":"Diagnostic plots","text":"<pre><code>az.style.use(\"arviz-darkgrid\")\naz.plot_rank(inference_data)\n</code></pre> <pre><code>array([&lt;AxesSubplot:title={'center':'theta\\n0'}, xlabel='Rank (all chains)', ylabel='Chain'&gt;,\n       &lt;AxesSubplot:title={'center':'theta\\n1'}, xlabel='Rank (all chains)', ylabel='Chain'&gt;],\n      dtype=object)\n</code></pre> <pre><code>az.plot_autocorr(inference_data);\n</code></pre> <pre><code>az.plot_trace(inference_data, compact=False);\n</code></pre> <pre><code>az.plot_ess(inference_data, kind=\"evolution\");\n</code></pre>"},{"location":"tutorial/15_mcmc_diagnostics_with_arviz/#posterior-density-plots","title":"Posterior density  plots","text":"<pre><code>az.plot_posterior(inference_data)\n</code></pre> <pre><code>array([&lt;AxesSubplot:title={'center':'theta\\n0'}&gt;,\n       &lt;AxesSubplot:title={'center':'theta\\n1'}&gt;], dtype=object)\n</code></pre> <pre><code>print(\n    f\"Given the {num_trials} we observed, the posterior is centered around true underlying parameters theta_o: {theta_o}\"\n)\n</code></pre> <pre><code>Given the 100 we observed, the posterior is centered around true underlying parameters theta_o: tensor([[1.9622, 0.7550]])\n</code></pre> <pre><code>az.plot_pair(inference_data)\n</code></pre> <pre><code>&lt;AxesSubplot:xlabel='theta\\n0', ylabel='theta\\n1'&gt;\n</code></pre> <pre><code>az.plot_pair(\n    inference_data,\n    var_names=[\"theta\"],\n    kind=\"hexbin\",\n    marginals=True,\n    figsize=(10, 10),\n)\n</code></pre> <pre><code>array([[&lt;AxesSubplot:&gt;, None],\n       [&lt;AxesSubplot:xlabel='theta\\n0', ylabel='theta\\n1'&gt;,\n        &lt;AxesSubplot:&gt;]], dtype=object)\n</code></pre> <pre><code>\n</code></pre>"},{"location":"tutorial/16_implemented_methods/","title":"API of implemented methods","text":"<p>This notebook spells out the API for all algorithms implemented in the <code>sbi</code> toolbox:</p> <ul> <li> <p>Posterior estimation (SNPE)</p> </li> <li> <p>Likelihood estimation (SNLE)</p> </li> <li> <p>Likelihood-ratio estimation (SNRE)</p> </li> <li> <p>Utilities  </p> </li> </ul>"},{"location":"tutorial/16_implemented_methods/#posterior-estimation-snpe","title":"Posterior estimation (SNPE)","text":"<p>Fast \u03b5-free Inference of Simulation Models with Bayesian Conditional Density Estimation by Papamakarios &amp; Murray (NeurIPS 2016) [PDF] [BibTeX]</p> <pre><code>from sbi.inference import SNPE_A\n\ninference = SNPE_A(prior)\nproposal = prior\nfor _ in range(rounds):\n    theta = proposal.sample((num_sims,))\n    x = simulator(theta)\n    _ = inference.append_simulations(theta, x, proposal=proposal).train()\n    posterior = inference.build_posterior().set_default_x(x_o)\n    proposal = posterior\n</code></pre> <p>Automatic posterior transformation for likelihood-free inferenceby Greenberg, Nonnenmacher &amp; Macke (ICML 2019) [PDF]</p> <pre><code>from sbi.inference import SNPE\n\ninference = SNPE(prior)\nproposal = prior\nfor _ in range(rounds):\n    theta = proposal.sample((num_sims,))\n    x = simulator(theta)\n    _ = inference.append_simulations(theta, x, proposal=proposal).train()\n    posterior = inference.build_posterior().set_default_x(x_o)\n    proposal = posterior\n</code></pre> <p>Truncated proposals for scalable and hassle-free simulation-based inference  by Deistler, Goncalves &amp; Macke (NeurIPS 2022) [Paper]</p> <pre><code>from sbi.inference import SNPE\nfrom sbi.utils import get_density_thresholder, RestrictedPrior\n\ninference = SNPE(prior)\nproposal = prior\nfor _ in range(rounds):\n    theta = proposal.sample((num_sims,))\n    x = simulator(theta)\n    _ = inference.append_simulations(theta, x).train(force_first_round_loss=True)\n    posterior = inference.build_posterior().set_default_x(x_o)\n\n    accept_reject_fn = get_density_thresholder(posterior, quantile=1e-4)\n    proposal = RestrictedPrior(prior, accept_reject_fn, sample_with=\"rejection\")\n</code></pre>"},{"location":"tutorial/16_implemented_methods/#likelihood-estimation-snle","title":"Likelihood estimation (SNLE)","text":"<p>Sequential neural likelihood: Fast likelihood-free inference with autoregressive flowsby Papamakarios, Sterratt &amp; Murray (AISTATS 2019) [PDF] [BibTeX]</p> <pre><code>from sbi.inference import SNLE\n\ninference = SNLE(prior)\nproposal = prior\nfor _ in range(rounds):\n    theta = proposal.sample((num_sims,))\n    x = simulator(theta)\n    _ = inference.append_simulations(theta, x).train()\n    posterior = inference.build_posterior().set_default_x(x_o)\n    proposal = posterior\n</code></pre> <p>Variational methods for simulation-based inference  by Gl\u00f6ckler, Deistler, Macke (ICLR 2022) [Paper]</p> <pre><code>from sbi.inference import SNLE\n\ninference = SNLE(prior)\nproposal = prior\nfor _ in range(rounds):\n    theta = proposal.sample((num_sims,))\n    x = simulator(theta)\n    _ = inference.append_simulations(theta, x).train()\n    posterior = inference.build_posterior(sample_with=\"vi\", vi_method=\"fKL\").set_default_x(x_o)\n    proposal = posterior\n</code></pre> <p>Flexible and efficient simulation-based inference for models of decision-making  by Boelts, Lueckmann, Gao, Macke (Elife 2022) [Paper]</p> <pre><code>from sbi.inference import MNLE\n\ninference = MNLE(prior)\ntheta = prior.sample((num_sims,))\nx = simulator(theta)\n_ = inference.append_simulations(theta, x).train()\nposterior = inference.build_posterior().set_default_x(x_o)\n</code></pre>"},{"location":"tutorial/16_implemented_methods/#likelihood-ratio-estimation-snre","title":"Likelihood-ratio estimation (SNRE)","text":"<p>Likelihood-free MCMC with Amortized Approximate Likelihood Ratiosby Hermans, Begy &amp; Louppe (ICML 2020) [PDF]</p> <pre><code>from sbi.inference import SNRE_A\n\ninference = SNRE_A(prior)\ntheta = prior.sample((num_sims,))\nx = simulator(theta)\n_ = inference.append_simulations(theta, x).train()\nposterior = inference.build_posterior().set_default_x(x_o)\n</code></pre> <p>On Contrastive Learning for Likelihood-free InferenceDurkan, Murray &amp; Papamakarios (ICML 2020) [PDF].</p> <pre><code>from sbi.inference import SNRE\n\ninference = SNRE(prior)\nproposal = prior\nfor _ in range(rounds):\n    theta = proposal.sample((num_sims,))\n    x = simulator(theta)\n    _ = inference.append_simulations(theta, x).train()\n    posterior = inference.build_posterior().set_default_x(x_o)\n    proposal = posterior\n</code></pre> <p>Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimationby Delaunoy, Hermans, Rozet, Wehenkel &amp; Louppe (NeurIPS 2022) [PDF]</p> <pre><code>from sbi.inference import BNRE\n\ninference = BNRE(prior)\ntheta = prior.sample((num_sims,))\nx = simulator(theta)\n_ = inference.append_simulations(theta, x).train(regularization_strength=100.)\nposterior = inference.build_posterior().set_default_x(x_o)\n</code></pre> <p>Contrastive Neural Ratio EstimationBenjamin Kurt Miller, Christoph Weniger, Patrick Forr\u00e9 (NeurIPS 2022) [PDF]</p> <pre><code># The main feature of NRE-C is producing an exact ratio of densities at optimum, even when using multiple contrastive pairs (classes).\n\nfrom sbi.inference import SNRE_C\n\n# Amortized inference\ninference = SNRE_C(prior)\nproposal = prior\ntheta = proposal.sample((num_sims,))\nx = simulator(theta)\n_ = inference.append_simulations(theta, x).train(\n    num_classes=5,  # SNRE_C sees `2 * num_classes - 1` marginally drawn contrastive pairs.\n    gamma=1.0,  # SNRE_C can control the weight between terms in its loss function.\n)\nposterior = inference.build_posterior().set_default_x(x_o)\n</code></pre>"},{"location":"tutorial/16_implemented_methods/#utilities","title":"Utilities","text":"<p>Simulation-based calibrationby Talts, Betancourt, Simpson, Vehtari, Gelman (arxiv 2018) [Paper])</p> <pre><code>from sbi.analysis import run_sbc, sbc_rank_plot\n\nthetas = prior.sample((1_000,))\nxs = simulator(thetas)\n\nranks, dap_samples = run_sbc(\n    thetas, xs, posterior, num_posterior_samples=1_000\n)\n\n_ = sbc_rank_plot(\n    ranks=ranks,\n    num_posterior_samples=num_posterior_samples,\n    plot_type=\"hist\",\n    num_bins=None,\n)\n</code></pre> <p>Restriction estimatorby Deistler, Macke &amp; Goncalves (PNAS 2022) [Paper]</p> <pre><code>from sbi.inference import SNPE\nfrom sbi.utils import RestrictionEstimator\n\nrestriction_estimator = RestrictionEstimator(prior=prior)\nproposal = prior\n\nfor _ in range(num_rounds):\n    theta = proposal.sample((num_sims,))\n    x = simulator(theta)\n    restriction_estimator.append_simulations(theta, x)\n    classifier = restriction_estimator.train()\n    proposal = restriction_estimator.restrict_prior()\n\nall_theta, all_x, _ = restriction_estimator.get_simulations()\n\ninference = SNPE(prior)\ndensity_estimator = inference.append_simulations(all_theta, all_x).train()\nposterior = inference.build_posterior()\n</code></pre> <p>Expected coverage (sample-based)as computed in Deistler, Goncalves, Macke (Neurips 2022) [Paper] and in Rozet, Louppe (2021) [Paper]</p> <pre><code>from sbi.analysis import run_sbc, sbc_rank_plot\n\nthetas = prior.sample((1_000,))\nxs = simulator(thetas)\n\nranks, dap_samples = run_sbc(\n    thetas, xs, posterior, num_posterior_samples=1_000, reduce_fns=posterior.log_prob\n)\n\n_ = sbc_rank_plot(\n    ranks=ranks,\n    num_posterior_samples=num_posterior_samples,\n    plot_type=\"hist\",\n    num_bins=None,\n)\n</code></pre>"},{"location":"tutorial/17_vi_posteriors/","title":"Using Variational Inference for Building Posteriors","text":"<p>In the previous tutorial, we saw how to build the posterior and how to specialize on one specific observation <code>x_o</code>. If one uses SNPE, then the posterior can be sampled from directly, yet this comes at the expense of necessary correction terms during training, since the samples are obtained from the \u201cwrong\u201d prior for <code>num_rounds &gt; 1</code>. For SNLE or SNRE, MCMC sampling is required, which is computationally expensive. With SNVI (sequential neural variational inference), it is possible to directly sample from the posterior without any corrections during training or without expensive MCMC for sampling. This is possible by learning the posterior with variational inference techniques. For this, an additional network (one for the likelihood or likelihood-to-evidence-ratio) must be trained first.</p>"},{"location":"tutorial/17_vi_posteriors/#main-syntax","title":"Main syntax","text":"<pre><code>inference = SNLE(prior)\n\nfor _ in range(num_rounds):\n    theta, x = simulate_for_sbi(simulator, proposal, num_simulations=500)\n\n    # In `SNLE` and `SNRE`, you should not pass the `proposal` to `\n    # .append_simulations()`.\n    likelihood_estimator = inference.append_simulations(\n        theta, x,\n    ).train()\n\n    # Obtain potential (learned likelihood * prior) and theta transformation.\n    potential_fn, theta_transform = likelihood_estimator_based_potential(\n        likelihood_estimator, prior, x_o\n    )\n\n    # Build posterior via variational inference.\n    posterior = VIPosterior(\n        potential_fn, prior, \"maf\", theta_transform, vi_method=\"fKL\",\n    ).train()\n    proposal = posterior\n</code></pre>"},{"location":"tutorial/17_vi_posteriors/#linear-gaussian-example","title":"Linear Gaussian example","text":"<p>Below, we give a full example of inferring the posterior distribution with SNVI over multiple rounds. For this, we take the same example as in the previous tutorial.</p> <pre><code>import torch\n\nfrom sbi.inference import (\n    likelihood_estimator_based_potential,\n    SNLE,\n    prepare_for_sbi,\n    simulate_for_sbi,\n    VIPosterior,\n)\nfrom sbi import utils as utils\nfrom sbi import analysis as analysis\n\n_ = torch.manual_seed(0)\n</code></pre> <pre><code>num_dim = 3\nprior = utils.BoxUniform(low=-2 * torch.ones(num_dim), high=2 * torch.ones(num_dim))\n</code></pre> <pre><code>def linear_gaussian(theta):\n    return theta + 1.0 + torch.randn_like(theta) * 0.1\n</code></pre> <pre><code>simulator, prior = prepare_for_sbi(linear_gaussian, prior)\n</code></pre> <p>Here, we decide to learn the likelihood directly, but learning the likelihood-to-evidence ratio would also be a viable option.</p> <pre><code>inference = SNLE(prior=prior)\n</code></pre> <p>Now we can run inference, where we first learn the likelihood, which is then in turn used to learn a posterior through variational inference.</p> <pre><code>num_rounds = 2\nx_o = torch.zeros(3,)\n\nposteriors = []\nproposal = prior\n\nfor _ in range(num_rounds):\n    theta, x = simulate_for_sbi(simulator, proposal, num_simulations=500)\n    likelihood_estimator = inference.append_simulations(\n        theta, x,\n    ).train()\n    potential_fn, theta_transform = likelihood_estimator_based_potential(\n        likelihood_estimator, prior, x_o\n    )\n\n    posterior = VIPosterior(\n        potential_fn, prior, \"maf\", theta_transform, vi_method=\"fKL\",\n    ).train()\n    posteriors.append(posterior)\n    proposal = posterior\n</code></pre> <pre><code>Running 500 simulations.:   0%|          | 0/500 [00:00&lt;?, ?it/s]\n\n\n Neural network successfully converged after 50 epochs.\n\n\n  0%|          | 0/2000 [00:00&lt;?, ?it/s]\n\n\n\nConverged with loss: -4.23\nQuality Score: -0.218    Good: Smaller than 0.5  Bad: Larger than 1.0            NOTE: Less sensitive to mode collapse.\n\n\n\nRunning 500 simulations.:   0%|          | 0/500 [00:00&lt;?, ?it/s]\n\n\n Neural network successfully converged after 47 epochs.\n\n\n  0%|          | 0/2000 [00:00&lt;?, ?it/s]\n\n\n\nConverged with loss: -4.2\nQuality Score: 0.025     Good: Smaller than 0.5  Bad: Larger than 1.0            NOTE: Less sensitive to mode collapse.\n</code></pre> <p>After having learned the posterior, we use <code>.pairplot()</code> to visualize the samples from the learned posterior.</p> <pre><code>posterior_samples = posterior.sample((10000,), x=x_o)\n\n# Plot posterior samples.\n_ = analysis.pairplot(\n    posterior_samples, limits=[[-2, 2], [-2, 2], [-2, 2]], figsize=(5, 5)\n)\n</code></pre> <p></p>"}]}
\ No newline at end of file
+{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"<code>sbi</code>: simulation-based inference","text":"<p><code>sbi</code>: A Python toolbox for simulation-based inference.</p> <p></p> <p>Inference can be run in a single line of code</p> <pre><code>posterior = infer(simulator, prior, method='SNPE', num_simulations=1000)\n</code></pre> <p>or in a few lines for more flexibility:</p> <pre><code>inference = SNPE(prior=prior)\n_ = inference.append_simulations(theta, x).train()\nposterior = inference.build_posterior()\n</code></pre> <p><code>sbi</code> lets you choose from a variety of amortized and sequential SBI methods:</p> <p>Amortized methods return a posterior that can be applied to many different observations without retraining, whereas sequential methods focus the inference on one particular observation to be more simulation-efficient. For an overview of implemented methods see below, or checkout or GitHub page.</p>"},{"location":"#overview","title":"Overview","text":"<ul> <li> <p>To learn about the general motivation behind simulation-based inference, and the   inference methods included in <code>sbi</code>, read on below.</p> </li> <li> <p>For example applications to canonical problems in neuroscience, browse the recent   research article Training deep neural density estimators to identify mechanistic models of neural dynamics.</p> </li> <li> <p>If you want to get started using <code>sbi</code> on your own problem, jump to   installation and then check out the tutorial.</p> </li> </ul>"},{"location":"#motivation-and-approach","title":"Motivation and approach","text":"<p>Many areas of science and engineering make extensive use of complex, stochastic, numerical simulations to describe the structure and dynamics of the processes being investigated.</p> <p>A key challenge in simulation-based science is constraining these simulation models\u2019 parameters, which are intepretable quantities, with observational data. Bayesian inference provides a general and powerful framework to invert the simulators, i.e. describe the parameters which are consistent both with empirical data and prior knowledge.</p> <p>In the case of simulators, a key quantity required for statistical inference, the likelihood of observed data given parameters, \\(\\mathcal{L}(\\theta) = p(x_o|\\theta)\\), is typically intractable, rendering conventional statistical approaches inapplicable.</p> <p><code>sbi</code> implements powerful machine-learning methods that address this problem. Roughly, these algorithms can be categorized as:</p> <ul> <li>Neural Posterior Estimation (amortized <code>NPE</code> and sequential <code>SNPE</code>),</li> <li>Neural Likelihood Estimation (<code>(S)NLE</code>), and</li> <li>Neural Ratio Estimation (<code>(S)NRE</code>).</li> </ul> <p>Depending on the characteristics of the problem, e.g. the dimensionalities of the parameter space and the observation space, one of the methods will be more suitable.</p> <p></p> <p>Goal: Algorithmically identify mechanistic models which are consistent with data.</p> <p>Each of the methods above needs three inputs: A candidate mechanistic model, prior knowledge or constraints on model parameters, and observational data (or summary statistics thereof).</p> <p>The methods then proceed by</p> <ol> <li>sampling parameters from the prior followed by simulating synthetic data from    these parameters,</li> <li>learning the (probabilistic) association between data (or    data features) and underlying parameters, i.e. to learn statistical inference from    simulated data. The way in which this association is learned differs between the    above methods, but all use deep neural networks.</li> <li>This learned neural network is then applied to empirical data to derive the full    space of parameters consistent with the data and the prior, i.e. the posterior    distribution. High posterior probability is assigned to parameters which are    consistent with both the data and the prior, low probability to inconsistent    parameters. While SNPE directly learns the posterior distribution, SNLE and SNRE need    an extra MCMC sampling step to construct a posterior.</li> <li>If needed, an initial estimate of the posterior can be used to adaptively generate    additional informative simulations.</li> </ol>"},{"location":"#publications","title":"Publications","text":"<p>See Cranmer, Brehmer, Louppe (2020) for a recent review on simulation-based inference.</p> <p>The following papers offer additional details on the inference methods implemented in <code>sbi</code>. You can find a tutorial on how to run each of these methods here.</p>"},{"location":"#posterior-estimation-snpe","title":"Posterior estimation (<code>(S)NPE</code>)","text":"<ul> <li> <p>Fast \u03b5-free Inference of Simulation Models with Bayesian Conditional Density Estimation by Papamakarios &amp; Murray (NeurIPS 2016) [PDF] [BibTeX]</p> </li> <li> <p>Flexible statistical inference for mechanistic models of neural dynamics  by Lueckmann, Goncalves, Bassetto, \u00d6cal, Nonnenmacher &amp; Macke (NeurIPS 2017) [PDF] [BibTeX]</p> </li> <li> <p>Automatic posterior transformation for likelihood-free inferenceby Greenberg, Nonnenmacher &amp; Macke (ICML 2019) [PDF] [BibTeX]</p> </li> <li> <p>Truncated proposals for scalable and hassle-free simulation-based inference  by Deistler, Goncalves &amp; Macke (NeurIPS 2022) [Paper]</p> </li> </ul>"},{"location":"#likelihood-estimation-snle","title":"Likelihood-estimation (<code>(S)NLE</code>)","text":"<ul> <li> <p>Sequential neural likelihood: Fast likelihood-free inference with autoregressive flowsby Papamakarios, Sterratt &amp; Murray (AISTATS 2019) [PDF] [BibTeX]</p> </li> <li> <p>Variational methods for simulation-based inference  by Gl\u00f6ckler, Deistler, Macke (ICLR 2022) [Paper]</p> </li> <li> <p>Flexible and efficient simulation-based inference for models of decision-making  by Boelts, Lueckmann, Gao, Macke (Elife 2022) [Paper]</p> </li> </ul>"},{"location":"#likelihood-ratio-estimation-snre","title":"Likelihood-ratio-estimation (<code>(S)NRE</code>)","text":"<ul> <li> <p>Likelihood-free MCMC with Amortized Approximate Likelihood Ratiosby Hermans, Begy &amp; Louppe (ICML 2020) [PDF]</p> </li> <li> <p>On Contrastive Learning for Likelihood-free InferenceDurkan, Murray &amp; Papamakarios (ICML 2020) [PDF]</p> </li> <li> <p>Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimationby Delaunoy, Hermans, Rozet, Wehenkel &amp; Louppe (NeurIPS 2022) [PDF]</p> </li> <li> <p>Contrastive Neural Ratio EstimationBenjamin Kurt Miller, Christoph Weniger, Patrick Forr\u00e9 (NeurIPS 2022) [PDF]</p> </li> </ul>"},{"location":"#utilities","title":"Utilities","text":"<ul> <li> <p>Restriction estimatorby Deistler, Macke &amp; Goncalves (PNAS 2022) [Paper]</p> </li> <li> <p>Simulation-based calibrationby Talts, Betancourt, Simpson, Vehtari, Gelman (arxiv 2018) [Paper])</p> </li> <li> <p>Expected coverage (sample-based)as computed in Deistler, Goncalves, Macke [Paper] and in Rozet, Louppe [Paper]</p> </li> </ul>"},{"location":"citation/","title":"Citation","text":"<p>If you use <code>sbi</code> consider citing the sbi software paper, in addition to the original research articles describing the specific sbi-algorithm(s) you are using. </p> <pre><code>@article{tejero-cantero2020sbi,\n  doi = {10.21105/joss.02505},\n  url = {https://doi.org/10.21105/joss.02505},\n  year = {2020},\n  publisher = {The Open Journal},\n  volume = {5},\n  number = {52},\n  pages = {2505},\n  author = {Alvaro Tejero-Cantero and Jan Boelts and Michael Deistler and Jan-Matthis Lueckmann and Conor Durkan and Pedro J. Gon\u00e7alves and David S. Greenberg and Jakob H. Macke},\n  title = {sbi: A toolkit for simulation-based inference},\n  journal = {Journal of Open Source Software}\n}\n</code></pre> <p>The above citation refers to the original version of the <code>sbi</code> project and has a persistent DOI. Additionally, new releases of <code>sbi</code> are citable via Zenodo, where we create a new DOI for every release.</p>"},{"location":"code_of_conduct/","title":"Contributor Covenant Code of Conduct","text":""},{"location":"code_of_conduct/#our-pledge","title":"Our Pledge","text":"<p>We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, or sexual identity and orientation.</p> <p>We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community.</p>"},{"location":"code_of_conduct/#our-standards","title":"Our Standards","text":"<p>Examples of behavior that contributes to a positive environment for our community include:</p> <ul> <li>Demonstrating empathy and kindness toward other people</li> <li>Being respectful of differing opinions, viewpoints, and experiences</li> <li>Giving and gracefully accepting constructive feedback</li> <li>Accepting responsibility and apologizing to those affected by our mistakes,   and learning from the experience</li> <li>Focusing on what is best not just for us as individuals, but for the overall   community</li> </ul> <p>Examples of unacceptable behavior include:</p> <ul> <li>The use of sexualized language or imagery, and sexual attention or advances of   any kind</li> <li>Trolling, insulting or derogatory comments, and personal or political attacks</li> <li>Public or private harassment</li> <li>Publishing others\u2019 private information, such as a physical or email address,   without their explicit permission</li> <li>Other conduct which could reasonably be considered inappropriate in a   professional setting</li> </ul>"},{"location":"code_of_conduct/#enforcement-responsibilities","title":"Enforcement Responsibilities","text":"<p>Community leaders are responsible for clarifying and enforcing our standards of acceptable behavior and will take appropriate and fair corrective action in response to any behavior that they deem inappropriate, threatening, offensive, or harmful.</p> <p>Community leaders have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, and will communicate reasons for moderation decisions when appropriate.</p>"},{"location":"code_of_conduct/#scope","title":"Scope","text":"<p>This Code of Conduct applies within all community spaces, and also applies when an individual is officially representing the community in public spaces. Examples of representing our community include using an official e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event.</p>"},{"location":"code_of_conduct/#enforcement","title":"Enforcement","text":"<p>Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting <code>sbi</code> developer Jan Boelts via email (jan.boelts@uni-tuebingen.de). All complaints will be reviewed and investigated promptly and fairly.</p> <p>All community leaders are obligated to respect the privacy and security of the reporter of any incident.</p>"},{"location":"code_of_conduct/#enforcement-guidelines","title":"Enforcement Guidelines","text":"<p>Community leaders will follow these Community Impact Guidelines in determining the consequences for any action they deem in violation of this Code of Conduct:</p>"},{"location":"code_of_conduct/#1-correction","title":"1. Correction","text":"<p>Community Impact: Use of inappropriate language or other behavior deemed unprofessional or unwelcome in the community.</p> <p>Consequence: A private, written warning from community leaders, providing clarity around the nature of the violation and an explanation of why the behavior was inappropriate. A public apology may be requested.</p>"},{"location":"code_of_conduct/#2-warning","title":"2. Warning","text":"<p>Community Impact: A violation through a single incident or series of actions.</p> <p>Consequence: A warning with consequences for continued behavior. No interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, for a specified period of time. This includes avoiding interactions in community spaces as well as external channels like social media. Violating these terms may lead to a temporary or permanent ban.</p>"},{"location":"code_of_conduct/#3-temporary-ban","title":"3. Temporary Ban","text":"<p>Community Impact: A serious violation of community standards, including sustained inappropriate behavior.</p> <p>Consequence: A temporary ban from any sort of interaction or public communication with the community for a specified period of time. No public or private interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, is allowed during this period. Violating these terms may lead to a permanent ban.</p>"},{"location":"code_of_conduct/#4-permanent-ban","title":"4. Permanent Ban","text":"<p>Community Impact: Demonstrating a pattern of violation of community standards, including sustained inappropriate behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals.</p> <p>Consequence: A permanent ban from any sort of public interaction within the community.</p>"},{"location":"code_of_conduct/#attribution","title":"Attribution","text":"<p>This Code of Conduct is adapted from the Contributor Covenant, version 2.1, available at https://www.contributor-covenant.org/version/2/1/code_of_conduct.html.</p> <p>Community Impact Guidelines were inspired by Mozilla\u2019s code of conduct enforcement ladder.</p> <p>For answers to common questions about this code of conduct, see the FAQ at https://www.contributor-covenant.org/faq. Translations are available at https://www.contributor-covenant.org/translations.</p>"},{"location":"contribute/","title":"Guide","text":""},{"location":"contribute/#user-experiences-bugs-and-feature-requests","title":"User experiences, bugs, and feature requests","text":"<p>If you are using <code>sbi</code> to infer the parameters of a simulator, we would be delighted to know how it worked for you. If it didn\u2019t work according to plan, please open up an issue and tell us more about your use case: the dimensionality of the input parameters and of the output, as well as the setup you used to run inference (i.e. number of simulations, number of rounds,\u2026).</p> <p>To report bugs and suggest features (including better documentation), please equally head over to issues on GitHub.</p>"},{"location":"contribute/#code-contributions","title":"Code contributions","text":"<p>Contributions to the <code>sbi</code> package are welcome! In general, we use pull requests to make changes to <code>sbi</code>. So, if you are planning to make a contribution, please fork, create a feature branch and then make a PR from your feature branch to the upstream <code>sbi</code> (details). To give credits to contributors, we consider adding contributors who repeatedly and substantially contributed to <code>sbi</code> to the list of authors of the package at the end of every year. Additionally, we mention all contributors in the releases.</p>"},{"location":"contribute/#development-environment","title":"Development environment","text":"<p>Clone the repo and install all the dependencies using the <code>environment.yml</code> file to create a conda environment: <code>conda env create -f environment.yml</code>. If you already have an <code>sbi</code> environment and want to refresh dependencies, just run <code>conda env update -f environment.yml --prune</code>.</p> <p>Alternatively, you can install via <code>setup.py</code> using <code>pip install -e \".[dev]\"</code> (the dev flag installs development and testing dependencies).</p>"},{"location":"contribute/#style-conventions","title":"Style conventions","text":"<p>For docstrings and comments, we use Google Style.</p> <p>Code needs to pass through the following tools, which are installed alongside <code>sbi</code>:</p> <p>black: Automatic code formatting for Python. You can run black manually from the console using <code>black .</code> in the top directory of the repository, which will format all files.</p> <p>isort: Used to consistently order imports. You can run isort manually from the console using <code>isort</code> in the top directory.</p> <p>pyright: Used for static type checking.</p> <p><code>black</code> and <code>isort</code> and <code>pyright</code> are checked as part of our CI actions. If these checks fail please make sure you have installed the latest versions for each of them and run them locally.</p>"},{"location":"contribute/#online-documentation","title":"Online documentation","text":"<p>Most of the documentation is written in markdown (basic markdown guide).</p> <p>You can directly fix mistakes and suggest clearer formulations in markdown files simply by initiating a PR on through GitHub. Click on documentation file and look for the little pencil at top right.</p>"},{"location":"credits/","title":"Credits","text":""},{"location":"credits/#license","title":"License","text":"<p><code>sbi</code> is licensed under the Affero General Public License version 3 (AGPLv3) and</p> <p>Copyright (C) 2020 \u00c1lvaro Tejero-Cantero, Jakob H. Macke, Jan-Matthis L\u00fcckmann, Michael Deistler, Jan F. B\u00f6lts.</p> <p>Copyright (C) 2020 Conor M. Durkan.</p>"},{"location":"credits/#support","title":"Support","text":"<p><code>sbi</code> has been supported by the German Federal Ministry of Education and Research (BMBF) through the project ADIMEM, FKZ 01IS18052 A-D). ADIMEM is a collaborative project between the groups of Jakob Macke (Uni T\u00fcbingen), Philipp Berens (Uni T\u00fcbingen), Philipp Hennig (Uni T\u00fcbingen) and Marcel Oberlaender (caesar Bonn) which aims to develop inference methods for mechanistic models.</p> <p></p>"},{"location":"credits/#important-dependencies-and-prior-art","title":"Important dependencies and prior art","text":"<ul> <li> <p><code>sbi</code> is the successor to <code>delfi</code>, a Theano-based   toolbox for sequential neural posterior estimation developed at mackelab. If you were   using <code>delfi</code>, we strongly recommend to move your inference over to <code>sbi</code>. Please open   issues if you find unexpected behaviour or missing features. We will consider these   bugs and give them priority.</p> </li> <li> <p><code>sbi</code> as a PyTorch-based toolbox started as a fork of   conormdurkan/lfi, by Conor   M.Durkan.</p> </li> <li> <p><code>sbi</code> uses density estimators from bayesiains/nflows by Conor M.Durkan, George Papamakarios and Artur Bekasov. These are proxied through <code>pyknos</code>, a package focused on density estimation.</p> </li> <li> <p><code>sbi</code> uses <code>PyTorch</code> and tries to align with the interfaces (e.g. for probability   distributions) adopted by <code>PyTorch</code>.</p> </li> <li> <p>See README.md for a list of   publications describing the methods implemented in <code>sbi</code>.</p> </li> </ul>"},{"location":"faq/","title":"Frequently asked questions","text":"<p>Can the algorithms deal with invalid data, e.g. NaN or inf?</p> <p>What should I do when my \u2018posterior samples are outside of the prior support\u2019 in SNPE?</p> <p>When using multiple workers, I get a pickling error. Can I still use multiprocessing?</p> <p>Can I use the GPU for training the density estimator?</p> <p>How should I save and load objects in <code>sbi</code>?</p> <p>Can I stop neural network training and resume it later?</p> <p>How can I use a prior that is not defined in PyTorch?</p>"},{"location":"install/","title":"Installation","text":"<p><code>sbi</code> requires Python 3.6 or higher. We recommend to use a <code>conda</code> virtual environment (Miniconda installation instructions). If <code>conda</code> is installed on the system, an environment for installing <code>sbi</code> can be created as follows: <pre><code># Create an environment for sbi (indicate Python 3.6 or higher); activate it\n$ conda create -n sbi_env python=3.7 &amp;&amp; conda activate sbi_env\n</code></pre></p> <p>Independent of whether you are using <code>conda</code> or not, <code>sbi</code> can be installed using <code>pip</code>: <pre><code>$ pip install sbi\n</code></pre></p> <p>To test the installation, drop into a python prompt and run <pre><code>from sbi.examples.minimal import simple\nposterior = simple()\nprint(posterior)\n</code></pre></p>"},{"location":"reference/","title":"API Reference","text":""},{"location":"reference/#inference","title":"Inference","text":"<p>Runs simulation-based inference and returns the posterior.</p> <p>This function provides a simple interface to run sbi. Inference is run for a single round and hence the returned posterior \\(p(\\theta|x)\\) can be sampled and evaluated for any \\(x\\) (i.e. it is amortized).</p> <p>The scope of this function is limited to the most essential features of sbi. For more flexibility (e.g. multi-round inference, different density estimators) please use the flexible interface described here: https://www.mackelab.org/sbi/tutorial/02_flexible_interface/</p> <p>Parameters:</p> Name Type Description Default <code>simulator</code> <code>Callable</code> <p>A function that takes parameters \\(\\theta\\) and maps them to simulations, or observations, <code>x</code>, \\(\\mathrm{sim}(\\theta)\\to x\\). Any regular Python callable (i.e. function or class with <code>__call__</code> method) can be used.</p> required <code>prior</code> <code>Distribution</code> <p>A probability distribution that expresses prior knowledge about the parameters, e.g. which ranges are meaningful for them. Any object with <code>.log_prob()</code>and <code>.sample()</code> (for example, a PyTorch distribution) can be used.</p> required <code>method</code> <code>str</code> <p>What inference method to use. Either of SNPE, SNLE or SNRE.</p> required <code>num_simulations</code> <code>int</code> <p>Number of simulation calls. More simulations means a longer runtime, but a better posterior estimate.</p> required <code>num_workers</code> <code>int</code> <p>Number of parallel workers to use for simulations.</p> <code>1</code> <p>Returns: Posterior over parameters conditional on observations (amortized).</p> Source code in <code>sbi/inference/base.py</code> <pre><code>def infer(\n    simulator: Callable,\n    prior: Distribution,\n    method: str,\n    num_simulations: int,\n    num_workers: int = 1,\n) -&gt; NeuralPosterior:\n    r\"\"\"Runs simulation-based inference and returns the posterior.\n\n    This function provides a simple interface to run sbi. Inference is run for a single\n    round and hence the returned posterior $p(\\theta|x)$ can be sampled and evaluated\n    for any $x$ (i.e. it is amortized).\n\n    The scope of this function is limited to the most essential features of sbi. For\n    more flexibility (e.g. multi-round inference, different density estimators) please\n    use the flexible interface described here:\n    https://www.mackelab.org/sbi/tutorial/02_flexible_interface/\n\n    Args:\n        simulator: A function that takes parameters $\\theta$ and maps them to\n            simulations, or observations, `x`, $\\mathrm{sim}(\\theta)\\to x$. Any\n            regular Python callable (i.e. function or class with `__call__` method)\n            can be used.\n        prior: A probability distribution that expresses prior knowledge about the\n            parameters, e.g. which ranges are meaningful for them. Any\n            object with `.log_prob()`and `.sample()` (for example, a PyTorch\n            distribution) can be used.\n        method: What inference method to use. Either of SNPE, SNLE or SNRE.\n        num_simulations: Number of simulation calls. More simulations means a longer\n            runtime, but a better posterior estimate.\n        num_workers: Number of parallel workers to use for simulations.\n\n    Returns: Posterior over parameters conditional on observations (amortized).\n    \"\"\"\n\n    try:\n        method_fun: Callable = getattr(sbi.inference, method.upper())\n    except AttributeError:\n        raise NameError(\n            \"Method not available. `method` must be one of 'SNPE', 'SNLE', 'SNRE'.\"\n        )\n\n    simulator, prior = prepare_for_sbi(simulator, prior)\n\n    inference = method_fun(prior=prior)\n    theta, x = simulate_for_sbi(\n        simulator=simulator,\n        proposal=prior,\n        num_simulations=num_simulations,\n        num_workers=num_workers,\n    )\n    _ = inference.append_simulations(theta, x).train()\n    posterior = inference.build_posterior()\n\n    return posterior\n</code></pre> <p>Prepare simulator and prior for usage in sbi.</p> <p>NOTE: This is a wrapper around <code>process_prior</code> and <code>process_simulator</code> which can be used in isolation as well.</p> <p>Attempts to meet the following requirements by reshaping and type-casting:</p> <ul> <li>the simulator function receives as input and returns a Tensor.</li> <li>the simulator can simulate batches of parameters and return batches of data.</li> <li>the prior does not produce batches and samples and evaluates to Tensor.</li> <li>the output shape is a <code>torch.Size((1,N))</code> (i.e, has a leading batch dimension 1).</li> </ul> <p>If this is not possible, a suitable exception will be raised.</p> <p>Parameters:</p> Name Type Description Default <code>simulator</code> <code>Callable</code> <p>Simulator as provided by the user.</p> required <code>prior</code> <p>Prior as provided by the user.</p> required <p>Returns:</p> Type Description <code>Tuple[Callable, Distribution]</code> <p>Tuple (simulator, prior) checked and matching the requirements of sbi.</p> Source code in <code>sbi/utils/user_input_checks.py</code> <pre><code>def prepare_for_sbi(simulator: Callable, prior) -&gt; Tuple[Callable, Distribution]:\n    \"\"\"Prepare simulator and prior for usage in sbi.\n\n    NOTE: This is a wrapper around `process_prior` and `process_simulator` which can be\n    used in isolation as well.\n\n    Attempts to meet the following requirements by reshaping and type-casting:\n\n    - the simulator function receives as input and returns a Tensor.&lt;br/&gt;\n    - the simulator can simulate batches of parameters and return batches of data.&lt;br/&gt;\n    - the prior does not produce batches and samples and evaluates to Tensor.&lt;br/&gt;\n    - the output shape is a `torch.Size((1,N))` (i.e, has a leading batch dimension 1).\n\n    If this is not possible, a suitable exception will be raised.\n\n    Args:\n        simulator: Simulator as provided by the user.\n        prior: Prior as provided by the user.\n\n    Returns:\n        Tuple (simulator, prior) checked and matching the requirements of sbi.\n    \"\"\"\n\n    # Check prior, return PyTorch prior.\n    prior, _, prior_returns_numpy = process_prior(prior)\n\n    # Check simulator, returns PyTorch simulator able to simulate batches.\n    simulator = process_simulator(simulator, prior, prior_returns_numpy)\n\n    # Consistency check after making ready for sbi.\n    check_sbi_inputs(simulator, prior)\n\n    return simulator, prior\n</code></pre> <p>Returns (\\(\\theta, x\\)) pairs obtained from sampling the proposal and simulating.</p> <p>This function performs two steps:</p> <ul> <li>Sample parameters \\(\\theta\\) from the <code>proposal</code>.</li> <li>Simulate these parameters to obtain \\(x\\).</li> </ul> <p>Parameters:</p> Name Type Description Default <code>simulator</code> <code>Callable</code> <p>A function that takes parameters \\(\\theta\\) and maps them to simulations, or observations, <code>x</code>, \\(\\text{sim}(\\theta)\\to x\\). Any regular Python callable (i.e. function or class with <code>__call__</code> method) can be used.</p> required <code>proposal</code> <code>Any</code> <p>Probability distribution that the parameters \\(\\theta\\) are sampled from.</p> required <code>num_simulations</code> <code>int</code> <p>Number of simulations that are run.</p> required <code>num_workers</code> <code>int</code> <p>Number of parallel workers to use for simulations.</p> <code>1</code> <code>simulation_batch_size</code> <code>int</code> <p>Number of parameter sets that the simulator maps to data x at once. If None, we simulate all parameter sets at the same time. If &gt;= 1, the simulator has to process data of shape (simulation_batch_size, parameter_dimension).</p> <code>1</code> <code>seed</code> <code>Optional[int]</code> <p>Seed for reproducibility.</p> <code>None</code> <code>show_progress_bar</code> <code>bool</code> <p>Whether to show a progress bar for simulating. This will not affect whether there will be a progressbar while drawing samples from the proposal.</p> <code>True</code> <p>Returns: Sampled parameters \\(\\theta\\) and simulation-outputs \\(x\\).</p> Source code in <code>sbi/inference/base.py</code> <pre><code>def simulate_for_sbi(\n    simulator: Callable,\n    proposal: Any,\n    num_simulations: int,\n    num_workers: int = 1,\n    simulation_batch_size: int = 1,\n    seed: Optional[int] = None,\n    show_progress_bar: bool = True,\n) -&gt; Tuple[Tensor, Tensor]:\n    r\"\"\"Returns ($\\theta, x$) pairs obtained from sampling the proposal and simulating.\n\n    This function performs two steps:\n\n    - Sample parameters $\\theta$ from the `proposal`.\n    - Simulate these parameters to obtain $x$.\n\n    Args:\n        simulator: A function that takes parameters $\\theta$ and maps them to\n            simulations, or observations, `x`, $\\text{sim}(\\theta)\\to x$. Any\n            regular Python callable (i.e. function or class with `__call__` method)\n            can be used.\n        proposal: Probability distribution that the parameters $\\theta$ are sampled\n            from.\n        num_simulations: Number of simulations that are run.\n        num_workers: Number of parallel workers to use for simulations.\n        simulation_batch_size: Number of parameter sets that the simulator\n            maps to data x at once. If None, we simulate all parameter sets at the\n            same time. If &gt;= 1, the simulator has to process data of shape\n            (simulation_batch_size, parameter_dimension).\n        seed: Seed for reproducibility.\n        show_progress_bar: Whether to show a progress bar for simulating. This will not\n            affect whether there will be a progressbar while drawing samples from the\n            proposal.\n\n    Returns: Sampled parameters $\\theta$ and simulation-outputs $x$.\n    \"\"\"\n\n    theta = proposal.sample((num_simulations,))\n\n    x = simulate_in_batches(\n        simulator=simulator,\n        theta=theta,\n        sim_batch_size=simulation_batch_size,\n        num_workers=num_workers,\n        seed=seed,\n        show_progress_bars=show_progress_bar,\n    )\n\n    return theta, x\n</code></pre> <p>             Bases: <code>PosteriorEstimator</code></p> Source code in <code>sbi/inference/snpe/snpe_a.py</code> <pre><code>class SNPE_A(PosteriorEstimator):\n    def __init__(\n        self,\n        prior: Optional[Distribution] = None,\n        density_estimator: Union[str, Callable] = \"mdn_snpe_a\",\n        num_components: int = 10,\n        device: str = \"cpu\",\n        logging_level: Union[int, str] = \"WARNING\",\n        summary_writer: Optional[TensorboardSummaryWriter] = None,\n        show_progress_bars: bool = True,\n    ):\n        r\"\"\"SNPE-A [1].\n\n        [1] _Fast epsilon-free Inference of Simulation Models with Bayesian Conditional\n            Density Estimation_, Papamakarios et al., NeurIPS 2016,\n            https://arxiv.org/abs/1605.06376.\n\n        This class implements SNPE-A. SNPE-A trains across multiple rounds with a\n        maximum-likelihood-loss. This will make training converge to the proposal\n        posterior instead of the true posterior. To correct for this, SNPE-A applies a\n        post-hoc correction after training. This correction has to be performed\n        analytically. Thus, SNPE-A is limited to Gaussian distributions for all but the\n        last round. In the last round, SNPE-A can use a Mixture of Gaussians.\n\n        Args:\n            prior: A probability distribution that expresses prior knowledge about the\n                parameters, e.g. which ranges are meaningful for them. Any\n                object with `.log_prob()`and `.sample()` (for example, a PyTorch\n                distribution) can be used.\n            density_estimator: If it is a string (only \"mdn_snpe_a\" is valid), use a\n                pre-configured mixture of densities network. Alternatively, a function\n                that builds a custom neural network can be provided. The function will\n                be called with the first batch of simulations (theta, x), which can\n                thus be used for shape inference and potentially for z-scoring. It\n                needs to return a PyTorch `nn.Module` implementing the density\n                estimator. The density estimator needs to provide the methods\n                `.log_prob` and `.sample()`. Note that until the last round only a\n                single (multivariate) Gaussian component is used for training (see\n                Algorithm 1 in [1]). In the last round, this component is replicated\n                `num_components` times, its parameters are perturbed with a very small\n                noise, and then the last training round is done with the expanded\n                Gaussian mixture as estimator for the proposal posterior.\n            num_components: Number of components of the mixture of Gaussians in the\n                last round. This overrides the `num_components` value passed to\n                `posterior_nn()`.\n            device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n            logging_level: Minimum severity of messages to log. One of the strings\n                INFO, WARNING, DEBUG, ERROR and CRITICAL.\n            summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n                file location (default is `&lt;current working directory&gt;/logs`.)\n            show_progress_bars: Whether to show a progressbar during training.\n        \"\"\"\n\n        # Catch invalid inputs.\n        if not ((density_estimator == \"mdn_snpe_a\") or callable(density_estimator)):\n            raise TypeError(\n                \"The `density_estimator` passed to SNPE_A needs to be a \"\n                \"callable or the string 'mdn_snpe_a'!\"\n            )\n\n        # `num_components` will be used to replicate the Gaussian in the last round.\n        self._num_components = num_components\n        self._ran_final_round = False\n\n        # WARNING: sneaky trick ahead. We proxy the parent's `train` here,\n        # requiring the signature to have `num_atoms`, save it for use below, and\n        # continue. It's sneaky because we are using the object (self) as a namespace\n        # to pass arguments between functions, and that's implicit state management.\n        kwargs = utils.del_entries(\n            locals(),\n            entries=(\"self\", \"__class__\", \"num_components\"),\n        )\n        super().__init__(**kwargs)\n\n    def train(\n        self,\n        final_round: bool = False,\n        training_batch_size: int = 50,\n        learning_rate: float = 5e-4,\n        validation_fraction: float = 0.1,\n        stop_after_epochs: int = 20,\n        max_num_epochs: int = 2**31 - 1,\n        clip_max_norm: Optional[float] = 5.0,\n        calibration_kernel: Optional[Callable] = None,\n        resume_training: bool = False,\n        retrain_from_scratch: bool = False,\n        show_train_summary: bool = False,\n        dataloader_kwargs: Optional[Dict] = None,\n        component_perturbation: float = 5e-3,\n    ) -&gt; nn.Module:\n        r\"\"\"Return density estimator that approximates the proposal posterior.\n\n        [1] _Fast epsilon-free Inference of Simulation Models with Bayesian Conditional\n            Density Estimation_, Papamakarios et al., NeurIPS 2016,\n            https://arxiv.org/abs/1605.06376.\n\n        Training is performed with maximum likelihood on samples from the latest round,\n        which leads the algorithm to converge to the proposal posterior.\n\n        Args:\n            final_round: Whether we are in the last round of training or not. For all\n                but the last round, Algorithm 1 from [1] is executed. In last the\n                round, Algorithm 2 from [1] is executed once.\n            training_batch_size: Training batch size.\n            learning_rate: Learning rate for Adam optimizer.\n            validation_fraction: The fraction of data to use for validation.\n            stop_after_epochs: The number of epochs to wait for improvement on the\n                validation set before terminating training.\n            max_num_epochs: Maximum number of epochs to run. If reached, we stop\n                training even when the validation loss is still decreasing. Otherwise,\n                we train until validation loss increases (see also `stop_after_epochs`).\n            clip_max_norm: Value at which to clip the total gradient norm in order to\n                prevent exploding gradients. Use None for no clipping.\n            calibration_kernel: A function to calibrate the loss with respect to the\n                simulations `x`. See Lueckmann, Gon\u00e7alves et al., NeurIPS 2017.\n            resume_training: Can be used in case training time is limited, e.g. on a\n                cluster. If `True`, the split between train and validation set, the\n                optimizer, the number of epochs, and the best validation log-prob will\n                be restored from the last time `.train()` was called.\n            force_first_round_loss: If `True`, train with maximum likelihood,\n                i.e., potentially ignoring the correction for using a proposal\n                distribution different from the prior.\n            retrain_from_scratch: Whether to retrain the conditional density\n                estimator for the posterior from scratch each round. Not supported for\n                SNPE-A.\n            show_train_summary: Whether to print the number of epochs and validation\n                loss and leakage after the training.\n            dataloader_kwargs: Additional or updated kwargs to be passed to the training\n                and validation dataloaders (like, e.g., a collate_fn)\n            component_perturbation: The standard deviation applied to all weights and\n                biases when, in the last round, the Mixture of Gaussians is build from\n                a single Gaussian. This value can be problem-specific and also depends\n                on the number of mixture components.\n\n        Returns:\n            Density estimator that approximates the distribution $p(\\theta|x)$.\n        \"\"\"\n\n        assert not retrain_from_scratch, \"\"\"Retraining from scratch is not supported in\n            SNPE-A yet. The reason for this is that, if we reininitialized the density\n            estimator, the z-scoring would change, which would break the posthoc\n            correction. This is a pure implementation issue.\"\"\"\n\n        kwargs = utils.del_entries(\n            locals(),\n            entries=(\n                \"self\",\n                \"__class__\",\n                \"final_round\",\n                \"component_perturbation\",\n            ),\n        )\n\n        # SNPE-A always discards the prior samples.\n        kwargs[\"discard_prior_samples\"] = True\n        kwargs[\"force_first_round_loss\"] = True\n\n        self._round = max(self._data_round_index)\n\n        if final_round:\n            # If there is (will be) only one round, train with Algorithm 2 from [1].\n            if self._round == 0:\n                self._build_neural_net = partial(\n                    self._build_neural_net, num_components=self._num_components\n                )\n            # Run Algorithm 2 from [1].\n            elif not self._ran_final_round:\n                # Now switch to the specified number of components. This method will\n                # only be used if `retrain_from_scratch=True`. Otherwise,\n                # the MDN will be built from replicating the single-component net for\n                # `num_component` times (via `_expand_mog()`).\n                self._build_neural_net = partial(\n                    self._build_neural_net, num_components=self._num_components\n                )\n\n                # Extend the MDN to the originally desired number of components.\n                self._expand_mog(eps=component_perturbation)\n            else:\n                warnings.warn(\n                    \"You have already run SNPE-A with `final_round=True`. Running it\"\n                    \"again with this setting will not allow computing the posthoc\"\n                    \"correction applied in SNPE-A. Thus, you will get an error when \"\n                    \"calling `.build_posterior()` after training.\",\n                    UserWarning,\n                )\n        else:\n            # Run Algorithm 1 from [1].\n            # Wrap the function that builds the MDN such that we can make\n            # sure that there is only one component when running.\n            self._build_neural_net = partial(self._build_neural_net, num_components=1)\n\n        if final_round:\n            self._ran_final_round = True\n\n        return super().train(**kwargs)\n\n    def correct_for_proposal(\n        self,\n        density_estimator: Optional[TorchModule] = None,\n    ) -&gt; \"SNPE_A_MDN\":\n        r\"\"\"Build mixture of Gaussians that approximates the posterior.\n\n        Returns a `SNPE_A_MDN` object, which applies the posthoc-correction required in\n        SNPE-A.\n\n        Args:\n            density_estimator: The density estimator that the posterior is based on.\n                If `None`, use the latest neural density estimator that was trained.\n\n        Returns:\n            Posterior $p(\\theta|x)$  with `.sample()` and `.log_prob()` methods.\n        \"\"\"\n        if density_estimator is None:\n            density_estimator = deepcopy(\n                self._neural_net\n            )  # PosteriorEstimator.train() also returns a deepcopy, mimic this here\n            # If internal net is used device is defined.\n            device = self._device\n        else:\n            # Otherwise, infer it from the device of the net parameters.\n            device = str(next(density_estimator.parameters()).device)\n\n        # Set proposal of the density estimator.\n        # This also evokes the z-scoring correction if necessary.\n        if (\n            self._proposal_roundwise[-1] is self._prior\n            or self._proposal_roundwise[-1] is None\n        ):\n            proposal = self._prior\n            assert isinstance(\n                proposal, (MultivariateNormal, utils.BoxUniform)\n            ), \"\"\"Prior must be `torch.distributions.MultivariateNormal` or `sbi.utils.\n                BoxUniform`\"\"\"\n        else:\n            assert isinstance(\n                self._proposal_roundwise[-1], DirectPosterior\n            ), \"\"\"The proposal you passed to `append_simulations` is neither the prior\n                nor a `DirectPosterior`. SNPE-A currently only supports these scenarios.\n                \"\"\"\n            proposal = self._proposal_roundwise[-1]\n\n        # Create the SNPE_A_MDN\n        wrapped_density_estimator = SNPE_A_MDN(\n            flow=density_estimator,  # type: ignore\n            proposal=proposal,\n            prior=self._prior,\n            device=device,\n        )\n        return wrapped_density_estimator\n\n    def build_posterior(\n        self,\n        density_estimator: Optional[TorchModule] = None,\n        prior: Optional[Distribution] = None,\n    ) -&gt; \"DirectPosterior\":\n        r\"\"\"Build posterior from the neural density estimator.\n\n        This method first corrects the estimated density with `correct_for_proposal`\n        and then returns a `DirectPosterior`.\n\n        Args:\n            density_estimator: The density estimator that the posterior is based on.\n                If `None`, use the latest neural density estimator that was trained.\n            prior: Prior distribution.\n\n        Returns:\n            Posterior $p(\\theta|x)$  with `.sample()` and `.log_prob()` methods.\n        \"\"\"\n        if prior is None:\n            assert (\n                self._prior is not None\n            ), \"\"\"You did not pass a prior. You have to pass the prior either at\n                initialization `inference = SNPE_A(prior)` or to `.build_posterior\n                (prior=prior)`.\"\"\"\n            prior = self._prior\n\n        wrapped_density_estimator = self.correct_for_proposal(\n            density_estimator=density_estimator\n        )\n        self._posterior = DirectPosterior(\n            posterior_estimator=wrapped_density_estimator,  # type: ignore\n            prior=prior,\n        )\n        return deepcopy(self._posterior)\n\n    def _log_prob_proposal_posterior(\n        self,\n        theta: Tensor,\n        x: Tensor,\n        masks: Tensor,\n        proposal: Optional[Any],\n    ) -&gt; Tensor:\n        \"\"\"Return the log-probability of the proposal posterior.\n\n        For SNPE-A this is the same as `self._neural_net.log_prob(theta, x)` in\n        `_loss()` to be found in `snpe_base.py`.\n\n        Args:\n            theta: Batch of parameters \u03b8.\n            x: Batch of data.\n            masks: Mask that is True for prior samples in the batch in order to train\n                them with prior loss.\n            proposal: Proposal distribution.\n\n        Returns: Log-probability of the proposal posterior.\n        \"\"\"\n        return self._neural_net.log_prob(theta, x)\n\n    def _expand_mog(self, eps: float = 1e-5):\n        \"\"\"\n        Replicate a singe Gaussian trained with Algorithm 1 before continuing\n        with Algorithm 2. The weights and biases of the associated MDN layers\n        are repeated `num_components` times, slightly perturbed to break the\n        symmetry such that the gradients in the subsequent training are not\n        all identical.\n\n        Args:\n            eps: Standard deviation for the random perturbation.\n        \"\"\"\n        assert isinstance(self._neural_net._distribution, MultivariateGaussianMDN)\n\n        # Increase the number of components\n        self._neural_net._distribution._num_components = self._num_components\n\n        # Expand the 1-dim Gaussian.\n        for name, param in self._neural_net.named_parameters():\n            if any(\n                key in name for key in [\"logits\", \"means\", \"unconstrained\", \"upper\"]\n            ):\n                if \"bias\" in name:\n                    param.data = param.data.repeat(self._num_components)\n                    param.data.add_(torch.randn_like(param.data) * eps)\n                    param.grad = None  # let autograd construct a new gradient\n                elif \"weight\" in name:\n                    param.data = param.data.repeat(self._num_components, 1)\n                    param.data.add_(torch.randn_like(param.data) * eps)\n                    param.grad = None  # let autograd construct a new gradient\n</code></pre> <p>             Bases: <code>PosteriorEstimator</code></p> Source code in <code>sbi/inference/snpe/snpe_c.py</code> <pre><code>class SNPE_C(PosteriorEstimator):\n    def __init__(\n        self,\n        prior: Optional[Distribution] = None,\n        density_estimator: Union[str, Callable] = \"maf\",\n        device: str = \"cpu\",\n        logging_level: Union[int, str] = \"WARNING\",\n        summary_writer: Optional[TensorboardSummaryWriter] = None,\n        show_progress_bars: bool = True,\n    ):\n        r\"\"\"SNPE-C / APT [1].\n\n        [1] _Automatic Posterior Transformation for Likelihood-free Inference_,\n            Greenberg et al., ICML 2019, https://arxiv.org/abs/1905.07488.\n\n        This class implements two loss variants of SNPE-C: the non-atomic and the atomic\n        version. The atomic loss of SNPE-C can be used for any density estimator,\n        i.e. also for normalizing flows. However, it suffers from leakage issues. On\n        the other hand, the non-atomic loss can only be used only if the proposal\n        distribution is a mixture of Gaussians, the density estimator is a mixture of\n        Gaussians, and the prior is either Gaussian or Uniform. It does not suffer from\n        leakage issues. At the beginning of each round, we print whether the non-atomic\n        or the atomic version is used.\n\n        In this codebase, we will automatically switch to the non-atomic loss if the\n        following criteria are fulfilled:&lt;br/&gt;\n        - proposal is a `DirectPosterior` with density_estimator `mdn`, as built\n            with `utils.sbi.posterior_nn()`.&lt;br/&gt;\n        - the density estimator is a `mdn`, as built with\n            `utils.sbi.posterior_nn()`.&lt;br/&gt;\n        - `isinstance(prior, MultivariateNormal)` (from `torch.distributions`) or\n            `isinstance(prior, sbi.utils.BoxUniform)`\n\n        Note that custom implementations of any of these densities (or estimators) will\n        not trigger the non-atomic loss, and the algorithm will fall back onto using\n        the atomic loss.\n\n        Args:\n            prior: A probability distribution that expresses prior knowledge about the\n                parameters, e.g. which ranges are meaningful for them.\n            density_estimator: If it is a string, use a pre-configured network of the\n                provided type (one of nsf, maf, mdn, made). Alternatively, a function\n                that builds a custom neural network can be provided. The function will\n                be called with the first batch of simulations (theta, x), which can\n                thus be used for shape inference and potentially for z-scoring. It\n                needs to return a PyTorch `nn.Module` implementing the density\n                estimator. The density estimator needs to provide the methods\n                `.log_prob` and `.sample()`.\n            device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n            logging_level: Minimum severity of messages to log. One of the strings\n                INFO, WARNING, DEBUG, ERROR and CRITICAL.\n            summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n                file location (default is `&lt;current working directory&gt;/logs`.)\n            show_progress_bars: Whether to show a progressbar during training.\n        \"\"\"\n\n        kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n        super().__init__(**kwargs)\n\n    def train(\n        self,\n        num_atoms: int = 10,\n        training_batch_size: int = 50,\n        learning_rate: float = 5e-4,\n        validation_fraction: float = 0.1,\n        stop_after_epochs: int = 20,\n        max_num_epochs: int = 2**31 - 1,\n        clip_max_norm: Optional[float] = 5.0,\n        calibration_kernel: Optional[Callable] = None,\n        resume_training: bool = False,\n        force_first_round_loss: bool = False,\n        discard_prior_samples: bool = False,\n        use_combined_loss: bool = False,\n        retrain_from_scratch: bool = False,\n        show_train_summary: bool = False,\n        dataloader_kwargs: Optional[Dict] = None,\n    ) -&gt; nn.Module:\n        r\"\"\"Return density estimator that approximates the distribution $p(\\theta|x)$.\n\n        Args:\n            num_atoms: Number of atoms to use for classification.\n            training_batch_size: Training batch size.\n            learning_rate: Learning rate for Adam optimizer.\n            validation_fraction: The fraction of data to use for validation.\n            stop_after_epochs: The number of epochs to wait for improvement on the\n                validation set before terminating training.\n            max_num_epochs: Maximum number of epochs to run. If reached, we stop\n                training even when the validation loss is still decreasing. Otherwise,\n                we train until validation loss increases (see also `stop_after_epochs`).\n            clip_max_norm: Value at which to clip the total gradient norm in order to\n                prevent exploding gradients. Use None for no clipping.\n            calibration_kernel: A function to calibrate the loss with respect to the\n                simulations `x`. See Lueckmann, Gon\u00e7alves et al., NeurIPS 2017.\n            resume_training: Can be used in case training time is limited, e.g. on a\n                cluster. If `True`, the split between train and validation set, the\n                optimizer, the number of epochs, and the best validation log-prob will\n                be restored from the last time `.train()` was called.\n            force_first_round_loss: If `True`, train with maximum likelihood,\n                i.e., potentially ignoring the correction for using a proposal\n                distribution different from the prior.\n            discard_prior_samples: Whether to discard samples simulated in round 1, i.e.\n                from the prior. Training may be sped up by ignoring such less targeted\n                samples.\n            use_combined_loss: Whether to train the neural net also on prior samples\n                using maximum likelihood in addition to training it on all samples using\n                atomic loss. The extra MLE loss helps prevent density leaking with\n                bounded priors.\n            retrain_from_scratch: Whether to retrain the conditional density\n                estimator for the posterior from scratch each round.\n            show_train_summary: Whether to print the number of epochs and validation\n                loss and leakage after the training.\n            dataloader_kwargs: Additional or updated kwargs to be passed to the training\n                and validation dataloaders (like, e.g., a collate_fn)\n\n        Returns:\n            Density estimator that approximates the distribution $p(\\theta|x)$.\n        \"\"\"\n\n        # WARNING: sneaky trick ahead. We proxy the parent's `train` here,\n        # requiring the signature to have `num_atoms`, save it for use below, and\n        # continue. It's sneaky because we are using the object (self) as a namespace\n        # to pass arguments between functions, and that's implicit state management.\n        self._num_atoms = num_atoms\n        self._use_combined_loss = use_combined_loss\n        kwargs = del_entries(\n            locals(),\n            entries=(\"self\", \"__class__\", \"num_atoms\", \"use_combined_loss\"),\n        )\n\n        self._round = max(self._data_round_index)\n\n        if self._round &gt; 0:\n            # Set the proposal to the last proposal that was passed by the user. For\n            # atomic SNPE, it does not matter what the proposal is. For non-atomic\n            # SNPE, we only use the latest data that was passed, i.e. the one from the\n            # last proposal.\n            proposal = self._proposal_roundwise[-1]\n            self.use_non_atomic_loss = (\n                isinstance(proposal, DirectPosterior)\n                and isinstance(proposal.posterior_estimator._distribution, mdn)\n                and isinstance(self._neural_net._distribution, mdn)\n                and check_dist_class(\n                    self._prior, class_to_check=(Uniform, MultivariateNormal)\n                )[0]\n            )\n\n            algorithm = \"non-atomic\" if self.use_non_atomic_loss else \"atomic\"\n            print(f\"Using SNPE-C with {algorithm} loss\")\n\n            if self.use_non_atomic_loss:\n                # Take care of z-scoring, pre-compute and store prior terms.\n                self._set_state_for_mog_proposal()\n\n        return super().train(**kwargs)\n\n    def _set_state_for_mog_proposal(self) -&gt; None:\n        \"\"\"Set state variables that are used at each training step of non-atomic SNPE-C.\n\n        Three things are computed:\n        1) Check if z-scoring was requested. To do so, we check if the `_transform`\n            argument of the net had been a `CompositeTransform`. See pyknos mdn.py.\n        2) Define a (potentially standardized) prior. It's standardized if z-scoring\n            had been requested.\n        3) Compute (Precision * mean) for the prior. This quantity is used at every\n            training step if the prior is Gaussian.\n        \"\"\"\n\n        self.z_score_theta = isinstance(self._neural_net._transform, CompositeTransform)\n\n        self._set_maybe_z_scored_prior()\n\n        if isinstance(self._maybe_z_scored_prior, MultivariateNormal):\n            self.prec_m_prod_prior = torch.mv(\n                self._maybe_z_scored_prior.precision_matrix,  # type: ignore\n                self._maybe_z_scored_prior.loc,  # type: ignore\n            )\n\n    def _set_maybe_z_scored_prior(self) -&gt; None:\n        r\"\"\"Compute and store potentially standardized prior (if z-scoring was done).\n\n        The proposal posterior is:\n        $pp(\\theta|x) = 1/Z * q(\\theta|x) * prop(\\theta) / p(\\theta)$\n\n        Let's denote z-scored theta by `a`: a = (theta - mean) / std\n        Then pp'(a|x) = 1/Z_2 * q'(a|x) * prop'(a) / p'(a)$\n\n        The ' indicates that the evaluation occurs in standardized space. The constant\n        scaling factor has been absorbed into Z_2.\n        From the above equation, we see that we need to evaluate the prior **in\n        standardized space**. We build the standardized prior in this function.\n\n        The standardize transform that is applied to the samples theta does not use\n        the exact prior mean and std (due to implementation issues). Hence, the z-scored\n        prior will not be exactly have mean=0 and std=1.\n        \"\"\"\n\n        if self.z_score_theta:\n            scale = self._neural_net._transform._transforms[0]._scale\n            shift = self._neural_net._transform._transforms[0]._shift\n\n            # Following the definintion of the linear transform in\n            # `standardizing_transform` in `sbiutils.py`:\n            # shift=-mean / std\n            # scale=1 / std\n            # Solving these equations for mean and std:\n            estim_prior_std = 1 / scale\n            estim_prior_mean = -shift * estim_prior_std\n\n            # Compute the discrepancy of the true prior mean and std and the mean and\n            # std that was empirically estimated from samples.\n            # N(theta|m,s) = N((theta-m_e)/s_e|(m-m_e)/s_e, s/s_e)\n            # Above: m,s are true prior mean and std. m_e,s_e are estimated prior mean\n            # and std (estimated from samples and used to build standardize transform).\n            almost_zero_mean = (self._prior.mean - estim_prior_mean) / estim_prior_std\n            almost_one_std = torch.sqrt(self._prior.variance) / estim_prior_std\n\n            if isinstance(self._prior, MultivariateNormal):\n                self._maybe_z_scored_prior = MultivariateNormal(\n                    almost_zero_mean, torch.diag(almost_one_std)\n                )\n            else:\n                range_ = torch.sqrt(almost_one_std * 3.0)\n                self._maybe_z_scored_prior = utils.BoxUniform(\n                    almost_zero_mean - range_, almost_zero_mean + range_\n                )\n        else:\n            self._maybe_z_scored_prior = self._prior\n\n    def _log_prob_proposal_posterior(\n        self,\n        theta: Tensor,\n        x: Tensor,\n        masks: Tensor,\n        proposal: DirectPosterior,\n    ) -&gt; Tensor:\n        \"\"\"Return the log-probability of the proposal posterior.\n\n        If the proposal is a MoG, the density estimator is a MoG, and the prior is\n        either Gaussian or uniform, we use non-atomic loss. Else, use atomic loss (which\n        suffers from leakage).\n\n        Args:\n            theta: Batch of parameters \u03b8.\n            x: Batch of data.\n            masks: Mask that is True for prior samples in the batch in order to train\n                them with prior loss.\n            proposal: Proposal distribution.\n\n        Returns: Log-probability of the proposal posterior.\n        \"\"\"\n\n        if self.use_non_atomic_loss:\n            return self._log_prob_proposal_posterior_mog(theta, x, proposal)\n        else:\n            return self._log_prob_proposal_posterior_atomic(theta, x, masks)\n\n    def _log_prob_proposal_posterior_atomic(\n        self, theta: Tensor, x: Tensor, masks: Tensor\n    ):\n        \"\"\"Return log probability of the proposal posterior for atomic proposals.\n\n        We have two main options when evaluating the proposal posterior.\n            (1) Generate atoms from the proposal prior.\n            (2) Generate atoms from a more targeted distribution, such as the most\n                recent posterior.\n        If we choose the latter, it is likely beneficial not to do this in the first\n        round, since we would be sampling from a randomly-initialized neural density\n        estimator.\n\n        Args:\n            theta: Batch of parameters \u03b8.\n            x: Batch of data.\n            masks: Mask that is True for prior samples in the batch in order to train\n                them with prior loss.\n\n        Returns:\n            Log-probability of the proposal posterior.\n        \"\"\"\n\n        batch_size = theta.shape[0]\n\n        num_atoms = int(\n            clamp_and_warn(\"num_atoms\", self._num_atoms, min_val=2, max_val=batch_size)\n        )\n\n        # Each set of parameter atoms is evaluated using the same x,\n        # so we repeat rows of the data x, e.g. [1, 2] -&gt; [1, 1, 2, 2]\n        repeated_x = repeat_rows(x, num_atoms)\n\n        # To generate the full set of atoms for a given item in the batch,\n        # we sample without replacement num_atoms - 1 times from the rest\n        # of the theta in the batch.\n        probs = ones(batch_size, batch_size) * (1 - eye(batch_size)) / (batch_size - 1)\n\n        choices = torch.multinomial(probs, num_samples=num_atoms - 1, replacement=False)\n        contrasting_theta = theta[choices]\n\n        # We can now create our sets of atoms from the contrasting parameter sets\n        # we have generated.\n        atomic_theta = torch.cat((theta[:, None, :], contrasting_theta), dim=1).reshape(\n            batch_size * num_atoms, -1\n        )\n\n        # Evaluate large batch giving (batch_size * num_atoms) log prob posterior evals.\n        log_prob_posterior = self._neural_net.log_prob(atomic_theta, repeated_x)\n        utils.assert_all_finite(log_prob_posterior, \"posterior eval\")\n        log_prob_posterior = log_prob_posterior.reshape(batch_size, num_atoms)\n\n        # Get (batch_size * num_atoms) log prob prior evals.\n        log_prob_prior = self._prior.log_prob(atomic_theta)\n        log_prob_prior = log_prob_prior.reshape(batch_size, num_atoms)\n        utils.assert_all_finite(log_prob_prior, \"prior eval\")\n\n        # Compute unnormalized proposal posterior.\n        unnormalized_log_prob = log_prob_posterior - log_prob_prior\n\n        # Normalize proposal posterior across discrete set of atoms.\n        log_prob_proposal_posterior = unnormalized_log_prob[:, 0] - torch.logsumexp(\n            unnormalized_log_prob, dim=-1\n        )\n        utils.assert_all_finite(log_prob_proposal_posterior, \"proposal posterior eval\")\n\n        # XXX This evaluates the posterior on _all_ prior samples\n        if self._use_combined_loss:\n            log_prob_posterior_non_atomic = self._neural_net.log_prob(theta, x)\n            masks = masks.reshape(-1)\n            log_prob_proposal_posterior = (\n                masks * log_prob_posterior_non_atomic + log_prob_proposal_posterior\n            )\n\n        return log_prob_proposal_posterior\n\n    def _log_prob_proposal_posterior_mog(\n        self, theta: Tensor, x: Tensor, proposal: DirectPosterior\n    ) -&gt; Tensor:\n        \"\"\"Return log-probability of the proposal posterior for MoG proposal.\n\n        For MoG proposals and MoG density estimators, this can be done in closed form\n        and does not require atomic loss (i.e. there will be no leakage issues).\n\n        Notation:\n\n        m are mean vectors.\n        prec are precision matrices.\n        cov are covariance matrices.\n\n        _p at the end indicates that it is the proposal.\n        _d indicates that it is the density estimator.\n        _pp indicates the proposal posterior.\n\n        All tensors will have shapes (batch_dim, num_components, ...)\n\n        Args:\n            theta: Batch of parameters \u03b8.\n            x: Batch of data.\n            proposal: Proposal distribution.\n\n        Returns:\n            Log-probability of the proposal posterior.\n        \"\"\"\n\n        # Evaluate the proposal. MDNs do not have functionality to run the embedding_net\n        # and then get the mixture_components (**without** calling log_prob()). Hence,\n        # we call them separately here.\n        encoded_x = proposal.posterior_estimator._embedding_net(proposal.default_x)\n        dist = (\n            proposal.posterior_estimator._distribution\n        )  # defined to avoid ugly black formatting.\n        logits_p, m_p, prec_p, _, _ = dist.get_mixture_components(encoded_x)\n        norm_logits_p = logits_p - torch.logsumexp(logits_p, dim=-1, keepdim=True)\n\n        # Evaluate the density estimator.\n        encoded_x = self._neural_net._embedding_net(x)\n        dist = self._neural_net._distribution  # defined to avoid black formatting.\n        logits_d, m_d, prec_d, _, _ = dist.get_mixture_components(encoded_x)\n        norm_logits_d = logits_d - torch.logsumexp(logits_d, dim=-1, keepdim=True)\n\n        # z-score theta if it z-scoring had been requested.\n        theta = self._maybe_z_score_theta(theta)\n\n        # Compute the MoG parameters of the proposal posterior.\n        (\n            logits_pp,\n            m_pp,\n            prec_pp,\n            cov_pp,\n        ) = self._automatic_posterior_transformation(\n            norm_logits_p, m_p, prec_p, norm_logits_d, m_d, prec_d\n        )\n\n        # Compute the log_prob of theta under the product.\n        log_prob_proposal_posterior = utils.mog_log_prob(\n            theta, logits_pp, m_pp, prec_pp\n        )\n        utils.assert_all_finite(\n            log_prob_proposal_posterior,\n            \"\"\"the evaluation of the MoG proposal posterior. This is likely due to a\n            numerical instability in the training procedure. Please create an issue on\n            Github.\"\"\",\n        )\n\n        return log_prob_proposal_posterior\n\n    def _automatic_posterior_transformation(\n        self,\n        logits_p: Tensor,\n        means_p: Tensor,\n        precisions_p: Tensor,\n        logits_d: Tensor,\n        means_d: Tensor,\n        precisions_d: Tensor,\n    ):\n        r\"\"\"Returns the MoG parameters of the proposal posterior.\n\n        The proposal posterior is:\n        $pp(\\theta|x) = 1/Z * q(\\theta|x) * prop(\\theta) / p(\\theta)$\n        In words: proposal posterior = posterior estimate * proposal / prior.\n\n        If the posterior estimate and the proposal are MoG and the prior is either\n        Gaussian or uniform, we can solve this in closed-form. The is implemented in\n        this function.\n\n        This function implements Appendix A1 from Greenberg et al. 2019.\n\n        We have to build L*K components. How do we do this?\n        Example: proposal has two components, density estimator has three components.\n        Let's call the two components of the proposal i,j and the three components\n        of the density estimator x,y,z. We have to multiply every component of the\n        proposal with every component of the density estimator. So, what we do is:\n        1) for the proposal, build: i,i,i,j,j,j. Done with torch.repeat_interleave()\n        2) for the density estimator, build: x,y,z,x,y,z. Done with torch.repeat()\n        3) Multiply them with simple matrix operations.\n\n        Args:\n            logits_p: Component weight of each Gaussian of the proposal.\n            means_p: Mean of each Gaussian of the proposal.\n            precisions_p: Precision matrix of each Gaussian of the proposal.\n            logits_d: Component weight for each Gaussian of the density estimator.\n            means_d: Mean of each Gaussian of the density estimator.\n            precisions_d: Precision matrix of each Gaussian of the density estimator.\n\n        Returns: (Component weight, mean, precision matrix, covariance matrix) of each\n            Gaussian of the proposal posterior. Has L*K terms (proposal has L terms,\n            density estimator has K terms).\n        \"\"\"\n\n        precisions_pp, covariances_pp = self._precisions_proposal_posterior(\n            precisions_p, precisions_d\n        )\n\n        means_pp = self._means_proposal_posterior(\n            covariances_pp, means_p, precisions_p, means_d, precisions_d\n        )\n\n        logits_pp = self._logits_proposal_posterior(\n            means_pp,\n            precisions_pp,\n            covariances_pp,\n            logits_p,\n            means_p,\n            precisions_p,\n            logits_d,\n            means_d,\n            precisions_d,\n        )\n\n        return logits_pp, means_pp, precisions_pp, covariances_pp\n\n    def _precisions_proposal_posterior(\n        self, precisions_p: Tensor, precisions_d: Tensor\n    ):\n        \"\"\"Return the precisions and covariances of the proposal posterior.\n\n        Args:\n            precisions_p: Precision matrices of the proposal distribution.\n            precisions_d: Precision matrices of the density estimator.\n\n        Returns: (Precisions, Covariances) of the proposal posterior. L*K terms.\n        \"\"\"\n\n        num_comps_p = precisions_p.shape[1]\n        num_comps_d = precisions_d.shape[1]\n\n        precisions_p_rep = precisions_p.repeat_interleave(num_comps_d, dim=1)\n        precisions_d_rep = precisions_d.repeat(1, num_comps_p, 1, 1)\n\n        precisions_pp = precisions_p_rep + precisions_d_rep\n        if isinstance(self._maybe_z_scored_prior, MultivariateNormal):\n            precisions_pp -= self._maybe_z_scored_prior.precision_matrix\n\n        covariances_pp = torch.inverse(precisions_pp)\n\n        return precisions_pp, covariances_pp\n\n    def _means_proposal_posterior(\n        self,\n        covariances_pp: Tensor,\n        means_p: Tensor,\n        precisions_p: Tensor,\n        means_d: Tensor,\n        precisions_d: Tensor,\n    ):\n        \"\"\"Return the means of the proposal posterior.\n\n        means_pp = C_ix * (P_i * m_i + P_x * m_x - P_o * m_o).\n\n        Args:\n            covariances_pp: Covariance matrices of the proposal posterior.\n            means_p: Means of the proposal distribution.\n            precisions_p: Precision matrices of the proposal distribution.\n            means_d: Means of the density estimator.\n            precisions_d: Precision matrices of the density estimator.\n\n        Returns: Means of the proposal posterior. L*K terms.\n        \"\"\"\n\n        num_comps_p = precisions_p.shape[1]\n        num_comps_d = precisions_d.shape[1]\n\n        # First, compute the product P_i * m_i and P_j * m_j\n        prec_m_prod_p = batched_mixture_mv(precisions_p, means_p)\n        prec_m_prod_d = batched_mixture_mv(precisions_d, means_d)\n\n        # Repeat them to allow for matrix operations: same trick as for the precisions.\n        prec_m_prod_p_rep = prec_m_prod_p.repeat_interleave(num_comps_d, dim=1)\n        prec_m_prod_d_rep = prec_m_prod_d.repeat(1, num_comps_p, 1)\n\n        # Means = C_ij * (P_i * m_i + P_x * m_x - P_o * m_o).\n        summed_cov_m_prod_rep = prec_m_prod_p_rep + prec_m_prod_d_rep\n        if isinstance(self._maybe_z_scored_prior, MultivariateNormal):\n            summed_cov_m_prod_rep -= self.prec_m_prod_prior\n\n        means_pp = batched_mixture_mv(covariances_pp, summed_cov_m_prod_rep)\n\n        return means_pp\n\n    @staticmethod\n    def _logits_proposal_posterior(\n        means_pp: Tensor,\n        precisions_pp: Tensor,\n        covariances_pp: Tensor,\n        logits_p: Tensor,\n        means_p: Tensor,\n        precisions_p: Tensor,\n        logits_d: Tensor,\n        means_d: Tensor,\n        precisions_d: Tensor,\n    ):\n        \"\"\"Return the component weights (i.e. logits) of the proposal posterior.\n\n        Args:\n            means_pp: Means of the proposal posterior.\n            precisions_pp: Precision matrices of the proposal posterior.\n            covariances_pp: Covariance matrices of the proposal posterior.\n            logits_p: Component weights (i.e. logits) of the proposal distribution.\n            means_p: Means of the proposal distribution.\n            precisions_p: Precision matrices of the proposal distribution.\n            logits_d: Component weights (i.e. logits) of the density estimator.\n            means_d: Means of the density estimator.\n            precisions_d: Precision matrices of the density estimator.\n\n        Returns: Component weights of the proposal posterior. L*K terms.\n        \"\"\"\n\n        num_comps_p = precisions_p.shape[1]\n        num_comps_d = precisions_d.shape[1]\n\n        # Compute log(alpha_i * beta_j)\n        logits_p_rep = logits_p.repeat_interleave(num_comps_d, dim=1)\n        logits_d_rep = logits_d.repeat(1, num_comps_p)\n        logit_factors = logits_p_rep + logits_d_rep\n\n        # Compute sqrt(det()/(det()*det()))\n        logdet_covariances_pp = torch.logdet(covariances_pp)\n        logdet_covariances_p = -torch.logdet(precisions_p)\n        logdet_covariances_d = -torch.logdet(precisions_d)\n\n        # Repeat the proposal and density estimator terms such that there are LK terms.\n        # Same trick as has been used above.\n        logdet_covariances_p_rep = logdet_covariances_p.repeat_interleave(\n            num_comps_d, dim=1\n        )\n        logdet_covariances_d_rep = logdet_covariances_d.repeat(1, num_comps_p)\n\n        log_sqrt_det_ratio = 0.5 * (\n            logdet_covariances_pp\n            - (logdet_covariances_p_rep + logdet_covariances_d_rep)\n        )\n\n        # Compute for proposal, density estimator, and proposal posterior:\n        # mu_i.T * P_i * mu_i\n        exponent_p = batched_mixture_vmv(precisions_p, means_p)\n        exponent_d = batched_mixture_vmv(precisions_d, means_d)\n        exponent_pp = batched_mixture_vmv(precisions_pp, means_pp)\n\n        # Extend proposal and density estimator exponents to get LK terms.\n        exponent_p_rep = exponent_p.repeat_interleave(num_comps_d, dim=1)\n        exponent_d_rep = exponent_d.repeat(1, num_comps_p)\n        exponent = -0.5 * (exponent_p_rep + exponent_d_rep - exponent_pp)\n\n        logits_pp = logit_factors + log_sqrt_det_ratio + exponent\n\n        return logits_pp\n\n    def _maybe_z_score_theta(self, theta: Tensor) -&gt; Tensor:\n        \"\"\"Return potentially standardized theta if z-scoring was requested.\"\"\"\n\n        if self.z_score_theta:\n            theta, _ = self._neural_net._transform(theta)\n\n        return theta\n</code></pre> <p>             Bases: <code>LikelihoodEstimator</code></p> Source code in <code>sbi/inference/snle/snle_a.py</code> <pre><code>class SNLE_A(LikelihoodEstimator):\n    def __init__(\n        self,\n        prior: Optional[Distribution] = None,\n        density_estimator: Union[str, Callable] = \"maf\",\n        device: str = \"cpu\",\n        logging_level: Union[int, str] = \"WARNING\",\n        summary_writer: Optional[TensorboardSummaryWriter] = None,\n        show_progress_bars: bool = True,\n    ):\n        r\"\"\"Sequential Neural Likelihood [1].\n\n        [1] Sequential Neural Likelihood: Fast Likelihood-free Inference with\n        Autoregressive Flows_, Papamakarios et al., AISTATS 2019,\n        https://arxiv.org/abs/1805.07226\n\n        Args:\n            prior: A probability distribution that expresses prior knowledge about the\n                parameters, e.g. which ranges are meaningful for them. If `None`, the\n                prior must be passed to `.build_posterior()`.\n            density_estimator: If it is a string, use a pre-configured network of the\n                provided type (one of nsf, maf, mdn, made). Alternatively, a function\n                that builds a custom neural network can be provided. The function will\n                be called with the first batch of simulations (theta, x), which can\n                thus be used for shape inference and potentially for z-scoring. It\n                needs to return a PyTorch `nn.Module` implementing the density\n                estimator. The density estimator needs to provide the methods\n                `.log_prob` and `.sample()`.\n            device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n            logging_level: Minimum severity of messages to log. One of the strings\n                INFO, WARNING, DEBUG, ERROR and CRITICAL.\n            summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n                file location (default is `&lt;current working directory&gt;/logs`.)\n            show_progress_bars: Whether to show a progressbar during simulation and\n                sampling.\n        \"\"\"\n\n        kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n        super().__init__(**kwargs)\n</code></pre> <p>             Bases: <code>RatioEstimator</code></p> Source code in <code>sbi/inference/snre/snre_a.py</code> <pre><code>class SNRE_A(RatioEstimator):\n    def __init__(\n        self,\n        prior: Optional[Distribution] = None,\n        classifier: Union[str, Callable] = \"resnet\",\n        device: str = \"cpu\",\n        logging_level: Union[int, str] = \"warning\",\n        summary_writer: Optional[TensorboardSummaryWriter] = None,\n        show_progress_bars: bool = True,\n    ):\n        r\"\"\"AALR[1], here known as SNRE_A.\n\n        [1] _Likelihood-free MCMC with Amortized Approximate Likelihood Ratios_, Hermans\n            et al., ICML 2020, https://arxiv.org/abs/1903.04057\n\n        Args:\n            prior: A probability distribution that expresses prior knowledge about the\n                parameters, e.g. which ranges are meaningful for them. If `None`, the\n                prior must be passed to `.build_posterior()`.\n            classifier: Classifier trained to approximate likelihood ratios. If it is\n                a string, use a pre-configured network of the provided type (one of\n                linear, mlp, resnet). Alternatively, a function that builds a custom\n                neural network can be provided. The function will be called with the\n                first batch of simulations (theta, x), which can thus be used for shape\n                inference and potentially for z-scoring. It needs to return a PyTorch\n                `nn.Module` implementing the classifier.\n            device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n            logging_level: Minimum severity of messages to log. One of the strings\n                INFO, WARNING, DEBUG, ERROR and CRITICAL.\n            summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n                file location (default is `&lt;current working directory&gt;/logs`.)\n            show_progress_bars: Whether to show a progressbar during simulation and\n                sampling.\n        \"\"\"\n\n        kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n        super().__init__(**kwargs)\n\n    def train(\n        self,\n        training_batch_size: int = 50,\n        learning_rate: float = 5e-4,\n        validation_fraction: float = 0.1,\n        stop_after_epochs: int = 20,\n        max_num_epochs: int = 2**31 - 1,\n        clip_max_norm: Optional[float] = 5.0,\n        resume_training: bool = False,\n        discard_prior_samples: bool = False,\n        retrain_from_scratch: bool = False,\n        show_train_summary: bool = False,\n        dataloader_kwargs: Optional[Dict] = None,\n        loss_kwargs: Dict[str, Any] = {},\n    ) -&gt; nn.Module:\n        r\"\"\"Return classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n\n        Args:\n            training_batch_size: Training batch size.\n            learning_rate: Learning rate for Adam optimizer.\n            validation_fraction: The fraction of data to use for validation.\n            stop_after_epochs: The number of epochs to wait for improvement on the\n                validation set before terminating training.\n            max_num_epochs: Maximum number of epochs to run. If reached, we stop\n                training even when the validation loss is still decreasing. Otherwise,\n                we train until validation loss increases (see also `stop_after_epochs`).\n            clip_max_norm: Value at which to clip the total gradient norm in order to\n                prevent exploding gradients. Use None for no clipping.\n            resume_training: Can be used in case training time is limited, e.g. on a\n                cluster. If `True`, the split between train and validation set, the\n                optimizer, the number of epochs, and the best validation log-prob will\n                be restored from the last time `.train()` was called.\n            discard_prior_samples: Whether to discard samples simulated in round 1, i.e.\n                from the prior. Training may be sped up by ignoring such less targeted\n                samples.\n            retrain_from_scratch: Whether to retrain the conditional density\n                estimator for the posterior from scratch each round.\n            show_train_summary: Whether to print the number of epochs and validation\n                loss and leakage after the training.\n            dataloader_kwargs: Additional or updated kwargs to be passed to the training\n                and validation dataloaders (like, e.g., a collate_fn)\n            loss_kwargs: Additional or updated kwargs to be passed to the self._loss fn.\n\n        Returns:\n            Classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n        \"\"\"\n\n        # AALR is defined for `num_atoms=2`.\n        # Proxy to `super().__call__` to ensure right parameter.\n        kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n        return super().train(**kwargs, num_atoms=2)\n\n    def _loss(self, theta: Tensor, x: Tensor, num_atoms: int) -&gt; Tensor:\n        \"\"\"Returns the binary cross-entropy loss for the trained classifier.\n\n        The classifier takes as input a $(\\theta,x)$ pair. It is trained to predict 1\n        if the pair was sampled from the joint $p(\\theta,x)$, and to predict 0 if the\n        pair was sampled from the marginals $p(\\theta)p(x)$.\n        \"\"\"\n\n        assert theta.shape[0] == x.shape[0], \"Batch sizes for theta and x must match.\"\n        batch_size = theta.shape[0]\n\n        logits = self._classifier_logits(theta, x, num_atoms)\n        likelihood = torch.sigmoid(logits).squeeze()\n\n        # Alternating pairs where there is one sampled from the joint and one\n        # sampled from the marginals. The first element is sampled from the\n        # joint p(theta, x) and is labelled 1. The second element is sampled\n        # from the marginals p(theta)p(x) and is labelled 0. And so on.\n        labels = ones(2 * batch_size, device=self._device)  # two atoms\n        labels[1::2] = 0.0\n\n        # Binary cross entropy to learn the likelihood (AALR-specific)\n        return nn.BCELoss()(likelihood, labels)\n</code></pre> <p>             Bases: <code>RatioEstimator</code></p> Source code in <code>sbi/inference/snre/snre_b.py</code> <pre><code>class SNRE_B(RatioEstimator):\n    def __init__(\n        self,\n        prior: Optional[Distribution] = None,\n        classifier: Union[str, Callable] = \"resnet\",\n        device: str = \"cpu\",\n        logging_level: Union[int, str] = \"warning\",\n        summary_writer: Optional[TensorboardSummaryWriter] = None,\n        show_progress_bars: bool = True,\n    ):\n        r\"\"\"SRE[1], here known as SNRE_B.\n\n        [1] _On Contrastive Learning for Likelihood-free Inference_, Durkan et al.,\n            ICML 2020, https://arxiv.org/pdf/2002.03712\n\n        Args:\n            prior: A probability distribution that expresses prior knowledge about the\n                parameters, e.g. which ranges are meaningful for them. If `None`, the\n                prior must be passed to `.build_posterior()`.\n            classifier: Classifier trained to approximate likelihood ratios. If it is\n                a string, use a pre-configured network of the provided type (one of\n                linear, mlp, resnet). Alternatively, a function that builds a custom\n                neural network can be provided. The function will be called with the\n                first batch of simulations (theta, x), which can thus be used for shape\n                inference and potentially for z-scoring. It needs to return a PyTorch\n                `nn.Module` implementing the classifier.\n            device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n            logging_level: Minimum severity of messages to log. One of the strings\n                INFO, WARNING, DEBUG, ERROR and CRITICAL.\n            summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n                file location (default is `&lt;current working directory&gt;/logs`.)\n            show_progress_bars: Whether to show a progressbar during simulation and\n                sampling.\n        \"\"\"\n\n        kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n        super().__init__(**kwargs)\n\n    def train(\n        self,\n        num_atoms: int = 10,\n        training_batch_size: int = 50,\n        learning_rate: float = 5e-4,\n        validation_fraction: float = 0.1,\n        stop_after_epochs: int = 20,\n        max_num_epochs: int = 2**31 - 1,\n        clip_max_norm: Optional[float] = 5.0,\n        resume_training: bool = False,\n        discard_prior_samples: bool = False,\n        retrain_from_scratch: bool = False,\n        show_train_summary: bool = False,\n        dataloader_kwargs: Optional[Dict] = None,\n    ) -&gt; nn.Module:\n        r\"\"\"Return classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n\n        Args:\n            num_atoms: Number of atoms to use for classification.\n            training_batch_size: Training batch size.\n            learning_rate: Learning rate for Adam optimizer.\n            validation_fraction: The fraction of data to use for validation.\n            stop_after_epochs: The number of epochs to wait for improvement on the\n                validation set before terminating training.\n            max_num_epochs: Maximum number of epochs to run. If reached, we stop\n                training even when the validation loss is still decreasing. Otherwise,\n                we train until validation loss increases (see also `stop_after_epochs`).\n            clip_max_norm: Value at which to clip the total gradient norm in order to\n                prevent exploding gradients. Use None for no clipping.\n            resume_training: Can be used in case training time is limited, e.g. on a\n                cluster. If `True`, the split between train and validation set, the\n                optimizer, the number of epochs, and the best validation log-prob will\n                be restored from the last time `.train()` was called.\n            discard_prior_samples: Whether to discard samples simulated in round 1, i.e.\n                from the prior. Training may be sped up by ignoring such less targeted\n                samples.\n            retrain_from_scratch: Whether to retrain the conditional density\n                estimator for the posterior from scratch each round.\n            show_train_summary: Whether to print the number of epochs and validation\n                loss and leakage after the training.\n            dataloader_kwargs: Additional or updated kwargs to be passed to the training\n                and validation dataloaders (like, e.g., a collate_fn)\n\n        Returns:\n            Classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n        \"\"\"\n        kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n        return super().train(**kwargs)\n\n    def _loss(self, theta: Tensor, x: Tensor, num_atoms: int) -&gt; Tensor:\n        r\"\"\"Return cross-entropy (via softmax activation) loss for 1-out-of-`num_atoms`\n        classification.\n\n        The classifier takes as input `num_atoms` $(\\theta,x)$ pairs. Out of these\n        pairs, one pair was sampled from the joint $p(\\theta,x)$ and all others from the\n        marginals $p(\\theta)p(x)$. The classifier is trained to predict which of the\n        pairs was sampled from the joint $p(\\theta,x)$.\n        \"\"\"\n\n        assert theta.shape[0] == x.shape[0], \"Batch sizes for theta and x must match.\"\n        batch_size = theta.shape[0]\n        logits = self._classifier_logits(theta, x, num_atoms)\n\n        # For 1-out-of-`num_atoms` classification each datapoint consists\n        # of `num_atoms` points, with one of them being the correct one.\n        # We have a batch of `batch_size` such datapoints.\n        logits = logits.reshape(batch_size, num_atoms)\n\n        # Index 0 is the theta-x-pair sampled from the joint p(theta,x) and hence the\n        # \"correct\" one for the 1-out-of-N classification.\n        log_prob = logits[:, 0] - torch.logsumexp(logits, dim=-1)\n\n        return -torch.mean(log_prob)\n</code></pre> <p>             Bases: <code>RatioEstimator</code></p> Source code in <code>sbi/inference/snre/snre_c.py</code> <pre><code>class SNRE_C(RatioEstimator):\n    def __init__(\n        self,\n        prior: Optional[Distribution] = None,\n        classifier: Union[str, Callable] = \"resnet\",\n        device: str = \"cpu\",\n        logging_level: Union[int, str] = \"warning\",\n        summary_writer: Optional[TensorboardSummaryWriter] = None,\n        show_progress_bars: bool = True,\n    ):\n        r\"\"\"NRE-C[1] is a generalization of the non-sequential (amortized) versions of\n        SNRE_A and SNRE_B. We call the algorithm SNRE_C within `sbi`.\n\n        NRE-C:\n        (1) like SNRE_B, features a \"multiclass\" loss function where several marginally\n            drawn parameter-data pairs are contrasted against a jointly drawn pair.\n        (2) like AALR/NRE_A, i.e., the non-sequential version of SNRE_A, it encourages\n            the approximate ratio $p(\\theta,x)/p(\\theta)p(x)$, accessed through\n            `.potential()` within `sbi`, to be exact at optimum. This addresses the\n            issue that SNRE_B estimates this ratio only up to an arbitrary function\n            (normalizing constant) of the data $x$.\n\n        Just like for all ratio estimation algorithms, the sequential version of SNRE_C\n        will be estimated only up to a function (normalizing constant) of the data $x$\n        in rounds after the first.\n\n        [1] _Contrastive Neural Ratio Estimation_, Benajmin Kurt Miller, et. al.,\n            NeurIPS 2022, https://arxiv.org/abs/2210.06170\n\n        Args:\n            prior: A probability distribution that expresses prior knowledge about the\n                parameters, e.g. which ranges are meaningful for them. If `None`, the\n                prior must be passed to `.build_posterior()`.\n            classifier: Classifier trained to approximate likelihood ratios. If it is\n                a string, use a pre-configured network of the provided type (one of\n                linear, mlp, resnet). Alternatively, a function that builds a custom\n                neural network can be provided. The function will be called with the\n                first batch of simulations (theta, x), which can thus be used for shape\n                inference and potentially for z-scoring. It needs to return a PyTorch\n                `nn.Module` implementing the classifier.\n            device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n            logging_level: Minimum severity of messages to log. One of the strings\n                INFO, WARNING, DEBUG, ERROR and CRITICAL.\n            summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n                file location (default is `&lt;current working directory&gt;/logs`.)\n            show_progress_bars: Whether to show a progressbar during simulation and\n                sampling.\n        \"\"\"\n\n        kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n        super().__init__(**kwargs)\n\n    def train(\n        self,\n        num_classes: int = 5,\n        gamma: float = 1.0,\n        training_batch_size: int = 50,\n        learning_rate: float = 5e-4,\n        validation_fraction: float = 0.1,\n        stop_after_epochs: int = 20,\n        max_num_epochs: int = 2**31 - 1,\n        clip_max_norm: Optional[float] = 5.0,\n        resume_training: bool = False,\n        discard_prior_samples: bool = False,\n        retrain_from_scratch: bool = False,\n        show_train_summary: bool = False,\n        dataloader_kwargs: Optional[Dict] = None,\n    ) -&gt; nn.Module:\n        r\"\"\"Return classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n\n        Args:\n            num_classes: Number of theta to classify against, corresponds to $K$ in\n                _Contrastive Neural Ratio Estimation_. Minimum value is 1. Similar to\n                `num_atoms` for SNRE_B except SNRE_C has an additional independently\n                drawn sample. The total number of alternative parameters `NRE-C` \"sees\"\n                is $2K-1$ or `2 * num_classes - 1` divided between two loss terms.\n            gamma: Determines the relative weight of the sum of all $K$ dependently\n                drawn classes against the marginally drawn one. Specifically,\n                $p(y=k) :=p_K$, $p(y=0) := p_0$, $p_0 = 1 - K p_K$, and finally\n                $\\gamma := K p_K / p_0$.\n            training_batch_size: Training batch size.\n            learning_rate: Learning rate for Adam optimizer.\n            validation_fraction: The fraction of data to use for validation.\n            stop_after_epochs: The number of epochs to wait for improvement on the\n                validation set before terminating training.\n            max_num_epochs: Maximum number of epochs to run. If reached, we stop\n                training even when the validation loss is still decreasing. Otherwise,\n                we train until validation loss increases (see also `stop_after_epochs`).\n            clip_max_norm: Value at which to clip the total gradient norm in order to\n                prevent exploding gradients. Use None for no clipping.\n            exclude_invalid_x: Whether to exclude simulation outputs `x=NaN` or `x=\u00b1\u221e`\n                during training. Expect errors, silent or explicit, when `False`.\n            resume_training: Can be used in case training time is limited, e.g. on a\n                cluster. If `True`, the split between train and validation set, the\n                optimizer, the number of epochs, and the best validation log-prob will\n                be restored from the last time `.train()` was called.\n            discard_prior_samples: Whether to discard samples simulated in round 1, i.e.\n                from the prior. Training may be sped up by ignoring such less targeted\n                samples.\n            retrain_from_scratch: Whether to retrain the conditional density\n                estimator for the posterior from scratch each round.\n            show_train_summary: Whether to print the number of epochs and validation\n                loss and leakage after the training.\n            dataloader_kwargs: Additional or updated kwargs to be passed to the training\n                and validation dataloaders (like, e.g., a collate_fn)\n\n        Returns:\n            Classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n        \"\"\"\n        kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n        kwargs[\"num_atoms\"] = kwargs.pop(\"num_classes\") + 1\n        kwargs[\"loss_kwargs\"] = {\"gamma\": kwargs.pop(\"gamma\")}\n        return super().train(**kwargs)\n\n    def _loss(\n        self, theta: Tensor, x: Tensor, num_atoms: int, gamma: float\n    ) -&gt; torch.Tensor:\n        r\"\"\"Return cross-entropy loss (via ''multi-class sigmoid'' activation) for\n        1-out-of-`K + 1` classification.\n\n        At optimum, this loss function returns the exact likelihood-to-evidence ratio\n        in the first round.\n        Details of loss computation are described in Contrastive Neural Ratio\n        Estimation[1]. The paper does not discuss the sequential case.\n\n        [1] _Contrastive Neural Ratio Estimation_, Benajmin Kurt Miller, et. al.,\n            NeurIPS 2022, https://arxiv.org/abs/2210.06170\n        \"\"\"\n\n        # Reminder: K = num_classes\n        # The algorithm is written with K, so we convert back to K format rather than\n        # reasoning in num_atoms.\n        num_classes = num_atoms - 1\n        assert num_classes &gt;= 1, f\"num_classes = {num_classes} must be greater than 1.\"\n\n        assert theta.shape[0] == x.shape[0], \"Batch sizes for theta and x must match.\"\n        batch_size = theta.shape[0]\n\n        # We append an contrastive theta to the marginal case because we will remove\n        # the jointly drawn\n        # sample in the logits_marginal[:, 0] position. That makes the remaining sample\n        # marginally drawn.\n        # We have a batch of `batch_size` datapoints.\n        logits_marginal = self._classifier_logits(theta, x, num_classes + 1).reshape(\n            batch_size, num_classes + 1\n        )\n        logits_joint = self._classifier_logits(theta, x, num_classes).reshape(\n            batch_size, num_classes\n        )\n\n        dtype = logits_marginal.dtype\n        device = logits_marginal.device\n\n        # Index 0 is the theta-x-pair sampled from the joint p(theta,x) and hence\n        # we remove the jointly drawn sample from the logits_marginal\n        logits_marginal = logits_marginal[:, 1:]\n        # ... and retain it in the logits_joint. Now we have two arrays with K choices.\n\n        # To use logsumexp, we extend the denominator logits with loggamma\n        loggamma = torch.tensor(gamma, dtype=dtype, device=device).log()\n        logK = torch.tensor(num_classes, dtype=dtype, device=device).log()\n        denominator_marginal = torch.concat(\n            [loggamma + logits_marginal, logK.expand((batch_size, 1))],\n            dim=-1,\n        )\n        denominator_joint = torch.concat(\n            [loggamma + logits_joint, logK.expand((batch_size, 1))],\n            dim=-1,\n        )\n\n        # Compute the contributions to the loss from each term in the classification.\n        log_prob_marginal = logK - torch.logsumexp(denominator_marginal, dim=-1)\n        log_prob_joint = (\n            loggamma + logits_joint[:, 0] - torch.logsumexp(denominator_joint, dim=-1)\n        )\n\n        # relative weights. p_marginal := p_0, and p_joint := p_K * K from the notation.\n        p_marginal, p_joint = self._get_prior_probs_marginal_and_joint(gamma)\n        return -torch.mean(p_marginal * log_prob_marginal + p_joint * log_prob_joint)\n\n    @staticmethod\n    def _get_prior_probs_marginal_and_joint(gamma: float) -&gt; Tuple[float, float]:\n        r\"\"\"Return a tuple (p_marginal, p_joint) where `p_marginal := `$p_0$,\n        `p_joint := `$p_K \\cdot K$.\n\n        We let the joint (dependently drawn) class to be equally likely across K\n        options. The marginal class is therefore restricted to get the remaining\n        probability.\n        \"\"\"\n        p_joint = gamma / (1 + gamma)\n        p_marginal = 1 / (1 + gamma)\n        return p_marginal, p_joint\n</code></pre> <p>             Bases: <code>SNRE_A</code></p> Source code in <code>sbi/inference/snre/bnre.py</code> <pre><code>class BNRE(SNRE_A):\n    def __init__(\n        self,\n        prior: Optional[Distribution] = None,\n        classifier: Union[str, Callable] = \"resnet\",\n        device: str = \"cpu\",\n        logging_level: Union[int, str] = \"warning\",\n        summary_writer: Optional[TensorboardSummaryWriter] = None,\n        show_progress_bars: bool = True,\n    ):\n        r\"\"\"Balanced neural ratio estimation (BNRE)[1]. BNRE is a variation of NRE\n        aiming to produce more conservative posterior approximations\n\n        [1] Delaunoy, A., Hermans, J., Rozet, F., Wehenkel, A., &amp; Louppe, G..\n        Towards Reliable Simulation-Based Inference with Balanced Neural Ratio\n        Estimation.\n        NeurIPS 2022. https://arxiv.org/abs/2208.13624\n\n        Args:\n            prior: A probability distribution that expresses prior knowledge about the\n                parameters, e.g. which ranges are meaningful for them. If `None`, the\n                prior must be passed to `.build_posterior()`.\n            classifier: Classifier trained to approximate likelihood ratios. If it is\n                a string, use a pre-configured network of the provided type (one of\n                linear, mlp, resnet). Alternatively, a function that builds a custom\n                neural network can be provided. The function will be called with the\n                first batch of simulations $(\\theta, x)$, which can thus be used for\n                shape inference and potentially for z-scoring. It needs to return a\n                PyTorch `nn.Module` implementing the classifier.\n            device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n            logging_level: Minimum severity of messages to log. One of the strings\n                INFO, WARNING, DEBUG, ERROR and CRITICAL.\n            summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n                file location (default is `&lt;current working directory&gt;/logs`.)\n            show_progress_bars: Whether to show a progressbar during simulation and\n                sampling.\n        \"\"\"\n\n        kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n        super().__init__(**kwargs)\n\n    def train(\n        self,\n        regularization_strength: float = 100.0,\n        training_batch_size: int = 50,\n        learning_rate: float = 5e-4,\n        validation_fraction: float = 0.1,\n        stop_after_epochs: int = 20,\n        max_num_epochs: int = 2**31 - 1,\n        clip_max_norm: Optional[float] = 5.0,\n        resume_training: bool = False,\n        discard_prior_samples: bool = False,\n        retrain_from_scratch: bool = False,\n        show_train_summary: bool = False,\n        dataloader_kwargs: Optional[Dict] = None,\n    ) -&gt; nn.Module:\n        r\"\"\"Return classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n        Args:\n\n            regularization_strength: The multiplicative coefficient applied to the\n                balancing regularizer ($\\lambda$).\n            training_batch_size: Training batch size.\n            learning_rate: Learning rate for Adam optimizer.\n            validation_fraction: The fraction of data to use for validation.\n            stop_after_epochs: The number of epochs to wait for improvement on the\n                validation set before terminating training.\n            max_num_epochs: Maximum number of epochs to run. If reached, we stop\n                training even when the validation loss is still decreasing. Otherwise,\n                we train until validation loss increases (see also `stop_after_epochs`).\n            clip_max_norm: Value at which to clip the total gradient norm in order to\n                prevent exploding gradients. Use None for no clipping.\n            exclude_invalid_x: Whether to exclude simulation outputs `x=NaN` or `x=\u00b1\u221e`\n                during training. Expect errors, silent or explicit, when `False`.\n            resume_training: Can be used in case training time is limited, e.g. on a\n                cluster. If `True`, the split between train and validation set, the\n                optimizer, the number of epochs, and the best validation log-prob will\n                be restored from the last time `.train()` was called.\n            discard_prior_samples: Whether to discard samples simulated in round 1, i.e.\n                from the prior. Training may be sped up by ignoring such less targeted\n                samples.\n            retrain_from_scratch: Whether to retrain the conditional density\n                estimator for the posterior from scratch each round.\n            show_train_summary: Whether to print the number of epochs and validation\n                loss and leakage after the training.\n            dataloader_kwargs: Additional or updated kwargs to be passed to the training\n                and validation dataloaders (like, e.g., a collate_fn)\n        Returns:\n            Classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n        \"\"\"\n        kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n        kwargs[\"loss_kwargs\"] = {\n            \"regularization_strength\": kwargs.pop(\"regularization_strength\")\n        }\n        return super().train(**kwargs)\n\n    def _loss(\n        self, theta: Tensor, x: Tensor, num_atoms: int, regularization_strength: float\n    ) -&gt; Tensor:\n        \"\"\"Returns the binary cross-entropy loss for the trained classifier.\n\n        The classifier takes as input a $(\\theta,x)$ pair. It is trained to predict 1\n        if the pair was sampled from the joint $p(\\theta,x)$, and to predict 0 if the\n        pair was sampled from the marginals $p(\\theta)p(x)$.\n        \"\"\"\n\n        assert theta.shape[0] == x.shape[0], \"Batch sizes for theta and x must match.\"\n        batch_size = theta.shape[0]\n\n        logits = self._classifier_logits(theta, x, num_atoms)\n        likelihood = torch.sigmoid(logits).squeeze()\n\n        # Alternating pairs where there is one sampled from the joint and one\n        # sampled from the marginals. The first element is sampled from the\n        # joint p(theta, x) and is labelled 1. The second element is sampled\n        # from the marginals p(theta)p(x) and is labelled 0. And so on.\n        labels = ones(2 * batch_size, device=self._device)  # two atoms\n        labels[1::2] = 0.0\n\n        # Binary cross entropy to learn the likelihood (AALR-specific)\n        bce = nn.BCELoss()(likelihood, labels)\n\n        # Balancing regularizer\n        regularizer = (\n            (torch.sigmoid(logits[0::2]) + torch.sigmoid(logits[1::2]) - 1)\n            .mean()\n            .square()\n        )\n\n        return bce + regularization_strength * regularizer\n</code></pre> <p>             Bases: <code>ABCBASE</code></p> Source code in <code>sbi/inference/abc/mcabc.py</code> <pre><code>class MCABC(ABCBASE):\n    def __init__(\n        self,\n        simulator: Callable,\n        prior,\n        distance: Union[str, Callable] = \"l2\",\n        num_workers: int = 1,\n        simulation_batch_size: int = 1,\n        show_progress_bars: bool = True,\n    ):\n        r\"\"\"Monte-Carlo Approximate Bayesian Computation (Rejection ABC) [1].\n\n        [1] Pritchard, J. K., Seielstad, M. T., Perez-Lezaun, A., &amp; Feldman, M. W.\n        (1999). Population growth of human Y chromosomes: a study of Y chromosome\n        microsatellites. Molecular biology and evolution, 16(12), 1791-1798.\n\n        Args:\n            simulator: A function that takes parameters $\\theta$ and maps them to\n                simulations, or observations, `x`, $\\mathrm{sim}(\\theta)\\to x$. Any\n                regular Python callable (i.e. function or class with `__call__` method)\n                can be used.\n            prior: A probability distribution that expresses prior knowledge about the\n                parameters, e.g. which ranges are meaningful for them. Any\n                object with `.log_prob()`and `.sample()` (for example, a PyTorch\n                distribution) can be used.\n            distance: Distance function to compare observed and simulated data. Can be\n                a custom function or one of `l1`, `l2`, `mse`.\n            num_workers: Number of parallel workers to use for simulations.\n            simulation_batch_size: Number of parameter sets that the simulator\n                maps to data x at once. If None, we simulate all parameter sets at the\n                same time. If &gt;= 1, the simulator has to process data of shape\n                (simulation_batch_size, parameter_dimension).\n            show_progress_bars: Whether to show a progressbar during simulation and\n                sampling.\n        \"\"\"\n\n        super().__init__(\n            simulator=simulator,\n            prior=prior,\n            distance=distance,\n            num_workers=num_workers,\n            simulation_batch_size=simulation_batch_size,\n            show_progress_bars=show_progress_bars,\n        )\n\n    def __call__(\n        self,\n        x_o: Union[Tensor, ndarray],\n        num_simulations: int,\n        eps: Optional[float] = None,\n        quantile: Optional[float] = None,\n        lra: bool = False,\n        sass: bool = False,\n        sass_fraction: float = 0.25,\n        sass_expansion_degree: int = 1,\n        kde: bool = False,\n        kde_kwargs: Dict[str, Any] = {},\n        return_summary: bool = False,\n    ) -&gt; Union[Tuple[Tensor, dict], Tuple[KDEWrapper, dict], Tensor, KDEWrapper]:\n        r\"\"\"Run MCABC and return accepted parameters or KDE object fitted on them.\n\n        Args:\n            x_o: Observed data.\n            num_simulations: Number of simulations to run.\n            eps: Acceptance threshold $\\epsilon$ for distance between observed and\n                simulated data.\n            quantile: Upper quantile of smallest distances for which the corresponding\n                parameters are returned, e.g, q=0.01 will return the top 1%. Exactly\n                one of quantile or `eps` have to be passed.\n            lra: Whether to run linear regression adjustment as in Beaumont et al. 2002\n            sass: Whether to determine semi-automatic summary statistics as in\n                Fearnhead &amp; Prangle 2012.\n            sass_fraction: Fraction of simulation budget used for the initial sass run.\n            sass_expansion_degree: Degree of the polynomial feature expansion for the\n                sass regression, default 1 - no expansion.\n            kde: Whether to run KDE on the accepted parameters to return a KDE\n                object from which one can sample.\n            kde_kwargs: kwargs for performing KDE:\n                'bandwidth='; either a float, or a string naming a bandwidth\n                heuristics, e.g., 'cv' (cross validation), 'silvermann' or 'scott',\n                default 'cv'.\n                'transform': transform applied to the parameters before doing KDE.\n                'sample_weights': weights associated with samples. See 'get_kde' for\n                more details\n            return_summary: Whether to return the distances and data corresponding to\n                the accepted parameters.\n\n        Returns:\n            theta (if kde False): accepted parameters\n            kde (if kde True): KDE object based on accepted parameters from which one\n                can .sample() and .log_prob().\n            summary (if summary True): dictionary containing the accepted paramters (if\n                kde True), distances and simulated data x.\n        \"\"\"\n\n        # Exactly one of eps or quantile need to be passed.\n        assert (eps is not None) ^ (\n            quantile is not None\n        ), \"Eps or quantile must be passed, but not both.\"\n\n        # Run SASS and change the simulator and x_o accordingly.\n        if sass:\n            num_pilot_simulations = int(sass_fraction * num_simulations)\n            self.logger.info(\n                f\"Running SASS with {num_pilot_simulations} pilot samples.\"\n            )\n            num_simulations -= num_pilot_simulations\n\n            pilot_theta = self.prior.sample((num_pilot_simulations,))\n            pilot_x = self._batched_simulator(pilot_theta)\n\n            sass_transform = self.get_sass_transform(\n                pilot_theta, pilot_x, sass_expansion_degree\n            )\n\n            simulator = lambda theta: sass_transform(self._batched_simulator(theta))\n            x_o = sass_transform(x_o)\n        else:\n            simulator = self._batched_simulator\n\n        # Simulate and calculate distances.\n        theta = self.prior.sample((num_simulations,))\n        x = simulator(theta)\n\n        # Infer shape of x to test and set x_o.\n        self.x_shape = x[0].unsqueeze(0).shape\n        self.x_o = process_x(x_o, self.x_shape)\n\n        distances = self.distance(self.x_o, x)\n\n        # Select based on acceptance threshold epsilon.\n        if eps is not None:\n            is_accepted = distances &lt; eps\n            num_accepted = is_accepted.sum().item()\n            assert num_accepted &gt; 0, f\"No parameters accepted, eps={eps} too small\"\n\n            theta_accepted = theta[is_accepted]\n            distances_accepted = distances[is_accepted]\n            x_accepted = x[is_accepted]\n\n        # Select based on quantile on sorted distances.\n        elif quantile is not None:\n            num_top_samples = int(num_simulations * quantile)\n            sort_idx = torch.argsort(distances)\n            theta_accepted = theta[sort_idx][:num_top_samples]\n            distances_accepted = distances[sort_idx][:num_top_samples]\n            x_accepted = x[sort_idx][:num_top_samples]\n\n        else:\n            raise ValueError(\"One of epsilon or quantile has to be passed.\")\n\n        # Maybe adjust theta with LRA.\n        if lra:\n            self.logger.info(\"Running Linear regression adjustment.\")\n            final_theta = self.run_lra(theta_accepted, x_accepted, observation=self.x_o)\n        else:\n            final_theta = theta_accepted\n\n        if kde:\n            self.logger.info(\n                f\"\"\"KDE on {final_theta.shape[0]} samples with bandwidth option\n                {kde_kwargs[\"bandwidth\"] if \"bandwidth\" in kde_kwargs else \"cv\"}.\n                Beware that KDE can give unreliable results when used with too few\n                samples and in high dimensions.\"\"\"\n            )\n\n            kde_dist = get_kde(final_theta, **kde_kwargs)\n\n            if return_summary:\n                return (\n                    kde_dist,\n                    dict(theta=final_theta, distances=distances_accepted, x=x_accepted),\n                )\n            else:\n                return kde_dist\n        elif return_summary:\n            return final_theta, dict(distances=distances_accepted, x=x_accepted)\n        else:\n            return final_theta\n</code></pre> <p>             Bases: <code>ABCBASE</code></p> Source code in <code>sbi/inference/abc/smcabc.py</code> <pre><code>class SMCABC(ABCBASE):\n    def __init__(\n        self,\n        simulator: Callable,\n        prior: Distribution,\n        distance: Union[str, Callable] = \"l2\",\n        num_workers: int = 1,\n        simulation_batch_size: int = 1,\n        show_progress_bars: bool = True,\n        kernel: Optional[str] = \"gaussian\",\n        algorithm_variant: str = \"C\",\n    ):\n        r\"\"\"Sequential Monte Carlo Approximate Bayesian Computation.\n\n        We distinguish between three different SMC methods here:\n            - A: Toni et al. 2010 (Phd Thesis)\n            - B: Sisson et al. 2007 (with correction from 2009)\n            - C: Beaumont et al. 2009\n\n        In Toni et al. 2010 we find an overview of the differences on page 34:\n            - B: same as A except for resampling of weights if the effective sampling\n                size is too small.\n            - C: same as A except for calculation of the covariance of the perturbation\n                kernel: the kernel covariance is a scaled version of the covariance of\n                the previous population.\n\n        Args:\n            simulator: A function that takes parameters $\\theta$ and maps them to\n                simulations, or observations, `x`, $\\mathrm{sim}(\\theta)\\to x$. Any\n                regular Python callable (i.e. function or class with `__call__` method)\n                can be used.\n            prior: A probability distribution that expresses prior knowledge about the\n                parameters, e.g. which ranges are meaningful for them. Any\n                object with `.log_prob()`and `.sample()` (for example, a PyTorch\n                distribution) can be used.\n            distance: Distance function to compare observed and simulated data. Can be\n                a custom function or one of `l1`, `l2`, `mse`.\n            num_workers: Number of parallel workers to use for simulations.\n            simulation_batch_size: Number of parameter sets that the simulator\n                maps to data x at once. If None, we simulate all parameter sets at the\n                same time. If &gt;= 1, the simulator has to process data of shape\n                (simulation_batch_size, parameter_dimension).\n            show_progress_bars: Whether to show a progressbar during simulation and\n                sampling.\n            kernel: Perturbation kernel.\n            algorithm_variant: Indicating the choice of algorithm variant, A, B, or C.\n\n        \"\"\"\n\n        super().__init__(\n            simulator=simulator,\n            prior=prior,\n            distance=distance,\n            num_workers=num_workers,\n            simulation_batch_size=simulation_batch_size,\n            show_progress_bars=show_progress_bars,\n        )\n\n        kernels = (\"gaussian\", \"uniform\")\n        assert (\n            kernel in kernels\n        ), f\"Kernel '{kernel}' not supported. Choose one from {kernels}.\"\n        self.kernel = kernel\n\n        algorithm_variants = (\"A\", \"B\", \"C\")\n        assert algorithm_variant in algorithm_variants, (\n            f\"SMCABC variant '{algorithm_variant}' not supported, choose one from\"\n            \" {algorithm_variants}.\"\n        )\n        self.algorithm_variant = algorithm_variant\n        self.distance_to_x0 = None\n        self.simulation_counter = 0\n        self.num_simulations = 0\n\n        # Define simulator that keeps track of budget.\n        def simulate_with_budget(theta):\n            self.simulation_counter += theta.shape[0]\n            return self._batched_simulator(theta)\n\n        self._simulate_with_budget = simulate_with_budget\n\n    def __call__(\n        self,\n        x_o: Union[Tensor, ndarray],\n        num_particles: int,\n        num_initial_pop: int,\n        num_simulations: int,\n        epsilon_decay: float,\n        distance_based_decay: bool = False,\n        ess_min: Optional[float] = None,\n        kernel_variance_scale: float = 1.0,\n        use_last_pop_samples: bool = True,\n        return_summary: bool = False,\n        kde: bool = False,\n        kde_kwargs: Dict[str, Any] = {},\n        kde_sample_weights: bool = False,\n        lra: bool = False,\n        lra_with_weights: bool = False,\n        sass: bool = False,\n        sass_fraction: float = 0.25,\n        sass_expansion_degree: int = 1,\n    ) -&gt; Union[Tensor, KDEWrapper, Tuple[Tensor, dict], Tuple[KDEWrapper, dict]]:\n        r\"\"\"Run SMCABC and return accepted parameters or KDE object fitted on them.\n\n        Args:\n            x_o: Observed data.\n            num_particles: Number of particles in each population.\n            num_initial_pop: Number of simulations used for initial population.\n            num_simulations: Total number of possible simulations.\n            epsilon_decay: Factor with which the acceptance threshold $\\epsilon$ decays.\n            distance_based_decay: Whether the $\\epsilon$ decay is constant over\n                populations or calculated from the previous populations distribution of\n                distances.\n            ess_min: Threshold of effective sampling size for resampling weights. Not\n                used when None (default).\n            kernel_variance_scale: Factor for scaling the perturbation kernel variance.\n            use_last_pop_samples: Whether to fill up the current population with\n                samples from the previous population when the budget is used up. If\n                False, the current population is discarded and the previous population\n                is returned.\n            lra: Whether to run linear regression adjustment as in Beaumont et al. 2002\n            lra_with_weights: Whether to run lra as weighted linear regression with SMC\n                weights\n            sass: Whether to determine semi-automatic summary statistics as in\n                Fearnhead &amp; Prangle 2012.\n            sass_fraction: Fraction of simulation budget used for the initial sass run.\n            sass_expansion_degree: Degree of the polynomial feature expansion for the\n                sass regression, default 1 - no expansion.\n            kde: Whether to run KDE on the accepted parameters to return a KDE\n                object from which one can sample.\n            kde_kwargs: kwargs for performing KDE:\n                'bandwidth='; either a float, or a string naming a bandwidth\n                heuristics, e.g., 'cv' (cross validation), 'silvermann' or 'scott',\n                default 'cv'.\n                'transform': transform applied to the parameters before doing KDE.\n                'sample_weights': weights associated with samples. See 'get_kde' for\n                more details\n            kde_sample_weights: Whether perform weighted KDE with SMC weights or on raw\n                particles.\n            return_summary: Whether to return a dictionary with all accepted particles,\n                weights, etc. at the end.\n\n        Returns:\n            theta (if kde False): accepted parameters of the last population.\n            kde (if kde True): KDE object fitted on accepted parameters, from which one\n                can .sample() and .log_prob().\n            summary (if return_summary True): dictionary containing the accepted\n                paramters (if kde True), distances and simulated data x of all\n                populations.\n        \"\"\"\n\n        pop_idx = 0\n        self.num_simulations = num_simulations\n\n        # Pilot run for SASS.\n        if sass:\n            num_pilot_simulations = int(sass_fraction * num_simulations)\n            self.logger.info(\n                f\"Running SASS with {num_pilot_simulations} pilot samples.\"\n            )\n            sass_transform = self.run_sass_set_xo(\n                num_particles, num_pilot_simulations, x_o, lra, sass_expansion_degree\n            )\n            # Udpate simulator and xo\n            x_o = sass_transform(self.x_o)\n\n            def sass_simulator(theta):\n                self.simulation_counter += theta.shape[0]\n                return sass_transform(self._batched_simulator(theta))\n\n            self._simulate_with_budget = sass_simulator\n\n        # run initial population\n        particles, epsilon, distances, x = self._set_xo_and_sample_initial_population(\n            x_o, num_particles, num_initial_pop\n        )\n        log_weights = torch.log(1 / num_particles * ones(num_particles))\n\n        self.logger.info(\n            (\n                f\"population={pop_idx}, eps={epsilon}, ess={1.0}, \"\n                f\"num_sims={num_initial_pop}\"\n            )\n        )\n\n        all_particles = [particles]\n        all_log_weights = [log_weights]\n        all_distances = [distances]\n        all_epsilons = [epsilon]\n        all_x = [x]\n\n        while self.simulation_counter &lt; self.num_simulations:\n            pop_idx += 1\n            # Decay based on quantile of distances from previous pop.\n            if distance_based_decay:\n                epsilon = self._get_next_epsilon(\n                    all_distances[pop_idx - 1], epsilon_decay\n                )\n            # Constant decay.\n            else:\n                epsilon *= epsilon_decay\n\n            # Get kernel variance from previous pop.\n            self.kernel_variance = self.get_kernel_variance(\n                all_particles[pop_idx - 1],\n                torch.exp(all_log_weights[pop_idx - 1]),\n                samples_per_dim=500,\n                kernel_variance_scale=kernel_variance_scale,\n            )\n            particles, log_weights, distances, x = self._sample_next_population(\n                particles=all_particles[pop_idx - 1],\n                log_weights=all_log_weights[pop_idx - 1],\n                distances=all_distances[pop_idx - 1],\n                epsilon=epsilon,\n                x=all_x[pop_idx - 1],\n                use_last_pop_samples=use_last_pop_samples,\n            )\n\n            # Resample population if effective sampling size is too small.\n            if ess_min is not None:\n                particles, log_weights = self.resample_if_ess_too_small(\n                    particles, log_weights, ess_min, pop_idx\n                )\n\n            self.logger.info(\n                (\n                    f\"population={pop_idx} done: eps={epsilon:.6f},\"\n                    f\" num_sims={self.simulation_counter}.\"\n                )\n            )\n\n            # collect results\n            all_particles.append(particles)\n            all_log_weights.append(log_weights)\n            all_distances.append(distances)\n            all_epsilons.append(epsilon)\n            all_x.append(x)\n\n        # Maybe run LRA and adjust weights.\n        if lra:\n            self.logger.info(\"Running Linear regression adjustment.\")\n            adjusted_particles, adjusted_weights = self.run_lra_update_weights(\n                particles=all_particles[-1],\n                xs=all_x[-1],\n                observation=process_x(x_o),\n                log_weights=all_log_weights[-1],\n                lra_with_weights=lra_with_weights,\n            )\n            final_particles = adjusted_particles\n        else:\n            final_particles = all_particles[-1]\n\n        if kde:\n            self.logger.info(\n                f\"\"\"KDE on {final_particles.shape[0]} samples with bandwidth option\n                {kde_kwargs[\"bandwidth\"] if \"bandwidth\" in kde_kwargs else \"cv\"}.\n                Beware that KDE can give unreliable results when used with too few\n                samples and in high dimensions.\"\"\"\n            )\n            # Maybe get particles weights from last population for weighted KDE.\n            if kde_sample_weights:\n                kde_kwargs[\"sample_weights\"] = all_log_weights[-1].exp()\n\n            kde_dist = get_kde(final_particles, **kde_kwargs)\n\n            if return_summary:\n                return (\n                    kde_dist,\n                    dict(\n                        particles=all_particles,\n                        weights=all_log_weights,\n                        epsilons=all_epsilons,\n                        distances=all_distances,\n                        xs=all_x,\n                    ),\n                )\n            else:\n                return kde_dist\n\n        if return_summary:\n            return (\n                final_particles,\n                dict(\n                    particles=all_particles,\n                    weights=all_log_weights,\n                    epsilons=all_epsilons,\n                    distances=all_distances,\n                    xs=all_x,\n                ),\n            )\n        else:\n            return final_particles\n\n    def _set_xo_and_sample_initial_population(\n        self,\n        x_o: Array,\n        num_particles: int,\n        num_initial_pop: int,\n    ) -&gt; Tuple[Tensor, float, Tensor, Tensor]:\n        \"\"\"Return particles, epsilon and distances of initial population.\"\"\"\n\n        assert (\n            num_particles &lt;= num_initial_pop\n        ), \"number of initial round simulations must be greater than population size\"\n\n        theta = self.prior.sample((num_initial_pop,))\n        x = self._simulate_with_budget(theta)\n\n        # Infer x shape to test and set x_o.\n        self.x_shape = x[0].unsqueeze(0).shape\n        self.x_o = process_x(x_o, self.x_shape)\n\n        distances = self.distance(self.x_o, x)\n        sortidx = torch.argsort(distances)\n        particles = theta[sortidx][:num_particles]\n        # Take last accepted distance as epsilon.\n        initial_epsilon = distances[sortidx][num_particles - 1]\n\n        if not torch.isfinite(initial_epsilon):\n            initial_epsilon = 1e8\n\n        return (\n            particles,\n            initial_epsilon,\n            distances[sortidx][:num_particles],\n            x[sortidx][:num_particles],\n        )\n\n    def _sample_next_population(\n        self,\n        particles: Tensor,\n        log_weights: Tensor,\n        distances: Tensor,\n        epsilon: float,\n        x: Tensor,\n        use_last_pop_samples: bool = True,\n    ) -&gt; Tuple[Tensor, Tensor, Tensor, Tensor]:\n        \"\"\"Return particles, weights and distances of new population.\"\"\"\n\n        new_particles = []\n        new_log_weights = []\n        new_distances = []\n        new_x = []\n\n        num_accepted_particles = 0\n        num_particles = particles.shape[0]\n\n        while num_accepted_particles &lt; num_particles:\n            # Upperbound for batch size to not exceed simulation budget.\n            num_batch = min(\n                num_particles - num_accepted_particles,\n                self.num_simulations - self.simulation_counter,\n            )\n\n            # Sample from previous population and perturb.\n            particle_candidates = self._sample_and_perturb(\n                particles, torch.exp(log_weights), num_samples=num_batch\n            )\n            # Simulate and select based on distance.\n            x_candidates = self._simulate_with_budget(particle_candidates)\n            dists = self.distance(self.x_o, x_candidates)\n            is_accepted = dists &lt;= epsilon\n            num_accepted_batch = is_accepted.sum().item()\n\n            if num_accepted_batch &gt; 0:\n                new_particles.append(particle_candidates[is_accepted])\n                new_log_weights.append(\n                    self._calculate_new_log_weights(\n                        particle_candidates[is_accepted],\n                        particles,\n                        log_weights,\n                    )\n                )\n                new_distances.append(dists[is_accepted])\n                new_x.append(x_candidates[is_accepted])\n                num_accepted_particles += num_accepted_batch\n\n            # If simulation budget was exceeded and we still need particles, take\n            # previous population or fill up with previous population.\n            if (\n                self.simulation_counter &gt;= self.num_simulations\n                and num_accepted_particles &lt; num_particles\n            ):\n                if use_last_pop_samples:\n                    num_remaining = num_particles - num_accepted_particles\n                    self.logger.info(\n                        f\"\"\"Simulation Budget exceeded, filling up with {num_remaining}\n                        samples from last population.\"\"\"\n                    )\n                    # Some new particles have been accepted already, therefore\n                    # fill up the remaining once with old particles and weights.\n                    new_particles.append(particles[:num_remaining, :])\n                    # Recalculate weights with new particles.\n                    new_log_weights = [\n                        self._calculate_new_log_weights(\n                            torch.cat(new_particles),\n                            particles,\n                            log_weights,\n                        )\n                    ]\n                    new_distances.append(distances[:num_remaining])\n                    new_x.append(x[:num_remaining])\n                else:\n                    self.logger.info(\n                        \"Simulation Budget exceeded, returning previous population.\"\n                    )\n                    new_particles = [particles]\n                    new_log_weights = [log_weights]\n                    new_distances = [distances]\n                    new_x = [x]\n\n                break\n\n        # collect lists of tensors into tensors\n        new_particles = torch.cat(new_particles)\n        new_log_weights = torch.cat(new_log_weights)\n        new_distances = torch.cat(new_distances)\n        new_x = torch.cat(new_x)\n\n        # normalize the new weights\n        new_log_weights -= torch.logsumexp(new_log_weights, dim=0)\n\n        # Return sorted wrt distances.\n        sort_idx = torch.argsort(new_distances)\n\n        return (\n            new_particles[sort_idx],\n            new_log_weights[sort_idx],\n            new_distances[sort_idx],\n            new_x[sort_idx],\n        )\n\n    def _get_next_epsilon(self, distances: Tensor, quantile: float) -&gt; float:\n        \"\"\"Return epsilon for next round based on quantile of this round's distances.\n\n        Note: distances are made unique to avoid repeated distances from simulations\n        that result in the same observation.\n\n        Args:\n            distances: The distances accepted in this round.\n            quantile: Quantile in the distance distribution to determine new epsilon.\n\n        Returns:\n            epsilon: Epsilon for the next population.\n        \"\"\"\n        # Take unique distances to skip same distances simulations (return is sorted).\n        distances = torch.unique(distances)\n        # Cumsum as cdf proxy.\n        distances_cdf = torch.cumsum(distances, dim=0) / distances.sum()\n        # Take the q quantile of distances.\n        try:\n            qidx = torch.where(distances_cdf &gt;= quantile)[0][0]\n        except IndexError:\n            self.logger.warning(\n                (\n                    f\"Accepted unique distances={distances} don't match \"\n                    f\"quantile={quantile:.2f}. Selecting last distance.\"\n                )\n            )\n            qidx = -1\n\n        # The new epsilon is given by that distance.\n        return distances[qidx].item()\n\n    def _calculate_new_log_weights(\n        self,\n        new_particles: Tensor,\n        old_particles: Tensor,\n        old_log_weights: Tensor,\n    ) -&gt; Tensor:\n        \"\"\"Return new log weights following formulas in publications A,B anc C.\"\"\"\n\n        # Prior can be batched across new particles.\n        prior_log_probs = self.prior.log_prob(new_particles)\n\n        # Contstruct function to get kernel log prob for given old particle.\n        # The kernel is centered on each old particle as in all three variants (A,B,C).\n        def kernel_log_prob(new_particle):\n            return self.get_new_kernel(old_particles).log_prob(new_particle)\n\n        # We still have to loop over particles here because\n        # the kernel log probs are already batched across old particles.\n        log_weighted_sum = tensor(\n            [\n                torch.logsumexp(old_log_weights + kernel_log_prob(new_particle), dim=0)\n                for new_particle in new_particles\n            ],\n            dtype=torch.float32,\n        )\n        # new weights are prior probs over weighted sum:\n        return prior_log_probs - log_weighted_sum\n\n    @staticmethod\n    def sample_from_population_with_weights(\n        particles: Tensor, weights: Tensor, num_samples: int = 1\n    ) -&gt; Tensor:\n        \"\"\"Return samples from particles sampled with weights.\"\"\"\n\n        # define multinomial with weights as probs\n        multi = Multinomial(probs=weights)\n        # sample num samples, with replacement\n        samples = multi.sample(sample_shape=torch.Size((num_samples,)))\n        # get indices of success trials\n        indices = torch.where(samples)[1]\n        # return those indices from trace\n        return particles[indices]\n\n    def _sample_and_perturb(\n        self, particles: Tensor, weights: Tensor, num_samples: int = 1\n    ) -&gt; Tensor:\n        \"\"\"Sample and perturb batch of new parameters from trace.\n\n        Reject sampled and perturbed parameters outside of prior.\n        \"\"\"\n\n        num_accepted = 0\n        parameters = []\n        while num_accepted &lt; num_samples:\n            parms = self.sample_from_population_with_weights(\n                particles, weights, num_samples=num_samples - num_accepted\n            )\n\n            # Create kernel on params and perturb.\n            parms_perturbed = self.get_new_kernel(parms).sample()\n\n            is_within_prior = within_support(self.prior, parms_perturbed)\n            num_accepted += int(is_within_prior.sum().item())\n\n            if num_accepted &gt; 0:\n                parameters.append(parms_perturbed[is_within_prior])\n\n        return torch.cat(parameters)\n\n    def get_kernel_variance(\n        self,\n        particles: Tensor,\n        weights: Tensor,\n        samples_per_dim: int = 100,\n        kernel_variance_scale: float = 1.0,\n    ) -&gt; Tensor:\n        if self.kernel == \"gaussian\":\n            # For variant C, Beaumont et al. 2009, the kernel variance comes from the\n            # previous population.\n            if self.algorithm_variant == \"C\":\n                # Calculate weighted covariance of particles.\n                population_cov = torch.tensor(\n                    np.atleast_2d(np.cov(particles, rowvar=False, aweights=weights)),\n                    dtype=torch.float32,\n                )\n                # Make sure variance is nonsingular.\n                try:\n                    torch.cholesky(kernel_variance_scale * population_cov)\n                except RuntimeError:\n                    self.logger.warning(\n                        \"\"\"\"Singular particle covariance, using unit covariance.\"\"\"\n                    )\n                    population_cov = torch.eye(particles.shape[1])\n                return kernel_variance_scale * population_cov\n            # While for Toni et al. and Sisson et al. it comes from the parameter\n            # ranges.\n            elif self.algorithm_variant in (\"A\", \"B\"):\n                particle_ranges = self.get_particle_ranges(\n                    particles, weights, samples_per_dim=samples_per_dim\n                )\n                return kernel_variance_scale * torch.diag(particle_ranges)\n            else:\n                raise ValueError(f\"Variant, '{self.algorithm_variant}' not supported.\")\n        elif self.kernel == \"uniform\":\n            # Variance spans the range of parameters for every dimension.\n            return kernel_variance_scale * self.get_particle_ranges(\n                particles, weights, samples_per_dim=samples_per_dim\n            )\n        else:\n            raise ValueError(f\"Kernel, '{self.kernel}' not supported.\")\n\n    def get_new_kernel(self, thetas: Tensor) -&gt; Distribution:\n        \"\"\"Return new kernel distribution for a given set of paramters.\"\"\"\n\n        if self.kernel == \"gaussian\":\n            assert self.kernel_variance.ndim == 2\n            return MultivariateNormal(\n                loc=thetas, covariance_matrix=self.kernel_variance\n            )\n\n        elif self.kernel == \"uniform\":\n            low = thetas - self.kernel_variance\n            high = thetas + self.kernel_variance\n            # Move batch shape to event shape to get Uniform that is multivariate in\n            # parameter dimension.\n            return Uniform(low=low, high=high).to_event(1)\n        else:\n            raise ValueError(f\"Kernel, '{self.kernel}' not supported.\")\n\n    def resample_if_ess_too_small(\n        self,\n        particles: Tensor,\n        log_weights: Tensor,\n        ess_min: float,\n        pop_idx: int,\n    ) -&gt; Tuple[Tensor, Tensor]:\n        \"\"\"Return resampled particles and uniform weights if effectice sampling size is\n        too small.\n        \"\"\"\n\n        num_particles = particles.shape[0]\n        ess = (1 / torch.sum(torch.exp(2.0 * log_weights), dim=0)) / num_particles\n        # Resampling of weights for low ESS only for Sisson et al. 2007.\n        if ess &lt; ess_min:\n            self.logger.info(f\"ESS={ess:.2f} too low, resampling pop {pop_idx}...\")\n            # First resample, then set to uniform weights as in Sisson et al. 2007.\n            particles = self.sample_from_population_with_weights(\n                particles, torch.exp(log_weights), num_samples=num_particles\n            )\n            log_weights = torch.log(1 / num_particles * ones(num_particles))\n\n        return particles, log_weights\n\n    def run_lra_update_weights(\n        self,\n        particles: Tensor,\n        xs: Tensor,\n        observation: Tensor,\n        log_weights: Tensor,\n        lra_with_weights: bool,\n    ) -&gt; Tuple[Tensor, Tensor]:\n        \"\"\"Return particles and weights adjusted with LRA.\n\n        Runs (weighted) linear regression from xs onto particles to adjust the\n        particles.\n\n        Updates the SMC weights according to the new particles.\n        \"\"\"\n\n        adjusted_particels = self.run_lra(\n            theta=particles,\n            x=xs,\n            observation=observation,\n            sample_weight=log_weights.exp() if lra_with_weights else None,\n        )\n\n        # Update SMC weights with LRA adjusted weights\n        adjusted_log_weights = self._calculate_new_log_weights(\n            new_particles=adjusted_particels,\n            old_particles=particles,\n            old_log_weights=log_weights,\n        )\n\n        return adjusted_particels, adjusted_log_weights\n\n    def run_sass_set_xo(\n        self,\n        num_particles: int,\n        num_pilot_simulations: int,\n        x_o,\n        lra: bool = False,\n        sass_expansion_degree: int = 1,\n    ) -&gt; Callable:\n        \"\"\"Return transform for semi-automatic summary statistics.\n\n        Runs an single round of rejection abc with fixed budget and accepts\n        num_particles simulations to run the regression for sass.\n\n        Sets self.x_o once the x_shape can be derived from simulations.\n        \"\"\"\n        (\n            pilot_particles,\n            _,\n            _,\n            pilot_xs,\n        ) = self._set_xo_and_sample_initial_population(\n            x_o, num_particles, num_pilot_simulations\n        )\n        # Adjust with LRA.\n        if lra:\n            pilot_particles = self.run_lra(pilot_particles, pilot_xs, self.x_o)\n        sass_transform = self.get_sass_transform(\n            pilot_particles,\n            pilot_xs,\n            expansion_degree=sass_expansion_degree,\n            sample_weight=None,\n        )\n        return sass_transform\n\n    def get_particle_ranges(\n        self, particles: Tensor, weights: Tensor, samples_per_dim: int = 100\n    ) -&gt; Tensor:\n        \"\"\"Return range of particles in each parameter dimension.\"\"\"\n\n        # get weighted samples\n        samples = self.sample_from_population_with_weights(\n            particles,\n            weights,\n            num_samples=samples_per_dim * particles.shape[1],\n        )\n\n        # Variance spans the range of particles for every dimension.\n        particle_ranges = samples.max(0).values - samples.min(0).values\n        assert particle_ranges.ndim &lt; 2\n        return particle_ranges\n</code></pre>"},{"location":"reference/#sbi.inference.snpe.snpe_a.SNPE_A.__init__","title":"<code>__init__(prior=None, density_estimator='mdn_snpe_a', num_components=10, device='cpu', logging_level='WARNING', summary_writer=None, show_progress_bars=True)</code>","text":"<p>SNPE-A [1].</p> <p>[1] Fast epsilon-free Inference of Simulation Models with Bayesian Conditional     Density Estimation, Papamakarios et al., NeurIPS 2016,     https://arxiv.org/abs/1605.06376.</p> <p>This class implements SNPE-A. SNPE-A trains across multiple rounds with a maximum-likelihood-loss. This will make training converge to the proposal posterior instead of the true posterior. To correct for this, SNPE-A applies a post-hoc correction after training. This correction has to be performed analytically. Thus, SNPE-A is limited to Gaussian distributions for all but the last round. In the last round, SNPE-A can use a Mixture of Gaussians.</p> <p>Parameters:</p> Name Type Description Default <code>prior</code> <code>Optional[Distribution]</code> <p>A probability distribution that expresses prior knowledge about the parameters, e.g. which ranges are meaningful for them. Any object with <code>.log_prob()</code>and <code>.sample()</code> (for example, a PyTorch distribution) can be used.</p> <code>None</code> <code>density_estimator</code> <code>Union[str, Callable]</code> <p>If it is a string (only \u201cmdn_snpe_a\u201d is valid), use a pre-configured mixture of densities network. Alternatively, a function that builds a custom neural network can be provided. The function will be called with the first batch of simulations (theta, x), which can thus be used for shape inference and potentially for z-scoring. It needs to return a PyTorch <code>nn.Module</code> implementing the density estimator. The density estimator needs to provide the methods <code>.log_prob</code> and <code>.sample()</code>. Note that until the last round only a single (multivariate) Gaussian component is used for training (see Algorithm 1 in [1]). In the last round, this component is replicated <code>num_components</code> times, its parameters are perturbed with a very small noise, and then the last training round is done with the expanded Gaussian mixture as estimator for the proposal posterior.</p> <code>'mdn_snpe_a'</code> <code>num_components</code> <code>int</code> <p>Number of components of the mixture of Gaussians in the last round. This overrides the <code>num_components</code> value passed to <code>posterior_nn()</code>.</p> <code>10</code> <code>device</code> <code>str</code> <p>Training device, e.g., \u201ccpu\u201d, \u201ccuda\u201d or \u201ccuda:{0, 1, \u2026}\u201d.</p> <code>'cpu'</code> <code>logging_level</code> <code>Union[int, str]</code> <p>Minimum severity of messages to log. One of the strings INFO, WARNING, DEBUG, ERROR and CRITICAL.</p> <code>'WARNING'</code> <code>summary_writer</code> <code>Optional[TensorboardSummaryWriter]</code> <p>A tensorboard <code>SummaryWriter</code> to control, among others, log file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p> <code>None</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during training.</p> <code>True</code> Source code in <code>sbi/inference/snpe/snpe_a.py</code> <pre><code>def __init__(\n    self,\n    prior: Optional[Distribution] = None,\n    density_estimator: Union[str, Callable] = \"mdn_snpe_a\",\n    num_components: int = 10,\n    device: str = \"cpu\",\n    logging_level: Union[int, str] = \"WARNING\",\n    summary_writer: Optional[TensorboardSummaryWriter] = None,\n    show_progress_bars: bool = True,\n):\n    r\"\"\"SNPE-A [1].\n\n    [1] _Fast epsilon-free Inference of Simulation Models with Bayesian Conditional\n        Density Estimation_, Papamakarios et al., NeurIPS 2016,\n        https://arxiv.org/abs/1605.06376.\n\n    This class implements SNPE-A. SNPE-A trains across multiple rounds with a\n    maximum-likelihood-loss. This will make training converge to the proposal\n    posterior instead of the true posterior. To correct for this, SNPE-A applies a\n    post-hoc correction after training. This correction has to be performed\n    analytically. Thus, SNPE-A is limited to Gaussian distributions for all but the\n    last round. In the last round, SNPE-A can use a Mixture of Gaussians.\n\n    Args:\n        prior: A probability distribution that expresses prior knowledge about the\n            parameters, e.g. which ranges are meaningful for them. Any\n            object with `.log_prob()`and `.sample()` (for example, a PyTorch\n            distribution) can be used.\n        density_estimator: If it is a string (only \"mdn_snpe_a\" is valid), use a\n            pre-configured mixture of densities network. Alternatively, a function\n            that builds a custom neural network can be provided. The function will\n            be called with the first batch of simulations (theta, x), which can\n            thus be used for shape inference and potentially for z-scoring. It\n            needs to return a PyTorch `nn.Module` implementing the density\n            estimator. The density estimator needs to provide the methods\n            `.log_prob` and `.sample()`. Note that until the last round only a\n            single (multivariate) Gaussian component is used for training (see\n            Algorithm 1 in [1]). In the last round, this component is replicated\n            `num_components` times, its parameters are perturbed with a very small\n            noise, and then the last training round is done with the expanded\n            Gaussian mixture as estimator for the proposal posterior.\n        num_components: Number of components of the mixture of Gaussians in the\n            last round. This overrides the `num_components` value passed to\n            `posterior_nn()`.\n        device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n        logging_level: Minimum severity of messages to log. One of the strings\n            INFO, WARNING, DEBUG, ERROR and CRITICAL.\n        summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n            file location (default is `&lt;current working directory&gt;/logs`.)\n        show_progress_bars: Whether to show a progressbar during training.\n    \"\"\"\n\n    # Catch invalid inputs.\n    if not ((density_estimator == \"mdn_snpe_a\") or callable(density_estimator)):\n        raise TypeError(\n            \"The `density_estimator` passed to SNPE_A needs to be a \"\n            \"callable or the string 'mdn_snpe_a'!\"\n        )\n\n    # `num_components` will be used to replicate the Gaussian in the last round.\n    self._num_components = num_components\n    self._ran_final_round = False\n\n    # WARNING: sneaky trick ahead. We proxy the parent's `train` here,\n    # requiring the signature to have `num_atoms`, save it for use below, and\n    # continue. It's sneaky because we are using the object (self) as a namespace\n    # to pass arguments between functions, and that's implicit state management.\n    kwargs = utils.del_entries(\n        locals(),\n        entries=(\"self\", \"__class__\", \"num_components\"),\n    )\n    super().__init__(**kwargs)\n</code></pre>"},{"location":"reference/#sbi.inference.snpe.snpe_a.SNPE_A.build_posterior","title":"<code>build_posterior(density_estimator=None, prior=None)</code>","text":"<p>Build posterior from the neural density estimator.</p> <p>This method first corrects the estimated density with <code>correct_for_proposal</code> and then returns a <code>DirectPosterior</code>.</p> <p>Parameters:</p> Name Type Description Default <code>density_estimator</code> <code>Optional[TorchModule]</code> <p>The density estimator that the posterior is based on. If <code>None</code>, use the latest neural density estimator that was trained.</p> <code>None</code> <code>prior</code> <code>Optional[Distribution]</code> <p>Prior distribution.</p> <code>None</code> <p>Returns:</p> Type Description <code>DirectPosterior</code> <p>Posterior \\(p(\\theta|x)\\)  with <code>.sample()</code> and <code>.log_prob()</code> methods.</p> Source code in <code>sbi/inference/snpe/snpe_a.py</code> <pre><code>def build_posterior(\n    self,\n    density_estimator: Optional[TorchModule] = None,\n    prior: Optional[Distribution] = None,\n) -&gt; \"DirectPosterior\":\n    r\"\"\"Build posterior from the neural density estimator.\n\n    This method first corrects the estimated density with `correct_for_proposal`\n    and then returns a `DirectPosterior`.\n\n    Args:\n        density_estimator: The density estimator that the posterior is based on.\n            If `None`, use the latest neural density estimator that was trained.\n        prior: Prior distribution.\n\n    Returns:\n        Posterior $p(\\theta|x)$  with `.sample()` and `.log_prob()` methods.\n    \"\"\"\n    if prior is None:\n        assert (\n            self._prior is not None\n        ), \"\"\"You did not pass a prior. You have to pass the prior either at\n            initialization `inference = SNPE_A(prior)` or to `.build_posterior\n            (prior=prior)`.\"\"\"\n        prior = self._prior\n\n    wrapped_density_estimator = self.correct_for_proposal(\n        density_estimator=density_estimator\n    )\n    self._posterior = DirectPosterior(\n        posterior_estimator=wrapped_density_estimator,  # type: ignore\n        prior=prior,\n    )\n    return deepcopy(self._posterior)\n</code></pre>"},{"location":"reference/#sbi.inference.snpe.snpe_a.SNPE_A.correct_for_proposal","title":"<code>correct_for_proposal(density_estimator=None)</code>","text":"<p>Build mixture of Gaussians that approximates the posterior.</p> <p>Returns a <code>SNPE_A_MDN</code> object, which applies the posthoc-correction required in SNPE-A.</p> <p>Parameters:</p> Name Type Description Default <code>density_estimator</code> <code>Optional[TorchModule]</code> <p>The density estimator that the posterior is based on. If <code>None</code>, use the latest neural density estimator that was trained.</p> <code>None</code> <p>Returns:</p> Type Description <code>SNPE_A_MDN</code> <p>Posterior \\(p(\\theta|x)\\)  with <code>.sample()</code> and <code>.log_prob()</code> methods.</p> Source code in <code>sbi/inference/snpe/snpe_a.py</code> <pre><code>def correct_for_proposal(\n    self,\n    density_estimator: Optional[TorchModule] = None,\n) -&gt; \"SNPE_A_MDN\":\n    r\"\"\"Build mixture of Gaussians that approximates the posterior.\n\n    Returns a `SNPE_A_MDN` object, which applies the posthoc-correction required in\n    SNPE-A.\n\n    Args:\n        density_estimator: The density estimator that the posterior is based on.\n            If `None`, use the latest neural density estimator that was trained.\n\n    Returns:\n        Posterior $p(\\theta|x)$  with `.sample()` and `.log_prob()` methods.\n    \"\"\"\n    if density_estimator is None:\n        density_estimator = deepcopy(\n            self._neural_net\n        )  # PosteriorEstimator.train() also returns a deepcopy, mimic this here\n        # If internal net is used device is defined.\n        device = self._device\n    else:\n        # Otherwise, infer it from the device of the net parameters.\n        device = str(next(density_estimator.parameters()).device)\n\n    # Set proposal of the density estimator.\n    # This also evokes the z-scoring correction if necessary.\n    if (\n        self._proposal_roundwise[-1] is self._prior\n        or self._proposal_roundwise[-1] is None\n    ):\n        proposal = self._prior\n        assert isinstance(\n            proposal, (MultivariateNormal, utils.BoxUniform)\n        ), \"\"\"Prior must be `torch.distributions.MultivariateNormal` or `sbi.utils.\n            BoxUniform`\"\"\"\n    else:\n        assert isinstance(\n            self._proposal_roundwise[-1], DirectPosterior\n        ), \"\"\"The proposal you passed to `append_simulations` is neither the prior\n            nor a `DirectPosterior`. SNPE-A currently only supports these scenarios.\n            \"\"\"\n        proposal = self._proposal_roundwise[-1]\n\n    # Create the SNPE_A_MDN\n    wrapped_density_estimator = SNPE_A_MDN(\n        flow=density_estimator,  # type: ignore\n        proposal=proposal,\n        prior=self._prior,\n        device=device,\n    )\n    return wrapped_density_estimator\n</code></pre>"},{"location":"reference/#sbi.inference.snpe.snpe_a.SNPE_A.train","title":"<code>train(final_round=False, training_batch_size=50, learning_rate=0.0005, validation_fraction=0.1, stop_after_epochs=20, max_num_epochs=2 ** 31 - 1, clip_max_norm=5.0, calibration_kernel=None, resume_training=False, retrain_from_scratch=False, show_train_summary=False, dataloader_kwargs=None, component_perturbation=0.005)</code>","text":"<p>Return density estimator that approximates the proposal posterior.</p> <p>[1] Fast epsilon-free Inference of Simulation Models with Bayesian Conditional     Density Estimation, Papamakarios et al., NeurIPS 2016,     https://arxiv.org/abs/1605.06376.</p> <p>Training is performed with maximum likelihood on samples from the latest round, which leads the algorithm to converge to the proposal posterior.</p> <p>Parameters:</p> Name Type Description Default <code>final_round</code> <code>bool</code> <p>Whether we are in the last round of training or not. For all but the last round, Algorithm 1 from [1] is executed. In last the round, Algorithm 2 from [1] is executed once.</p> <code>False</code> <code>training_batch_size</code> <code>int</code> <p>Training batch size.</p> <code>50</code> <code>learning_rate</code> <code>float</code> <p>Learning rate for Adam optimizer.</p> <code>0.0005</code> <code>validation_fraction</code> <code>float</code> <p>The fraction of data to use for validation.</p> <code>0.1</code> <code>stop_after_epochs</code> <code>int</code> <p>The number of epochs to wait for improvement on the validation set before terminating training.</p> <code>20</code> <code>max_num_epochs</code> <code>int</code> <p>Maximum number of epochs to run. If reached, we stop training even when the validation loss is still decreasing. Otherwise, we train until validation loss increases (see also <code>stop_after_epochs</code>).</p> <code>2 ** 31 - 1</code> <code>clip_max_norm</code> <code>Optional[float]</code> <p>Value at which to clip the total gradient norm in order to prevent exploding gradients. Use None for no clipping.</p> <code>5.0</code> <code>calibration_kernel</code> <code>Optional[Callable]</code> <p>A function to calibrate the loss with respect to the simulations <code>x</code>. See Lueckmann, Gon\u00e7alves et al., NeurIPS 2017.</p> <code>None</code> <code>resume_training</code> <code>bool</code> <p>Can be used in case training time is limited, e.g. on a cluster. If <code>True</code>, the split between train and validation set, the optimizer, the number of epochs, and the best validation log-prob will be restored from the last time <code>.train()</code> was called.</p> <code>False</code> <code>force_first_round_loss</code> <p>If <code>True</code>, train with maximum likelihood, i.e., potentially ignoring the correction for using a proposal distribution different from the prior.</p> required <code>retrain_from_scratch</code> <code>bool</code> <p>Whether to retrain the conditional density estimator for the posterior from scratch each round. Not supported for SNPE-A.</p> <code>False</code> <code>show_train_summary</code> <code>bool</code> <p>Whether to print the number of epochs and validation loss and leakage after the training.</p> <code>False</code> <code>dataloader_kwargs</code> <code>Optional[Dict]</code> <p>Additional or updated kwargs to be passed to the training and validation dataloaders (like, e.g., a collate_fn)</p> <code>None</code> <code>component_perturbation</code> <code>float</code> <p>The standard deviation applied to all weights and biases when, in the last round, the Mixture of Gaussians is build from a single Gaussian. This value can be problem-specific and also depends on the number of mixture components.</p> <code>0.005</code> <p>Returns:</p> Type Description <code>Module</code> <p>Density estimator that approximates the distribution \\(p(\\theta|x)\\).</p> Source code in <code>sbi/inference/snpe/snpe_a.py</code> <pre><code>def train(\n    self,\n    final_round: bool = False,\n    training_batch_size: int = 50,\n    learning_rate: float = 5e-4,\n    validation_fraction: float = 0.1,\n    stop_after_epochs: int = 20,\n    max_num_epochs: int = 2**31 - 1,\n    clip_max_norm: Optional[float] = 5.0,\n    calibration_kernel: Optional[Callable] = None,\n    resume_training: bool = False,\n    retrain_from_scratch: bool = False,\n    show_train_summary: bool = False,\n    dataloader_kwargs: Optional[Dict] = None,\n    component_perturbation: float = 5e-3,\n) -&gt; nn.Module:\n    r\"\"\"Return density estimator that approximates the proposal posterior.\n\n    [1] _Fast epsilon-free Inference of Simulation Models with Bayesian Conditional\n        Density Estimation_, Papamakarios et al., NeurIPS 2016,\n        https://arxiv.org/abs/1605.06376.\n\n    Training is performed with maximum likelihood on samples from the latest round,\n    which leads the algorithm to converge to the proposal posterior.\n\n    Args:\n        final_round: Whether we are in the last round of training or not. For all\n            but the last round, Algorithm 1 from [1] is executed. In last the\n            round, Algorithm 2 from [1] is executed once.\n        training_batch_size: Training batch size.\n        learning_rate: Learning rate for Adam optimizer.\n        validation_fraction: The fraction of data to use for validation.\n        stop_after_epochs: The number of epochs to wait for improvement on the\n            validation set before terminating training.\n        max_num_epochs: Maximum number of epochs to run. If reached, we stop\n            training even when the validation loss is still decreasing. Otherwise,\n            we train until validation loss increases (see also `stop_after_epochs`).\n        clip_max_norm: Value at which to clip the total gradient norm in order to\n            prevent exploding gradients. Use None for no clipping.\n        calibration_kernel: A function to calibrate the loss with respect to the\n            simulations `x`. See Lueckmann, Gon\u00e7alves et al., NeurIPS 2017.\n        resume_training: Can be used in case training time is limited, e.g. on a\n            cluster. If `True`, the split between train and validation set, the\n            optimizer, the number of epochs, and the best validation log-prob will\n            be restored from the last time `.train()` was called.\n        force_first_round_loss: If `True`, train with maximum likelihood,\n            i.e., potentially ignoring the correction for using a proposal\n            distribution different from the prior.\n        retrain_from_scratch: Whether to retrain the conditional density\n            estimator for the posterior from scratch each round. Not supported for\n            SNPE-A.\n        show_train_summary: Whether to print the number of epochs and validation\n            loss and leakage after the training.\n        dataloader_kwargs: Additional or updated kwargs to be passed to the training\n            and validation dataloaders (like, e.g., a collate_fn)\n        component_perturbation: The standard deviation applied to all weights and\n            biases when, in the last round, the Mixture of Gaussians is build from\n            a single Gaussian. This value can be problem-specific and also depends\n            on the number of mixture components.\n\n    Returns:\n        Density estimator that approximates the distribution $p(\\theta|x)$.\n    \"\"\"\n\n    assert not retrain_from_scratch, \"\"\"Retraining from scratch is not supported in\n        SNPE-A yet. The reason for this is that, if we reininitialized the density\n        estimator, the z-scoring would change, which would break the posthoc\n        correction. This is a pure implementation issue.\"\"\"\n\n    kwargs = utils.del_entries(\n        locals(),\n        entries=(\n            \"self\",\n            \"__class__\",\n            \"final_round\",\n            \"component_perturbation\",\n        ),\n    )\n\n    # SNPE-A always discards the prior samples.\n    kwargs[\"discard_prior_samples\"] = True\n    kwargs[\"force_first_round_loss\"] = True\n\n    self._round = max(self._data_round_index)\n\n    if final_round:\n        # If there is (will be) only one round, train with Algorithm 2 from [1].\n        if self._round == 0:\n            self._build_neural_net = partial(\n                self._build_neural_net, num_components=self._num_components\n            )\n        # Run Algorithm 2 from [1].\n        elif not self._ran_final_round:\n            # Now switch to the specified number of components. This method will\n            # only be used if `retrain_from_scratch=True`. Otherwise,\n            # the MDN will be built from replicating the single-component net for\n            # `num_component` times (via `_expand_mog()`).\n            self._build_neural_net = partial(\n                self._build_neural_net, num_components=self._num_components\n            )\n\n            # Extend the MDN to the originally desired number of components.\n            self._expand_mog(eps=component_perturbation)\n        else:\n            warnings.warn(\n                \"You have already run SNPE-A with `final_round=True`. Running it\"\n                \"again with this setting will not allow computing the posthoc\"\n                \"correction applied in SNPE-A. Thus, you will get an error when \"\n                \"calling `.build_posterior()` after training.\",\n                UserWarning,\n            )\n    else:\n        # Run Algorithm 1 from [1].\n        # Wrap the function that builds the MDN such that we can make\n        # sure that there is only one component when running.\n        self._build_neural_net = partial(self._build_neural_net, num_components=1)\n\n    if final_round:\n        self._ran_final_round = True\n\n    return super().train(**kwargs)\n</code></pre>"},{"location":"reference/#sbi.inference.snpe.snpe_c.SNPE_C.__init__","title":"<code>__init__(prior=None, density_estimator='maf', device='cpu', logging_level='WARNING', summary_writer=None, show_progress_bars=True)</code>","text":"<p>SNPE-C / APT [1].</p> <p>[1] Automatic Posterior Transformation for Likelihood-free Inference,     Greenberg et al., ICML 2019, https://arxiv.org/abs/1905.07488.</p> <p>This class implements two loss variants of SNPE-C: the non-atomic and the atomic version. The atomic loss of SNPE-C can be used for any density estimator, i.e. also for normalizing flows. However, it suffers from leakage issues. On the other hand, the non-atomic loss can only be used only if the proposal distribution is a mixture of Gaussians, the density estimator is a mixture of Gaussians, and the prior is either Gaussian or Uniform. It does not suffer from leakage issues. At the beginning of each round, we print whether the non-atomic or the atomic version is used.</p> <p>In this codebase, we will automatically switch to the non-atomic loss if the following criteria are fulfilled: - proposal is a <code>DirectPosterior</code> with density_estimator <code>mdn</code>, as built     with <code>utils.sbi.posterior_nn()</code>. - the density estimator is a <code>mdn</code>, as built with     <code>utils.sbi.posterior_nn()</code>. - <code>isinstance(prior, MultivariateNormal)</code> (from <code>torch.distributions</code>) or     <code>isinstance(prior, sbi.utils.BoxUniform)</code></p> <p>Note that custom implementations of any of these densities (or estimators) will not trigger the non-atomic loss, and the algorithm will fall back onto using the atomic loss.</p> <p>Parameters:</p> Name Type Description Default <code>prior</code> <code>Optional[Distribution]</code> <p>A probability distribution that expresses prior knowledge about the parameters, e.g. which ranges are meaningful for them.</p> <code>None</code> <code>density_estimator</code> <code>Union[str, Callable]</code> <p>If it is a string, use a pre-configured network of the provided type (one of nsf, maf, mdn, made). Alternatively, a function that builds a custom neural network can be provided. The function will be called with the first batch of simulations (theta, x), which can thus be used for shape inference and potentially for z-scoring. It needs to return a PyTorch <code>nn.Module</code> implementing the density estimator. The density estimator needs to provide the methods <code>.log_prob</code> and <code>.sample()</code>.</p> <code>'maf'</code> <code>device</code> <code>str</code> <p>Training device, e.g., \u201ccpu\u201d, \u201ccuda\u201d or \u201ccuda:{0, 1, \u2026}\u201d.</p> <code>'cpu'</code> <code>logging_level</code> <code>Union[int, str]</code> <p>Minimum severity of messages to log. One of the strings INFO, WARNING, DEBUG, ERROR and CRITICAL.</p> <code>'WARNING'</code> <code>summary_writer</code> <code>Optional[TensorboardSummaryWriter]</code> <p>A tensorboard <code>SummaryWriter</code> to control, among others, log file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p> <code>None</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during training.</p> <code>True</code> Source code in <code>sbi/inference/snpe/snpe_c.py</code> <pre><code>def __init__(\n    self,\n    prior: Optional[Distribution] = None,\n    density_estimator: Union[str, Callable] = \"maf\",\n    device: str = \"cpu\",\n    logging_level: Union[int, str] = \"WARNING\",\n    summary_writer: Optional[TensorboardSummaryWriter] = None,\n    show_progress_bars: bool = True,\n):\n    r\"\"\"SNPE-C / APT [1].\n\n    [1] _Automatic Posterior Transformation for Likelihood-free Inference_,\n        Greenberg et al., ICML 2019, https://arxiv.org/abs/1905.07488.\n\n    This class implements two loss variants of SNPE-C: the non-atomic and the atomic\n    version. The atomic loss of SNPE-C can be used for any density estimator,\n    i.e. also for normalizing flows. However, it suffers from leakage issues. On\n    the other hand, the non-atomic loss can only be used only if the proposal\n    distribution is a mixture of Gaussians, the density estimator is a mixture of\n    Gaussians, and the prior is either Gaussian or Uniform. It does not suffer from\n    leakage issues. At the beginning of each round, we print whether the non-atomic\n    or the atomic version is used.\n\n    In this codebase, we will automatically switch to the non-atomic loss if the\n    following criteria are fulfilled:&lt;br/&gt;\n    - proposal is a `DirectPosterior` with density_estimator `mdn`, as built\n        with `utils.sbi.posterior_nn()`.&lt;br/&gt;\n    - the density estimator is a `mdn`, as built with\n        `utils.sbi.posterior_nn()`.&lt;br/&gt;\n    - `isinstance(prior, MultivariateNormal)` (from `torch.distributions`) or\n        `isinstance(prior, sbi.utils.BoxUniform)`\n\n    Note that custom implementations of any of these densities (or estimators) will\n    not trigger the non-atomic loss, and the algorithm will fall back onto using\n    the atomic loss.\n\n    Args:\n        prior: A probability distribution that expresses prior knowledge about the\n            parameters, e.g. which ranges are meaningful for them.\n        density_estimator: If it is a string, use a pre-configured network of the\n            provided type (one of nsf, maf, mdn, made). Alternatively, a function\n            that builds a custom neural network can be provided. The function will\n            be called with the first batch of simulations (theta, x), which can\n            thus be used for shape inference and potentially for z-scoring. It\n            needs to return a PyTorch `nn.Module` implementing the density\n            estimator. The density estimator needs to provide the methods\n            `.log_prob` and `.sample()`.\n        device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n        logging_level: Minimum severity of messages to log. One of the strings\n            INFO, WARNING, DEBUG, ERROR and CRITICAL.\n        summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n            file location (default is `&lt;current working directory&gt;/logs`.)\n        show_progress_bars: Whether to show a progressbar during training.\n    \"\"\"\n\n    kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n    super().__init__(**kwargs)\n</code></pre>"},{"location":"reference/#sbi.inference.snpe.snpe_c.SNPE_C.train","title":"<code>train(num_atoms=10, training_batch_size=50, learning_rate=0.0005, validation_fraction=0.1, stop_after_epochs=20, max_num_epochs=2 ** 31 - 1, clip_max_norm=5.0, calibration_kernel=None, resume_training=False, force_first_round_loss=False, discard_prior_samples=False, use_combined_loss=False, retrain_from_scratch=False, show_train_summary=False, dataloader_kwargs=None)</code>","text":"<p>Return density estimator that approximates the distribution \\(p(\\theta|x)\\).</p> <p>Parameters:</p> Name Type Description Default <code>num_atoms</code> <code>int</code> <p>Number of atoms to use for classification.</p> <code>10</code> <code>training_batch_size</code> <code>int</code> <p>Training batch size.</p> <code>50</code> <code>learning_rate</code> <code>float</code> <p>Learning rate for Adam optimizer.</p> <code>0.0005</code> <code>validation_fraction</code> <code>float</code> <p>The fraction of data to use for validation.</p> <code>0.1</code> <code>stop_after_epochs</code> <code>int</code> <p>The number of epochs to wait for improvement on the validation set before terminating training.</p> <code>20</code> <code>max_num_epochs</code> <code>int</code> <p>Maximum number of epochs to run. If reached, we stop training even when the validation loss is still decreasing. Otherwise, we train until validation loss increases (see also <code>stop_after_epochs</code>).</p> <code>2 ** 31 - 1</code> <code>clip_max_norm</code> <code>Optional[float]</code> <p>Value at which to clip the total gradient norm in order to prevent exploding gradients. Use None for no clipping.</p> <code>5.0</code> <code>calibration_kernel</code> <code>Optional[Callable]</code> <p>A function to calibrate the loss with respect to the simulations <code>x</code>. See Lueckmann, Gon\u00e7alves et al., NeurIPS 2017.</p> <code>None</code> <code>resume_training</code> <code>bool</code> <p>Can be used in case training time is limited, e.g. on a cluster. If <code>True</code>, the split between train and validation set, the optimizer, the number of epochs, and the best validation log-prob will be restored from the last time <code>.train()</code> was called.</p> <code>False</code> <code>force_first_round_loss</code> <code>bool</code> <p>If <code>True</code>, train with maximum likelihood, i.e., potentially ignoring the correction for using a proposal distribution different from the prior.</p> <code>False</code> <code>discard_prior_samples</code> <code>bool</code> <p>Whether to discard samples simulated in round 1, i.e. from the prior. Training may be sped up by ignoring such less targeted samples.</p> <code>False</code> <code>use_combined_loss</code> <code>bool</code> <p>Whether to train the neural net also on prior samples using maximum likelihood in addition to training it on all samples using atomic loss. The extra MLE loss helps prevent density leaking with bounded priors.</p> <code>False</code> <code>retrain_from_scratch</code> <code>bool</code> <p>Whether to retrain the conditional density estimator for the posterior from scratch each round.</p> <code>False</code> <code>show_train_summary</code> <code>bool</code> <p>Whether to print the number of epochs and validation loss and leakage after the training.</p> <code>False</code> <code>dataloader_kwargs</code> <code>Optional[Dict]</code> <p>Additional or updated kwargs to be passed to the training and validation dataloaders (like, e.g., a collate_fn)</p> <code>None</code> <p>Returns:</p> Type Description <code>Module</code> <p>Density estimator that approximates the distribution \\(p(\\theta|x)\\).</p> Source code in <code>sbi/inference/snpe/snpe_c.py</code> <pre><code>def train(\n    self,\n    num_atoms: int = 10,\n    training_batch_size: int = 50,\n    learning_rate: float = 5e-4,\n    validation_fraction: float = 0.1,\n    stop_after_epochs: int = 20,\n    max_num_epochs: int = 2**31 - 1,\n    clip_max_norm: Optional[float] = 5.0,\n    calibration_kernel: Optional[Callable] = None,\n    resume_training: bool = False,\n    force_first_round_loss: bool = False,\n    discard_prior_samples: bool = False,\n    use_combined_loss: bool = False,\n    retrain_from_scratch: bool = False,\n    show_train_summary: bool = False,\n    dataloader_kwargs: Optional[Dict] = None,\n) -&gt; nn.Module:\n    r\"\"\"Return density estimator that approximates the distribution $p(\\theta|x)$.\n\n    Args:\n        num_atoms: Number of atoms to use for classification.\n        training_batch_size: Training batch size.\n        learning_rate: Learning rate for Adam optimizer.\n        validation_fraction: The fraction of data to use for validation.\n        stop_after_epochs: The number of epochs to wait for improvement on the\n            validation set before terminating training.\n        max_num_epochs: Maximum number of epochs to run. If reached, we stop\n            training even when the validation loss is still decreasing. Otherwise,\n            we train until validation loss increases (see also `stop_after_epochs`).\n        clip_max_norm: Value at which to clip the total gradient norm in order to\n            prevent exploding gradients. Use None for no clipping.\n        calibration_kernel: A function to calibrate the loss with respect to the\n            simulations `x`. See Lueckmann, Gon\u00e7alves et al., NeurIPS 2017.\n        resume_training: Can be used in case training time is limited, e.g. on a\n            cluster. If `True`, the split between train and validation set, the\n            optimizer, the number of epochs, and the best validation log-prob will\n            be restored from the last time `.train()` was called.\n        force_first_round_loss: If `True`, train with maximum likelihood,\n            i.e., potentially ignoring the correction for using a proposal\n            distribution different from the prior.\n        discard_prior_samples: Whether to discard samples simulated in round 1, i.e.\n            from the prior. Training may be sped up by ignoring such less targeted\n            samples.\n        use_combined_loss: Whether to train the neural net also on prior samples\n            using maximum likelihood in addition to training it on all samples using\n            atomic loss. The extra MLE loss helps prevent density leaking with\n            bounded priors.\n        retrain_from_scratch: Whether to retrain the conditional density\n            estimator for the posterior from scratch each round.\n        show_train_summary: Whether to print the number of epochs and validation\n            loss and leakage after the training.\n        dataloader_kwargs: Additional or updated kwargs to be passed to the training\n            and validation dataloaders (like, e.g., a collate_fn)\n\n    Returns:\n        Density estimator that approximates the distribution $p(\\theta|x)$.\n    \"\"\"\n\n    # WARNING: sneaky trick ahead. We proxy the parent's `train` here,\n    # requiring the signature to have `num_atoms`, save it for use below, and\n    # continue. It's sneaky because we are using the object (self) as a namespace\n    # to pass arguments between functions, and that's implicit state management.\n    self._num_atoms = num_atoms\n    self._use_combined_loss = use_combined_loss\n    kwargs = del_entries(\n        locals(),\n        entries=(\"self\", \"__class__\", \"num_atoms\", \"use_combined_loss\"),\n    )\n\n    self._round = max(self._data_round_index)\n\n    if self._round &gt; 0:\n        # Set the proposal to the last proposal that was passed by the user. For\n        # atomic SNPE, it does not matter what the proposal is. For non-atomic\n        # SNPE, we only use the latest data that was passed, i.e. the one from the\n        # last proposal.\n        proposal = self._proposal_roundwise[-1]\n        self.use_non_atomic_loss = (\n            isinstance(proposal, DirectPosterior)\n            and isinstance(proposal.posterior_estimator._distribution, mdn)\n            and isinstance(self._neural_net._distribution, mdn)\n            and check_dist_class(\n                self._prior, class_to_check=(Uniform, MultivariateNormal)\n            )[0]\n        )\n\n        algorithm = \"non-atomic\" if self.use_non_atomic_loss else \"atomic\"\n        print(f\"Using SNPE-C with {algorithm} loss\")\n\n        if self.use_non_atomic_loss:\n            # Take care of z-scoring, pre-compute and store prior terms.\n            self._set_state_for_mog_proposal()\n\n    return super().train(**kwargs)\n</code></pre>"},{"location":"reference/#sbi.inference.snle.snle_a.SNLE_A.__init__","title":"<code>__init__(prior=None, density_estimator='maf', device='cpu', logging_level='WARNING', summary_writer=None, show_progress_bars=True)</code>","text":"<p>Sequential Neural Likelihood [1].</p> <p>[1] Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows_, Papamakarios et al., AISTATS 2019, https://arxiv.org/abs/1805.07226</p> <p>Parameters:</p> Name Type Description Default <code>prior</code> <code>Optional[Distribution]</code> <p>A probability distribution that expresses prior knowledge about the parameters, e.g. which ranges are meaningful for them. If <code>None</code>, the prior must be passed to <code>.build_posterior()</code>.</p> <code>None</code> <code>density_estimator</code> <code>Union[str, Callable]</code> <p>If it is a string, use a pre-configured network of the provided type (one of nsf, maf, mdn, made). Alternatively, a function that builds a custom neural network can be provided. The function will be called with the first batch of simulations (theta, x), which can thus be used for shape inference and potentially for z-scoring. It needs to return a PyTorch <code>nn.Module</code> implementing the density estimator. The density estimator needs to provide the methods <code>.log_prob</code> and <code>.sample()</code>.</p> <code>'maf'</code> <code>device</code> <code>str</code> <p>Training device, e.g., \u201ccpu\u201d, \u201ccuda\u201d or \u201ccuda:{0, 1, \u2026}\u201d.</p> <code>'cpu'</code> <code>logging_level</code> <code>Union[int, str]</code> <p>Minimum severity of messages to log. One of the strings INFO, WARNING, DEBUG, ERROR and CRITICAL.</p> <code>'WARNING'</code> <code>summary_writer</code> <code>Optional[TensorboardSummaryWriter]</code> <p>A tensorboard <code>SummaryWriter</code> to control, among others, log file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p> <code>None</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during simulation and sampling.</p> <code>True</code> Source code in <code>sbi/inference/snle/snle_a.py</code> <pre><code>def __init__(\n    self,\n    prior: Optional[Distribution] = None,\n    density_estimator: Union[str, Callable] = \"maf\",\n    device: str = \"cpu\",\n    logging_level: Union[int, str] = \"WARNING\",\n    summary_writer: Optional[TensorboardSummaryWriter] = None,\n    show_progress_bars: bool = True,\n):\n    r\"\"\"Sequential Neural Likelihood [1].\n\n    [1] Sequential Neural Likelihood: Fast Likelihood-free Inference with\n    Autoregressive Flows_, Papamakarios et al., AISTATS 2019,\n    https://arxiv.org/abs/1805.07226\n\n    Args:\n        prior: A probability distribution that expresses prior knowledge about the\n            parameters, e.g. which ranges are meaningful for them. If `None`, the\n            prior must be passed to `.build_posterior()`.\n        density_estimator: If it is a string, use a pre-configured network of the\n            provided type (one of nsf, maf, mdn, made). Alternatively, a function\n            that builds a custom neural network can be provided. The function will\n            be called with the first batch of simulations (theta, x), which can\n            thus be used for shape inference and potentially for z-scoring. It\n            needs to return a PyTorch `nn.Module` implementing the density\n            estimator. The density estimator needs to provide the methods\n            `.log_prob` and `.sample()`.\n        device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n        logging_level: Minimum severity of messages to log. One of the strings\n            INFO, WARNING, DEBUG, ERROR and CRITICAL.\n        summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n            file location (default is `&lt;current working directory&gt;/logs`.)\n        show_progress_bars: Whether to show a progressbar during simulation and\n            sampling.\n    \"\"\"\n\n    kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n    super().__init__(**kwargs)\n</code></pre>"},{"location":"reference/#sbi.inference.snre.snre_a.SNRE_A.__init__","title":"<code>__init__(prior=None, classifier='resnet', device='cpu', logging_level='warning', summary_writer=None, show_progress_bars=True)</code>","text":"<p>AALR[1], here known as SNRE_A.</p> <p>[1] Likelihood-free MCMC with Amortized Approximate Likelihood Ratios, Hermans     et al., ICML 2020, https://arxiv.org/abs/1903.04057</p> <p>Parameters:</p> Name Type Description Default <code>prior</code> <code>Optional[Distribution]</code> <p>A probability distribution that expresses prior knowledge about the parameters, e.g. which ranges are meaningful for them. If <code>None</code>, the prior must be passed to <code>.build_posterior()</code>.</p> <code>None</code> <code>classifier</code> <code>Union[str, Callable]</code> <p>Classifier trained to approximate likelihood ratios. If it is a string, use a pre-configured network of the provided type (one of linear, mlp, resnet). Alternatively, a function that builds a custom neural network can be provided. The function will be called with the first batch of simulations (theta, x), which can thus be used for shape inference and potentially for z-scoring. It needs to return a PyTorch <code>nn.Module</code> implementing the classifier.</p> <code>'resnet'</code> <code>device</code> <code>str</code> <p>Training device, e.g., \u201ccpu\u201d, \u201ccuda\u201d or \u201ccuda:{0, 1, \u2026}\u201d.</p> <code>'cpu'</code> <code>logging_level</code> <code>Union[int, str]</code> <p>Minimum severity of messages to log. One of the strings INFO, WARNING, DEBUG, ERROR and CRITICAL.</p> <code>'warning'</code> <code>summary_writer</code> <code>Optional[TensorboardSummaryWriter]</code> <p>A tensorboard <code>SummaryWriter</code> to control, among others, log file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p> <code>None</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during simulation and sampling.</p> <code>True</code> Source code in <code>sbi/inference/snre/snre_a.py</code> <pre><code>def __init__(\n    self,\n    prior: Optional[Distribution] = None,\n    classifier: Union[str, Callable] = \"resnet\",\n    device: str = \"cpu\",\n    logging_level: Union[int, str] = \"warning\",\n    summary_writer: Optional[TensorboardSummaryWriter] = None,\n    show_progress_bars: bool = True,\n):\n    r\"\"\"AALR[1], here known as SNRE_A.\n\n    [1] _Likelihood-free MCMC with Amortized Approximate Likelihood Ratios_, Hermans\n        et al., ICML 2020, https://arxiv.org/abs/1903.04057\n\n    Args:\n        prior: A probability distribution that expresses prior knowledge about the\n            parameters, e.g. which ranges are meaningful for them. If `None`, the\n            prior must be passed to `.build_posterior()`.\n        classifier: Classifier trained to approximate likelihood ratios. If it is\n            a string, use a pre-configured network of the provided type (one of\n            linear, mlp, resnet). Alternatively, a function that builds a custom\n            neural network can be provided. The function will be called with the\n            first batch of simulations (theta, x), which can thus be used for shape\n            inference and potentially for z-scoring. It needs to return a PyTorch\n            `nn.Module` implementing the classifier.\n        device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n        logging_level: Minimum severity of messages to log. One of the strings\n            INFO, WARNING, DEBUG, ERROR and CRITICAL.\n        summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n            file location (default is `&lt;current working directory&gt;/logs`.)\n        show_progress_bars: Whether to show a progressbar during simulation and\n            sampling.\n    \"\"\"\n\n    kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n    super().__init__(**kwargs)\n</code></pre>"},{"location":"reference/#sbi.inference.snre.snre_a.SNRE_A.train","title":"<code>train(training_batch_size=50, learning_rate=0.0005, validation_fraction=0.1, stop_after_epochs=20, max_num_epochs=2 ** 31 - 1, clip_max_norm=5.0, resume_training=False, discard_prior_samples=False, retrain_from_scratch=False, show_train_summary=False, dataloader_kwargs=None, loss_kwargs={})</code>","text":"<p>Return classifier that approximates the ratio \\(p(\\theta,x)/p(\\theta)p(x)\\).</p> <p>Parameters:</p> Name Type Description Default <code>training_batch_size</code> <code>int</code> <p>Training batch size.</p> <code>50</code> <code>learning_rate</code> <code>float</code> <p>Learning rate for Adam optimizer.</p> <code>0.0005</code> <code>validation_fraction</code> <code>float</code> <p>The fraction of data to use for validation.</p> <code>0.1</code> <code>stop_after_epochs</code> <code>int</code> <p>The number of epochs to wait for improvement on the validation set before terminating training.</p> <code>20</code> <code>max_num_epochs</code> <code>int</code> <p>Maximum number of epochs to run. If reached, we stop training even when the validation loss is still decreasing. Otherwise, we train until validation loss increases (see also <code>stop_after_epochs</code>).</p> <code>2 ** 31 - 1</code> <code>clip_max_norm</code> <code>Optional[float]</code> <p>Value at which to clip the total gradient norm in order to prevent exploding gradients. Use None for no clipping.</p> <code>5.0</code> <code>resume_training</code> <code>bool</code> <p>Can be used in case training time is limited, e.g. on a cluster. If <code>True</code>, the split between train and validation set, the optimizer, the number of epochs, and the best validation log-prob will be restored from the last time <code>.train()</code> was called.</p> <code>False</code> <code>discard_prior_samples</code> <code>bool</code> <p>Whether to discard samples simulated in round 1, i.e. from the prior. Training may be sped up by ignoring such less targeted samples.</p> <code>False</code> <code>retrain_from_scratch</code> <code>bool</code> <p>Whether to retrain the conditional density estimator for the posterior from scratch each round.</p> <code>False</code> <code>show_train_summary</code> <code>bool</code> <p>Whether to print the number of epochs and validation loss and leakage after the training.</p> <code>False</code> <code>dataloader_kwargs</code> <code>Optional[Dict]</code> <p>Additional or updated kwargs to be passed to the training and validation dataloaders (like, e.g., a collate_fn)</p> <code>None</code> <code>loss_kwargs</code> <code>Dict[str, Any]</code> <p>Additional or updated kwargs to be passed to the self._loss fn.</p> <code>{}</code> <p>Returns:</p> Type Description <code>Module</code> <p>Classifier that approximates the ratio \\(p(\\theta,x)/p(\\theta)p(x)\\).</p> Source code in <code>sbi/inference/snre/snre_a.py</code> <pre><code>def train(\n    self,\n    training_batch_size: int = 50,\n    learning_rate: float = 5e-4,\n    validation_fraction: float = 0.1,\n    stop_after_epochs: int = 20,\n    max_num_epochs: int = 2**31 - 1,\n    clip_max_norm: Optional[float] = 5.0,\n    resume_training: bool = False,\n    discard_prior_samples: bool = False,\n    retrain_from_scratch: bool = False,\n    show_train_summary: bool = False,\n    dataloader_kwargs: Optional[Dict] = None,\n    loss_kwargs: Dict[str, Any] = {},\n) -&gt; nn.Module:\n    r\"\"\"Return classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n\n    Args:\n        training_batch_size: Training batch size.\n        learning_rate: Learning rate for Adam optimizer.\n        validation_fraction: The fraction of data to use for validation.\n        stop_after_epochs: The number of epochs to wait for improvement on the\n            validation set before terminating training.\n        max_num_epochs: Maximum number of epochs to run. If reached, we stop\n            training even when the validation loss is still decreasing. Otherwise,\n            we train until validation loss increases (see also `stop_after_epochs`).\n        clip_max_norm: Value at which to clip the total gradient norm in order to\n            prevent exploding gradients. Use None for no clipping.\n        resume_training: Can be used in case training time is limited, e.g. on a\n            cluster. If `True`, the split between train and validation set, the\n            optimizer, the number of epochs, and the best validation log-prob will\n            be restored from the last time `.train()` was called.\n        discard_prior_samples: Whether to discard samples simulated in round 1, i.e.\n            from the prior. Training may be sped up by ignoring such less targeted\n            samples.\n        retrain_from_scratch: Whether to retrain the conditional density\n            estimator for the posterior from scratch each round.\n        show_train_summary: Whether to print the number of epochs and validation\n            loss and leakage after the training.\n        dataloader_kwargs: Additional or updated kwargs to be passed to the training\n            and validation dataloaders (like, e.g., a collate_fn)\n        loss_kwargs: Additional or updated kwargs to be passed to the self._loss fn.\n\n    Returns:\n        Classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n    \"\"\"\n\n    # AALR is defined for `num_atoms=2`.\n    # Proxy to `super().__call__` to ensure right parameter.\n    kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n    return super().train(**kwargs, num_atoms=2)\n</code></pre>"},{"location":"reference/#sbi.inference.snre.snre_b.SNRE_B.__init__","title":"<code>__init__(prior=None, classifier='resnet', device='cpu', logging_level='warning', summary_writer=None, show_progress_bars=True)</code>","text":"<p>SRE[1], here known as SNRE_B.</p> <p>[1] On Contrastive Learning for Likelihood-free Inference, Durkan et al.,     ICML 2020, https://arxiv.org/pdf/2002.03712</p> <p>Parameters:</p> Name Type Description Default <code>prior</code> <code>Optional[Distribution]</code> <p>A probability distribution that expresses prior knowledge about the parameters, e.g. which ranges are meaningful for them. If <code>None</code>, the prior must be passed to <code>.build_posterior()</code>.</p> <code>None</code> <code>classifier</code> <code>Union[str, Callable]</code> <p>Classifier trained to approximate likelihood ratios. If it is a string, use a pre-configured network of the provided type (one of linear, mlp, resnet). Alternatively, a function that builds a custom neural network can be provided. The function will be called with the first batch of simulations (theta, x), which can thus be used for shape inference and potentially for z-scoring. It needs to return a PyTorch <code>nn.Module</code> implementing the classifier.</p> <code>'resnet'</code> <code>device</code> <code>str</code> <p>Training device, e.g., \u201ccpu\u201d, \u201ccuda\u201d or \u201ccuda:{0, 1, \u2026}\u201d.</p> <code>'cpu'</code> <code>logging_level</code> <code>Union[int, str]</code> <p>Minimum severity of messages to log. One of the strings INFO, WARNING, DEBUG, ERROR and CRITICAL.</p> <code>'warning'</code> <code>summary_writer</code> <code>Optional[TensorboardSummaryWriter]</code> <p>A tensorboard <code>SummaryWriter</code> to control, among others, log file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p> <code>None</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during simulation and sampling.</p> <code>True</code> Source code in <code>sbi/inference/snre/snre_b.py</code> <pre><code>def __init__(\n    self,\n    prior: Optional[Distribution] = None,\n    classifier: Union[str, Callable] = \"resnet\",\n    device: str = \"cpu\",\n    logging_level: Union[int, str] = \"warning\",\n    summary_writer: Optional[TensorboardSummaryWriter] = None,\n    show_progress_bars: bool = True,\n):\n    r\"\"\"SRE[1], here known as SNRE_B.\n\n    [1] _On Contrastive Learning for Likelihood-free Inference_, Durkan et al.,\n        ICML 2020, https://arxiv.org/pdf/2002.03712\n\n    Args:\n        prior: A probability distribution that expresses prior knowledge about the\n            parameters, e.g. which ranges are meaningful for them. If `None`, the\n            prior must be passed to `.build_posterior()`.\n        classifier: Classifier trained to approximate likelihood ratios. If it is\n            a string, use a pre-configured network of the provided type (one of\n            linear, mlp, resnet). Alternatively, a function that builds a custom\n            neural network can be provided. The function will be called with the\n            first batch of simulations (theta, x), which can thus be used for shape\n            inference and potentially for z-scoring. It needs to return a PyTorch\n            `nn.Module` implementing the classifier.\n        device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n        logging_level: Minimum severity of messages to log. One of the strings\n            INFO, WARNING, DEBUG, ERROR and CRITICAL.\n        summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n            file location (default is `&lt;current working directory&gt;/logs`.)\n        show_progress_bars: Whether to show a progressbar during simulation and\n            sampling.\n    \"\"\"\n\n    kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n    super().__init__(**kwargs)\n</code></pre>"},{"location":"reference/#sbi.inference.snre.snre_b.SNRE_B.train","title":"<code>train(num_atoms=10, training_batch_size=50, learning_rate=0.0005, validation_fraction=0.1, stop_after_epochs=20, max_num_epochs=2 ** 31 - 1, clip_max_norm=5.0, resume_training=False, discard_prior_samples=False, retrain_from_scratch=False, show_train_summary=False, dataloader_kwargs=None)</code>","text":"<p>Return classifier that approximates the ratio \\(p(\\theta,x)/p(\\theta)p(x)\\).</p> <p>Parameters:</p> Name Type Description Default <code>num_atoms</code> <code>int</code> <p>Number of atoms to use for classification.</p> <code>10</code> <code>training_batch_size</code> <code>int</code> <p>Training batch size.</p> <code>50</code> <code>learning_rate</code> <code>float</code> <p>Learning rate for Adam optimizer.</p> <code>0.0005</code> <code>validation_fraction</code> <code>float</code> <p>The fraction of data to use for validation.</p> <code>0.1</code> <code>stop_after_epochs</code> <code>int</code> <p>The number of epochs to wait for improvement on the validation set before terminating training.</p> <code>20</code> <code>max_num_epochs</code> <code>int</code> <p>Maximum number of epochs to run. If reached, we stop training even when the validation loss is still decreasing. Otherwise, we train until validation loss increases (see also <code>stop_after_epochs</code>).</p> <code>2 ** 31 - 1</code> <code>clip_max_norm</code> <code>Optional[float]</code> <p>Value at which to clip the total gradient norm in order to prevent exploding gradients. Use None for no clipping.</p> <code>5.0</code> <code>resume_training</code> <code>bool</code> <p>Can be used in case training time is limited, e.g. on a cluster. If <code>True</code>, the split between train and validation set, the optimizer, the number of epochs, and the best validation log-prob will be restored from the last time <code>.train()</code> was called.</p> <code>False</code> <code>discard_prior_samples</code> <code>bool</code> <p>Whether to discard samples simulated in round 1, i.e. from the prior. Training may be sped up by ignoring such less targeted samples.</p> <code>False</code> <code>retrain_from_scratch</code> <code>bool</code> <p>Whether to retrain the conditional density estimator for the posterior from scratch each round.</p> <code>False</code> <code>show_train_summary</code> <code>bool</code> <p>Whether to print the number of epochs and validation loss and leakage after the training.</p> <code>False</code> <code>dataloader_kwargs</code> <code>Optional[Dict]</code> <p>Additional or updated kwargs to be passed to the training and validation dataloaders (like, e.g., a collate_fn)</p> <code>None</code> <p>Returns:</p> Type Description <code>Module</code> <p>Classifier that approximates the ratio \\(p(\\theta,x)/p(\\theta)p(x)\\).</p> Source code in <code>sbi/inference/snre/snre_b.py</code> <pre><code>def train(\n    self,\n    num_atoms: int = 10,\n    training_batch_size: int = 50,\n    learning_rate: float = 5e-4,\n    validation_fraction: float = 0.1,\n    stop_after_epochs: int = 20,\n    max_num_epochs: int = 2**31 - 1,\n    clip_max_norm: Optional[float] = 5.0,\n    resume_training: bool = False,\n    discard_prior_samples: bool = False,\n    retrain_from_scratch: bool = False,\n    show_train_summary: bool = False,\n    dataloader_kwargs: Optional[Dict] = None,\n) -&gt; nn.Module:\n    r\"\"\"Return classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n\n    Args:\n        num_atoms: Number of atoms to use for classification.\n        training_batch_size: Training batch size.\n        learning_rate: Learning rate for Adam optimizer.\n        validation_fraction: The fraction of data to use for validation.\n        stop_after_epochs: The number of epochs to wait for improvement on the\n            validation set before terminating training.\n        max_num_epochs: Maximum number of epochs to run. If reached, we stop\n            training even when the validation loss is still decreasing. Otherwise,\n            we train until validation loss increases (see also `stop_after_epochs`).\n        clip_max_norm: Value at which to clip the total gradient norm in order to\n            prevent exploding gradients. Use None for no clipping.\n        resume_training: Can be used in case training time is limited, e.g. on a\n            cluster. If `True`, the split between train and validation set, the\n            optimizer, the number of epochs, and the best validation log-prob will\n            be restored from the last time `.train()` was called.\n        discard_prior_samples: Whether to discard samples simulated in round 1, i.e.\n            from the prior. Training may be sped up by ignoring such less targeted\n            samples.\n        retrain_from_scratch: Whether to retrain the conditional density\n            estimator for the posterior from scratch each round.\n        show_train_summary: Whether to print the number of epochs and validation\n            loss and leakage after the training.\n        dataloader_kwargs: Additional or updated kwargs to be passed to the training\n            and validation dataloaders (like, e.g., a collate_fn)\n\n    Returns:\n        Classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n    \"\"\"\n    kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n    return super().train(**kwargs)\n</code></pre>"},{"location":"reference/#sbi.inference.snre.snre_c.SNRE_C.__init__","title":"<code>__init__(prior=None, classifier='resnet', device='cpu', logging_level='warning', summary_writer=None, show_progress_bars=True)</code>","text":"<p>NRE-C[1] is a generalization of the non-sequential (amortized) versions of SNRE_A and SNRE_B. We call the algorithm SNRE_C within <code>sbi</code>.</p> <p>NRE-C: (1) like SNRE_B, features a \u201cmulticlass\u201d loss function where several marginally     drawn parameter-data pairs are contrasted against a jointly drawn pair. (2) like AALR/NRE_A, i.e., the non-sequential version of SNRE_A, it encourages     the approximate ratio \\(p(\\theta,x)/p(\\theta)p(x)\\), accessed through     <code>.potential()</code> within <code>sbi</code>, to be exact at optimum. This addresses the     issue that SNRE_B estimates this ratio only up to an arbitrary function     (normalizing constant) of the data \\(x\\).</p> <p>Just like for all ratio estimation algorithms, the sequential version of SNRE_C will be estimated only up to a function (normalizing constant) of the data \\(x\\) in rounds after the first.</p> <p>[1] Contrastive Neural Ratio Estimation, Benajmin Kurt Miller, et. al.,     NeurIPS 2022, https://arxiv.org/abs/2210.06170</p> <p>Parameters:</p> Name Type Description Default <code>prior</code> <code>Optional[Distribution]</code> <p>A probability distribution that expresses prior knowledge about the parameters, e.g. which ranges are meaningful for them. If <code>None</code>, the prior must be passed to <code>.build_posterior()</code>.</p> <code>None</code> <code>classifier</code> <code>Union[str, Callable]</code> <p>Classifier trained to approximate likelihood ratios. If it is a string, use a pre-configured network of the provided type (one of linear, mlp, resnet). Alternatively, a function that builds a custom neural network can be provided. The function will be called with the first batch of simulations (theta, x), which can thus be used for shape inference and potentially for z-scoring. It needs to return a PyTorch <code>nn.Module</code> implementing the classifier.</p> <code>'resnet'</code> <code>device</code> <code>str</code> <p>Training device, e.g., \u201ccpu\u201d, \u201ccuda\u201d or \u201ccuda:{0, 1, \u2026}\u201d.</p> <code>'cpu'</code> <code>logging_level</code> <code>Union[int, str]</code> <p>Minimum severity of messages to log. One of the strings INFO, WARNING, DEBUG, ERROR and CRITICAL.</p> <code>'warning'</code> <code>summary_writer</code> <code>Optional[TensorboardSummaryWriter]</code> <p>A tensorboard <code>SummaryWriter</code> to control, among others, log file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p> <code>None</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during simulation and sampling.</p> <code>True</code> Source code in <code>sbi/inference/snre/snre_c.py</code> <pre><code>def __init__(\n    self,\n    prior: Optional[Distribution] = None,\n    classifier: Union[str, Callable] = \"resnet\",\n    device: str = \"cpu\",\n    logging_level: Union[int, str] = \"warning\",\n    summary_writer: Optional[TensorboardSummaryWriter] = None,\n    show_progress_bars: bool = True,\n):\n    r\"\"\"NRE-C[1] is a generalization of the non-sequential (amortized) versions of\n    SNRE_A and SNRE_B. We call the algorithm SNRE_C within `sbi`.\n\n    NRE-C:\n    (1) like SNRE_B, features a \"multiclass\" loss function where several marginally\n        drawn parameter-data pairs are contrasted against a jointly drawn pair.\n    (2) like AALR/NRE_A, i.e., the non-sequential version of SNRE_A, it encourages\n        the approximate ratio $p(\\theta,x)/p(\\theta)p(x)$, accessed through\n        `.potential()` within `sbi`, to be exact at optimum. This addresses the\n        issue that SNRE_B estimates this ratio only up to an arbitrary function\n        (normalizing constant) of the data $x$.\n\n    Just like for all ratio estimation algorithms, the sequential version of SNRE_C\n    will be estimated only up to a function (normalizing constant) of the data $x$\n    in rounds after the first.\n\n    [1] _Contrastive Neural Ratio Estimation_, Benajmin Kurt Miller, et. al.,\n        NeurIPS 2022, https://arxiv.org/abs/2210.06170\n\n    Args:\n        prior: A probability distribution that expresses prior knowledge about the\n            parameters, e.g. which ranges are meaningful for them. If `None`, the\n            prior must be passed to `.build_posterior()`.\n        classifier: Classifier trained to approximate likelihood ratios. If it is\n            a string, use a pre-configured network of the provided type (one of\n            linear, mlp, resnet). Alternatively, a function that builds a custom\n            neural network can be provided. The function will be called with the\n            first batch of simulations (theta, x), which can thus be used for shape\n            inference and potentially for z-scoring. It needs to return a PyTorch\n            `nn.Module` implementing the classifier.\n        device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n        logging_level: Minimum severity of messages to log. One of the strings\n            INFO, WARNING, DEBUG, ERROR and CRITICAL.\n        summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n            file location (default is `&lt;current working directory&gt;/logs`.)\n        show_progress_bars: Whether to show a progressbar during simulation and\n            sampling.\n    \"\"\"\n\n    kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n    super().__init__(**kwargs)\n</code></pre>"},{"location":"reference/#sbi.inference.snre.snre_c.SNRE_C.train","title":"<code>train(num_classes=5, gamma=1.0, training_batch_size=50, learning_rate=0.0005, validation_fraction=0.1, stop_after_epochs=20, max_num_epochs=2 ** 31 - 1, clip_max_norm=5.0, resume_training=False, discard_prior_samples=False, retrain_from_scratch=False, show_train_summary=False, dataloader_kwargs=None)</code>","text":"<p>Return classifier that approximates the ratio \\(p(\\theta,x)/p(\\theta)p(x)\\).</p> <p>Parameters:</p> Name Type Description Default <code>num_classes</code> <code>int</code> <p>Number of theta to classify against, corresponds to \\(K\\) in Contrastive Neural Ratio Estimation. Minimum value is 1. Similar to <code>num_atoms</code> for SNRE_B except SNRE_C has an additional independently drawn sample. The total number of alternative parameters <code>NRE-C</code> \u201csees\u201d is \\(2K-1\\) or <code>2 * num_classes - 1</code> divided between two loss terms.</p> <code>5</code> <code>gamma</code> <code>float</code> <p>Determines the relative weight of the sum of all \\(K\\) dependently drawn classes against the marginally drawn one. Specifically, \\(p(y=k) :=p_K\\), \\(p(y=0) := p_0\\), \\(p_0 = 1 - K p_K\\), and finally \\(\\gamma := K p_K / p_0\\).</p> <code>1.0</code> <code>training_batch_size</code> <code>int</code> <p>Training batch size.</p> <code>50</code> <code>learning_rate</code> <code>float</code> <p>Learning rate for Adam optimizer.</p> <code>0.0005</code> <code>validation_fraction</code> <code>float</code> <p>The fraction of data to use for validation.</p> <code>0.1</code> <code>stop_after_epochs</code> <code>int</code> <p>The number of epochs to wait for improvement on the validation set before terminating training.</p> <code>20</code> <code>max_num_epochs</code> <code>int</code> <p>Maximum number of epochs to run. If reached, we stop training even when the validation loss is still decreasing. Otherwise, we train until validation loss increases (see also <code>stop_after_epochs</code>).</p> <code>2 ** 31 - 1</code> <code>clip_max_norm</code> <code>Optional[float]</code> <p>Value at which to clip the total gradient norm in order to prevent exploding gradients. Use None for no clipping.</p> <code>5.0</code> <code>exclude_invalid_x</code> <p>Whether to exclude simulation outputs <code>x=NaN</code> or <code>x=\u00b1\u221e</code> during training. Expect errors, silent or explicit, when <code>False</code>.</p> required <code>resume_training</code> <code>bool</code> <p>Can be used in case training time is limited, e.g. on a cluster. If <code>True</code>, the split between train and validation set, the optimizer, the number of epochs, and the best validation log-prob will be restored from the last time <code>.train()</code> was called.</p> <code>False</code> <code>discard_prior_samples</code> <code>bool</code> <p>Whether to discard samples simulated in round 1, i.e. from the prior. Training may be sped up by ignoring such less targeted samples.</p> <code>False</code> <code>retrain_from_scratch</code> <code>bool</code> <p>Whether to retrain the conditional density estimator for the posterior from scratch each round.</p> <code>False</code> <code>show_train_summary</code> <code>bool</code> <p>Whether to print the number of epochs and validation loss and leakage after the training.</p> <code>False</code> <code>dataloader_kwargs</code> <code>Optional[Dict]</code> <p>Additional or updated kwargs to be passed to the training and validation dataloaders (like, e.g., a collate_fn)</p> <code>None</code> <p>Returns:</p> Type Description <code>Module</code> <p>Classifier that approximates the ratio \\(p(\\theta,x)/p(\\theta)p(x)\\).</p> Source code in <code>sbi/inference/snre/snre_c.py</code> <pre><code>def train(\n    self,\n    num_classes: int = 5,\n    gamma: float = 1.0,\n    training_batch_size: int = 50,\n    learning_rate: float = 5e-4,\n    validation_fraction: float = 0.1,\n    stop_after_epochs: int = 20,\n    max_num_epochs: int = 2**31 - 1,\n    clip_max_norm: Optional[float] = 5.0,\n    resume_training: bool = False,\n    discard_prior_samples: bool = False,\n    retrain_from_scratch: bool = False,\n    show_train_summary: bool = False,\n    dataloader_kwargs: Optional[Dict] = None,\n) -&gt; nn.Module:\n    r\"\"\"Return classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n\n    Args:\n        num_classes: Number of theta to classify against, corresponds to $K$ in\n            _Contrastive Neural Ratio Estimation_. Minimum value is 1. Similar to\n            `num_atoms` for SNRE_B except SNRE_C has an additional independently\n            drawn sample. The total number of alternative parameters `NRE-C` \"sees\"\n            is $2K-1$ or `2 * num_classes - 1` divided between two loss terms.\n        gamma: Determines the relative weight of the sum of all $K$ dependently\n            drawn classes against the marginally drawn one. Specifically,\n            $p(y=k) :=p_K$, $p(y=0) := p_0$, $p_0 = 1 - K p_K$, and finally\n            $\\gamma := K p_K / p_0$.\n        training_batch_size: Training batch size.\n        learning_rate: Learning rate for Adam optimizer.\n        validation_fraction: The fraction of data to use for validation.\n        stop_after_epochs: The number of epochs to wait for improvement on the\n            validation set before terminating training.\n        max_num_epochs: Maximum number of epochs to run. If reached, we stop\n            training even when the validation loss is still decreasing. Otherwise,\n            we train until validation loss increases (see also `stop_after_epochs`).\n        clip_max_norm: Value at which to clip the total gradient norm in order to\n            prevent exploding gradients. Use None for no clipping.\n        exclude_invalid_x: Whether to exclude simulation outputs `x=NaN` or `x=\u00b1\u221e`\n            during training. Expect errors, silent or explicit, when `False`.\n        resume_training: Can be used in case training time is limited, e.g. on a\n            cluster. If `True`, the split between train and validation set, the\n            optimizer, the number of epochs, and the best validation log-prob will\n            be restored from the last time `.train()` was called.\n        discard_prior_samples: Whether to discard samples simulated in round 1, i.e.\n            from the prior. Training may be sped up by ignoring such less targeted\n            samples.\n        retrain_from_scratch: Whether to retrain the conditional density\n            estimator for the posterior from scratch each round.\n        show_train_summary: Whether to print the number of epochs and validation\n            loss and leakage after the training.\n        dataloader_kwargs: Additional or updated kwargs to be passed to the training\n            and validation dataloaders (like, e.g., a collate_fn)\n\n    Returns:\n        Classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n    \"\"\"\n    kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n    kwargs[\"num_atoms\"] = kwargs.pop(\"num_classes\") + 1\n    kwargs[\"loss_kwargs\"] = {\"gamma\": kwargs.pop(\"gamma\")}\n    return super().train(**kwargs)\n</code></pre>"},{"location":"reference/#sbi.inference.snre.bnre.BNRE.__init__","title":"<code>__init__(prior=None, classifier='resnet', device='cpu', logging_level='warning', summary_writer=None, show_progress_bars=True)</code>","text":"<p>Balanced neural ratio estimation (BNRE)[1]. BNRE is a variation of NRE aiming to produce more conservative posterior approximations</p> <p>[1] Delaunoy, A., Hermans, J., Rozet, F., Wehenkel, A., &amp; Louppe, G.. Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation. NeurIPS 2022. https://arxiv.org/abs/2208.13624</p> <p>Parameters:</p> Name Type Description Default <code>prior</code> <code>Optional[Distribution]</code> <p>A probability distribution that expresses prior knowledge about the parameters, e.g. which ranges are meaningful for them. If <code>None</code>, the prior must be passed to <code>.build_posterior()</code>.</p> <code>None</code> <code>classifier</code> <code>Union[str, Callable]</code> <p>Classifier trained to approximate likelihood ratios. If it is a string, use a pre-configured network of the provided type (one of linear, mlp, resnet). Alternatively, a function that builds a custom neural network can be provided. The function will be called with the first batch of simulations \\((\\theta, x)\\), which can thus be used for shape inference and potentially for z-scoring. It needs to return a PyTorch <code>nn.Module</code> implementing the classifier.</p> <code>'resnet'</code> <code>device</code> <code>str</code> <p>Training device, e.g., \u201ccpu\u201d, \u201ccuda\u201d or \u201ccuda:{0, 1, \u2026}\u201d.</p> <code>'cpu'</code> <code>logging_level</code> <code>Union[int, str]</code> <p>Minimum severity of messages to log. One of the strings INFO, WARNING, DEBUG, ERROR and CRITICAL.</p> <code>'warning'</code> <code>summary_writer</code> <code>Optional[TensorboardSummaryWriter]</code> <p>A tensorboard <code>SummaryWriter</code> to control, among others, log file location (default is <code>&lt;current working directory&gt;/logs</code>.)</p> <code>None</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during simulation and sampling.</p> <code>True</code> Source code in <code>sbi/inference/snre/bnre.py</code> <pre><code>def __init__(\n    self,\n    prior: Optional[Distribution] = None,\n    classifier: Union[str, Callable] = \"resnet\",\n    device: str = \"cpu\",\n    logging_level: Union[int, str] = \"warning\",\n    summary_writer: Optional[TensorboardSummaryWriter] = None,\n    show_progress_bars: bool = True,\n):\n    r\"\"\"Balanced neural ratio estimation (BNRE)[1]. BNRE is a variation of NRE\n    aiming to produce more conservative posterior approximations\n\n    [1] Delaunoy, A., Hermans, J., Rozet, F., Wehenkel, A., &amp; Louppe, G..\n    Towards Reliable Simulation-Based Inference with Balanced Neural Ratio\n    Estimation.\n    NeurIPS 2022. https://arxiv.org/abs/2208.13624\n\n    Args:\n        prior: A probability distribution that expresses prior knowledge about the\n            parameters, e.g. which ranges are meaningful for them. If `None`, the\n            prior must be passed to `.build_posterior()`.\n        classifier: Classifier trained to approximate likelihood ratios. If it is\n            a string, use a pre-configured network of the provided type (one of\n            linear, mlp, resnet). Alternatively, a function that builds a custom\n            neural network can be provided. The function will be called with the\n            first batch of simulations $(\\theta, x)$, which can thus be used for\n            shape inference and potentially for z-scoring. It needs to return a\n            PyTorch `nn.Module` implementing the classifier.\n        device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:{0, 1, ...}\".\n        logging_level: Minimum severity of messages to log. One of the strings\n            INFO, WARNING, DEBUG, ERROR and CRITICAL.\n        summary_writer: A tensorboard `SummaryWriter` to control, among others, log\n            file location (default is `&lt;current working directory&gt;/logs`.)\n        show_progress_bars: Whether to show a progressbar during simulation and\n            sampling.\n    \"\"\"\n\n    kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n    super().__init__(**kwargs)\n</code></pre>"},{"location":"reference/#sbi.inference.snre.bnre.BNRE.train","title":"<code>train(regularization_strength=100.0, training_batch_size=50, learning_rate=0.0005, validation_fraction=0.1, stop_after_epochs=20, max_num_epochs=2 ** 31 - 1, clip_max_norm=5.0, resume_training=False, discard_prior_samples=False, retrain_from_scratch=False, show_train_summary=False, dataloader_kwargs=None)</code>","text":"<p>Return classifier that approximates the ratio \\(p(\\theta,x)/p(\\theta)p(x)\\). Args:</p> <pre><code>regularization_strength: The multiplicative coefficient applied to the\n    balancing regularizer ($\\lambda$).\ntraining_batch_size: Training batch size.\nlearning_rate: Learning rate for Adam optimizer.\nvalidation_fraction: The fraction of data to use for validation.\nstop_after_epochs: The number of epochs to wait for improvement on the\n    validation set before terminating training.\nmax_num_epochs: Maximum number of epochs to run. If reached, we stop\n    training even when the validation loss is still decreasing. Otherwise,\n    we train until validation loss increases (see also `stop_after_epochs`).\nclip_max_norm: Value at which to clip the total gradient norm in order to\n    prevent exploding gradients. Use None for no clipping.\nexclude_invalid_x: Whether to exclude simulation outputs `x=NaN` or `x=\u00b1\u221e`\n    during training. Expect errors, silent or explicit, when `False`.\nresume_training: Can be used in case training time is limited, e.g. on a\n    cluster. If `True`, the split between train and validation set, the\n    optimizer, the number of epochs, and the best validation log-prob will\n    be restored from the last time `.train()` was called.\ndiscard_prior_samples: Whether to discard samples simulated in round 1, i.e.\n    from the prior. Training may be sped up by ignoring such less targeted\n    samples.\nretrain_from_scratch: Whether to retrain the conditional density\n    estimator for the posterior from scratch each round.\nshow_train_summary: Whether to print the number of epochs and validation\n    loss and leakage after the training.\ndataloader_kwargs: Additional or updated kwargs to be passed to the training\n    and validation dataloaders (like, e.g., a collate_fn)\n</code></pre> <p>Returns:     Classifier that approximates the ratio \\(p(\\theta,x)/p(\\theta)p(x)\\).</p> Source code in <code>sbi/inference/snre/bnre.py</code> <pre><code>def train(\n    self,\n    regularization_strength: float = 100.0,\n    training_batch_size: int = 50,\n    learning_rate: float = 5e-4,\n    validation_fraction: float = 0.1,\n    stop_after_epochs: int = 20,\n    max_num_epochs: int = 2**31 - 1,\n    clip_max_norm: Optional[float] = 5.0,\n    resume_training: bool = False,\n    discard_prior_samples: bool = False,\n    retrain_from_scratch: bool = False,\n    show_train_summary: bool = False,\n    dataloader_kwargs: Optional[Dict] = None,\n) -&gt; nn.Module:\n    r\"\"\"Return classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n    Args:\n\n        regularization_strength: The multiplicative coefficient applied to the\n            balancing regularizer ($\\lambda$).\n        training_batch_size: Training batch size.\n        learning_rate: Learning rate for Adam optimizer.\n        validation_fraction: The fraction of data to use for validation.\n        stop_after_epochs: The number of epochs to wait for improvement on the\n            validation set before terminating training.\n        max_num_epochs: Maximum number of epochs to run. If reached, we stop\n            training even when the validation loss is still decreasing. Otherwise,\n            we train until validation loss increases (see also `stop_after_epochs`).\n        clip_max_norm: Value at which to clip the total gradient norm in order to\n            prevent exploding gradients. Use None for no clipping.\n        exclude_invalid_x: Whether to exclude simulation outputs `x=NaN` or `x=\u00b1\u221e`\n            during training. Expect errors, silent or explicit, when `False`.\n        resume_training: Can be used in case training time is limited, e.g. on a\n            cluster. If `True`, the split between train and validation set, the\n            optimizer, the number of epochs, and the best validation log-prob will\n            be restored from the last time `.train()` was called.\n        discard_prior_samples: Whether to discard samples simulated in round 1, i.e.\n            from the prior. Training may be sped up by ignoring such less targeted\n            samples.\n        retrain_from_scratch: Whether to retrain the conditional density\n            estimator for the posterior from scratch each round.\n        show_train_summary: Whether to print the number of epochs and validation\n            loss and leakage after the training.\n        dataloader_kwargs: Additional or updated kwargs to be passed to the training\n            and validation dataloaders (like, e.g., a collate_fn)\n    Returns:\n        Classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\n    \"\"\"\n    kwargs = del_entries(locals(), entries=(\"self\", \"__class__\"))\n    kwargs[\"loss_kwargs\"] = {\n        \"regularization_strength\": kwargs.pop(\"regularization_strength\")\n    }\n    return super().train(**kwargs)\n</code></pre>"},{"location":"reference/#sbi.inference.abc.mcabc.MCABC.__call__","title":"<code>__call__(x_o, num_simulations, eps=None, quantile=None, lra=False, sass=False, sass_fraction=0.25, sass_expansion_degree=1, kde=False, kde_kwargs={}, return_summary=False)</code>","text":"<p>Run MCABC and return accepted parameters or KDE object fitted on them.</p> <p>Parameters:</p> Name Type Description Default <code>x_o</code> <code>Union[Tensor, ndarray]</code> <p>Observed data.</p> required <code>num_simulations</code> <code>int</code> <p>Number of simulations to run.</p> required <code>eps</code> <code>Optional[float]</code> <p>Acceptance threshold \\(\\epsilon\\) for distance between observed and simulated data.</p> <code>None</code> <code>quantile</code> <code>Optional[float]</code> <p>Upper quantile of smallest distances for which the corresponding parameters are returned, e.g, q=0.01 will return the top 1%. Exactly one of quantile or <code>eps</code> have to be passed.</p> <code>None</code> <code>lra</code> <code>bool</code> <p>Whether to run linear regression adjustment as in Beaumont et al. 2002</p> <code>False</code> <code>sass</code> <code>bool</code> <p>Whether to determine semi-automatic summary statistics as in Fearnhead &amp; Prangle 2012.</p> <code>False</code> <code>sass_fraction</code> <code>float</code> <p>Fraction of simulation budget used for the initial sass run.</p> <code>0.25</code> <code>sass_expansion_degree</code> <code>int</code> <p>Degree of the polynomial feature expansion for the sass regression, default 1 - no expansion.</p> <code>1</code> <code>kde</code> <code>bool</code> <p>Whether to run KDE on the accepted parameters to return a KDE object from which one can sample.</p> <code>False</code> <code>kde_kwargs</code> <code>Dict[str, Any]</code> <p>kwargs for performing KDE: \u2018bandwidth=\u2019; either a float, or a string naming a bandwidth heuristics, e.g., \u2018cv\u2019 (cross validation), \u2018silvermann\u2019 or \u2018scott\u2019, default \u2018cv\u2019. \u2018transform\u2019: transform applied to the parameters before doing KDE. \u2018sample_weights\u2019: weights associated with samples. See \u2018get_kde\u2019 for more details</p> <code>{}</code> <code>return_summary</code> <code>bool</code> <p>Whether to return the distances and data corresponding to the accepted parameters.</p> <code>False</code> <p>Returns:</p> Name Type Description <code>theta</code> <code>if kde False</code> <p>accepted parameters</p> <code>kde</code> <code>if kde True</code> <p>KDE object based on accepted parameters from which one can .sample() and .log_prob().</p> <code>summary</code> <code>if summary True</code> <p>dictionary containing the accepted paramters (if kde True), distances and simulated data x.</p> Source code in <code>sbi/inference/abc/mcabc.py</code> <pre><code>def __call__(\n    self,\n    x_o: Union[Tensor, ndarray],\n    num_simulations: int,\n    eps: Optional[float] = None,\n    quantile: Optional[float] = None,\n    lra: bool = False,\n    sass: bool = False,\n    sass_fraction: float = 0.25,\n    sass_expansion_degree: int = 1,\n    kde: bool = False,\n    kde_kwargs: Dict[str, Any] = {},\n    return_summary: bool = False,\n) -&gt; Union[Tuple[Tensor, dict], Tuple[KDEWrapper, dict], Tensor, KDEWrapper]:\n    r\"\"\"Run MCABC and return accepted parameters or KDE object fitted on them.\n\n    Args:\n        x_o: Observed data.\n        num_simulations: Number of simulations to run.\n        eps: Acceptance threshold $\\epsilon$ for distance between observed and\n            simulated data.\n        quantile: Upper quantile of smallest distances for which the corresponding\n            parameters are returned, e.g, q=0.01 will return the top 1%. Exactly\n            one of quantile or `eps` have to be passed.\n        lra: Whether to run linear regression adjustment as in Beaumont et al. 2002\n        sass: Whether to determine semi-automatic summary statistics as in\n            Fearnhead &amp; Prangle 2012.\n        sass_fraction: Fraction of simulation budget used for the initial sass run.\n        sass_expansion_degree: Degree of the polynomial feature expansion for the\n            sass regression, default 1 - no expansion.\n        kde: Whether to run KDE on the accepted parameters to return a KDE\n            object from which one can sample.\n        kde_kwargs: kwargs for performing KDE:\n            'bandwidth='; either a float, or a string naming a bandwidth\n            heuristics, e.g., 'cv' (cross validation), 'silvermann' or 'scott',\n            default 'cv'.\n            'transform': transform applied to the parameters before doing KDE.\n            'sample_weights': weights associated with samples. See 'get_kde' for\n            more details\n        return_summary: Whether to return the distances and data corresponding to\n            the accepted parameters.\n\n    Returns:\n        theta (if kde False): accepted parameters\n        kde (if kde True): KDE object based on accepted parameters from which one\n            can .sample() and .log_prob().\n        summary (if summary True): dictionary containing the accepted paramters (if\n            kde True), distances and simulated data x.\n    \"\"\"\n\n    # Exactly one of eps or quantile need to be passed.\n    assert (eps is not None) ^ (\n        quantile is not None\n    ), \"Eps or quantile must be passed, but not both.\"\n\n    # Run SASS and change the simulator and x_o accordingly.\n    if sass:\n        num_pilot_simulations = int(sass_fraction * num_simulations)\n        self.logger.info(\n            f\"Running SASS with {num_pilot_simulations} pilot samples.\"\n        )\n        num_simulations -= num_pilot_simulations\n\n        pilot_theta = self.prior.sample((num_pilot_simulations,))\n        pilot_x = self._batched_simulator(pilot_theta)\n\n        sass_transform = self.get_sass_transform(\n            pilot_theta, pilot_x, sass_expansion_degree\n        )\n\n        simulator = lambda theta: sass_transform(self._batched_simulator(theta))\n        x_o = sass_transform(x_o)\n    else:\n        simulator = self._batched_simulator\n\n    # Simulate and calculate distances.\n    theta = self.prior.sample((num_simulations,))\n    x = simulator(theta)\n\n    # Infer shape of x to test and set x_o.\n    self.x_shape = x[0].unsqueeze(0).shape\n    self.x_o = process_x(x_o, self.x_shape)\n\n    distances = self.distance(self.x_o, x)\n\n    # Select based on acceptance threshold epsilon.\n    if eps is not None:\n        is_accepted = distances &lt; eps\n        num_accepted = is_accepted.sum().item()\n        assert num_accepted &gt; 0, f\"No parameters accepted, eps={eps} too small\"\n\n        theta_accepted = theta[is_accepted]\n        distances_accepted = distances[is_accepted]\n        x_accepted = x[is_accepted]\n\n    # Select based on quantile on sorted distances.\n    elif quantile is not None:\n        num_top_samples = int(num_simulations * quantile)\n        sort_idx = torch.argsort(distances)\n        theta_accepted = theta[sort_idx][:num_top_samples]\n        distances_accepted = distances[sort_idx][:num_top_samples]\n        x_accepted = x[sort_idx][:num_top_samples]\n\n    else:\n        raise ValueError(\"One of epsilon or quantile has to be passed.\")\n\n    # Maybe adjust theta with LRA.\n    if lra:\n        self.logger.info(\"Running Linear regression adjustment.\")\n        final_theta = self.run_lra(theta_accepted, x_accepted, observation=self.x_o)\n    else:\n        final_theta = theta_accepted\n\n    if kde:\n        self.logger.info(\n            f\"\"\"KDE on {final_theta.shape[0]} samples with bandwidth option\n            {kde_kwargs[\"bandwidth\"] if \"bandwidth\" in kde_kwargs else \"cv\"}.\n            Beware that KDE can give unreliable results when used with too few\n            samples and in high dimensions.\"\"\"\n        )\n\n        kde_dist = get_kde(final_theta, **kde_kwargs)\n\n        if return_summary:\n            return (\n                kde_dist,\n                dict(theta=final_theta, distances=distances_accepted, x=x_accepted),\n            )\n        else:\n            return kde_dist\n    elif return_summary:\n        return final_theta, dict(distances=distances_accepted, x=x_accepted)\n    else:\n        return final_theta\n</code></pre>"},{"location":"reference/#sbi.inference.abc.mcabc.MCABC.__init__","title":"<code>__init__(simulator, prior, distance='l2', num_workers=1, simulation_batch_size=1, show_progress_bars=True)</code>","text":"<p>Monte-Carlo Approximate Bayesian Computation (Rejection ABC) [1].</p> <p>[1] Pritchard, J. K., Seielstad, M. T., Perez-Lezaun, A., &amp; Feldman, M. W. (1999). Population growth of human Y chromosomes: a study of Y chromosome microsatellites. Molecular biology and evolution, 16(12), 1791-1798.</p> <p>Parameters:</p> Name Type Description Default <code>simulator</code> <code>Callable</code> <p>A function that takes parameters \\(\\theta\\) and maps them to simulations, or observations, <code>x</code>, \\(\\mathrm{sim}(\\theta)\\to x\\). Any regular Python callable (i.e. function or class with <code>__call__</code> method) can be used.</p> required <code>prior</code> <p>A probability distribution that expresses prior knowledge about the parameters, e.g. which ranges are meaningful for them. Any object with <code>.log_prob()</code>and <code>.sample()</code> (for example, a PyTorch distribution) can be used.</p> required <code>distance</code> <code>Union[str, Callable]</code> <p>Distance function to compare observed and simulated data. Can be a custom function or one of <code>l1</code>, <code>l2</code>, <code>mse</code>.</p> <code>'l2'</code> <code>num_workers</code> <code>int</code> <p>Number of parallel workers to use for simulations.</p> <code>1</code> <code>simulation_batch_size</code> <code>int</code> <p>Number of parameter sets that the simulator maps to data x at once. If None, we simulate all parameter sets at the same time. If &gt;= 1, the simulator has to process data of shape (simulation_batch_size, parameter_dimension).</p> <code>1</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during simulation and sampling.</p> <code>True</code> Source code in <code>sbi/inference/abc/mcabc.py</code> <pre><code>def __init__(\n    self,\n    simulator: Callable,\n    prior,\n    distance: Union[str, Callable] = \"l2\",\n    num_workers: int = 1,\n    simulation_batch_size: int = 1,\n    show_progress_bars: bool = True,\n):\n    r\"\"\"Monte-Carlo Approximate Bayesian Computation (Rejection ABC) [1].\n\n    [1] Pritchard, J. K., Seielstad, M. T., Perez-Lezaun, A., &amp; Feldman, M. W.\n    (1999). Population growth of human Y chromosomes: a study of Y chromosome\n    microsatellites. Molecular biology and evolution, 16(12), 1791-1798.\n\n    Args:\n        simulator: A function that takes parameters $\\theta$ and maps them to\n            simulations, or observations, `x`, $\\mathrm{sim}(\\theta)\\to x$. Any\n            regular Python callable (i.e. function or class with `__call__` method)\n            can be used.\n        prior: A probability distribution that expresses prior knowledge about the\n            parameters, e.g. which ranges are meaningful for them. Any\n            object with `.log_prob()`and `.sample()` (for example, a PyTorch\n            distribution) can be used.\n        distance: Distance function to compare observed and simulated data. Can be\n            a custom function or one of `l1`, `l2`, `mse`.\n        num_workers: Number of parallel workers to use for simulations.\n        simulation_batch_size: Number of parameter sets that the simulator\n            maps to data x at once. If None, we simulate all parameter sets at the\n            same time. If &gt;= 1, the simulator has to process data of shape\n            (simulation_batch_size, parameter_dimension).\n        show_progress_bars: Whether to show a progressbar during simulation and\n            sampling.\n    \"\"\"\n\n    super().__init__(\n        simulator=simulator,\n        prior=prior,\n        distance=distance,\n        num_workers=num_workers,\n        simulation_batch_size=simulation_batch_size,\n        show_progress_bars=show_progress_bars,\n    )\n</code></pre>"},{"location":"reference/#sbi.inference.abc.smcabc.SMCABC.__call__","title":"<code>__call__(x_o, num_particles, num_initial_pop, num_simulations, epsilon_decay, distance_based_decay=False, ess_min=None, kernel_variance_scale=1.0, use_last_pop_samples=True, return_summary=False, kde=False, kde_kwargs={}, kde_sample_weights=False, lra=False, lra_with_weights=False, sass=False, sass_fraction=0.25, sass_expansion_degree=1)</code>","text":"<p>Run SMCABC and return accepted parameters or KDE object fitted on them.</p> <p>Parameters:</p> Name Type Description Default <code>x_o</code> <code>Union[Tensor, ndarray]</code> <p>Observed data.</p> required <code>num_particles</code> <code>int</code> <p>Number of particles in each population.</p> required <code>num_initial_pop</code> <code>int</code> <p>Number of simulations used for initial population.</p> required <code>num_simulations</code> <code>int</code> <p>Total number of possible simulations.</p> required <code>epsilon_decay</code> <code>float</code> <p>Factor with which the acceptance threshold \\(\\epsilon\\) decays.</p> required <code>distance_based_decay</code> <code>bool</code> <p>Whether the \\(\\epsilon\\) decay is constant over populations or calculated from the previous populations distribution of distances.</p> <code>False</code> <code>ess_min</code> <code>Optional[float]</code> <p>Threshold of effective sampling size for resampling weights. Not used when None (default).</p> <code>None</code> <code>kernel_variance_scale</code> <code>float</code> <p>Factor for scaling the perturbation kernel variance.</p> <code>1.0</code> <code>use_last_pop_samples</code> <code>bool</code> <p>Whether to fill up the current population with samples from the previous population when the budget is used up. If False, the current population is discarded and the previous population is returned.</p> <code>True</code> <code>lra</code> <code>bool</code> <p>Whether to run linear regression adjustment as in Beaumont et al. 2002</p> <code>False</code> <code>lra_with_weights</code> <code>bool</code> <p>Whether to run lra as weighted linear regression with SMC weights</p> <code>False</code> <code>sass</code> <code>bool</code> <p>Whether to determine semi-automatic summary statistics as in Fearnhead &amp; Prangle 2012.</p> <code>False</code> <code>sass_fraction</code> <code>float</code> <p>Fraction of simulation budget used for the initial sass run.</p> <code>0.25</code> <code>sass_expansion_degree</code> <code>int</code> <p>Degree of the polynomial feature expansion for the sass regression, default 1 - no expansion.</p> <code>1</code> <code>kde</code> <code>bool</code> <p>Whether to run KDE on the accepted parameters to return a KDE object from which one can sample.</p> <code>False</code> <code>kde_kwargs</code> <code>Dict[str, Any]</code> <p>kwargs for performing KDE: \u2018bandwidth=\u2019; either a float, or a string naming a bandwidth heuristics, e.g., \u2018cv\u2019 (cross validation), \u2018silvermann\u2019 or \u2018scott\u2019, default \u2018cv\u2019. \u2018transform\u2019: transform applied to the parameters before doing KDE. \u2018sample_weights\u2019: weights associated with samples. See \u2018get_kde\u2019 for more details</p> <code>{}</code> <code>kde_sample_weights</code> <code>bool</code> <p>Whether perform weighted KDE with SMC weights or on raw particles.</p> <code>False</code> <code>return_summary</code> <code>bool</code> <p>Whether to return a dictionary with all accepted particles, weights, etc. at the end.</p> <code>False</code> <p>Returns:</p> Name Type Description <code>theta</code> <code>if kde False</code> <p>accepted parameters of the last population.</p> <code>kde</code> <code>if kde True</code> <p>KDE object fitted on accepted parameters, from which one can .sample() and .log_prob().</p> <code>summary</code> <code>if return_summary True</code> <p>dictionary containing the accepted paramters (if kde True), distances and simulated data x of all populations.</p> Source code in <code>sbi/inference/abc/smcabc.py</code> <pre><code>def __call__(\n    self,\n    x_o: Union[Tensor, ndarray],\n    num_particles: int,\n    num_initial_pop: int,\n    num_simulations: int,\n    epsilon_decay: float,\n    distance_based_decay: bool = False,\n    ess_min: Optional[float] = None,\n    kernel_variance_scale: float = 1.0,\n    use_last_pop_samples: bool = True,\n    return_summary: bool = False,\n    kde: bool = False,\n    kde_kwargs: Dict[str, Any] = {},\n    kde_sample_weights: bool = False,\n    lra: bool = False,\n    lra_with_weights: bool = False,\n    sass: bool = False,\n    sass_fraction: float = 0.25,\n    sass_expansion_degree: int = 1,\n) -&gt; Union[Tensor, KDEWrapper, Tuple[Tensor, dict], Tuple[KDEWrapper, dict]]:\n    r\"\"\"Run SMCABC and return accepted parameters or KDE object fitted on them.\n\n    Args:\n        x_o: Observed data.\n        num_particles: Number of particles in each population.\n        num_initial_pop: Number of simulations used for initial population.\n        num_simulations: Total number of possible simulations.\n        epsilon_decay: Factor with which the acceptance threshold $\\epsilon$ decays.\n        distance_based_decay: Whether the $\\epsilon$ decay is constant over\n            populations or calculated from the previous populations distribution of\n            distances.\n        ess_min: Threshold of effective sampling size for resampling weights. Not\n            used when None (default).\n        kernel_variance_scale: Factor for scaling the perturbation kernel variance.\n        use_last_pop_samples: Whether to fill up the current population with\n            samples from the previous population when the budget is used up. If\n            False, the current population is discarded and the previous population\n            is returned.\n        lra: Whether to run linear regression adjustment as in Beaumont et al. 2002\n        lra_with_weights: Whether to run lra as weighted linear regression with SMC\n            weights\n        sass: Whether to determine semi-automatic summary statistics as in\n            Fearnhead &amp; Prangle 2012.\n        sass_fraction: Fraction of simulation budget used for the initial sass run.\n        sass_expansion_degree: Degree of the polynomial feature expansion for the\n            sass regression, default 1 - no expansion.\n        kde: Whether to run KDE on the accepted parameters to return a KDE\n            object from which one can sample.\n        kde_kwargs: kwargs for performing KDE:\n            'bandwidth='; either a float, or a string naming a bandwidth\n            heuristics, e.g., 'cv' (cross validation), 'silvermann' or 'scott',\n            default 'cv'.\n            'transform': transform applied to the parameters before doing KDE.\n            'sample_weights': weights associated with samples. See 'get_kde' for\n            more details\n        kde_sample_weights: Whether perform weighted KDE with SMC weights or on raw\n            particles.\n        return_summary: Whether to return a dictionary with all accepted particles,\n            weights, etc. at the end.\n\n    Returns:\n        theta (if kde False): accepted parameters of the last population.\n        kde (if kde True): KDE object fitted on accepted parameters, from which one\n            can .sample() and .log_prob().\n        summary (if return_summary True): dictionary containing the accepted\n            paramters (if kde True), distances and simulated data x of all\n            populations.\n    \"\"\"\n\n    pop_idx = 0\n    self.num_simulations = num_simulations\n\n    # Pilot run for SASS.\n    if sass:\n        num_pilot_simulations = int(sass_fraction * num_simulations)\n        self.logger.info(\n            f\"Running SASS with {num_pilot_simulations} pilot samples.\"\n        )\n        sass_transform = self.run_sass_set_xo(\n            num_particles, num_pilot_simulations, x_o, lra, sass_expansion_degree\n        )\n        # Udpate simulator and xo\n        x_o = sass_transform(self.x_o)\n\n        def sass_simulator(theta):\n            self.simulation_counter += theta.shape[0]\n            return sass_transform(self._batched_simulator(theta))\n\n        self._simulate_with_budget = sass_simulator\n\n    # run initial population\n    particles, epsilon, distances, x = self._set_xo_and_sample_initial_population(\n        x_o, num_particles, num_initial_pop\n    )\n    log_weights = torch.log(1 / num_particles * ones(num_particles))\n\n    self.logger.info(\n        (\n            f\"population={pop_idx}, eps={epsilon}, ess={1.0}, \"\n            f\"num_sims={num_initial_pop}\"\n        )\n    )\n\n    all_particles = [particles]\n    all_log_weights = [log_weights]\n    all_distances = [distances]\n    all_epsilons = [epsilon]\n    all_x = [x]\n\n    while self.simulation_counter &lt; self.num_simulations:\n        pop_idx += 1\n        # Decay based on quantile of distances from previous pop.\n        if distance_based_decay:\n            epsilon = self._get_next_epsilon(\n                all_distances[pop_idx - 1], epsilon_decay\n            )\n        # Constant decay.\n        else:\n            epsilon *= epsilon_decay\n\n        # Get kernel variance from previous pop.\n        self.kernel_variance = self.get_kernel_variance(\n            all_particles[pop_idx - 1],\n            torch.exp(all_log_weights[pop_idx - 1]),\n            samples_per_dim=500,\n            kernel_variance_scale=kernel_variance_scale,\n        )\n        particles, log_weights, distances, x = self._sample_next_population(\n            particles=all_particles[pop_idx - 1],\n            log_weights=all_log_weights[pop_idx - 1],\n            distances=all_distances[pop_idx - 1],\n            epsilon=epsilon,\n            x=all_x[pop_idx - 1],\n            use_last_pop_samples=use_last_pop_samples,\n        )\n\n        # Resample population if effective sampling size is too small.\n        if ess_min is not None:\n            particles, log_weights = self.resample_if_ess_too_small(\n                particles, log_weights, ess_min, pop_idx\n            )\n\n        self.logger.info(\n            (\n                f\"population={pop_idx} done: eps={epsilon:.6f},\"\n                f\" num_sims={self.simulation_counter}.\"\n            )\n        )\n\n        # collect results\n        all_particles.append(particles)\n        all_log_weights.append(log_weights)\n        all_distances.append(distances)\n        all_epsilons.append(epsilon)\n        all_x.append(x)\n\n    # Maybe run LRA and adjust weights.\n    if lra:\n        self.logger.info(\"Running Linear regression adjustment.\")\n        adjusted_particles, adjusted_weights = self.run_lra_update_weights(\n            particles=all_particles[-1],\n            xs=all_x[-1],\n            observation=process_x(x_o),\n            log_weights=all_log_weights[-1],\n            lra_with_weights=lra_with_weights,\n        )\n        final_particles = adjusted_particles\n    else:\n        final_particles = all_particles[-1]\n\n    if kde:\n        self.logger.info(\n            f\"\"\"KDE on {final_particles.shape[0]} samples with bandwidth option\n            {kde_kwargs[\"bandwidth\"] if \"bandwidth\" in kde_kwargs else \"cv\"}.\n            Beware that KDE can give unreliable results when used with too few\n            samples and in high dimensions.\"\"\"\n        )\n        # Maybe get particles weights from last population for weighted KDE.\n        if kde_sample_weights:\n            kde_kwargs[\"sample_weights\"] = all_log_weights[-1].exp()\n\n        kde_dist = get_kde(final_particles, **kde_kwargs)\n\n        if return_summary:\n            return (\n                kde_dist,\n                dict(\n                    particles=all_particles,\n                    weights=all_log_weights,\n                    epsilons=all_epsilons,\n                    distances=all_distances,\n                    xs=all_x,\n                ),\n            )\n        else:\n            return kde_dist\n\n    if return_summary:\n        return (\n            final_particles,\n            dict(\n                particles=all_particles,\n                weights=all_log_weights,\n                epsilons=all_epsilons,\n                distances=all_distances,\n                xs=all_x,\n            ),\n        )\n    else:\n        return final_particles\n</code></pre>"},{"location":"reference/#sbi.inference.abc.smcabc.SMCABC.__init__","title":"<code>__init__(simulator, prior, distance='l2', num_workers=1, simulation_batch_size=1, show_progress_bars=True, kernel='gaussian', algorithm_variant='C')</code>","text":"<p>Sequential Monte Carlo Approximate Bayesian Computation.</p> We distinguish between three different SMC methods here <ul> <li>A: Toni et al. 2010 (Phd Thesis)</li> <li>B: Sisson et al. 2007 (with correction from 2009)</li> <li>C: Beaumont et al. 2009</li> </ul> <p>In Toni et al. 2010 we find an overview of the differences on page 34:     - B: same as A except for resampling of weights if the effective sampling         size is too small.     - C: same as A except for calculation of the covariance of the perturbation         kernel: the kernel covariance is a scaled version of the covariance of         the previous population.</p> <p>Parameters:</p> Name Type Description Default <code>simulator</code> <code>Callable</code> <p>A function that takes parameters \\(\\theta\\) and maps them to simulations, or observations, <code>x</code>, \\(\\mathrm{sim}(\\theta)\\to x\\). Any regular Python callable (i.e. function or class with <code>__call__</code> method) can be used.</p> required <code>prior</code> <code>Distribution</code> <p>A probability distribution that expresses prior knowledge about the parameters, e.g. which ranges are meaningful for them. Any object with <code>.log_prob()</code>and <code>.sample()</code> (for example, a PyTorch distribution) can be used.</p> required <code>distance</code> <code>Union[str, Callable]</code> <p>Distance function to compare observed and simulated data. Can be a custom function or one of <code>l1</code>, <code>l2</code>, <code>mse</code>.</p> <code>'l2'</code> <code>num_workers</code> <code>int</code> <p>Number of parallel workers to use for simulations.</p> <code>1</code> <code>simulation_batch_size</code> <code>int</code> <p>Number of parameter sets that the simulator maps to data x at once. If None, we simulate all parameter sets at the same time. If &gt;= 1, the simulator has to process data of shape (simulation_batch_size, parameter_dimension).</p> <code>1</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during simulation and sampling.</p> <code>True</code> <code>kernel</code> <code>Optional[str]</code> <p>Perturbation kernel.</p> <code>'gaussian'</code> <code>algorithm_variant</code> <code>str</code> <p>Indicating the choice of algorithm variant, A, B, or C.</p> <code>'C'</code> Source code in <code>sbi/inference/abc/smcabc.py</code> <pre><code>def __init__(\n    self,\n    simulator: Callable,\n    prior: Distribution,\n    distance: Union[str, Callable] = \"l2\",\n    num_workers: int = 1,\n    simulation_batch_size: int = 1,\n    show_progress_bars: bool = True,\n    kernel: Optional[str] = \"gaussian\",\n    algorithm_variant: str = \"C\",\n):\n    r\"\"\"Sequential Monte Carlo Approximate Bayesian Computation.\n\n    We distinguish between three different SMC methods here:\n        - A: Toni et al. 2010 (Phd Thesis)\n        - B: Sisson et al. 2007 (with correction from 2009)\n        - C: Beaumont et al. 2009\n\n    In Toni et al. 2010 we find an overview of the differences on page 34:\n        - B: same as A except for resampling of weights if the effective sampling\n            size is too small.\n        - C: same as A except for calculation of the covariance of the perturbation\n            kernel: the kernel covariance is a scaled version of the covariance of\n            the previous population.\n\n    Args:\n        simulator: A function that takes parameters $\\theta$ and maps them to\n            simulations, or observations, `x`, $\\mathrm{sim}(\\theta)\\to x$. Any\n            regular Python callable (i.e. function or class with `__call__` method)\n            can be used.\n        prior: A probability distribution that expresses prior knowledge about the\n            parameters, e.g. which ranges are meaningful for them. Any\n            object with `.log_prob()`and `.sample()` (for example, a PyTorch\n            distribution) can be used.\n        distance: Distance function to compare observed and simulated data. Can be\n            a custom function or one of `l1`, `l2`, `mse`.\n        num_workers: Number of parallel workers to use for simulations.\n        simulation_batch_size: Number of parameter sets that the simulator\n            maps to data x at once. If None, we simulate all parameter sets at the\n            same time. If &gt;= 1, the simulator has to process data of shape\n            (simulation_batch_size, parameter_dimension).\n        show_progress_bars: Whether to show a progressbar during simulation and\n            sampling.\n        kernel: Perturbation kernel.\n        algorithm_variant: Indicating the choice of algorithm variant, A, B, or C.\n\n    \"\"\"\n\n    super().__init__(\n        simulator=simulator,\n        prior=prior,\n        distance=distance,\n        num_workers=num_workers,\n        simulation_batch_size=simulation_batch_size,\n        show_progress_bars=show_progress_bars,\n    )\n\n    kernels = (\"gaussian\", \"uniform\")\n    assert (\n        kernel in kernels\n    ), f\"Kernel '{kernel}' not supported. Choose one from {kernels}.\"\n    self.kernel = kernel\n\n    algorithm_variants = (\"A\", \"B\", \"C\")\n    assert algorithm_variant in algorithm_variants, (\n        f\"SMCABC variant '{algorithm_variant}' not supported, choose one from\"\n        \" {algorithm_variants}.\"\n    )\n    self.algorithm_variant = algorithm_variant\n    self.distance_to_x0 = None\n    self.simulation_counter = 0\n    self.num_simulations = 0\n\n    # Define simulator that keeps track of budget.\n    def simulate_with_budget(theta):\n        self.simulation_counter += theta.shape[0]\n        return self._batched_simulator(theta)\n\n    self._simulate_with_budget = simulate_with_budget\n</code></pre>"},{"location":"reference/#sbi.inference.abc.smcabc.SMCABC.get_new_kernel","title":"<code>get_new_kernel(thetas)</code>","text":"<p>Return new kernel distribution for a given set of paramters.</p> Source code in <code>sbi/inference/abc/smcabc.py</code> <pre><code>def get_new_kernel(self, thetas: Tensor) -&gt; Distribution:\n    \"\"\"Return new kernel distribution for a given set of paramters.\"\"\"\n\n    if self.kernel == \"gaussian\":\n        assert self.kernel_variance.ndim == 2\n        return MultivariateNormal(\n            loc=thetas, covariance_matrix=self.kernel_variance\n        )\n\n    elif self.kernel == \"uniform\":\n        low = thetas - self.kernel_variance\n        high = thetas + self.kernel_variance\n        # Move batch shape to event shape to get Uniform that is multivariate in\n        # parameter dimension.\n        return Uniform(low=low, high=high).to_event(1)\n    else:\n        raise ValueError(f\"Kernel, '{self.kernel}' not supported.\")\n</code></pre>"},{"location":"reference/#sbi.inference.abc.smcabc.SMCABC.get_particle_ranges","title":"<code>get_particle_ranges(particles, weights, samples_per_dim=100)</code>","text":"<p>Return range of particles in each parameter dimension.</p> Source code in <code>sbi/inference/abc/smcabc.py</code> <pre><code>def get_particle_ranges(\n    self, particles: Tensor, weights: Tensor, samples_per_dim: int = 100\n) -&gt; Tensor:\n    \"\"\"Return range of particles in each parameter dimension.\"\"\"\n\n    # get weighted samples\n    samples = self.sample_from_population_with_weights(\n        particles,\n        weights,\n        num_samples=samples_per_dim * particles.shape[1],\n    )\n\n    # Variance spans the range of particles for every dimension.\n    particle_ranges = samples.max(0).values - samples.min(0).values\n    assert particle_ranges.ndim &lt; 2\n    return particle_ranges\n</code></pre>"},{"location":"reference/#sbi.inference.abc.smcabc.SMCABC.resample_if_ess_too_small","title":"<code>resample_if_ess_too_small(particles, log_weights, ess_min, pop_idx)</code>","text":"<p>Return resampled particles and uniform weights if effectice sampling size is too small.</p> Source code in <code>sbi/inference/abc/smcabc.py</code> <pre><code>def resample_if_ess_too_small(\n    self,\n    particles: Tensor,\n    log_weights: Tensor,\n    ess_min: float,\n    pop_idx: int,\n) -&gt; Tuple[Tensor, Tensor]:\n    \"\"\"Return resampled particles and uniform weights if effectice sampling size is\n    too small.\n    \"\"\"\n\n    num_particles = particles.shape[0]\n    ess = (1 / torch.sum(torch.exp(2.0 * log_weights), dim=0)) / num_particles\n    # Resampling of weights for low ESS only for Sisson et al. 2007.\n    if ess &lt; ess_min:\n        self.logger.info(f\"ESS={ess:.2f} too low, resampling pop {pop_idx}...\")\n        # First resample, then set to uniform weights as in Sisson et al. 2007.\n        particles = self.sample_from_population_with_weights(\n            particles, torch.exp(log_weights), num_samples=num_particles\n        )\n        log_weights = torch.log(1 / num_particles * ones(num_particles))\n\n    return particles, log_weights\n</code></pre>"},{"location":"reference/#sbi.inference.abc.smcabc.SMCABC.run_lra_update_weights","title":"<code>run_lra_update_weights(particles, xs, observation, log_weights, lra_with_weights)</code>","text":"<p>Return particles and weights adjusted with LRA.</p> <p>Runs (weighted) linear regression from xs onto particles to adjust the particles.</p> <p>Updates the SMC weights according to the new particles.</p> Source code in <code>sbi/inference/abc/smcabc.py</code> <pre><code>def run_lra_update_weights(\n    self,\n    particles: Tensor,\n    xs: Tensor,\n    observation: Tensor,\n    log_weights: Tensor,\n    lra_with_weights: bool,\n) -&gt; Tuple[Tensor, Tensor]:\n    \"\"\"Return particles and weights adjusted with LRA.\n\n    Runs (weighted) linear regression from xs onto particles to adjust the\n    particles.\n\n    Updates the SMC weights according to the new particles.\n    \"\"\"\n\n    adjusted_particels = self.run_lra(\n        theta=particles,\n        x=xs,\n        observation=observation,\n        sample_weight=log_weights.exp() if lra_with_weights else None,\n    )\n\n    # Update SMC weights with LRA adjusted weights\n    adjusted_log_weights = self._calculate_new_log_weights(\n        new_particles=adjusted_particels,\n        old_particles=particles,\n        old_log_weights=log_weights,\n    )\n\n    return adjusted_particels, adjusted_log_weights\n</code></pre>"},{"location":"reference/#sbi.inference.abc.smcabc.SMCABC.run_sass_set_xo","title":"<code>run_sass_set_xo(num_particles, num_pilot_simulations, x_o, lra=False, sass_expansion_degree=1)</code>","text":"<p>Return transform for semi-automatic summary statistics.</p> <p>Runs an single round of rejection abc with fixed budget and accepts num_particles simulations to run the regression for sass.</p> <p>Sets self.x_o once the x_shape can be derived from simulations.</p> Source code in <code>sbi/inference/abc/smcabc.py</code> <pre><code>def run_sass_set_xo(\n    self,\n    num_particles: int,\n    num_pilot_simulations: int,\n    x_o,\n    lra: bool = False,\n    sass_expansion_degree: int = 1,\n) -&gt; Callable:\n    \"\"\"Return transform for semi-automatic summary statistics.\n\n    Runs an single round of rejection abc with fixed budget and accepts\n    num_particles simulations to run the regression for sass.\n\n    Sets self.x_o once the x_shape can be derived from simulations.\n    \"\"\"\n    (\n        pilot_particles,\n        _,\n        _,\n        pilot_xs,\n    ) = self._set_xo_and_sample_initial_population(\n        x_o, num_particles, num_pilot_simulations\n    )\n    # Adjust with LRA.\n    if lra:\n        pilot_particles = self.run_lra(pilot_particles, pilot_xs, self.x_o)\n    sass_transform = self.get_sass_transform(\n        pilot_particles,\n        pilot_xs,\n        expansion_degree=sass_expansion_degree,\n        sample_weight=None,\n    )\n    return sass_transform\n</code></pre>"},{"location":"reference/#sbi.inference.abc.smcabc.SMCABC.sample_from_population_with_weights","title":"<code>sample_from_population_with_weights(particles, weights, num_samples=1)</code>  <code>staticmethod</code>","text":"<p>Return samples from particles sampled with weights.</p> Source code in <code>sbi/inference/abc/smcabc.py</code> <pre><code>@staticmethod\ndef sample_from_population_with_weights(\n    particles: Tensor, weights: Tensor, num_samples: int = 1\n) -&gt; Tensor:\n    \"\"\"Return samples from particles sampled with weights.\"\"\"\n\n    # define multinomial with weights as probs\n    multi = Multinomial(probs=weights)\n    # sample num samples, with replacement\n    samples = multi.sample(sample_shape=torch.Size((num_samples,)))\n    # get indices of success trials\n    indices = torch.where(samples)[1]\n    # return those indices from trace\n    return particles[indices]\n</code></pre>"},{"location":"reference/#posteriors","title":"Posteriors","text":"<p>             Bases: <code>NeuralPosterior</code></p> <p>Posterior \\(p(\\theta|x_o)\\) with <code>log_prob()</code> and <code>sample()</code> methods, only applicable to SNPE. SNPE trains a neural network to directly approximate the posterior distribution. However, for bounded priors, the neural network can have leakage: it puts non-zero mass in regions where the prior is zero. The <code>DirectPosterior</code> class wraps the trained network to deal with these cases. Specifically, this class offers the following functionality: - correct the calculation of the log probability such that it compensates for the   leakage. - reject samples that lie outside of the prior bounds. This class can not be used in combination with SNLE or SNRE.</p> Source code in <code>sbi/inference/posteriors/direct_posterior.py</code> <pre><code>class DirectPosterior(NeuralPosterior):\n    r\"\"\"Posterior $p(\\theta|x_o)$ with `log_prob()` and `sample()` methods, only\n    applicable to SNPE.&lt;br/&gt;&lt;br/&gt;\n    SNPE trains a neural network to directly approximate the posterior distribution.\n    However, for bounded priors, the neural network can have leakage: it puts non-zero\n    mass in regions where the prior is zero. The `DirectPosterior` class wraps the\n    trained network to deal with these cases.&lt;br/&gt;&lt;br/&gt;\n    Specifically, this class offers the following functionality:&lt;br/&gt;\n    - correct the calculation of the log probability such that it compensates for the\n      leakage.&lt;br/&gt;\n    - reject samples that lie outside of the prior bounds.&lt;br/&gt;&lt;br/&gt;\n    This class can not be used in combination with SNLE or SNRE.\n    \"\"\"\n\n    def __init__(\n        self,\n        posterior_estimator: flows.Flow,\n        prior: Distribution,\n        max_sampling_batch_size: int = 10_000,\n        device: Optional[str] = None,\n        x_shape: Optional[torch.Size] = None,\n        enable_transform: bool = True,\n    ):\n        \"\"\"\n        Args:\n            prior: Prior distribution with `.log_prob()` and `.sample()`.\n            posterior_estimator: The trained neural posterior.\n            max_sampling_batch_size: Batchsize of samples being drawn from\n                the proposal at every iteration.\n            device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:0\". If None,\n                `potential_fn.device` is used.\n            x_shape: Shape of a single simulator output. If passed, it is used to check\n                the shape of the observed data and give a descriptive error.\n            enable_transform: Whether to transform parameters to unconstrained space\n                during MAP optimization. When False, an identity transform will be\n                returned for `theta_transform`.\n        \"\"\"\n        # Because `DirectPosterior` does not take the `potential_fn` as input, it\n        # builds it itself. The `potential_fn` and `theta_transform` are used only for\n        # obtaining the MAP.\n        check_prior(prior)\n        potential_fn, theta_transform = posterior_estimator_based_potential(\n            posterior_estimator,\n            prior,\n            x_o=None,\n            enable_transform=enable_transform,\n        )\n\n        super().__init__(\n            potential_fn=potential_fn,\n            theta_transform=theta_transform,\n            device=device,\n            x_shape=x_shape,\n        )\n\n        self.prior = prior\n        self.posterior_estimator = posterior_estimator\n\n        self.max_sampling_batch_size = max_sampling_batch_size\n        self._leakage_density_correction_factor = None\n\n        self._purpose = \"\"\"It samples the posterior network and rejects samples that\n            lie outside of the prior bounds.\"\"\"\n\n    def sample(\n        self,\n        sample_shape: Shape = torch.Size(),\n        x: Optional[Tensor] = None,\n        max_sampling_batch_size: int = 10_000,\n        sample_with: Optional[str] = None,\n        show_progress_bars: bool = True,\n    ) -&gt; Tensor:\n        r\"\"\"Return samples from posterior distribution $p(\\theta|x)$.\n\n        Args:\n            sample_shape: Desired shape of samples that are drawn from posterior. If\n                sample_shape is multidimensional we simply draw `sample_shape.numel()`\n                samples and then reshape into the desired shape.\n            sample_with: This argument only exists to keep backward-compatibility with\n                `sbi` v0.17.2 or older. If it is set, we instantly raise an error.\n            show_progress_bars: Whether to show sampling progress monitor.\n        \"\"\"\n\n        num_samples = torch.Size(sample_shape).numel()\n        x = self._x_else_default_x(x)\n        max_sampling_batch_size = (\n            self.max_sampling_batch_size\n            if max_sampling_batch_size is None\n            else max_sampling_batch_size\n        )\n\n        if sample_with is not None:\n            raise ValueError(\n                f\"You set `sample_with={sample_with}`. As of sbi v0.18.0, setting \"\n                f\"`sample_with` is no longer supported. You have to rerun \"\n                f\"`.build_posterior(sample_with={sample_with}).`\"\n            )\n\n        samples = accept_reject_sample(\n            proposal=self.posterior_estimator,\n            accept_reject_fn=lambda theta: within_support(self.prior, theta),\n            num_samples=num_samples,\n            show_progress_bars=show_progress_bars,\n            max_sampling_batch_size=max_sampling_batch_size,\n            proposal_sampling_kwargs={\"context\": x},\n            alternative_method=\"build_posterior(..., sample_with='mcmc')\",\n        )[0]\n        return samples\n\n    def log_prob(\n        self,\n        theta: Tensor,\n        x: Optional[Tensor] = None,\n        norm_posterior: bool = True,\n        track_gradients: bool = False,\n        leakage_correction_params: Optional[dict] = None,\n    ) -&gt; Tensor:\n        r\"\"\"Returns the log-probability of the posterior $p(\\theta|x)$.\n\n        Args:\n            theta: Parameters $\\theta$.\n            norm_posterior: Whether to enforce a normalized posterior density.\n                Renormalization of the posterior is useful when some\n                probability falls out or leaks out of the prescribed prior support.\n                The normalizing factor is calculated via rejection sampling, so if you\n                need speedier but unnormalized log posterior estimates set here\n                `norm_posterior=False`. The returned log posterior is set to\n                -\u221e outside of the prior support regardless of this setting.\n            track_gradients: Whether the returned tensor supports tracking gradients.\n                This can be helpful for e.g. sensitivity analysis, but increases memory\n                consumption.\n            leakage_correction_params: A `dict` of keyword arguments to override the\n                default values of `leakage_correction()`. Possible options are:\n                `num_rejection_samples`, `force_update`, `show_progress_bars`, and\n                `rejection_sampling_batch_size`.\n                These parameters only have an effect if `norm_posterior=True`.\n\n        Returns:\n            `(len(\u03b8),)`-shaped log posterior probability $\\log p(\\theta|x)$ for \u03b8 in the\n            support of the prior, -\u221e (corresponding to 0 probability) outside.\n        \"\"\"\n        x = self._x_else_default_x(x)\n\n        # TODO Train exited here, entered after sampling?\n        self.posterior_estimator.eval()\n\n        theta = ensure_theta_batched(torch.as_tensor(theta))\n        theta_repeated, x_repeated = match_theta_and_x_batch_shapes(theta, x)\n\n        with torch.set_grad_enabled(track_gradients):\n            # Evaluate on device, move back to cpu for comparison with prior.\n            unnorm_log_prob = self.posterior_estimator.log_prob(\n                theta_repeated, context=x_repeated\n            )\n\n            # Force probability to be zero outside prior support.\n            in_prior_support = within_support(self.prior, theta_repeated)\n\n            masked_log_prob = torch.where(\n                in_prior_support,\n                unnorm_log_prob,\n                torch.tensor(float(\"-inf\"), dtype=torch.float32, device=self._device),\n            )\n\n            if leakage_correction_params is None:\n                leakage_correction_params = dict()  # use defaults\n            log_factor = (\n                log(self.leakage_correction(x=x, **leakage_correction_params))\n                if norm_posterior\n                else 0\n            )\n\n            return masked_log_prob - log_factor\n\n    @torch.no_grad()\n    def leakage_correction(\n        self,\n        x: Tensor,\n        num_rejection_samples: int = 10_000,\n        force_update: bool = False,\n        show_progress_bars: bool = False,\n        rejection_sampling_batch_size: int = 10_000,\n    ) -&gt; Tensor:\n        r\"\"\"Return leakage correction factor for a leaky posterior density estimate.\n\n        The factor is estimated from the acceptance probability during rejection\n        sampling from the posterior.\n\n        This is to avoid re-estimating the acceptance probability from scratch\n        whenever `log_prob` is called and `norm_posterior=True`. Here, it\n        is estimated only once for `self.default_x` and saved for later. We\n        re-evaluate only whenever a new `x` is passed.\n\n        Arguments:\n            num_rejection_samples: Number of samples used to estimate correction factor.\n            show_progress_bars: Whether to show a progress bar during sampling.\n            rejection_sampling_batch_size: Batch size for rejection sampling.\n\n        Returns:\n            Saved or newly-estimated correction factor (as a scalar `Tensor`).\n        \"\"\"\n\n        def acceptance_at(x: Tensor) -&gt; Tensor:\n            return accept_reject_sample(\n                proposal=self.posterior_estimator,\n                accept_reject_fn=lambda theta: within_support(self.prior, theta),\n                num_samples=num_rejection_samples,\n                show_progress_bars=show_progress_bars,\n                sample_for_correction_factor=True,\n                max_sampling_batch_size=rejection_sampling_batch_size,\n                proposal_sampling_kwargs={\"context\": x},\n            )[1]\n\n        # Check if the provided x matches the default x (short-circuit on identity).\n        is_new_x = self.default_x is None or (\n            x is not self.default_x and (x != self.default_x).any()\n        )\n\n        not_saved_at_default_x = self._leakage_density_correction_factor is None\n\n        if is_new_x:  # Calculate at x; don't save.\n            return acceptance_at(x)\n        elif not_saved_at_default_x or force_update:  # Calculate at default_x; save.\n            assert self.default_x is not None\n            self._leakage_density_correction_factor = acceptance_at(self.default_x)\n\n        return self._leakage_density_correction_factor  # type: ignore\n\n    def map(\n        self,\n        x: Optional[Tensor] = None,\n        num_iter: int = 1_000,\n        num_to_optimize: int = 100,\n        learning_rate: float = 0.01,\n        init_method: Union[str, Tensor] = \"posterior\",\n        num_init_samples: int = 1_000,\n        save_best_every: int = 10,\n        show_progress_bars: bool = False,\n        force_update: bool = False,\n    ) -&gt; Tensor:\n        r\"\"\"Returns the maximum-a-posteriori estimate (MAP).\n\n        The method can be interrupted (Ctrl-C) when the user sees that the\n        log-probability converges. The best estimate will be saved in `self._map` and\n        can be accessed with `self.map()`. The MAP is obtained by running gradient\n        ascent from a given number of starting positions (samples from the posterior\n        with the highest log-probability). After the optimization is done, we select the\n        parameter set that has the highest log-probability after the optimization.\n\n        Warning: The default values used by this function are not well-tested. They\n        might require hand-tuning for the problem at hand.\n\n        For developers: if the prior is a `BoxUniform`, we carry out the optimization\n        in unbounded space and transform the result back into bounded space.\n\n        Args:\n            x: Deprecated - use `.set_default_x()` prior to `.map()`.\n            num_iter: Number of optimization steps that the algorithm takes\n                to find the MAP.\n            learning_rate: Learning rate of the optimizer.\n            init_method: How to select the starting parameters for the optimization. If\n                it is a string, it can be either [`posterior`, `prior`], which samples\n                the respective distribution `num_init_samples` times. If it is a\n                tensor, the tensor will be used as init locations.\n            num_init_samples: Draw this number of samples from the posterior and\n                evaluate the log-probability of all of them.\n            num_to_optimize: From the drawn `num_init_samples`, use the\n                `num_to_optimize` with highest log-probability as the initial points\n                for the optimization.\n            save_best_every: The best log-probability is computed, saved in the\n                `map`-attribute, and printed every `save_best_every`-th iteration.\n                Computing the best log-probability creates a significant overhead\n                (thus, the default is `10`.)\n            show_progress_bars: Whether to show a progressbar during sampling from the\n                posterior.\n            force_update: Whether to re-calculate the MAP when x is unchanged and\n                have a cached value.\n            log_prob_kwargs: Will be empty for SNLE and SNRE. Will contain\n                {'norm_posterior': True} for SNPE.\n\n        Returns:\n            The MAP estimate.\n        \"\"\"\n        return super().map(\n            x=x,\n            num_iter=num_iter,\n            num_to_optimize=num_to_optimize,\n            learning_rate=learning_rate,\n            init_method=init_method,\n            num_init_samples=num_init_samples,\n            save_best_every=save_best_every,\n            show_progress_bars=show_progress_bars,\n            force_update=force_update,\n        )\n</code></pre> <p>             Bases: <code>NeuralPosterior</code></p> <p>Provides importance sampling to sample from the posterior. SNLE or SNRE train neural networks to approximate the likelihood(-ratios). <code>ImportanceSamplingPosterior</code> allows to estimate the posterior log-probability by estimating the normlalization constant with importance sampling. It also allows to perform importance sampling (with <code>.sample()</code>) and to draw approximate samples with sampling-importance-resampling (SIR) (with <code>.sir_sample()</code>)</p> Source code in <code>sbi/inference/posteriors/importance_posterior.py</code> <pre><code>class ImportanceSamplingPosterior(NeuralPosterior):\n    r\"\"\"Provides importance sampling to sample from the posterior.&lt;br/&gt;&lt;br/&gt;\n    SNLE or SNRE train neural networks to approximate the likelihood(-ratios).\n    `ImportanceSamplingPosterior` allows to estimate the posterior log-probability by\n    estimating the normlalization constant with importance sampling. It also allows to\n    perform importance sampling (with `.sample()`) and to draw approximate samples with\n    sampling-importance-resampling (SIR) (with `.sir_sample()`)\n    \"\"\"\n\n    def __init__(\n        self,\n        potential_fn: Callable,\n        proposal: Any,\n        theta_transform: Optional[TorchTransform] = None,\n        method: str = \"sir\",\n        oversampling_factor: int = 32,\n        max_sampling_batch_size: int = 10_000,\n        device: Optional[str] = None,\n        x_shape: Optional[torch.Size] = None,\n    ):\n        \"\"\"\n        Args:\n            potential_fn: The potential function from which to draw samples.\n            proposal: The proposal distribution.\n            theta_transform: Transformation that is applied to parameters. Is not used\n                during but only when calling `.map()`.\n            method: Either of [`sir`|`importance`]. This sets the behavior of the\n                `.sample()` method. With `sir`, approximate posterior samples are\n                generated with sampling importance resampling (SIR). With\n                `importance`, the `.sample()` method returns a tuple of samples and\n                corresponding importance weights.\n            oversampling_factor: Number of proposed samples from which only one is\n                selected based on its importance weight.\n            max_sampling_batch_size: The batch size of samples being drawn from the\n                proposal at every iteration.\n            device: Device on which to sample, e.g., \"cpu\", \"cuda\" or \"cuda:0\". If\n                None, `potential_fn.device` is used.\n            x_shape: Shape of a single simulator output. If passed, it is used to check\n                the shape of the observed data and give a descriptive error.\n        \"\"\"\n        super().__init__(\n            potential_fn,\n            theta_transform=theta_transform,\n            device=device,\n            x_shape=x_shape,\n        )\n\n        self.proposal = proposal\n        self._normalization_constant = None\n        self.method = method\n\n        self.oversampling_factor = oversampling_factor\n        self.max_sampling_batch_size = max_sampling_batch_size\n\n        self._purpose = (\n            \"It provides sampling-importance resampling (SIR) to .sample() from the \"\n            \"posterior and can evaluate the _unnormalized_ posterior density with \"\n            \".log_prob().\"\n        )\n\n    def log_prob(\n        self,\n        theta: Tensor,\n        x: Optional[Tensor] = None,\n        track_gradients: bool = False,\n        normalization_constant_params: Optional[dict] = None,\n    ) -&gt; Tensor:\n        r\"\"\"Returns the log-probability of theta under the posterior.\n\n        The normalization constant is estimated with importance sampling.\n\n        Args:\n            theta: Parameters $\\theta$.\n            track_gradients: Whether the returned tensor supports tracking gradients.\n                This can be helpful for e.g. sensitivity analysis, but increases memory\n                consumption.\n            normalization_constant_params: Parameters passed on to\n                `estimate_normalization_constant()`.\n\n        Returns:\n            `len($\\theta$)`-shaped log-probability.\n        \"\"\"\n        x = self._x_else_default_x(x)\n        self.potential_fn.set_x(x)\n\n        theta = ensure_theta_batched(torch.as_tensor(theta))\n\n        with torch.set_grad_enabled(track_gradients):\n            potential_values = self.potential_fn(\n                theta.to(self._device), track_gradients=track_gradients\n            )\n\n            if normalization_constant_params is None:\n                normalization_constant_params = dict()  # use defaults\n            normalization_constant = self.estimate_normalization_constant(\n                x, **normalization_constant_params\n            )\n\n            return (potential_values - torch.log(normalization_constant)).to(\n                self._device\n            )\n\n    @torch.no_grad()\n    def estimate_normalization_constant(\n        self, x: Tensor, num_samples: int = 10_000, force_update: bool = False\n    ) -&gt; Tensor:\n        \"\"\"Returns the normalization constant via importance sampling.\n\n        Args:\n            num_samples: Number of importance samples used for the estimate.\n            force_update: Whether to re-calculate the normlization constant when x is\n                unchanged and have a cached value.\n        \"\"\"\n        # Check if the provided x matches the default x (short-circuit on identity).\n        is_new_x = self.default_x is None or (\n            x is not self.default_x and (x != self.default_x).any()\n        )\n\n        not_saved_at_default_x = self._normalization_constant is None\n\n        if is_new_x:  # Calculate at x; don't save.\n            _, log_importance_weights = importance_sample(\n                self.potential_fn,\n                proposal=self.proposal,\n                num_samples=num_samples,\n            )\n            return torch.mean(torch.exp(log_importance_weights))\n        elif not_saved_at_default_x or force_update:  # Calculate at default_x; save.\n            assert self.default_x is not None\n            _, log_importance_weights = importance_sample(\n                self.potential_fn,\n                proposal=self.proposal,\n                num_samples=num_samples,\n            )\n            self._normalization_constant = torch.mean(torch.exp(log_importance_weights))\n\n        return self._normalization_constant.to(self._device)  # type: ignore\n\n    def sample(\n        self,\n        sample_shape: Shape = torch.Size(),\n        x: Optional[Tensor] = None,\n        oversampling_factor: int = 32,\n        max_sampling_batch_size: int = 10_000,\n        sample_with: Optional[str] = None,\n    ) -&gt; Union[Tensor, Tuple[Tensor, Tensor]]:\n        \"\"\"Return samples from the approximate posterior distribution.\n\n        Args:\n            sample_shape: _description_\n            x: _description_\n        \"\"\"\n        if sample_with is not None:\n            raise ValueError(\n                f\"You set `sample_with={sample_with}`. As of sbi v0.18.0, setting \"\n                f\"`sample_with` is no longer supported. You have to rerun \"\n                f\"`.build_posterior(sample_with={sample_with}).`\"\n            )\n\n        self.potential_fn.set_x(self._x_else_default_x(x))\n\n        if self.method == \"sir\":\n            return self._sir_sample(\n                sample_shape,\n                oversampling_factor=oversampling_factor,\n                max_sampling_batch_size=max_sampling_batch_size,\n            )\n        elif self.method == \"importance\":\n            return self._importance_sample(sample_shape)\n        else:\n            raise NameError\n\n    def _importance_sample(\n        self,\n        sample_shape: Shape = torch.Size(),\n    ) -&gt; Tuple[Tensor, Tensor]:\n        \"\"\"Returns samples from the proposal and log of their importance weights.\n\n        Args:\n            sample_shape: Desired shape of samples that are drawn from posterior.\n            sample_with: This argument only exists to keep backward-compatibility with\n                `sbi` v0.17.2 or older. If it is set, we instantly raise an error.\n\n        Returns:\n            Samples and logarithm of corresponding importance weights.\n        \"\"\"\n        num_samples = torch.Size(sample_shape).numel()\n        samples, log_importance_weights = importance_sample(\n            self.potential_fn,\n            proposal=self.proposal,\n            num_samples=num_samples,\n        )\n\n        samples = samples.reshape((*sample_shape, -1)).to(self._device)\n        return samples, log_importance_weights.to(self._device)\n\n    def _sir_sample(\n        self,\n        sample_shape: Shape = torch.Size(),\n        oversampling_factor: int = 32,\n        max_sampling_batch_size: int = 10_000,\n        show_progress_bars: bool = True,\n    ):\n        r\"\"\"Returns approximate samples from posterior $p(\\theta|x)$ via SIR.\n\n        Args:\n            sample_shape: Desired shape of samples that are drawn from posterior. If\n                sample_shape is multidimensional we simply draw `sample_shape.numel()`\n                samples and then reshape into the desired shape.\n            x: Observed data.\n            sample_with: This argument only exists to keep backward-compatibility with\n                `sbi` v0.17.2 or older. If it is set, we instantly raise an error.\n            oversampling_factor: Number of proposed samples form which only one is\n                selected based on its importance weight.\n            max_sampling_batch_size: The batchsize of samples being drawn from\n                the proposal at every iteration. Used only in `sir_sample()`.\n            show_progress_bars: Whether to show sampling progress monitor.\n\n        Returns:\n            Samples from posterior.\n        \"\"\"\n        # Replace arguments that were not passed with their default.\n        oversampling_factor = (\n            self.oversampling_factor\n            if oversampling_factor is None\n            else oversampling_factor\n        )\n        max_sampling_batch_size = (\n            self.max_sampling_batch_size\n            if max_sampling_batch_size is None\n            else max_sampling_batch_size\n        )\n\n        num_samples = torch.Size(sample_shape).numel()\n        samples = sampling_importance_resampling(\n            self.potential_fn,\n            proposal=self.proposal,\n            num_samples=num_samples,\n            oversampling_factor=oversampling_factor,\n            show_progress_bars=show_progress_bars,\n            max_sampling_batch_size=max_sampling_batch_size,\n            device=self._device,\n        )\n\n        return samples.reshape((*sample_shape, -1)).to(self._device)\n\n    def map(\n        self,\n        x: Optional[Tensor] = None,\n        num_iter: int = 1_000,\n        num_to_optimize: int = 100,\n        learning_rate: float = 0.01,\n        init_method: Union[str, Tensor] = \"proposal\",\n        num_init_samples: int = 1_000,\n        save_best_every: int = 10,\n        show_progress_bars: bool = False,\n        force_update: bool = False,\n    ) -&gt; Tensor:\n        r\"\"\"Returns the maximum-a-posteriori estimate (MAP).\n\n        The method can be interrupted (Ctrl-C) when the user sees that the\n        log-probability converges. The best estimate will be saved in `self._map` and\n        can be accessed with `self.map()`. The MAP is obtained by running gradient\n        ascent from a given number of starting positions (samples from the posterior\n        with the highest log-probability). After the optimization is done, we select the\n        parameter set that has the highest log-probability after the optimization.\n\n        Warning: The default values used by this function are not well-tested. They\n        might require hand-tuning for the problem at hand.\n\n        For developers: if the prior is a `BoxUniform`, we carry out the optimization\n        in unbounded space and transform the result back into bounded space.\n\n        Args:\n            x: Deprecated - use `.set_default_x()` prior to `.map()`.\n            num_iter: Number of optimization steps that the algorithm takes\n                to find the MAP.\n            learning_rate: Learning rate of the optimizer.\n            init_method: How to select the starting parameters for the optimization. If\n                it is a string, it can be either [`posterior`, `prior`], which samples\n                the respective distribution `num_init_samples` times. If it is a\n                tensor, the tensor will be used as init locations.\n            num_init_samples: Draw this number of samples from the posterior and\n                evaluate the log-probability of all of them.\n            num_to_optimize: From the drawn `num_init_samples`, use the\n                `num_to_optimize` with highest log-probability as the initial points\n                for the optimization.\n            save_best_every: The best log-probability is computed, saved in the\n                `map`-attribute, and printed every `save_best_every`-th iteration.\n                Computing the best log-probability creates a significant overhead\n                (thus, the default is `10`.)\n            show_progress_bars: Whether to show a progressbar during sampling from the\n                posterior.\n            force_update: Whether to re-calculate the MAP when x is unchanged and\n                have a cached value.\n            log_prob_kwargs: Will be empty for SNLE and SNRE. Will contain\n                {'norm_posterior': True} for SNPE.\n\n        Returns:\n            The MAP estimate.\n        \"\"\"\n        return super().map(\n            x=x,\n            num_iter=num_iter,\n            num_to_optimize=num_to_optimize,\n            learning_rate=learning_rate,\n            init_method=init_method,\n            num_init_samples=num_init_samples,\n            save_best_every=save_best_every,\n            show_progress_bars=show_progress_bars,\n            force_update=force_update,\n        )\n</code></pre> <p>             Bases: <code>NeuralPosterior</code></p> <p>Provides MCMC to sample from the posterior. SNLE or SNRE train neural networks to approximate the likelihood(-ratios). <code>MCMCPosterior</code> allows to sample from the posterior with MCMC.</p> Source code in <code>sbi/inference/posteriors/mcmc_posterior.py</code> <pre><code>class MCMCPosterior(NeuralPosterior):\n    r\"\"\"Provides MCMC to sample from the posterior.&lt;br/&gt;&lt;br/&gt;\n    SNLE or SNRE train neural networks to approximate the likelihood(-ratios).\n    `MCMCPosterior` allows to sample from the posterior with MCMC.\n    \"\"\"\n\n    def __init__(\n        self,\n        potential_fn: Callable,\n        proposal: Any,\n        theta_transform: Optional[TorchTransform] = None,\n        method: str = \"slice_np\",\n        thin: int = 10,\n        warmup_steps: int = 10,\n        num_chains: int = 1,\n        init_strategy: str = \"resample\",\n        init_strategy_parameters: Dict[str, Any] = {},\n        init_strategy_num_candidates: Optional[int] = None,\n        num_workers: int = 1,\n        device: Optional[str] = None,\n        x_shape: Optional[torch.Size] = None,\n    ):\n        \"\"\"\n        Args:\n            potential_fn: The potential function from which to draw samples.\n            proposal: Proposal distribution that is used to initialize the MCMC chain.\n            theta_transform: Transformation that will be applied during sampling.\n                Allows to perform MCMC in unconstrained space.\n            method: Method used for MCMC sampling, one of `slice_np`,\n                `slice_np_vectorized`, `slice`, `hmc`, `nuts`. `slice_np` is a custom\n                numpy implementation of slice sampling. `slice_np_vectorized` is\n                identical to `slice_np`, but if `num_chains&gt;1`, the chains are\n                vectorized for `slice_np_vectorized` whereas they are run sequentially\n                for `slice_np`. The samplers `hmc`, `nuts` or `slice` sample with Pyro.\n            thin: The thinning factor for the chain.\n            warmup_steps: The initial number of samples to discard.\n            num_chains: The number of chains.\n            init_strategy: The initialisation strategy for chains; `proposal` will draw\n                init locations from `proposal`, whereas `sir` will use Sequential-\n                Importance-Resampling (SIR). SIR initially samples\n                `init_strategy_num_candidates` from the `proposal`, evaluates all of\n                them under the `potential_fn` and `proposal`, and then resamples the\n                initial locations with weights proportional to `exp(potential_fn -\n                proposal.log_prob`. `resample` is the same as `sir` but\n                uses `exp(potential_fn)` as weights.\n            init_strategy_parameters: Dictionary of keyword arguments passed to the\n                init strategy, e.g., for `init_strategy=sir` this could be\n                `num_candidate_samples`, i.e., the number of candidates to to find init\n                locations (internal default is `1000`), or `device`.\n            init_strategy_num_candidates: Number of candidates to to find init\n                 locations in `init_strategy=sir` (deprecated, use\n                 init_strategy_parameters instead).\n            num_workers: number of cpu cores used to parallelize mcmc\n            device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:0\". If None,\n                `potential_fn.device` is used.\n            x_shape: Shape of a single simulator output. If passed, it is used to check\n                the shape of the observed data and give a descriptive error.\n        \"\"\"\n\n        super().__init__(\n            potential_fn,\n            theta_transform=theta_transform,\n            device=device,\n            x_shape=x_shape,\n        )\n\n        self.proposal = proposal\n        self.method = method\n        self.thin = thin\n        self.warmup_steps = warmup_steps\n        self.num_chains = num_chains\n        self.init_strategy = init_strategy\n        self.init_strategy_parameters = init_strategy_parameters\n        self.num_workers = num_workers\n        self._posterior_sampler = None\n        # Hardcode parameter name to reduce clutter kwargs.\n        self.param_name = \"theta\"\n\n        if init_strategy_num_candidates is not None:\n            warn(\n                \"\"\"Passing `init_strategy_num_candidates` is deprecated as of sbi\n                v0.19.0. Instead, use e.g.,\n                `init_strategy_parameters={\"num_candidate_samples\": 1000}`\"\"\"\n            )\n            self.init_strategy_parameters[\n                \"num_candidate_samples\"\n            ] = init_strategy_num_candidates\n\n        self.potential_ = self._prepare_potential(method)\n\n        self._purpose = (\n            \"It provides MCMC to .sample() from the posterior and \"\n            \"can evaluate the _unnormalized_ posterior density with .log_prob().\"\n        )\n\n    @property\n    def mcmc_method(self) -&gt; str:\n        \"\"\"Returns MCMC method.\"\"\"\n        return self._mcmc_method\n\n    @mcmc_method.setter\n    def mcmc_method(self, method: str) -&gt; None:\n        \"\"\"See `set_mcmc_method`.\"\"\"\n        self.set_mcmc_method(method)\n\n    @property\n    def posterior_sampler(self):\n        \"\"\"Returns sampler created by `sample`.\"\"\"\n        return self._posterior_sampler\n\n    def set_mcmc_method(self, method: str) -&gt; \"NeuralPosterior\":\n        \"\"\"Sets sampling method to for MCMC and returns `NeuralPosterior`.\n\n        Args:\n            method: Method to use.\n\n        Returns:\n            `NeuralPosterior` for chainable calls.\n        \"\"\"\n        self._mcmc_method = method\n        return self\n\n    def log_prob(\n        self, theta: Tensor, x: Optional[Tensor] = None, track_gradients: bool = False\n    ) -&gt; Tensor:\n        r\"\"\"Returns the log-probability of theta under the posterior.\n\n        Args:\n            theta: Parameters $\\theta$.\n            track_gradients: Whether the returned tensor supports tracking gradients.\n                This can be helpful for e.g. sensitivity analysis, but increases memory\n                consumption.\n\n        Returns:\n            `len($\\theta$)`-shaped log-probability.\n        \"\"\"\n        warn(\n            \"\"\"`.log_prob()` is deprecated for methods that can only evaluate the\n            log-probability up to a normalizing constant. Use `.potential()` instead.\"\"\"\n        )\n        warn(\"The log-probability is unnormalized!\")\n\n        self.potential_fn.set_x(self._x_else_default_x(x))\n\n        theta = ensure_theta_batched(torch.as_tensor(theta))\n        return self.potential_fn(\n            theta.to(self._device), track_gradients=track_gradients\n        )\n\n    def sample(\n        self,\n        sample_shape: Shape = torch.Size(),\n        x: Optional[Tensor] = None,\n        method: Optional[str] = None,\n        thin: Optional[int] = None,\n        warmup_steps: Optional[int] = None,\n        num_chains: Optional[int] = None,\n        init_strategy: Optional[str] = None,\n        init_strategy_parameters: Optional[Dict[str, Any]] = None,\n        init_strategy_num_candidates: Optional[int] = None,\n        mcmc_parameters: Dict = {},\n        mcmc_method: Optional[str] = None,\n        sample_with: Optional[str] = None,\n        num_workers: Optional[int] = None,\n        show_progress_bars: bool = True,\n    ) -&gt; Tensor:\n        r\"\"\"Return samples from posterior distribution $p(\\theta|x)$ with MCMC.\n\n        Check the `__init__()` method for a description of all arguments as well as\n        their default values.\n\n        Args:\n            sample_shape: Desired shape of samples that are drawn from posterior. If\n                sample_shape is multidimensional we simply draw `sample_shape.numel()`\n                samples and then reshape into the desired shape.\n            mcmc_parameters: Dictionary that is passed only to support the API of\n                `sbi` v0.17.2 or older.\n            mcmc_method: This argument only exists to keep backward-compatibility with\n                `sbi` v0.17.2 or older. Please use `method` instead.\n            sample_with: This argument only exists to keep backward-compatibility with\n                `sbi` v0.17.2 or older. If it is set, we instantly raise an error.\n            show_progress_bars: Whether to show sampling progress monitor.\n\n        Returns:\n            Samples from posterior.\n        \"\"\"\n        self.potential_fn.set_x(self._x_else_default_x(x))\n\n        # Replace arguments that were not passed with their default.\n        method = self.method if method is None else method\n        thin = self.thin if thin is None else thin\n        warmup_steps = self.warmup_steps if warmup_steps is None else warmup_steps\n        num_chains = self.num_chains if num_chains is None else num_chains\n        init_strategy = self.init_strategy if init_strategy is None else init_strategy\n        num_workers = self.num_workers if num_workers is None else num_workers\n        init_strategy_parameters = (\n            self.init_strategy_parameters\n            if init_strategy_parameters is None\n            else init_strategy_parameters\n        )\n        if init_strategy_num_candidates is not None:\n            warn(\n                \"\"\"Passing `init_strategy_num_candidates` is deprecated as of sbi\n                v0.19.0. Instead, use e.g.,\n                `init_strategy_parameters={\"num_candidate_samples\": 1000}`\"\"\"\n            )\n            self.init_strategy_parameters[\n                \"num_candidate_samples\"\n            ] = init_strategy_num_candidates\n        if sample_with is not None:\n            raise ValueError(\n                f\"You set `sample_with={sample_with}`. As of sbi v0.18.0, setting \"\n                f\"`sample_with` is no longer supported. You have to rerun \"\n                f\"`.build_posterior(sample_with={sample_with}).`\"\n            )\n        if mcmc_method is not None:\n            warn(\n                \"You passed `mcmc_method` to `.sample()`. As of sbi v0.18.0, this \"\n                \"is deprecated and will be removed in a future release. Use `method` \"\n                \"instead of `mcmc_method`.\"\n            )\n            method = mcmc_method\n        if mcmc_parameters:\n            warn(\n                \"You passed `mcmc_parameters` to `.sample()`. As of sbi v0.18.0, this \"\n                \"is deprecated and will be removed in a future release. Instead, pass \"\n                \"the variable to `.sample()` directly, e.g. \"\n                \"`posterior.sample((1,), num_chains=5)`.\"\n            )\n        # The following lines are only for backwards compatibility with sbi v0.17.2 or\n        # older.\n        m_p = mcmc_parameters  # define to shorten the variable name\n        method = _maybe_use_dict_entry(method, \"mcmc_method\", m_p)\n        thin = _maybe_use_dict_entry(thin, \"thin\", m_p)\n        warmup_steps = _maybe_use_dict_entry(warmup_steps, \"warmup_steps\", m_p)\n        num_chains = _maybe_use_dict_entry(num_chains, \"num_chains\", m_p)\n        init_strategy = _maybe_use_dict_entry(init_strategy, \"init_strategy\", m_p)\n        self.potential_ = self._prepare_potential(method)  # type: ignore\n\n        initial_params = self._get_initial_params(\n            init_strategy,  # type: ignore\n            num_chains,  # type: ignore\n            num_workers,\n            show_progress_bars,\n            **init_strategy_parameters,\n        )\n        num_samples = torch.Size(sample_shape).numel()\n\n        track_gradients = method in (\"hmc\", \"nuts\")\n        with torch.set_grad_enabled(track_gradients):\n            if method in (\"slice_np\", \"slice_np_vectorized\"):\n                transformed_samples = self._slice_np_mcmc(\n                    num_samples=num_samples,\n                    potential_function=self.potential_,\n                    initial_params=initial_params,\n                    thin=thin,  # type: ignore\n                    warmup_steps=warmup_steps,  # type: ignore\n                    vectorized=(method == \"slice_np_vectorized\"),\n                    num_workers=num_workers,\n                    show_progress_bars=show_progress_bars,\n                )\n            elif method in (\"hmc\", \"nuts\", \"slice\"):\n                transformed_samples = self._pyro_mcmc(\n                    num_samples=num_samples,\n                    potential_function=self.potential_,\n                    initial_params=initial_params,\n                    mcmc_method=method,  # type: ignore\n                    thin=thin,  # type: ignore\n                    warmup_steps=warmup_steps,  # type: ignore\n                    num_chains=num_chains,\n                    show_progress_bars=show_progress_bars,\n                )\n            else:\n                raise NameError\n\n        samples = self.theta_transform.inv(transformed_samples)\n\n        return samples.reshape((*sample_shape, -1))  # type: ignore\n\n    def _build_mcmc_init_fn(\n        self,\n        proposal: Any,\n        potential_fn: Callable,\n        transform: torch_tf.Transform,\n        init_strategy: str,\n        **kwargs,\n    ) -&gt; Callable:\n        \"\"\"Return function that, when called, creates an initial parameter set for MCMC.\n\n        Args:\n            proposal: Proposal distribution.\n            potential_fn: Potential function that the candidate samples are weighted\n                with.\n            init_strategy: Specifies the initialization method. Either of\n                [`proposal`|`sir`|`resample`|`latest_sample`].\n            kwargs: Passed on to init function. This way, init specific keywords can\n                be set through `mcmc_parameters`. Unused arguments will be absorbed by\n                the intitialization method.\n\n        Returns: Initialization function.\n        \"\"\"\n        if init_strategy == \"proposal\" or init_strategy == \"prior\":\n            if init_strategy == \"prior\":\n                warn(\n                    \"You set `init_strategy=prior`. As of sbi v0.18.0, this is \"\n                    \"deprecated and it will be removed in a future release. Use \"\n                    \"`init_strategy=proposal` instead.\"\n                )\n            return lambda: proposal_init(proposal, transform=transform, **kwargs)\n        elif init_strategy == \"sir\":\n            warn(\n                \"As of sbi v0.19.0, the behavior of the SIR initialization for MCMC \"\n                \"has changed. If you wish to restore the behavior of sbi v0.18.0, set \"\n                \"`init_strategy='resample'.`\"\n            )\n            return lambda: sir_init(\n                proposal, potential_fn, transform=transform, **kwargs\n            )\n        elif init_strategy == \"resample\":\n            return lambda: resample_given_potential_fn(\n                proposal, potential_fn, transform=transform, **kwargs\n            )\n        elif init_strategy == \"latest_sample\":\n            latest_sample = IterateParameters(self._mcmc_init_params, **kwargs)\n            return latest_sample\n        else:\n            raise NotImplementedError\n\n    def _get_initial_params(\n        self,\n        init_strategy: str,\n        num_chains: int,\n        num_workers: int,\n        show_progress_bars: bool,\n        **kwargs,\n    ) -&gt; Tensor:\n        \"\"\"Return initial parameters for MCMC obtained with given init strategy.\n\n        Parallelizes across CPU cores only for SIR.\n\n        Args:\n            init_strategy: Specifies the initialization method. Either of\n                [`proposal`|`sir`|`resample`|`latest_sample`].\n            num_chains: number of MCMC chains, generates initial params for each\n            num_workers: number of CPU cores for parallization\n            show_progress_bars: whether to show progress bars for SIR init\n            kwargs: Passed on to `_build_mcmc_init_fn`.\n\n        Returns:\n            Tensor: initial parameters, one for each chain\n        \"\"\"\n        # Build init function\n        init_fn = self._build_mcmc_init_fn(\n            self.proposal,\n            self.potential_fn,\n            transform=self.theta_transform,\n            init_strategy=init_strategy,  # type: ignore\n            **kwargs,\n        )\n\n        # Parallelize inits for resampling only.\n        if num_workers &gt; 1 and (init_strategy == \"resample\" or init_strategy == \"sir\"):\n\n            def seeded_init_fn(seed):\n                torch.manual_seed(seed)\n                return init_fn()\n\n            seeds = torch.randint(high=2**31, size=(num_chains,))\n\n            # Generate initial params parallelized over num_workers.\n            with tqdm_joblib(\n                tqdm(\n                    range(num_chains),  # type: ignore\n                    disable=not show_progress_bars,\n                    desc=f\"\"\"Generating {num_chains} MCMC inits with {num_workers}\n                         workers.\"\"\",\n                    total=num_chains,\n                )\n            ):\n                initial_params = torch.cat(\n                    Parallel(n_jobs=num_workers)(\n                        delayed(seeded_init_fn)(seed) for seed in seeds\n                    )\n                )\n        else:\n            initial_params = torch.cat(\n                [init_fn() for _ in range(num_chains)]  # type: ignore\n            )\n\n        return initial_params\n\n    def _slice_np_mcmc(\n        self,\n        num_samples: int,\n        potential_function: Callable,\n        initial_params: Tensor,\n        thin: int,\n        warmup_steps: int,\n        vectorized: bool = False,\n        num_workers: int = 1,\n        init_width: Union[float, ndarray] = 0.01,\n        show_progress_bars: bool = True,\n    ) -&gt; Tensor:\n        \"\"\"Custom implementation of slice sampling using Numpy.\n\n        Args:\n            num_samples: Desired number of samples.\n            potential_function: A callable **class**.\n            initial_params: Initial parameters for MCMC chain.\n            thin: Thinning (subsampling) factor.\n            warmup_steps: Initial number of samples to discard.\n            vectorized: Whether to use a vectorized implementation of the Slice sampler.\n            num_workers: Number of CPU cores to use.\n            init_width: Inital width of brackets.\n            show_progress_bars: Whether to show a progressbar during sampling;\n                can only be turned off for vectorized sampler.\n\n        Returns:\n            Tensor of shape (num_samples, shape_of_single_theta).\n        \"\"\"\n\n        num_chains, dim_samples = initial_params.shape\n\n        if not vectorized:\n            SliceSamplerMultiChain = SliceSamplerSerial\n        else:\n            SliceSamplerMultiChain = SliceSamplerVectorized\n\n        posterior_sampler = SliceSamplerMultiChain(\n            init_params=tensor2numpy(initial_params),\n            log_prob_fn=potential_function,\n            num_chains=num_chains,\n            thin=thin,\n            verbose=show_progress_bars,\n            num_workers=num_workers,\n            init_width=init_width,\n        )\n        warmup_ = warmup_steps * thin\n        num_samples_ = ceil((num_samples * thin) / num_chains)\n        # Run mcmc including warmup\n        samples = posterior_sampler.run(warmup_ + num_samples_)\n        samples = samples[:, warmup_steps:, :]  # discard warmup steps\n        samples = torch.from_numpy(samples)  # chains x samples x dim\n\n        # Save posterior sampler.\n        self._posterior_sampler = posterior_sampler\n\n        # Save sample as potential next init (if init_strategy == 'latest_sample').\n        self._mcmc_init_params = samples[:, -1, :].reshape(num_chains, dim_samples)\n\n        # Collect samples from all chains.\n        samples = samples.reshape(-1, dim_samples)[:num_samples, :]\n        assert samples.shape[0] == num_samples\n\n        return samples.type(torch.float32).to(self._device)\n\n    def _pyro_mcmc(\n        self,\n        num_samples: int,\n        potential_function: Callable,\n        initial_params: Tensor,\n        mcmc_method: str = \"slice\",\n        thin: int = 10,\n        warmup_steps: int = 200,\n        num_chains: Optional[int] = 1,\n        show_progress_bars: bool = True,\n    ) -&gt; Tensor:\n        r\"\"\"Return samples obtained using Pyro HMC, NUTS for slice kernels.\n\n        Args:\n            num_samples: Desired number of samples.\n            potential_function: A callable **class**. A class, but not a function,\n                is picklable for Pyro MCMC to use it across chains in parallel,\n                even when the potential function requires evaluating a neural network.\n            mcmc_method: One of `hmc`, `nuts` or `slice`.\n            thin: Thinning (subsampling) factor.\n            warmup_steps: Initial number of samples to discard.\n            num_chains: Whether to sample in parallel. If None, use all but one CPU.\n            show_progress_bars: Whether to show a progressbar during sampling.\n\n        Returns:\n            Tensor of shape (num_samples, shape_of_single_theta).\n        \"\"\"\n        num_chains = mp.cpu_count() - 1 if num_chains is None else num_chains\n\n        kernels = dict(slice=Slice, hmc=HMC, nuts=NUTS)\n\n        sampler = MCMC(\n            kernel=kernels[mcmc_method](potential_fn=potential_function),\n            num_samples=(thin * num_samples) // num_chains + num_chains,\n            warmup_steps=warmup_steps,\n            initial_params={self.param_name: initial_params},\n            num_chains=num_chains,\n            mp_context=\"spawn\",\n            disable_progbar=not show_progress_bars,\n            transforms={},\n        )\n        sampler.run()\n        samples = next(iter(sampler.get_samples().values())).reshape(\n            -1, initial_params.shape[1]  # .shape[1] = dim of theta\n        )\n\n        # Save posterior sampler.\n        self._posterior_sampler = sampler\n\n        samples = samples[::thin][:num_samples]\n        assert samples.shape[0] == num_samples\n\n        return samples.detach()\n\n    def _prepare_potential(self, method: str) -&gt; Callable:\n        \"\"\"Combines potential and transform and takes care of gradients and pyro.\n\n        Args:\n            method: Which MCMC method to use.\n\n        Returns:\n            A potential function that is ready to be used in MCMC.\n        \"\"\"\n        if method == \"slice\":\n            track_gradients = False\n            pyro = True\n        elif method in (\"hmc\", \"nuts\"):\n            track_gradients = True\n            pyro = True\n        elif \"slice_np\" in method:\n            track_gradients = False\n            pyro = False\n        else:\n            raise NotImplementedError\n\n        prepared_potential = partial(\n            transformed_potential,\n            potential_fn=self.potential_fn,\n            theta_transform=self.theta_transform,\n            device=self._device,\n            track_gradients=track_gradients,\n        )\n        if pyro:\n            prepared_potential = partial(\n                pyro_potential_wrapper, potential=prepared_potential\n            )\n\n        return prepared_potential\n\n    def map(\n        self,\n        x: Optional[Tensor] = None,\n        num_iter: int = 1_000,\n        num_to_optimize: int = 100,\n        learning_rate: float = 0.01,\n        init_method: Union[str, Tensor] = \"proposal\",\n        num_init_samples: int = 1_000,\n        save_best_every: int = 10,\n        show_progress_bars: bool = False,\n        force_update: bool = False,\n    ) -&gt; Tensor:\n        r\"\"\"Returns the maximum-a-posteriori estimate (MAP).\n\n        The method can be interrupted (Ctrl-C) when the user sees that the\n        log-probability converges. The best estimate will be saved in `self._map` and\n        can be accessed with `self.map()`. The MAP is obtained by running gradient\n        ascent from a given number of starting positions (samples from the posterior\n        with the highest log-probability). After the optimization is done, we select the\n        parameter set that has the highest log-probability after the optimization.\n\n        Warning: The default values used by this function are not well-tested. They\n        might require hand-tuning for the problem at hand.\n\n        For developers: if the prior is a `BoxUniform`, we carry out the optimization\n        in unbounded space and transform the result back into bounded space.\n\n        Args:\n            x: Deprecated - use `.set_default_x()` prior to `.map()`.\n            num_iter: Number of optimization steps that the algorithm takes\n                to find the MAP.\n            learning_rate: Learning rate of the optimizer.\n            init_method: How to select the starting parameters for the optimization. If\n                it is a string, it can be either [`posterior`, `prior`], which samples\n                the respective distribution `num_init_samples` times. If it is a\n                tensor, the tensor will be used as init locations.\n            num_init_samples: Draw this number of samples from the posterior and\n                evaluate the log-probability of all of them.\n            num_to_optimize: From the drawn `num_init_samples`, use the\n                `num_to_optimize` with highest log-probability as the initial points\n                for the optimization.\n            save_best_every: The best log-probability is computed, saved in the\n                `map`-attribute, and printed every `save_best_every`-th iteration.\n                Computing the best log-probability creates a significant overhead\n                (thus, the default is `10`.)\n            show_progress_bars: Whether to show a progressbar during sampling from\n                the posterior.\n            force_update: Whether to re-calculate the MAP when x is unchanged and\n                have a cached value.\n            log_prob_kwargs: Will be empty for SNLE and SNRE. Will contain\n                {'norm_posterior': True} for SNPE.\n\n        Returns:\n            The MAP estimate.\n        \"\"\"\n        return super().map(\n            x=x,\n            num_iter=num_iter,\n            num_to_optimize=num_to_optimize,\n            learning_rate=learning_rate,\n            init_method=init_method,\n            num_init_samples=num_init_samples,\n            save_best_every=save_best_every,\n            show_progress_bars=show_progress_bars,\n            force_update=force_update,\n        )\n\n    def get_arviz_inference_data(self) -&gt; InferenceData:\n        \"\"\"Returns arviz InferenceData object constructed most recent samples.\n\n        Note: the InferenceData is constructed using the posterior samples generated in\n        most recent call to `.sample(...)`.\n\n        For Pyro HMC and NUTS kernels InferenceData will contain diagnostics, for Pyro\n        Slice or sbi slice sampling samples, only the samples are added.\n\n        Returns:\n            inference_data: Arviz InferenceData object.\n        \"\"\"\n        assert (\n            self._posterior_sampler is not None\n        ), \"\"\"No samples have been generated, call .sample() first.\"\"\"\n\n        sampler: Union[\n            MCMC, SliceSamplerSerial, SliceSamplerVectorized\n        ] = self._posterior_sampler\n\n        # If Pyro sampler and samples not transformed, use arviz' from_pyro.\n        # Exclude 'slice' kernel as it lacks the 'divergence' diagnostics key.\n        if isinstance(self._posterior_sampler, (HMC, NUTS)) and isinstance(\n            self.theta_transform, torch_tf.IndependentTransform\n        ):\n            inference_data = az.from_pyro(sampler)\n\n        # otherwise get samples from sampler and transform to original space.\n        else:\n            transformed_samples = sampler.get_samples(group_by_chain=True)\n            # Pyro samplers returns dicts, get values.\n            if isinstance(transformed_samples, Dict):\n                # popitem gets last items, [1] get the values as tensor.\n                transformed_samples = transformed_samples.popitem()[1]\n            # Our slice samplers return numpy arrays.\n            elif isinstance(transformed_samples, ndarray):\n                transformed_samples = torch.from_numpy(transformed_samples).type(\n                    torch.float32\n                )\n            # For MultipleIndependent priors transforms first dim must be batch dim.\n            # thus, reshape back and forth to have batch dim in front.\n            samples_shape = transformed_samples.shape\n            samples = self.theta_transform.inv(  # type: ignore\n                transformed_samples.reshape(-1, samples_shape[-1])\n            ).reshape(  # type: ignore\n                *samples_shape\n            )\n\n            inference_data = az.convert_to_inference_data(\n                {f\"{self.param_name}\": samples}\n            )\n\n        return inference_data\n</code></pre> <p>             Bases: <code>NeuralPosterior</code></p> <p>Provides rejection sampling to sample from the posterior. SNLE or SNRE train neural networks to approximate the likelihood(-ratios). <code>RejectionPosterior</code> allows to sample from the posterior with rejection sampling.</p> Source code in <code>sbi/inference/posteriors/rejection_posterior.py</code> <pre><code>class RejectionPosterior(NeuralPosterior):\n    r\"\"\"Provides rejection sampling to sample from the posterior.&lt;br/&gt;&lt;br/&gt;\n    SNLE or SNRE train neural networks to approximate the likelihood(-ratios).\n    `RejectionPosterior` allows to sample from the posterior with rejection sampling.\n    \"\"\"\n\n    def __init__(\n        self,\n        potential_fn: Callable,\n        proposal: Any,\n        theta_transform: Optional[TorchTransform] = None,\n        max_sampling_batch_size: int = 10_000,\n        num_samples_to_find_max: int = 10_000,\n        num_iter_to_find_max: int = 100,\n        m: float = 1.2,\n        device: Optional[str] = None,\n        x_shape: Optional[torch.Size] = None,\n    ):\n        \"\"\"\n        Args:\n            potential_fn: The potential function from which to draw samples.\n            proposal: The proposal distribution.\n            theta_transform: Transformation that is applied to parameters. Is not used\n                during but only when calling `.map()`.\n            max_sampling_batch_size: The batchsize of samples being drawn from\n                the proposal at every iteration.\n            num_samples_to_find_max: The number of samples that are used to find the\n                maximum of the `potential_fn / proposal` ratio.\n            num_iter_to_find_max: The number of gradient ascent iterations to find the\n                maximum of the `potential_fn / proposal` ratio.\n            m: Multiplier to the `potential_fn / proposal` ratio.\n            device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:0\". If None,\n                `potential_fn.device` is used.\n            x_shape: Shape of a single simulator output. If passed, it is used to check\n                the shape of the observed data and give a descriptive error.\n        \"\"\"\n        super().__init__(\n            potential_fn,\n            theta_transform=theta_transform,\n            device=device,\n            x_shape=x_shape,\n        )\n\n        self.proposal = proposal\n        self.max_sampling_batch_size = max_sampling_batch_size\n        self.num_samples_to_find_max = num_samples_to_find_max\n        self.num_iter_to_find_max = num_iter_to_find_max\n        self.m = m\n\n        self._purpose = (\n            \"It provides rejection sampling to .sample() from the posterior and \"\n            \"can evaluate the _unnormalized_ posterior density with .log_prob().\"\n        )\n\n    def log_prob(\n        self, theta: Tensor, x: Optional[Tensor] = None, track_gradients: bool = False\n    ) -&gt; Tensor:\n        r\"\"\"Returns the log-probability of theta under the posterior.\n\n        Args:\n            theta: Parameters $\\theta$.\n            track_gradients: Whether the returned tensor supports tracking gradients.\n                This can be helpful for e.g. sensitivity analysis, but increases memory\n                consumption.\n\n        Returns:\n            `len($\\theta$)`-shaped log-probability.\n        \"\"\"\n        warn(\n            \"`.log_prob()` is deprecated for methods that can only evaluate the log-probability up to a normalizing constant. Use `.potential()` instead.\"\n        )\n        warn(\"The log-probability is unnormalized!\")\n\n        self.potential_fn.set_x(self._x_else_default_x(x))\n\n        theta = ensure_theta_batched(torch.as_tensor(theta))\n        return self.potential_fn(\n            theta.to(self._device), track_gradients=track_gradients\n        )\n\n    def sample(\n        self,\n        sample_shape: Shape = torch.Size(),\n        x: Optional[Tensor] = None,\n        max_sampling_batch_size: Optional[int] = None,\n        num_samples_to_find_max: Optional[int] = None,\n        num_iter_to_find_max: Optional[int] = None,\n        m: Optional[float] = None,\n        sample_with: Optional[str] = None,\n        show_progress_bars: bool = True,\n    ):\n        r\"\"\"Return samples from posterior $p(\\theta|x)$ via rejection sampling.\n\n        Args:\n            sample_shape: Desired shape of samples that are drawn from posterior. If\n                sample_shape is multidimensional we simply draw `sample_shape.numel()`\n                samples and then reshape into the desired shape.\n            sample_with: This argument only exists to keep backward-compatibility with\n                `sbi` v0.17.2 or older. If it is set, we instantly raise an error.\n            show_progress_bars: Whether to show sampling progress monitor.\n\n        Returns:\n            Samples from posterior.\n        \"\"\"\n        num_samples = torch.Size(sample_shape).numel()\n        self.potential_fn.set_x(self._x_else_default_x(x))\n\n        potential = partial(self.potential_fn, track_gradients=True)\n\n        if sample_with is not None:\n            raise ValueError(\n                f\"You set `sample_with={sample_with}`. As of sbi v0.18.0, setting \"\n                f\"`sample_with` is no longer supported. You have to rerun \"\n                f\"`.build_posterior(sample_with={sample_with}).`\"\n            )\n        # Replace arguments that were not passed with their default.\n        max_sampling_batch_size = (\n            self.max_sampling_batch_size\n            if max_sampling_batch_size is None\n            else max_sampling_batch_size\n        )\n        num_samples_to_find_max = (\n            self.num_samples_to_find_max\n            if num_samples_to_find_max is None\n            else num_samples_to_find_max\n        )\n        num_iter_to_find_max = (\n            self.num_iter_to_find_max\n            if num_iter_to_find_max is None\n            else num_iter_to_find_max\n        )\n        m = self.m if m is None else m\n\n        samples, _ = rejection_sample(\n            potential,\n            proposal=self.proposal,\n            num_samples=num_samples,\n            show_progress_bars=show_progress_bars,\n            warn_acceptance=0.01,\n            max_sampling_batch_size=max_sampling_batch_size,\n            num_samples_to_find_max=num_samples_to_find_max,\n            num_iter_to_find_max=num_iter_to_find_max,\n            m=m,\n            device=self._device,\n        )\n\n        return samples.reshape((*sample_shape, -1))\n\n    def map(\n        self,\n        x: Optional[Tensor] = None,\n        num_iter: int = 1_000,\n        num_to_optimize: int = 100,\n        learning_rate: float = 0.01,\n        init_method: Union[str, Tensor] = \"proposal\",\n        num_init_samples: int = 1_000,\n        save_best_every: int = 10,\n        show_progress_bars: bool = False,\n        force_update: bool = False,\n    ) -&gt; Tensor:\n        r\"\"\"Returns the maximum-a-posteriori estimate (MAP).\n\n        The method can be interrupted (Ctrl-C) when the user sees that the\n        log-probability converges. The best estimate will be saved in `self._map` and\n        can be accessed with `self.map()`. The MAP is obtained by running gradient\n        ascent from a given number of starting positions (samples from the posterior\n        with the highest log-probability). After the optimization is done, we select the\n        parameter set that has the highest log-probability after the optimization.\n\n        Warning: The default values used by this function are not well-tested. They\n        might require hand-tuning for the problem at hand.\n\n        For developers: if the prior is a `BoxUniform`, we carry out the optimization\n        in unbounded space and transform the result back into bounded space.\n\n        Args:\n            x: Deprecated - use `.set_default_x()` prior to `.map()`.\n            num_iter: Number of optimization steps that the algorithm takes\n                to find the MAP.\n            learning_rate: Learning rate of the optimizer.\n            init_method: How to select the starting parameters for the optimization. If\n                it is a string, it can be either [`posterior`, `prior`], which samples\n                the respective distribution `num_init_samples` times. If it is a\n                tensor, the tensor will be used as init locations.\n            num_init_samples: Draw this number of samples from the posterior and\n                evaluate the log-probability of all of them.\n            num_to_optimize: From the drawn `num_init_samples`, use the\n                `num_to_optimize` with highest log-probability as the initial points\n                for the optimization.\n            save_best_every: The best log-probability is computed, saved in the\n                `map`-attribute, and printed every `save_best_every`-th iteration.\n                Computing the best log-probability creates a significant overhead\n                (thus, the default is `10`.)\n            show_progress_bars: Whether to show a progressbar during sampling from\n                the posterior.\n            force_update: Whether to re-calculate the MAP when x is unchanged and\n                have a cached value.\n            log_prob_kwargs: Will be empty for SNLE and SNRE. Will contain\n                {'norm_posterior': True} for SNPE.\n\n        Returns:\n            The MAP estimate.\n        \"\"\"\n        return super().map(\n            x=x,\n            num_iter=num_iter,\n            num_to_optimize=num_to_optimize,\n            learning_rate=learning_rate,\n            init_method=init_method,\n            num_init_samples=num_init_samples,\n            save_best_every=save_best_every,\n            show_progress_bars=show_progress_bars,\n            force_update=force_update,\n        )\n</code></pre> <p>             Bases: <code>NeuralPosterior</code></p> <p>Provides VI (Variational Inference) to sample from the posterior. SNLE or SNRE train neural networks to approximate the likelihood(-ratios). <code>VIPosterior</code> allows to learn a tractable variational posterior \\(q(\\theta)\\) which approximates the true posterior \\(p(\\theta|x_o)\\). After this second training stage, we can produce approximate posterior samples, by just sampling from q with no additional cost. For additional information see [1] and [2]. References: [1] Variational methods for simulation-based inference, Manuel Gl\u00f6ckler, Michael Deistler, Jakob Macke, 2022, https://openreview.net/forum?id=kZ0UYdhqkNY [2] Sequential Neural Posterior and Likelihood Approximation, Samuel Wiqvist, Jes Frellsen, Umberto Picchini, 2021, https://arxiv.org/abs/2102.06522</p> Source code in <code>sbi/inference/posteriors/vi_posterior.py</code> <pre><code>class VIPosterior(NeuralPosterior):\n    r\"\"\"Provides VI (Variational Inference) to sample from the posterior.&lt;br/&gt;&lt;br/&gt;\n    SNLE or SNRE train neural networks to approximate the likelihood(-ratios).\n    `VIPosterior` allows to learn a tractable variational posterior $q(\\theta)$ which\n    approximates the true posterior $p(\\theta|x_o)$. After this second training stage,\n    we can produce approximate posterior samples, by just sampling from q with no\n    additional cost. For additional information see [1] and [2].&lt;br/&gt;&lt;br/&gt;\n    References:&lt;br/&gt;\n    [1] Variational methods for simulation-based inference, Manuel Gl\u00f6ckler, Michael\n    Deistler, Jakob Macke, 2022, https://openreview.net/forum?id=kZ0UYdhqkNY&lt;br/&gt;\n    [2] Sequential Neural Posterior and Likelihood Approximation, Samuel Wiqvist, Jes\n    Frellsen, Umberto Picchini, 2021, https://arxiv.org/abs/2102.06522\n    \"\"\"\n\n    def __init__(\n        self,\n        potential_fn: Callable,\n        prior: Optional[TorchDistribution] = None,\n        q: Union[str, PyroTransformedDistribution, \"VIPosterior\", Callable] = \"maf\",\n        theta_transform: Optional[TorchTransform] = None,\n        vi_method: str = \"rKL\",\n        device: str = \"cpu\",\n        x_shape: Optional[torch.Size] = None,\n        parameters: Iterable = [],\n        modules: Iterable = [],\n    ):\n        \"\"\"\n        Args:\n            potential_fn: The potential function from which to draw samples.\n            prior: This is the prior distribution. Note that this is only\n                used to check/construct the variational distribution or within some\n                quality metrics. Please make sure that this matches with the prior\n                within the potential_fn. If `None` is given, we will try to infer it\n                from potential_fn or q, if this fails we raise an Error.\n            q: Variational distribution, either string, `TransformedDistribution`, or a\n                `VIPosterior` object. This specifies a parametric class of distribution\n                over which the best possible posterior approximation is searched. For\n                string input, we currently support [nsf, scf, maf, mcf, gaussian,\n                gaussian_diag]. You can also specify your own variational family by\n                passing a pyro `TransformedDistribution`.\n                Additionally, we allow a `Callable`, which allows you the pass a\n                `builder` function, which if called returns a distribution. This may be\n                useful for setting the hyperparameters e.g. `num_transfroms` within the\n                `get_flow_builder` method specifying the number of transformations\n                within a normalizing flow. If q is already a `VIPosterior`, then the\n                arguments will be copied from it (relevant for multi-round training).\n            theta_transform: Maps form prior support to unconstrained space. The\n                inverse is used here to ensure that the posterior support is equal to\n                that of the prior.\n            vi_method: This specifies the variational methods which are used to fit q to\n                the posterior. We currently support [rKL, fKL, IW, alpha]. Note that\n                some of the divergences are `mode seeking` i.e. they underestimate\n                variance and collapse on multimodal targets (`rKL`, `alpha` for alpha &gt;\n                1) and some are `mass covering` i.e. they overestimate variance but\n                typically cover all modes (`fKL`, `IW`, `alpha` for alpha &lt; 1).\n            device: Training device, e.g., `cpu`, `cuda` or `cuda:0`. We will ensure\n                that all other objects are also on this device.\n            x_shape: Shape of a single simulator output. If passed, it is used to check\n                the shape of the observed data and give a descriptive error.\n            parameters: List of parameters of the variational posterior. This is only\n                required for user-defined q i.e. if q does not have a `parameters`\n                attribute.\n            modules: List of modules of the variational posterior. This is only\n                required for user-defined q i.e. if q does not have a `modules`\n                attribute.\n        \"\"\"\n        super().__init__(potential_fn, theta_transform, device, x_shape=x_shape)\n\n        # Especially the prior may be on another device -&gt; move it...\n        self._device = device\n        self.potential_fn.device = device\n        move_all_tensor_to_device(self.potential_fn, device)\n\n        # Get prior and previous builds\n        if prior is not None:\n            self._prior = prior\n        elif hasattr(self.potential_fn, \"prior\") and isinstance(\n            self.potential_fn.prior, Distribution\n        ):\n            self._prior = self.potential_fn.prior\n        elif isinstance(q, VIPosterior) and isinstance(q._prior, Distribution):\n            self._prior = q._prior\n        else:\n            raise ValueError(\n                \"We could not find a suitable prior distribution within `potential_fn`\"\n                \"or `q` (if a VIPosterior is given). Please explicitly specify a prior.\"\n            )\n        move_all_tensor_to_device(self._prior, device)\n        self._optimizer = None\n\n        # In contrast to MCMC we want to project into constrained space.\n        if theta_transform is None:\n            self.link_transform = mcmc_transform(self._prior).inv\n        else:\n            self.link_transform = theta_transform.inv\n\n        # This will set the variational distribution and VI method\n        self.set_q(q, parameters=parameters, modules=modules)\n        self.set_vi_method(vi_method)\n\n        self._purpose = (\n            \"It provides Variational inference to .sample() from the posterior and \"\n            \"can evaluate the _normalized_ posterior density with .log_prob().\"\n        )\n\n    @property\n    def q(self) -&gt; Distribution:\n        \"\"\"Returns the variational posterior.\"\"\"\n        return self._q\n\n    @q.setter\n    def q(\n        self,\n        q: Union[str, Distribution, \"VIPosterior\", Callable],\n    ) -&gt; None:\n        \"\"\"Sets the variational distribution. If the distribution does not admit access\n        through `parameters` and `modules` function, please use `set_q` if you want to\n        explicitly specify the parameters and modules.\n\n\n        Args:\n            q: Variational distribution, either string, distribution, or a VIPosterior\n                object. This specifies a parametric class of distribution over which\n                the best possible posterior approximation is searched. For string input,\n                we currently support [nsf, scf, maf, mcf, gaussian, gaussian_diag]. Of\n                course, you can also specify your own variational family by passing a\n                `parameterized` distribution object i.e. a torch.distributions\n                Distribution with methods `parameters` returning an iterable of all\n                parameters (you can pass them within the paramters/modules attribute).\n                Additionally, we allow a `Callable`, which allows you the pass a\n                `builder` function, which if called returns an distribution. This may be\n                useful for setting the hyperparameters e.g. `num_transfroms:int` by\n                using the `get_flow_builder` method specifying the hyperparameters. If q\n                is already a `VIPosterior`, then the arguments will be copied from it\n                (relevant for multi-round training).\n\n\n        \"\"\"\n        self.set_q(q)\n\n    def set_q(\n        self,\n        q: Union[str, PyroTransformedDistribution, \"VIPosterior\", Callable],\n        parameters: Iterable = [],\n        modules: Iterable = [],\n    ) -&gt; None:\n        \"\"\"Defines the variational family.\n\n        You can specify over which parameters/modules we optimize. This is required for\n        custom distributions which e.g. do not inherit nn.Modules or has the function\n        `parameters` or `modules` to give direct access to trainable parameters.\n        Further, you can pass a function, which constructs a variational distribution\n        if called.\n\n        Args:\n            q: Variational distribution, either string, distribution, or a VIPosterior\n                object. This specifies a parametric class of distribution over which\n                the best possible posterior approximation is searched. For string input,\n                we currently support [nsf, scf, maf, mcf, gaussian, gaussian_diag]. Of\n                course, you can also specify your own variational family by passing a\n                `parameterized` distribution object i.e. a torch.distributions\n                Distribution with methods `parameters` returning an iterable of all\n                parameters (you can pass them within the paramters/modules attribute).\n                Additionally, we allow a `Callable`, which allows you the pass a\n                `builder` function, which if called returns an distribution. This may be\n                useful for setting the hyperparameters e.g. `num_transfroms:int` by\n                using the `get_flow_builder` method specifying the hyperparameters. If q\n                is already a `VIPosterior`, then the arguments will be copied from it\n                (relevant for multi-round training).\n            parameters: List of parameters associated with the distribution object.\n            modules: List of modules associated with the distribution object.\n\n        \"\"\"\n        self._q_arg = q\n        if isinstance(q, Distribution):\n            q = adapt_variational_distribution(\n                q,\n                self._prior,\n                self.link_transform,\n                parameters=parameters,\n                modules=modules,\n            )\n            make_object_deepcopy_compatible(q)\n            self_custom_q_init_cache = deepcopy(q)\n            self._q_build_fn = lambda *args, **kwargs: self_custom_q_init_cache\n            self._trained_on = None\n        elif isinstance(q, str) or isinstance(q, Callable):\n            if isinstance(q, str):\n                self._q_build_fn = get_flow_builder(q)\n            else:\n                self._q_build_fn = q\n\n            q = self._q_build_fn(\n                self._prior.event_shape,\n                self.link_transform,\n                device=self._device,\n            )\n            make_object_deepcopy_compatible(q)\n            self._trained_on = None\n        elif isinstance(q, VIPosterior):\n            self._q_build_fn = q._q_build_fn\n            self._trained_on = q._trained_on\n            self.vi_method = q.vi_method  # type: ignore\n            self._device = q._device\n            self._prior = q._prior\n            self._x = q._x\n            self._q_arg = q._q_arg\n            make_object_deepcopy_compatible(q.q)\n            q = deepcopy(q.q)\n        move_all_tensor_to_device(q, self._device)\n        assert isinstance(\n            q, Distribution\n        ), \"\"\"Something went wrong when initializing the variational distribution.\n            Please create an issue on github https://github.com/mackelab/sbi/issues\"\"\"\n        check_variational_distribution(q, self._prior)\n        self._q = q\n\n    @property\n    def vi_method(self) -&gt; str:\n        \"\"\"Variational inference method e.g. one of [rKL, fKL, IW, alpha].\"\"\"\n        return self._vi_method\n\n    @vi_method.setter\n    def vi_method(self, method: str) -&gt; None:\n        \"\"\"See `set_vi_method`.\"\"\"\n        self.set_vi_method(method)\n\n    def set_vi_method(self, method: str) -&gt; \"VIPosterior\":\n        \"\"\"Sets variational inference method.\n\n        Args:\n            method: One of [rKL, fKL, IW, alpha].\n\n        Returns:\n            `VIPosterior` for chainable calls.\n        \"\"\"\n        self._vi_method = method\n        self._optimizer_builder = get_VI_method(method)\n        return self\n\n    def sample(\n        self,\n        sample_shape: Shape = torch.Size(),\n        x: Optional[Tensor] = None,\n        **kwargs,\n    ) -&gt; Tensor:\n        \"\"\"Samples from the variational posterior distribution.\n\n        Args:\n            sample_shape: Shape of samples\n\n        Returns:\n            Samples from posterior.\n        \"\"\"\n        x = self._x_else_default_x(x)\n        if self._trained_on is None or (x != self._trained_on).all():\n            raise AttributeError(\n                f\"The variational posterior was not fit on the specified `default_x` \"\n                f\"{x}. Please train using `posterior.train()`.\"\n            )\n        samples = self.q.sample(torch.Size(sample_shape))\n        return samples.reshape((*sample_shape, samples.shape[-1]))\n\n    def log_prob(\n        self,\n        theta: Tensor,\n        x: Optional[Tensor] = None,\n        track_gradients: bool = False,\n    ) -&gt; Tensor:\n        r\"\"\"Returns the log-probability of theta under the variational posterior.\n\n        Args:\n            theta: Parameters\n            track_gradients: Whether the returned tensor supports tracking gradients.\n                This can be helpful for e.g. sensitivity analysis but increases memory\n                consumption.\n\n        Returns:\n            `len($\\theta$)`-shaped log-probability.\n        \"\"\"\n        x = self._x_else_default_x(x)\n        if self._trained_on is None or (x != self._trained_on).all():\n            raise AttributeError(\n                f\"The variational posterior was not fit using observation {x}.\\\n                     Please train.\"\n            )\n        with torch.set_grad_enabled(track_gradients):\n            theta = ensure_theta_batched(torch.as_tensor(theta))\n            return self.q.log_prob(theta)\n\n    def train(\n        self,\n        x: Optional[TorchTensor] = None,\n        n_particles: int = 256,\n        learning_rate: float = 1e-3,\n        gamma: float = 0.999,\n        max_num_iters: int = 2000,\n        min_num_iters: int = 10,\n        clip_value: float = 10.0,\n        warm_up_rounds: int = 100,\n        retrain_from_scratch: bool = False,\n        reset_optimizer: bool = False,\n        show_progress_bar: bool = True,\n        check_for_convergence: bool = True,\n        quality_control: bool = True,\n        quality_control_metric: str = \"psis\",\n        **kwargs,\n    ) -&gt; \"VIPosterior\":\n        \"\"\"This method trains the variational posterior.\n\n        Args:\n            x: The observation.\n            n_particles: Number of samples to approximate expectations within the\n                variational bounds. The larger the more accurate are gradient\n                estimates, but the computational cost per iteration increases.\n            learning_rate: Learning rate of the optimizer.\n            gamma: Learning rate decay per iteration. We use an exponential decay\n                scheduler.\n            max_num_iters: Maximum number of iterations.\n            min_num_iters: Minimum number of iterations.\n            clip_value: Gradient clipping value, decreasing may help if you see invalid\n                values.\n            warm_up_rounds: Initialize the posterior as the prior.\n            retrain_from_scratch: Retrain the variational distributions from scratch.\n            reset_optimizer: Reset the divergence optimizer\n            show_progress_bar: If any progress report should be displayed.\n            quality_control: If False quality control is skipped.\n            quality_control_metric: Which metric to use for evaluating the quality.\n            kwargs: Hyperparameters check corresponding `DivergenceOptimizer` for detail\n                eps: Determines sensitivity of convergence check.\n                retain_graph: Boolean which decides whether to retain the computation\n                    graph. This may be required for some `exotic` user-specified q's.\n                optimizer: A PyTorch Optimizer class e.g. Adam or SGD. See\n                    `DivergenceOptimizer` for details.\n                scheduler: A PyTorch learning rate scheduler. See\n                    `DivergenceOptimizer` for details.\n                alpha: Only used if vi_method=`alpha`. Determines the alpha divergence.\n                K: Only used if vi_method=`IW`. Determines the number of importance\n                    weighted particles.\n                stick_the_landing: If one should use the STL estimator (only for rKL,\n                    IW, alpha).\n                dreg: If one should use the DREG estimator (only for rKL, IW, alpha).\n                weight_transform: Callable applied to importance weights (only for fKL)\n        Returns:\n            VIPosterior: `VIPosterior` (can be used to chain calls).\n        \"\"\"\n        # Update optimizer with current arguments.\n        if self._optimizer is not None:\n            self._optimizer.update({**locals(), **kwargs})\n\n        # Init q and the optimizer if necessary\n        if retrain_from_scratch:\n            self.q = self._q_build_fn()  # type: ignore\n            self._optimizer = self._optimizer_builder(\n                self.potential_fn,\n                self.q,\n                lr=learning_rate,\n                clip_value=clip_value,\n                gamma=gamma,\n                n_particles=n_particles,\n                prior=self._prior,\n                **kwargs,\n            )\n\n        if (\n            reset_optimizer\n            or self._optimizer is None\n            or not isinstance(self._optimizer, self._optimizer_builder)\n        ):\n            self._optimizer = self._optimizer_builder(\n                self.potential_fn,\n                self.q,\n                lr=learning_rate,\n                clip_value=clip_value,\n                gamma=gamma,\n                n_particles=n_particles,\n                prior=self._prior,\n                **kwargs,\n            )\n\n        # Check context\n        x = atleast_2d_float32_tensor(self._x_else_default_x(x)).to(  # type: ignore\n            self._device\n        )\n\n        already_trained = self._trained_on is not None and (x == self._trained_on).all()\n\n        # Optimize\n        optimizer = self._optimizer\n        optimizer.to(self._device)\n        optimizer.reset_loss_stats()\n\n        if show_progress_bar:\n            iters = tqdm(range(max_num_iters))\n        else:\n            iters = range(max_num_iters)\n\n        # Warmup before training\n        if reset_optimizer or (not optimizer.warm_up_was_done and not already_trained):\n            if show_progress_bar:\n                iters.set_description(  # type: ignore\n                    \"Warmup phase, this may take a few seconds...\"\n                )\n            optimizer.warm_up(warm_up_rounds)\n\n        for i in iters:\n            optimizer.step(x)\n            mean_loss, std_loss = optimizer.get_loss_stats()\n            # Update progress bar\n            if show_progress_bar:\n                assert isinstance(iters, tqdm)\n                iters.set_description(  # type: ignore\n                    f\"Loss: {np.round(float(mean_loss), 2)}\"\n                    f\"Std: {np.round(float(std_loss), 2)}\"\n                )\n            # Check for convergence\n            if check_for_convergence and i &gt; min_num_iters:\n                if optimizer.converged():\n                    if show_progress_bar:\n                        print(f\"\\nConverged with loss: {np.round(float(mean_loss), 2)}\")\n                    break\n        # Training finished:\n        self._trained_on = x\n\n        # Evaluate quality\n        if quality_control:\n            try:\n                self.evaluate(quality_control_metric=quality_control_metric)\n            except Exception as e:\n                print(\n                    f\"Quality control did not work, we reset the variational \\\n                        posterior,please check your setting. \\\n                        \\n Following error occured {e}\"\n                )\n                self.train(\n                    learning_rate=learning_rate * 0.1,\n                    retrain_from_scratch=True,\n                    reset_optimizer=True,\n                )\n\n        return self\n\n    def evaluate(self, quality_control_metric: str = \"psis\", N: int = int(5e4)) -&gt; None:\n        \"\"\"This function will evaluate the quality of the variational posterior\n        distribution. We currently support two different metrics of type `psis`, which\n        checks the quality based on the tails of importance weights (there should not be\n        much with a large one), or `prop` which checks the proportionality between q\n        and potential_fn.\n\n        NOTE: In our experience `prop` is sensitive to distinguish ``good`` from ``ok``\n        whereas `psis` is more sensitive in distinguishing `very bad` from `ok`.\n\n        Args:\n            quality_control_metric: The metric of choice, we currently support [psis,\n                prop, prop_prior].\n            N: Number of samples which is used to evaluate the metric.\n        \"\"\"\n        quality_control_fn, quality_control_msg = get_quality_metric(\n            quality_control_metric\n        )\n        metric = round(float(quality_control_fn(self, N=N)), 3)\n        print(f\"Quality Score: {metric} \" + quality_control_msg)\n\n    def map(\n        self,\n        x: Optional[TorchTensor] = None,\n        num_iter: int = 1_000,\n        num_to_optimize: int = 100,\n        learning_rate: float = 0.01,\n        init_method: Union[str, TorchTensor] = \"proposal\",\n        num_init_samples: int = 10_000,\n        save_best_every: int = 10,\n        show_progress_bars: bool = False,\n        force_update: bool = False,\n    ) -&gt; Tensor:\n        r\"\"\"Returns the maximum-a-posteriori estimate (MAP).\n\n        The method can be interrupted (Ctrl-C) when the user sees that the\n        log-probability converges. The best estimate will be saved in `self._map` and\n        can be accessed with `self.map()`. The MAP is obtained by running gradient\n        ascent from a given number of starting positions (samples from the posterior\n        with the highest log-probability). After the optimization is done, we select the\n        parameter set that has the highest log-probability after the optimization.\n\n        Warning: The default values used by this function are not well-tested. They\n        might require hand-tuning for the problem at hand.\n\n        For developers: if the prior is a `BoxUniform`, we carry out the optimization\n        in unbounded space and transform the result back into bounded space.\n\n        Args:\n            x: Deprecated - use `.set_default_x()` prior to `.map()`.\n            num_iter: Number of optimization steps that the algorithm takes\n                to find the MAP.\n            learning_rate: Learning rate of the optimizer.\n            init_method: How to select the starting parameters for the optimization. If\n                it is a string, it can be either [`posterior`, `prior`], which samples\n                the respective distribution `num_init_samples` times. If it is a\n                tensor, the tensor will be used as init locations.\n            num_init_samples: Draw this number of samples from the posterior and\n                evaluate the log-probability of all of them.\n            num_to_optimize: From the drawn `num_init_samples`, use the\n                `num_to_optimize` with highest log-probability as the initial points\n                for the optimization.\n            save_best_every: The best log-probability is computed, saved in the\n                `map`-attribute, and printed every `save_best_every`-th iteration.\n                Computing the best log-probability creates a significant overhead\n                (thus, the default is `10`.)\n            show_progress_bars: Whether to show a progressbar during sampling from\n                the posterior.\n            force_update: Whether to re-calculate the MAP when x is unchanged and\n                have a cached value.\n            log_prob_kwargs: Will be empty for SNLE and SNRE. Will contain\n                {'norm_posterior': True} for SNPE.\n\n        Returns:\n            The MAP estimate.\n        \"\"\"\n        self.proposal = self.q\n        return super().map(\n            x=x,\n            num_iter=num_iter,\n            num_to_optimize=num_to_optimize,\n            learning_rate=learning_rate,\n            init_method=init_method,\n            num_init_samples=num_init_samples,\n            save_best_every=save_best_every,\n            show_progress_bars=show_progress_bars,\n            force_update=force_update,\n        )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.direct_posterior.DirectPosterior.__init__","title":"<code>__init__(posterior_estimator, prior, max_sampling_batch_size=10000, device=None, x_shape=None, enable_transform=True)</code>","text":"<p>Parameters:</p> Name Type Description Default <code>prior</code> <code>Distribution</code> <p>Prior distribution with <code>.log_prob()</code> and <code>.sample()</code>.</p> required <code>posterior_estimator</code> <code>Flow</code> <p>The trained neural posterior.</p> required <code>max_sampling_batch_size</code> <code>int</code> <p>Batchsize of samples being drawn from the proposal at every iteration.</p> <code>10000</code> <code>device</code> <code>Optional[str]</code> <p>Training device, e.g., \u201ccpu\u201d, \u201ccuda\u201d or \u201ccuda:0\u201d. If None, <code>potential_fn.device</code> is used.</p> <code>None</code> <code>x_shape</code> <code>Optional[Size]</code> <p>Shape of a single simulator output. If passed, it is used to check the shape of the observed data and give a descriptive error.</p> <code>None</code> <code>enable_transform</code> <code>bool</code> <p>Whether to transform parameters to unconstrained space during MAP optimization. When False, an identity transform will be returned for <code>theta_transform</code>.</p> <code>True</code> Source code in <code>sbi/inference/posteriors/direct_posterior.py</code> <pre><code>def __init__(\n    self,\n    posterior_estimator: flows.Flow,\n    prior: Distribution,\n    max_sampling_batch_size: int = 10_000,\n    device: Optional[str] = None,\n    x_shape: Optional[torch.Size] = None,\n    enable_transform: bool = True,\n):\n    \"\"\"\n    Args:\n        prior: Prior distribution with `.log_prob()` and `.sample()`.\n        posterior_estimator: The trained neural posterior.\n        max_sampling_batch_size: Batchsize of samples being drawn from\n            the proposal at every iteration.\n        device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:0\". If None,\n            `potential_fn.device` is used.\n        x_shape: Shape of a single simulator output. If passed, it is used to check\n            the shape of the observed data and give a descriptive error.\n        enable_transform: Whether to transform parameters to unconstrained space\n            during MAP optimization. When False, an identity transform will be\n            returned for `theta_transform`.\n    \"\"\"\n    # Because `DirectPosterior` does not take the `potential_fn` as input, it\n    # builds it itself. The `potential_fn` and `theta_transform` are used only for\n    # obtaining the MAP.\n    check_prior(prior)\n    potential_fn, theta_transform = posterior_estimator_based_potential(\n        posterior_estimator,\n        prior,\n        x_o=None,\n        enable_transform=enable_transform,\n    )\n\n    super().__init__(\n        potential_fn=potential_fn,\n        theta_transform=theta_transform,\n        device=device,\n        x_shape=x_shape,\n    )\n\n    self.prior = prior\n    self.posterior_estimator = posterior_estimator\n\n    self.max_sampling_batch_size = max_sampling_batch_size\n    self._leakage_density_correction_factor = None\n\n    self._purpose = \"\"\"It samples the posterior network and rejects samples that\n        lie outside of the prior bounds.\"\"\"\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.direct_posterior.DirectPosterior.leakage_correction","title":"<code>leakage_correction(x, num_rejection_samples=10000, force_update=False, show_progress_bars=False, rejection_sampling_batch_size=10000)</code>","text":"<p>Return leakage correction factor for a leaky posterior density estimate.</p> <p>The factor is estimated from the acceptance probability during rejection sampling from the posterior.</p> <p>This is to avoid re-estimating the acceptance probability from scratch whenever <code>log_prob</code> is called and <code>norm_posterior=True</code>. Here, it is estimated only once for <code>self.default_x</code> and saved for later. We re-evaluate only whenever a new <code>x</code> is passed.</p> <p>Parameters:</p> Name Type Description Default <code>num_rejection_samples</code> <code>int</code> <p>Number of samples used to estimate correction factor.</p> <code>10000</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progress bar during sampling.</p> <code>False</code> <code>rejection_sampling_batch_size</code> <code>int</code> <p>Batch size for rejection sampling.</p> <code>10000</code> <p>Returns:</p> Type Description <code>Tensor</code> <p>Saved or newly-estimated correction factor (as a scalar <code>Tensor</code>).</p> Source code in <code>sbi/inference/posteriors/direct_posterior.py</code> <pre><code>@torch.no_grad()\ndef leakage_correction(\n    self,\n    x: Tensor,\n    num_rejection_samples: int = 10_000,\n    force_update: bool = False,\n    show_progress_bars: bool = False,\n    rejection_sampling_batch_size: int = 10_000,\n) -&gt; Tensor:\n    r\"\"\"Return leakage correction factor for a leaky posterior density estimate.\n\n    The factor is estimated from the acceptance probability during rejection\n    sampling from the posterior.\n\n    This is to avoid re-estimating the acceptance probability from scratch\n    whenever `log_prob` is called and `norm_posterior=True`. Here, it\n    is estimated only once for `self.default_x` and saved for later. We\n    re-evaluate only whenever a new `x` is passed.\n\n    Arguments:\n        num_rejection_samples: Number of samples used to estimate correction factor.\n        show_progress_bars: Whether to show a progress bar during sampling.\n        rejection_sampling_batch_size: Batch size for rejection sampling.\n\n    Returns:\n        Saved or newly-estimated correction factor (as a scalar `Tensor`).\n    \"\"\"\n\n    def acceptance_at(x: Tensor) -&gt; Tensor:\n        return accept_reject_sample(\n            proposal=self.posterior_estimator,\n            accept_reject_fn=lambda theta: within_support(self.prior, theta),\n            num_samples=num_rejection_samples,\n            show_progress_bars=show_progress_bars,\n            sample_for_correction_factor=True,\n            max_sampling_batch_size=rejection_sampling_batch_size,\n            proposal_sampling_kwargs={\"context\": x},\n        )[1]\n\n    # Check if the provided x matches the default x (short-circuit on identity).\n    is_new_x = self.default_x is None or (\n        x is not self.default_x and (x != self.default_x).any()\n    )\n\n    not_saved_at_default_x = self._leakage_density_correction_factor is None\n\n    if is_new_x:  # Calculate at x; don't save.\n        return acceptance_at(x)\n    elif not_saved_at_default_x or force_update:  # Calculate at default_x; save.\n        assert self.default_x is not None\n        self._leakage_density_correction_factor = acceptance_at(self.default_x)\n\n    return self._leakage_density_correction_factor  # type: ignore\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.direct_posterior.DirectPosterior.log_prob","title":"<code>log_prob(theta, x=None, norm_posterior=True, track_gradients=False, leakage_correction_params=None)</code>","text":"<p>Returns the log-probability of the posterior \\(p(\\theta|x)\\).</p> <p>Parameters:</p> Name Type Description Default <code>theta</code> <code>Tensor</code> <p>Parameters \\(\\theta\\).</p> required <code>norm_posterior</code> <code>bool</code> <p>Whether to enforce a normalized posterior density. Renormalization of the posterior is useful when some probability falls out or leaks out of the prescribed prior support. The normalizing factor is calculated via rejection sampling, so if you need speedier but unnormalized log posterior estimates set here <code>norm_posterior=False</code>. The returned log posterior is set to -\u221e outside of the prior support regardless of this setting.</p> <code>True</code> <code>track_gradients</code> <code>bool</code> <p>Whether the returned tensor supports tracking gradients. This can be helpful for e.g. sensitivity analysis, but increases memory consumption.</p> <code>False</code> <code>leakage_correction_params</code> <code>Optional[dict]</code> <p>A <code>dict</code> of keyword arguments to override the default values of <code>leakage_correction()</code>. Possible options are: <code>num_rejection_samples</code>, <code>force_update</code>, <code>show_progress_bars</code>, and <code>rejection_sampling_batch_size</code>. These parameters only have an effect if <code>norm_posterior=True</code>.</p> <code>None</code> <p>Returns:</p> Type Description <code>Tensor</code> <p><code>(len(\u03b8),)</code>-shaped log posterior probability \\(\\log p(\\theta|x)\\) for \u03b8 in the</p> <code>Tensor</code> <p>support of the prior, -\u221e (corresponding to 0 probability) outside.</p> Source code in <code>sbi/inference/posteriors/direct_posterior.py</code> <pre><code>def log_prob(\n    self,\n    theta: Tensor,\n    x: Optional[Tensor] = None,\n    norm_posterior: bool = True,\n    track_gradients: bool = False,\n    leakage_correction_params: Optional[dict] = None,\n) -&gt; Tensor:\n    r\"\"\"Returns the log-probability of the posterior $p(\\theta|x)$.\n\n    Args:\n        theta: Parameters $\\theta$.\n        norm_posterior: Whether to enforce a normalized posterior density.\n            Renormalization of the posterior is useful when some\n            probability falls out or leaks out of the prescribed prior support.\n            The normalizing factor is calculated via rejection sampling, so if you\n            need speedier but unnormalized log posterior estimates set here\n            `norm_posterior=False`. The returned log posterior is set to\n            -\u221e outside of the prior support regardless of this setting.\n        track_gradients: Whether the returned tensor supports tracking gradients.\n            This can be helpful for e.g. sensitivity analysis, but increases memory\n            consumption.\n        leakage_correction_params: A `dict` of keyword arguments to override the\n            default values of `leakage_correction()`. Possible options are:\n            `num_rejection_samples`, `force_update`, `show_progress_bars`, and\n            `rejection_sampling_batch_size`.\n            These parameters only have an effect if `norm_posterior=True`.\n\n    Returns:\n        `(len(\u03b8),)`-shaped log posterior probability $\\log p(\\theta|x)$ for \u03b8 in the\n        support of the prior, -\u221e (corresponding to 0 probability) outside.\n    \"\"\"\n    x = self._x_else_default_x(x)\n\n    # TODO Train exited here, entered after sampling?\n    self.posterior_estimator.eval()\n\n    theta = ensure_theta_batched(torch.as_tensor(theta))\n    theta_repeated, x_repeated = match_theta_and_x_batch_shapes(theta, x)\n\n    with torch.set_grad_enabled(track_gradients):\n        # Evaluate on device, move back to cpu for comparison with prior.\n        unnorm_log_prob = self.posterior_estimator.log_prob(\n            theta_repeated, context=x_repeated\n        )\n\n        # Force probability to be zero outside prior support.\n        in_prior_support = within_support(self.prior, theta_repeated)\n\n        masked_log_prob = torch.where(\n            in_prior_support,\n            unnorm_log_prob,\n            torch.tensor(float(\"-inf\"), dtype=torch.float32, device=self._device),\n        )\n\n        if leakage_correction_params is None:\n            leakage_correction_params = dict()  # use defaults\n        log_factor = (\n            log(self.leakage_correction(x=x, **leakage_correction_params))\n            if norm_posterior\n            else 0\n        )\n\n        return masked_log_prob - log_factor\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.direct_posterior.DirectPosterior.map","title":"<code>map(x=None, num_iter=1000, num_to_optimize=100, learning_rate=0.01, init_method='posterior', num_init_samples=1000, save_best_every=10, show_progress_bars=False, force_update=False)</code>","text":"<p>Returns the maximum-a-posteriori estimate (MAP).</p> <p>The method can be interrupted (Ctrl-C) when the user sees that the log-probability converges. The best estimate will be saved in <code>self._map</code> and can be accessed with <code>self.map()</code>. The MAP is obtained by running gradient ascent from a given number of starting positions (samples from the posterior with the highest log-probability). After the optimization is done, we select the parameter set that has the highest log-probability after the optimization.</p> <p>Warning: The default values used by this function are not well-tested. They might require hand-tuning for the problem at hand.</p> <p>For developers: if the prior is a <code>BoxUniform</code>, we carry out the optimization in unbounded space and transform the result back into bounded space.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>Optional[Tensor]</code> <p>Deprecated - use <code>.set_default_x()</code> prior to <code>.map()</code>.</p> <code>None</code> <code>num_iter</code> <code>int</code> <p>Number of optimization steps that the algorithm takes to find the MAP.</p> <code>1000</code> <code>learning_rate</code> <code>float</code> <p>Learning rate of the optimizer.</p> <code>0.01</code> <code>init_method</code> <code>Union[str, Tensor]</code> <p>How to select the starting parameters for the optimization. If it is a string, it can be either [<code>posterior</code>, <code>prior</code>], which samples the respective distribution <code>num_init_samples</code> times. If it is a tensor, the tensor will be used as init locations.</p> <code>'posterior'</code> <code>num_init_samples</code> <code>int</code> <p>Draw this number of samples from the posterior and evaluate the log-probability of all of them.</p> <code>1000</code> <code>num_to_optimize</code> <code>int</code> <p>From the drawn <code>num_init_samples</code>, use the <code>num_to_optimize</code> with highest log-probability as the initial points for the optimization.</p> <code>100</code> <code>save_best_every</code> <code>int</code> <p>The best log-probability is computed, saved in the <code>map</code>-attribute, and printed every <code>save_best_every</code>-th iteration. Computing the best log-probability creates a significant overhead (thus, the default is <code>10</code>.)</p> <code>10</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during sampling from the posterior.</p> <code>False</code> <code>force_update</code> <code>bool</code> <p>Whether to re-calculate the MAP when x is unchanged and have a cached value.</p> <code>False</code> <code>log_prob_kwargs</code> <p>Will be empty for SNLE and SNRE. Will contain {\u2018norm_posterior\u2019: True} for SNPE.</p> required <p>Returns:</p> Type Description <code>Tensor</code> <p>The MAP estimate.</p> Source code in <code>sbi/inference/posteriors/direct_posterior.py</code> <pre><code>def map(\n    self,\n    x: Optional[Tensor] = None,\n    num_iter: int = 1_000,\n    num_to_optimize: int = 100,\n    learning_rate: float = 0.01,\n    init_method: Union[str, Tensor] = \"posterior\",\n    num_init_samples: int = 1_000,\n    save_best_every: int = 10,\n    show_progress_bars: bool = False,\n    force_update: bool = False,\n) -&gt; Tensor:\n    r\"\"\"Returns the maximum-a-posteriori estimate (MAP).\n\n    The method can be interrupted (Ctrl-C) when the user sees that the\n    log-probability converges. The best estimate will be saved in `self._map` and\n    can be accessed with `self.map()`. The MAP is obtained by running gradient\n    ascent from a given number of starting positions (samples from the posterior\n    with the highest log-probability). After the optimization is done, we select the\n    parameter set that has the highest log-probability after the optimization.\n\n    Warning: The default values used by this function are not well-tested. They\n    might require hand-tuning for the problem at hand.\n\n    For developers: if the prior is a `BoxUniform`, we carry out the optimization\n    in unbounded space and transform the result back into bounded space.\n\n    Args:\n        x: Deprecated - use `.set_default_x()` prior to `.map()`.\n        num_iter: Number of optimization steps that the algorithm takes\n            to find the MAP.\n        learning_rate: Learning rate of the optimizer.\n        init_method: How to select the starting parameters for the optimization. If\n            it is a string, it can be either [`posterior`, `prior`], which samples\n            the respective distribution `num_init_samples` times. If it is a\n            tensor, the tensor will be used as init locations.\n        num_init_samples: Draw this number of samples from the posterior and\n            evaluate the log-probability of all of them.\n        num_to_optimize: From the drawn `num_init_samples`, use the\n            `num_to_optimize` with highest log-probability as the initial points\n            for the optimization.\n        save_best_every: The best log-probability is computed, saved in the\n            `map`-attribute, and printed every `save_best_every`-th iteration.\n            Computing the best log-probability creates a significant overhead\n            (thus, the default is `10`.)\n        show_progress_bars: Whether to show a progressbar during sampling from the\n            posterior.\n        force_update: Whether to re-calculate the MAP when x is unchanged and\n            have a cached value.\n        log_prob_kwargs: Will be empty for SNLE and SNRE. Will contain\n            {'norm_posterior': True} for SNPE.\n\n    Returns:\n        The MAP estimate.\n    \"\"\"\n    return super().map(\n        x=x,\n        num_iter=num_iter,\n        num_to_optimize=num_to_optimize,\n        learning_rate=learning_rate,\n        init_method=init_method,\n        num_init_samples=num_init_samples,\n        save_best_every=save_best_every,\n        show_progress_bars=show_progress_bars,\n        force_update=force_update,\n    )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.direct_posterior.DirectPosterior.sample","title":"<code>sample(sample_shape=torch.Size(), x=None, max_sampling_batch_size=10000, sample_with=None, show_progress_bars=True)</code>","text":"<p>Return samples from posterior distribution \\(p(\\theta|x)\\).</p> <p>Parameters:</p> Name Type Description Default <code>sample_shape</code> <code>Shape</code> <p>Desired shape of samples that are drawn from posterior. If sample_shape is multidimensional we simply draw <code>sample_shape.numel()</code> samples and then reshape into the desired shape.</p> <code>Size()</code> <code>sample_with</code> <code>Optional[str]</code> <p>This argument only exists to keep backward-compatibility with <code>sbi</code> v0.17.2 or older. If it is set, we instantly raise an error.</p> <code>None</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show sampling progress monitor.</p> <code>True</code> Source code in <code>sbi/inference/posteriors/direct_posterior.py</code> <pre><code>def sample(\n    self,\n    sample_shape: Shape = torch.Size(),\n    x: Optional[Tensor] = None,\n    max_sampling_batch_size: int = 10_000,\n    sample_with: Optional[str] = None,\n    show_progress_bars: bool = True,\n) -&gt; Tensor:\n    r\"\"\"Return samples from posterior distribution $p(\\theta|x)$.\n\n    Args:\n        sample_shape: Desired shape of samples that are drawn from posterior. If\n            sample_shape is multidimensional we simply draw `sample_shape.numel()`\n            samples and then reshape into the desired shape.\n        sample_with: This argument only exists to keep backward-compatibility with\n            `sbi` v0.17.2 or older. If it is set, we instantly raise an error.\n        show_progress_bars: Whether to show sampling progress monitor.\n    \"\"\"\n\n    num_samples = torch.Size(sample_shape).numel()\n    x = self._x_else_default_x(x)\n    max_sampling_batch_size = (\n        self.max_sampling_batch_size\n        if max_sampling_batch_size is None\n        else max_sampling_batch_size\n    )\n\n    if sample_with is not None:\n        raise ValueError(\n            f\"You set `sample_with={sample_with}`. As of sbi v0.18.0, setting \"\n            f\"`sample_with` is no longer supported. You have to rerun \"\n            f\"`.build_posterior(sample_with={sample_with}).`\"\n        )\n\n    samples = accept_reject_sample(\n        proposal=self.posterior_estimator,\n        accept_reject_fn=lambda theta: within_support(self.prior, theta),\n        num_samples=num_samples,\n        show_progress_bars=show_progress_bars,\n        max_sampling_batch_size=max_sampling_batch_size,\n        proposal_sampling_kwargs={\"context\": x},\n        alternative_method=\"build_posterior(..., sample_with='mcmc')\",\n    )[0]\n    return samples\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.__init__","title":"<code>__init__(potential_fn, proposal, theta_transform=None, method='sir', oversampling_factor=32, max_sampling_batch_size=10000, device=None, x_shape=None)</code>","text":"<p>Parameters:</p> Name Type Description Default <code>potential_fn</code> <code>Callable</code> <p>The potential function from which to draw samples.</p> required <code>proposal</code> <code>Any</code> <p>The proposal distribution.</p> required <code>theta_transform</code> <code>Optional[TorchTransform]</code> <p>Transformation that is applied to parameters. Is not used during but only when calling <code>.map()</code>.</p> <code>None</code> <code>method</code> <code>str</code> <p>Either of [<code>sir</code>|<code>importance</code>]. This sets the behavior of the <code>.sample()</code> method. With <code>sir</code>, approximate posterior samples are generated with sampling importance resampling (SIR). With <code>importance</code>, the <code>.sample()</code> method returns a tuple of samples and corresponding importance weights.</p> <code>'sir'</code> <code>oversampling_factor</code> <code>int</code> <p>Number of proposed samples from which only one is selected based on its importance weight.</p> <code>32</code> <code>max_sampling_batch_size</code> <code>int</code> <p>The batch size of samples being drawn from the proposal at every iteration.</p> <code>10000</code> <code>device</code> <code>Optional[str]</code> <p>Device on which to sample, e.g., \u201ccpu\u201d, \u201ccuda\u201d or \u201ccuda:0\u201d. If None, <code>potential_fn.device</code> is used.</p> <code>None</code> <code>x_shape</code> <code>Optional[Size]</code> <p>Shape of a single simulator output. If passed, it is used to check the shape of the observed data and give a descriptive error.</p> <code>None</code> Source code in <code>sbi/inference/posteriors/importance_posterior.py</code> <pre><code>def __init__(\n    self,\n    potential_fn: Callable,\n    proposal: Any,\n    theta_transform: Optional[TorchTransform] = None,\n    method: str = \"sir\",\n    oversampling_factor: int = 32,\n    max_sampling_batch_size: int = 10_000,\n    device: Optional[str] = None,\n    x_shape: Optional[torch.Size] = None,\n):\n    \"\"\"\n    Args:\n        potential_fn: The potential function from which to draw samples.\n        proposal: The proposal distribution.\n        theta_transform: Transformation that is applied to parameters. Is not used\n            during but only when calling `.map()`.\n        method: Either of [`sir`|`importance`]. This sets the behavior of the\n            `.sample()` method. With `sir`, approximate posterior samples are\n            generated with sampling importance resampling (SIR). With\n            `importance`, the `.sample()` method returns a tuple of samples and\n            corresponding importance weights.\n        oversampling_factor: Number of proposed samples from which only one is\n            selected based on its importance weight.\n        max_sampling_batch_size: The batch size of samples being drawn from the\n            proposal at every iteration.\n        device: Device on which to sample, e.g., \"cpu\", \"cuda\" or \"cuda:0\". If\n            None, `potential_fn.device` is used.\n        x_shape: Shape of a single simulator output. If passed, it is used to check\n            the shape of the observed data and give a descriptive error.\n    \"\"\"\n    super().__init__(\n        potential_fn,\n        theta_transform=theta_transform,\n        device=device,\n        x_shape=x_shape,\n    )\n\n    self.proposal = proposal\n    self._normalization_constant = None\n    self.method = method\n\n    self.oversampling_factor = oversampling_factor\n    self.max_sampling_batch_size = max_sampling_batch_size\n\n    self._purpose = (\n        \"It provides sampling-importance resampling (SIR) to .sample() from the \"\n        \"posterior and can evaluate the _unnormalized_ posterior density with \"\n        \".log_prob().\"\n    )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.estimate_normalization_constant","title":"<code>estimate_normalization_constant(x, num_samples=10000, force_update=False)</code>","text":"<p>Returns the normalization constant via importance sampling.</p> <p>Parameters:</p> Name Type Description Default <code>num_samples</code> <code>int</code> <p>Number of importance samples used for the estimate.</p> <code>10000</code> <code>force_update</code> <code>bool</code> <p>Whether to re-calculate the normlization constant when x is unchanged and have a cached value.</p> <code>False</code> Source code in <code>sbi/inference/posteriors/importance_posterior.py</code> <pre><code>@torch.no_grad()\ndef estimate_normalization_constant(\n    self, x: Tensor, num_samples: int = 10_000, force_update: bool = False\n) -&gt; Tensor:\n    \"\"\"Returns the normalization constant via importance sampling.\n\n    Args:\n        num_samples: Number of importance samples used for the estimate.\n        force_update: Whether to re-calculate the normlization constant when x is\n            unchanged and have a cached value.\n    \"\"\"\n    # Check if the provided x matches the default x (short-circuit on identity).\n    is_new_x = self.default_x is None or (\n        x is not self.default_x and (x != self.default_x).any()\n    )\n\n    not_saved_at_default_x = self._normalization_constant is None\n\n    if is_new_x:  # Calculate at x; don't save.\n        _, log_importance_weights = importance_sample(\n            self.potential_fn,\n            proposal=self.proposal,\n            num_samples=num_samples,\n        )\n        return torch.mean(torch.exp(log_importance_weights))\n    elif not_saved_at_default_x or force_update:  # Calculate at default_x; save.\n        assert self.default_x is not None\n        _, log_importance_weights = importance_sample(\n            self.potential_fn,\n            proposal=self.proposal,\n            num_samples=num_samples,\n        )\n        self._normalization_constant = torch.mean(torch.exp(log_importance_weights))\n\n    return self._normalization_constant.to(self._device)  # type: ignore\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.log_prob","title":"<code>log_prob(theta, x=None, track_gradients=False, normalization_constant_params=None)</code>","text":"<p>Returns the log-probability of theta under the posterior.</p> <p>The normalization constant is estimated with importance sampling.</p> <p>Parameters:</p> Name Type Description Default <code>theta</code> <code>Tensor</code> <p>Parameters \\(\\theta\\).</p> required <code>track_gradients</code> <code>bool</code> <p>Whether the returned tensor supports tracking gradients. This can be helpful for e.g. sensitivity analysis, but increases memory consumption.</p> <code>False</code> <code>normalization_constant_params</code> <code>Optional[dict]</code> <p>Parameters passed on to <code>estimate_normalization_constant()</code>.</p> <code>None</code> <p>Returns:</p> Type Description <code>Tensor</code> <p><code>len($\\theta$)</code>-shaped log-probability.</p> Source code in <code>sbi/inference/posteriors/importance_posterior.py</code> <pre><code>def log_prob(\n    self,\n    theta: Tensor,\n    x: Optional[Tensor] = None,\n    track_gradients: bool = False,\n    normalization_constant_params: Optional[dict] = None,\n) -&gt; Tensor:\n    r\"\"\"Returns the log-probability of theta under the posterior.\n\n    The normalization constant is estimated with importance sampling.\n\n    Args:\n        theta: Parameters $\\theta$.\n        track_gradients: Whether the returned tensor supports tracking gradients.\n            This can be helpful for e.g. sensitivity analysis, but increases memory\n            consumption.\n        normalization_constant_params: Parameters passed on to\n            `estimate_normalization_constant()`.\n\n    Returns:\n        `len($\\theta$)`-shaped log-probability.\n    \"\"\"\n    x = self._x_else_default_x(x)\n    self.potential_fn.set_x(x)\n\n    theta = ensure_theta_batched(torch.as_tensor(theta))\n\n    with torch.set_grad_enabled(track_gradients):\n        potential_values = self.potential_fn(\n            theta.to(self._device), track_gradients=track_gradients\n        )\n\n        if normalization_constant_params is None:\n            normalization_constant_params = dict()  # use defaults\n        normalization_constant = self.estimate_normalization_constant(\n            x, **normalization_constant_params\n        )\n\n        return (potential_values - torch.log(normalization_constant)).to(\n            self._device\n        )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.map","title":"<code>map(x=None, num_iter=1000, num_to_optimize=100, learning_rate=0.01, init_method='proposal', num_init_samples=1000, save_best_every=10, show_progress_bars=False, force_update=False)</code>","text":"<p>Returns the maximum-a-posteriori estimate (MAP).</p> <p>The method can be interrupted (Ctrl-C) when the user sees that the log-probability converges. The best estimate will be saved in <code>self._map</code> and can be accessed with <code>self.map()</code>. The MAP is obtained by running gradient ascent from a given number of starting positions (samples from the posterior with the highest log-probability). After the optimization is done, we select the parameter set that has the highest log-probability after the optimization.</p> <p>Warning: The default values used by this function are not well-tested. They might require hand-tuning for the problem at hand.</p> <p>For developers: if the prior is a <code>BoxUniform</code>, we carry out the optimization in unbounded space and transform the result back into bounded space.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>Optional[Tensor]</code> <p>Deprecated - use <code>.set_default_x()</code> prior to <code>.map()</code>.</p> <code>None</code> <code>num_iter</code> <code>int</code> <p>Number of optimization steps that the algorithm takes to find the MAP.</p> <code>1000</code> <code>learning_rate</code> <code>float</code> <p>Learning rate of the optimizer.</p> <code>0.01</code> <code>init_method</code> <code>Union[str, Tensor]</code> <p>How to select the starting parameters for the optimization. If it is a string, it can be either [<code>posterior</code>, <code>prior</code>], which samples the respective distribution <code>num_init_samples</code> times. If it is a tensor, the tensor will be used as init locations.</p> <code>'proposal'</code> <code>num_init_samples</code> <code>int</code> <p>Draw this number of samples from the posterior and evaluate the log-probability of all of them.</p> <code>1000</code> <code>num_to_optimize</code> <code>int</code> <p>From the drawn <code>num_init_samples</code>, use the <code>num_to_optimize</code> with highest log-probability as the initial points for the optimization.</p> <code>100</code> <code>save_best_every</code> <code>int</code> <p>The best log-probability is computed, saved in the <code>map</code>-attribute, and printed every <code>save_best_every</code>-th iteration. Computing the best log-probability creates a significant overhead (thus, the default is <code>10</code>.)</p> <code>10</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during sampling from the posterior.</p> <code>False</code> <code>force_update</code> <code>bool</code> <p>Whether to re-calculate the MAP when x is unchanged and have a cached value.</p> <code>False</code> <code>log_prob_kwargs</code> <p>Will be empty for SNLE and SNRE. Will contain {\u2018norm_posterior\u2019: True} for SNPE.</p> required <p>Returns:</p> Type Description <code>Tensor</code> <p>The MAP estimate.</p> Source code in <code>sbi/inference/posteriors/importance_posterior.py</code> <pre><code>def map(\n    self,\n    x: Optional[Tensor] = None,\n    num_iter: int = 1_000,\n    num_to_optimize: int = 100,\n    learning_rate: float = 0.01,\n    init_method: Union[str, Tensor] = \"proposal\",\n    num_init_samples: int = 1_000,\n    save_best_every: int = 10,\n    show_progress_bars: bool = False,\n    force_update: bool = False,\n) -&gt; Tensor:\n    r\"\"\"Returns the maximum-a-posteriori estimate (MAP).\n\n    The method can be interrupted (Ctrl-C) when the user sees that the\n    log-probability converges. The best estimate will be saved in `self._map` and\n    can be accessed with `self.map()`. The MAP is obtained by running gradient\n    ascent from a given number of starting positions (samples from the posterior\n    with the highest log-probability). After the optimization is done, we select the\n    parameter set that has the highest log-probability after the optimization.\n\n    Warning: The default values used by this function are not well-tested. They\n    might require hand-tuning for the problem at hand.\n\n    For developers: if the prior is a `BoxUniform`, we carry out the optimization\n    in unbounded space and transform the result back into bounded space.\n\n    Args:\n        x: Deprecated - use `.set_default_x()` prior to `.map()`.\n        num_iter: Number of optimization steps that the algorithm takes\n            to find the MAP.\n        learning_rate: Learning rate of the optimizer.\n        init_method: How to select the starting parameters for the optimization. If\n            it is a string, it can be either [`posterior`, `prior`], which samples\n            the respective distribution `num_init_samples` times. If it is a\n            tensor, the tensor will be used as init locations.\n        num_init_samples: Draw this number of samples from the posterior and\n            evaluate the log-probability of all of them.\n        num_to_optimize: From the drawn `num_init_samples`, use the\n            `num_to_optimize` with highest log-probability as the initial points\n            for the optimization.\n        save_best_every: The best log-probability is computed, saved in the\n            `map`-attribute, and printed every `save_best_every`-th iteration.\n            Computing the best log-probability creates a significant overhead\n            (thus, the default is `10`.)\n        show_progress_bars: Whether to show a progressbar during sampling from the\n            posterior.\n        force_update: Whether to re-calculate the MAP when x is unchanged and\n            have a cached value.\n        log_prob_kwargs: Will be empty for SNLE and SNRE. Will contain\n            {'norm_posterior': True} for SNPE.\n\n    Returns:\n        The MAP estimate.\n    \"\"\"\n    return super().map(\n        x=x,\n        num_iter=num_iter,\n        num_to_optimize=num_to_optimize,\n        learning_rate=learning_rate,\n        init_method=init_method,\n        num_init_samples=num_init_samples,\n        save_best_every=save_best_every,\n        show_progress_bars=show_progress_bars,\n        force_update=force_update,\n    )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior.sample","title":"<code>sample(sample_shape=torch.Size(), x=None, oversampling_factor=32, max_sampling_batch_size=10000, sample_with=None)</code>","text":"<p>Return samples from the approximate posterior distribution.</p> <p>Parameters:</p> Name Type Description Default <code>sample_shape</code> <code>Shape</code> <p>description</p> <code>Size()</code> <code>x</code> <code>Optional[Tensor]</code> <p>description</p> <code>None</code> Source code in <code>sbi/inference/posteriors/importance_posterior.py</code> <pre><code>def sample(\n    self,\n    sample_shape: Shape = torch.Size(),\n    x: Optional[Tensor] = None,\n    oversampling_factor: int = 32,\n    max_sampling_batch_size: int = 10_000,\n    sample_with: Optional[str] = None,\n) -&gt; Union[Tensor, Tuple[Tensor, Tensor]]:\n    \"\"\"Return samples from the approximate posterior distribution.\n\n    Args:\n        sample_shape: _description_\n        x: _description_\n    \"\"\"\n    if sample_with is not None:\n        raise ValueError(\n            f\"You set `sample_with={sample_with}`. As of sbi v0.18.0, setting \"\n            f\"`sample_with` is no longer supported. You have to rerun \"\n            f\"`.build_posterior(sample_with={sample_with}).`\"\n        )\n\n    self.potential_fn.set_x(self._x_else_default_x(x))\n\n    if self.method == \"sir\":\n        return self._sir_sample(\n            sample_shape,\n            oversampling_factor=oversampling_factor,\n            max_sampling_batch_size=max_sampling_batch_size,\n        )\n    elif self.method == \"importance\":\n        return self._importance_sample(sample_shape)\n    else:\n        raise NameError\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.mcmc_method","title":"<code>mcmc_method: str</code>  <code>property</code> <code>writable</code>","text":"<p>Returns MCMC method.</p>"},{"location":"reference/#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.posterior_sampler","title":"<code>posterior_sampler</code>  <code>property</code>","text":"<p>Returns sampler created by <code>sample</code>.</p>"},{"location":"reference/#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.__init__","title":"<code>__init__(potential_fn, proposal, theta_transform=None, method='slice_np', thin=10, warmup_steps=10, num_chains=1, init_strategy='resample', init_strategy_parameters={}, init_strategy_num_candidates=None, num_workers=1, device=None, x_shape=None)</code>","text":"<p>Parameters:</p> Name Type Description Default <code>potential_fn</code> <code>Callable</code> <p>The potential function from which to draw samples.</p> required <code>proposal</code> <code>Any</code> <p>Proposal distribution that is used to initialize the MCMC chain.</p> required <code>theta_transform</code> <code>Optional[TorchTransform]</code> <p>Transformation that will be applied during sampling. Allows to perform MCMC in unconstrained space.</p> <code>None</code> <code>method</code> <code>str</code> <p>Method used for MCMC sampling, one of <code>slice_np</code>, <code>slice_np_vectorized</code>, <code>slice</code>, <code>hmc</code>, <code>nuts</code>. <code>slice_np</code> is a custom numpy implementation of slice sampling. <code>slice_np_vectorized</code> is identical to <code>slice_np</code>, but if <code>num_chains&gt;1</code>, the chains are vectorized for <code>slice_np_vectorized</code> whereas they are run sequentially for <code>slice_np</code>. The samplers <code>hmc</code>, <code>nuts</code> or <code>slice</code> sample with Pyro.</p> <code>'slice_np'</code> <code>thin</code> <code>int</code> <p>The thinning factor for the chain.</p> <code>10</code> <code>warmup_steps</code> <code>int</code> <p>The initial number of samples to discard.</p> <code>10</code> <code>num_chains</code> <code>int</code> <p>The number of chains.</p> <code>1</code> <code>init_strategy</code> <code>str</code> <p>The initialisation strategy for chains; <code>proposal</code> will draw init locations from <code>proposal</code>, whereas <code>sir</code> will use Sequential- Importance-Resampling (SIR). SIR initially samples <code>init_strategy_num_candidates</code> from the <code>proposal</code>, evaluates all of them under the <code>potential_fn</code> and <code>proposal</code>, and then resamples the initial locations with weights proportional to <code>exp(potential_fn - proposal.log_prob</code>. <code>resample</code> is the same as <code>sir</code> but uses <code>exp(potential_fn)</code> as weights.</p> <code>'resample'</code> <code>init_strategy_parameters</code> <code>Dict[str, Any]</code> <p>Dictionary of keyword arguments passed to the init strategy, e.g., for <code>init_strategy=sir</code> this could be <code>num_candidate_samples</code>, i.e., the number of candidates to to find init locations (internal default is <code>1000</code>), or <code>device</code>.</p> <code>{}</code> <code>init_strategy_num_candidates</code> <code>Optional[int]</code> <p>Number of candidates to to find init  locations in <code>init_strategy=sir</code> (deprecated, use  init_strategy_parameters instead).</p> <code>None</code> <code>num_workers</code> <code>int</code> <p>number of cpu cores used to parallelize mcmc</p> <code>1</code> <code>device</code> <code>Optional[str]</code> <p>Training device, e.g., \u201ccpu\u201d, \u201ccuda\u201d or \u201ccuda:0\u201d. If None, <code>potential_fn.device</code> is used.</p> <code>None</code> <code>x_shape</code> <code>Optional[Size]</code> <p>Shape of a single simulator output. If passed, it is used to check the shape of the observed data and give a descriptive error.</p> <code>None</code> Source code in <code>sbi/inference/posteriors/mcmc_posterior.py</code> <pre><code>def __init__(\n    self,\n    potential_fn: Callable,\n    proposal: Any,\n    theta_transform: Optional[TorchTransform] = None,\n    method: str = \"slice_np\",\n    thin: int = 10,\n    warmup_steps: int = 10,\n    num_chains: int = 1,\n    init_strategy: str = \"resample\",\n    init_strategy_parameters: Dict[str, Any] = {},\n    init_strategy_num_candidates: Optional[int] = None,\n    num_workers: int = 1,\n    device: Optional[str] = None,\n    x_shape: Optional[torch.Size] = None,\n):\n    \"\"\"\n    Args:\n        potential_fn: The potential function from which to draw samples.\n        proposal: Proposal distribution that is used to initialize the MCMC chain.\n        theta_transform: Transformation that will be applied during sampling.\n            Allows to perform MCMC in unconstrained space.\n        method: Method used for MCMC sampling, one of `slice_np`,\n            `slice_np_vectorized`, `slice`, `hmc`, `nuts`. `slice_np` is a custom\n            numpy implementation of slice sampling. `slice_np_vectorized` is\n            identical to `slice_np`, but if `num_chains&gt;1`, the chains are\n            vectorized for `slice_np_vectorized` whereas they are run sequentially\n            for `slice_np`. The samplers `hmc`, `nuts` or `slice` sample with Pyro.\n        thin: The thinning factor for the chain.\n        warmup_steps: The initial number of samples to discard.\n        num_chains: The number of chains.\n        init_strategy: The initialisation strategy for chains; `proposal` will draw\n            init locations from `proposal`, whereas `sir` will use Sequential-\n            Importance-Resampling (SIR). SIR initially samples\n            `init_strategy_num_candidates` from the `proposal`, evaluates all of\n            them under the `potential_fn` and `proposal`, and then resamples the\n            initial locations with weights proportional to `exp(potential_fn -\n            proposal.log_prob`. `resample` is the same as `sir` but\n            uses `exp(potential_fn)` as weights.\n        init_strategy_parameters: Dictionary of keyword arguments passed to the\n            init strategy, e.g., for `init_strategy=sir` this could be\n            `num_candidate_samples`, i.e., the number of candidates to to find init\n            locations (internal default is `1000`), or `device`.\n        init_strategy_num_candidates: Number of candidates to to find init\n             locations in `init_strategy=sir` (deprecated, use\n             init_strategy_parameters instead).\n        num_workers: number of cpu cores used to parallelize mcmc\n        device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:0\". If None,\n            `potential_fn.device` is used.\n        x_shape: Shape of a single simulator output. If passed, it is used to check\n            the shape of the observed data and give a descriptive error.\n    \"\"\"\n\n    super().__init__(\n        potential_fn,\n        theta_transform=theta_transform,\n        device=device,\n        x_shape=x_shape,\n    )\n\n    self.proposal = proposal\n    self.method = method\n    self.thin = thin\n    self.warmup_steps = warmup_steps\n    self.num_chains = num_chains\n    self.init_strategy = init_strategy\n    self.init_strategy_parameters = init_strategy_parameters\n    self.num_workers = num_workers\n    self._posterior_sampler = None\n    # Hardcode parameter name to reduce clutter kwargs.\n    self.param_name = \"theta\"\n\n    if init_strategy_num_candidates is not None:\n        warn(\n            \"\"\"Passing `init_strategy_num_candidates` is deprecated as of sbi\n            v0.19.0. Instead, use e.g.,\n            `init_strategy_parameters={\"num_candidate_samples\": 1000}`\"\"\"\n        )\n        self.init_strategy_parameters[\n            \"num_candidate_samples\"\n        ] = init_strategy_num_candidates\n\n    self.potential_ = self._prepare_potential(method)\n\n    self._purpose = (\n        \"It provides MCMC to .sample() from the posterior and \"\n        \"can evaluate the _unnormalized_ posterior density with .log_prob().\"\n    )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.get_arviz_inference_data","title":"<code>get_arviz_inference_data()</code>","text":"<p>Returns arviz InferenceData object constructed most recent samples.</p> <p>Note: the InferenceData is constructed using the posterior samples generated in most recent call to <code>.sample(...)</code>.</p> <p>For Pyro HMC and NUTS kernels InferenceData will contain diagnostics, for Pyro Slice or sbi slice sampling samples, only the samples are added.</p> <p>Returns:</p> Name Type Description <code>inference_data</code> <code>InferenceData</code> <p>Arviz InferenceData object.</p> Source code in <code>sbi/inference/posteriors/mcmc_posterior.py</code> <pre><code>def get_arviz_inference_data(self) -&gt; InferenceData:\n    \"\"\"Returns arviz InferenceData object constructed most recent samples.\n\n    Note: the InferenceData is constructed using the posterior samples generated in\n    most recent call to `.sample(...)`.\n\n    For Pyro HMC and NUTS kernels InferenceData will contain diagnostics, for Pyro\n    Slice or sbi slice sampling samples, only the samples are added.\n\n    Returns:\n        inference_data: Arviz InferenceData object.\n    \"\"\"\n    assert (\n        self._posterior_sampler is not None\n    ), \"\"\"No samples have been generated, call .sample() first.\"\"\"\n\n    sampler: Union[\n        MCMC, SliceSamplerSerial, SliceSamplerVectorized\n    ] = self._posterior_sampler\n\n    # If Pyro sampler and samples not transformed, use arviz' from_pyro.\n    # Exclude 'slice' kernel as it lacks the 'divergence' diagnostics key.\n    if isinstance(self._posterior_sampler, (HMC, NUTS)) and isinstance(\n        self.theta_transform, torch_tf.IndependentTransform\n    ):\n        inference_data = az.from_pyro(sampler)\n\n    # otherwise get samples from sampler and transform to original space.\n    else:\n        transformed_samples = sampler.get_samples(group_by_chain=True)\n        # Pyro samplers returns dicts, get values.\n        if isinstance(transformed_samples, Dict):\n            # popitem gets last items, [1] get the values as tensor.\n            transformed_samples = transformed_samples.popitem()[1]\n        # Our slice samplers return numpy arrays.\n        elif isinstance(transformed_samples, ndarray):\n            transformed_samples = torch.from_numpy(transformed_samples).type(\n                torch.float32\n            )\n        # For MultipleIndependent priors transforms first dim must be batch dim.\n        # thus, reshape back and forth to have batch dim in front.\n        samples_shape = transformed_samples.shape\n        samples = self.theta_transform.inv(  # type: ignore\n            transformed_samples.reshape(-1, samples_shape[-1])\n        ).reshape(  # type: ignore\n            *samples_shape\n        )\n\n        inference_data = az.convert_to_inference_data(\n            {f\"{self.param_name}\": samples}\n        )\n\n    return inference_data\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.log_prob","title":"<code>log_prob(theta, x=None, track_gradients=False)</code>","text":"<p>Returns the log-probability of theta under the posterior.</p> <p>Parameters:</p> Name Type Description Default <code>theta</code> <code>Tensor</code> <p>Parameters \\(\\theta\\).</p> required <code>track_gradients</code> <code>bool</code> <p>Whether the returned tensor supports tracking gradients. This can be helpful for e.g. sensitivity analysis, but increases memory consumption.</p> <code>False</code> <p>Returns:</p> Type Description <code>Tensor</code> <p><code>len($\\theta$)</code>-shaped log-probability.</p> Source code in <code>sbi/inference/posteriors/mcmc_posterior.py</code> <pre><code>def log_prob(\n    self, theta: Tensor, x: Optional[Tensor] = None, track_gradients: bool = False\n) -&gt; Tensor:\n    r\"\"\"Returns the log-probability of theta under the posterior.\n\n    Args:\n        theta: Parameters $\\theta$.\n        track_gradients: Whether the returned tensor supports tracking gradients.\n            This can be helpful for e.g. sensitivity analysis, but increases memory\n            consumption.\n\n    Returns:\n        `len($\\theta$)`-shaped log-probability.\n    \"\"\"\n    warn(\n        \"\"\"`.log_prob()` is deprecated for methods that can only evaluate the\n        log-probability up to a normalizing constant. Use `.potential()` instead.\"\"\"\n    )\n    warn(\"The log-probability is unnormalized!\")\n\n    self.potential_fn.set_x(self._x_else_default_x(x))\n\n    theta = ensure_theta_batched(torch.as_tensor(theta))\n    return self.potential_fn(\n        theta.to(self._device), track_gradients=track_gradients\n    )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.map","title":"<code>map(x=None, num_iter=1000, num_to_optimize=100, learning_rate=0.01, init_method='proposal', num_init_samples=1000, save_best_every=10, show_progress_bars=False, force_update=False)</code>","text":"<p>Returns the maximum-a-posteriori estimate (MAP).</p> <p>The method can be interrupted (Ctrl-C) when the user sees that the log-probability converges. The best estimate will be saved in <code>self._map</code> and can be accessed with <code>self.map()</code>. The MAP is obtained by running gradient ascent from a given number of starting positions (samples from the posterior with the highest log-probability). After the optimization is done, we select the parameter set that has the highest log-probability after the optimization.</p> <p>Warning: The default values used by this function are not well-tested. They might require hand-tuning for the problem at hand.</p> <p>For developers: if the prior is a <code>BoxUniform</code>, we carry out the optimization in unbounded space and transform the result back into bounded space.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>Optional[Tensor]</code> <p>Deprecated - use <code>.set_default_x()</code> prior to <code>.map()</code>.</p> <code>None</code> <code>num_iter</code> <code>int</code> <p>Number of optimization steps that the algorithm takes to find the MAP.</p> <code>1000</code> <code>learning_rate</code> <code>float</code> <p>Learning rate of the optimizer.</p> <code>0.01</code> <code>init_method</code> <code>Union[str, Tensor]</code> <p>How to select the starting parameters for the optimization. If it is a string, it can be either [<code>posterior</code>, <code>prior</code>], which samples the respective distribution <code>num_init_samples</code> times. If it is a tensor, the tensor will be used as init locations.</p> <code>'proposal'</code> <code>num_init_samples</code> <code>int</code> <p>Draw this number of samples from the posterior and evaluate the log-probability of all of them.</p> <code>1000</code> <code>num_to_optimize</code> <code>int</code> <p>From the drawn <code>num_init_samples</code>, use the <code>num_to_optimize</code> with highest log-probability as the initial points for the optimization.</p> <code>100</code> <code>save_best_every</code> <code>int</code> <p>The best log-probability is computed, saved in the <code>map</code>-attribute, and printed every <code>save_best_every</code>-th iteration. Computing the best log-probability creates a significant overhead (thus, the default is <code>10</code>.)</p> <code>10</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during sampling from the posterior.</p> <code>False</code> <code>force_update</code> <code>bool</code> <p>Whether to re-calculate the MAP when x is unchanged and have a cached value.</p> <code>False</code> <code>log_prob_kwargs</code> <p>Will be empty for SNLE and SNRE. Will contain {\u2018norm_posterior\u2019: True} for SNPE.</p> required <p>Returns:</p> Type Description <code>Tensor</code> <p>The MAP estimate.</p> Source code in <code>sbi/inference/posteriors/mcmc_posterior.py</code> <pre><code>def map(\n    self,\n    x: Optional[Tensor] = None,\n    num_iter: int = 1_000,\n    num_to_optimize: int = 100,\n    learning_rate: float = 0.01,\n    init_method: Union[str, Tensor] = \"proposal\",\n    num_init_samples: int = 1_000,\n    save_best_every: int = 10,\n    show_progress_bars: bool = False,\n    force_update: bool = False,\n) -&gt; Tensor:\n    r\"\"\"Returns the maximum-a-posteriori estimate (MAP).\n\n    The method can be interrupted (Ctrl-C) when the user sees that the\n    log-probability converges. The best estimate will be saved in `self._map` and\n    can be accessed with `self.map()`. The MAP is obtained by running gradient\n    ascent from a given number of starting positions (samples from the posterior\n    with the highest log-probability). After the optimization is done, we select the\n    parameter set that has the highest log-probability after the optimization.\n\n    Warning: The default values used by this function are not well-tested. They\n    might require hand-tuning for the problem at hand.\n\n    For developers: if the prior is a `BoxUniform`, we carry out the optimization\n    in unbounded space and transform the result back into bounded space.\n\n    Args:\n        x: Deprecated - use `.set_default_x()` prior to `.map()`.\n        num_iter: Number of optimization steps that the algorithm takes\n            to find the MAP.\n        learning_rate: Learning rate of the optimizer.\n        init_method: How to select the starting parameters for the optimization. If\n            it is a string, it can be either [`posterior`, `prior`], which samples\n            the respective distribution `num_init_samples` times. If it is a\n            tensor, the tensor will be used as init locations.\n        num_init_samples: Draw this number of samples from the posterior and\n            evaluate the log-probability of all of them.\n        num_to_optimize: From the drawn `num_init_samples`, use the\n            `num_to_optimize` with highest log-probability as the initial points\n            for the optimization.\n        save_best_every: The best log-probability is computed, saved in the\n            `map`-attribute, and printed every `save_best_every`-th iteration.\n            Computing the best log-probability creates a significant overhead\n            (thus, the default is `10`.)\n        show_progress_bars: Whether to show a progressbar during sampling from\n            the posterior.\n        force_update: Whether to re-calculate the MAP when x is unchanged and\n            have a cached value.\n        log_prob_kwargs: Will be empty for SNLE and SNRE. Will contain\n            {'norm_posterior': True} for SNPE.\n\n    Returns:\n        The MAP estimate.\n    \"\"\"\n    return super().map(\n        x=x,\n        num_iter=num_iter,\n        num_to_optimize=num_to_optimize,\n        learning_rate=learning_rate,\n        init_method=init_method,\n        num_init_samples=num_init_samples,\n        save_best_every=save_best_every,\n        show_progress_bars=show_progress_bars,\n        force_update=force_update,\n    )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.sample","title":"<code>sample(sample_shape=torch.Size(), x=None, method=None, thin=None, warmup_steps=None, num_chains=None, init_strategy=None, init_strategy_parameters=None, init_strategy_num_candidates=None, mcmc_parameters={}, mcmc_method=None, sample_with=None, num_workers=None, show_progress_bars=True)</code>","text":"<p>Return samples from posterior distribution \\(p(\\theta|x)\\) with MCMC.</p> <p>Check the <code>__init__()</code> method for a description of all arguments as well as their default values.</p> <p>Parameters:</p> Name Type Description Default <code>sample_shape</code> <code>Shape</code> <p>Desired shape of samples that are drawn from posterior. If sample_shape is multidimensional we simply draw <code>sample_shape.numel()</code> samples and then reshape into the desired shape.</p> <code>Size()</code> <code>mcmc_parameters</code> <code>Dict</code> <p>Dictionary that is passed only to support the API of <code>sbi</code> v0.17.2 or older.</p> <code>{}</code> <code>mcmc_method</code> <code>Optional[str]</code> <p>This argument only exists to keep backward-compatibility with <code>sbi</code> v0.17.2 or older. Please use <code>method</code> instead.</p> <code>None</code> <code>sample_with</code> <code>Optional[str]</code> <p>This argument only exists to keep backward-compatibility with <code>sbi</code> v0.17.2 or older. If it is set, we instantly raise an error.</p> <code>None</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show sampling progress monitor.</p> <code>True</code> <p>Returns:</p> Type Description <code>Tensor</code> <p>Samples from posterior.</p> Source code in <code>sbi/inference/posteriors/mcmc_posterior.py</code> <pre><code>def sample(\n    self,\n    sample_shape: Shape = torch.Size(),\n    x: Optional[Tensor] = None,\n    method: Optional[str] = None,\n    thin: Optional[int] = None,\n    warmup_steps: Optional[int] = None,\n    num_chains: Optional[int] = None,\n    init_strategy: Optional[str] = None,\n    init_strategy_parameters: Optional[Dict[str, Any]] = None,\n    init_strategy_num_candidates: Optional[int] = None,\n    mcmc_parameters: Dict = {},\n    mcmc_method: Optional[str] = None,\n    sample_with: Optional[str] = None,\n    num_workers: Optional[int] = None,\n    show_progress_bars: bool = True,\n) -&gt; Tensor:\n    r\"\"\"Return samples from posterior distribution $p(\\theta|x)$ with MCMC.\n\n    Check the `__init__()` method for a description of all arguments as well as\n    their default values.\n\n    Args:\n        sample_shape: Desired shape of samples that are drawn from posterior. If\n            sample_shape is multidimensional we simply draw `sample_shape.numel()`\n            samples and then reshape into the desired shape.\n        mcmc_parameters: Dictionary that is passed only to support the API of\n            `sbi` v0.17.2 or older.\n        mcmc_method: This argument only exists to keep backward-compatibility with\n            `sbi` v0.17.2 or older. Please use `method` instead.\n        sample_with: This argument only exists to keep backward-compatibility with\n            `sbi` v0.17.2 or older. If it is set, we instantly raise an error.\n        show_progress_bars: Whether to show sampling progress monitor.\n\n    Returns:\n        Samples from posterior.\n    \"\"\"\n    self.potential_fn.set_x(self._x_else_default_x(x))\n\n    # Replace arguments that were not passed with their default.\n    method = self.method if method is None else method\n    thin = self.thin if thin is None else thin\n    warmup_steps = self.warmup_steps if warmup_steps is None else warmup_steps\n    num_chains = self.num_chains if num_chains is None else num_chains\n    init_strategy = self.init_strategy if init_strategy is None else init_strategy\n    num_workers = self.num_workers if num_workers is None else num_workers\n    init_strategy_parameters = (\n        self.init_strategy_parameters\n        if init_strategy_parameters is None\n        else init_strategy_parameters\n    )\n    if init_strategy_num_candidates is not None:\n        warn(\n            \"\"\"Passing `init_strategy_num_candidates` is deprecated as of sbi\n            v0.19.0. Instead, use e.g.,\n            `init_strategy_parameters={\"num_candidate_samples\": 1000}`\"\"\"\n        )\n        self.init_strategy_parameters[\n            \"num_candidate_samples\"\n        ] = init_strategy_num_candidates\n    if sample_with is not None:\n        raise ValueError(\n            f\"You set `sample_with={sample_with}`. As of sbi v0.18.0, setting \"\n            f\"`sample_with` is no longer supported. You have to rerun \"\n            f\"`.build_posterior(sample_with={sample_with}).`\"\n        )\n    if mcmc_method is not None:\n        warn(\n            \"You passed `mcmc_method` to `.sample()`. As of sbi v0.18.0, this \"\n            \"is deprecated and will be removed in a future release. Use `method` \"\n            \"instead of `mcmc_method`.\"\n        )\n        method = mcmc_method\n    if mcmc_parameters:\n        warn(\n            \"You passed `mcmc_parameters` to `.sample()`. As of sbi v0.18.0, this \"\n            \"is deprecated and will be removed in a future release. Instead, pass \"\n            \"the variable to `.sample()` directly, e.g. \"\n            \"`posterior.sample((1,), num_chains=5)`.\"\n        )\n    # The following lines are only for backwards compatibility with sbi v0.17.2 or\n    # older.\n    m_p = mcmc_parameters  # define to shorten the variable name\n    method = _maybe_use_dict_entry(method, \"mcmc_method\", m_p)\n    thin = _maybe_use_dict_entry(thin, \"thin\", m_p)\n    warmup_steps = _maybe_use_dict_entry(warmup_steps, \"warmup_steps\", m_p)\n    num_chains = _maybe_use_dict_entry(num_chains, \"num_chains\", m_p)\n    init_strategy = _maybe_use_dict_entry(init_strategy, \"init_strategy\", m_p)\n    self.potential_ = self._prepare_potential(method)  # type: ignore\n\n    initial_params = self._get_initial_params(\n        init_strategy,  # type: ignore\n        num_chains,  # type: ignore\n        num_workers,\n        show_progress_bars,\n        **init_strategy_parameters,\n    )\n    num_samples = torch.Size(sample_shape).numel()\n\n    track_gradients = method in (\"hmc\", \"nuts\")\n    with torch.set_grad_enabled(track_gradients):\n        if method in (\"slice_np\", \"slice_np_vectorized\"):\n            transformed_samples = self._slice_np_mcmc(\n                num_samples=num_samples,\n                potential_function=self.potential_,\n                initial_params=initial_params,\n                thin=thin,  # type: ignore\n                warmup_steps=warmup_steps,  # type: ignore\n                vectorized=(method == \"slice_np_vectorized\"),\n                num_workers=num_workers,\n                show_progress_bars=show_progress_bars,\n            )\n        elif method in (\"hmc\", \"nuts\", \"slice\"):\n            transformed_samples = self._pyro_mcmc(\n                num_samples=num_samples,\n                potential_function=self.potential_,\n                initial_params=initial_params,\n                mcmc_method=method,  # type: ignore\n                thin=thin,  # type: ignore\n                warmup_steps=warmup_steps,  # type: ignore\n                num_chains=num_chains,\n                show_progress_bars=show_progress_bars,\n            )\n        else:\n            raise NameError\n\n    samples = self.theta_transform.inv(transformed_samples)\n\n    return samples.reshape((*sample_shape, -1))  # type: ignore\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.mcmc_posterior.MCMCPosterior.set_mcmc_method","title":"<code>set_mcmc_method(method)</code>","text":"<p>Sets sampling method to for MCMC and returns <code>NeuralPosterior</code>.</p> <p>Parameters:</p> Name Type Description Default <code>method</code> <code>str</code> <p>Method to use.</p> required <p>Returns:</p> Type Description <code>NeuralPosterior</code> <p><code>NeuralPosterior</code> for chainable calls.</p> Source code in <code>sbi/inference/posteriors/mcmc_posterior.py</code> <pre><code>def set_mcmc_method(self, method: str) -&gt; \"NeuralPosterior\":\n    \"\"\"Sets sampling method to for MCMC and returns `NeuralPosterior`.\n\n    Args:\n        method: Method to use.\n\n    Returns:\n        `NeuralPosterior` for chainable calls.\n    \"\"\"\n    self._mcmc_method = method\n    return self\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.rejection_posterior.RejectionPosterior.__init__","title":"<code>__init__(potential_fn, proposal, theta_transform=None, max_sampling_batch_size=10000, num_samples_to_find_max=10000, num_iter_to_find_max=100, m=1.2, device=None, x_shape=None)</code>","text":"<p>Parameters:</p> Name Type Description Default <code>potential_fn</code> <code>Callable</code> <p>The potential function from which to draw samples.</p> required <code>proposal</code> <code>Any</code> <p>The proposal distribution.</p> required <code>theta_transform</code> <code>Optional[TorchTransform]</code> <p>Transformation that is applied to parameters. Is not used during but only when calling <code>.map()</code>.</p> <code>None</code> <code>max_sampling_batch_size</code> <code>int</code> <p>The batchsize of samples being drawn from the proposal at every iteration.</p> <code>10000</code> <code>num_samples_to_find_max</code> <code>int</code> <p>The number of samples that are used to find the maximum of the <code>potential_fn / proposal</code> ratio.</p> <code>10000</code> <code>num_iter_to_find_max</code> <code>int</code> <p>The number of gradient ascent iterations to find the maximum of the <code>potential_fn / proposal</code> ratio.</p> <code>100</code> <code>m</code> <code>float</code> <p>Multiplier to the <code>potential_fn / proposal</code> ratio.</p> <code>1.2</code> <code>device</code> <code>Optional[str]</code> <p>Training device, e.g., \u201ccpu\u201d, \u201ccuda\u201d or \u201ccuda:0\u201d. If None, <code>potential_fn.device</code> is used.</p> <code>None</code> <code>x_shape</code> <code>Optional[Size]</code> <p>Shape of a single simulator output. If passed, it is used to check the shape of the observed data and give a descriptive error.</p> <code>None</code> Source code in <code>sbi/inference/posteriors/rejection_posterior.py</code> <pre><code>def __init__(\n    self,\n    potential_fn: Callable,\n    proposal: Any,\n    theta_transform: Optional[TorchTransform] = None,\n    max_sampling_batch_size: int = 10_000,\n    num_samples_to_find_max: int = 10_000,\n    num_iter_to_find_max: int = 100,\n    m: float = 1.2,\n    device: Optional[str] = None,\n    x_shape: Optional[torch.Size] = None,\n):\n    \"\"\"\n    Args:\n        potential_fn: The potential function from which to draw samples.\n        proposal: The proposal distribution.\n        theta_transform: Transformation that is applied to parameters. Is not used\n            during but only when calling `.map()`.\n        max_sampling_batch_size: The batchsize of samples being drawn from\n            the proposal at every iteration.\n        num_samples_to_find_max: The number of samples that are used to find the\n            maximum of the `potential_fn / proposal` ratio.\n        num_iter_to_find_max: The number of gradient ascent iterations to find the\n            maximum of the `potential_fn / proposal` ratio.\n        m: Multiplier to the `potential_fn / proposal` ratio.\n        device: Training device, e.g., \"cpu\", \"cuda\" or \"cuda:0\". If None,\n            `potential_fn.device` is used.\n        x_shape: Shape of a single simulator output. If passed, it is used to check\n            the shape of the observed data and give a descriptive error.\n    \"\"\"\n    super().__init__(\n        potential_fn,\n        theta_transform=theta_transform,\n        device=device,\n        x_shape=x_shape,\n    )\n\n    self.proposal = proposal\n    self.max_sampling_batch_size = max_sampling_batch_size\n    self.num_samples_to_find_max = num_samples_to_find_max\n    self.num_iter_to_find_max = num_iter_to_find_max\n    self.m = m\n\n    self._purpose = (\n        \"It provides rejection sampling to .sample() from the posterior and \"\n        \"can evaluate the _unnormalized_ posterior density with .log_prob().\"\n    )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.rejection_posterior.RejectionPosterior.log_prob","title":"<code>log_prob(theta, x=None, track_gradients=False)</code>","text":"<p>Returns the log-probability of theta under the posterior.</p> <p>Parameters:</p> Name Type Description Default <code>theta</code> <code>Tensor</code> <p>Parameters \\(\\theta\\).</p> required <code>track_gradients</code> <code>bool</code> <p>Whether the returned tensor supports tracking gradients. This can be helpful for e.g. sensitivity analysis, but increases memory consumption.</p> <code>False</code> <p>Returns:</p> Type Description <code>Tensor</code> <p><code>len($\\theta$)</code>-shaped log-probability.</p> Source code in <code>sbi/inference/posteriors/rejection_posterior.py</code> <pre><code>def log_prob(\n    self, theta: Tensor, x: Optional[Tensor] = None, track_gradients: bool = False\n) -&gt; Tensor:\n    r\"\"\"Returns the log-probability of theta under the posterior.\n\n    Args:\n        theta: Parameters $\\theta$.\n        track_gradients: Whether the returned tensor supports tracking gradients.\n            This can be helpful for e.g. sensitivity analysis, but increases memory\n            consumption.\n\n    Returns:\n        `len($\\theta$)`-shaped log-probability.\n    \"\"\"\n    warn(\n        \"`.log_prob()` is deprecated for methods that can only evaluate the log-probability up to a normalizing constant. Use `.potential()` instead.\"\n    )\n    warn(\"The log-probability is unnormalized!\")\n\n    self.potential_fn.set_x(self._x_else_default_x(x))\n\n    theta = ensure_theta_batched(torch.as_tensor(theta))\n    return self.potential_fn(\n        theta.to(self._device), track_gradients=track_gradients\n    )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.rejection_posterior.RejectionPosterior.map","title":"<code>map(x=None, num_iter=1000, num_to_optimize=100, learning_rate=0.01, init_method='proposal', num_init_samples=1000, save_best_every=10, show_progress_bars=False, force_update=False)</code>","text":"<p>Returns the maximum-a-posteriori estimate (MAP).</p> <p>The method can be interrupted (Ctrl-C) when the user sees that the log-probability converges. The best estimate will be saved in <code>self._map</code> and can be accessed with <code>self.map()</code>. The MAP is obtained by running gradient ascent from a given number of starting positions (samples from the posterior with the highest log-probability). After the optimization is done, we select the parameter set that has the highest log-probability after the optimization.</p> <p>Warning: The default values used by this function are not well-tested. They might require hand-tuning for the problem at hand.</p> <p>For developers: if the prior is a <code>BoxUniform</code>, we carry out the optimization in unbounded space and transform the result back into bounded space.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>Optional[Tensor]</code> <p>Deprecated - use <code>.set_default_x()</code> prior to <code>.map()</code>.</p> <code>None</code> <code>num_iter</code> <code>int</code> <p>Number of optimization steps that the algorithm takes to find the MAP.</p> <code>1000</code> <code>learning_rate</code> <code>float</code> <p>Learning rate of the optimizer.</p> <code>0.01</code> <code>init_method</code> <code>Union[str, Tensor]</code> <p>How to select the starting parameters for the optimization. If it is a string, it can be either [<code>posterior</code>, <code>prior</code>], which samples the respective distribution <code>num_init_samples</code> times. If it is a tensor, the tensor will be used as init locations.</p> <code>'proposal'</code> <code>num_init_samples</code> <code>int</code> <p>Draw this number of samples from the posterior and evaluate the log-probability of all of them.</p> <code>1000</code> <code>num_to_optimize</code> <code>int</code> <p>From the drawn <code>num_init_samples</code>, use the <code>num_to_optimize</code> with highest log-probability as the initial points for the optimization.</p> <code>100</code> <code>save_best_every</code> <code>int</code> <p>The best log-probability is computed, saved in the <code>map</code>-attribute, and printed every <code>save_best_every</code>-th iteration. Computing the best log-probability creates a significant overhead (thus, the default is <code>10</code>.)</p> <code>10</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during sampling from the posterior.</p> <code>False</code> <code>force_update</code> <code>bool</code> <p>Whether to re-calculate the MAP when x is unchanged and have a cached value.</p> <code>False</code> <code>log_prob_kwargs</code> <p>Will be empty for SNLE and SNRE. Will contain {\u2018norm_posterior\u2019: True} for SNPE.</p> required <p>Returns:</p> Type Description <code>Tensor</code> <p>The MAP estimate.</p> Source code in <code>sbi/inference/posteriors/rejection_posterior.py</code> <pre><code>def map(\n    self,\n    x: Optional[Tensor] = None,\n    num_iter: int = 1_000,\n    num_to_optimize: int = 100,\n    learning_rate: float = 0.01,\n    init_method: Union[str, Tensor] = \"proposal\",\n    num_init_samples: int = 1_000,\n    save_best_every: int = 10,\n    show_progress_bars: bool = False,\n    force_update: bool = False,\n) -&gt; Tensor:\n    r\"\"\"Returns the maximum-a-posteriori estimate (MAP).\n\n    The method can be interrupted (Ctrl-C) when the user sees that the\n    log-probability converges. The best estimate will be saved in `self._map` and\n    can be accessed with `self.map()`. The MAP is obtained by running gradient\n    ascent from a given number of starting positions (samples from the posterior\n    with the highest log-probability). After the optimization is done, we select the\n    parameter set that has the highest log-probability after the optimization.\n\n    Warning: The default values used by this function are not well-tested. They\n    might require hand-tuning for the problem at hand.\n\n    For developers: if the prior is a `BoxUniform`, we carry out the optimization\n    in unbounded space and transform the result back into bounded space.\n\n    Args:\n        x: Deprecated - use `.set_default_x()` prior to `.map()`.\n        num_iter: Number of optimization steps that the algorithm takes\n            to find the MAP.\n        learning_rate: Learning rate of the optimizer.\n        init_method: How to select the starting parameters for the optimization. If\n            it is a string, it can be either [`posterior`, `prior`], which samples\n            the respective distribution `num_init_samples` times. If it is a\n            tensor, the tensor will be used as init locations.\n        num_init_samples: Draw this number of samples from the posterior and\n            evaluate the log-probability of all of them.\n        num_to_optimize: From the drawn `num_init_samples`, use the\n            `num_to_optimize` with highest log-probability as the initial points\n            for the optimization.\n        save_best_every: The best log-probability is computed, saved in the\n            `map`-attribute, and printed every `save_best_every`-th iteration.\n            Computing the best log-probability creates a significant overhead\n            (thus, the default is `10`.)\n        show_progress_bars: Whether to show a progressbar during sampling from\n            the posterior.\n        force_update: Whether to re-calculate the MAP when x is unchanged and\n            have a cached value.\n        log_prob_kwargs: Will be empty for SNLE and SNRE. Will contain\n            {'norm_posterior': True} for SNPE.\n\n    Returns:\n        The MAP estimate.\n    \"\"\"\n    return super().map(\n        x=x,\n        num_iter=num_iter,\n        num_to_optimize=num_to_optimize,\n        learning_rate=learning_rate,\n        init_method=init_method,\n        num_init_samples=num_init_samples,\n        save_best_every=save_best_every,\n        show_progress_bars=show_progress_bars,\n        force_update=force_update,\n    )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.rejection_posterior.RejectionPosterior.sample","title":"<code>sample(sample_shape=torch.Size(), x=None, max_sampling_batch_size=None, num_samples_to_find_max=None, num_iter_to_find_max=None, m=None, sample_with=None, show_progress_bars=True)</code>","text":"<p>Return samples from posterior \\(p(\\theta|x)\\) via rejection sampling.</p> <p>Parameters:</p> Name Type Description Default <code>sample_shape</code> <code>Shape</code> <p>Desired shape of samples that are drawn from posterior. If sample_shape is multidimensional we simply draw <code>sample_shape.numel()</code> samples and then reshape into the desired shape.</p> <code>Size()</code> <code>sample_with</code> <code>Optional[str]</code> <p>This argument only exists to keep backward-compatibility with <code>sbi</code> v0.17.2 or older. If it is set, we instantly raise an error.</p> <code>None</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show sampling progress monitor.</p> <code>True</code> <p>Returns:</p> Type Description <p>Samples from posterior.</p> Source code in <code>sbi/inference/posteriors/rejection_posterior.py</code> <pre><code>def sample(\n    self,\n    sample_shape: Shape = torch.Size(),\n    x: Optional[Tensor] = None,\n    max_sampling_batch_size: Optional[int] = None,\n    num_samples_to_find_max: Optional[int] = None,\n    num_iter_to_find_max: Optional[int] = None,\n    m: Optional[float] = None,\n    sample_with: Optional[str] = None,\n    show_progress_bars: bool = True,\n):\n    r\"\"\"Return samples from posterior $p(\\theta|x)$ via rejection sampling.\n\n    Args:\n        sample_shape: Desired shape of samples that are drawn from posterior. If\n            sample_shape is multidimensional we simply draw `sample_shape.numel()`\n            samples and then reshape into the desired shape.\n        sample_with: This argument only exists to keep backward-compatibility with\n            `sbi` v0.17.2 or older. If it is set, we instantly raise an error.\n        show_progress_bars: Whether to show sampling progress monitor.\n\n    Returns:\n        Samples from posterior.\n    \"\"\"\n    num_samples = torch.Size(sample_shape).numel()\n    self.potential_fn.set_x(self._x_else_default_x(x))\n\n    potential = partial(self.potential_fn, track_gradients=True)\n\n    if sample_with is not None:\n        raise ValueError(\n            f\"You set `sample_with={sample_with}`. As of sbi v0.18.0, setting \"\n            f\"`sample_with` is no longer supported. You have to rerun \"\n            f\"`.build_posterior(sample_with={sample_with}).`\"\n        )\n    # Replace arguments that were not passed with their default.\n    max_sampling_batch_size = (\n        self.max_sampling_batch_size\n        if max_sampling_batch_size is None\n        else max_sampling_batch_size\n    )\n    num_samples_to_find_max = (\n        self.num_samples_to_find_max\n        if num_samples_to_find_max is None\n        else num_samples_to_find_max\n    )\n    num_iter_to_find_max = (\n        self.num_iter_to_find_max\n        if num_iter_to_find_max is None\n        else num_iter_to_find_max\n    )\n    m = self.m if m is None else m\n\n    samples, _ = rejection_sample(\n        potential,\n        proposal=self.proposal,\n        num_samples=num_samples,\n        show_progress_bars=show_progress_bars,\n        warn_acceptance=0.01,\n        max_sampling_batch_size=max_sampling_batch_size,\n        num_samples_to_find_max=num_samples_to_find_max,\n        num_iter_to_find_max=num_iter_to_find_max,\n        m=m,\n        device=self._device,\n    )\n\n    return samples.reshape((*sample_shape, -1))\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.vi_posterior.VIPosterior.q","title":"<code>q: Distribution</code>  <code>property</code> <code>writable</code>","text":"<p>Returns the variational posterior.</p>"},{"location":"reference/#sbi.inference.posteriors.vi_posterior.VIPosterior.vi_method","title":"<code>vi_method: str</code>  <code>property</code> <code>writable</code>","text":"<p>Variational inference method e.g. one of [rKL, fKL, IW, alpha].</p>"},{"location":"reference/#sbi.inference.posteriors.vi_posterior.VIPosterior.__init__","title":"<code>__init__(potential_fn, prior=None, q='maf', theta_transform=None, vi_method='rKL', device='cpu', x_shape=None, parameters=[], modules=[])</code>","text":"<p>Parameters:</p> Name Type Description Default <code>potential_fn</code> <code>Callable</code> <p>The potential function from which to draw samples.</p> required <code>prior</code> <code>Optional[TorchDistribution]</code> <p>This is the prior distribution. Note that this is only used to check/construct the variational distribution or within some quality metrics. Please make sure that this matches with the prior within the potential_fn. If <code>None</code> is given, we will try to infer it from potential_fn or q, if this fails we raise an Error.</p> <code>None</code> <code>q</code> <code>Union[str, PyroTransformedDistribution, VIPosterior, Callable]</code> <p>Variational distribution, either string, <code>TransformedDistribution</code>, or a <code>VIPosterior</code> object. This specifies a parametric class of distribution over which the best possible posterior approximation is searched. For string input, we currently support [nsf, scf, maf, mcf, gaussian, gaussian_diag]. You can also specify your own variational family by passing a pyro <code>TransformedDistribution</code>. Additionally, we allow a <code>Callable</code>, which allows you the pass a <code>builder</code> function, which if called returns a distribution. This may be useful for setting the hyperparameters e.g. <code>num_transfroms</code> within the <code>get_flow_builder</code> method specifying the number of transformations within a normalizing flow. If q is already a <code>VIPosterior</code>, then the arguments will be copied from it (relevant for multi-round training).</p> <code>'maf'</code> <code>theta_transform</code> <code>Optional[TorchTransform]</code> <p>Maps form prior support to unconstrained space. The inverse is used here to ensure that the posterior support is equal to that of the prior.</p> <code>None</code> <code>vi_method</code> <code>str</code> <p>This specifies the variational methods which are used to fit q to the posterior. We currently support [rKL, fKL, IW, alpha]. Note that some of the divergences are <code>mode seeking</code> i.e. they underestimate variance and collapse on multimodal targets (<code>rKL</code>, <code>alpha</code> for alpha &gt; 1) and some are <code>mass covering</code> i.e. they overestimate variance but typically cover all modes (<code>fKL</code>, <code>IW</code>, <code>alpha</code> for alpha &lt; 1).</p> <code>'rKL'</code> <code>device</code> <code>str</code> <p>Training device, e.g., <code>cpu</code>, <code>cuda</code> or <code>cuda:0</code>. We will ensure that all other objects are also on this device.</p> <code>'cpu'</code> <code>x_shape</code> <code>Optional[Size]</code> <p>Shape of a single simulator output. If passed, it is used to check the shape of the observed data and give a descriptive error.</p> <code>None</code> <code>parameters</code> <code>Iterable</code> <p>List of parameters of the variational posterior. This is only required for user-defined q i.e. if q does not have a <code>parameters</code> attribute.</p> <code>[]</code> <code>modules</code> <code>Iterable</code> <p>List of modules of the variational posterior. This is only required for user-defined q i.e. if q does not have a <code>modules</code> attribute.</p> <code>[]</code> Source code in <code>sbi/inference/posteriors/vi_posterior.py</code> <pre><code>def __init__(\n    self,\n    potential_fn: Callable,\n    prior: Optional[TorchDistribution] = None,\n    q: Union[str, PyroTransformedDistribution, \"VIPosterior\", Callable] = \"maf\",\n    theta_transform: Optional[TorchTransform] = None,\n    vi_method: str = \"rKL\",\n    device: str = \"cpu\",\n    x_shape: Optional[torch.Size] = None,\n    parameters: Iterable = [],\n    modules: Iterable = [],\n):\n    \"\"\"\n    Args:\n        potential_fn: The potential function from which to draw samples.\n        prior: This is the prior distribution. Note that this is only\n            used to check/construct the variational distribution or within some\n            quality metrics. Please make sure that this matches with the prior\n            within the potential_fn. If `None` is given, we will try to infer it\n            from potential_fn or q, if this fails we raise an Error.\n        q: Variational distribution, either string, `TransformedDistribution`, or a\n            `VIPosterior` object. This specifies a parametric class of distribution\n            over which the best possible posterior approximation is searched. For\n            string input, we currently support [nsf, scf, maf, mcf, gaussian,\n            gaussian_diag]. You can also specify your own variational family by\n            passing a pyro `TransformedDistribution`.\n            Additionally, we allow a `Callable`, which allows you the pass a\n            `builder` function, which if called returns a distribution. This may be\n            useful for setting the hyperparameters e.g. `num_transfroms` within the\n            `get_flow_builder` method specifying the number of transformations\n            within a normalizing flow. If q is already a `VIPosterior`, then the\n            arguments will be copied from it (relevant for multi-round training).\n        theta_transform: Maps form prior support to unconstrained space. The\n            inverse is used here to ensure that the posterior support is equal to\n            that of the prior.\n        vi_method: This specifies the variational methods which are used to fit q to\n            the posterior. We currently support [rKL, fKL, IW, alpha]. Note that\n            some of the divergences are `mode seeking` i.e. they underestimate\n            variance and collapse on multimodal targets (`rKL`, `alpha` for alpha &gt;\n            1) and some are `mass covering` i.e. they overestimate variance but\n            typically cover all modes (`fKL`, `IW`, `alpha` for alpha &lt; 1).\n        device: Training device, e.g., `cpu`, `cuda` or `cuda:0`. We will ensure\n            that all other objects are also on this device.\n        x_shape: Shape of a single simulator output. If passed, it is used to check\n            the shape of the observed data and give a descriptive error.\n        parameters: List of parameters of the variational posterior. This is only\n            required for user-defined q i.e. if q does not have a `parameters`\n            attribute.\n        modules: List of modules of the variational posterior. This is only\n            required for user-defined q i.e. if q does not have a `modules`\n            attribute.\n    \"\"\"\n    super().__init__(potential_fn, theta_transform, device, x_shape=x_shape)\n\n    # Especially the prior may be on another device -&gt; move it...\n    self._device = device\n    self.potential_fn.device = device\n    move_all_tensor_to_device(self.potential_fn, device)\n\n    # Get prior and previous builds\n    if prior is not None:\n        self._prior = prior\n    elif hasattr(self.potential_fn, \"prior\") and isinstance(\n        self.potential_fn.prior, Distribution\n    ):\n        self._prior = self.potential_fn.prior\n    elif isinstance(q, VIPosterior) and isinstance(q._prior, Distribution):\n        self._prior = q._prior\n    else:\n        raise ValueError(\n            \"We could not find a suitable prior distribution within `potential_fn`\"\n            \"or `q` (if a VIPosterior is given). Please explicitly specify a prior.\"\n        )\n    move_all_tensor_to_device(self._prior, device)\n    self._optimizer = None\n\n    # In contrast to MCMC we want to project into constrained space.\n    if theta_transform is None:\n        self.link_transform = mcmc_transform(self._prior).inv\n    else:\n        self.link_transform = theta_transform.inv\n\n    # This will set the variational distribution and VI method\n    self.set_q(q, parameters=parameters, modules=modules)\n    self.set_vi_method(vi_method)\n\n    self._purpose = (\n        \"It provides Variational inference to .sample() from the posterior and \"\n        \"can evaluate the _normalized_ posterior density with .log_prob().\"\n    )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.vi_posterior.VIPosterior.evaluate","title":"<code>evaluate(quality_control_metric='psis', N=int(50000.0))</code>","text":"<p>This function will evaluate the quality of the variational posterior distribution. We currently support two different metrics of type <code>psis</code>, which checks the quality based on the tails of importance weights (there should not be much with a large one), or <code>prop</code> which checks the proportionality between q and potential_fn.</p> <p>NOTE: In our experience <code>prop</code> is sensitive to distinguish <code>good</code> from <code>ok</code> whereas <code>psis</code> is more sensitive in distinguishing <code>very bad</code> from <code>ok</code>.</p> <p>Parameters:</p> Name Type Description Default <code>quality_control_metric</code> <code>str</code> <p>The metric of choice, we currently support [psis, prop, prop_prior].</p> <code>'psis'</code> <code>N</code> <code>int</code> <p>Number of samples which is used to evaluate the metric.</p> <code>int(50000.0)</code> Source code in <code>sbi/inference/posteriors/vi_posterior.py</code> <pre><code>def evaluate(self, quality_control_metric: str = \"psis\", N: int = int(5e4)) -&gt; None:\n    \"\"\"This function will evaluate the quality of the variational posterior\n    distribution. We currently support two different metrics of type `psis`, which\n    checks the quality based on the tails of importance weights (there should not be\n    much with a large one), or `prop` which checks the proportionality between q\n    and potential_fn.\n\n    NOTE: In our experience `prop` is sensitive to distinguish ``good`` from ``ok``\n    whereas `psis` is more sensitive in distinguishing `very bad` from `ok`.\n\n    Args:\n        quality_control_metric: The metric of choice, we currently support [psis,\n            prop, prop_prior].\n        N: Number of samples which is used to evaluate the metric.\n    \"\"\"\n    quality_control_fn, quality_control_msg = get_quality_metric(\n        quality_control_metric\n    )\n    metric = round(float(quality_control_fn(self, N=N)), 3)\n    print(f\"Quality Score: {metric} \" + quality_control_msg)\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.vi_posterior.VIPosterior.log_prob","title":"<code>log_prob(theta, x=None, track_gradients=False)</code>","text":"<p>Returns the log-probability of theta under the variational posterior.</p> <p>Parameters:</p> Name Type Description Default <code>theta</code> <code>Tensor</code> <p>Parameters</p> required <code>track_gradients</code> <code>bool</code> <p>Whether the returned tensor supports tracking gradients. This can be helpful for e.g. sensitivity analysis but increases memory consumption.</p> <code>False</code> <p>Returns:</p> Type Description <code>Tensor</code> <p><code>len($\\theta$)</code>-shaped log-probability.</p> Source code in <code>sbi/inference/posteriors/vi_posterior.py</code> <pre><code>def log_prob(\n    self,\n    theta: Tensor,\n    x: Optional[Tensor] = None,\n    track_gradients: bool = False,\n) -&gt; Tensor:\n    r\"\"\"Returns the log-probability of theta under the variational posterior.\n\n    Args:\n        theta: Parameters\n        track_gradients: Whether the returned tensor supports tracking gradients.\n            This can be helpful for e.g. sensitivity analysis but increases memory\n            consumption.\n\n    Returns:\n        `len($\\theta$)`-shaped log-probability.\n    \"\"\"\n    x = self._x_else_default_x(x)\n    if self._trained_on is None or (x != self._trained_on).all():\n        raise AttributeError(\n            f\"The variational posterior was not fit using observation {x}.\\\n                 Please train.\"\n        )\n    with torch.set_grad_enabled(track_gradients):\n        theta = ensure_theta_batched(torch.as_tensor(theta))\n        return self.q.log_prob(theta)\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.vi_posterior.VIPosterior.map","title":"<code>map(x=None, num_iter=1000, num_to_optimize=100, learning_rate=0.01, init_method='proposal', num_init_samples=10000, save_best_every=10, show_progress_bars=False, force_update=False)</code>","text":"<p>Returns the maximum-a-posteriori estimate (MAP).</p> <p>The method can be interrupted (Ctrl-C) when the user sees that the log-probability converges. The best estimate will be saved in <code>self._map</code> and can be accessed with <code>self.map()</code>. The MAP is obtained by running gradient ascent from a given number of starting positions (samples from the posterior with the highest log-probability). After the optimization is done, we select the parameter set that has the highest log-probability after the optimization.</p> <p>Warning: The default values used by this function are not well-tested. They might require hand-tuning for the problem at hand.</p> <p>For developers: if the prior is a <code>BoxUniform</code>, we carry out the optimization in unbounded space and transform the result back into bounded space.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>Optional[TorchTensor]</code> <p>Deprecated - use <code>.set_default_x()</code> prior to <code>.map()</code>.</p> <code>None</code> <code>num_iter</code> <code>int</code> <p>Number of optimization steps that the algorithm takes to find the MAP.</p> <code>1000</code> <code>learning_rate</code> <code>float</code> <p>Learning rate of the optimizer.</p> <code>0.01</code> <code>init_method</code> <code>Union[str, TorchTensor]</code> <p>How to select the starting parameters for the optimization. If it is a string, it can be either [<code>posterior</code>, <code>prior</code>], which samples the respective distribution <code>num_init_samples</code> times. If it is a tensor, the tensor will be used as init locations.</p> <code>'proposal'</code> <code>num_init_samples</code> <code>int</code> <p>Draw this number of samples from the posterior and evaluate the log-probability of all of them.</p> <code>10000</code> <code>num_to_optimize</code> <code>int</code> <p>From the drawn <code>num_init_samples</code>, use the <code>num_to_optimize</code> with highest log-probability as the initial points for the optimization.</p> <code>100</code> <code>save_best_every</code> <code>int</code> <p>The best log-probability is computed, saved in the <code>map</code>-attribute, and printed every <code>save_best_every</code>-th iteration. Computing the best log-probability creates a significant overhead (thus, the default is <code>10</code>.)</p> <code>10</code> <code>show_progress_bars</code> <code>bool</code> <p>Whether to show a progressbar during sampling from the posterior.</p> <code>False</code> <code>force_update</code> <code>bool</code> <p>Whether to re-calculate the MAP when x is unchanged and have a cached value.</p> <code>False</code> <code>log_prob_kwargs</code> <p>Will be empty for SNLE and SNRE. Will contain {\u2018norm_posterior\u2019: True} for SNPE.</p> required <p>Returns:</p> Type Description <code>Tensor</code> <p>The MAP estimate.</p> Source code in <code>sbi/inference/posteriors/vi_posterior.py</code> <pre><code>def map(\n    self,\n    x: Optional[TorchTensor] = None,\n    num_iter: int = 1_000,\n    num_to_optimize: int = 100,\n    learning_rate: float = 0.01,\n    init_method: Union[str, TorchTensor] = \"proposal\",\n    num_init_samples: int = 10_000,\n    save_best_every: int = 10,\n    show_progress_bars: bool = False,\n    force_update: bool = False,\n) -&gt; Tensor:\n    r\"\"\"Returns the maximum-a-posteriori estimate (MAP).\n\n    The method can be interrupted (Ctrl-C) when the user sees that the\n    log-probability converges. The best estimate will be saved in `self._map` and\n    can be accessed with `self.map()`. The MAP is obtained by running gradient\n    ascent from a given number of starting positions (samples from the posterior\n    with the highest log-probability). After the optimization is done, we select the\n    parameter set that has the highest log-probability after the optimization.\n\n    Warning: The default values used by this function are not well-tested. They\n    might require hand-tuning for the problem at hand.\n\n    For developers: if the prior is a `BoxUniform`, we carry out the optimization\n    in unbounded space and transform the result back into bounded space.\n\n    Args:\n        x: Deprecated - use `.set_default_x()` prior to `.map()`.\n        num_iter: Number of optimization steps that the algorithm takes\n            to find the MAP.\n        learning_rate: Learning rate of the optimizer.\n        init_method: How to select the starting parameters for the optimization. If\n            it is a string, it can be either [`posterior`, `prior`], which samples\n            the respective distribution `num_init_samples` times. If it is a\n            tensor, the tensor will be used as init locations.\n        num_init_samples: Draw this number of samples from the posterior and\n            evaluate the log-probability of all of them.\n        num_to_optimize: From the drawn `num_init_samples`, use the\n            `num_to_optimize` with highest log-probability as the initial points\n            for the optimization.\n        save_best_every: The best log-probability is computed, saved in the\n            `map`-attribute, and printed every `save_best_every`-th iteration.\n            Computing the best log-probability creates a significant overhead\n            (thus, the default is `10`.)\n        show_progress_bars: Whether to show a progressbar during sampling from\n            the posterior.\n        force_update: Whether to re-calculate the MAP when x is unchanged and\n            have a cached value.\n        log_prob_kwargs: Will be empty for SNLE and SNRE. Will contain\n            {'norm_posterior': True} for SNPE.\n\n    Returns:\n        The MAP estimate.\n    \"\"\"\n    self.proposal = self.q\n    return super().map(\n        x=x,\n        num_iter=num_iter,\n        num_to_optimize=num_to_optimize,\n        learning_rate=learning_rate,\n        init_method=init_method,\n        num_init_samples=num_init_samples,\n        save_best_every=save_best_every,\n        show_progress_bars=show_progress_bars,\n        force_update=force_update,\n    )\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.vi_posterior.VIPosterior.sample","title":"<code>sample(sample_shape=torch.Size(), x=None, **kwargs)</code>","text":"<p>Samples from the variational posterior distribution.</p> <p>Parameters:</p> Name Type Description Default <code>sample_shape</code> <code>Shape</code> <p>Shape of samples</p> <code>Size()</code> <p>Returns:</p> Type Description <code>Tensor</code> <p>Samples from posterior.</p> Source code in <code>sbi/inference/posteriors/vi_posterior.py</code> <pre><code>def sample(\n    self,\n    sample_shape: Shape = torch.Size(),\n    x: Optional[Tensor] = None,\n    **kwargs,\n) -&gt; Tensor:\n    \"\"\"Samples from the variational posterior distribution.\n\n    Args:\n        sample_shape: Shape of samples\n\n    Returns:\n        Samples from posterior.\n    \"\"\"\n    x = self._x_else_default_x(x)\n    if self._trained_on is None or (x != self._trained_on).all():\n        raise AttributeError(\n            f\"The variational posterior was not fit on the specified `default_x` \"\n            f\"{x}. Please train using `posterior.train()`.\"\n        )\n    samples = self.q.sample(torch.Size(sample_shape))\n    return samples.reshape((*sample_shape, samples.shape[-1]))\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.vi_posterior.VIPosterior.set_q","title":"<code>set_q(q, parameters=[], modules=[])</code>","text":"<p>Defines the variational family.</p> <p>You can specify over which parameters/modules we optimize. This is required for custom distributions which e.g. do not inherit nn.Modules or has the function <code>parameters</code> or <code>modules</code> to give direct access to trainable parameters. Further, you can pass a function, which constructs a variational distribution if called.</p> <p>Parameters:</p> Name Type Description Default <code>q</code> <code>Union[str, PyroTransformedDistribution, VIPosterior, Callable]</code> <p>Variational distribution, either string, distribution, or a VIPosterior object. This specifies a parametric class of distribution over which the best possible posterior approximation is searched. For string input, we currently support [nsf, scf, maf, mcf, gaussian, gaussian_diag]. Of course, you can also specify your own variational family by passing a <code>parameterized</code> distribution object i.e. a torch.distributions Distribution with methods <code>parameters</code> returning an iterable of all parameters (you can pass them within the paramters/modules attribute). Additionally, we allow a <code>Callable</code>, which allows you the pass a <code>builder</code> function, which if called returns an distribution. This may be useful for setting the hyperparameters e.g. <code>num_transfroms:int</code> by using the <code>get_flow_builder</code> method specifying the hyperparameters. If q is already a <code>VIPosterior</code>, then the arguments will be copied from it (relevant for multi-round training).</p> required <code>parameters</code> <code>Iterable</code> <p>List of parameters associated with the distribution object.</p> <code>[]</code> <code>modules</code> <code>Iterable</code> <p>List of modules associated with the distribution object.</p> <code>[]</code> Source code in <code>sbi/inference/posteriors/vi_posterior.py</code> <pre><code>def set_q(\n    self,\n    q: Union[str, PyroTransformedDistribution, \"VIPosterior\", Callable],\n    parameters: Iterable = [],\n    modules: Iterable = [],\n) -&gt; None:\n    \"\"\"Defines the variational family.\n\n    You can specify over which parameters/modules we optimize. This is required for\n    custom distributions which e.g. do not inherit nn.Modules or has the function\n    `parameters` or `modules` to give direct access to trainable parameters.\n    Further, you can pass a function, which constructs a variational distribution\n    if called.\n\n    Args:\n        q: Variational distribution, either string, distribution, or a VIPosterior\n            object. This specifies a parametric class of distribution over which\n            the best possible posterior approximation is searched. For string input,\n            we currently support [nsf, scf, maf, mcf, gaussian, gaussian_diag]. Of\n            course, you can also specify your own variational family by passing a\n            `parameterized` distribution object i.e. a torch.distributions\n            Distribution with methods `parameters` returning an iterable of all\n            parameters (you can pass them within the paramters/modules attribute).\n            Additionally, we allow a `Callable`, which allows you the pass a\n            `builder` function, which if called returns an distribution. This may be\n            useful for setting the hyperparameters e.g. `num_transfroms:int` by\n            using the `get_flow_builder` method specifying the hyperparameters. If q\n            is already a `VIPosterior`, then the arguments will be copied from it\n            (relevant for multi-round training).\n        parameters: List of parameters associated with the distribution object.\n        modules: List of modules associated with the distribution object.\n\n    \"\"\"\n    self._q_arg = q\n    if isinstance(q, Distribution):\n        q = adapt_variational_distribution(\n            q,\n            self._prior,\n            self.link_transform,\n            parameters=parameters,\n            modules=modules,\n        )\n        make_object_deepcopy_compatible(q)\n        self_custom_q_init_cache = deepcopy(q)\n        self._q_build_fn = lambda *args, **kwargs: self_custom_q_init_cache\n        self._trained_on = None\n    elif isinstance(q, str) or isinstance(q, Callable):\n        if isinstance(q, str):\n            self._q_build_fn = get_flow_builder(q)\n        else:\n            self._q_build_fn = q\n\n        q = self._q_build_fn(\n            self._prior.event_shape,\n            self.link_transform,\n            device=self._device,\n        )\n        make_object_deepcopy_compatible(q)\n        self._trained_on = None\n    elif isinstance(q, VIPosterior):\n        self._q_build_fn = q._q_build_fn\n        self._trained_on = q._trained_on\n        self.vi_method = q.vi_method  # type: ignore\n        self._device = q._device\n        self._prior = q._prior\n        self._x = q._x\n        self._q_arg = q._q_arg\n        make_object_deepcopy_compatible(q.q)\n        q = deepcopy(q.q)\n    move_all_tensor_to_device(q, self._device)\n    assert isinstance(\n        q, Distribution\n    ), \"\"\"Something went wrong when initializing the variational distribution.\n        Please create an issue on github https://github.com/mackelab/sbi/issues\"\"\"\n    check_variational_distribution(q, self._prior)\n    self._q = q\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.vi_posterior.VIPosterior.set_vi_method","title":"<code>set_vi_method(method)</code>","text":"<p>Sets variational inference method.</p> <p>Parameters:</p> Name Type Description Default <code>method</code> <code>str</code> <p>One of [rKL, fKL, IW, alpha].</p> required <p>Returns:</p> Type Description <code>VIPosterior</code> <p><code>VIPosterior</code> for chainable calls.</p> Source code in <code>sbi/inference/posteriors/vi_posterior.py</code> <pre><code>def set_vi_method(self, method: str) -&gt; \"VIPosterior\":\n    \"\"\"Sets variational inference method.\n\n    Args:\n        method: One of [rKL, fKL, IW, alpha].\n\n    Returns:\n        `VIPosterior` for chainable calls.\n    \"\"\"\n    self._vi_method = method\n    self._optimizer_builder = get_VI_method(method)\n    return self\n</code></pre>"},{"location":"reference/#sbi.inference.posteriors.vi_posterior.VIPosterior.train","title":"<code>train(x=None, n_particles=256, learning_rate=0.001, gamma=0.999, max_num_iters=2000, min_num_iters=10, clip_value=10.0, warm_up_rounds=100, retrain_from_scratch=False, reset_optimizer=False, show_progress_bar=True, check_for_convergence=True, quality_control=True, quality_control_metric='psis', **kwargs)</code>","text":"<p>This method trains the variational posterior.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>Optional[TorchTensor]</code> <p>The observation.</p> <code>None</code> <code>n_particles</code> <code>int</code> <p>Number of samples to approximate expectations within the variational bounds. The larger the more accurate are gradient estimates, but the computational cost per iteration increases.</p> <code>256</code> <code>learning_rate</code> <code>float</code> <p>Learning rate of the optimizer.</p> <code>0.001</code> <code>gamma</code> <code>float</code> <p>Learning rate decay per iteration. We use an exponential decay scheduler.</p> <code>0.999</code> <code>max_num_iters</code> <code>int</code> <p>Maximum number of iterations.</p> <code>2000</code> <code>min_num_iters</code> <code>int</code> <p>Minimum number of iterations.</p> <code>10</code> <code>clip_value</code> <code>float</code> <p>Gradient clipping value, decreasing may help if you see invalid values.</p> <code>10.0</code> <code>warm_up_rounds</code> <code>int</code> <p>Initialize the posterior as the prior.</p> <code>100</code> <code>retrain_from_scratch</code> <code>bool</code> <p>Retrain the variational distributions from scratch.</p> <code>False</code> <code>reset_optimizer</code> <code>bool</code> <p>Reset the divergence optimizer</p> <code>False</code> <code>show_progress_bar</code> <code>bool</code> <p>If any progress report should be displayed.</p> <code>True</code> <code>quality_control</code> <code>bool</code> <p>If False quality control is skipped.</p> <code>True</code> <code>quality_control_metric</code> <code>str</code> <p>Which metric to use for evaluating the quality.</p> <code>'psis'</code> <code>kwargs</code> <p>Hyperparameters check corresponding <code>DivergenceOptimizer</code> for detail eps: Determines sensitivity of convergence check. retain_graph: Boolean which decides whether to retain the computation     graph. This may be required for some <code>exotic</code> user-specified q\u2019s. optimizer: A PyTorch Optimizer class e.g. Adam or SGD. See     <code>DivergenceOptimizer</code> for details. scheduler: A PyTorch learning rate scheduler. See     <code>DivergenceOptimizer</code> for details. alpha: Only used if vi_method=<code>alpha</code>. Determines the alpha divergence. K: Only used if vi_method=<code>IW</code>. Determines the number of importance     weighted particles. stick_the_landing: If one should use the STL estimator (only for rKL,     IW, alpha). dreg: If one should use the DREG estimator (only for rKL, IW, alpha). weight_transform: Callable applied to importance weights (only for fKL)</p> <code>{}</code> <p>Returns:     VIPosterior: <code>VIPosterior</code> (can be used to chain calls).</p> Source code in <code>sbi/inference/posteriors/vi_posterior.py</code> <pre><code>def train(\n    self,\n    x: Optional[TorchTensor] = None,\n    n_particles: int = 256,\n    learning_rate: float = 1e-3,\n    gamma: float = 0.999,\n    max_num_iters: int = 2000,\n    min_num_iters: int = 10,\n    clip_value: float = 10.0,\n    warm_up_rounds: int = 100,\n    retrain_from_scratch: bool = False,\n    reset_optimizer: bool = False,\n    show_progress_bar: bool = True,\n    check_for_convergence: bool = True,\n    quality_control: bool = True,\n    quality_control_metric: str = \"psis\",\n    **kwargs,\n) -&gt; \"VIPosterior\":\n    \"\"\"This method trains the variational posterior.\n\n    Args:\n        x: The observation.\n        n_particles: Number of samples to approximate expectations within the\n            variational bounds. The larger the more accurate are gradient\n            estimates, but the computational cost per iteration increases.\n        learning_rate: Learning rate of the optimizer.\n        gamma: Learning rate decay per iteration. We use an exponential decay\n            scheduler.\n        max_num_iters: Maximum number of iterations.\n        min_num_iters: Minimum number of iterations.\n        clip_value: Gradient clipping value, decreasing may help if you see invalid\n            values.\n        warm_up_rounds: Initialize the posterior as the prior.\n        retrain_from_scratch: Retrain the variational distributions from scratch.\n        reset_optimizer: Reset the divergence optimizer\n        show_progress_bar: If any progress report should be displayed.\n        quality_control: If False quality control is skipped.\n        quality_control_metric: Which metric to use for evaluating the quality.\n        kwargs: Hyperparameters check corresponding `DivergenceOptimizer` for detail\n            eps: Determines sensitivity of convergence check.\n            retain_graph: Boolean which decides whether to retain the computation\n                graph. This may be required for some `exotic` user-specified q's.\n            optimizer: A PyTorch Optimizer class e.g. Adam or SGD. See\n                `DivergenceOptimizer` for details.\n            scheduler: A PyTorch learning rate scheduler. See\n                `DivergenceOptimizer` for details.\n            alpha: Only used if vi_method=`alpha`. Determines the alpha divergence.\n            K: Only used if vi_method=`IW`. Determines the number of importance\n                weighted particles.\n            stick_the_landing: If one should use the STL estimator (only for rKL,\n                IW, alpha).\n            dreg: If one should use the DREG estimator (only for rKL, IW, alpha).\n            weight_transform: Callable applied to importance weights (only for fKL)\n    Returns:\n        VIPosterior: `VIPosterior` (can be used to chain calls).\n    \"\"\"\n    # Update optimizer with current arguments.\n    if self._optimizer is not None:\n        self._optimizer.update({**locals(), **kwargs})\n\n    # Init q and the optimizer if necessary\n    if retrain_from_scratch:\n        self.q = self._q_build_fn()  # type: ignore\n        self._optimizer = self._optimizer_builder(\n            self.potential_fn,\n            self.q,\n            lr=learning_rate,\n            clip_value=clip_value,\n            gamma=gamma,\n            n_particles=n_particles,\n            prior=self._prior,\n            **kwargs,\n        )\n\n    if (\n        reset_optimizer\n        or self._optimizer is None\n        or not isinstance(self._optimizer, self._optimizer_builder)\n    ):\n        self._optimizer = self._optimizer_builder(\n            self.potential_fn,\n            self.q,\n            lr=learning_rate,\n            clip_value=clip_value,\n            gamma=gamma,\n            n_particles=n_particles,\n            prior=self._prior,\n            **kwargs,\n        )\n\n    # Check context\n    x = atleast_2d_float32_tensor(self._x_else_default_x(x)).to(  # type: ignore\n        self._device\n    )\n\n    already_trained = self._trained_on is not None and (x == self._trained_on).all()\n\n    # Optimize\n    optimizer = self._optimizer\n    optimizer.to(self._device)\n    optimizer.reset_loss_stats()\n\n    if show_progress_bar:\n        iters = tqdm(range(max_num_iters))\n    else:\n        iters = range(max_num_iters)\n\n    # Warmup before training\n    if reset_optimizer or (not optimizer.warm_up_was_done and not already_trained):\n        if show_progress_bar:\n            iters.set_description(  # type: ignore\n                \"Warmup phase, this may take a few seconds...\"\n            )\n        optimizer.warm_up(warm_up_rounds)\n\n    for i in iters:\n        optimizer.step(x)\n        mean_loss, std_loss = optimizer.get_loss_stats()\n        # Update progress bar\n        if show_progress_bar:\n            assert isinstance(iters, tqdm)\n            iters.set_description(  # type: ignore\n                f\"Loss: {np.round(float(mean_loss), 2)}\"\n                f\"Std: {np.round(float(std_loss), 2)}\"\n            )\n        # Check for convergence\n        if check_for_convergence and i &gt; min_num_iters:\n            if optimizer.converged():\n                if show_progress_bar:\n                    print(f\"\\nConverged with loss: {np.round(float(mean_loss), 2)}\")\n                break\n    # Training finished:\n    self._trained_on = x\n\n    # Evaluate quality\n    if quality_control:\n        try:\n            self.evaluate(quality_control_metric=quality_control_metric)\n        except Exception as e:\n            print(\n                f\"Quality control did not work, we reset the variational \\\n                    posterior,please check your setting. \\\n                    \\n Following error occured {e}\"\n            )\n            self.train(\n                learning_rate=learning_rate * 0.1,\n                retrain_from_scratch=True,\n                reset_optimizer=True,\n            )\n\n    return self\n</code></pre>"},{"location":"reference/#models","title":"Models","text":"<p>Returns a function that builds a density estimator for learning the posterior.</p> <p>This function will usually be used for SNPE. The returned function is to be passed to the inference class when using the flexible interface.</p> <p>Parameters:</p> Name Type Description Default <code>model</code> <code>str</code> <p>The type of density estimator that will be created. One of [<code>mdn</code>, <code>made</code>, <code>maf</code>, <code>maf_rqs</code>, <code>nsf</code>].</p> required <code>z_score_theta</code> <code>Optional[str]</code> <p>Whether to z-score parameters \\(\\theta\\) before passing them into the network, can take one of the following: - <code>none</code>, or None: do not z-score. - <code>independent</code>: z-score each dimension independently. - <code>structured</code>: treat dimensions as related, therefore compute mean and std over the entire batch, instead of per-dimension. Should be used when each sample is, for example, a time series or an image.</p> <code>'independent'</code> <code>z_score_x</code> <code>Optional[str]</code> <p>Whether to z-score simulation outputs \\(x\\) before passing them into the network, same options as z_score_theta.</p> <code>'independent'</code> <code>hidden_features</code> <code>int</code> <p>Number of hidden features.</p> <code>50</code> <code>num_transforms</code> <code>int</code> <p>Number of transforms when a flow is used. Only relevant if density estimator is a normalizing flow (i.e. currently either a <code>maf</code> or a <code>nsf</code>). Ignored if density estimator is a <code>mdn</code> or <code>made</code>.</p> <code>5</code> <code>num_bins</code> <code>int</code> <p>Number of bins used for the splines in <code>nsf</code>. Ignored if density estimator not <code>nsf</code>.</p> <code>10</code> <code>embedding_net</code> <code>Module</code> <p>Optional embedding network for simulation outputs \\(x\\). This embedding net allows to learn features from potentially high-dimensional simulation outputs.</p> <code>Identity()</code> <code>num_components</code> <code>int</code> <p>Number of mixture components for a mixture of Gaussians. Ignored if density estimator is not an mdn.</p> <code>10</code> <code>kwargs</code> <p>additional custom arguments passed to downstream build functions.</p> <code>{}</code> Source code in <code>sbi/utils/get_nn_models.py</code> <pre><code>def posterior_nn(\n    model: str,\n    z_score_theta: Optional[str] = \"independent\",\n    z_score_x: Optional[str] = \"independent\",\n    hidden_features: int = 50,\n    num_transforms: int = 5,\n    num_bins: int = 10,\n    embedding_net: nn.Module = nn.Identity(),\n    num_components: int = 10,\n    **kwargs,\n) -&gt; Callable:\n    r\"\"\"\n    Returns a function that builds a density estimator for learning the posterior.\n\n    This function will usually be used for SNPE. The returned function is to be passed\n    to the inference class when using the flexible interface.\n\n    Args:\n        model: The type of density estimator that will be created. One of [`mdn`,\n            `made`, `maf`, `maf_rqs`, `nsf`].\n        z_score_theta: Whether to z-score parameters $\\theta$ before passing them into\n            the network, can take one of the following:\n            - `none`, or None: do not z-score.\n            - `independent`: z-score each dimension independently.\n            - `structured`: treat dimensions as related, therefore compute mean and std\n            over the entire batch, instead of per-dimension. Should be used when each\n            sample is, for example, a time series or an image.\n        z_score_x: Whether to z-score simulation outputs $x$ before passing them into\n            the network, same options as z_score_theta.\n        hidden_features: Number of hidden features.\n        num_transforms: Number of transforms when a flow is used. Only relevant if\n            density estimator is a normalizing flow (i.e. currently either a `maf` or a\n            `nsf`). Ignored if density estimator is a `mdn` or `made`.\n        num_bins: Number of bins used for the splines in `nsf`. Ignored if density\n            estimator not `nsf`.\n        embedding_net: Optional embedding network for simulation outputs $x$. This\n            embedding net allows to learn features from potentially high-dimensional\n            simulation outputs.\n        num_components: Number of mixture components for a mixture of Gaussians.\n            Ignored if density estimator is not an mdn.\n        kwargs: additional custom arguments passed to downstream build functions.\n    \"\"\"\n\n    kwargs = dict(\n        zip(\n            (\n                \"z_score_x\",\n                \"z_score_y\",\n                \"hidden_features\",\n                \"num_transforms\",\n                \"num_bins\",\n                \"embedding_net\",\n                \"num_components\",\n            ),\n            (\n                z_score_theta,\n                z_score_x,\n                hidden_features,\n                num_transforms,\n                num_bins,\n                embedding_net,\n                num_components,\n            ),\n        ),\n        **kwargs,\n    )\n\n    def build_fn_snpe_a(batch_theta, batch_x, num_components):\n        \"\"\"Build function for SNPE-A\n\n        Extract the number of components from the kwargs, such that they are exposed as\n        a kwargs, offering the possibility to later override this kwarg with\n        `functools.partial`. This is necessary in order to make sure that the MDN in\n        SNPE-A only has one component when running the Algorithm 1 part.\n        \"\"\"\n        return build_mdn(\n            batch_x=batch_theta,\n            batch_y=batch_x,\n            num_components=num_components,\n            **kwargs,\n        )\n\n    def build_fn(batch_theta, batch_x):\n        if model == \"mdn\":\n            return build_mdn(batch_x=batch_theta, batch_y=batch_x, **kwargs)\n        elif model == \"made\":\n            return build_made(batch_x=batch_theta, batch_y=batch_x, **kwargs)\n        elif model == \"maf\":\n            return build_maf(batch_x=batch_theta, batch_y=batch_x, **kwargs)\n        elif model == \"maf_rqs\":\n            return build_maf_rqs(batch_x=batch_theta, batch_y=batch_x, **kwargs)\n        elif model == \"nsf\":\n            return build_nsf(batch_x=batch_theta, batch_y=batch_x, **kwargs)\n        else:\n            raise NotImplementedError\n\n    if model == \"mdn_snpe_a\":\n        if num_components != 10:\n            raise ValueError(\n                \"You set `num_components`. For SNPE-A, this has to be done at \"\n                \"instantiation of the inference object, i.e. \"\n                \"`inference = SNPE_A(..., num_components=20)`\"\n            )\n        kwargs.pop(\"num_components\")\n\n    return build_fn_snpe_a if model == \"mdn_snpe_a\" else build_fn\n</code></pre> <p>Returns a function that builds a density estimator for learning the likelihood.</p> <p>This function will usually be used for SNLE. The returned function is to be passed to the inference class when using the flexible interface.</p> <p>Parameters:</p> Name Type Description Default <code>model</code> <code>str</code> <p>The type of density estimator that will be created. One of [<code>mdn</code>, <code>made</code>, <code>maf</code>, <code>maf_rqs</code>, <code>nsf</code>].</p> required <code>z_score_theta</code> <code>Optional[str]</code> <p>Whether to z-score parameters \\(\\theta\\) before passing them into the network, can take one of the following: - <code>none</code>, or None: do not z-score. - <code>independent</code>: z-score each dimension independently. - <code>structured</code>: treat dimensions as related, therefore compute mean and std over the entire batch, instead of per-dimension. Should be used when each sample is, for example, a time series or an image.</p> <code>'independent'</code> <code>z_score_x</code> <code>Optional[str]</code> <p>Whether to z-score simulation outputs \\(x\\) before passing them into the network, same options as z_score_theta.</p> <code>'independent'</code> <code>hidden_features</code> <code>int</code> <p>Number of hidden features.</p> <code>50</code> <code>num_transforms</code> <code>int</code> <p>Number of transforms when a flow is used. Only relevant if density estimator is a normalizing flow (i.e. currently either a <code>maf</code> or a <code>nsf</code>). Ignored if density estimator is a <code>mdn</code> or <code>made</code>.</p> <code>5</code> <code>num_bins</code> <code>int</code> <p>Number of bins used for the splines in <code>nsf</code>. Ignored if density estimator not <code>nsf</code>.</p> <code>10</code> <code>embedding_net</code> <code>Module</code> <p>Optional embedding network for parameters \\(\\theta\\).</p> <code>Identity()</code> <code>num_components</code> <code>int</code> <p>Number of mixture components for a mixture of Gaussians. Ignored if density estimator is not an mdn.</p> <code>10</code> <code>kwargs</code> <p>additional custom arguments passed to downstream build functions.</p> <code>{}</code> Source code in <code>sbi/utils/get_nn_models.py</code> <pre><code>def likelihood_nn(\n    model: str,\n    z_score_theta: Optional[str] = \"independent\",\n    z_score_x: Optional[str] = \"independent\",\n    hidden_features: int = 50,\n    num_transforms: int = 5,\n    num_bins: int = 10,\n    embedding_net: nn.Module = nn.Identity(),\n    num_components: int = 10,\n    **kwargs,\n) -&gt; Callable:\n    r\"\"\"\n    Returns a function that builds a density estimator for learning the likelihood.\n\n    This function will usually be used for SNLE. The returned function is to be passed\n    to the inference class when using the flexible interface.\n\n    Args:\n        model: The type of density estimator that will be created. One of [`mdn`,\n            `made`, `maf`, `maf_rqs`, `nsf`].\n        z_score_theta: Whether to z-score parameters $\\theta$ before passing them into\n            the network, can take one of the following:\n            - `none`, or None: do not z-score.\n            - `independent`: z-score each dimension independently.\n            - `structured`: treat dimensions as related, therefore compute mean and std\n            over the entire batch, instead of per-dimension. Should be used when each\n            sample is, for example, a time series or an image.\n        z_score_x: Whether to z-score simulation outputs $x$ before passing them into\n            the network, same options as z_score_theta.\n        hidden_features: Number of hidden features.\n        num_transforms: Number of transforms when a flow is used. Only relevant if\n            density estimator is a normalizing flow (i.e. currently either a `maf` or a\n            `nsf`). Ignored if density estimator is a `mdn` or `made`.\n        num_bins: Number of bins used for the splines in `nsf`. Ignored if density\n            estimator not `nsf`.\n        embedding_net: Optional embedding network for parameters $\\theta$.\n        num_components: Number of mixture components for a mixture of Gaussians.\n            Ignored if density estimator is not an mdn.\n        kwargs: additional custom arguments passed to downstream build functions.\n    \"\"\"\n\n    kwargs = dict(\n        zip(\n            (\n                \"z_score_x\",\n                \"z_score_y\",\n                \"hidden_features\",\n                \"num_transforms\",\n                \"num_bins\",\n                \"embedding_net\",\n                \"num_components\",\n            ),\n            (\n                z_score_x,\n                z_score_theta,\n                hidden_features,\n                num_transforms,\n                num_bins,\n                embedding_net,\n                num_components,\n            ),\n        ),\n        **kwargs,\n    )\n\n    def build_fn(batch_theta, batch_x):\n        if model == \"mdn\":\n            return build_mdn(batch_x=batch_x, batch_y=batch_theta, **kwargs)\n        elif model == \"made\":\n            return build_made(batch_x=batch_x, batch_y=batch_theta, **kwargs)\n        elif model == \"maf\":\n            return build_maf(batch_x=batch_x, batch_y=batch_theta, **kwargs)\n        elif model == \"maf_rqs\":\n            return build_maf_rqs(batch_x=batch_x, batch_y=batch_theta, **kwargs)\n        elif model == \"nsf\":\n            return build_nsf(batch_x=batch_x, batch_y=batch_theta, **kwargs)\n        elif model == \"mnle\":\n            return build_mnle(batch_x=batch_x, batch_y=batch_theta, **kwargs)\n        else:\n            raise NotImplementedError\n\n    return build_fn\n</code></pre> <p>Returns a function that builds a classifier for learning density ratios.</p> <p>This function will usually be used for SNRE. The returned function is to be passed to the inference class when using the flexible interface.</p> <p>Note that in the view of the SNRE classifier we build below, x=theta and y=x.</p> <p>Parameters:</p> Name Type Description Default <code>model</code> <code>str</code> <p>The type of classifier that will be created. One of [<code>linear</code>, <code>mlp</code>, <code>resnet</code>].</p> required <code>z_score_theta</code> <code>Optional[str]</code> <p>Whether to z-score parameters \\(\\theta\\) before passing them into the network, can take one of the following: - <code>none</code>, or None: do not z-score. - <code>independent</code>: z-score each dimension independently. - <code>structured</code>: treat dimensions as related, therefore compute mean and std over the entire batch, instead of per-dimension. Should be used when each sample is, for example, a time series or an image.</p> <code>'independent'</code> <code>z_score_x</code> <code>Optional[str]</code> <p>Whether to z-score simulation outputs \\(x\\) before passing them into the network, same options as z_score_theta.</p> <code>'independent'</code> <code>hidden_features</code> <code>int</code> <p>Number of hidden features.</p> <code>50</code> <code>embedding_net_theta</code> <code>Module</code> <p>Optional embedding network for parameters \\(\\theta\\).</p> <code>Identity()</code> <code>embedding_net_x</code> <code>Module</code> <p>Optional embedding network for simulation outputs \\(x\\). This embedding net allows to learn features from potentially high-dimensional simulation outputs.</p> <code>Identity()</code> <code>kwargs</code> <p>additional custom arguments passed to downstream build functions.</p> <code>{}</code> Source code in <code>sbi/utils/get_nn_models.py</code> <pre><code>def classifier_nn(\n    model: str,\n    z_score_theta: Optional[str] = \"independent\",\n    z_score_x: Optional[str] = \"independent\",\n    hidden_features: int = 50,\n    embedding_net_theta: nn.Module = nn.Identity(),\n    embedding_net_x: nn.Module = nn.Identity(),\n    **kwargs,\n) -&gt; Callable:\n    r\"\"\"\n    Returns a function that builds a classifier for learning density ratios.\n\n    This function will usually be used for SNRE. The returned function is to be passed\n    to the inference class when using the flexible interface.\n\n    Note that in the view of the SNRE classifier we build below, x=theta and y=x.\n\n    Args:\n        model: The type of classifier that will be created. One of [`linear`, `mlp`,\n            `resnet`].\n        z_score_theta: Whether to z-score parameters $\\theta$ before passing them into\n            the network, can take one of the following:\n            - `none`, or None: do not z-score.\n            - `independent`: z-score each dimension independently.\n            - `structured`: treat dimensions as related, therefore compute mean and std\n            over the entire batch, instead of per-dimension. Should be used when each\n            sample is, for example, a time series or an image.\n        z_score_x: Whether to z-score simulation outputs $x$ before passing them into\n            the network, same options as z_score_theta.\n        hidden_features: Number of hidden features.\n        embedding_net_theta:  Optional embedding network for parameters $\\theta$.\n        embedding_net_x:  Optional embedding network for simulation outputs $x$. This\n            embedding net allows to learn features from potentially high-dimensional\n            simulation outputs.\n        kwargs: additional custom arguments passed to downstream build functions.\n    \"\"\"\n\n    kwargs = dict(\n        zip(\n            (\n                \"z_score_x\",\n                \"z_score_y\",\n                \"hidden_features\",\n                \"embedding_net_x\",\n                \"embedding_net_y\",\n            ),\n            (\n                z_score_theta,\n                z_score_x,\n                hidden_features,\n                embedding_net_theta,\n                embedding_net_x,\n            ),\n        ),\n        **kwargs,\n    )\n\n    def build_fn(batch_theta, batch_x):\n        if model == \"linear\":\n            return build_linear_classifier(\n                batch_x=batch_theta, batch_y=batch_x, **kwargs\n            )\n        if model == \"mlp\":\n            return build_mlp_classifier(batch_x=batch_theta, batch_y=batch_x, **kwargs)\n        if model == \"resnet\":\n            return build_resnet_classifier(\n                batch_x=batch_theta, batch_y=batch_x, **kwargs\n            )\n        else:\n            raise NotImplementedError\n\n    return build_fn\n</code></pre>"},{"location":"reference/#potentials","title":"Potentials","text":"<p>Returns the potential for posterior-based methods.</p> <p>It also returns a transformation that can be used to transform the potential into unconstrained space.</p> <p>The potential is the same as the log-probability of the <code>posterior_estimator</code>, but it is set to \\(-\\inf\\) outside of the prior bounds.</p> <p>Parameters:</p> Name Type Description Default <code>posterior_estimator</code> <code>Module</code> <p>The neural network modelling the posterior.</p> required <code>prior</code> <code>Distribution</code> <p>The prior distribution.</p> required <code>x_o</code> <code>Optional[Tensor]</code> <p>The observed data at which to evaluate the posterior.</p> required <code>enable_transform</code> <code>bool</code> <p>Whether to transform parameters to unconstrained space. When False, an identity transform will be returned for <code>theta_transform</code>.</p> <code>True</code> <p>Returns:</p> Type Description <code>Callable</code> <p>The potential function and a transformation that maps</p> <code>TorchTransform</code> <p>to unconstrained space.</p> Source code in <code>sbi/inference/potentials/posterior_based_potential.py</code> <pre><code>def posterior_estimator_based_potential(\n    posterior_estimator: nn.Module,\n    prior: Distribution,\n    x_o: Optional[Tensor],\n    enable_transform: bool = True,\n) -&gt; Tuple[Callable, TorchTransform]:\n    r\"\"\"Returns the potential for posterior-based methods.\n\n    It also returns a transformation that can be used to transform the potential into\n    unconstrained space.\n\n    The potential is the same as the log-probability of the `posterior_estimator`, but\n    it is set to $-\\inf$ outside of the prior bounds.\n\n    Args:\n        posterior_estimator: The neural network modelling the posterior.\n        prior: The prior distribution.\n        x_o: The observed data at which to evaluate the posterior.\n        enable_transform: Whether to transform parameters to unconstrained space.\n            When False, an identity transform will be returned for `theta_transform`.\n\n    Returns:\n        The potential function and a transformation that maps\n        to unconstrained space.\n    \"\"\"\n\n    device = str(next(posterior_estimator.parameters()).device)\n\n    potential_fn = PosteriorBasedPotential(\n        posterior_estimator, prior, x_o, device=device\n    )\n\n    theta_transform = mcmc_transform(\n        prior, device=device, enable_transform=enable_transform\n    )\n\n    return potential_fn, theta_transform\n</code></pre> <p>Returns potential \\(\\log(p(x_o|\\theta)p(\\theta))\\) for likelihood-based methods.</p> <p>It also returns a transformation that can be used to transform the potential into unconstrained space.</p> <p>Parameters:</p> Name Type Description Default <code>likelihood_estimator</code> <code>Module</code> <p>The neural network modelling the likelihood.</p> required <code>prior</code> <code>Distribution</code> <p>The prior distribution.</p> required <code>x_o</code> <code>Optional[Tensor]</code> <p>The observed data at which to evaluate the likelihood.</p> required <code>enable_transform</code> <code>bool</code> <p>Whether to transform parameters to unconstrained space.  When False, an identity transform will be returned for <code>theta_transform</code>.</p> <code>True</code> <p>Returns:</p> Type Description <code>Callable</code> <p>The potential function \\(p(x_o|\\theta)p(\\theta)\\) and a transformation that maps</p> <code>TorchTransform</code> <p>to unconstrained space.</p> Source code in <code>sbi/inference/potentials/likelihood_based_potential.py</code> <pre><code>def likelihood_estimator_based_potential(\n    likelihood_estimator: nn.Module,\n    prior: Distribution,\n    x_o: Optional[Tensor],\n    enable_transform: bool = True,\n) -&gt; Tuple[Callable, TorchTransform]:\n    r\"\"\"Returns potential $\\log(p(x_o|\\theta)p(\\theta))$ for likelihood-based methods.\n\n    It also returns a transformation that can be used to transform the potential into\n    unconstrained space.\n\n    Args:\n        likelihood_estimator: The neural network modelling the likelihood.\n        prior: The prior distribution.\n        x_o: The observed data at which to evaluate the likelihood.\n        enable_transform: Whether to transform parameters to unconstrained space.\n             When False, an identity transform will be returned for `theta_transform`.\n\n    Returns:\n        The potential function $p(x_o|\\theta)p(\\theta)$ and a transformation that maps\n        to unconstrained space.\n    \"\"\"\n\n    device = str(next(likelihood_estimator.parameters()).device)\n\n    potential_fn = LikelihoodBasedPotential(\n        likelihood_estimator, prior, x_o, device=device\n    )\n    theta_transform = mcmc_transform(\n        prior, device=device, enable_transform=enable_transform\n    )\n\n    return potential_fn, theta_transform\n</code></pre> <p>Returns the potential for ratio-based methods.</p> <p>It also returns a transformation that can be used to transform the potential into unconstrained space.</p> <p>Parameters:</p> Name Type Description Default <code>ratio_estimator</code> <code>Module</code> <p>The neural network modelling likelihood-to-evidence ratio.</p> required <code>prior</code> <code>Distribution</code> <p>The prior distribution.</p> required <code>x_o</code> <code>Optional[Tensor]</code> <p>The observed data at which to evaluate the likelihood-to-evidence ratio.</p> required <code>enable_transform</code> <code>bool</code> <p>Whether to transform parameters to unconstrained space. When False, an identity transform will be returned for <code>theta_transform</code>.</p> <code>True</code> <p>Returns:</p> Type Description <code>Callable</code> <p>The potential function and a transformation that maps</p> <code>TorchTransform</code> <p>to unconstrained space.</p> Source code in <code>sbi/inference/potentials/ratio_based_potential.py</code> <pre><code>def ratio_estimator_based_potential(\n    ratio_estimator: nn.Module,\n    prior: Distribution,\n    x_o: Optional[Tensor],\n    enable_transform: bool = True,\n) -&gt; Tuple[Callable, TorchTransform]:\n    r\"\"\"Returns the potential for ratio-based methods.\n\n    It also returns a transformation that can be used to transform the potential into\n    unconstrained space.\n\n    Args:\n        ratio_estimator: The neural network modelling likelihood-to-evidence ratio.\n        prior: The prior distribution.\n        x_o: The observed data at which to evaluate the likelihood-to-evidence ratio.\n        enable_transform: Whether to transform parameters to unconstrained space.\n            When False, an identity transform will be returned for `theta_transform`.\n\n    Returns:\n        The potential function and a transformation that maps\n        to unconstrained space.\n    \"\"\"\n\n    device = str(next(ratio_estimator.parameters()).device)\n\n    potential_fn = RatioBasedPotential(ratio_estimator, prior, x_o, device=device)\n    theta_transform = mcmc_transform(\n        prior, device=device, enable_transform=enable_transform\n    )\n\n    return potential_fn, theta_transform\n</code></pre>"},{"location":"reference/#analysis","title":"Analysis","text":"<p>Plot samples in a 2D grid showing marginals and pairwise marginals.</p> <p>Each of the diagonal plots can be interpreted as a 1D-marginal of the distribution that the samples were drawn from. Each upper-diagonal plot can be interpreted as a 2D-marginal of the distribution.</p> <p>Parameters:</p> Name Type Description Default <code>samples</code> <code>Union[List[ndarray], List[Tensor], ndarray, Tensor]</code> <p>Samples used to build the histogram.</p> required <code>points</code> <code>Optional[Union[List[ndarray], List[Tensor], ndarray, Tensor]]</code> <p>List of additional points to scatter.</p> <code>None</code> <code>limits</code> <code>Optional[Union[List, Tensor]]</code> <p>Array containing the plot xlim for each parameter dimension. If None, just use the min and max of the passed samples</p> <code>None</code> <code>subset</code> <code>Optional[List[int]]</code> <p>List containing the dimensions to plot. E.g. subset=[1,3] will plot plot only the 1<sup>st</sup> and 3<sup>rd</sup> dimension but will discard the 0<sup>th</sup> and 2<sup>nd</sup> (and, if they exist, the 4<sup>th</sup>, 5<sup>th</sup> and so on).</p> <code>None</code> <code>offdiag</code> <code>Optional[Union[List[str], str]]</code> <p>Plotting style for upper diagonal, {hist, scatter, contour, cond, None}.</p> <code>'hist'</code> <code>upper</code> <code>Optional[str]</code> <p>deprecated, use offdiag instead.</p> <code>None</code> <code>diag</code> <code>Optional[Union[List[str], str]]</code> <p>Plotting style for diagonal, {hist, cond, None}.</p> <code>'hist'</code> <code>figsize</code> <code>Tuple</code> <p>Size of the entire figure.</p> <code>(10, 10)</code> <code>labels</code> <code>Optional[List[str]]</code> <p>List of strings specifying the names of the parameters.</p> <code>None</code> <code>ticks</code> <code>Union[List, Tensor]</code> <p>Position of the ticks.</p> <code>[]</code> <code>fig</code> <p>matplotlib figure to plot on.</p> <code>None</code> <code>axes</code> <p>matplotlib axes corresponding to fig.</p> <code>None</code> <code>**kwargs</code> <p>Additional arguments to adjust the plot, e.g., <code>samples_colors</code>, <code>points_colors</code> and many more, see the source code in <code>_get_default_opts()</code> in <code>sbi.analysis.plot</code> for details.</p> <code>{}</code> <p>Returns: figure and axis of posterior distribution plot</p> Source code in <code>sbi/analysis/plot.py</code> <pre><code>def pairplot(\n    samples: Union[List[np.ndarray], List[torch.Tensor], np.ndarray, torch.Tensor],\n    points: Optional[\n        Union[List[np.ndarray], List[torch.Tensor], np.ndarray, torch.Tensor]\n    ] = None,\n    limits: Optional[Union[List, torch.Tensor]] = None,\n    subset: Optional[List[int]] = None,\n    offdiag: Optional[Union[List[str], str]] = \"hist\",\n    diag: Optional[Union[List[str], str]] = \"hist\",\n    figsize: Tuple = (10, 10),\n    labels: Optional[List[str]] = None,\n    ticks: Union[List, torch.Tensor] = [],\n    upper: Optional[str] = None,\n    fig=None,\n    axes=None,\n    **kwargs,\n):\n    \"\"\"\n    Plot samples in a 2D grid showing marginals and pairwise marginals.\n\n    Each of the diagonal plots can be interpreted as a 1D-marginal of the distribution\n    that the samples were drawn from. Each upper-diagonal plot can be interpreted as a\n    2D-marginal of the distribution.\n\n    Args:\n        samples: Samples used to build the histogram.\n        points: List of additional points to scatter.\n        limits: Array containing the plot xlim for each parameter dimension. If None,\n            just use the min and max of the passed samples\n        subset: List containing the dimensions to plot. E.g. subset=[1,3] will plot\n            plot only the 1st and 3rd dimension but will discard the 0th and 2nd (and,\n            if they exist, the 4th, 5th and so on).\n        offdiag: Plotting style for upper diagonal, {hist, scatter, contour, cond,\n            None}.\n        upper: deprecated, use offdiag instead.\n        diag: Plotting style for diagonal, {hist, cond, None}.\n        figsize: Size of the entire figure.\n        labels: List of strings specifying the names of the parameters.\n        ticks: Position of the ticks.\n        fig: matplotlib figure to plot on.\n        axes: matplotlib axes corresponding to fig.\n        **kwargs: Additional arguments to adjust the plot, e.g., `samples_colors`,\n            `points_colors` and many more, see the source code in `_get_default_opts()`\n            in `sbi.analysis.plot` for details.\n\n    Returns: figure and axis of posterior distribution plot\n    \"\"\"\n\n    # TODO: add color map support\n    # TODO: automatically determine good bin sizes for histograms\n    # TODO: add legend (if legend is True)\n\n    opts = _get_default_opts()\n    # update the defaults dictionary by the current values of the variables (passed by\n    # the user)\n\n    opts = _update(opts, locals())\n    opts = _update(opts, kwargs)\n\n    samples, dim, limits = prepare_for_plot(samples, limits)\n\n    # checks.\n    if opts[\"legend\"]:\n        assert len(opts[\"samples_labels\"]) &gt;= len(\n            samples\n        ), \"Provide at least as many labels as samples.\"\n    if opts[\"upper\"] is not None:\n        warn(\"upper is deprecated, use offdiag instead.\")\n        opts[\"offdiag\"] = opts[\"upper\"]\n\n    # Prepare diag/upper/lower\n    if type(opts[\"diag\"]) is not list:\n        opts[\"diag\"] = [opts[\"diag\"] for _ in range(len(samples))]\n    if type(opts[\"offdiag\"]) is not list:\n        opts[\"offdiag\"] = [opts[\"offdiag\"] for _ in range(len(samples))]\n    # if type(opts['lower']) is not list:\n    #    opts['lower'] = [opts['lower'] for _ in range(len(samples))]\n    opts[\"lower\"] = None\n\n    diag_func = get_diag_func(samples, limits, opts, **kwargs)\n\n    def offdiag_func(row, col, limits, **kwargs):\n        if len(samples) &gt; 0:\n            for n, v in enumerate(samples):\n                if opts[\"offdiag\"][n] == \"hist\" or opts[\"offdiag\"][n] == \"hist2d\":\n                    hist, xedges, yedges = np.histogram2d(\n                        v[:, col],\n                        v[:, row],\n                        range=[\n                            [limits[col][0], limits[col][1]],\n                            [limits[row][0], limits[row][1]],\n                        ],\n                        **opts[\"hist_offdiag\"],\n                    )\n                    plt.imshow(\n                        hist.T,\n                        origin=\"lower\",\n                        extent=(\n                            xedges[0],\n                            xedges[-1],\n                            yedges[0],\n                            yedges[-1],\n                        ),\n                        aspect=\"auto\",\n                    )\n\n                elif opts[\"offdiag\"][n] in [\n                    \"kde\",\n                    \"kde2d\",\n                    \"contour\",\n                    \"contourf\",\n                ]:\n                    density = gaussian_kde(\n                        v[:, [col, row]].T,\n                        bw_method=opts[\"kde_offdiag\"][\"bw_method\"],\n                    )\n                    X, Y = np.meshgrid(\n                        np.linspace(\n                            limits[col][0],\n                            limits[col][1],\n                            opts[\"kde_offdiag\"][\"bins\"],\n                        ),\n                        np.linspace(\n                            limits[row][0],\n                            limits[row][1],\n                            opts[\"kde_offdiag\"][\"bins\"],\n                        ),\n                    )\n                    positions = np.vstack([X.ravel(), Y.ravel()])\n                    Z = np.reshape(density(positions).T, X.shape)\n\n                    if opts[\"offdiag\"][n] == \"kde\" or opts[\"offdiag\"][n] == \"kde2d\":\n                        plt.imshow(\n                            Z,\n                            extent=(\n                                limits[col][0],\n                                limits[col][1],\n                                limits[row][0],\n                                limits[row][1],\n                            ),\n                            origin=\"lower\",\n                            aspect=\"auto\",\n                        )\n                    elif opts[\"offdiag\"][n] == \"contour\":\n                        if opts[\"contour_offdiag\"][\"percentile\"]:\n                            Z = probs2contours(Z, opts[\"contour_offdiag\"][\"levels\"])\n                        else:\n                            Z = (Z - Z.min()) / (Z.max() - Z.min())\n                        plt.contour(\n                            X,\n                            Y,\n                            Z,\n                            origin=\"lower\",\n                            extent=[\n                                limits[col][0],\n                                limits[col][1],\n                                limits[row][0],\n                                limits[row][1],\n                            ],\n                            colors=opts[\"samples_colors\"][n],\n                            levels=opts[\"contour_offdiag\"][\"levels\"],\n                        )\n                    else:\n                        pass\n                elif opts[\"offdiag\"][n] == \"scatter\":\n                    plt.scatter(\n                        v[:, col],\n                        v[:, row],\n                        color=opts[\"samples_colors\"][n],\n                        **opts[\"scatter_offdiag\"],\n                    )\n                elif opts[\"offdiag\"][n] == \"plot\":\n                    plt.plot(\n                        v[:, col],\n                        v[:, row],\n                        color=opts[\"samples_colors\"][n],\n                        **opts[\"plot_offdiag\"],\n                    )\n                else:\n                    pass\n\n    return _arrange_plots(\n        diag_func, offdiag_func, dim, limits, points, opts, fig=fig, axes=axes\n    )\n</code></pre> <p>Plot samples in a row showing 1D marginals of selected dimensions.</p> <p>Each of the plots can be interpreted as a 1D-marginal of the distribution that the samples were drawn from.</p> <p>Parameters:</p> Name Type Description Default <code>samples</code> <code>Union[List[ndarray], List[Tensor], ndarray, Tensor]</code> <p>Samples used to build the histogram.</p> required <code>points</code> <code>Optional[Union[List[ndarray], List[Tensor], ndarray, Tensor]]</code> <p>List of additional points to scatter.</p> <code>None</code> <code>limits</code> <code>Optional[Union[List, Tensor]]</code> <p>Array containing the plot xlim for each parameter dimension. If None, just use the min and max of the passed samples</p> <code>None</code> <code>subset</code> <code>Optional[List[int]]</code> <p>List containing the dimensions to plot. E.g. subset=[1,3] will plot plot only the 1<sup>st</sup> and 3<sup>rd</sup> dimension but will discard the 0<sup>th</sup> and 2<sup>nd</sup> (and, if they exist, the 4<sup>th</sup>, 5<sup>th</sup> and so on).</p> <code>None</code> <code>diag</code> <code>Optional[str]</code> <p>Plotting style for 1D marginals, {hist, kde cond, None}.</p> <code>'hist'</code> <code>figsize</code> <code>Tuple</code> <p>Size of the entire figure.</p> <code>(10, 10)</code> <code>labels</code> <code>Optional[List[str]]</code> <p>List of strings specifying the names of the parameters.</p> <code>None</code> <code>ticks</code> <code>Union[List, Tensor]</code> <p>Position of the ticks.</p> <code>[]</code> <code>points_colors</code> <p>Colors of the <code>points</code>.</p> required <code>fig</code> <p>matplotlib figure to plot on.</p> <code>None</code> <code>axes</code> <p>matplotlib axes corresponding to fig.</p> <code>None</code> <code>**kwargs</code> <p>Additional arguments to adjust the plot, e.g., <code>samples_colors</code>, <code>points_colors</code> and many more, see the source code in <code>_get_default_opts()</code> in <code>sbi.analysis.plot</code> for details.</p> <code>{}</code> <p>Returns: figure and axis of posterior distribution plot</p> Source code in <code>sbi/analysis/plot.py</code> <pre><code>def marginal_plot(\n    samples: Union[List[np.ndarray], List[torch.Tensor], np.ndarray, torch.Tensor],\n    points: Optional[\n        Union[List[np.ndarray], List[torch.Tensor], np.ndarray, torch.Tensor]\n    ] = None,\n    limits: Optional[Union[List, torch.Tensor]] = None,\n    subset: Optional[List[int]] = None,\n    diag: Optional[str] = \"hist\",\n    figsize: Tuple = (10, 10),\n    labels: Optional[List[str]] = None,\n    ticks: Union[List, torch.Tensor] = [],\n    fig=None,\n    axes=None,\n    **kwargs,\n):\n    \"\"\"\n    Plot samples in a row showing 1D marginals of selected dimensions.\n\n    Each of the plots can be interpreted as a 1D-marginal of the distribution\n    that the samples were drawn from.\n\n    Args:\n        samples: Samples used to build the histogram.\n        points: List of additional points to scatter.\n        limits: Array containing the plot xlim for each parameter dimension. If None,\n            just use the min and max of the passed samples\n        subset: List containing the dimensions to plot. E.g. subset=[1,3] will plot\n            plot only the 1st and 3rd dimension but will discard the 0th and 2nd (and,\n            if they exist, the 4th, 5th and so on).\n        diag: Plotting style for 1D marginals, {hist, kde cond, None}.\n        figsize: Size of the entire figure.\n        labels: List of strings specifying the names of the parameters.\n        ticks: Position of the ticks.\n        points_colors: Colors of the `points`.\n        fig: matplotlib figure to plot on.\n        axes: matplotlib axes corresponding to fig.\n        **kwargs: Additional arguments to adjust the plot, e.g., `samples_colors`,\n            `points_colors` and many more, see the source code in `_get_default_opts()`\n            in `sbi.analysis.plot` for details.\n\n    Returns: figure and axis of posterior distribution plot\n    \"\"\"\n\n    opts = _get_default_opts()\n    # update the defaults dictionary by the current values of the variables (passed by\n    # the user)\n\n    opts = _update(opts, locals())\n    opts = _update(opts, kwargs)\n\n    samples, dim, limits = prepare_for_plot(samples, limits)\n\n    # Prepare diag/upper/lower\n    if type(opts[\"diag\"]) is not list:\n        opts[\"diag\"] = [opts[\"diag\"] for _ in range(len(samples))]\n\n    diag_func = get_diag_func(samples, limits, opts, **kwargs)\n\n    return _arrange_plots(\n        diag_func, None, dim, limits, points, opts, fig=fig, axes=axes\n    )\n</code></pre> <p>Plot conditional distribution given all other parameters.</p> <p>The conditionals can be interpreted as slices through the <code>density</code> at a location given by <code>condition</code>.</p> <p>For example: Say we have a 3D density with parameters \\(\\theta_0\\), \\(\\theta_1\\), \\(\\theta_2\\) and a condition \\(c\\) passed by the user in the <code>condition</code> argument. For the plot of \\(\\theta_0\\) on the diagonal, this will plot the conditional \\(p(\\theta_0 | \\theta_1=c[1], \\theta_2=c[2])\\). For the upper diagonal of \\(\\theta_1\\) and \\(\\theta_2\\), it will plot \\(p(\\theta_1, \\theta_2 | \\theta_0=c[0])\\). All other diagonals and upper-diagonals are built in the corresponding way.</p> <p>Parameters:</p> Name Type Description Default <code>density</code> <code>Any</code> <p>Probability density with a <code>log_prob()</code> method.</p> required <code>condition</code> <code>Tensor</code> <p>Condition that all but the one/two regarded parameters are fixed to. The condition should be of shape (1, dim_theta), i.e. it could e.g. be a sample from the posterior distribution.</p> required <code>limits</code> <code>Union[List, Tensor]</code> <p>Limits in between which each parameter will be evaluated.</p> required <code>points</code> <code>Optional[Union[List[ndarray], List[Tensor], ndarray, Tensor]]</code> <p>Additional points to scatter.</p> <code>None</code> <code>subset</code> <code>Optional[List[int]]</code> <p>List containing the dimensions to plot. E.g. subset=[1,3] will plot plot only the 1<sup>st</sup> and 3<sup>rd</sup> dimension but will discard the 0<sup>th</sup> and 2<sup>nd</sup> (and, if they exist, the 4<sup>th</sup>, 5<sup>th</sup> and so on)</p> <code>None</code> <code>resolution</code> <code>int</code> <p>Resolution of the grid at which we evaluate the <code>pdf</code>.</p> <code>50</code> <code>figsize</code> <code>Tuple</code> <p>Size of the entire figure.</p> <code>(10, 10)</code> <code>labels</code> <code>Optional[List[str]]</code> <p>List of strings specifying the names of the parameters.</p> <code>None</code> <code>ticks</code> <code>Union[List, Tensor]</code> <p>Position of the ticks.</p> <code>[]</code> <code>points_colors</code> <p>Colors of the <code>points</code>.</p> required <code>fig</code> <p>matplotlib figure to plot on.</p> <code>None</code> <code>axes</code> <p>matplotlib axes corresponding to fig.</p> <code>None</code> <code>**kwargs</code> <p>Additional arguments to adjust the plot, e.g., <code>samples_colors</code>, <code>points_colors</code> and many more, see the source code in <code>_get_default_opts()</code> in <code>sbi.analysis.plot</code> for details.</p> <code>{}</code> <p>Returns: figure and axis of posterior distribution plot</p> Source code in <code>sbi/analysis/plot.py</code> <pre><code>def conditional_pairplot(\n    density: Any,\n    condition: torch.Tensor,\n    limits: Union[List, torch.Tensor],\n    points: Optional[\n        Union[List[np.ndarray], List[torch.Tensor], np.ndarray, torch.Tensor]\n    ] = None,\n    subset: Optional[List[int]] = None,\n    resolution: int = 50,\n    figsize: Tuple = (10, 10),\n    labels: Optional[List[str]] = None,\n    ticks: Union[List, torch.Tensor] = [],\n    fig=None,\n    axes=None,\n    **kwargs,\n):\n    r\"\"\"\n    Plot conditional distribution given all other parameters.\n\n    The conditionals can be interpreted as slices through the `density` at a location\n    given by `condition`.\n\n    For example:\n    Say we have a 3D density with parameters $\\theta_0$, $\\theta_1$, $\\theta_2$ and\n    a condition $c$ passed by the user in the `condition` argument.\n    For the plot of $\\theta_0$ on the diagonal, this will plot the conditional\n    $p(\\theta_0 | \\theta_1=c[1], \\theta_2=c[2])$. For the upper\n    diagonal of $\\theta_1$ and $\\theta_2$, it will plot\n    $p(\\theta_1, \\theta_2 | \\theta_0=c[0])$. All other diagonals and upper-diagonals\n    are built in the corresponding way.\n\n    Args:\n        density: Probability density with a `log_prob()` method.\n        condition: Condition that all but the one/two regarded parameters are fixed to.\n            The condition should be of shape (1, dim_theta), i.e. it could e.g. be\n            a sample from the posterior distribution.\n        limits: Limits in between which each parameter will be evaluated.\n        points: Additional points to scatter.\n        subset: List containing the dimensions to plot. E.g. subset=[1,3] will plot\n            plot only the 1st and 3rd dimension but will discard the 0th and 2nd (and,\n            if they exist, the 4th, 5th and so on)\n        resolution: Resolution of the grid at which we evaluate the `pdf`.\n        figsize: Size of the entire figure.\n        labels: List of strings specifying the names of the parameters.\n        ticks: Position of the ticks.\n        points_colors: Colors of the `points`.\n\n        fig: matplotlib figure to plot on.\n        axes: matplotlib axes corresponding to fig.\n        **kwargs: Additional arguments to adjust the plot, e.g., `samples_colors`,\n            `points_colors` and many more, see the source code in `_get_default_opts()`\n            in `sbi.analysis.plot` for details.\n\n    Returns: figure and axis of posterior distribution plot\n    \"\"\"\n    device = density._device if hasattr(density, \"_device\") else \"cpu\"\n\n    # Setting these is required because _pairplot_scaffold will check if opts['diag'] is\n    # `None`. This would break if opts has no key 'diag'. Same for 'upper'.\n    diag = \"cond\"\n    offdiag = \"cond\"\n\n    opts = _get_default_opts()\n    # update the defaults dictionary by the current values of the variables (passed by\n    # the user)\n    opts = _update(opts, locals())\n    opts = _update(opts, kwargs)\n    opts[\"lower\"] = None\n\n    dim, limits, eps_margins = prepare_for_conditional_plot(condition, opts)\n    diag_func = get_conditional_diag_func(opts, limits, eps_margins, resolution)\n\n    def offdiag_func(row, col, **kwargs):\n        p_image = (\n            eval_conditional_density(\n                opts[\"density\"],\n                opts[\"condition\"].to(device),\n                limits.to(device),\n                row,\n                col,\n                resolution=resolution,\n                eps_margins1=eps_margins[row],\n                eps_margins2=eps_margins[col],\n            )\n            .to(\"cpu\")\n            .numpy()\n        )\n        plt.imshow(\n            p_image.T,\n            origin=\"lower\",\n            extent=(\n                limits[col, 0].item(),\n                limits[col, 1].item(),\n                limits[row, 0].item(),\n                limits[row, 1].item(),\n            ),\n            aspect=\"auto\",\n        )\n\n    return _arrange_plots(\n        diag_func, offdiag_func, dim, limits, points, opts, fig=fig, axes=axes\n    )\n</code></pre> <p>Returns the conditional correlation matrix of a distribution.</p> <p>To compute the conditional distribution, we condition all but two parameters to values from <code>condition</code>, and then compute the Pearson correlation coefficient \\(\\rho\\) between the remaining two parameters under the distribution <code>density</code>. We do so for any pair of parameters specified in <code>subset</code>, thus creating a matrix containing conditional correlations between any pair of parameters.</p> <p>If <code>condition</code> is a batch of conditions, this function computes the conditional correlation matrix for each one of them and returns the mean.</p> <p>Parameters:</p> Name Type Description Default <code>density</code> <code>Any</code> <p>Probability density function with <code>.log_prob()</code> function.</p> required <code>limits</code> <code>Tensor</code> <p>Limits within which to evaluate the <code>density</code>.</p> required <code>condition</code> <code>Tensor</code> <p>Values to condition the <code>density</code> on. If a batch of conditions is passed, we compute the conditional correlation matrix for each of them and return the average conditional correlation matrix.</p> required <code>subset</code> <code>Optional[List[int]]</code> <p>Evaluate the conditional distribution only on a subset of dimensions. If <code>None</code> this function uses all dimensions.</p> <code>None</code> <code>resolution</code> <code>int</code> <p>Number of grid points on which the conditional distribution is evaluated. A higher value increases the accuracy of the estimated correlation but also increases the computational cost.</p> <code>50</code> <p>Returns: Average conditional correlation matrix of shape either <code>(num_dim, num_dim)</code> or <code>(len(subset), len(subset))</code> if <code>subset</code> was specified.</p> Source code in <code>sbi/analysis/conditional_density.py</code> <pre><code>def conditional_corrcoeff(\n    density: Any,\n    limits: Tensor,\n    condition: Tensor,\n    subset: Optional[List[int]] = None,\n    resolution: int = 50,\n) -&gt; Tensor:\n    r\"\"\"Returns the conditional correlation matrix of a distribution.\n\n    To compute the conditional distribution, we condition all but two parameters to\n    values from `condition`, and then compute the Pearson correlation\n    coefficient $\\rho$ between the remaining two parameters under the distribution\n    `density`. We do so for any pair of parameters specified in `subset`, thus\n    creating a matrix containing conditional correlations between any pair of\n    parameters.\n\n    If `condition` is a batch of conditions, this function computes the conditional\n    correlation matrix for each one of them and returns the mean.\n\n    Args:\n        density: Probability density function with `.log_prob()` function.\n        limits: Limits within which to evaluate the `density`.\n        condition: Values to condition the `density` on. If a batch of conditions is\n            passed, we compute the conditional correlation matrix for each of them and\n            return the average conditional correlation matrix.\n        subset: Evaluate the conditional distribution only on a subset of dimensions.\n            If `None` this function uses all dimensions.\n        resolution: Number of grid points on which the conditional distribution is\n            evaluated. A higher value increases the accuracy of the estimated\n            correlation but also increases the computational cost.\n\n    Returns: Average conditional correlation matrix of shape either `(num_dim, num_dim)`\n    or `(len(subset), len(subset))` if `subset` was specified.\n    \"\"\"\n\n    device = density._device if hasattr(density, \"_device\") else \"cpu\"\n\n    subset_ = subset if subset is not None else range(condition.shape[1])\n\n    correlation_matrices = []\n    for cond in condition:\n        correlation_matrices.append(\n            torch.stack(\n                [\n                    compute_corrcoeff(\n                        eval_conditional_density(\n                            density,\n                            cond.to(device),\n                            limits.to(device),\n                            dim1=dim1,\n                            dim2=dim2,\n                            resolution=resolution,\n                        ),\n                        limits[[dim1, dim2]].to(device),\n                    )\n                    for dim1 in subset_\n                    for dim2 in subset_\n                    if dim1 &lt; dim2\n                ]\n            )\n        )\n\n    average_correlations = torch.mean(torch.stack(correlation_matrices), dim=0)\n\n    # `average_correlations` is still a vector containing the upper triangular entries.\n    # Below, assemble them into a matrix:\n    av_correlation_matrix = torch.zeros((len(subset_), len(subset_)), device=device)\n    triu_indices = torch.triu_indices(\n        row=len(subset_), col=len(subset_), offset=1, device=device\n    )\n    av_correlation_matrix[triu_indices[0], triu_indices[1]] = average_correlations\n\n    # Make the matrix symmetric by copying upper diagonal to lower diagonal.\n    av_correlation_matrix = torch.triu(av_correlation_matrix) + torch.tril(\n        av_correlation_matrix.T\n    )\n\n    av_correlation_matrix.fill_diagonal_(1.0)\n    return av_correlation_matrix\n</code></pre>"},{"location":"examples/00_HH_simulator/","title":"Inference on Hodgkin-Huxley model: tutorial","text":"<p>In this tutorial, we use <code>sbi</code> to do inference on a Hodgkin-Huxley model from neuroscience (Hodgkin and Huxley, 1952). We will learn two parameters (\\(\\bar g_{Na}\\),\\(\\bar g_K\\)) based on a current-clamp recording, that we generate synthetically (in practice, this would be an experimental observation).</p> <p>Note, you find the original version of this notebook at https://github.com/sbi-dev/sbi/blob/main/examples/00_HH_simulator.ipynb in the <code>sbi</code> repository.</p> <p>First we are going to import basic packages.</p> <pre><code>import numpy as np\nimport torch\n\n# visualization\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\n\n# sbi\nfrom sbi import utils as utils\nfrom sbi import analysis as analysis\nfrom sbi.inference.base import infer\n</code></pre> <pre><code># remove top and right axis from plots\nmpl.rcParams[\"axes.spines.right\"] = False\nmpl.rcParams[\"axes.spines.top\"] = False\n</code></pre>"},{"location":"examples/00_HH_simulator/#different-required-components","title":"Different required components","text":"<p>Before running inference, let us define the different required components:</p> <ol> <li>observed data</li> <li>prior over model parameters</li> <li>simulator</li> </ol>"},{"location":"examples/00_HH_simulator/#1-observed-data","title":"1. Observed data","text":"<p>Let us assume we current-clamped a neuron and recorded the following voltage trace:</p> <p> </p> <p>In fact, this voltage trace was not measured experimentally but synthetically generated by simulating a Hodgkin-Huxley model with particular parameters (\\(\\bar g_{Na}\\),\\(\\bar g_K\\)). We will come back to this point later in the tutorial.</p>"},{"location":"examples/00_HH_simulator/#2-simulator","title":"2. Simulator","text":"<p>We would like to infer the posterior over the two parameters (\\(\\bar g_{Na}\\),\\(\\bar g_K\\)) of a Hodgkin-Huxley model, given the observed electrophysiological recording above. The model has channel kinetics as in Pospischil et al. 2008, and is defined by the following set of differential equations (parameters of interest highlighted in orange):</p> \\[ \\scriptsize \\begin{align} C_m\\frac{dV}{dt}&amp;=g_1\\left(E_1-V\\right)+                     \\color{orange}{\\bar{g}_{Na}}m^3h\\left(E_{Na}-V\\right)+                     \\color{orange}{\\bar{g}_{K}}n^4\\left(E_K-V\\right)+                     \\bar{g}_Mp\\left(E_K-V\\right)+                     I_{inj}+                     \\sigma\\eta\\left(t\\right)\\\\ \\frac{dq}{dt}&amp;=\\frac{q_\\infty\\left(V\\right)-q}{\\tau_q\\left(V\\right)},\\;q\\in\\{m,h,n,p\\} \\end{align} \\] <p>Above, \\(V\\) represents the membrane potential, \\(C_m\\) is the membrane capacitance, \\(g_{\\text{l}}\\) is the leak conductance, \\(E_{\\text{l}}\\) is the membrane reversal potential, \\(\\bar{g}_c\\) is the density of channels of type \\(c\\) (\\(\\text{Na}^+\\), \\(\\text{K}^+\\), M), \\(E_c\\) is the reversal potential of \\(c\\), (\\(m\\), \\(h\\), \\(n\\), \\(p\\)) are the respective channel gating kinetic variables, and \\(\\sigma \\eta(t)\\) is the intrinsic neural noise. The right hand side of the voltage dynamics is composed of a leak current, a voltage-dependent \\(\\text{Na}^+\\) current, a delayed-rectifier \\(\\text{K}^+\\) current, a slow voltage-dependent \\(\\text{K}^+\\) current responsible for spike-frequency adaptation, and an injected current \\(I_{\\text{inj}}\\). Channel gating variables \\(q\\) have dynamics fully characterized by the neuron membrane potential \\(V\\), given the respective steady-state \\(q_{\\infty}(V)\\) and time constant \\(\\tau_{q}(V)\\) (details in Pospischil et al. 2008).</p> <p>The input current \\(I_{\\text{inj}}\\) is defined as</p> <pre><code>from HH_helper_functions import syn_current\n\nI, t_on, t_off, dt, t, A_soma = syn_current()\n</code></pre> <p>The Hodgkin-Huxley simulator is given by:</p> <pre><code>from HH_helper_functions import HHsimulator\n</code></pre> <p>Putting the input current and the simulator together:</p> <pre><code>def run_HH_model(params):\n\n    params = np.asarray(params)\n\n    # input current, time step\n    I, t_on, t_off, dt, t, A_soma = syn_current()\n\n    t = np.arange(0, len(I), 1) * dt\n\n    # initial voltage\n    V0 = -70\n\n    states = HHsimulator(V0, params.reshape(1, -1), dt, t, I)\n\n    return dict(data=states.reshape(-1), time=t, dt=dt, I=I.reshape(-1))\n</code></pre> <p>To get an idea of the output of the Hodgkin-Huxley model, let us generate some voltage traces for different parameters (\\(\\bar g_{Na}\\),\\(\\bar g_K\\)), given the input current \\(I_{\\text{inj}}\\):</p> <pre><code># three sets of (g_Na, g_K)\nparams = np.array([[50.0, 1.0], [4.0, 1.5], [20.0, 15.0]])\n\nnum_samples = len(params[:, 0])\nsim_samples = np.zeros((num_samples, len(I)))\nfor i in range(num_samples):\n    sim_samples[i, :] = run_HH_model(params=params[i, :])[\"data\"]\n</code></pre> <pre><code># colors for traces\ncol_min = 2\nnum_colors = num_samples + col_min\ncm1 = mpl.cm.Blues\ncol1 = [cm1(1.0 * i / num_colors) for i in range(col_min, num_colors)]\n\nfig = plt.figure(figsize=(7, 5))\ngs = mpl.gridspec.GridSpec(2, 1, height_ratios=[4, 1])\nax = plt.subplot(gs[0])\nfor i in range(num_samples):\n    plt.plot(t, sim_samples[i, :], color=col1[i], lw=2)\nplt.ylabel(\"voltage (mV)\")\nax.set_xticks([])\nax.set_yticks([-80, -20, 40])\n\nax = plt.subplot(gs[1])\nplt.plot(t, I * A_soma * 1e3, \"k\", lw=2)\nplt.xlabel(\"time (ms)\")\nplt.ylabel(\"input (nA)\")\n\nax.set_xticks([0, max(t) / 2, max(t)])\nax.set_yticks([0, 1.1 * np.max(I * A_soma * 1e3)])\nax.yaxis.set_major_formatter(mpl.ticker.FormatStrFormatter(\"%.2f\"))\nplt.show()\n</code></pre> <p></p> <p>As can be seen, the voltage traces can be quite diverse for different parameter values.</p> <p>Often, we are not interested in matching the exact trace, but only in matching certain features thereof. In this example of the Hodgkin-Huxley model, the summary features are the number of spikes, the mean resting potential, the standard deviation of the resting potential, and the first four voltage moments: mean, standard deviation, skewness and kurtosis. Using the function <code>calculate_summary_statistics()</code> imported below, we obtain these statistics from the output of the Hodgkin Huxley simulator. </p> <pre><code>from HH_helper_functions import calculate_summary_statistics\n</code></pre> <p>Lastly, we define a function that performs all of the above steps at once. The function <code>simulation_wrapper</code> takes in conductance values, runs the Hodgkin Huxley model and then returns the summary statistics.</p> <pre><code>def simulation_wrapper(params):\n    \"\"\"\n    Returns summary statistics from conductance values in `params`.\n\n    Summarizes the output of the HH simulator and converts it to `torch.Tensor`.\n    \"\"\"\n    obs = run_HH_model(params)\n    summstats = torch.as_tensor(calculate_summary_statistics(obs))\n    return summstats\n</code></pre> <p><code>sbi</code> takes any function as simulator. Thus, <code>sbi</code> also has the flexibility to use simulators that utilize external packages, e.g., Brian (http://briansimulator.org/), nest (https://www.nest-simulator.org/), or NEURON (https://neuron.yale.edu/neuron/). External simulators do not even need to be Python-based as long as they store simulation outputs in a format that can be read from Python. All that is necessary is to wrap your external simulator of choice into a Python callable that takes a parameter set and outputs a set of summary statistics we want to fit the parameters to.</p>"},{"location":"examples/00_HH_simulator/#3-prior-over-model-parameters","title":"3. Prior over model parameters","text":"<p>Now that we have the simulator, we need to define a function with the prior over the model parameters (\\(\\bar g_{Na}\\),\\(\\bar g_K\\)), which in this case is chosen to be a Uniform distribution:</p> <pre><code>prior_min = [0.5, 1e-4]\nprior_max = [80.0, 15.0]\nprior = utils.torchutils.BoxUniform(\n    low=torch.as_tensor(prior_min), high=torch.as_tensor(prior_max)\n)\n</code></pre>"},{"location":"examples/00_HH_simulator/#inference","title":"Inference","text":"<p>Now that we have all the required components, we can run inference with SNPE to identify parameters whose activity matches this trace.</p> <pre><code>posterior = infer(\n    simulation_wrapper, prior, method=\"SNPE\", num_simulations=300, num_workers=4\n)\n</code></pre> <pre><code>HBox(children=(FloatProgress(value=0.0, description='Running 300 simulations in 300 batches.', max=300.0, styl\u2026\n\n\n\nNeural network successfully converged after 233 epochs.\n</code></pre> <p>Note <code>sbi</code> can parallelize your simulator. If you experience problems with parallelization, try setting <code>num_workers=1</code> and please give us an error report as a GitHub issue.</p>"},{"location":"examples/00_HH_simulator/#coming-back-to-the-observed-data","title":"Coming back to the observed data","text":"<p>As mentioned at the beginning of the tutorial, the observed data are generated by the Hodgkin-Huxley model with a set of known parameters (\\(\\bar g_{Na}\\),\\(\\bar g_K\\)). To illustrate how to compute the summary statistics of the observed data, let us regenerate the observed data:</p> <pre><code># true parameters and respective labels\ntrue_params = np.array([50.0, 5.0])\nlabels_params = [r\"$g_{Na}$\", r\"$g_{K}$\"]\n</code></pre> <pre><code>observation_trace = run_HH_model(true_params)\nobservation_summary_statistics = calculate_summary_statistics(observation_trace)\n</code></pre> <p>As we already shown above, the observed voltage traces look as follows:</p> <pre><code>fig = plt.figure(figsize=(7, 5))\ngs = mpl.gridspec.GridSpec(2, 1, height_ratios=[4, 1])\nax = plt.subplot(gs[0])\nplt.plot(observation_trace[\"time\"], observation_trace[\"data\"])\nplt.ylabel(\"voltage (mV)\")\nplt.title(\"observed data\")\nplt.setp(ax, xticks=[], yticks=[-80, -20, 40])\n\nax = plt.subplot(gs[1])\nplt.plot(observation_trace[\"time\"], I * A_soma * 1e3, \"k\", lw=2)\nplt.xlabel(\"time (ms)\")\nplt.ylabel(\"input (nA)\")\n\nax.set_xticks([0, max(observation_trace[\"time\"]) / 2, max(observation_trace[\"time\"])])\nax.set_yticks([0, 1.1 * np.max(I * A_soma * 1e3)])\nax.yaxis.set_major_formatter(mpl.ticker.FormatStrFormatter(\"%.2f\"))\n</code></pre> <p></p>"},{"location":"examples/00_HH_simulator/#analysis-of-the-posterior-given-the-observed-data","title":"Analysis of the posterior given the observed data","text":"<p>After running the inference algorithm, let us inspect the inferred posterior distribution over the parameters (\\(\\bar g_{Na}\\),\\(\\bar g_K\\)), given the observed trace. To do so, we first draw samples (i.e. consistent parameter sets) from the posterior:</p> <pre><code>samples = posterior.sample((10000,), x=observation_summary_statistics)\n</code></pre> <pre><code>HBox(children=(FloatProgress(value=0.0, description='Drawing 10000 posterior samples', max=10000.0, style=Prog\u2026\n</code></pre> <pre><code>fig, axes = analysis.pairplot(\n    samples,\n    limits=[[0.5, 80], [1e-4, 15.0]],\n    ticks=[[0.5, 80], [1e-4, 15.0]],\n    figsize=(5, 5),\n    points=true_params,\n    points_offdiag={\"markersize\": 6},\n    points_colors=\"r\",\n);\n</code></pre> <p></p> <p>As can be seen, the inferred posterior contains the ground-truth parameters (red) in a high-probability region. Now, let us sample parameters from the posterior distribution, simulate the Hodgkin-Huxley model for this parameter set and compare the simulations with the observed data:</p> <pre><code># Draw a sample from the posterior and convert to numpy for plotting.\nposterior_sample = posterior.sample((1,), x=observation_summary_statistics).numpy()\n</code></pre> <pre><code>HBox(children=(FloatProgress(value=0.0, description='Drawing 1 posterior samples', max=1.0, style=ProgressStyl\u2026\n</code></pre> <pre><code>fig = plt.figure(figsize=(7, 5))\n\n# plot observation\nt = observation_trace[\"time\"]\ny_obs = observation_trace[\"data\"]\nplt.plot(t, y_obs, lw=2, label=\"observation\")\n\n# simulate and plot samples\nx = run_HH_model(posterior_sample)\nplt.plot(t, x[\"data\"], \"--\", lw=2, label=\"posterior sample\")\n\nplt.xlabel(\"time (ms)\")\nplt.ylabel(\"voltage (mV)\")\n\nax = plt.gca()\nhandles, labels = ax.get_legend_handles_labels()\nax.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.3, 1), loc=\"upper right\")\n\nax.set_xticks([0, 60, 120])\nax.set_yticks([-80, -20, 40]);\n</code></pre> <p></p> <p>As can be seen, the sample from the inferred posterior leads to simulations that closely resemble the observed data, confirming that <code>SNPE</code> did a good job at capturing the observed data in this simple case.</p>"},{"location":"examples/00_HH_simulator/#references","title":"References","text":"<p>A. L. Hodgkin and A. F. Huxley. A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 117(4):500\u2013544, 1952.</p> <p>M. Pospischil, M. Toledo-Rodriguez, C. Monier, Z. Piwkowska, T. Bal, Y. Fr\u00e9gnac, H. Markram, and A. Destexhe. Minimal Hodgkin-Huxley type models for different classes of cortical and thalamic neurons. Biological Cybernetics, 99(4-5), 2008.</p>"},{"location":"examples/01_decision_making_model/","title":"SBI for decision-making models","text":"<p>In a previous tutorial, we showed how to use SBI with trial-based iid data. Such scenarios can arise, for example, in models of perceptual decision making. In addition to trial-based iid data points, these models often come with mixed data types and varying experimental conditions. Here, we show how <code>sbi</code> can be used to perform inference in such models with the <code>MNLE</code> method.</p>"},{"location":"examples/01_decision_making_model/#trial-based-sbi-with-mixed-data-types","title":"Trial-based SBI with mixed data types","text":"<p>In some cases, models with trial-based data additionally return data with mixed data types, e.g., continous and discrete data. For example, most computational models of decision-making have continuous reaction times and discrete choices as output. </p> <p>This can induce a problem when performing trial-based SBI that relies on learning a neural likelihood: It is challenging for most density estimators to handle both, continuous and discrete data at the same time.  However, there is a recent SBI method for solving this problem, it\u2019s called Mixed Neural Likelihood Estimation (MNLE). It works just like NLE, but with mixed data types. The trick is that it learns two separate density estimators, one for the discrete part of the data, and one for the continuous part, and combines the two to obtain the final neural likelihood. Crucially, the continuous density estimator is trained conditioned on the output of the discrete one, such that statistical dependencies between the discrete and continuous data (e.g., between choices and reaction times) are modeled as well. The interested reader is referred to the original paper available here.</p> <p>MNLE was recently added to <code>sbi</code> (see this PR and also issue) and follows the same API as <code>SNLE</code>.</p> <p>In this tutorial we will show how to apply <code>MNLE</code> to mixed data, and how to deal with varying experimental conditions. </p>"},{"location":"examples/01_decision_making_model/#toy-problem-for-mnle","title":"Toy problem for <code>MNLE</code>","text":"<p>To illustrate <code>MNLE</code> we set up a toy simulator that outputs mixed data and for which we know the likelihood such we can obtain reference posterior samples via MCMC.</p> <p>Simulator: To simulate mixed data we do the following</p> <ul> <li>Sample reaction time from <code>inverse Gamma</code></li> <li>Sample choices from <code>Binomial</code></li> <li>Return reaction time \\(rt \\in (0, \\infty)\\) and choice index \\(c \\in \\{0, 1\\}\\)</li> </ul> \\[ c \\sim \\text{Binomial}(\\rho) \\\\ rt \\sim \\text{InverseGamma}(\\alpha=2, \\beta) \\\\ \\] <p>Prior: The priors of the two parameters \\(\\rho\\) and \\(\\beta\\) are independent. We define a <code>Beta</code> prior over the probabilty parameter of the <code>Binomial</code> used in the simulator and a <code>Gamma</code> prior over the shape-parameter of the <code>inverse Gamma</code> used in the simulator:</p> \\[ p(\\beta, \\rho) = p(\\beta) \\; p(\\rho) ; \\\\ p(\\beta) = \\text{Gamma}(1, 0.5) \\\\ p(\\text{probs}) = \\text{Beta}(2, 2)  \\] <p>Because the <code>InverseGamma</code> and the <code>Binomial</code> likelihoods are well-defined we can perform MCMC on this problem and obtain reference-posterior samples.</p> <pre><code>import matplotlib.pyplot as plt\nimport torch\nfrom torch import Tensor\n\n\nfrom sbi.inference import MNLE\nfrom pyro.distributions import InverseGamma\nfrom torch.distributions import Beta, Binomial, Categorical, Gamma\nfrom sbi.utils import MultipleIndependent\nfrom sbi.utils.metrics import c2st\n\nfrom sbi.analysis import pairplot\nfrom sbi.inference import MCMCPosterior\nfrom sbi.utils.torchutils import atleast_2d\n\nfrom sbi.inference.potentials.likelihood_based_potential import (\n    MixedLikelihoodBasedPotential,\n)\nfrom sbi.utils.conditional_density_utils import ConditionedPotential\n\nfrom sbi.utils import mcmc_transform\nfrom sbi.inference.potentials.base_potential import BasePotential\n</code></pre> <pre><code># Toy simulator for mixed data\ndef mixed_simulator(theta: Tensor, concentration_scaling: float = 1.0):\n    \"\"\"Returns a sample from a mixed distribution given parameters theta.\n\n    Args:\n        theta: batch of parameters, shape (batch_size, 2)\n        concentration_scaling: scaling factor for the concentration parameter of the InverseGamma distribution, mimics an experimental condition.\n\n    \"\"\"\n    beta, ps = theta[:, :1], theta[:, 1:]\n\n    choices = Binomial(probs=ps).sample()\n    rts = InverseGamma(\n        concentration=concentration_scaling * torch.ones_like(beta), rate=beta\n    ).sample()\n\n    return torch.cat((rts, choices), dim=1)\n\n\n# The potential function defines the ground truth likelihood and allows us to obtain reference posterior samples via MCMC.\nclass PotentialFunctionProvider(BasePotential):\n    allow_iid_x = True  # type: ignore\n\n    def __init__(self, prior, x_o, concentration_scaling=1.0, device=\"cpu\"):\n        super().__init__(prior, x_o, device)\n        self.concentration_scaling = concentration_scaling\n\n    def __call__(self, theta, track_gradients: bool = True):\n        theta = atleast_2d(theta)\n\n        with torch.set_grad_enabled(track_gradients):\n            iid_ll = self.iid_likelihood(theta)\n\n        return iid_ll + self.prior.log_prob(theta)\n\n    def iid_likelihood(self, theta):\n        lp_choices = torch.stack(\n            [\n                Binomial(probs=th.reshape(1, -1)).log_prob(self.x_o[:, 1:])\n                for th in theta[:, 1:]\n            ],\n            dim=1,\n        )\n\n        lp_rts = torch.stack(\n            [\n                InverseGamma(\n                    concentration=self.concentration_scaling * torch.ones_like(beta_i),\n                    rate=beta_i,\n                ).log_prob(self.x_o[:, :1])\n                for beta_i in theta[:, :1]\n            ],\n            dim=1,\n        )\n\n        joint_likelihood = (lp_choices + lp_rts).squeeze()\n\n        assert joint_likelihood.shape == torch.Size([self.x_o.shape[0], theta.shape[0]])\n        return joint_likelihood.sum(0)\n</code></pre> <pre><code># Define independent prior.\nprior = MultipleIndependent(\n    [\n        Gamma(torch.tensor([1.0]), torch.tensor([0.5])),\n        Beta(torch.tensor([2.0]), torch.tensor([2.0])),\n    ],\n    validate_args=False,\n)\n</code></pre>"},{"location":"examples/01_decision_making_model/#obtain-reference-posterior-samples-via-analytical-likelihood-and-mcmc","title":"Obtain reference-posterior samples via analytical likelihood and MCMC","text":"<pre><code>torch.manual_seed(42)\nnum_trials = 10\nnum_samples = 1000\ntheta_o = prior.sample((1,))\nx_o = mixed_simulator(theta_o.repeat(num_trials, 1))\n</code></pre> <pre><code>mcmc_kwargs = dict(\n    num_chains=20,\n    warmup_steps=50,\n    method=\"slice_np_vectorized\",\n    init_strategy=\"proposal\",\n)\n\ntrue_posterior = MCMCPosterior(\n    potential_fn=PotentialFunctionProvider(prior, x_o),\n    proposal=prior,\n    theta_transform=mcmc_transform(prior, enable_transform=True),\n    **mcmc_kwargs,\n)\ntrue_samples = true_posterior.sample((num_samples,))\n</code></pre> <pre><code>/Users/janbolts/qode/sbi/sbi/utils/sbiutils.py:342: UserWarning: An x with a batch size of 10 was passed. It will be interpreted as a batch of independent and identically\n            distributed data X={x_1, ..., x_n}, i.e., data generated based on the\n            same underlying (unknown) parameter. The resulting posterior will be with\n            respect to entire batch, i.e,. p(theta | X).\n  warnings.warn(\n\n\n\nRunning vectorized MCMC with 20 chains:   0%|          | 0/20000 [00:00&lt;?, ?it/s]\n</code></pre>"},{"location":"examples/01_decision_making_model/#train-mnle-and-generate-samples-via-mcmc","title":"Train MNLE and generate samples via MCMC","text":"<pre><code># Training data\nnum_simulations = 20000\n# For training the MNLE emulator we need to define a proposal distribution, the prior is\n# a good choice.\nproposal = prior\ntheta = proposal.sample((num_simulations,))\nx = mixed_simulator(theta)\n\n# Train MNLE and obtain MCMC-based posterior.\ntrainer = MNLE()\nestimator = trainer.append_simulations(theta, x).train(training_batch_size=1000)\n</code></pre> <pre><code>/Users/janbolts/qode/sbi/sbi/neural_nets/mnle.py:60: UserWarning: The mixed neural likelihood estimator assumes that x contains\n        continuous data in the first n-1 columns (e.g., reaction times) and\n        categorical data in the last column (e.g., corresponding choices). If\n        this is not the case for the passed `x` do not use this function.\n  warnings.warn(\n\n\n Neural network successfully converged after 73 epochs.\n</code></pre> <pre><code># Build posterior from the trained estimator and prior.\nmnle_posterior = trainer.build_posterior(prior=prior)\n\nmnle_samples = mnle_posterior.sample((num_samples,), x=x_o, **mcmc_kwargs)\n</code></pre> <pre><code>Running vectorized MCMC with 20 chains:   0%|          | 0/20000 [00:00&lt;?, ?it/s]\n</code></pre>"},{"location":"examples/01_decision_making_model/#compare-mnle-and-reference-posterior","title":"Compare MNLE and reference posterior","text":"<pre><code># Plot them in one pairplot as contours (obtained via KDE on the samples).\nfig, ax = pairplot(\n    [\n        prior.sample((1000,)),\n        true_samples,\n        mnle_samples,\n    ],\n    points=theta_o,\n    diag=\"kde\",\n    upper=\"contour\",\n    kde_offdiag=dict(bins=50),\n    kde_diag=dict(bins=100),\n    contour_offdiag=dict(levels=[0.95]),\n    points_colors=[\"k\"],\n    points_offdiag=dict(marker=\"*\", markersize=10),\n    labels=[r\"$\\beta$\", r\"$\\rho$\"],\n)\n\nplt.sca(ax[1, 1])\nplt.legend(\n    [\"Prior\", \"Reference\", \"MNLE\", r\"$\\theta_o$\"],\n    frameon=False,\n    fontsize=12,\n);\n</code></pre> <p>We see that the inferred <code>MNLE</code> posterior nicely matches the reference posterior, and how both inferred a posterior that is quite different from the prior.</p> <p>Because MNLE training is amortized we can obtain another posterior given a different observation with potentially a different number of trials, just by running MCMC again (without re-training <code>MNLE</code>):</p>"},{"location":"examples/01_decision_making_model/#repeat-inference-with-different-x_o-that-contains-more-trials","title":"Repeat inference with different <code>x_o</code> that contains more trials","text":"<pre><code>num_trials = 50\nx_o = mixed_simulator(theta_o.repeat(num_trials, 1))\ntrue_samples = true_posterior.sample((num_samples,), x=x_o, **mcmc_kwargs)\nmnle_samples = mnle_posterior.sample((num_samples,), x=x_o, **mcmc_kwargs)\n</code></pre> <pre><code>/Users/janbolts/qode/sbi/sbi/utils/sbiutils.py:342: UserWarning: An x with a batch size of 50 was passed. It will be interpreted as a batch of independent and identically\n            distributed data X={x_1, ..., x_n}, i.e., data generated based on the\n            same underlying (unknown) parameter. The resulting posterior will be with\n            respect to entire batch, i.e,. p(theta | X).\n  warnings.warn(\n\n\n\nRunning vectorized MCMC with 20 chains:   0%|          | 0/20000 [00:00&lt;?, ?it/s]\n\n\n\nRunning vectorized MCMC with 20 chains:   0%|          | 0/20000 [00:00&lt;?, ?it/s]\n</code></pre> <pre><code># Plot them in one pairplot as contours (obtained via KDE on the samples).\nfig, ax = pairplot(\n    [\n        prior.sample((1000,)),\n        true_samples,\n        mnle_samples,\n    ],\n    points=theta_o,\n    diag=\"kde\",\n    upper=\"contour\",\n    kde_offdiag=dict(bins=50),\n    kde_diag=dict(bins=100),\n    contour_offdiag=dict(levels=[0.95]),\n    points_colors=[\"k\"],\n    points_offdiag=dict(marker=\"*\", markersize=10),\n    labels=[r\"$\\beta$\", r\"$\\rho$\"],\n)\n\nplt.sca(ax[1, 1])\nplt.legend(\n    [\"Prior\", \"Reference\", \"MNLE\", r\"$\\theta_o$\"],\n    frameon=False,\n    fontsize=12,\n);\n</code></pre> <pre><code>print(c2st(true_samples, mnle_samples)[0])\n</code></pre> <pre><code>tensor(0.5565)\n</code></pre> <p>Again we can see that the posteriors match nicely. In addition, we observe that the posterior\u2019s (epistemic) uncertainty reduces as we increase the number of trials. </p> <p>Note: <code>MNLE</code> is trained on single-trial data. Theoretically, density estimation is perfectly accurate only in the limit of infinite training data. Thus, training with a finite amount of training data naturally induces a small bias in the density estimator.  As we observed above, this bias is so small that we don\u2019t really notice it, e.g., the <code>c2st</code> scores were close to 0.5.  However, when we increase the number of trials in <code>x_o</code> dramatically (on the order of 1000s) the small bias can accumulate over the trials and inference with <code>MNLE</code> can become less accurate.</p>"},{"location":"examples/01_decision_making_model/#mnle-with-experimental-conditions","title":"MNLE with experimental conditions","text":"<p>In the perceptual decision-making research it is common to design experiments with varying experimental decisions, e.g., to vary the difficulty of the task.  During parameter inference, it can be beneficial to incorporate the experimental conditions.  In MNLE, we are learning an emulator that should be able to generate synthetic experimental data including reaction times and choices given different experimental conditions.  Thus, to make MNLE work with experimental conditions, we need to include them in the training process, i.e., treat them like auxiliary parameters of the simulator: </p> <pre><code># define a simulator wrapper in which the experimental condition are contained in theta and passed to the simulator.\ndef sim_wrapper(theta):\n    # simulate with experiment conditions\n    return mixed_simulator(\n        theta=theta[:, :2],\n        concentration_scaling=theta[:, 2:]\n        + 1,  # add 1 to deal with 0 values from Categorical distribution\n    )\n</code></pre> <pre><code># Define a proposal that contains both, priors for the parameters and a discrte prior over experimental conditions.\nproposal = MultipleIndependent(\n    [\n        Gamma(torch.tensor([1.0]), torch.tensor([0.5])),\n        Beta(torch.tensor([2.0]), torch.tensor([2.0])),\n        Categorical(probs=torch.ones(1, 3)),\n    ],\n    validate_args=False,\n)\n\n# Simulated data\nnum_simulations = 10000\nnum_samples = 1000\ntheta = proposal.sample((num_simulations,))\nx = sim_wrapper(theta)\nassert x.shape == (num_simulations, 2)\n\n# simulate observed data and define ground truth parameters\nnum_trials = 10\ntheta_o = proposal.sample((1,))\ntheta_o[0, 2] = 2.0  # set condition to 2 as in original simulator.\nx_o = sim_wrapper(theta_o.repeat(num_trials, 1))\n</code></pre>"},{"location":"examples/01_decision_making_model/#obtain-ground-truth-posterior-via-mcmc","title":"Obtain ground truth posterior via MCMC","text":"<p>We obtain a ground-truth posterior via MCMC by using the PotentialFunctionProvider.</p> <p>For that, we first the define the actual prior, i.e., the distribution over the parameter we want to infer (not the proposal).</p> <p>Thus, we leave out the discrete prior over experimental conditions.</p> <pre><code>prior = MultipleIndependent(\n    [\n        Gamma(torch.tensor([1.0]), torch.tensor([0.5])),\n        Beta(torch.tensor([2.0]), torch.tensor([2.0])),\n    ],\n    validate_args=False,\n)\nprior_transform = mcmc_transform(prior)\n\n# We can now use the PotentialFunctionProvider to obtain a ground-truth posterior via MCMC.\ntrue_posterior_samples = MCMCPosterior(\n    PotentialFunctionProvider(\n        prior,\n        x_o,\n        concentration_scaling=float(theta_o[0, 2])\n        + 1.0,  # add one because the sim_wrapper adds one (see above)\n    ),\n    theta_transform=prior_transform,\n    proposal=prior,\n    **mcmc_kwargs,\n).sample((num_samples,), show_progress_bars=True)\n</code></pre> <pre><code>/Users/janbolts/qode/sbi/sbi/utils/sbiutils.py:342: UserWarning: An x with a batch size of 10 was passed. It will be interpreted as a batch of independent and identically\n            distributed data X={x_1, ..., x_n}, i.e., data generated based on the\n            same underlying (unknown) parameter. The resulting posterior will be with\n            respect to entire batch, i.e,. p(theta | X).\n  warnings.warn(\n\n\n\nRunning vectorized MCMC with 20 chains:   0%|          | 0/20000 [00:00&lt;?, ?it/s]\n</code></pre>"},{"location":"examples/01_decision_making_model/#train-mnle-including-experimental-conditions","title":"Train MNLE including experimental conditions","text":"<pre><code>trainer = MNLE(proposal)\nestimator = trainer.append_simulations(theta, x).train(training_batch_size=100)\n</code></pre> <pre><code>/Users/janbolts/qode/sbi/sbi/neural_nets/mnle.py:60: UserWarning: The mixed neural likelihood estimator assumes that x contains\n        continuous data in the first n-1 columns (e.g., reaction times) and\n        categorical data in the last column (e.g., corresponding choices). If\n        this is not the case for the passed `x` do not use this function.\n  warnings.warn(\n\n\n Neural network successfully converged after 73 epochs.\n</code></pre>"},{"location":"examples/01_decision_making_model/#construct-conditional-potential-function","title":"Construct conditional potential function","text":"<p>To obtain posterior samples conditioned on a particular experimental condition (and on x_o), we need to construct a corresponding potential function.</p> <pre><code># First, we define the potential function for the complete, unconditional MNLE-likelihood.\npotential_fn = MixedLikelihoodBasedPotential(estimator, proposal, x_o)\n\n# Then we use the potential to construct the conditional potential function.\n# Here, we tell the constructor to condition on the last dimension (index 2) by passing dims_to_sample=[0, 1].\nconditioned_potential_fn = ConditionedPotential(\n    potential_fn,\n    condition=theta_o,\n    dims_to_sample=[0, 1],\n    allow_iid_x=True,  # we also need to explicitly tell that MNLE allows iid_x\n)\n\n# Using this potential function, we can now obtain conditional samples.\nmnle_posterior = MCMCPosterior(\n    potential_fn=conditioned_potential_fn,\n    theta_transform=prior_transform,\n    proposal=prior,\n    **mcmc_kwargs\n)\nconditional_samples = mnle_posterior.sample((num_samples,), x=x_o)\n</code></pre> <pre><code>Running vectorized MCMC with 20 chains:   0%|          | 0/20000 [00:00&lt;?, ?it/s]\n</code></pre> <pre><code># Finally, we can compare the ground truth conditional posterior with the MNLE-conditional posterior.\nfig, ax = pairplot(\n    [\n        prior.sample((1000,)),\n        true_posterior_samples,\n        conditional_samples,\n    ],\n    points=theta_o,\n    diag=\"kde\",\n    upper=\"contour\",\n    kde_offdiag=dict(bins=50),\n    kde_diag=dict(bins=100),\n    contour_offdiag=dict(levels=[0.95]),\n    points_colors=[\"k\"],\n    points_offdiag=dict(marker=\"*\", markersize=10),\n    labels=[r\"$\\beta$\", r\"$\\rho$\"],\n)\n\nplt.sca(ax[1, 1])\nplt.legend(\n    [\"Prior\", \"Reference\", \"MNLE\", r\"$\\theta_o$\"],\n    frameon=False,\n    fontsize=12,\n);\n</code></pre> <p></p> <p>They match accurately, showing that we can indeed post-hoc condition the trained MNLE likelihood on different experimental conditions.</p> <pre><code>\n</code></pre>"},{"location":"faq/question_01/","title":"What should I do when my \u2018posterior samples are outside of the prior support\u2019 in SNPE?","text":"<p>When working with multi-round SNPE, you might have experienced the following warning:  <pre><code>Only x% posterior samples are within the prior support. It may take a long time to collect the remaining 10000 samples. Consider interrupting (Ctrl-C) and switching to 'sample_with_mcmc=True'.\n</code></pre></p> <p>This reason for this issue is described in more detail  here and here.  The following fixes are possible:  </p> <ul> <li> <p>sample with MCMC: <code>samples = posterior((num_samples,), x=x_o, sample_with_mcmc=True)</code>. This will make sampling slower, but samples will not \u2018leak\u2019.  </p> </li> <li> <p>resort to single-round SNPE and (if necessary) increase your simulation budget.  </p> </li> <li> <p>if your prior is either Gaussian (torch.distributions.multivariateNormal) or Uniform  (sbi.utils.BoxUniform), you can avoid leakage by using a mixture density network as  density estimator. I.e., using the  flexible interface, set  <code>density_estimator='mdn'</code>. When running inference, there should be a print statement  \u201cUsing SNPE-C with non-atomic loss\u201d</p> </li> <li> <p>use a different algorithm, e.g. SNRE and SNLE. Note, however, that these algorithms can have different issues and potential pitfalls.  </p> </li> </ul>"},{"location":"faq/question_02/","title":"Can the algorithms deal with invalid data, e.g. NaN or inf?","text":"<p>Yes. By default, whenever a simulation returns at least one <code>NaN</code> or <code>inf</code>, it is  completely excluded from the training data. In other words, the simulation is simply  discarded.</p> <p>In cases where a very large fraction of simulations return <code>NaN</code> or <code>inf</code>, discarding many simulations can be wasteful. There are two options to deal with this: Either, you use the <code>RestrictionEstimator</code> to learn regions in parameter space that do not produce <code>NaN</code> or <code>inf</code>, see here. Alternatively, you can manually substitute the \u2018invalid\u2019 values with a reasonable replacement.  I.e., at the end of your simulation code, you search for invalid entries and replace  them with a floating point number. Importantly, in order for neural network training  work well, the floating point number should still be in a reasonable range, i.e. maybe a  few standard deviations outside of \u2018good\u2019 values.</p> <p>If you are running multi-round SNPE, however, things can go fully wrong if invalid  data are encountered. In that case, you will get the following warning <pre><code>When invalid simulations are excluded, multi-round SNPE-C can leak into the regions where parameters led to invalid simulations. This can lead to poor results.\n</code></pre> Hence, if you are running multi-round SNPE and a significant fraction of simulations returns at least one invalid number, we strongly recommend to manually replace the value  in your simulation code as described above (or resort to single-round SNPE or use a   different method).</p>"},{"location":"faq/question_03/","title":"When using multiple workers, I get a pickling error. Can I still use multiprocessing?","text":"<p>Yes, but you will have to make a few adjustments to your code. </p> <p>Some background: when using <code>num_workers &gt; 1</code>, you might experience an error that a  certain object from your simulator could not be pickled (an example can be found here).</p> <p>This can be fixed by forcing <code>sbi</code> to pickle with <code>dill</code> instead of the default  <code>cloudpickle</code>. To do so, adjust your code as  follows:</p> <ul> <li>Install <code>dill</code>: <pre><code>pip install dill\n</code></pre></li> <li>At the very beginning of your python script, set the pickler to <code>dill</code>: <pre><code>from joblib.externals.loky import set_loky_pickler\nset_loky_pickler(\"dill\")\n</code></pre></li> <li>Move all imports required by your simulator into the simulator: <pre><code># Imports specified outside of the simulator will break dill:\nimport torch\ndef my_simulator(parameters):\n    return torch.ones(1,10)\n\n# Therefore, move the imports into the simulator:\ndef my_simulator(parameters):\n    import torch\n    return torch.ones(1,10)\n</code></pre></li> </ul>"},{"location":"faq/question_03/#alternative-parallelize-yourself","title":"Alternative: parallelize yourself","text":"<p>You can also write your own code to parallelize simulations with whatever  multiprocessing framework you prefer. You can then simulate your data outside of <code>sbi</code> and pass the simulated data as shown in the  flexible interface: </p>"},{"location":"faq/question_03/#some-more-background","title":"Some more background","text":"<p><code>sbi</code> uses <code>joblib</code> to parallelize simulations, which in turn uses <code>pickle</code> or  <code>cloudpickle</code> to serialize the simulator. Almost all simulators will be picklable with  <code>cloudpickle</code>, but we have experienced issues e.g. with <code>neuron</code> simulators, see here.</p>"},{"location":"faq/question_04/","title":"Can I use the GPU for training the density estimator?","text":"<p>TLDR; Yes, by passing <code>device=\"cuda\"</code> and by passing a prior that lives on the device name your passed. But no speed-ups for default density estimators.</p> <p>Yes. When creating the inference object in the flexible interface, you can pass the <code>device</code> as an argument, e.g.,</p> <pre><code>inference = SNPE(prior, device=\"cuda\", density_estimator=\"maf\")\n</code></pre> <p>The device is set to <code>\"cpu\"</code> by default, and it can be set to anything, as long as it maps to an existing PyTorch CUDA device. <code>sbi</code> will take care of copying the <code>net</code> and the training data to and from the <code>device</code>.  Note that the prior must be on the training device already, e.g., when passing <code>device=\"cuda:0\"</code>, make sure to pass a prior object that was created on that device, e.g.,  <code>prior = torch.distributions.MultivariateNormal(loc=torch.zeros(2, device=\"cuda:0\"),                                                  covariance_matrix=torch.eye(2, device=\"cuda:0\"))</code>.</p>"},{"location":"faq/question_04/#performance","title":"Performance","text":"<p>Whether or not you reduce your training time when training on a GPU depends on the problem at hand. We provide a couple of default density estimators for <code>SNPE</code>, <code>SNLE</code> and <code>SNRE</code>, e.g., a mixture density network (<code>density_estimator=\"mdn\"</code>) or a Masked Autoregressive Flow (<code>density_estimator=\"maf\"</code>). For those default density estimators we do not expect a speed up. This is because the underlying neural networks are quite shallow and not tall, e.g., they do not have many parameters or matrix operations that profit a lot from being executed on the GPU. </p> <p>A speed up through training on the GPU will most likely become visible when you are using convolutional modules in your neural networks. E.g., when passing an embedding net for image processing like in this example: https://github.com/sbi-dev/sbi/blob/main/tutorials/05_embedding_net.ipynb. </p>"},{"location":"faq/question_05/","title":"How should I save and load objects in <code>sbi</code>?","text":"<p><code>NeuralPosterior</code> objects are picklable. <pre><code>import pickle\n\n# ... run inference\nposterior = inference.build_posterior()\n\nwith open(\"/path/to/my_posterior.pkl\", \"wb\") as handle:\n    pickle.dump(posterior, handle)\n</code></pre></p> <p>Note: posterior objects that were saved under <code>sbi v0.17.2</code> or older can not be loaded under <code>sbi v0.18.0</code> or newer.</p> <p>Note: if you try to load a posterior that was saved under <code>sbi v0.14.x</code> or earlier under <code>sbi v0.15.x</code> until <code>sbi v0.17.x</code>, you have to add: <pre><code>import sys\nfrom sbi.utils import user_input_checks_utils\n\nsys.modules[\"sbi.user_input.user_input_checks_utils\"] = user_input_checks_utils\n</code></pre> to your script before loading the posterior.</p> <p>As of <code>sbi v0.18.0</code>, <code>NeuralInference</code> objects are also picklable. <pre><code>import pickle\n\n# ... run inference\nposterior = inference.build_posterior()\n\nwith open(\"/path/to/my_inference.pkl\", \"wb\") as handle:\n    pickle.dump(inference, handle)\n</code></pre> However, saving and loading the <code>inference</code> object will slightly modify the object (in order to make it serializable). These modifications lead to the following two changes in behaviour: 1) Retraining from scratch is not supported, i.e. <code>.train(..., retrain_from_scratch=True)</code> does not work. 2) When the loaded object calls the <code>.train()</code> method, it generates a new tensorboard summary writer (instead of appending to the current one).</p>"},{"location":"faq/question_06/","title":"Can I stop neural network training and resume it later?","text":"<p>Many clusters have a time limit and <code>sbi</code> might exceed this limit. You can circumvent this problem by using the flexible interface. After simulations are finished, <code>sbi</code> trains a neural network. If this process takes too long, you can stop training and resume it later. The syntax is:</p> <pre><code>inference = SNPE(prior=prior)\ninference = inference.append_simulations(theta, x)\ninference.train(max_num_epochs=300)  # Pick `max_num_epochs` such that it does not exceed the runtime.\n\nwith open(\"path/to/my/inference.pkl\", \"wb\") as handle:\n    pickle.dump(inference, handle)\n\n# To resume training:\nwith open(\"path/to/my/inference.pkl\", \"rb\") as handle:\n    inference_from_disk = pickle.load(handle)\ninference_from_disk.train(resume_training=True, max_num_epochs=600)  # Run epochs 301 until 600 (or stop early).\nposterior = inference_from_disk.build_posterior()\n</code></pre>"},{"location":"faq/question_07/","title":"Can I use a custom prior with sbi?","text":"<p><code>sbi</code> works with torch distributions only so we recommend to use those whenever possible. For example, when you are used to using <code>scipy.stats</code> distributions as priors then we recommend using the corresponding <code>torch.distributions</code>, most common distributions are implemented there. </p> <p>In case you want to use a custom prior that is not in the set of common distributions that\u2019s possible as well: You need to write a prior class that mimicks the behaviour of a <code>torch.distributions.Distribution</code> class. Then <code>sbi</code> will wrap this class to make it a fully functional torch <code>Distribution</code>.</p> <p>Essentially, the class needs two methods:</p> <ul> <li><code>.sample(sample_shape)</code>, where sample_shape is a shape tuple, e.g., <code>(n,)</code>, and returns a batch of n samples, e.g., of shape (n, 2)` for a two dimenional prior.</li> <li><code>.log_prob(value)</code> method that returns the \u201clog probs\u201d of parameters under the prior, e.g., for a batches of n parameters with shape <code>(n, ndims)</code> it should return a log probs array of shape <code>(n,)</code>.</li> </ul> <p>For sbi &gt; 0.17.2 this could look like the following:</p> <pre><code>class CustomUniformPrior:\n    \"\"\"User defined numpy uniform prior.\n\n    Custom prior with user-defined valid .sample and .log_prob methods.\n    \"\"\"\n\n    def __init__(self, lower: Tensor, upper: Tensor, return_numpy: bool = False):\n        self.lower = lower\n        self.upper = upper\n        self.dist = BoxUniform(lower, upper)\n        self.return_numpy = return_numpy\n\n    def sample(self, sample_shape=torch.Size([])):\n        samples = self.dist.sample(sample_shape)\n        return samples.numpy() if self.return_numpy else samples\n\n    def log_prob(self, values):\n        if self.return_numpy:\n            values = torch.as_tensor(values)\n        log_probs = self.dist.log_prob(values)\n        return log_probs.numpy() if self.return_numpy else log_probs\n</code></pre> <p>Once you have such a class you can wrap into a <code>Distribution</code> using the <code>process_prior</code> function <code>sbi</code> provides:</p> <pre><code>from sbi.utils import process_prior\n\ncustom_prior = CustomUniformPrior(torch.zeros(2), torch.ones(2))\nprior, *_ = process_prior(custom_prior)  # Keeping only the first return.\n# use this wrapped prior in sbi...\n</code></pre> <p>In <code>sbi</code> it is sometimes required to check the support of the prior, e.g., when the prior support is bounded and one wants to reject samples from the posterior density estimator that lie outside the prior support. In torch <code>Distributions</code> this is handled automatically, however, when using a custom prior it is not. Thus,  if your prior has bounded support (like the one above) it makes sense to pass the bounds to the wrapper function such that <code>sbi</code> can pass them to torch <code>Distributions</code>:</p> <pre><code>from sbi.utils import process_prior\n\ncustom_prior = CustomUniformPrior(torch.zeros(2), torch.ones(2))\nprior = process_prior(custom_prior, \n                      custom_prior_wrapper_kwargs=dict(lower_bound=torch.zeros(2), \n                                                       upper_bound=torch.ones(2)))\n# use this wrapped prior in sbi...\n</code></pre> <p>Note that in <code>custom_prior_wrapper_kwargs</code> you can pass additinal arguments for the wrapper, e.g., <code>validate_args</code> or <code>arg_constraints</code> see the <code>Distribution</code> documentation for more details. </p> <p>If you are running sbi &lt; 0.17.2 and use <code>SNLE</code> the code above will produce a <code>NotImplementedError</code> (see #581). In this case you need to update to a newer version of <code>sbi</code> or use <code>SNPE</code> instead. </p>"},{"location":"tutorial/00_getting_started/","title":"Getting started with <code>sbi</code>","text":"<p>Note, you can find the original version of this notebook at https://github.com/sbi-dev/sbi/blob/main/tutorials/00_getting_started.ipynb in the <code>sbi</code> repository.</p> <pre><code>import torch\nfrom sbi import utils as utils\nfrom sbi import analysis as analysis\nfrom sbi.inference.base import infer\n</code></pre>"},{"location":"tutorial/00_getting_started/#running-the-inference-procedure","title":"Running the inference procedure","text":"<p><code>sbi</code> provides a simple interface to run state-of-the-art algorithms for simulation-based inference.</p> <p>For inference, you need to provide two ingredients:</p> <p>1) a prior distribution that allows to sample parameter sets. 2) a simulator that takes parameter sets and produces simulation outputs.</p> <p>For example, we can have a 3-dimensional parameter space with a uniform prior between [-1,1] and a simple simulator that for the sake of example adds 1.0 and some Gaussian noise to the parameter set:</p> <pre><code>num_dim = 3\nprior = utils.BoxUniform(low=-2 * torch.ones(num_dim), high=2 * torch.ones(num_dim))\n\ndef simulator(parameter_set):\n    return 1.0 + parameter_set + torch.randn(parameter_set.shape) * 0.1\n</code></pre> <p><code>sbi</code> can then run inference:</p> <pre><code># Other methods are \"SNLE\" or \"SNRE\".\nposterior = infer(simulator, prior, method=\"SNPE\", num_simulations=1000)\n</code></pre> <pre><code>Running 1000 simulations.:   0%|          | 0/1000 [00:00&lt;?, ?it/s]\n\n\n Neural network successfully converged after 119 epochs.\n</code></pre> <p>Let\u2019s say we have made some observation \\(x\\):</p> <pre><code>observation = torch.zeros(3)\n</code></pre> <p>Given this observation, we can then sample from the posterior \\(p(\\theta|x)\\), evaluate its log-probability, or plot it.</p> <pre><code>samples = posterior.sample((10000,), x=observation)\nlog_probability = posterior.log_prob(samples, x=observation)\n_ = analysis.pairplot(samples, limits=[[-2, 2], [-2, 2], [-2, 2]], figsize=(6, 6))\n</code></pre> <pre><code>Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00&lt;?, ?it/s]\n</code></pre> <p></p>"},{"location":"tutorial/00_getting_started/#next-steps","title":"Next steps","text":"<p>The single-line interface described above provides an easy entry for using <code>sbi</code>. However, on almost any real-world problem that goes beyond a simple demonstration, we strongly recommend using the flexible interface.</p>"},{"location":"tutorial/01_gaussian_amortized/","title":"Amortized posterior inference on Gaussian example","text":"<p>Note, you can find the original version of this notebook at https://github.com/sbi-dev/sbi/blob/main/tutorials/01_gaussian_amortized.ipynb in the <code>sbi</code> repository.</p> <p>In this tutorial, we will demonstrate how <code>sbi</code> can infer an amortized posterior for a simple toy model with a uniform prior and Gaussian likelihood.</p> <pre><code>import torch\nimport numpy as np\n\nfrom sbi import utils as utils\nfrom sbi import analysis as analysis\nfrom sbi.inference.base import infer\n</code></pre>"},{"location":"tutorial/01_gaussian_amortized/#defining-prior-simulator-and-running-inference","title":"Defining prior, simulator, and running inference","text":"<p>Say we have a 3-dimensional parameter space, and the prior is uniformly distributed between <code>-2</code> and <code>2</code> in each dimension, i.e. \\(\\theta \\in [-2,2], y\\in [-2,2], z \\in [-2,2]\\).</p> <pre><code>num_dim = 3\nprior = utils.BoxUniform(low=-2 * torch.ones(num_dim), high=2 * torch.ones(num_dim))\n</code></pre> <p>Our simulator takes the input parameters, adds <code>1.0</code> in each dimension, and then adds some Gaussian noise:</p> <pre><code>def linear_gaussian(theta):\n    return theta + 1.0 + torch.randn_like(theta) * 0.1\n</code></pre> <p>We can then run inference (either with the simple interface or with the flexible interface):</p> <pre><code>posterior = infer(linear_gaussian, prior, \"SNPE\", num_simulations=1000)\n</code></pre>"},{"location":"tutorial/01_gaussian_amortized/#amortized-inference","title":"Amortized inference","text":"<p>Note that we have not yet provided an observation to the inference procedure. In fact, we can evaluate the posterior for different observations without having to re-run inference. This is called amortization. An amortized posterior is one that is not focused on any particular observation. Naturally, if the diversity of observations is large, any of the inference methods will need to run a sufficient number of simulations for the resulting posterior to perform well across these diverse observations.</p> <p>Let\u2019s say we have two observations <code>x_o_1 = [0,0,0]</code> and <code>x_o_2 = [2,2,2]</code>:</p> <pre><code>x_o_1 = torch.zeros(3,)\nx_o_2 = 2.0 * torch.ones(3,)\n</code></pre> <p>We can draw samples from the posterior given <code>x_o_1</code> and then plot them:</p> <pre><code>posterior_samples_1 = posterior.sample((10000,), x=x_o_1)\n\n# plot posterior samples\n_ = analysis.pairplot(\n    posterior_samples_1, limits=[[-2, 2], [-2, 2], [-2, 2]], figsize=(5, 5)\n)\n</code></pre> <pre><code>Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00&lt;?, ?it/s]\n</code></pre> <p></p> <p>As it can be seen, the posterior samples are centered around <code>[-1,-1,-1]</code> in each dimension.  This makes sense because the simulator always adds <code>1.0</code> in each dimension and we have observed <code>x_o_1 = [0,0,0]</code>.</p> <p>Since the learned posterior is amortized, we can also draw samples from the posterior given the second observation without having to re-run inference:</p> <pre><code>posterior_samples_2 = posterior.sample((10000,), x=x_o_2)\n\n# plot posterior samples\n_ = analysis.pairplot(\n    posterior_samples_2, limits=[[-2, 2], [-2, 2], [-2, 2]], figsize=(5, 5)\n)\n</code></pre> <pre><code>Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00&lt;?, ?it/s]\n</code></pre> <p></p> <p>So, if we observed <code>x_o_2 = [2,2,2]</code>, the posterior is centered around <code>[1,1,1]</code> \u2013 again, this makes sense because the simulator adds <code>1.0</code> in each dimension.</p>"},{"location":"tutorial/02_flexible_interface/","title":"The flexible interface","text":"<p>In the previous tutorial, we have demonstrated how <code>sbi</code> can be used to run simulation-based inference with just a single line of code.</p> <p>In addition to this simple interface, <code>sbi</code> also provides a flexible interface which provides several additional features implemented in <code>sbi</code>.</p> <p>Note, you can find the original version of this notebook at https://github.com/sbi-dev/sbi/blob/main/tutorials/02_flexible_interface.ipynb in the <code>sbi</code> repository.</p>"},{"location":"tutorial/02_flexible_interface/#features","title":"Features","text":"<p>The flexible interface offers the following features (and many more):</p> <ul> <li>performing sequential posterior estimation by focusing on a particular observation over multiple rounds. This can decrease the number of simulations one has to run, but the inference procedure is no longer amortized (tutorial).    </li> <li>specify your own density estimator, or change hyperparameters of existing ones (e.g. number of hidden units for NSF) (tutorial).    </li> <li>use an <code>embedding_net</code> to learn summary features from high-dimensional simulation outputs (tutorial).  </li> <li>provide presimulated data  </li> <li>choose between different methods to sample from the posterior.  </li> <li>use calibration kernels as proposed by Lueckmann, Goncalves et al. 2017.</li> </ul>"},{"location":"tutorial/02_flexible_interface/#main-syntax","title":"Main syntax","text":"<pre><code>from sbi.inference import SNPE, prepare_for_sbi, simulate_for_sbi\n\nsimulator, prior = prepare_for_sbi(simulator, prior)\ninference = SNPE(prior)\n\ntheta, x = simulate_for_sbi(simulator, proposal=prior, num_simulations=1000)\ndensity_estimator = inference.append_simulations(theta, x).train()\nposterior = inference.build_posterior(density_estimator)\n</code></pre>"},{"location":"tutorial/02_flexible_interface/#linear-gaussian-example","title":"Linear Gaussian example","text":"<p>We will show an example of how we can use the flexible interface to infer the posterior for an example with a Gaussian likelihood (same example as before). First, we import the inference method we want to use (<code>SNPE</code>, <code>SNLE</code>, or <code>SNRE</code>) and other helper functions.</p> <pre><code>import torch\n\nfrom sbi.inference import SNPE, prepare_for_sbi, simulate_for_sbi\nfrom sbi.utils.get_nn_models import posterior_nn\nfrom sbi import utils as utils\nfrom sbi import analysis as analysis\n</code></pre> <p>Next, we define the prior and simulator:</p> <pre><code>num_dim = 3\nprior = utils.BoxUniform(low=-2 * torch.ones(num_dim), high=2 * torch.ones(num_dim))\n</code></pre> <pre><code>def linear_gaussian(theta):\n    return theta + 1.0 + torch.randn_like(theta) * 0.1\n</code></pre> <p>In the flexible interface, you have to ensure that your simulator and prior adhere the requirements of <code>sbi</code>. You can do so with the <code>prepare_for_sbi()</code> function.</p> <pre><code>simulator, prior = prepare_for_sbi(linear_gaussian, prior)\n</code></pre> <p>Then, we instantiate the inference object:</p> <pre><code>inference = SNPE(prior=prior)\n</code></pre> <p>Next, we run simulations. You can do so either by yourself by sampling from the prior and running the simulator (e.g. on a compute cluster), or you can use a helper function provided by <code>sbi</code> called <code>simulate_for_sbi</code>. This function allows to parallelize your code with <code>joblib</code>.</p> <pre><code>theta, x = simulate_for_sbi(simulator, proposal=prior, num_simulations=2000)\n</code></pre> <pre><code>Running 2000 simulations.:   0%|          | 0/2000 [00:00&lt;?, ?it/s]\n</code></pre> <p>We then pass the simulated data to the inference object. <code>theta</code> and <code>x</code> should both be a <code>torch.Tensor</code> of type <code>float32</code>.</p> <pre><code>inference = inference.append_simulations(theta, x)\n</code></pre> <p>Next, we train the neural density estimator.</p> <pre><code>density_estimator = inference.train()\n</code></pre> <pre><code> Neural network successfully converged after 73 epochs.\n</code></pre> <p>Lastly, we can use this density estimator to build the posterior:</p> <pre><code>posterior = inference.build_posterior(density_estimator)\n</code></pre> <p>Once we have obtained the posterior, we can <code>.sample()</code>, <code>.log_prob()</code>, or <code>.pairplot()</code> in the same way as for the simple interface.</p> <pre><code>x_o = torch.zeros(3,)\n</code></pre> <pre><code>posterior_samples = posterior.sample((10000,), x=x_o)\n\n# plot posterior samples\n_ = analysis.pairplot(\n    posterior_samples, limits=[[-2, 2], [-2, 2], [-2, 2]], figsize=(5, 5)\n)\n</code></pre> <pre><code>Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00&lt;?, ?it/s]\n</code></pre> <p></p> <p>We can always print the posterior to know how it was trained:</p> <pre><code>print(posterior)\n</code></pre> <pre><code>Posterior conditional density p(\u03b8|x) of type DirectPosterior. It samples the posterior network and rejects samples that\n            lie outside of the prior bounds.\n</code></pre>"},{"location":"tutorial/03_multiround_inference/","title":"Multi-round inference","text":"<p>In the previous tutorials, we have inferred the posterior using single-round inference. In single-round inference, we draw parameters from the prior, simulate the corresponding data, and then train a neural network to obtain the posterior. However,  if one is interested in only one particular observation <code>x_o</code> sampling from the prior can be inefficient in the number of simulations because one is effectively learning a posterior estimate for all observations in the prior space. In this tutorial, we show how one can alleviate this issue by performing multi-round inference with <code>sbi</code>.  </p> <p>Multi-round inference also starts by drawing parameters from the prior, simulating them, and training a neural network to estimate the posterior distribution. Afterwards, however, it continues inference in multiple rounds, focusing on a particular observation <code>x_o</code>. In each new round of inference, it draws samples from the obtained posterior distribution conditioned at <code>x_o</code> (instead of from the prior), simulates these, and trains the network again. This process can be repeated arbitrarily often to get increasingly good approximations to the true posterior distribution at <code>x_o</code>.</p> <p>Running multi-round inference can be more efficient in the number of simulations, but it will lead to the posterior no longer being amortized (i.e. it will be accurate only for a specific observation <code>x_o</code>, not for any <code>x</code>).</p> <p>Note, you can find the original version of this notebook at https://github.com/sbi-dev/sbi/blob/main/tutorials/03_multiround_inference.ipynb in the <code>sbi</code> repository.</p>"},{"location":"tutorial/03_multiround_inference/#main-syntax","title":"Main syntax","text":"<pre><code># 2 rounds: first round simulates from the prior, second round simulates parameter set\n# that were sampled from the obtained posterior.\nnum_rounds = 2\n# The specific observation we want to focus the inference on.\nx_o = torch.zeros(\n    3,\n)\n\nposteriors = []\nproposal = prior\n\nfor _ in range(num_rounds):\n    theta, x = simulate_for_sbi(simulator, proposal, num_simulations=500)\n\n    # In `SNLE` and `SNRE`, you should not pass the `proposal` to `.append_simulations()`\n    density_estimator = inference.append_simulations(\n        theta, x, proposal=proposal\n    ).train()\n    posterior = inference.build_posterior(density_estimator)\n    posteriors.append(posterior)\n    proposal = posterior.set_default_x(x_o)\n</code></pre>"},{"location":"tutorial/03_multiround_inference/#linear-gaussian-example","title":"Linear Gaussian example","text":"<p>Below, we give a full example of inferring the posterior distribution over multiple rounds.</p> <pre><code>import torch\n\nfrom sbi.inference import SNPE, prepare_for_sbi, simulate_for_sbi\nfrom sbi.utils.get_nn_models import posterior_nn\nfrom sbi import utils as utils\nfrom sbi import analysis as analysis\n\n_ = torch.manual_seed(0)\n</code></pre> <p>First, we define a simple prior and simulator and ensure that they comply with <code>sbi</code> by using <code>prepare_for_sbi</code>:</p> <pre><code>num_dim = 3\nprior = utils.BoxUniform(low=-2 * torch.ones(num_dim), high=2 * torch.ones(num_dim))\n</code></pre> <pre><code>def linear_gaussian(theta):\n    return theta + 1.0 + torch.randn_like(theta) * 0.1\n</code></pre> <pre><code>simulator, prior = prepare_for_sbi(linear_gaussian, prior)\n</code></pre> <p>Then, we instantiate the inference object:</p> <pre><code>inference = SNPE(prior=prior)\n</code></pre> <p>And we can run inference. In this example, we will run inference over <code>2</code> rounds, potentially leading to a more focused posterior around the observation <code>x_o</code>.</p> <pre><code>num_rounds = 2\nx_o = torch.zeros(\n    3,\n)\n\nposteriors = []\nproposal = prior\n\nfor _ in range(num_rounds):\n    theta, x = simulate_for_sbi(simulator, proposal, num_simulations=500)\n    density_estimator = inference.append_simulations(\n        theta, x, proposal=proposal\n    ).train()\n    posterior = inference.build_posterior(density_estimator)\n    posteriors.append(posterior)\n    proposal = posterior.set_default_x(x_o)\n</code></pre> <pre><code>Running 500 simulations.:   0%|          | 0/500 [00:00&lt;?, ?it/s]\n\n\n Neural network successfully converged after 157 epochs.\n\n\nDrawing 500 posterior samples:   0%|          | 0/500 [00:00&lt;?, ?it/s]\n\n\n\nRunning 500 simulations.:   0%|          | 0/500 [00:00&lt;?, ?it/s]\n\n\nUsing SNPE-C with atomic loss\n Neural network successfully converged after 58 epochs.\n</code></pre> <p>Note that, for <code>num_rounds&gt;1</code>, the posterior is no longer amortized: it will give good results when sampled around <code>x=observation</code>, but possibly bad results for other <code>x</code>.</p> <p>Once we have obtained the posterior, we can <code>.sample()</code>, <code>.log_prob()</code>, or <code>.pairplot()</code> in the same way as for the simple interface.</p> <pre><code>posterior_samples = posterior.sample((10000,), x=x_o)\n\n# plot posterior samples\n_ = analysis.pairplot(\n    posterior_samples, limits=[[-2, 2], [-2, 2], [-2, 2]], figsize=(5, 5)\n)\n</code></pre> <pre><code>Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00&lt;?, ?it/s]\n</code></pre> <p></p>"},{"location":"tutorial/04_density_estimators/","title":"Customizing the density estimator","text":"<p><code>sbi</code> allows to specify a custom density estimator for each of the implemented methods. For all options, check the API reference here.</p>"},{"location":"tutorial/04_density_estimators/#changing-the-type-of-density-estimator","title":"Changing the type of density estimator","text":"<p>One option is to use one of set of preconfigured density estimators by passing a string in the <code>density_estimator</code> keyword argument to the inference object (<code>SNPE</code> or <code>SNLE</code>), e.g., \u201cmaf\u201d to use a Masked Autoregressive Flow, of \u201cnsf\u201d to use a Neural Spline Flow with default hyperparameters.</p> <pre><code>inference = SNPE(prior=prior, density_estimator=\"maf\")\n</code></pre> <p>In the case of <code>SNRE</code>, the argument is called <code>classifier</code>:</p> <pre><code>inference = SNRE(prior=prior, classifier=\"resnet\")\n</code></pre>"},{"location":"tutorial/04_density_estimators/#changing-hyperparameters-of-density-estimators","title":"Changing hyperparameters of density estimators","text":"<p>Alternatively, you can use a set of utils functions to configure a density estimator yourself, e.g., use a MAF with hyperparameters chosen for your problem at hand.</p> <p>Here, because we want to use SN*P*E, we specifiy a neural network targeting the posterior (using the utils function <code>posterior_nn</code>). In this example, we will create a neural spline flow (<code>'nsf'</code>) with <code>60</code> hidden units and <code>3</code> transform layers:</p> <pre><code>from sbi.utils.get_nn_models import (\n    posterior_nn,\n)  # For SNLE: likelihood_nn(). For SNRE: classifier_nn()\n\ndensity_estimator_build_fun = posterior_nn(\n    model=\"nsf\", hidden_features=60, num_transforms=3\n)\ninference = SNPE(prior=prior, density_estimator=density_estimator_build_fun)\n</code></pre> <p>It is also possible to pass an <code>embedding_net</code> to <code>posterior_nn()</code> which learn summary statistics from high-dimensional simulation outputs. You can find a more detailed tutorial on this here.</p>"},{"location":"tutorial/04_density_estimators/#building-new-density-estimators-from-scratch","title":"Building new density estimators from scratch","text":"<p>Finally, it is also possible to implement your own density estimator from scratch, e.g., including embedding nets to preprocess data, or to a density estimator architecture of your choice. </p> <p>For this, the <code>density_estimator</code> argument needs to be a function that takes <code>theta</code> and <code>x</code> batches as arguments to then construct the density estimator after the first set of simulations was generated. Our utils functions in <code>sbi/utils/get_nn_models.py</code> return such a function. </p>"},{"location":"tutorial/05_embedding_net/","title":"Learning summary statistics with a neural net","text":"<p>When doing simulation-based inference, it is very important to use well-chosen summary statistics for describing the data generated by the simulator. Usually, these statistics take into account domain knowledge. For instance, in the example of the Hodgkin-Huxley model, the summary statistics are defined by a function which takes a 120 ms recording as input (a 12000-dimensional input vector) and outputs a 7-dimensional feature vector containing different statistical descriptors of the recording (e.g., number of spikes, average value, etc.). </p> <p>However, in other cases, it might be of interest to actually learn from the data which summary statistics to use, e.g., because the raw data is highly complex and domain knowledge is not available or not applicable. </p> <p><code>sbi</code> offers functionality to learn summary statistics from (potentially high-dimensional) simulation outputs with a neural network. In <code>sbi</code>, this neural network is referred to as <code>embedding_net</code>. If an <code>embedding_net</code> is specified, the simulation outputs are passed through the <code>embedding_net</code>, whose outputs are then passed to the neural density estimator. The parameters of the <code>embedding_net</code> are learned together with the parameters of the neural density estimator.</p> <p>NB: only <code>SNPE</code> and <code>SNRE</code> methods can use an <code>embedding_net</code> to learn summary statistics from simulation outputs. <code>SNLE</code> does not offer this functionality since the simulation outputs \\(x\\) are the outputs of the neural density estimator.</p> <p>In the example that follows, we illustrate a situation where the data points generated by the simulator model are high-dimensional (32 by 32 images) and we use a convolutional neural network as summary statistics extractor.</p> <p>Note, you can find the original version of this notebook at https://github.com/sbi-dev/sbi/blob/main/tutorials/05_embedding_net.ipynb in the <code>sbi</code> repository.</p> <p>First of all, we import all the packages required for running the tutorial</p> <pre><code>import matplotlib.pyplot as plt\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom sbi import utils\nfrom sbi import analysis\nfrom sbi import inference\nfrom sbi.inference import SNPE, simulate_for_sbi, prepare_for_sbi\n\nseed = 0\ntorch.manual_seed(seed)\n</code></pre> <pre><code>&lt;torch._C.Generator at 0x7f124d5d04b0&gt;\n</code></pre>"},{"location":"tutorial/05_embedding_net/#the-simulator-model","title":"The simulator model","text":"<p>The simulator model that we consider has two parameters: \\(r\\) and \\(\\theta\\). On each run, it generates 100 two-dimensional points centered around \\((r \\cos(\\theta), r \\sin(\\theta))\\) and perturbed by a Gaussian noise with variance 0.01. Instead of simply outputting the \\((x,y)\\) coordinates of each data point, the model generates a grayscale image of the scattered points with dimensions 32 by 32. This image is further perturbed by an uniform noise with values betweeen 0 and 0.2. The code below defines such model.</p> <pre><code>def simulator_model(parameter, return_points=False):\n    \"\"\"Simulator model with two-dimensional input parameter and 1024-dimensional output\n\n    This simulator serves as a basic example for using a neural net for learning summary features.\n    It has only two input parameters but generates high-dimensional output vectors.\n    The data is generated as follows:\n        (-) Input:  parameter = [r, theta]\n        (1) Generate 100 two-dimensional points centered around (r cos(theta),r sin(theta))\n            and perturbed by a Gaussian noise with variance 0.01\n        (2) Create a grayscale image I of the scattered points with dimensions 32 by 32\n        (3) Perturb I with an uniform noise with values betweeen 0 and 0.2\n        (-) Output: I\n\n    Parameters\n    ----------\n    parameter : array-like, shape (2)\n        The two input parameters of the model, ordered as [r, theta]\n    return_points : bool (default: False)\n        Whether the simulator should return the coordinates of the simulated data points as well\n\n    Returns\n    -------\n    I: torch tensor, shape (1, 1024)\n        Output flattened image\n    (optional) points: array-like, shape (100, 2)\n        Coordinates of the 2D simulated data points\n\n    \"\"\"\n    r = parameter[0]\n    theta = parameter[1]\n\n    sigma_points = 0.10\n    npoints = 100\n    points = []\n    for _ in range(npoints):\n        x = r * torch.cos(theta) + sigma_points * torch.randn(1)\n        y = r * torch.sin(theta) + sigma_points * torch.randn(1)\n        points.append([x, y])\n    points = torch.as_tensor(points)\n\n    nx = 32\n    ny = 32\n    sigma_image = 0.20\n    I = torch.zeros(nx, ny)\n    for point in points:\n        pi = int((point[0] - (-1)) / ((+1) - (-1)) * nx)\n        pj = int((point[1] - (-1)) / ((+1) - (-1)) * ny)\n        if (pi &lt; nx) and (pj &lt; ny):\n            I[pi, pj] = 1\n    I = I + sigma_image * torch.rand(nx, ny)\n    I = I.T\n    I = I.reshape(1, -1)\n\n    if return_points:\n        return I, points\n    else:\n        return I\n</code></pre> <p>The figure below shows an example of the output of the simulator when \\(r = 0.70\\) and \\(\\theta = \\pi/4\\)</p> <pre><code># simulate samples\ntrue_parameter = torch.tensor([0.70, torch.pi / 4])\nx_observed, x_points = simulator_model(true_parameter, return_points=True)\n\n# plot the observation\nfig, ax = plt.subplots(\n    facecolor=\"white\", figsize=(11.15, 5.61), ncols=2, constrained_layout=True\n)\ncircle = plt.Circle((0, 0), 1.0, color=\"k\", ls=\"--\", lw=0.8, fill=False)\nax[0].add_artist(circle)\nax[0].scatter(x_points[:, 0], x_points[:, 1], s=20)\nax[0].set_xlabel(\"x\")\nax[0].set_ylabel(\"y\")\nax[0].set_xlim(-1, +1)\nax[0].set_xticks([-1, 0.0, +1.0])\nax[0].set_ylim(-1, +1)\nax[0].set_yticks([-1, 0.0, +1.0])\nax[0].set_title(r\"original simulated points with $r = 0.70$ and $\\theta = \\pi/4$\")\nax[1].imshow(x_observed.view(32, 32), origin=\"lower\", cmap=\"gray\")\nax[1].set_xticks([])\nax[1].set_yticks([])\nax[1].set_title(\"noisy observed data (gray image with 32 x 32 pixels)\")\n</code></pre> <pre><code>Text(0.5, 1.0, 'noisy observed data (gray image with 32 x 32 pixels)')\n</code></pre> <p></p>"},{"location":"tutorial/05_embedding_net/#defining-an-embedding_net","title":"Defining an <code>embedding_net</code>","text":"<p>An inference procedure applied to the output data from this simulator model determines the posterior distribution of \\(r\\) and \\(\\theta\\) given an observation of \\(x\\), which lives in a 1024 dimensional space (32 x 32 = 1024). To avoid working directly on these high-dimensional vectors, one can use a convolutional neural network (CNN) that takes the 32x32 images as input and encodes them into 8-dimensional feature vectors. This CNN is trained along with the neural density estimator of the inference procedure and serves as an automatic summary statistics extractor. </p> <p>We define and instantiate the CNN as follows:</p> <pre><code>class SummaryNet(nn.Module):\n    def __init__(self):\n        super().__init__()\n        # 2D convolutional layer\n        self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, padding=2)\n        # Maxpool layer that reduces 32x32 image to 4x4\n        self.pool = nn.MaxPool2d(kernel_size=8, stride=8)\n        # Fully connected layer taking as input the 6 flattened output arrays from the maxpooling layer\n        self.fc = nn.Linear(in_features=6 * 4 * 4, out_features=8)\n\n    def forward(self, x):\n        x = x.view(-1, 1, 32, 32)\n        x = self.pool(F.relu(self.conv1(x)))\n        x = x.view(-1, 6 * 4 * 4)\n        x = F.relu(self.fc(x))\n        return x\n\n\nembedding_net = SummaryNet()\n</code></pre>"},{"location":"tutorial/05_embedding_net/#the-inference-procedure","title":"The inference procedure","text":"<p>With the <code>embedding_net</code> defined and instantiated, we can follow the usual workflow of an inference procedure in <code>sbi</code>. The <code>embedding_net</code> object appears as an input argument when instantiating the neural density estimator with <code>utils.posterior_nn</code>.</p> <pre><code># set prior distribution for the parameters\nprior = utils.BoxUniform(\n    low=torch.tensor([0.0, 0.0]), high=torch.tensor([1.0, 2 * torch.pi])\n)\n\n# make a SBI-wrapper on the simulator object for compatibility\nsimulator_wrapper, prior = prepare_for_sbi(simulator_model, prior)\n</code></pre> <pre><code># instantiate the neural density estimator\nneural_posterior = utils.posterior_nn(\n    model=\"maf\", embedding_net=embedding_net, hidden_features=10, num_transforms=2\n)\n\n# setup the inference procedure with the SNPE-C procedure\ninference = SNPE(prior=prior, density_estimator=neural_posterior)\n</code></pre> <pre><code># run the inference procedure on one round and 10000 simulated data points\ntheta, x = simulate_for_sbi(simulator_wrapper, prior, num_simulations=10000)\n</code></pre> <pre><code>Running 10000 simulations.:   0%|          | 0/10000 [00:00&lt;?, ?it/s]\n</code></pre> <pre><code>density_estimator = inference.append_simulations(theta, x).train()\nposterior = inference.build_posterior(density_estimator)\n</code></pre> <pre><code> Neural network successfully converged after 76 epochs.\n</code></pre>"},{"location":"tutorial/05_embedding_net/#visualizing-the-results","title":"Visualizing the results","text":"<p>We now generate 50000 samples of the posterior distribution of \\(r\\) and \\(\\theta\\) when observing an input data point \\(x\\) generated from the <code>simulator model</code> with \\(r = 0.70\\) and \\(\\theta = \\pi/4\\). </p> <pre><code># generate posterior samples\ntrue_parameter = torch.tensor([0.70, torch.pi / 4])\nx_observed = simulator_model(true_parameter)\nsamples = posterior.set_default_x(x_observed).sample((50000,))\n</code></pre> <pre><code>Drawing 50000 posterior samples:   0%|          | 0/50000 [00:00&lt;?, ?it/s]\n</code></pre> <p>The figure below shows the statistics of the generated samples.</p> <pre><code># create the figure\nfig, ax = analysis.pairplot(\n    samples,\n    points=true_parameter,\n    labels=[\"r\", r\"$\\theta$\"],\n    limits=[[0, 1], [0, 2 * torch.pi]],\n    points_colors=\"r\",\n    points_offdiag={\"markersize\": 6},\n    figsize=(5, 5),\n)\n</code></pre> <p></p> <pre><code>\n</code></pre>"},{"location":"tutorial/07_conditional_distributions/","title":"Analysing variability and compensation mechansims with conditional distributions","text":"<p>A central advantage of <code>sbi</code> over parameter search methods such as genetic algorithms is that the posterior captures all models that can reproduce experimental data. This allows us to analyse whether parameters can be variable or have to be narrowly tuned, and to analyse compensation mechanisms between different parameters. See also Marder and Taylor, 2011 for further motivation to identify all models that capture experimental data.  </p> <p>In this tutorial, we will show how one can use the posterior distribution to identify whether parameters can be variable or have to be finely tuned, and how we can use the posterior to find potential compensation mechanisms between model parameters. To investigate this, we will extract conditional distributions from the posterior inferred with <code>sbi</code>.</p> <p>Note, you can find the original version of this notebook at https://github.com/sbi-dev/sbi/blob/main/tutorials/07_conditional_distributions.ipynb in the <code>sbi</code> repository.</p>"},{"location":"tutorial/07_conditional_distributions/#main-syntax","title":"Main syntax","text":"<pre><code>from sbi.analysis import conditional_pairplot, conditional_corrcoeff\n\n# Plot slices through posterior, i.e. conditionals.\n_ = conditional_pairplot(\n    density=posterior,\n    condition=posterior.sample((1,)),\n    limits=torch.tensor([[-2.0, 2.0], [-2.0, 2.0]]),\n)\n\n# Compute the matrix of correlation coefficients of the slices.\ncond_coeff_mat = conditional_corrcoeff(\n    density=posterior,\n    condition=posterior.sample((1,)),\n    limits=torch.tensor([[-2.0, 2.0], [-2.0, 2.0]]),\n)\nplt.imshow(cond_coeff_mat, clim=[-1, 1])\n</code></pre>"},{"location":"tutorial/07_conditional_distributions/#analysing-variability-and-compensation-mechanisms-in-a-toy-example","title":"Analysing variability and compensation mechanisms in a toy example","text":"<p>Below, we use a simple toy example to demonstrate the above described features. For an application of these features to a neuroscience problem, see figure 6 in Gon\u00e7alves, Lueckmann, Deistler et al., 2019.</p> <pre><code>from sbi import utils as utils\nfrom sbi.analysis import pairplot, conditional_pairplot, conditional_corrcoeff\nimport torch\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib import animation, rc\nfrom IPython.display import HTML, Image\n\n_ = torch.manual_seed(0)\n</code></pre> <p>Let\u2019s say we have used SNPE to obtain a posterior distribution over three parameters. In this tutorial, we just load the posterior from a file:</p> <pre><code>from toy_posterior_for_07_cc import ExamplePosterior\n\nposterior = ExamplePosterior()\n</code></pre> <p>First, we specify the experimental observation \\(x_o\\) at which we want to evaluate and sample the posterior \\(p(\\theta|x_o)\\):</p> <pre><code>x_o = torch.ones(1, 20)  # simulator output was 20-dimensional\nposterior.set_default_x(x_o)\n</code></pre> <p>As always, we can inspect the posterior marginals with the <code>pairplot()</code> function:</p> <pre><code>posterior_samples = posterior.sample((5000,))\n\nfig, ax = pairplot(\n    samples=posterior_samples,\n    limits=torch.tensor([[-2.0, 2.0]] * 3),\n    offdiag=[\"kde\"],\n    diag=[\"kde\"],\n    figsize=(5, 5),\n)\n</code></pre> <p></p> <p>The 1D and 2D marginals of the posterior fill almost the entire parameter space! Also, the Pearson correlation coefficient matrix of the marginal shows rather weak interactions (low correlations):</p> <pre><code>corr_matrix_marginal = np.corrcoef(posterior_samples.T)\nfig, ax = plt.subplots(1, 1, figsize=(4, 4))\nim = plt.imshow(corr_matrix_marginal, clim=[-1, 1], cmap=\"PiYG\")\n_ = fig.colorbar(im)\n</code></pre> <p></p> <p>It might be tempting to conclude that the experimental data barely constrains our parameters and that almost all parameter combinations can reproduce the experimental data. As we will show below, this is not the case.</p> <p>Because our toy posterior has only three parameters, we can plot posterior samples in a 3D plot:</p> <pre><code>rc(\"animation\", html=\"html5\")\n\n# First set up the figure, the axis, and the plot element we want to animate\nfig = plt.figure(figsize=(6, 6))\nax = fig.add_subplot(111, projection=\"3d\")\n\nax.set_xlim((-2, 2))\nax.set_ylim((-2, 2))\n\n\ndef init():\n    (line,) = ax.plot([], [], lw=2)\n    line.set_data([], [])\n    return (line,)\n\n\ndef animate(angle):\n    num_samples_vis = 1000\n    line = ax.scatter(\n        posterior_samples[:num_samples_vis, 0],\n        posterior_samples[:num_samples_vis, 1],\n        posterior_samples[:num_samples_vis, 2],\n        zdir=\"z\",\n        s=15,\n        c=\"#2171b5\",\n        depthshade=False,\n    )\n    ax.view_init(20, angle)\n    return (line,)\n\n\nanim = animation.FuncAnimation(\n    fig, animate, init_func=init, frames=range(0, 360, 5), interval=150, blit=True\n)\n\nplt.close()\n</code></pre> <pre><code>HTML(anim.to_html5_video())\n</code></pre>    Your browser does not support the video tag.  <p>Clearly, the range of admissible parameters is constrained to a narrow region in parameter space, which had not been evident from the marginals.</p> <p>If the posterior has more than three dimensions, inspecting all dimensions at once will not be possible anymore. One way to still reveal structures in high-dimensional posteriors is to inspect 2D-slices through the posterior. In <code>sbi</code>, this can be done with the <code>conditional_pairplot()</code> function, which computes the conditional distributions within the posterior. We can slice (i.e. condition) the posterior at any location, given by the <code>condition</code>. In the plot below, for all upper diagonal plots, we keep all but two parameters constant at values sampled from the posterior, and inspect what combinations of the remaining two parameters can reproduce experimental data. For the plots on the diagonal (the 1D conditionals), we keep all but one parameter constant.</p> <pre><code>condition = posterior.sample((1,))\n\n_ = conditional_pairplot(\n    density=posterior,\n    condition=condition,\n    limits=torch.tensor([[-2.0, 2.0]] * 3),\n    figsize=(5, 5),\n)\n</code></pre> <p></p> <p>This plot looks completely different from the marginals obtained with <code>pairplot()</code>. As it can be seen on the diagonal plots, if all parameters but one are kept constant, the remaining parameter has to be tuned to a narrow region in parameter space. In addition, the upper diagonal plots show strong correlations: deviations in one parameter can be compensated through changes in another parameter.</p> <p>We can summarize these correlations in a conditional correlation matrix, which computes the Pearson correlation coefficient of each of these pairwise plots. This matrix (below) shows strong correlations between many parameters, which can be interpreted as potential compensation mechansims:</p> <pre><code>cond_coeff_mat = conditional_corrcoeff(\n    density=posterior,\n    condition=condition,\n    limits=torch.tensor([[-2.0, 2.0]] * 3),\n)\nfig, ax = plt.subplots(1, 1, figsize=(4, 4))\nim = plt.imshow(cond_coeff_mat, clim=[-1, 1], cmap=\"PiYG\")\n_ = fig.colorbar(im)\n</code></pre> <p></p> <p>So far, we have investigated the conditional distribution only at a specific <code>condition</code> sampled from the posterior. In many applications, it makes sense to repeat the above analyses with a different <code>condition</code> (another sample from the posterior), which can be interpreted as slicing the posterior at a different location. Note that <code>conditional_corrcoeff()</code> can directly compute the matrix for several <code>conditions</code> and then outputs the average over them. This can be done by passing a batch of \\(N\\) conditions as the <code>condition</code> argument.</p>"},{"location":"tutorial/07_conditional_distributions/#sampling-conditional-distributions","title":"Sampling conditional distributions","text":"<p>So far, we have demonstrated how one can plot 2D conditional distributions with <code>conditional_pairplot()</code> and how one can compute the pairwise conditional correlation coefficient with <code>conditional_corrcoeff()</code>. In some cases, it can be useful to keep a subset of parameters fixed and to vary more than two parameters. This can be done by sampling the conditonal posterior \\(p(\\theta_i | \\theta_{j \\neq i}, x_o)\\). As of <code>sbi</code> <code>v0.18.0</code>, this functionality requires using the sampler interface. In this tutorial, we demonstrate this functionality on a linear gaussian simulator with four parameters. We would like to fix the forth parameter to \\(\\theta_4=0.2\\) and sample the first three parameters given that value, i.e. we want to sample \\(p(\\theta_1, \\theta_2, \\theta_3 | \\theta_4 = 0.2, x_o)\\). For an application in neuroscience, see Deistler, Gon\u00e7alves, Macke, 2021.</p> <p>In this tutorial, we will use SNPE, but the same also works for SNLE and SNRE. First, we define the prior and the simulator and train the deep neural density estimator:</p> <pre><code>from sbi.inference import SNPE, prepare_for_sbi, simulate_for_sbi\nfrom sbi.inference import posterior_estimator_based_potential, MCMCPosterior\nfrom sbi.utils import BoxUniform\nimport torch\n\nnum_dim = 4\nprior = BoxUniform(low=-2 * torch.ones(num_dim), high=2 * torch.ones(num_dim))\n\n\ndef linear_gaussian(theta):\n    return theta + 1.0 + torch.randn_like(theta) * 0.1\n\n\nsimulator, prior = prepare_for_sbi(linear_gaussian, prior)\n\ninference = SNPE()\ntheta, x = simulate_for_sbi(simulator, prior, 1000)\nposterior_estimator = inference.append_simulations(theta, x).train()\n</code></pre> <pre><code>Running 1000 simulations.:   0%|          | 0/1000 [00:00&lt;?, ?it/s]\n\n\n Neural network successfully converged after 147 epochs.\n</code></pre> <p>Next, we follow the sampler interface and create a potential_function. The observation in this example is <code>x_o=[1, 1, 1, 1]</code>.</p> <pre><code>potential_fn, theta_transform = posterior_estimator_based_potential(\n    posterior_estimator, prior=prior, x_o=torch.ones(4)\n)\n</code></pre> <p>Now we want to build the conditional potential (please read throught the sampler interface tutorial for an explanation of potential functions). For this, we have to pass a <code>condition</code>. In our case, we want to condition the forth parameter on \\(\\theta_4=0.2\\). Regardless of how many parameters one wants to condition on, in <code>sbi</code>, one has to pass a <code>condition</code> value for all parameters. The first three values will simply be ignored. We can tell the algorithm which parameters should be kept fixed and which ones should be sampled with the argument <code>dims_to_sample</code>.</p> <pre><code>from sbi.analysis import conditional_potential\n\nconditioned_potential_fn, restricted_tf, restricted_prior = conditional_potential(\n    potential_fn=potential_fn,\n    theta_transform=theta_transform,\n    prior=prior,\n    condition=torch.as_tensor(\n        [0.0, 0.0, 0.0, 0.2]\n    ),  # the first three values are arbitrary and are ignored internally\n    dims_to_sample=[0, 1, 2],\n)\n</code></pre> <p>Finally, we have to build a sampler for the <code>conditioned_potential_fn</code>. E.g., we can sample the conditional posterior with MCMC:</p> <pre><code>mcmc_posterior = MCMCPosterior(\n    potential_fn=conditioned_potential_fn,\n    theta_transform=restricted_tf,\n    proposal=restricted_prior,\n)\ncond_samples = mcmc_posterior.sample((100,))\n</code></pre> <pre><code>  0%|          | 0/50 [00:00&lt;?, ?it/s]\n\n\n\n  0%|          | 0/10 [00:00&lt;?, ?it/s]\n\n\n\n  0%|          | 0/100 [00:00&lt;?, ?it/s]\n</code></pre> <p>The resulting samples are 3-dimensional, corresponding to \\([\\theta_1, \\theta_2, \\theta_3]\\), sampled from \\(p(\\theta_1, \\theta_2, \\theta_3 | \\theta_4=0.2, x_o)\\).</p> <pre><code>print(cond_samples.shape)\n</code></pre> <pre><code>torch.Size([100, 3])\n</code></pre> <p>We can also plot them with <code>pairplot</code>:</p> <pre><code>from sbi.analysis import pairplot\n\n_ = pairplot(cond_samples, limits=[[-2, 2], [-2, 2], [-2, 2], [-2, 2]], figsize=(4, 4))\n</code></pre> <p></p>"},{"location":"tutorial/08_restriction_estimator/","title":"Efficient handling of invalid simulation outputs","text":"<p>For many simulators, the output of the simulator can be ill-defined or it can have non-sensical values. For example, in neuroscience models, if a specific parameter set does not produce a spike, features such as the spike shape can not be computed. When using <code>sbi</code>, such simulations that have <code>NaN</code> or <code>inf</code> in their output are discarded during neural network training. This can lead to inefficetive use of simulation budget: we carry out many simulations, but a potentially large fraction of them is discarded. </p> <p>In this tutorial, we show how we can use <code>sbi</code> to learn regions in parameter space that produce <code>valid</code> simulation outputs, and thereby improve the sampling efficiency. The key idea of the method is to use a classifier to distinguish parameters that lead to <code>valid</code> simulations from regions that lead to <code>invalid</code> simulations. After we have obtained the region in parameter space that produes <code>valid</code> simulation outputs, we train the deep neural density estimator used in <code>SNPE</code>. The method was originally proposed in Lueckmann, Goncalves et al. 2017 and later used in Deistler et al. 2021.</p>"},{"location":"tutorial/08_restriction_estimator/#main-syntax","title":"Main syntax","text":"<pre><code>from sbi.inference import SNPE\nfrom sbi.utils import RestrictionEstimator\n\nrestriction_estimator = RestrictionEstimator(prior=prior)\nproposals = [prior]\n\nfor r in range(num_rounds):\n    theta, x = simulate_for_sbi(simulator, proposals[-1], 1000)\n    restriction_estimator.append_simulations(theta, x)\n    if (\n        r &lt; num_rounds - 1\n    ):  # training not needed in last round because classifier will not be used anymore.\n        classifier = restriction_estimator.train()\n    proposals.append(restriction_estimator.restrict_prior())\n\nall_theta, all_x, _ = restriction_estimator.get_simulations()\n\ninference = SNPE(prior=prior)\ndensity_estimator = inference.append_simulations(all_theta, all_x).train()\nposterior = inference.build_posterior()\n</code></pre>"},{"location":"tutorial/08_restriction_estimator/#further-explanation-in-a-toy-example","title":"Further explanation in a toy example","text":"<pre><code>from sbi.inference import SNPE, simulate_for_sbi\nfrom sbi.utils import RestrictionEstimator, BoxUniform\nfrom sbi.analysis import pairplot\nimport torch\n\n_ = torch.manual_seed(2)\n</code></pre> <p>We will define a simulator with two parameters and two simulation outputs. The simulator produces <code>NaN</code> whenever the first parameter is below <code>0.0</code>. If it is above <code>0.0</code> the simulator simply perturbs the parameter set with Gaussian noise:</p> <pre><code>def simulator(theta):\n    perturbed_theta = theta + 0.5 * torch.randn(2)\n    perturbed_theta[theta[:, 0] &lt; 0.0] = torch.as_tensor([float(\"nan\"), float(\"nan\")])\n    return perturbed_theta\n</code></pre> <p>The prior is a uniform distribution in [-2, 2]:</p> <pre><code>prior = BoxUniform(-2 * torch.ones(2), 2 * torch.ones(2))\n</code></pre> <p>We then begin by drawing samples from the prior and simulating them. Looking at the simulation outputs, half of them contain <code>NaN</code>:</p> <pre><code>theta, x = simulate_for_sbi(simulator, prior, 1000)\nprint(\"Simulation outputs: \", x)\n</code></pre> <pre><code>Running 1000 simulations.:   0%|          | 0/1000 [00:00&lt;?, ?it/s]\n\n\nSimulation outputs:  tensor([[ 0.0411, -0.5656],\n        [ 0.0096, -1.0841],\n        [ 1.2937,  0.9448],\n        ...,\n        [    nan,     nan],\n        [    nan,     nan],\n        [ 2.7940,  0.6461]])\n</code></pre> <p>The simulations that contain <code>NaN</code> are wasted, and we want to learn to \u201crestrict\u201d the prior such that it produces only <code>valid</code> simulation outputs. To do so, we set up the <code>RestrictionEstimator</code>:</p> <pre><code>restriction_estimator = RestrictionEstimator(prior=prior)\n</code></pre> <p>The <code>RestrictionEstimator</code> trains a classifier to distinguish parameters that lead to <code>valid</code> simulation outputs from parameters that lead to <code>invalid</code> simulation outputs </p> <pre><code>restriction_estimator.append_simulations(theta, x)\nclassifier = restriction_estimator.train()\n</code></pre> <pre><code>Training neural network. Epochs trained:  35\n</code></pre> <p>We can inspect the <code>restricted_prior</code>, i.e. the parameters that the classifier believes will lead to <code>valid</code> simulation outputs, with:</p> <pre><code>restricted_prior = restriction_estimator.restrict_prior()\nsamples = restricted_prior.sample((10_000,))\n_ = pairplot(samples, limits=[[-2, 2], [-2, 2]], fig_size=(4, 4))\n</code></pre> <pre><code>The classifier rejected 51.6% of all samples. You will get a speed-up of 106.5%.\n</code></pre> <p></p> <p>Indeed, parameter sets sampled from the <code>restricted_prior</code> always have a first parameter larger than <code>0.0</code>. These are the ones that produce <code>valid</code> simulation outputs (see our definition of the simulator above). We can then use the <code>restricted_prior</code> to generate more simulations. Almost all of them will have <code>valid</code> simulation outputs:</p> <pre><code>new_theta, new_x = simulate_for_sbi(simulator, restricted_prior, 1000)\nprint(\"Simulation outputs: \", new_x)\n</code></pre> <pre><code>The classifier rejected 50.9% of all samples. You will get a speed-up of 103.6%.\n\n\n\nRunning 1000 simulations.:   0%|          | 0/1000 [00:00&lt;?, ?it/s]\n\n\nSimulation outputs:  tensor([[ 0.6834, -0.2415],\n        [ 1.3459,  1.5373],\n        [ 2.1092,  1.9180],\n        ...,\n        [ 0.8845,  0.4036],\n        [ 1.9111,  1.2526],\n        [ 0.8320,  2.3755]])\n</code></pre> <p>We can now use all simulations and run <code>SNPE</code> as always:</p> <pre><code>restriction_estimator.append_simulations(\n    new_theta, new_x\n)  # Gather the new simulations in the `restriction_estimator`.\n(\n    all_theta,\n    all_x,\n    _,\n) = restriction_estimator.get_simulations()  # Get all simulations run so far.\n\ninference = SNPE(prior=prior)\ndensity_estimator = inference.append_simulations(all_theta, all_x).train()\nposterior = inference.build_posterior()\n\nposterior_samples = posterior.sample((10_000,), x=torch.ones(2))\n_ = pairplot(posterior_samples, limits=[[-2, 2], [-2, 2]], fig_size=(3, 3))\n</code></pre> <pre><code>WARNING:root:Found 523 NaN simulations and 0 Inf simulations. They will be excluded from training.\n\n\n Neural network successfully converged after 118 epochs.\n\n\nDrawing 10000 posterior samples:   0%|          | 0/10000 [00:00&lt;?, ?it/s]\n</code></pre> <p></p>"},{"location":"tutorial/08_restriction_estimator/#further-options-for-tuning-the-algorithm","title":"Further options for tuning the algorithm","text":"<ul> <li>the whole procedure can be repeated many times (see the loop shown in \u201cMain syntax\u201d in this tutorial)  </li> <li>the classifier is trained to be relatively conservative, i.e. it will try to be very sure that a specific parameter set can indeed not produce <code>valid</code> simulation outputs. If you are ok with the restricted prior potentially ignoring a small fraction of parameter sets that might have produced <code>valid</code> data, you can use <code>restriction_estimator.restrict_prior(allowed_false_negatives=...)</code>. The argument <code>allowed_false_negatives</code> sets the fraction of potentially ignored parameter sets. A higher value will lead to more <code>valid</code> simulations.  </li> <li>By default, the algorithm considers simulations that have at least one <code>NaN</code> of <code>inf</code> as <code>invalid</code>. You can specify custom criterions with <code>RestrictionEstimator(decision_criterion=...)</code></li> </ul>"},{"location":"tutorial/09_sensitivity_analysis/","title":"Active subspaces for sensitivity analysis","text":"<p>A standard method to analyse dynamical systems such as models of neural dynamics is to use a sensitivity analysis. We can use the posterior obtained with <code>sbi</code>, to perform such analyses.</p>"},{"location":"tutorial/09_sensitivity_analysis/#main-syntax","title":"Main syntax","text":"<pre><code>from sbi.analysis import ActiveSubspace\n\nsensitivity = ActiveSubspace(posterior.set_default_x(x_o))\ne_vals, e_vecs = sensitivity.find_directions(posterior_log_prob_as_property=True)\nprojected_data = sensitivity.project(theta_project, num_dimensions=1)\n</code></pre>"},{"location":"tutorial/09_sensitivity_analysis/#example-and-further-explanation","title":"Example and further explanation","text":"<pre><code>import torch\nfrom torch.distributions import MultivariateNormal\n\nfrom sbi.analysis import ActiveSubspace, pairplot\nfrom sbi.simulators import linear_gaussian\nfrom sbi.inference import simulate_for_sbi, infer\n\n_ = torch.manual_seed(0)\n</code></pre> <p>Let\u2019s define a simple Gaussian toy example:</p> <pre><code>prior = MultivariateNormal(0.0 * torch.ones(2), 2 * torch.eye(2))\n\n\ndef simulator(theta):\n    return linear_gaussian(\n        theta, -0.8 * torch.ones(2), torch.tensor([[1.0, 0.98], [0.98, 1.0]])\n    )\n\n\nposterior = infer(simulator, prior, num_simulations=2000, method=\"SNPE\").set_default_x(\n    torch.zeros(2)\n)\n</code></pre> <pre><code>Running 2000 simulations.:   0%|          | 0/2000 [00:00&lt;?, ?it/s]\n\n\n Neural network successfully converged after 117 epochs.\n</code></pre> <pre><code>posterior_samples = posterior.sample((2000,))\n</code></pre> <pre><code>Drawing 2000 posterior samples:   0%|          | 0/2000 [00:00&lt;?, ?it/s]\n</code></pre> <pre><code>_ = pairplot(posterior_samples, limits=[[-3, 3], [-3, 3]], figsize=(4, 4))\n</code></pre> <p></p> <p>When performing a sensitivity analysis on this model, we would expect that there is one direction that is less sensitive (from bottom left to top right, along the vector [1, 1]) and one direction that is more sensitive (from top left to bottom right, along [1, -1]). We can recover these directions with the <code>ActiveSubspace</code> module in <code>sbi</code>.</p> <pre><code>sensitivity = ActiveSubspace(posterior)\ne_vals, e_vecs = sensitivity.find_directions(posterior_log_prob_as_property=True)\n</code></pre> <pre><code>Drawing 1000 posterior samples:   0%|          | 0/1000 [00:00&lt;?, ?it/s]\n</code></pre> <p>The method <code>.find_active()</code> returns eigenvalues and the corresponding eigenvectors. It does so by computing the matrix:</p> <p>\\(M = \\mathbb{E}_{p(\\theta|x_o)}[\\nabla_{\\theta}p(\\theta|x_o)^T \\nabla_{\\theta}p(\\theta|x_o)\\)]  </p> <p>It then does an eigendecomposition: \\(M = Q \\Lambda Q^{-1}\\) </p> <p>A strong eigenvalue indicates that the gradient of the posterior density is large, i.e. the system output is sensitive to changes along the direction of the corresponding eigenvector (or <code>active</code>). The eigenvalue corresponding to the vector <code>[0.68, -0.73]</code> is much larger than the eigenvalue of <code>[0.73, 0.67]</code>. This matches the intuition we developed above.</p> <pre><code>print(\"Eigenvalues: \\n\", e_vals, \"\\n\")\nprint(\"Eigenvectors: \\n\", e_vecs)\n</code></pre> <pre><code>Eigenvalues: \n tensor([2.3552e-06, 7.0754e-05])\n\nEigenvectors: \n tensor([[-0.7066, -0.7076],\n        [-0.7076,  0.7066]])\n</code></pre> <p>Lastly, we can project the data into the active dimensions. In this case, we will just use one active dimension:</p> <pre><code>projected_data = sensitivity.project(posterior_samples, num_dimensions=1)\n</code></pre>"},{"location":"tutorial/09_sensitivity_analysis/#some-technical-details","title":"Some technical details","text":"<ul> <li>The gradients and thus the eigenvectors are computed in z-scored space. The mean and standard deviation are computed w.r.t. the prior distribution. Thus, the gradients (and thus the eigenvales) reflect changes on the scale of the prior.</li> <li>The expected value used to compute the matrix \\(M\\) is estimated using <code>1000</code> posterior samples. This value can be set with the <code>.find_active(num_monte_carlo_samples=...)</code> variable.</li> <li>How does this relate to Principal Component Analysis (PCA)? In the example above, the results of PCA would be very similar. However, there are two main differences to PCA: First, PCA ignores local changes in the posterior, whereas the active subspace can change a lot (since it computes the gradient, which is a local quantity). Second, active subspaces can be used characterize the sensitivity of any other quantity w.r.t. circuit parameters. This is outlined below:</li> </ul>"},{"location":"tutorial/09_sensitivity_analysis/#computing-the-sensitivity-of-a-specific-summary-statistic","title":"Computing the sensitivity of a specific summary statistic","text":"<p>Above, we have shown how to identify directions along which the posterior probability changes rapidly. Notably, the posterior probability reflects how consistent a specific parameter set is with all summary statistics, i.e. the entire \\(x_o\\). Sometimes, we might be interested in investigating how a specific features is influenced by the parameters. This feature could be one of the values of \\(x_o\\), but it could also be a different property.</p> <p>As a neuroscience example, in Deistler et al. 2021, we obtained the posterior distribution given burst durations and delays between bursts. After having obtained the posterior, we then wanted to analyse the sensitivity of metabolic cost w.r.t. circuit parameters. The framework we presented above can easily be extended to study such questions.</p> <pre><code>prior = MultivariateNormal(0.0 * torch.ones(2), 2 * torch.eye(2))\n\n\ndef simulator(theta):\n    return linear_gaussian(theta, -0.8 * torch.ones(2), torch.eye(2))\n\n\nposterior = infer(simulator, prior, num_simulations=2000, method=\"SNPE\").set_default_x(\n    torch.zeros(2)\n)\n</code></pre> <pre><code>Running 2000 simulations.:   0%|          | 0/2000 [00:00&lt;?, ?it/s]\n\n\n Neural network successfully converged after 139 epochs.\n</code></pre> <pre><code>_ = pairplot(posterior.sample((10_000,)), limits=[[-3, 3], [-3, 3]], figsize=(4, 4))\n</code></pre> <pre><code>Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00&lt;?, ?it/s]\n</code></pre> <p></p> <pre><code>sensitivity = ActiveSubspace(posterior)\n</code></pre> <p>This time, we begin by drawing samples from the posterior and then computing the desired property for each of the samples (i.e. you will probably have to run simulations for each theta and extract the property from the simulation output). As an example, we assume that the property is just the cube of the first dimension of the simulation output:</p> <pre><code>theta, x = simulate_for_sbi(simulator, posterior, 5000)\nproperty_ = x[:, :1] ** 3  # E.g. metabolic cost.\n</code></pre> <pre><code>Drawing 5000 posterior samples:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n\n\n\nRunning 5000 simulations.:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n</code></pre> <p>To investigate the sensitivity of a given parameter, we train a neural network to predict the <code>property_</code> from the parameters and then analyse the neural network as above:</p> <p>\\(M = \\mathbb{E}_{p(\\theta|x_o)}[\\nabla_{\\theta}f(\\theta)^T \\nabla_{\\theta}f(\\theta)\\)]  </p> <p>where \\(f(\\cdot)\\) is the trained neural network.</p> <pre><code>_ = sensitivity.add_property(theta, property_).train()\ne_vals, e_vecs = sensitivity.find_directions()\n</code></pre> <pre><code> Training neural network. Epochs trained:  24\n\n\nDrawing 1000 posterior samples:   0%|          | 0/1000 [00:00&lt;?, ?it/s]\n</code></pre> <pre><code>print(\"Eigenvalues: \\n\", e_vals, \"\\n\")\nprint(\"Eigenvectors: \\n\", e_vecs)\n</code></pre> <pre><code>Eigenvalues: \n tensor([2.8801e-06, 6.1131e-05])\n\nEigenvectors: \n tensor([[ 0.0362,  0.9993],\n        [ 0.9993, -0.0362]])\n</code></pre> <p>As we can see, one of the eigenvalues is much smaller than the other one. The larger eigenvalue represents (approximately) the vector <code>[1.0, 0.0]</code>. This makes sense, because only the <code>property_</code> is influenced only by the first output which, in turn, is influenced only by the first parameter.</p>"},{"location":"tutorial/10_crafting_summary_statistics/","title":"Crafting summary statistics","text":"<p>Many simulators produce outputs that are high-dimesional. For example, a simulator might generate a time series or an image. In a previous tutorial, we discussed how a neural networks can be used to learn summary statistics from such data. In this notebook, we will instead focus on hand-crafting summary statistics. We demonstrate that the choice of summary statistics can be crucial for the performance of the inference algorithm.</p> <pre><code>import numpy as np\nimport torch\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\n\n# sbi\nimport sbi.utils as utils\nfrom sbi.inference.base import infer\nfrom sbi.inference import SNPE, prepare_for_sbi, simulate_for_sbi\nfrom sbi.utils.get_nn_models import posterior_nn\nfrom sbi.analysis import pairplot\n</code></pre> <pre><code># remove top and right axis from plots\nmpl.rcParams[\"axes.spines.right\"] = False\nmpl.rcParams[\"axes.spines.top\"] = False\n</code></pre> <p>This notebook is not intended to provide a one-fits-all approach. In fact it argues against this: it argues for the user to carefully construct their summary statistics to (i) further help the user understand his observed data, (ii) help them understand exactly what they want the model to recover from the observation and (iii) help the inference framework itself.</p>"},{"location":"tutorial/10_crafting_summary_statistics/#example-1-the-quadratic-function","title":"Example 1: The quadratic function","text":"<p>Assume we have a simulator that is given by a quadratic function:</p> <p>\\(x(t) = a\\cdot t^2 + b\\cdot t + c + \\epsilon\\),</p> <p>where \\(\\epsilon\\) is Gaussian observation noise and \\(\\theta = \\{a, b, c\\}\\) are the parameters. Given an observed quadratic function \\(x_o\\), we would like to recover the posterior over parameters \\(a_o\\), \\(b_o\\) and \\(c_o\\).</p>"},{"location":"tutorial/10_crafting_summary_statistics/#11-prior-over-parameters","title":"1.1 Prior over parameters","text":"<p>First we define a prior distribution over parameters \\(a\\), \\(b\\) and \\(c\\). Here, we use a uniform prior for \\(a\\), \\(b\\) and \\(c\\) to go from \\(-1\\) to \\(1\\).</p> <pre><code>prior_min = [-1, -1, -1]\nprior_max = [1, 1, 1]\nprior = utils.torchutils.BoxUniform(\n    low=torch.as_tensor(prior_min), high=torch.as_tensor(prior_max)\n)\n</code></pre>"},{"location":"tutorial/10_crafting_summary_statistics/#12-simulator","title":"1.2 Simulator","text":"<p>Defining some helper functions first:</p> <pre><code>def create_t_x(theta, seed=None):\n    \"\"\"Return an t, x array for plotting based on params\"\"\"\n    if theta.ndim == 1:\n        theta = theta[np.newaxis, :]\n\n    if seed is not None:\n        rng = np.random.RandomState(seed)\n    else:\n        rng = np.random.RandomState()\n\n    t = np.linspace(-1, 1, 200)\n    ts = np.repeat(t[:, np.newaxis], theta.shape[0], axis=1)\n    x = (\n        theta[:, 0] * ts**2\n        + theta[:, 1] * ts\n        + theta[:, 2]\n        + 0.01 * rng.randn(ts.shape[0], theta.shape[0])\n    )\n    return t, x\n\n\ndef eval(theta, t, seed=None):\n    \"\"\"Evaluate the quadratic function at `t`\"\"\"\n\n    if theta.ndim == 1:\n        theta = theta[np.newaxis, :]\n\n    if seed is not None:\n        rng = np.random.RandomState(seed)\n    else:\n        rng = np.random.RandomState()\n\n    return theta[:, 0] * t**2 + theta[:, 1] * t + theta[:, 2] + 0.01 * rng.randn(1)\n</code></pre> <p>In this example, we generate the observation \\(x_o\\) from parameters \\(\\theta_o=(a_o, b_o, c_o)=(0.3, -0.2, -0.1)\\). The observation as follows.</p> <pre><code>theta_o = np.array([0.3, -0.2, -0.1])\nt, x = create_t_x(theta_o)\nplt.plot(t, x, \"k\")\n</code></pre> <pre><code>[&lt;matplotlib.lines.Line2D at 0x7f828b191d60&gt;]\n</code></pre> <p></p>"},{"location":"tutorial/10_crafting_summary_statistics/#13-summary-statistics","title":"1.3 Summary statistics","text":"<p>We will compare two methods for defining summary statistics. One method uses three summary statistics which are function evaluations at three points in time. The other method uses a single summary statistic: the mean squared error between the observed and the simulated trace. In the second case, one then tries to obtain the posterior \\(p(\\theta | 0)\\), i.e. the error being zero. These two methods are implemented below:  \\(\\textbf{get_3_values()}\\) returns 3 function evaluations at \\(x=-0.5, x=0\\) and \\(x=0.75\\).  \\(\\textbf{get_MSE()}\\) returns the mean squared error between true and a quadratic function corresponding to a prior distributions sample.</p> <pre><code>def get_3_values(theta, seed=None):\n    \"\"\"\n    Return 3 'y' values corresponding to x=-0.5,0,0.75 as summary statistic vector\n    \"\"\"\n    return np.array(\n        [\n            eval(theta, -0.5, seed=seed),\n            eval(theta, 0, seed=seed),\n            eval(theta, 0.75, seed=seed),\n        ]\n    ).T\n</code></pre> <pre><code>def get_MSE(theta, theta_o, seed=None):\n    \"\"\"\n    Return the mean-squared error (MSE) i.e. Euclidean distance from the observation function\n    \"\"\"\n    _, y = create_t_x(theta_o, seed=seed)  # truth\n    _, y_ = create_t_x(theta, seed=seed)  # simulations\n    return np.mean(np.square(y_ - y), axis=0, keepdims=True).T  # MSE\n</code></pre> <p>Let\u2019s try a couple of samples from our prior and see their summary statistics. Notice that these indeed change in small amounts every time you rerun it due to the noise, except if you set the seed.</p>"},{"location":"tutorial/10_crafting_summary_statistics/#14-simulating-data","title":"1.4 Simulating data","text":"<p>Let us see various plots of prior samples and their summary statistics versus the truth, i.e. our artificial observation.</p> <pre><code>t, x_truth = create_t_x(theta_o)\nplt.plot(t, x_truth, \"k\", zorder=1, label=\"truth\")\nn_samples = 100\ntheta = prior.sample((n_samples,))\nt, x = create_t_x(theta.numpy())\nplt.plot(t, x, \"grey\", zorder=0)\nplt.legend()\n</code></pre> <pre><code>&lt;matplotlib.legend.Legend at 0x7f8289154eb0&gt;\n</code></pre> <p></p> <p>In summary, we defined reasonable summary statistics and, a priori, there might be an appararent reason why one method would be better than another. When we do inference, we\u2019d like our posterior to focus around parameter samples that have their simulated MSE very close to 0 (i.e. the truth MSE summary statistic) or their 3 extracted \\((t, x)\\) coordinates to be the truthful ones.</p>"},{"location":"tutorial/10_crafting_summary_statistics/#15-inference","title":"1.5 Inference","text":""},{"location":"tutorial/10_crafting_summary_statistics/#151-using-the-mse","title":"1.5.1 Using the MSE","text":"<p>Let\u2019s see if we can use the MSE to recover the true observation parameters \\(\\theta_o=(a_0,b_0,c_0)\\).</p> <pre><code>theta = prior.sample((1000,))\nx = get_MSE(theta.numpy(), theta_o)\n\ntheta = torch.as_tensor(theta, dtype=torch.float32)\nx = torch.as_tensor(x, dtype=torch.float32)\n</code></pre> <pre><code>inference = SNPE(prior)\n_ = inference.append_simulations(theta, x).train()\nposterior = inference.build_posterior()\n</code></pre> <pre><code> Neural network successfully converged after 181 epochs.\n</code></pre> <p>Now that we\u2019ve build the posterior as such, we can see how likely it finds certain parameters given that we tell it that we\u2019ve observed a certain summary statistic (in this case the MSE). We can then sample from it.</p> <pre><code>x_o = torch.as_tensor(\n    [\n        [\n            0.0,\n        ]\n    ]\n)\ntheta_p = posterior.sample((10000,), x=x_o)\n</code></pre> <pre><code>Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00&lt;?, ?it/s]\n</code></pre> <pre><code>fig, axes = pairplot(\n    theta_p,\n    limits=list(zip(prior_min, prior_max)),\n    ticks=list(zip(prior_min, prior_max)),\n    figsize=(7, 7),\n    labels=[\"a\", \"b\", \"c\"],\n    points_offdiag={\"markersize\": 6},\n    points_colors=\"r\",\n    points=theta_o,\n);\n</code></pre> <p></p> <p>The posterior seems to pretty broad: i.e. it is not so certain about the \u2018true\u2019 parameters (here showcased in red).</p> <pre><code>x_o_t, x_o_x = create_t_x(theta_o)\nplt.plot(x_o_t, x_o_x, \"k\", zorder=1, label=\"truth\")\n\ntheta_p = posterior.sample((10,), x=x_o)\nx_t, x_x = create_t_x(theta_p.numpy())\nplt.plot(x_t, x_x, \"grey\", zorder=0)\nplt.legend()\n</code></pre> <pre><code>Drawing 10 posterior samples:   0%|          | 0/10 [00:00&lt;?, ?it/s]\n\n\n\n\n\n&lt;matplotlib.legend.Legend at 0x7f82882cd670&gt;\n</code></pre> <p></p> <p>The functions are a bit closer to the observation than prior samples, but many posterior samples generate activity that is very far off from the observation. We would expect <code>sbi</code> do better on such a simple example. So what\u2019s going on? Do we need more simulations? Feel free to try, but below we will show that one can use the same number of simulation samples with different summary statistics and do much better.</p>"},{"location":"tutorial/10_crafting_summary_statistics/#152-using-3-coordinates-as-summary-statistics","title":"1.5.2 Using 3 coordinates as summary statistics","text":"<pre><code>x = get_3_values(theta.numpy())\nx = torch.as_tensor(x, dtype=torch.float32)\n</code></pre> <pre><code>inference = SNPE(prior)\n\n_ = inference.append_simulations(theta, x).train()\nposterior = inference.build_posterior()\n</code></pre> <pre><code> Neural network successfully converged after 127 epochs.\n</code></pre> <p>The observation is now given by the values of the observed trace at three different coordinates:</p> <pre><code>x_o = torch.as_tensor(get_3_values(theta_o), dtype=float)\n</code></pre> <pre><code>theta_p = posterior.sample((10000,), x=x_o)\n\nfig, axes = pairplot(\n    theta_p,\n    limits=list(zip(prior_min, prior_max)),\n    ticks=list(zip(prior_min, prior_max)),\n    figsize=(7, 7),\n    labels=[\"a\", \"b\", \"c\"],\n    points_offdiag={\"markersize\": 6},\n    points_colors=\"r\",\n    points=theta_o,\n);\n</code></pre> <pre><code>Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00&lt;?, ?it/s]\n</code></pre> <p></p> <pre><code>x_o_x, x_o_y = create_t_x(theta_o)\nplt.plot(x_o_x, x_o_y, \"k\", zorder=1, label=\"truth\")\ntheta_p = posterior.sample((100,), x=x_o)\nind_10_highest = np.argsort(np.array(posterior.log_prob(theta=theta_p, x=x_o)))[-10:]\ntheta_p_considered = theta_p[ind_10_highest, :]\nx_x, x_y = create_t_x(theta_p_considered.numpy())\nplt.plot(x_x, x_y, \"grey\", zorder=0)\nplt.legend()\n</code></pre> <pre><code>Drawing 100 posterior samples:   0%|          | 0/100 [00:00&lt;?, ?it/s]\n\n\n\n\n\n&lt;matplotlib.legend.Legend at 0x7f82885b4af0&gt;\n</code></pre> <p></p> <p>Ok this definitely seems to work! The posterior correctly focuses on the true parameters with greater confidence. You can experiment yourself how this improves further with more training samples or you could try to see how many you\u2019d exactly need to keep having a satisfyingly looking posterior and high posterior sample simulations.</p> <p>So, what\u2019s up with the MSE? Why does it not seem so informative to constrain the posterior? In 1.6, we\u2019ll see both the power and pitfalls of summary statistics.</p>"},{"location":"tutorial/10_crafting_summary_statistics/#16-prior-simulations-summary-statistics-vs-observed-summary-statistics","title":"1.6 Prior simulations\u2019 summary statistics vs observed summary statistics","text":"<p>Let\u2019s try to understand this\u2026Let\u2019s look at a histogram of the four summary statistics we\u2019ve experimented with, and see how they compare to our observed truth summary statistic vector:</p> <pre><code>stats = np.concatenate(\n    (get_3_values(theta.numpy()), get_MSE(theta.numpy(), theta_o)), axis=1\n)\nx_o = np.concatenate((get_3_values(theta_o), np.asarray([[0.0]])), axis=1)\n\nfeatures = [\"y @ x=-0.5\", \"y @ x=0\", \"y @ x=0.7\", \"MSE\"]\nfig, axes = plt.subplots(1, 4, figsize=(10, 3))\nxlabelfontsize = 10\nfor i, ax in enumerate(axes.reshape(-1)):\n    ax.hist(\n        stats[:, i],\n        color=[\"grey\"],\n        alpha=0.5,\n        bins=30,\n        density=True,\n        histtype=\"stepfilled\",\n        label=[\"simulations\"],\n    )\n    ax.axvline(x_o[:, i], label=\"observation\")\n    ax.set_xlabel(features[i], fontsize=xlabelfontsize)\n    if i == 3:\n        ax.legend()\nplt.tight_layout()\n</code></pre> <p></p> <p>We see that for the coordinates (three plots on the left), simulations cover the observation. That is: it covers it from the left and right side in each case. For the MSE, simulations never truly reach the observation \\(0.0\\).</p> <p>For the trained neural network, it is strongly preferable if the simulations cover the observation. In that case, the neural network can interpolate between simulated data. Contrary to that, for the MSE, the neural network has to extrapolate: it never observes a simulation that is to the left of the observation and has to extrapolate to the region of MSE=\\(0.0\\). This seems like a technical point but, as we saw above, it makes a huge difference in performance.</p>"},{"location":"tutorial/10_crafting_summary_statistics/#17-explicit-recommendations","title":"1.7 Explicit recommendations","text":"<p>We give some explicit recommendation when using summary statistics</p> <ul> <li> <p>Visualize the histogram of each summary statistic and plot the value of the observation. If, for some summary statistics, the observation is not covered (or is at the very border, e.g. the MSE above), the trained neural network will struggle.</p> </li> <li> <p>Do not use an \u201cerror\u201d as summary statistic. This is common in optimization (e.g. genetic algorithms), but it often leads to trouble in <code>sbi</code> due to the reason above.</p> </li> <li> <p>Only use summary statistics that are necessary. The less summary statistics you use, the less can go wrong with them. Of course, you have to ensure that the summary statistics describe the raw data sufficiently well.</p> </li> </ul>"},{"location":"tutorial/11_sampler_interface/","title":"Sampling algorithms in <code>sbi</code>","text":"<p>Note: this tutorial requires that the user is already familiar with the flexible interface.</p> <p><code>sbi</code> implements three methods: SNPE, SNLE, and SNRE. When using SNPE, the trained neural network directly approximates the posterior. Thus, sampling from the posterior can be done by sampling from the trained neural network. The neural networks trained in SNLE and SNRE approximate the likelihood(-ratio). Thus, in order to draw samples from the posterior, one has to perform additional sampling steps, e.g. Markov-chain Monte-Carlo (MCMC). In <code>sbi</code>, the implemented samplers are:</p> <ul> <li> <p>Markov-chain Monte-Carlo (MCMC)</p> </li> <li> <p>Rejection sampling  </p> </li> <li> <p>Variational inference (VI)</p> </li> </ul> <p>Below, we will demonstrate how these samplers can be used in <code>sbi</code>. First, we train the neural network as always:</p> <pre><code>import torch\nfrom sbi.inference import SNLE\n\n# dummy Gaussian simulator for demonstration\nnum_dim = 2\nprior = torch.distributions.MultivariateNormal(torch.zeros(num_dim), torch.eye(num_dim))\ntheta = prior.sample((1000,))\nx = theta + torch.randn((1000, num_dim))\nx_o = torch.randn((1, num_dim))\n\ninference = SNLE(prior=prior, show_progress_bars=False)\nlikelihood_estimator = inference.append_simulations(theta, x).train()\n</code></pre> <p>And then we pass the options for which sampling method to use to the <code>build_posterior()</code> method:</p> <pre><code># Sampling with MCMC\nsampling_algorithm = \"mcmc\"\nmcmc_method = \"slice_np\"  # or nuts, or hmc\nposterior = inference.build_posterior(sample_with=sampling_algorithm, mcmc_method=mcmc_method)\n\n# Sampling with variational inference\nsampling_algorithm = \"vi\"\nvi_method = \"rKL\"  # or fKL\nposterior = inference.build_posterior(sample_with=sampling_algorithm, vi_method=vi_method)\n# Unlike other methods, vi needs a training step for every observation.\nposterior = posterior.set_default_x(x_o).train()\n\n# Sampling with rejection sampling\nsampling_algorithm = \"rejection\"\nposterior = inference.build_posterior(sample_with=sampling_algorithm)\n</code></pre>"},{"location":"tutorial/11_sampler_interface/#more-flexibility-in-adjusting-the-sampler","title":"More flexibility in adjusting the sampler","text":"<p>With the above syntax, you can easily try out different sampling algorithms. However, in many cases, you might want to customize your sampler. Below, we demonstrate how you can change hyperparameters of the samplers (e.g. number of warm-up steps of MCMC) or how you can write your own sampler from scratch.</p>"},{"location":"tutorial/11_sampler_interface/#main-syntax-for-snle-and-snre","title":"Main syntax (for SNLE and SNRE)","text":"<p>As above, we begin by training the neural network as always:</p> <pre><code>import torch\n\nfrom sbi.inference import SNLE\n\n# dummy Gaussian simulator for demonstration\nnum_dim = 2\nprior = torch.distributions.MultivariateNormal(torch.zeros(num_dim), torch.eye(num_dim))\ntheta = prior.sample((1000,))\nx = theta + torch.randn((1000, num_dim))\nx_o = torch.randn((1, num_dim))\n\ninference = SNLE(show_progress_bars=False)\nlikelihood_estimator = inference.append_simulations(theta, x).train()\n</code></pre> <pre><code> Neural network successfully converged after 52 epochs.\n</code></pre> <p>Then, for full flexibility on using the sampler, we do not use the <code>.build_posterior()</code> method, but instead we explicitly define the potential function and the sampling algorithm (see below for explanation):</p> <pre><code>from sbi.inference import likelihood_estimator_based_potential, MCMCPosterior\n\npotential_fn, parameter_transform = likelihood_estimator_based_potential(\n    likelihood_estimator, prior, x_o\n)\nposterior = MCMCPosterior(\n    potential_fn, proposal=prior, theta_transform=parameter_transform, warmup_steps=10\n)\n</code></pre> <p>If you want to use variational inference or rejection sampling, you have to replace the last line with <code>VIPosterior</code> or <code>RejectionPosterior</code>:</p> <pre><code># For VI, we have to train.\nposterior = VIPosterior(\n    potential_fn, proposal=prior, theta_transform=parameter_transform\n).train()\n\nposterior = RejectionPosterior(\n    potential_fn, proposal=prior, theta_transform=parameter_transform\n)\n</code></pre> <p>At this point, you could also plug the <code>potential_fn</code> into any sampler of your choice and not rely on any of the in-built <code>sbi</code>-samplers.</p>"},{"location":"tutorial/11_sampler_interface/#further-explanation","title":"Further explanation","text":"<p>The first lines are the same as for the flexible interface:</p> <pre><code>inference = SNLE()\nlikelihood_estimator = inference.append_simulations(theta, x).train()\n</code></pre> <pre><code> Neural network successfully converged after 33 epochs.\n</code></pre> <p>Next, we obtain the potential function. A potential function is a function of the parameter \\(f(\\theta)\\). The posterior is proportional to the product of likelihood and prior: \\(p(\\theta | x_o) \\propto p(x_o | \\theta)p(\\theta)\\). The potential function is the logarithm of the right-hand side of this equation: \\(f(\\theta) = \\log(p(x_o | \\theta)p(\\theta))\\)</p> <pre><code>potential_fn, parameter_transform = likelihood_estimator_based_potential(\n    likelihood_estimator, prior, x_o\n)\n</code></pre> <p>By calling the <code>potential_fn</code>, you can evaluate the potential:</p> <pre><code># Assuming that your parameters are 1D.\npotential = potential_fn(\n    torch.zeros(1, num_dim)\n)  # -&gt; returns f(0) = log( p(x_o|0) p(0) )\n</code></pre> <p>The other object that is returned by <code>likelihood_estimator_based_potential</code> is a <code>parameter_transform</code>. The <code>parameter_transform</code> is a pytorch transform. The <code>parameter_transform</code> is a fixed transform that is can be applied to parameter <code>theta</code>. It transforms the parameters into unconstrained space (if the prior is bounded, e.g. <code>BoxUniform</code>), and standardizes the parameters (i.e. zero mean, one std). Using <code>parameter_transform</code> during sampling is optional, but it usually improves the performance of MCMC.</p> <pre><code>theta_tf = parameter_transform(torch.zeros(1, num_dim))\ntheta_original = parameter_transform.inv(theta_tf)\nprint(theta_original)  # -&gt; tensor([[0.0]])\n</code></pre> <pre><code>tensor([[0., 0.]])\n</code></pre> <p>After having obtained the <code>potential_fn</code>, we can sample from the posterior with MCMC or rejection sampling:</p> <pre><code>from sbi.inference import MCMCPosterior, RejectionPosterior\n\nposterior = MCMCPosterior(\n    potential_fn, proposal=prior, theta_transform=parameter_transform\n)\nposterior = RejectionPosterior(potential_fn, proposal=prior)\n</code></pre>"},{"location":"tutorial/11_sampler_interface/#main-syntax-for-snpe","title":"Main syntax for SNPE","text":"<p>SNPE usually does not require MCMC or rejection sampling (if you still need it, you can use the same syntax as above with the <code>posterior_estimator_based_potential</code> function). Instead, SNPE samples from the neural network. If the support of the prior is bounded, some samples can lie outside of the support of the prior. The <code>DirectPosterior</code> class automatically rejects these samples:</p> <pre><code>from sbi.inference import SNPE\nfrom sbi.inference import DirectPosterior\n\ninference = SNPE()\nposterior_estimator = inference.append_simulations(theta, x).train()\n\nposterior = DirectPosterior(posterior_estimator, prior=prior)\n</code></pre> <pre><code> Neural network successfully converged after 57 epochs.\n</code></pre> <pre><code>\n</code></pre>"},{"location":"tutorial/12_diagnostics_posterior_predictive_check/","title":"Posterior Predictive Checks (PPC) in SBI","text":"<p>A common safety check performed as part of inference are Posterior Predictive Checks (PPC). A PPC compares data \\(x_{\\text{pp}}\\) generated using the parameters \\(\\theta_{\\text{posterior}}\\) sampled from the posterior with the observed data \\(x_o\\). The general concept is that -if the inference is correct- the generated data \\(x_{\\text{pp}}\\) should \u201clook similar\u201d the oberved data \\(x_0\\). Said differently, \\(x_o\\) should be within the support of \\(x_{\\text{pp}}\\).</p> <p>A PPC usually shouldn\u2019t be used as a validation metric. Nonetheless a PPC is a good start for an inference diagnosis and can provide with an intuition about any bias introduced in inference: does \\(x_{\\text{pp}}\\) systematically differ from \\(x_o\\)?</p>"},{"location":"tutorial/12_diagnostics_posterior_predictive_check/#main-syntax","title":"Main syntax","text":"<pre><code>from sbi.analysis import pairplot\n\n# A PPC is performed after we trained or neural posterior\nposterior.set_default_x(x_o)\n\n# We draw theta samples from the posterior. This part is not in the scope of SBI\nposterior_samples = posterior.sample((5_000,))\n\n# We use posterior theta samples to generate x data\nx_pp = simulator(posterior_samples)\n\n# We verify if the observed data falls within the support of the generated data\n_ = pairplot(\n    samples=x_pp,\n    points=x_o\n)\n</code></pre>"},{"location":"tutorial/12_diagnostics_posterior_predictive_check/#performing-a-ppc-over-a-toy-example","title":"Performing a PPC over a toy example","text":"<p>Below we provide an example Posterior Predictive Check (PPC) over some toy example:</p> <pre><code>from sbi.analysis import pairplot\nimport torch\n\n_ = torch.manual_seed(0)\n</code></pre> <p>We work on an inference problem over three parameters using any of the techniques implemented in <code>sbi</code>. In this tutorial, we load the dummy posterior:</p> <pre><code>from toy_posterior_for_07_cc import ExamplePosterior\n\nposterior = ExamplePosterior()\n</code></pre> <p>Let us say that we are observing the data point \\(x_o\\):</p> <pre><code>D = 5  # simulator output was 5-dimensional\nx_o = torch.ones(1, D)\nposterior.set_default_x(x_o)\n</code></pre> <p>The posterior can be used to draw \\(\\theta_{\\text{posterior}}\\) samples:</p> <pre><code>posterior_samples = posterior.sample((5_000,))\n\nfig, ax = pairplot(\n    samples=posterior_samples,\n    limits=torch.tensor([[-2.5, 2.5]] * 3),\n    offdiag=[\"kde\"],\n    diag=[\"kde\"],\n    figsize=(5, 5),\n    labels=[rf\"$\\theta_{d}$\" for d in range(3)],\n)\n</code></pre> <p></p> <p>Now we can use our simulator to generate some data \\(x_{\\text{PP}}\\), using as input parameters the poterior samples \\(\\theta_{\\text{posterior}}\\). Note that the simulation part is not in the <code>sbi</code> scope, so any simulator -including a non-Python one- can be used at this stage. In our case we\u2019ll use a dummy simulator:</p> <pre><code>def dummy_simulator(posterior_samples: torch.Tensor, *args, **kwargs) -&gt; torch.Tensor:\n    sample_size = posterior_samples.shape[0]\n    scale = 1.0\n\n    shift = torch.distributions.Gumbel(loc=torch.zeros(D), scale=scale / 2).sample()\n    return torch.distributions.Gumbel(loc=x_o[0] + shift, scale=scale).sample(\n        (sample_size,)\n    )\n\n\nx_pp = dummy_simulator(posterior_samples)\n</code></pre> <p>Plotting \\(x_o\\) against the \\(x_{\\text{pp}}\\), we perform a PPC that plays the role of a sanity check. In this case, the check indicates that \\(x_o\\) falls right within the support of \\(x_{\\text{pp}}\\), which should make the experimenter rather confident about the estimated <code>posterior</code>:</p> <pre><code>_ = pairplot(\n    samples=x_pp,\n    points=x_o[0],\n    limits=torch.tensor([[-2.0, 5.0]] * 5),\n    points_colors=\"red\",\n    figsize=(8, 8),\n    offdiag=\"scatter\",\n    scatter_offdiag=dict(marker=\".\", s=5),\n    points_offdiag=dict(marker=\"+\", markersize=20),\n    labels=[rf\"$x_{d}$\" for d in range(D)],\n)\n</code></pre> <p></p> <p>In contrast, \\(x_o\\) falling well outside the support of \\(x_{\\text{pp}}\\) is indicative of a failure to estimate the correct posterior. Here we simulate such a failure mode:</p> <pre><code>error_shift = -2.0 * torch.ones(1, 5)\n\n_ = pairplot(\n    samples=x_pp,\n    points=x_o[0] + error_shift,\n    limits=torch.tensor([[-2.0, 5.0]] * 5),\n    points_colors=\"red\",\n    figsize=(8, 8),\n    offdiag=\"scatter\",\n    scatter_offdiag=dict(marker=\".\", s=5),\n    points_offdiag=dict(marker=\"+\", markersize=20),\n    labels=[rf\"$x_{d}$\" for d in range(D)],\n)\n</code></pre> <p></p> <p>A typical way to investigate this issue would be to run a prior* predictive check, applying the same plotting strategy, but drawing \\(\\theta\\) from the prior instead of the posterior. **The support for \\(x_{\\text{pp}}\\) should be larger and should contain \\(x_o\\)*. If this check is successful, the \u201cblame\u201d can then be shifted to the inference (method used, convergence of density estimators, number of sequential rounds, etc\u2026).</p>"},{"location":"tutorial/13_diagnostics_simulation_based_calibration/","title":"Simulation-based Calibration in SBI","text":"<p>After a density estimator has been trained with simulated data to obtain a posterior, the estimator should be made subject to several diagnostic tests, before being used for inference given the actual observed data. Posterior Predictive Checks (see previous tutorial) provide one way to \u201ccritique\u201d a trained estimator via its predictive performance. Another important approach to such diagnostics is simulation-based calibration as developed by Cook et al, 2006 and Talts et al, 2018.</p> <p>Simulation-based calibration (SBC) provides a (qualitative) view and a quantitive measure to check, whether the uncertainties of the posterior are balanced, i.e., neither over-confident nor under-confident. As such, SBC can be viewed as a necessary condition (but not sufficient) for a valid inference algorithm: If SBC checks fail, this tells you that your inference is invalid. If SBC checks pass, this is no guarantee that the posterior estimation is working.</p>"},{"location":"tutorial/13_diagnostics_simulation_based_calibration/#in-a-nutshell","title":"In a nutshell","text":"<p>To run SBC,  </p> <ol> <li>we sample <code>theta_o_i</code> values from the prior of the problem at hand</li> <li>we simulate \u201cobservations\u201d from these parameters: <code>x_o_i = simulator(theta_o_i)</code> </li> <li>we perform inference given each observation <code>x_o_i</code>.</li> </ol> <p>This produces a separate posterior \\(p_i(\\theta | x_{o,i})\\) for each of <code>x_o_i</code>. The key step for SBC is to generate a set of posterior samples \\(\\{\\theta\\}_i\\) from each posterior (let\u2019s call this <code>theta_i_s</code>, referring to <code>s</code> samples from posterior \\(p_i(\\theta | x_{o,i})\\)), and to rank the corresponding <code>theta_o_i</code> under this set of samples. A rank is computed by counting how many samples <code>theta_i_s</code> fall below their corresponding <code>theta_o_i</code> (see section 4.1 in Talts et al.). These ranks are then used to perform the SBC check.</p>"},{"location":"tutorial/13_diagnostics_simulation_based_calibration/#key-ideas-behind-sbc","title":"Key ideas behind SBC","text":"<p>The core idea behind SBC is two fold: </p> <ul> <li> <p>SBC ranks of ground truth parameters under the inferred posterior samples follow a uniform distribution. (If the SBC ranks are not uniformly distributed, the posterior is not well calibrated.)</p> </li> <li> <p>samples from the data averaged posterior (ensemble of randomly chosen posterior samples given multiple distinct observations <code>x_o</code>) are distributed according to the prior</p> </li> </ul>"},{"location":"tutorial/13_diagnostics_simulation_based_calibration/#what-can-sbc-diagnose","title":"What can SBC diagnose?","text":"<p>SBC can inform us whether we are not wrong. However, it cannot tell us whether we are right, i.e., SBC checks a necessary condition. For example, imagine you run SBC using the prior as a posterior. The ranks would be perfectly uniform. But the inference would be wrong.</p> <p>The Posterior Predictive Checks (see tutorial 12) can be seen as the complementary sufficient check for the posterior (only as a methaphor, no theoretical guarantees here). Using the prior as a posterior and then doing predictive checks would clearly show that inference failed. </p> <p>To summarize SBC can:</p> <ul> <li>tell us whether the SBI method applied to the problem at hand produces posteriors that have well-calibrated uncertainties,</li> <li>and if not, what kind of systematic bias it has: negative or positive bias (shift in the mean of the predictions) or over- or underdispersion (too large or too small variance)</li> </ul>"},{"location":"tutorial/13_diagnostics_simulation_based_calibration/#a-healthy-posterior","title":"A healthy posterior","text":"<p>Let\u2019s take the gaussian linear simulator from the previous tutorials and run inference with NPE on it. </p> <p>Note: SBC requires running inference several times. Using SBC with amortized methods like NPE is hence a justified endavour: repeated inference is cheap and SBC can be performed with little runtime penalty. This does not hold for sequential methods or anything relying on MCMC or VI (here, parallelization is your friend, <code>num_workers&gt;1</code>).</p> <pre><code>import torch\n\nfrom torch import eye, ones, zeros\nfrom torch.distributions import MultivariateNormal\n\nfrom sbi.analysis import check_sbc, run_sbc, get_nltp, sbc_rank_plot\nfrom sbi.inference import SNPE, SNPE_C, prepare_for_sbi, simulate_for_sbi\nfrom sbi.simulators import linear_gaussian, diagonal_linear_gaussian\n</code></pre> <pre><code>num_dim = 2\nnum_simulations = 10_000\n\nprior_mean = ones(num_dim)\nprior_cov = 2 * eye(num_dim)\nprior = MultivariateNormal(\n    loc=prior_mean, covariance_matrix=prior_cov, validate_args=False\n)\n</code></pre>"},{"location":"tutorial/13_diagnostics_simulation_based_calibration/#an-ideal-case","title":"An ideal case","text":"<p>To explore SBC, we make our life easy and assume that we deal with a problem where the likelihood is modelled by an identity mapping and a bit of smear. But to start, we only use an almost vanishing smear of <code>0.01</code>.</p> <pre><code>default_likelihood_loc = 0.0  # let's start with 0 shift\ndefault_likelihood_scale = 0.01  # let's smear theta only by a little bit\n\n\ndef simulator(theta, loc=default_likelihood_loc, scale=default_likelihood_scale):\n    \"\"\"linear gaussian inspired by sbibm\n    https://github.com/sbi-benchmark/sbibm/blob/15f068a08a938383116ffd92b92de50c580810a3/sbibm/tasks/gaussian_linear/task.py#L74\n    \"\"\"\n    num_dim = theta.shape[-1]\n    cov_ = scale * eye(num_dim)  # always positively semi-definite\n\n    # using validate_args=False disables sanity checks on `covariance_matrix`\n    # for the sake of speed\n    value = MultivariateNormal(\n        loc=(theta + loc), covariance_matrix=cov_, validate_args=False\n    ).sample()\n    return value\n\n\n_ = torch.manual_seed(3)\ntheta, x = simulate_for_sbi(simulator, prior, num_simulations)\n</code></pre> <pre><code>Running 10000 simulations.:   0%|          | 0/10000 [00:00&lt;?, ?it/s]\n</code></pre> <pre><code>_ = torch.manual_seed(1)\n\n# let's obtain an observation\ntheta_o = prior.sample((1,))\nx_o = simulator(theta_o)\nprint(\"theta:\", theta_o.numpy())\nprint(\"x    :\", x_o.numpy())\n</code></pre> <pre><code>theta: [[1.9352932 1.3774877]]\nx    : [[1.941461  1.4396194]]\n</code></pre> <pre><code>_ = torch.manual_seed(2)\n\n# we use a mdn model to have a fast turnaround with training.\ninferer = SNPE(prior, density_estimator=\"mdn\")\n# append simulations and run training.\ninferer.append_simulations(theta, x).train();\n</code></pre> <pre><code> Neural network successfully converged after 63 epochs.\n</code></pre> <pre><code>posterior = inferer.build_posterior()\nposterior_samples = posterior.sample((15_000,), x=x_o)\n# Generate predictive samples by simulating from posterior samples.\nposterior_predictive_samples = simulator(posterior_samples)\n</code></pre> <pre><code>Drawing 15000 posterior samples:   0%|          | 0/15000 [00:00&lt;?, ?it/s]\n</code></pre> <pre><code># let's do some posterior predictive checks to see if the\n# posterior predictive samples cluster aournd the observation `x_o`.\nfrom sbi.analysis import pairplot\n\nfig, ax = pairplot(\n    samples=posterior_predictive_samples,\n    points=x_o,\n    limits=list(zip(x_o.flatten() - 1.0, x_o.flatten() + 1.0)),\n    offdiag=\"kde\",\n    diag=\"kde\",\n    figsize=(5, 5),\n    labels=[rf\"$x_{d}$\" for d in range(3)],\n)\n</code></pre> <p></p> <p>The observation <code>x_o</code> falls into the support of the predicted posterior samples, i.e. it is within <code>simulator(posterior_samples)</code>. Given the simulator, this is indicative that our posterior estimates the data well.</p>"},{"location":"tutorial/13_diagnostics_simulation_based_calibration/#running-sbc","title":"Running SBC","text":"<p>We have a working and trained posterior at this point! Hurray! Let\u2019s look at the SBC metrics now.</p> <pre><code>num_sbc_runs = 1_000  # choose a number of sbc runs, should be ~100s or ideally 1000\n# generate ground truth parameters and corresponding simulated observations for SBC.\nthetas = prior.sample((num_sbc_runs,))\nxs = simulator(thetas)\n</code></pre> <p>SBC is implemented in <code>sbi</code> for your use on any <code>sbi</code> posterior. To run it, we only need to call <code>run_sbc</code> with appropriate parameters. </p> <p>Note: For amortized neural posteriors (like in this tutorial), execution of <code>sbc</code> is expected to be fast. For posteriors that conduct inference with MCMC and hence are slow, <code>run_sbc</code> exposes the use of multiple internal parallel workers to the user. To use this feature, add <code>num_workers = 2</code> to the parameters for use of two workers. See the API documentation for details.</p> <pre><code># run SBC: for each inference we draw 1000 posterior samples.\nnum_posterior_samples = 1_000\nranks, dap_samples = run_sbc(\n    thetas, xs, posterior, num_posterior_samples=num_posterior_samples\n)\n</code></pre> <pre><code>Running 1000 sbc samples.:   0%|          | 0/1000 [00:00&lt;?, ?it/s]\n</code></pre> <p><code>sbi</code> establishes two methods to do simulation-based calibration:</p> <ul> <li>metrics to compare the sbc ranks with a uniform distribution </li> <li>control plots for visual inspections like fig. 1 or 2 in Talts et al, 2018</li> </ul> <p>The <code>ranks</code> count is performed per dimension of <code>theta</code>, i.e. on the 1-D marginal posterior estimates. According to theory, the distribution of these ranks (per dimension of <code>theta</code>) should turn out to be uniformly distributed. </p> <p>The data average posterior <code>dap</code> (see equation 1 of Talts et al, 2018) is yet another metric of interest. It is built from singular random samples of the estimated posterior samples for each <code>xs</code> above. The <code>dap</code> is expected to match the prior distribution used (see equation 1 in Talts et al, 2018 too). </p> <pre><code>check_stats = check_sbc(\n    ranks, thetas, dap_samples, num_posterior_samples=num_posterior_samples\n)\n</code></pre> <p>The <code>check_stats</code> variable created contains a dictionary with 3 metrics that help to judge our posterior. The \u201cfirst\u201d two compare the ranks to a uniform distribution.</p>"},{"location":"tutorial/13_diagnostics_simulation_based_calibration/#ranks-versus-uniform-distribution","title":"Ranks versus Uniform distribution","text":"<pre><code>print(\n    f\"kolmogorov-smirnov p-values \\ncheck_stats['ks_pvals'] = {check_stats['ks_pvals'].numpy()}\"\n)\n</code></pre> <pre><code>kolmogorov-smirnov p-values \ncheck_stats['ks_pvals'] = [0.28580528 0.12618546]\n</code></pre> <p>The Kolmogorov-Smirnov (KS test, see also here) as used by <code>check_sbc</code> provides p-values <code>pvals</code> on the null hypothesis that the samples from <code>ranks</code> are drawn from a uniform distribution (in other words <code>H_0: PDF(ranks) == PDF(uniform)</code>). We are provided two values as our problem is two-dimensional - one p-value for each dimension. </p> <p>The null hypothesis (of both distributions being equal) is rejected if the p-values fall below a significance threshold (usually <code>&lt; 0.05</code>). Therefor, vanishing p-values (<code>ks_pvals=0</code>) are interpreted to indicate a vanishing false positive rate to (mistakenly) consider both distrubtions being \u201cequal\u201d. Both values are above <code>0.05</code> and we, therefore, cannot claim that inference clearly went wrong. Nonetheless, we can add additional checks:</p> <pre><code>print(\n    f\"c2st accuracies \\ncheck_stats['c2st_ranks'] = {check_stats['c2st_ranks'].numpy()}\"\n)\n</code></pre> <pre><code>c2st accuracies \ncheck_stats['c2st_ranks'] = [0.5765 0.5735]\n</code></pre> <p>The second tier of metrics comparing <code>ranks</code> with a uniform distributions is a <code>c2st</code> test (see here for details). This is a nonparametric two sample test based on training a classifier to differented one of the ensembles (<code>ranks</code> versus samples from a uniform distribution) by being trained on the other. The values reported are the accuracies from cross-validation. If you see values around <code>0.5</code>, the classifier was unable to differentiate both ensembles, i.e. <code>ranks</code> are very uniform. If the values are high towards <code>1</code>, this matches the case where <code>ranks</code> is very unlike a uniform distribution.</p>"},{"location":"tutorial/13_diagnostics_simulation_based_calibration/#data-averaged-posterior-dap-versus-prior","title":"Data averaged posterior (DAP) versus prior","text":"<pre><code>print(f\"- c2st accuracies check_stats['c2st_dap'] = {check_stats['c2st_dap'].numpy()}\")\n</code></pre> <pre><code>- c2st accuracies check_stats['c2st_dap'] = [0.474 0.488]\n</code></pre> <p>The last metric reported is again based on <code>c2st</code> computed per dimension of <code>theta</code>. If you see values around <code>0.5</code>, the <code>c2st</code> classifier was unable to differentiate both ensembles for each dimension of <code>theta</code>, i.e. <code>dap</code> are much like (if not identical to) the prior. If the values are very high towards <code>1</code>, this represents the case where <code>dap</code> is very unlike the prior distribution.</p>"},{"location":"tutorial/13_diagnostics_simulation_based_calibration/#visual-inspection","title":"Visual Inspection","text":"<pre><code>from sbi.analysis import sbc_rank_plot\n\nf, ax = sbc_rank_plot(\n    ranks=ranks,\n    num_posterior_samples=num_posterior_samples,\n    plot_type=\"hist\",\n    num_bins=None,  # by passing None we use a heuristic for the number of bins.\n)\n</code></pre> <p>The two plots visualize the distribution of <code>ranks</code> (here depicted in red) in each dimension. Highlighted in grey you see the 99% confidence interval of a uniform distribution given the number of samples provided. In plain english: for a uniform distribution, we would expect 1 out of 100 (red) bars to lie outside the grey area.</p> <p>We also observe, that the entries fluctuate to some degree. This can be considered a hint that <code>sbc</code> should be conducted with a lot more samples than 1000. A good rule of thumb is that given the number of bins <code>B</code> and the number of SBC samples <code>N</code> (chosed to be <code>1_000</code> here) should amount to <code>N / B ~ 20</code>.</p> <pre><code>f, ax = sbc_rank_plot(ranks, 1_000, plot_type=\"cdf\")\n</code></pre> <p></p> <p>The above provides a visual representation of the cumulative density function (CDF) of <code>ranks</code> (blue and orange for each dimension of <code>theta</code>) with respect to the 95% confidence interval of a uniform distribution (grey).</p>"},{"location":"tutorial/13_diagnostics_simulation_based_calibration/#when-things-go-haywire","title":"When things go haywire","text":"<p>Next, we would like to explore some pathologies visible in sbc plots which can hint at our estimated posterior being somewhat wrong or completely off.</p>"},{"location":"tutorial/13_diagnostics_simulation_based_calibration/#a-shifted-posterior-mean","title":"A shifted posterior mean","text":"<p>In this scenario we emulate the situation that our posterior estimates incorrectly with a constant shift. We reuse our trained NPE posterior from above and wrap it so that all samples returned expose a constant shift by <code>+0.1</code>.</p> <pre><code>from utils_13_diagnosis_sbc import BiasedPosterior\n\n# this posterior shifts the expected value of the prior by .1\nposterior_ = BiasedPosterior(posterior, shift=0.1)\n</code></pre> <pre><code>ranks, dap_samples = run_sbc(thetas, xs, posterior_)\ncheck_stats = check_sbc(ranks, thetas, dap_samples, 1_000)\nprint(check_stats)\n</code></pre> <pre><code>Running 1000 sbc samples.:   0%|          | 0/1000 [00:00&lt;?, ?it/s]\n\n\n{'ks_pvals': tensor([0., 0.]), 'c2st_ranks': tensor([0.6815, 0.6730]), 'c2st_dap': tensor([0.5025, 0.4935])}\n</code></pre> <p>We can see that the Kolmogorv-Smirnov p-values vanish (<code>'ks_pvals': tensor([0., 0.])</code>). Thus, we can reject the hypothesis that the <code>ranks</code> PDF is the uniform PDF. The <code>c2st</code> accuracies show values higher than <code>0.5</code>. This is indicative that the <code>ranks</code> distribution is not a uniform PDF as well.</p> <pre><code>f, ax = sbc_rank_plot(ranks, 1_000, plot_type=\"hist\", num_bins=30)\n</code></pre> <p></p> <p>Inspecting the histograms for both dimenions, the rank distribution is clearly tilted to low rank values for both dimensions. Because we have shifted the expected value of the posterior to higher values (by <code>0.1</code>), we see more entries at low rank values.</p> <p>Let\u2019s try to shift all posterior samples in the opposite direction. We shift the expectation value by <code>-0.1</code>:</p> <pre><code>posterior_ = BiasedPosterior(posterior, shift=-0.1)\n</code></pre> <pre><code>ranks, dap_samples = run_sbc(thetas, xs, posterior_)\ncheck_stats = check_sbc(ranks, thetas, dap_samples, 1_000)\nprint(check_stats)\nf, ax = sbc_rank_plot(ranks, 1_000, plot_type=\"hist\", num_bins=30)\n</code></pre> <pre><code>Running 1000 sbc samples.:   0%|          | 0/1000 [00:00&lt;?, ?it/s]\n\n\n{'ks_pvals': tensor([0., 0.]), 'c2st_ranks': tensor([0.6795, 0.6955]), 'c2st_dap': tensor([0.4910, 0.4955])}\n</code></pre> <p></p> <p>A similar behavior is observed, but this time we see an upshot of ranks to higher values. Because we have shifted the expected value of the posterior to smaller values, we see an upshot in high rank counts.</p> <p>It is interesting to see that the historgams obtained provide very convincing evidence that this is not a uniform distribution. </p> <p>To conlude at this point, the rank distribution is capable of identifying pathologies of the estimated posterior:</p> <ul> <li>a left-skewed rank distribution shows a systematic underestimation of the posterior mean (we shifted the posterior by <code>0.1</code>)</li> <li>a rank-skewed rank distribution shows a systematic overestimation of the posterior mean (we shifted the posterior by <code>-0.1</code>)</li> </ul>"},{"location":"tutorial/13_diagnostics_simulation_based_calibration/#a-dispersed-posterior","title":"A dispersed posterior","text":"<p>In this scenario we emulate the situation if our posterior estimates incorrectly with a dispersion, i.e. the posterior is too wide or too thin. We reuse our trained NPE posterior from above and wrap it so that all samples return a dispersion by 100% more wide (<code>2</code>), i.e. the variance is overestimated by a factor of 2.</p> <pre><code>from utils_13_diagnosis_sbc import DispersedPosterior\n\n# this posterior which disperses the expected posterior value of the prior by 2.\nposterior_ = DispersedPosterior(posterior, dispersion=2.0)\n</code></pre> <pre><code>ranks, dap_samples = run_sbc(thetas, xs, posterior_)\ncheck_stats = check_sbc(ranks, thetas, dap_samples, 1_000)\nprint(check_stats)\nf, ax = sbc_rank_plot(ranks, 1_000, plot_type=\"hist\", num_bins=30)\n</code></pre> <pre><code>Running 1000 sbc samples.:   0%|          | 0/1000 [00:00&lt;?, ?it/s]\n\n\n{'ks_pvals': tensor([8.2151e-09, 6.9635e-07]), 'c2st_ranks': tensor([0.6150, 0.6160]), 'c2st_dap': tensor([0.5050, 0.4905])}\n</code></pre> <p></p> <p>The rank histograms now look more like a very wide gaussian distribution centered in the middle. The KS p-values again vanish unsurprisingly (we must reject the hypothesis that both distributions are from the same uniform PDF) and the c2st_ranks indicate that the rank histogram is not uniform too. As our posterior samples are distributed too broad now, we obtain more \u201cmedium\u201d range ranks and hence produce the peak of ranks in the center of the histogram.</p> <p>We can repeat this exercise by making our posterior too thin, i.e. the variance of the posterior is too small. Let\u2019s cut it by half.</p> <pre><code>posterior_ = DispersedPosterior(posterior, dispersion=0.5)\n</code></pre> <pre><code>ranks, dap_samples = run_sbc(thetas, xs, posterior_)\ncheck_stats = check_sbc(ranks, thetas, dap_samples, 1_000)\nprint(check_stats)\nf, ax = sbc_rank_plot(ranks, 1_000, plot_type=\"hist\", num_bins=30)\n</code></pre> <pre><code>Running 1000 sbc samples.:   0%|          | 0/1000 [00:00&lt;?, ?it/s]\n\n\n{'ks_pvals': tensor([1.1753e-07, 1.7929e-08]), 'c2st_ranks': tensor([0.5755, 0.6125]), 'c2st_dap': tensor([0.4980, 0.5075])}\n</code></pre> <p></p> <p>The histogram of ranks now shoots above the allowed (greyed) area for a uniform distributed around the extrema. We made the posterior samples too thin, so we received more extreme counts of ranks. The KS p-values vanish again and the <code>c2st</code> metric of the ranks is also larger than <code>.5</code> which underlines that our rank distribution is not uniformly distributed.</p> <p>We again see, the rank distribution is capable of identifying pathologies of the estimated posterior:</p> <ul> <li>a centrally peaked rank distribution shows a systematic over-estimation of the posterior variance (we dispersed the variance of the posterior by a factor of <code>2</code>)</li> <li>a U shaped rank distribution shows a systematic under-estimation of the posterior variance (we dispersed the variance of the posterior by a factor of <code>.5</code>)</li> </ul> <p>Simulation-based calibration offers a direct handle on which pathology an estimated posterior examines. Outside of this tutorial, you may very well encounter situations with mixtures of effects (a shifted mean and over-estimated variance). Moreover, uncovering a malignant posterior is only the first step to fix your analysis. </p> <pre><code>\n</code></pre>"},{"location":"tutorial/14_iid_data_and_permutation_invariant_embeddings/","title":"SBI with iid data and permutation-invariant embeddings","text":"<p>There are scenarios in which we observe multiple data points per experiment and we can assume that they are independent and identically distributed (iid, i.e., they are assumed to have the same underlying model parameters).  For example, in decision-making experiments, the experiment is often repeated in trials with the same experimental settings and conditions. The corresponding set of trials is then assumed to be \u201ciid\u201d given a single parameter set.  In such a scenario, we may want to obtain the posterior given a set of observation \\(p(\\theta | X=\\{x_i\\}_i^N)\\). </p>"},{"location":"tutorial/14_iid_data_and_permutation_invariant_embeddings/#amortization-of-neural-network-training-iid-inference-with-nle-nre","title":"Amortization of neural network training: iid-inference with NLE / NRE","text":"<p>For some SBI variants the iid assumption can be exploited: when using a likelihood-based SBI method (<code>SNLE</code>, <code>SNRE</code>) one can train the density or ratio estimator on single-trial data, and then perform inference with <code>MCMC</code> or variational inference (<code>VI</code>). Crucially, because the data is iid and the estimator is trained on single-trial data, one can repeat the inference with a different <code>x_o</code> (a different set of trials, or different number of trials) without having to retrain the density estimator. One can interpet this as amortization of the SBI training: we can obtain a neural likelihood, or likelihood-ratio estimate for new <code>x_o</code>s without retraining, but we still have to run <code>MCMC</code> or <code>VI</code> to do inference. </p> <p>In addition, one cannot only change the number of trials of a new <code>x_o</code>, but also the entire inference setting.  For example, one can apply hierarchical inference with changing hierarchical denpendencies between the model parameters\u2013all without having to retrain the density estimator because it estimates single-trail likelihoods.</p>"},{"location":"tutorial/14_iid_data_and_permutation_invariant_embeddings/#full-amortization-iid-inference-with-npe-and-permutation-invariant-embedding-nets","title":"Full amortization: iid-inference with NPE and permutation-invariant embedding nets","text":"<p>When performing neural posterior estimation (<code>SNPE</code>) we cannot exploit the iid assumption directly.  Thus, the underlying neural network takes <code>x</code> as input and predicts the parameters of the density estimator.  As a consequence, if <code>x</code> is a set of iid observations \\(X=\\{x_i\\}_i^N\\) then the neural network has to be invariant to permutations of this set, i.e., it has to be permutation invariant. In addition, the neural network has to be able to consume a varying number of iid datapoints in order to be amortized over the number of trials. Therefore, in order to use <code>SNPE</code> for inference on iid data, we need to provide a corresponding embedding network that handles the iid-data.  This will likely require some hyperparameter tuning and more training data for inference to work accurately. But once we have this, inference is fully amortized, i.e., we can get new posterior samples almost instantly without retraining and without running <code>MCMC</code> or <code>VI</code>. </p>"},{"location":"tutorial/14_iid_data_and_permutation_invariant_embeddings/#sbi-with-trial-based-data","title":"SBI with trial-based data","text":"<p>For illustration, we use a simple linear Gaussian simulator, as in previous tutorials. The simulator takes a single parameter (vector) which is the mean of a Gaussian. The simulator then adds noise with a fixed variance (set to one).  We define a Gaussian prior over the mean and perform inference. </p> <p>The observed data is also sampled from a Gaussian with some fixed \u201cground-truth\u201d parameter \\(\\theta_o\\).  Crucially, the observed data <code>x_o</code> can consist of multiple samples given the same ground-truth parameters and these samples are iid given \\(\\theta\\): </p> \\[  \\theta \\sim \\mathcal{N}(\\mu_0,\\; \\Sigma_0) \\\\ x | \\theta \\sim \\mathcal{N}(\\theta,\\; \\Sigma=I) \\\\ \\mathbf{x_o} = \\{x_o^i\\}_{i=1}^N \\sim  \\mathcal{N}(\\theta_o,\\; \\Sigma=I) \\] <p>For this toy problem, the ground-truth posterior is well defined, it is again a Gaussian, centered on the mean of \\(\\mathbf{x_o}\\) and with variance scaled by the number of trials \\(N\\), i.e., the more trials we observe, the more information about the underlying \\(\\theta_o\\) we have and the more concentrated the posteriors becomes.</p> <p>We will illustrate this below:</p> <pre><code>import torch\nimport matplotlib.pyplot as plt\n\nfrom torch import zeros, ones, eye\nfrom torch.distributions import MultivariateNormal\nfrom sbi.inference import SNLE, SNPE, prepare_for_sbi, simulate_for_sbi\nfrom sbi.analysis import pairplot\nfrom sbi.utils.metrics import c2st\n\nfrom sbi.simulators.linear_gaussian import (\n    linear_gaussian,\n    true_posterior_linear_gaussian_mvn_prior,\n)\n\n# Seeding\ntorch.manual_seed(1);\n</code></pre> <pre><code># Gaussian simulator\ntheta_dim = 2\nx_dim = theta_dim\n\n# likelihood_mean will be likelihood_shift+theta\nlikelihood_shift = -1.0 * zeros(x_dim)\nlikelihood_cov = 0.3 * eye(x_dim)\n\nprior_mean = zeros(theta_dim)\nprior_cov = eye(theta_dim)\nprior = MultivariateNormal(loc=prior_mean, covariance_matrix=prior_cov)\n\n# Define Gaussian simulator\nsimulator, prior = prepare_for_sbi(\n    lambda theta: linear_gaussian(theta, likelihood_shift, likelihood_cov), prior\n)\n\n\n# Use built-in function to obtain ground-truth posterior given x_o\ndef get_true_posterior_samples(x_o, num_samples=1):\n    return true_posterior_linear_gaussian_mvn_prior(\n        x_o, likelihood_shift, likelihood_cov, prior_mean, prior_cov\n    ).sample((num_samples,))\n</code></pre>"},{"location":"tutorial/14_iid_data_and_permutation_invariant_embeddings/#the-analytical-posterior-concentrates-around-true-parameters-with-increasing-number-of-iid-trials","title":"The analytical posterior concentrates around true parameters with increasing number of IID trials","text":"<pre><code>num_trials = [1, 5, 15, 20]\ntheta_o = zeros(1, theta_dim)\n\n# Generate multiple x_os with increasing number of trials.\nxos = [theta_o.repeat(nt, 1) for nt in num_trials]\n\n# Obtain analytical posterior samples for each of them.\ntrue_samples = [get_true_posterior_samples(xo, 5000) for xo in xos]\n</code></pre> <pre><code># Plot them in one pairplot as contours (obtained via KDE on the samples).\nfig, ax = pairplot(\n    true_samples,\n    points=theta_o,\n    diag=\"kde\",\n    upper=\"contour\",\n    kde_offdiag=dict(bins=50),\n    kde_diag=dict(bins=100),\n    contour_offdiag=dict(levels=[0.95]),\n    points_colors=[\"k\"],\n    points_offdiag=dict(marker=\"*\", markersize=10),\n)\nplt.sca(ax[1, 1])\nplt.legend(\n    [f\"{nt} trials\" if nt &gt; 1 else f\"{nt} trial\" for nt in num_trials]\n    + [r\"$\\theta_o$\"],\n    frameon=False,\n    fontsize=12,\n);\n</code></pre> <p>Indeed, with increasing number of trials the posterior density concentrates around the true underlying parameter.</p>"},{"location":"tutorial/14_iid_data_and_permutation_invariant_embeddings/#iid-inference-with-nle","title":"IID inference with NLE","text":"<p>(S)NLE and (S)NRE can perform inference given multiple IID obserations by using only single-trial training data (i.e., for training, we run the simulator only once per parameter set). Once the likelihood is learned on single trials (i.e., a neural network that predicts the likelihood of a single observation given a parameter set), one can sample the posterior for any number of trials. This works because, given a single-trial neural likelihood from (S)NLE or (S)NRE, we can calculate the joint likelihoods of all trials by multiplying them together (or adding them in log-space). The joint likelihood can then be plugged into <code>MCMC</code> or <code>VI</code>. <code>sbi</code> takes care of all of these steps, so you do not have to implement anything yourself:</p> <pre><code># Train SNLE.\ninferer = SNLE(prior, show_progress_bars=True, density_estimator=\"mdn\")\ntheta, x = simulate_for_sbi(simulator, prior, 10000, simulation_batch_size=1000)\ninferer.append_simulations(theta, x).train(training_batch_size=1000);\n</code></pre> <pre><code>Running 10000 simulations.:   0%|          | 0/10000 [00:00&lt;?, ?it/s]\n\n\n Neural network successfully converged after 43 epochs.\n</code></pre> <pre><code># Obtain posterior samples for different number of iid xos.\nnle_samples = []\nnum_samples = 5000\n\nmcmc_parameters = dict(\n    num_chains=50,\n    thin=10,\n    warmup_steps=50,\n    init_strategy=\"proposal\",\n)\nmcmc_method = \"slice_np_vectorized\"\n\nposterior = inferer.build_posterior(\n    mcmc_method=mcmc_method,\n    mcmc_parameters=mcmc_parameters,\n)\n\n# Generate samples with MCMC given the same set of x_os as above.\nfor xo in xos:\n    nle_samples.append(posterior.sample(sample_shape=(num_samples,), x=xo))\n</code></pre> <pre><code>Running vectorized MCMC with 50 chains:   0%|          | 0/75000 [00:00&lt;?, ?it/s]\n\n\n/Users/janbolts/qode/sbi/sbi/utils/sbiutils.py:342: UserWarning: An x with a batch size of 5 was passed. It will be interpreted as a batch of independent and identically\n            distributed data X={x_1, ..., x_n}, i.e., data generated based on the\n            same underlying (unknown) parameter. The resulting posterior will be with\n            respect to entire batch, i.e,. p(theta | X).\n  warnings.warn(\n\n\n\nRunning vectorized MCMC with 50 chains:   0%|          | 0/75000 [00:00&lt;?, ?it/s]\n\n\n/Users/janbolts/qode/sbi/sbi/utils/sbiutils.py:342: UserWarning: An x with a batch size of 15 was passed. It will be interpreted as a batch of independent and identically\n            distributed data X={x_1, ..., x_n}, i.e., data generated based on the\n            same underlying (unknown) parameter. The resulting posterior will be with\n            respect to entire batch, i.e,. p(theta | X).\n  warnings.warn(\n\n\n\nRunning vectorized MCMC with 50 chains:   0%|          | 0/75000 [00:00&lt;?, ?it/s]\n\n\n/Users/janbolts/qode/sbi/sbi/utils/sbiutils.py:342: UserWarning: An x with a batch size of 20 was passed. It will be interpreted as a batch of independent and identically\n            distributed data X={x_1, ..., x_n}, i.e., data generated based on the\n            same underlying (unknown) parameter. The resulting posterior will be with\n            respect to entire batch, i.e,. p(theta | X).\n  warnings.warn(\n\n\n\nRunning vectorized MCMC with 50 chains:   0%|          | 0/75000 [00:00&lt;?, ?it/s]\n</code></pre> <p>Note that <code>sbi</code> warns about <code>iid-x</code> with increasing number of trial here. We ignore the warning because that\u2019s exactly what we want to do.</p> <pre><code># Plot them in one pairplot as contours (obtained via KDE on the samples).\nfig, ax = pairplot(\n    nle_samples,\n    points=theta_o,\n    diag=\"kde\",\n    upper=\"contour\",\n    kde_offdiag=dict(bins=50),\n    kde_diag=dict(bins=100),\n    contour_offdiag=dict(levels=[0.95]),\n    points_colors=[\"k\"],\n    points_offdiag=dict(marker=\"*\", markersize=10),\n)\nplt.sca(ax[1, 1])\nplt.legend(\n    [f\"{nt} trials\" if nt &gt; 1 else f\"{nt} trial\" for nt in num_trials]\n    + [r\"$\\theta_o$\"],\n    frameon=False,\n    fontsize=12,\n);\n</code></pre> <p></p> <p>The pairplot above already indicates that (S)NLE is well able to obtain accurate posterior samples also for increasing number of trials (note that we trained the single-round version of SNLE so that we did not have to re-train it for new \\(x_o\\)). </p> <p>Quantitatively we can measure the accuracy of SNLE by calculating the <code>c2st</code> score between SNLE and the true posterior samples, where the best accuracy is perfect for <code>0.5</code>:</p> <pre><code>cs = [\n    c2st(torch.from_numpy(s1), torch.from_numpy(s2))\n    for s1, s2 in zip(true_samples, nle_samples)\n]\n\nfor _ in range(len(num_trials)):\n    print(f\"c2st score for num_trials={num_trials[_]}: {cs[_].item():.2f}\")\n</code></pre> <pre><code>c2st score for num_trials=1: 0.50\nc2st score for num_trials=5: 0.51\nc2st score for num_trials=15: 0.51\nc2st score for num_trials=20: 0.51\n</code></pre>"},{"location":"tutorial/14_iid_data_and_permutation_invariant_embeddings/#iid-inference-with-npe-using-permutation-invariant-embedding-nets","title":"IID inference with NPE using permutation-invariant embedding nets","text":"<p>For NPE we need to define an embedding net that handles the set-like structure of iid-data, i.e., that it permutation invariant and can handle different number of trials.  </p> <p>We implemented several embedding net classes that allow to construct such a permutation- and number-of-trials invariant embedding net. </p> <p>To become permutation invariant, the neural net first learns embeddings for single trials and then performs a permutation invariant operation on those embeddings, e.g., by taking the sum or the mean (Chen et al. 2018, Radev et al. 2021).</p> <p>To become invariant w.r.t. the number-of-trials, we train the net with varying number of trials for each parameter setting. This means that, unlike for (S)NLE and (S)NRE, (S)NPE requires to run the simulator multiple times for individual parameter sets to generate the training data.</p> <p>In order to implement this in <code>sbi</code>, \u201cunobserved\u201d trials in the training dataset have to be masked by NaNs (and ignore the resulting SBI warning about NaNs in the training data).</p>"},{"location":"tutorial/14_iid_data_and_permutation_invariant_embeddings/#construct-training-data-set","title":"Construct training data set.","text":"<pre><code># we need to fix the maximum number of trials.\nmax_num_trials = 20\n\n# construct training data set: we want to cover the full range of possible number of\n# trials\nnum_training_samples = 5000\ntheta = prior.sample((num_training_samples,))\n\n# there are certainly smarter ways to construct the training data set, but we go with a\n# for loop here for illustration purposes.\nx = torch.ones(num_training_samples * max_num_trials, max_num_trials, x_dim) * float(\n    \"nan\"\n)\nfor i in range(num_training_samples):\n    xi = simulator(theta[i].repeat(max_num_trials, 1))\n    for j in range(max_num_trials):\n        x[i * max_num_trials + j, : j + 1, :] = xi[: j + 1, :]\n\ntheta = theta.repeat_interleave(max_num_trials, dim=0)\n</code></pre>"},{"location":"tutorial/14_iid_data_and_permutation_invariant_embeddings/#build-embedding-net","title":"Build embedding net","text":"<pre><code>from sbi.neural_nets.embedding_nets import (\n    FCEmbedding,\n    PermutationInvariantEmbedding,\n)\nfrom sbi.utils import posterior_nn\n\n# embedding\nlatent_dim = 10\nsingle_trial_net = FCEmbedding(\n    input_dim=theta_dim,\n    num_hiddens=40,\n    num_layers=2,\n    output_dim=latent_dim,\n)\nembedding_net = PermutationInvariantEmbedding(\n    single_trial_net,\n    trial_net_output_dim=latent_dim,\n    # NOTE: post-embedding is not needed really.\n    num_layers=1,\n    num_hiddens=10,\n    output_dim=10,\n)\n\n# we choose a simple MDN as the density estimator.\n# NOTE: we turn off z-scoring of the data, as we used NaNs for the missing trials.\ndensity_estimator = posterior_nn(\"mdn\", embedding_net=embedding_net, z_score_x=\"none\")\n</code></pre>"},{"location":"tutorial/14_iid_data_and_permutation_invariant_embeddings/#run-training","title":"Run training","text":"<pre><code>inference = SNPE(prior, density_estimator=density_estimator)\n# NOTE: we don't exclude invalid x because we used NaNs for the missing trials.\ninference.append_simulations(\n    theta,\n    x,\n    exclude_invalid_x=False,\n).train(training_batch_size=1000)\nposterior = inference.build_posterior()\n</code></pre> <pre><code>WARNING:root:Found 95000 NaN simulations and 0 Inf simulations. They are not excluded from training due to `exclude_invalid_x=False`.Training will likely fail, we strongly recommend `exclude_invalid_x=True` for Single-round NPE.\n\n\n Neural network successfully converged after 168 epochs.\n</code></pre>"},{"location":"tutorial/14_iid_data_and_permutation_invariant_embeddings/#amortized-inference","title":"Amortized inference","text":"<p>Comparing runtimes, we see that the NPE training takes a bit longer than the training on single trials for <code>NLE</code> above. </p> <p>However, we trained the density estimator such that it can handle multiple and changing number of iid trials (up to 20). </p> <p>Thus, we can obtain posterior samples for different <code>x_o</code> with just a single forward pass instead of having to run <code>MCMC</code> for each new observation.</p> <p>As you can see below, the c2st score for increasing number of observed trials remains close to the ideal <code>0.5</code>. </p> <pre><code>npe_samples = []\nfor xo in xos:\n    # we need to pad the x_os with NaNs to match the shape of the training data.\n    xoi = torch.ones(1, max_num_trials, x_dim) * float(\"nan\")\n    xoi[0, : len(xo), :] = xo\n    npe_samples.append(posterior.sample(sample_shape=(num_samples,), x=xoi))\n\ncs = [c2st(torch.from_numpy(s1), s2) for s1, s2 in zip(true_samples, npe_samples)]\n\nfor _ in range(len(num_trials)):\n    print(f\"c2st score for num_trials={num_trials[_]}: {cs[_].item():.2f}\")\n</code></pre> <pre><code>Drawing 5000 posterior samples:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n\n\n\nDrawing 5000 posterior samples:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n\n\n\nDrawing 5000 posterior samples:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n\n\n\nDrawing 5000 posterior samples:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n\n\nc2st score for num_trials=1: 0.50\nc2st score for num_trials=5: 0.50\nc2st score for num_trials=15: 0.52\nc2st score for num_trials=20: 0.55\n</code></pre> <pre><code>num_trials = [1, 5, 15, 20]\nxos = [theta_o.repeat(nt, 1) for nt in num_trials]\n\nnpe_samples = []\nfor xo in xos:\n    # we need to pad the x_os with NaNs to match the shape of the training data.\n    xoi = torch.ones(1, max_num_trials, x_dim) * float(\"nan\")\n    xoi[0, : len(xo), :] = xo\n    npe_samples.append(posterior.sample(sample_shape=(num_samples,), x=xoi))\n\n\n# Plot them in one pairplot as contours (obtained via KDE on the samples).\nfig, ax = pairplot(\n    npe_samples,\n    points=theta_o,\n    diag=\"kde\",\n    upper=\"contour\",\n    kde_offdiag=dict(bins=50),\n    kde_diag=dict(bins=100),\n    contour_offdiag=dict(levels=[0.95]),\n    points_colors=[\"k\"],\n    points_offdiag=dict(marker=\"*\", markersize=10),\n)\nplt.sca(ax[1, 1])\nplt.legend(\n    [f\"{nt} trials\" if nt &gt; 1 else f\"{nt} trial\" for nt in num_trials]\n    + [r\"$\\theta_o$\"],\n    frameon=False,\n    fontsize=12,\n);\n</code></pre> <pre><code>Drawing 5000 posterior samples:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n\n\n\nDrawing 5000 posterior samples:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n\n\n\nDrawing 5000 posterior samples:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n\n\n\nDrawing 5000 posterior samples:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n</code></pre> <p></p> <pre><code># We can easily obtain posteriors for many different x_os, instantly, because NPE is fully amortized:\nnum_trials = [2, 4, 6, 8, 12, 14, 18]\nnpe_samples = []\nfor xo in xos:\n    # we need to pad the x_os with NaNs to match the shape of the training data.\n    xoi = torch.ones(1, max_num_trials, x_dim) * float(\"nan\")\n    xoi[0, : len(xo), :] = xo\n    npe_samples.append(posterior.sample(sample_shape=(num_samples,), x=xoi))\n\n\n# Plot them in one pairplot as contours (obtained via KDE on the samples).\nfig, ax = pairplot(\n    npe_samples,\n    points=theta_o,\n    diag=\"kde\",\n    upper=\"contour\",\n    kde_offdiag=dict(bins=50),\n    kde_diag=dict(bins=100),\n    contour_offdiag=dict(levels=[0.95]),\n    points_colors=[\"k\"],\n    points_offdiag=dict(marker=\"*\", markersize=10),\n)\nplt.sca(ax[1, 1])\nplt.legend(\n    [f\"{nt} trials\" if nt &gt; 1 else f\"{nt} trial\" for nt in num_trials]\n    + [r\"$\\theta_o$\"],\n    frameon=False,\n    fontsize=12,\n);\n</code></pre> <pre><code>Drawing 5000 posterior samples:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n\n\n\nDrawing 5000 posterior samples:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n\n\n\nDrawing 5000 posterior samples:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n\n\n\nDrawing 5000 posterior samples:   0%|          | 0/5000 [00:00&lt;?, ?it/s]\n</code></pre> <p></p> <pre><code>\n</code></pre>"},{"location":"tutorial/15_mcmc_diagnostics_with_arviz/","title":"MCMC diagnostics with Arviz","text":"<p>This tutorial shows how to evaluate the quality of MCMC samples generated via <code>sbi</code> using the <code>arviz</code> package. </p> <p>Outline:</p> <p>1) Train MNLE to approximate the likelihood underlying the simulator 2) Run MCMC using <code>pyro</code> MCMC samplers via <code>sbi</code> interface 3) Use <code>arviz</code> to visualize the posterior, predictive distributions and MCMC diagnostics. </p> <pre><code>import arviz as az\nimport torch\n\nfrom sbi.inference import MNLE, likelihood_estimator_based_potential\nfrom pyro.distributions import InverseGamma\nfrom torch.distributions import Beta, Binomial, Gamma\nfrom sbi.utils import MultipleIndependent\n\nfrom sbi.inference import MCMCPosterior\n\n# Seeding\ntorch.manual_seed(1);\n</code></pre> <pre><code># Toy simulator for mixed data\ndef mixed_simulator(theta):\n    beta, ps = theta[:, :1], theta[:, 1:]\n\n    choices = Binomial(probs=ps).sample()\n    rts = InverseGamma(concentration=2 * torch.ones_like(beta), rate=beta).sample()\n\n    return torch.cat((rts, choices), dim=1)\n\n\n# Define independent priors for each dimension.\nprior = MultipleIndependent(\n    [\n        Gamma(torch.tensor([1.0]), torch.tensor([0.5])),\n        Beta(torch.tensor([2.0]), torch.tensor([2.0])),\n    ],\n    validate_args=False,\n)\n</code></pre>"},{"location":"tutorial/15_mcmc_diagnostics_with_arviz/#train-mnle-to-approximate-the-likelihood","title":"Train MNLE to approximate the likelihood","text":"<p>For this tutorial, we will use a simple simulator with two parameters. For details see the example on the decision making model. </p> <p>Here, we pass <code>mcmc_method=\"nuts\"</code> in order to use the underlying <code>pyro</code> No-U-turn sampler, but it would work as well with other samplers (e.g. \u201cslice_np_vectorized\u201d, \u201chmc\u201d). </p> <p>Additionally, when calling <code>posterior.sample(...)</code> we pass <code>return_arviz=True</code> so that the <code>Arviz InferenceData</code> object is returned. This object gives us access to the wealth of MCMC diagnostics tool provided by <code>arviz</code>.</p> <pre><code># Generate training data and train MNLE.\nnum_simulations = 10000\ntheta = prior.sample((num_simulations,))\nx = mixed_simulator(theta)\n\ntrainer = MNLE(prior)\nlikelihood_estimator = trainer.append_simulations(theta, x).train()\n</code></pre> <pre><code>/Users/janbolts/qode/sbi/sbi/neural_nets/mnle.py:60: UserWarning: The mixed neural likelihood estimator assumes that x contains\n        continuous data in the first n-1 columns (e.g., reaction times) and\n        categorical data in the last column (e.g., corresponding choices). If\n        this is not the case for the passed `x` do not use this function.\n  warnings.warn(\n\n\n Neural network successfully converged after 65 epochs.\n</code></pre>"},{"location":"tutorial/15_mcmc_diagnostics_with_arviz/#run-pyro-nuts-mcmc-and-obtain-arviz-inferencedata-object","title":"Run Pyro NUTS MCMC and obtain <code>arviz InferenceData</code> object","text":"<pre><code># Simulate \"observed\" data x_o\ntorch.manual_seed(42)\nnum_trials = 100\ntheta_o = prior.sample((1,))\nx_o = mixed_simulator(theta_o.repeat(num_trials, 1))\n\n# Set MCMC parameters and run Pyro NUTS.\nmcmc_parameters = dict(\n    num_chains=4,\n    thin=5,\n    warmup_steps=50,\n    init_strategy=\"proposal\",\n    method=\"nuts\",\n)\nnum_samples = 1000\n\n# get the potential function and parameter transform for constructing the posterior\npotential_fn, parameter_transform = likelihood_estimator_based_potential(\n    likelihood_estimator, prior, x_o\n)\nmnle_posterior = MCMCPosterior(\n    potential_fn, proposal=prior, theta_transform=parameter_transform, **mcmc_parameters\n)\n\nmnle_samples = mnle_posterior.sample(\n    (num_samples,), x=x_o, show_progress_bars=False\n)\n# get arviz InferenceData object from posterior\ninference_data = mnle_posterior.get_arviz_inference_data()\n</code></pre> <pre><code>/Users/janbolts/qode/sbi/sbi/utils/sbiutils.py:280: UserWarning: An x with a batch size of 100 was passed. It will be interpreted as a batch of independent and identically\n            distributed data X={x_1, ..., x_n}, i.e., data generated based on the\n            same underlying (unknown) parameter. The resulting posterior will be with\n            respect to entire batch, i.e,. p(theta | X).\n  warnings.warn(\n</code></pre>"},{"location":"tutorial/15_mcmc_diagnostics_with_arviz/#generate-arviz-plots","title":"Generate <code>arviz</code> plots","text":"<p>The resulting <code>InferenceData</code> object can be passed to most <code>arviz</code> plotting functions, and there are plenty see here for an overview.</p> <p>To get a better understanding of the <code>InferenceData</code> object see here. </p> <p>Below and overview of common MCMC diagnostics plot, see the corresponding <code>arviz</code> documentation for interpretation of the plots. </p> <p>We will a full use-case using the SBI-MCMC-arviz workflow soon.</p> <pre><code>print(inference_data.posterior)\n</code></pre> <pre><code>&lt;xarray.Dataset&gt;\nDimensions:      (chain: 4, draw: 1254, theta_dim_0: 2)\nCoordinates:\n  * chain        (chain) int64 0 1 2 3\n  * draw         (draw) int64 0 1 2 3 4 5 6 ... 1248 1249 1250 1251 1252 1253\n  * theta_dim_0  (theta_dim_0) int64 0 1\nData variables:\n    theta        (chain, draw, theta_dim_0) float32 2.125 0.8092 ... 0.8088\nAttributes:\n    created_at:     2022-08-10T14:02:41.300799\n    arviz_version:  0.11.2\n</code></pre>"},{"location":"tutorial/15_mcmc_diagnostics_with_arviz/#diagnostic-plots","title":"Diagnostic plots","text":"<pre><code>az.style.use(\"arviz-darkgrid\")\naz.plot_rank(inference_data)\n</code></pre> <pre><code>array([&lt;AxesSubplot:title={'center':'theta\\n0'}, xlabel='Rank (all chains)', ylabel='Chain'&gt;,\n       &lt;AxesSubplot:title={'center':'theta\\n1'}, xlabel='Rank (all chains)', ylabel='Chain'&gt;],\n      dtype=object)\n</code></pre> <pre><code>az.plot_autocorr(inference_data);\n</code></pre> <pre><code>az.plot_trace(inference_data, compact=False);\n</code></pre> <pre><code>az.plot_ess(inference_data, kind=\"evolution\");\n</code></pre>"},{"location":"tutorial/15_mcmc_diagnostics_with_arviz/#posterior-density-plots","title":"Posterior density  plots","text":"<pre><code>az.plot_posterior(inference_data)\n</code></pre> <pre><code>array([&lt;AxesSubplot:title={'center':'theta\\n0'}&gt;,\n       &lt;AxesSubplot:title={'center':'theta\\n1'}&gt;], dtype=object)\n</code></pre> <pre><code>print(\n    f\"Given the {num_trials} we observed, the posterior is centered around true underlying parameters theta_o: {theta_o}\"\n)\n</code></pre> <pre><code>Given the 100 we observed, the posterior is centered around true underlying parameters theta_o: tensor([[1.9622, 0.7550]])\n</code></pre> <pre><code>az.plot_pair(inference_data)\n</code></pre> <pre><code>&lt;AxesSubplot:xlabel='theta\\n0', ylabel='theta\\n1'&gt;\n</code></pre> <pre><code>az.plot_pair(\n    inference_data,\n    var_names=[\"theta\"],\n    kind=\"hexbin\",\n    marginals=True,\n    figsize=(10, 10),\n)\n</code></pre> <pre><code>array([[&lt;AxesSubplot:&gt;, None],\n       [&lt;AxesSubplot:xlabel='theta\\n0', ylabel='theta\\n1'&gt;,\n        &lt;AxesSubplot:&gt;]], dtype=object)\n</code></pre> <pre><code>\n</code></pre>"},{"location":"tutorial/16_implemented_methods/","title":"API of implemented methods","text":"<p>This notebook spells out the API for all algorithms implemented in the <code>sbi</code> toolbox:</p> <ul> <li> <p>Posterior estimation (SNPE)</p> </li> <li> <p>Likelihood estimation (SNLE)</p> </li> <li> <p>Likelihood-ratio estimation (SNRE)</p> </li> <li> <p>Utilities  </p> </li> </ul>"},{"location":"tutorial/16_implemented_methods/#posterior-estimation-snpe","title":"Posterior estimation (SNPE)","text":"<p>Fast \u03b5-free Inference of Simulation Models with Bayesian Conditional Density Estimation by Papamakarios &amp; Murray (NeurIPS 2016) [PDF] [BibTeX]</p> <pre><code>from sbi.inference import SNPE_A\n\ninference = SNPE_A(prior)\nproposal = prior\nfor _ in range(rounds):\n    theta = proposal.sample((num_sims,))\n    x = simulator(theta)\n    _ = inference.append_simulations(theta, x, proposal=proposal).train()\n    posterior = inference.build_posterior().set_default_x(x_o)\n    proposal = posterior\n</code></pre> <p>Automatic posterior transformation for likelihood-free inferenceby Greenberg, Nonnenmacher &amp; Macke (ICML 2019) [PDF]</p> <pre><code>from sbi.inference import SNPE\n\ninference = SNPE(prior)\nproposal = prior\nfor _ in range(rounds):\n    theta = proposal.sample((num_sims,))\n    x = simulator(theta)\n    _ = inference.append_simulations(theta, x, proposal=proposal).train()\n    posterior = inference.build_posterior().set_default_x(x_o)\n    proposal = posterior\n</code></pre> <p>Truncated proposals for scalable and hassle-free simulation-based inference  by Deistler, Goncalves &amp; Macke (NeurIPS 2022) [Paper]</p> <pre><code>from sbi.inference import SNPE\nfrom sbi.utils import get_density_thresholder, RestrictedPrior\n\ninference = SNPE(prior)\nproposal = prior\nfor _ in range(rounds):\n    theta = proposal.sample((num_sims,))\n    x = simulator(theta)\n    _ = inference.append_simulations(theta, x).train(force_first_round_loss=True)\n    posterior = inference.build_posterior().set_default_x(x_o)\n\n    accept_reject_fn = get_density_thresholder(posterior, quantile=1e-4)\n    proposal = RestrictedPrior(prior, accept_reject_fn, sample_with=\"rejection\")\n</code></pre>"},{"location":"tutorial/16_implemented_methods/#likelihood-estimation-snle","title":"Likelihood estimation (SNLE)","text":"<p>Sequential neural likelihood: Fast likelihood-free inference with autoregressive flowsby Papamakarios, Sterratt &amp; Murray (AISTATS 2019) [PDF] [BibTeX]</p> <pre><code>from sbi.inference import SNLE\n\ninference = SNLE(prior)\nproposal = prior\nfor _ in range(rounds):\n    theta = proposal.sample((num_sims,))\n    x = simulator(theta)\n    _ = inference.append_simulations(theta, x).train()\n    posterior = inference.build_posterior().set_default_x(x_o)\n    proposal = posterior\n</code></pre> <p>Variational methods for simulation-based inference  by Gl\u00f6ckler, Deistler, Macke (ICLR 2022) [Paper]</p> <pre><code>from sbi.inference import SNLE\n\ninference = SNLE(prior)\nproposal = prior\nfor _ in range(rounds):\n    theta = proposal.sample((num_sims,))\n    x = simulator(theta)\n    _ = inference.append_simulations(theta, x).train()\n    posterior = inference.build_posterior(sample_with=\"vi\", vi_method=\"fKL\").set_default_x(x_o)\n    proposal = posterior\n</code></pre> <p>Flexible and efficient simulation-based inference for models of decision-making  by Boelts, Lueckmann, Gao, Macke (Elife 2022) [Paper]</p> <pre><code>from sbi.inference import MNLE\n\ninference = MNLE(prior)\ntheta = prior.sample((num_sims,))\nx = simulator(theta)\n_ = inference.append_simulations(theta, x).train()\nposterior = inference.build_posterior().set_default_x(x_o)\n</code></pre>"},{"location":"tutorial/16_implemented_methods/#likelihood-ratio-estimation-snre","title":"Likelihood-ratio estimation (SNRE)","text":"<p>Likelihood-free MCMC with Amortized Approximate Likelihood Ratiosby Hermans, Begy &amp; Louppe (ICML 2020) [PDF]</p> <pre><code>from sbi.inference import SNRE_A\n\ninference = SNRE_A(prior)\ntheta = prior.sample((num_sims,))\nx = simulator(theta)\n_ = inference.append_simulations(theta, x).train()\nposterior = inference.build_posterior().set_default_x(x_o)\n</code></pre> <p>On Contrastive Learning for Likelihood-free InferenceDurkan, Murray &amp; Papamakarios (ICML 2020) [PDF].</p> <pre><code>from sbi.inference import SNRE\n\ninference = SNRE(prior)\nproposal = prior\nfor _ in range(rounds):\n    theta = proposal.sample((num_sims,))\n    x = simulator(theta)\n    _ = inference.append_simulations(theta, x).train()\n    posterior = inference.build_posterior().set_default_x(x_o)\n    proposal = posterior\n</code></pre> <p>Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimationby Delaunoy, Hermans, Rozet, Wehenkel &amp; Louppe (NeurIPS 2022) [PDF]</p> <pre><code>from sbi.inference import BNRE\n\ninference = BNRE(prior)\ntheta = prior.sample((num_sims,))\nx = simulator(theta)\n_ = inference.append_simulations(theta, x).train(regularization_strength=100.)\nposterior = inference.build_posterior().set_default_x(x_o)\n</code></pre> <p>Contrastive Neural Ratio EstimationBenjamin Kurt Miller, Christoph Weniger, Patrick Forr\u00e9 (NeurIPS 2022) [PDF]</p> <pre><code># The main feature of NRE-C is producing an exact ratio of densities at optimum, even when using multiple contrastive pairs (classes).\n\nfrom sbi.inference import SNRE_C\n\n# Amortized inference\ninference = SNRE_C(prior)\nproposal = prior\ntheta = proposal.sample((num_sims,))\nx = simulator(theta)\n_ = inference.append_simulations(theta, x).train(\n    num_classes=5,  # SNRE_C sees `2 * num_classes - 1` marginally drawn contrastive pairs.\n    gamma=1.0,  # SNRE_C can control the weight between terms in its loss function.\n)\nposterior = inference.build_posterior().set_default_x(x_o)\n</code></pre>"},{"location":"tutorial/16_implemented_methods/#utilities","title":"Utilities","text":"<p>Simulation-based calibrationby Talts, Betancourt, Simpson, Vehtari, Gelman (arxiv 2018) [Paper])</p> <pre><code>from sbi.analysis import run_sbc, sbc_rank_plot\n\nthetas = prior.sample((1_000,))\nxs = simulator(thetas)\n\nranks, dap_samples = run_sbc(\n    thetas, xs, posterior, num_posterior_samples=1_000\n)\n\n_ = sbc_rank_plot(\n    ranks=ranks,\n    num_posterior_samples=num_posterior_samples,\n    plot_type=\"hist\",\n    num_bins=None,\n)\n</code></pre> <p>Restriction estimatorby Deistler, Macke &amp; Goncalves (PNAS 2022) [Paper]</p> <pre><code>from sbi.inference import SNPE\nfrom sbi.utils import RestrictionEstimator\n\nrestriction_estimator = RestrictionEstimator(prior=prior)\nproposal = prior\n\nfor _ in range(num_rounds):\n    theta = proposal.sample((num_sims,))\n    x = simulator(theta)\n    restriction_estimator.append_simulations(theta, x)\n    classifier = restriction_estimator.train()\n    proposal = restriction_estimator.restrict_prior()\n\nall_theta, all_x, _ = restriction_estimator.get_simulations()\n\ninference = SNPE(prior)\ndensity_estimator = inference.append_simulations(all_theta, all_x).train()\nposterior = inference.build_posterior()\n</code></pre> <p>Expected coverage (sample-based)as computed in Deistler, Goncalves, Macke (Neurips 2022) [Paper] and in Rozet, Louppe (2021) [Paper]</p> <pre><code>from sbi.analysis import run_sbc, sbc_rank_plot\n\nthetas = prior.sample((1_000,))\nxs = simulator(thetas)\n\nranks, dap_samples = run_sbc(\n    thetas, xs, posterior, num_posterior_samples=1_000, reduce_fns=posterior.log_prob\n)\n\n_ = sbc_rank_plot(\n    ranks=ranks,\n    num_posterior_samples=num_posterior_samples,\n    plot_type=\"hist\",\n    num_bins=None,\n)\n</code></pre>"}]}
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       <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-.27A6.516 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>
       <div class="md-search" data-md-component="search" role="dialog">
@@ -127,12 +133,15 @@
     <form class="md-search__form" name="search">
       <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-.27A6.516 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 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
         <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 12 19 6.41Z"/></svg>
         </button>
       </nav>
@@ -156,7 +165,7 @@
         <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -186,6 +195,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href="../.." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -200,7 +210,7 @@
       <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -212,14 +222,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../.." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
       </a>
     </li>
   
@@ -227,14 +241,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
       </a>
     </li>
   
@@ -242,66 +260,82 @@
     
       
       
-      
-
   
   
     
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
-      
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-        <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
-          Tutorials and Examples
-          <span class="md-nav__icon md-icon"></span>
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-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
-        <label class="md-nav__title" for="__nav_3">
-          <span class="md-nav__icon md-icon"></span>
-          Tutorials and Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Tutorials and Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            Tutorials and Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
     
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" checked>
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
-          Introduction
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="true">
-        <label class="md-nav__title" for="__nav_3_1">
-          <span class="md-nav__icon md-icon"></span>
-          Introduction
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" checked>
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
     
@@ -316,12 +350,24 @@
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          Getting started
+          
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <a href="./" class="md-nav__link md-nav__link--active">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
       </a>
       
         
@@ -360,468 +406,636 @@
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
-          Advanced
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_2">
-          <span class="md-nav__icon md-icon"></span>
-          Advanced
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Advanced
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
-      
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-        <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
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-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
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-          <span class="md-nav__icon md-icon"></span>
-          Diagnostics
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Diagnostics
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../12_diagnostics_posterior_predictive_check/" class="md-nav__link">
-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
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-      
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-          <span class="md-nav__icon md-icon"></span>
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-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Analysis
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Analysis
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
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       <a href="../07_conditional_distributions/" class="md-nav__link">
-        Conditional distributions
+        
+  
+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
+
       </a>
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
+  
+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
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+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
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-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
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-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
-          Examples
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-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_5">
-          <span class="md-nav__icon md-icon"></span>
-          Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
+        
           
+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_5">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/00_HH_simulator/" class="md-nav__link">
-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
-          Contributing
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_4">
-          <span class="md-nav__icon md-icon"></span>
-          Contributing
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Contributing
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Contributing
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../contribute/" class="md-nav__link">
-        Guide
+        
+  
+  <span class="md-ellipsis">
+    Guide
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../code_of_conduct/" class="md-nav__link">
-        Code of Conduct
+        
+  
+  <span class="md-ellipsis">
+    Code of Conduct
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../reference/" class="md-nav__link">
-        API Reference
+        
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -829,14 +1043,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../faq/" class="md-nav__link">
-        FAQ
+        
+  
+  <span class="md-ellipsis">
+    FAQ
+  </span>
+  
+
       </a>
     </li>
   
@@ -844,14 +1062,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../credits/" class="md-nav__link">
-        Credits
+        
+  
+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
       </a>
     </li>
   
@@ -996,7 +1218,7 @@ <h2 id="next-steps">Next steps<a class="headerlink" href="#next-steps" title="Pe
       
     
     <a href="https://github.com/sbi-dev/sbi" target="_blank" rel="noopener" title="github.com" class="md-social__link">
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
     </a>
   
 </div>
@@ -1010,10 +1232,11 @@ <h2 id="next-steps">Next steps<a class="headerlink" href="#next-steps" title="Pe
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.208ed371.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": "../../assets/javascripts/workers/search.f886a092.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.fac441b0.min.js"></script>
+      <script src="../../assets/javascripts/bundle.81fa17fe.min.js"></script>
       
         <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML"></script>
       
diff --git a/tutorial/01_gaussian_amortized/index.html b/tutorial/01_gaussian_amortized/index.html
index f7c5b796f..83550ef2e 100644
--- a/tutorial/01_gaussian_amortized/index.html
+++ b/tutorial/01_gaussian_amortized/index.html
@@ -16,8 +16,9 @@
       
         <link rel="next" href="../16_implemented_methods/">
       
+      
       <link rel="icon" href="../../assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.1.9">
+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.4.8">
     
     
       
@@ -25,15 +26,18 @@
       
     
     
-      <link rel="stylesheet" href="../../assets/stylesheets/main.85bb2934.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.4b4a2bd9.min.css">
       
         
-        <link rel="stylesheet" href="../../assets/stylesheets/palette.a6bdf11c.min.css">
+        <link rel="stylesheet" href="../../assets/stylesheets/palette.356b1318.min.css">
       
       
 
+
     
     
+      
+    
     
       
         
@@ -95,6 +99,7 @@
 
     </a>
     <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 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
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     <div class="md-header__title" data-md-component="header-title">
@@ -119,6 +124,7 @@
     
     
       <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-.27A6.516 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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@@ -127,12 +133,15 @@
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       <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-.27A6.516 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 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
         <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 12 19 6.41Z"/></svg>
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@@ -156,7 +165,7 @@
         <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -186,6 +195,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href="../.." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -200,7 +210,7 @@
       <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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@@ -212,14 +222,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../.." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
       </a>
     </li>
   
@@ -227,14 +241,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
       </a>
     </li>
   
@@ -242,94 +260,122 @@
     
       
       
-      
-
   
   
     
   
   
     
+    
+    
+    
+    
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-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
-          Tutorials and Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
-        <label class="md-nav__title" for="__nav_3">
-          <span class="md-nav__icon md-icon"></span>
-          Tutorials and Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Tutorials and Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            Tutorials and Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
     
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" checked>
-      
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+        
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+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="true">
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       <a href="../00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
       </a>
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-            
-          
+              
             
               
+                
   
   
     
@@ -344,12 +390,24 @@
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          Amortized inference
+          
+  
+  <span class="md-ellipsis">
+    Amortized inference
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+
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         </label>
       
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-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
       
         
@@ -388,440 +446,596 @@
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-            
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+              
             
               
+                
   
   
   
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       <a href="../16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
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+  </span>
+  
+
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-      </nav>
+          </ul>
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+        
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+    Multi-round inference
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+    SBI with trial-based data
+  </span>
+  
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+    Density plots and MCMC diagnostics with ArviZ
+  </span>
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@@ -829,14 +1043,18 @@
     
       
       
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       <a href="../../faq/" class="md-nav__link">
-        FAQ
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@@ -844,14 +1062,18 @@
     
       
       
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-        Credits
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+
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@@ -934,7 +1156,7 @@ <h2 id="defining-prior-simulator-and-running-inference">Defining prior, simulato
 <div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">linear_gaussian</span><span class="p">(</span><span class="n">theta</span><span class="p">):</span>
     <span class="k">return</span> <span class="n">theta</span> <span class="o">+</span> <span class="mf">1.0</span> <span class="o">+</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn_like</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.1</span>
 </code></pre></div>
-<p>We can then run inference (either with the simple interface of with the flexible interface):</p>
+<p>We can then run inference (either with the simple interface or with the flexible interface):</p>
 <div class="highlight"><pre><span></span><code><span class="n">posterior</span> <span class="o">=</span> <span class="n">infer</span><span class="p">(</span><span class="n">linear_gaussian</span><span class="p">,</span> <span class="n">prior</span><span class="p">,</span> <span class="s2">&quot;SNPE&quot;</span><span class="p">,</span> <span class="n">num_simulations</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
 </code></pre></div>
 <h2 id="amortized-inference">Amortized inference<a class="headerlink" href="#amortized-inference" title="Permanent link">&para;</a></h2>
@@ -1008,7 +1230,7 @@ <h2 id="amortized-inference">Amortized inference<a class="headerlink" href="#amo
       
     
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     </a>
   
 </div>
@@ -1022,10 +1244,11 @@ <h2 id="amortized-inference">Amortized inference<a class="headerlink" href="#amo
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.208ed371.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": "../../assets/javascripts/workers/search.f886a092.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.fac441b0.min.js"></script>
+      <script src="../../assets/javascripts/bundle.81fa17fe.min.js"></script>
       
         <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML"></script>
       
diff --git a/tutorial/02_flexible_interface/index.html b/tutorial/02_flexible_interface/index.html
index 4f403981a..66a269de3 100644
--- a/tutorial/02_flexible_interface/index.html
+++ b/tutorial/02_flexible_interface/index.html
@@ -16,8 +16,9 @@
       
         <link rel="next" href="../01_gaussian_amortized/">
       
+      
       <link rel="icon" href="../../assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.1.9">
+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.4.8">
     
     
       
@@ -25,15 +26,18 @@
       
     
     
-      <link rel="stylesheet" href="../../assets/stylesheets/main.85bb2934.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.4b4a2bd9.min.css">
       
         
-        <link rel="stylesheet" href="../../assets/stylesheets/palette.a6bdf11c.min.css">
+        <link rel="stylesheet" href="../../assets/stylesheets/palette.356b1318.min.css">
       
       
 
+
     
     
+      
+    
     
       
         
@@ -95,6 +99,7 @@
 
     </a>
     <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 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
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     <div class="md-header__title" data-md-component="header-title">
@@ -119,6 +124,7 @@
     
     
       <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-.27A6.516 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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@@ -127,12 +133,15 @@
     <form class="md-search__form" name="search">
       <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-.27A6.516 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 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
         <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 12 19 6.41Z"/></svg>
         </button>
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@@ -156,7 +165,7 @@
         <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -186,6 +195,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href="../.." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -200,7 +210,7 @@
       <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -212,14 +222,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../.." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
       </a>
     </li>
   
@@ -227,14 +241,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
       </a>
     </li>
   
@@ -242,80 +260,102 @@
     
       
       
-      
-
   
   
     
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
-          Tutorials and Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
-        <label class="md-nav__title" for="__nav_3">
-          <span class="md-nav__icon md-icon"></span>
-          Tutorials and Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Tutorials and Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            Tutorials and Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
     
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" checked>
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
-          Introduction
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="true">
-        <label class="md-nav__title" for="__nav_3_1">
-          <span class="md-nav__icon md-icon"></span>
-          Introduction
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" checked>
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
     
@@ -330,12 +370,24 @@
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          Flexible interface
+          
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <a href="./" class="md-nav__link md-nav__link--active">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
       </a>
       
         
@@ -381,454 +433,616 @@
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
-          Advanced
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_2">
-          <span class="md-nav__icon md-icon"></span>
-          Advanced
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Advanced
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
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-            
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+              
             
               
+                
   
   
   
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       <a href="../14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
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-            
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+              
             
               
+                
   
   
   
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       <a href="../08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
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+              
             
               
+                
   
   
   
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-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
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+              
             
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+          </ul>
+        </nav>
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+    
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+        
+        
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+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
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+  <span class="md-ellipsis">
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+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
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+            <span class="md-nav__icon md-icon"></span>
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-        Posterior predictive checks
+        
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+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
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+                
   
   
   
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-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
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-            
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       <a href="../15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
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+              
             
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-      </nav>
+          </ul>
+        </nav>
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+    
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+    
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-          Analysis
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+        
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+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
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+  <span class="md-ellipsis">
+    Analysis
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+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Analysis
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
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+        
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+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
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-            
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+              
             
               
+                
   
   
   
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       <a href="../09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
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+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
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+              
             
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+          </ul>
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+    
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+        
           
+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
+            
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+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_5">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
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+                
   
   
   
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-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
       </a>
     </li>
   
 
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+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
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+              
             
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-          <span class="md-nav__icon md-icon"></span>
-          Contributing
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-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Contributing
+  </span>
+  
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+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Contributing
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../contribute/" class="md-nav__link">
-        Guide
+        
+  
+  <span class="md-ellipsis">
+    Guide
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../code_of_conduct/" class="md-nav__link">
-        Code of Conduct
+        
+  
+  <span class="md-ellipsis">
+    Code of Conduct
+  </span>
+  
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       <a href="../../reference/" class="md-nav__link">
-        API Reference
+        
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
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@@ -836,14 +1050,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../faq/" class="md-nav__link">
-        FAQ
+        
+  
+  <span class="md-ellipsis">
+    FAQ
+  </span>
+  
+
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@@ -851,14 +1069,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../credits/" class="md-nav__link">
-        Credits
+        
+  
+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
       </a>
     </li>
   
@@ -1051,7 +1273,7 @@ <h2 id="linear-gaussian-example">Linear Gaussian example<a class="headerlink" hr
       
     
     <a href="https://github.com/sbi-dev/sbi" target="_blank" rel="noopener" title="github.com" class="md-social__link">
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+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
     </a>
   
 </div>
@@ -1065,10 +1287,11 @@ <h2 id="linear-gaussian-example">Linear Gaussian example<a class="headerlink" hr
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.208ed371.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": "../../assets/javascripts/workers/search.f886a092.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.fac441b0.min.js"></script>
+      <script src="../../assets/javascripts/bundle.81fa17fe.min.js"></script>
       
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diff --git a/tutorial/03_multiround_inference/index.html b/tutorial/03_multiround_inference/index.html
index f0d593250..9f11cd284 100644
--- a/tutorial/03_multiround_inference/index.html
+++ b/tutorial/03_multiround_inference/index.html
@@ -16,8 +16,9 @@
       
         <link rel="next" href="../11_sampler_interface/">
       
+      
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-      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.1.9">
+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.4.8">
     
     
       
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-      <link rel="stylesheet" href="../../assets/stylesheets/main.85bb2934.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.4b4a2bd9.min.css">
       
         
-        <link rel="stylesheet" href="../../assets/stylesheets/palette.a6bdf11c.min.css">
+        <link rel="stylesheet" href="../../assets/stylesheets/palette.356b1318.min.css">
       
       
 
+
     
     
+      
+    
     
       
         
@@ -95,6 +99,7 @@
 
     </a>
     <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 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
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@@ -119,6 +124,7 @@
     
     
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+        
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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-.27A6.516 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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         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
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@@ -156,7 +165,7 @@
         <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -186,6 +195,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href="../.." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -200,7 +210,7 @@
       <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -212,14 +222,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../.." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
       </a>
     </li>
   
@@ -227,14 +241,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
       </a>
     </li>
   
@@ -242,156 +260,206 @@
     
       
       
-      
-
   
   
     
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
-          Tutorials and Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
-        <label class="md-nav__title" for="__nav_3">
-          <span class="md-nav__icon md-icon"></span>
-          Tutorials and Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Tutorials and Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            Tutorials and Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
-          Introduction
-          <span class="md-nav__icon md-icon"></span>
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-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_1">
-          <span class="md-nav__icon md-icon"></span>
-          Introduction
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
     
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" checked>
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
-          Advanced
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="true">
-        <label class="md-nav__title" for="__nav_3_2">
-          <span class="md-nav__icon md-icon"></span>
-          Advanced
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" checked>
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Advanced
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
     
@@ -406,12 +474,24 @@
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          Multi-round inference
+          
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <a href="./" class="md-nav__link md-nav__link--active">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
       </a>
       
         
@@ -450,378 +530,512 @@
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
-          Diagnostics
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_3">
-          <span class="md-nav__icon md-icon"></span>
-          Diagnostics
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Diagnostics
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../12_diagnostics_posterior_predictive_check/" class="md-nav__link">
-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
-          Analysis
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
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-          <span class="md-nav__icon md-icon"></span>
-          Analysis
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Analysis
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Analysis
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../07_conditional_distributions/" class="md-nav__link">
-        Conditional distributions
+        
+  
+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
+  
+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
-      
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-          Examples
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-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
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-          <span class="md-nav__icon md-icon"></span>
-          Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
+        
           
+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_5">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/00_HH_simulator/" class="md-nav__link">
-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
-          Contributing
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_4">
-          <span class="md-nav__icon md-icon"></span>
-          Contributing
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Contributing
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Contributing
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../contribute/" class="md-nav__link">
-        Guide
+        
+  
+  <span class="md-ellipsis">
+    Guide
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../code_of_conduct/" class="md-nav__link">
-        Code of Conduct
+        
+  
+  <span class="md-ellipsis">
+    Code of Conduct
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../reference/" class="md-nav__link">
-        API Reference
+        
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -829,14 +1043,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../faq/" class="md-nav__link">
-        FAQ
+        
+  
+  <span class="md-ellipsis">
+    FAQ
+  </span>
+  
+
       </a>
     </li>
   
@@ -844,14 +1062,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../credits/" class="md-nav__link">
-        Credits
+        
+  
+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
       </a>
     </li>
   
@@ -1052,7 +1274,7 @@ <h2 id="linear-gaussian-example">Linear Gaussian example<a class="headerlink" hr
       
     
     <a href="https://github.com/sbi-dev/sbi" target="_blank" rel="noopener" title="github.com" class="md-social__link">
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
     </a>
   
 </div>
@@ -1066,10 +1288,11 @@ <h2 id="linear-gaussian-example">Linear Gaussian example<a class="headerlink" hr
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.208ed371.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": "../../assets/javascripts/workers/search.f886a092.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.fac441b0.min.js"></script>
+      <script src="../../assets/javascripts/bundle.81fa17fe.min.js"></script>
       
         <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML"></script>
       
diff --git a/tutorial/04_density_estimators/index.html b/tutorial/04_density_estimators/index.html
index ca6399a4a..781a475ea 100644
--- a/tutorial/04_density_estimators/index.html
+++ b/tutorial/04_density_estimators/index.html
@@ -16,8 +16,9 @@
       
         <link rel="next" href="../05_embedding_net/">
       
+      
       <link rel="icon" href="../../assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.1.9">
+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.4.8">
     
     
       
@@ -25,15 +26,18 @@
       
     
     
-      <link rel="stylesheet" href="../../assets/stylesheets/main.85bb2934.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.4b4a2bd9.min.css">
       
         
-        <link rel="stylesheet" href="../../assets/stylesheets/palette.a6bdf11c.min.css">
+        <link rel="stylesheet" href="../../assets/stylesheets/palette.356b1318.min.css">
       
       
 
+
     
     
+      
+    
     
       
         
@@ -95,6 +99,7 @@
 
     </a>
     <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 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
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     <div class="md-header__title" data-md-component="header-title">
@@ -119,6 +124,7 @@
     
     
       <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-.27A6.516 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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@@ -127,12 +133,15 @@
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       <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-.27A6.516 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 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
         <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 12 19 6.41Z"/></svg>
         </button>
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@@ -156,7 +165,7 @@
         <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -186,6 +195,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href="../.." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -200,7 +210,7 @@
       <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -212,14 +222,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../.." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
       </a>
     </li>
   
@@ -227,14 +241,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
       </a>
     </li>
   
@@ -242,184 +260,246 @@
     
       
       
-      
-
   
   
     
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
-          Tutorials and Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
-        <label class="md-nav__title" for="__nav_3">
-          <span class="md-nav__icon md-icon"></span>
-          Tutorials and Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Tutorials and Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            Tutorials and Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
-          Introduction
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_1">
-          <span class="md-nav__icon md-icon"></span>
-          Introduction
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
     
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" checked>
-      
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-          <span class="md-nav__icon md-icon"></span>
-          Advanced
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" checked>
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Advanced
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
     
@@ -434,12 +514,24 @@
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          Custom density estimators
+          
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <a href="./" class="md-nav__link md-nav__link--active">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
       </a>
       
         
@@ -485,350 +577,472 @@
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
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-      
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-      
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-          <span class="md-nav__icon md-icon"></span>
-          Diagnostics
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-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Diagnostics
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../12_diagnostics_posterior_predictive_check/" class="md-nav__link">
-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
       </a>
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
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+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
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-            
-          
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+    
+    
+    
+    
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-      
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-          Analysis
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+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
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+  <span class="md-ellipsis">
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+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Analysis
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
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-        Conditional distributions
+        
+  
+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
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       <a href="../09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
+  
+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
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+              
             
-          
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-      </nav>
+          </ul>
+        </nav>
+      
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+    
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+        
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+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
+        
           
+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
+            
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+  <span class="md-ellipsis">
+    Examples
+  </span>
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+
+            <span class="md-nav__icon md-icon"></span>
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+        
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+          <label class="md-nav__title" for="__nav_3_5">
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+            Examples
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+                
   
   
   
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-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
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+              
             
               
+                
   
   
   
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       <a href="../../examples/01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
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+              
             
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-          <span class="md-nav__icon md-icon"></span>
-          Contributing
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-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Contributing
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Contributing
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../contribute/" class="md-nav__link">
-        Guide
+        
+  
+  <span class="md-ellipsis">
+    Guide
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../code_of_conduct/" class="md-nav__link">
-        Code of Conduct
+        
+  
+  <span class="md-ellipsis">
+    Code of Conduct
+  </span>
+  
+
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+              
             
-          
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+          </ul>
+        </nav>
+      
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-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../reference/" class="md-nav__link">
-        API Reference
+        
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -836,14 +1050,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../faq/" class="md-nav__link">
-        FAQ
+        
+  
+  <span class="md-ellipsis">
+    FAQ
+  </span>
+  
+
       </a>
     </li>
   
@@ -851,14 +1069,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../credits/" class="md-nav__link">
-        Credits
+        
+  
+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
       </a>
     </li>
   
@@ -992,7 +1214,7 @@ <h2 id="building-new-density-estimators-from-scratch">Building new density estim
       
     
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@@ -1006,10 +1228,11 @@ <h2 id="building-new-density-estimators-from-scratch">Building new density estim
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     </div>
     
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+    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.f886a092.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.fac441b0.min.js"></script>
+      <script src="../../assets/javascripts/bundle.81fa17fe.min.js"></script>
       
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diff --git a/tutorial/05_embedding_net/index.html b/tutorial/05_embedding_net/index.html
index b80ee5ae8..bdf4cc360 100644
--- a/tutorial/05_embedding_net/index.html
+++ b/tutorial/05_embedding_net/index.html
@@ -16,8 +16,9 @@
       
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-      <link rel="stylesheet" href="../../assets/stylesheets/main.85bb2934.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.4b4a2bd9.min.css">
       
         
-        <link rel="stylesheet" href="../../assets/stylesheets/palette.a6bdf11c.min.css">
+        <link rel="stylesheet" href="../../assets/stylesheets/palette.356b1318.min.css">
       
       
 
+
     
     
+      
+    
     
       
         
@@ -95,6 +99,7 @@
 
     </a>
     <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 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
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     <div class="md-header__title" data-md-component="header-title">
@@ -119,6 +124,7 @@
     
     
       <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-.27A6.516 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>
       <div class="md-search" data-md-component="search" role="dialog">
@@ -127,12 +133,15 @@
     <form class="md-search__form" name="search">
       <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-.27A6.516 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 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
         <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 12 19 6.41Z"/></svg>
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       </nav>
@@ -156,7 +165,7 @@
         <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -186,6 +195,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href="../.." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -200,7 +210,7 @@
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   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
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@@ -212,14 +222,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../.." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
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@@ -227,14 +241,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
       </a>
     </li>
   
@@ -242,198 +260,266 @@
     
       
       
-      
-
   
   
     
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
-      
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-        <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
-          Tutorials and Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
-        <label class="md-nav__title" for="__nav_3">
-          <span class="md-nav__icon md-icon"></span>
-          Tutorials and Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Tutorials and Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            Tutorials and Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
-          Introduction
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_1">
-          <span class="md-nav__icon md-icon"></span>
-          Introduction
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
     
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" checked>
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
-          Advanced
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="true">
-        <label class="md-nav__title" for="__nav_3_2">
-          <span class="md-nav__icon md-icon"></span>
-          Advanced
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" checked>
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Advanced
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
     
@@ -448,12 +534,24 @@
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          Learning summary statistics
+          
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <a href="./" class="md-nav__link md-nav__link--active">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
       </a>
       
         
@@ -506,336 +604,452 @@
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
-          Diagnostics
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_3">
-          <span class="md-nav__icon md-icon"></span>
-          Diagnostics
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Diagnostics
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../12_diagnostics_posterior_predictive_check/" class="md-nav__link">
-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
       </a>
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+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
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-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
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-      
-      
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-      
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-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
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-          Analysis
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+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
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+  <span class="md-ellipsis">
+    Analysis
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+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
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+            Analysis
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+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
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+                
   
   
   
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+        
+  
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+  
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-      
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+  
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+        
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+    
+    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.f886a092.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>
     
     
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+        <link rel="stylesheet" href="../../assets/stylesheets/palette.356b1318.min.css">
       
       
 
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+  <span class="md-ellipsis">
+    Tutorials and Examples
+  </span>
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+
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+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
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+            Tutorials and Examples
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+                
   
   
   
     
+    
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+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
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+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
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-        Getting started
+        
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+  <span class="md-ellipsis">
+    Getting started
+  </span>
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+
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-            
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+              
             
               
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+        
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+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
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-            
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+              
             
               
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-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
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-        Implemented algorithms
+        
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+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
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+              
             
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-      </nav>
+          </ul>
+        </nav>
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+    
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+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Advanced
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
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       <a href="../03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
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       <a href="../05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
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       <a href="../10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
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+              
             
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-      </nav>
+          </ul>
+        </nav>
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+        
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+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
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+  
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+        
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+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
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       <a href="../12_diagnostics_posterior_predictive_check/" class="md-nav__link">
-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
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+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
     
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-      
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-      
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-          Analysis
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" checked>
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Analysis
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="true">
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+            Analysis
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
     
@@ -614,12 +762,24 @@
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          Conditional distributions
+          
+  
+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
+
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <a href="./" class="md-nav__link md-nav__link--active">
-        Conditional distributions
+        
+  
+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
+
       </a>
       
         
@@ -665,170 +825,224 @@
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
+  
+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
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+              
             
               
+                
   
   
   
     
+    
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+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
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+  <span class="md-ellipsis">
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-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
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+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
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+    Decision making model
+  </span>
+  
+
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+        
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+            Contributing
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-        Guide
+        
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+  <span class="md-ellipsis">
+    Guide
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+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../code_of_conduct/" class="md-nav__link">
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+    Code of Conduct
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-        API Reference
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+  <span class="md-ellipsis">
+    API Reference
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@@ -836,14 +1050,18 @@
     
       
       
-      
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       <a href="../../faq/" class="md-nav__link">
-        FAQ
+        
+  
+  <span class="md-ellipsis">
+    FAQ
+  </span>
+  
+
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@@ -851,14 +1069,18 @@
     
       
       
-      
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-        Credits
+        
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+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
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@@ -17175,7 +17397,7 @@ <h2 id="sampling-conditional-distributions">Sampling conditional distributions<a
       
     
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@@ -17189,10 +17411,11 @@ <h2 id="sampling-conditional-distributions">Sampling conditional distributions<a
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+    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.f886a092.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>
     
     
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diff --git a/tutorial/08_restriction_estimator/index.html b/tutorial/08_restriction_estimator/index.html
index cdde7fdc4..45bbdcab5 100644
--- a/tutorial/08_restriction_estimator/index.html
+++ b/tutorial/08_restriction_estimator/index.html
@@ -16,8 +16,9 @@
       
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+      
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-      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.1.9">
+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.4.8">
     
     
       
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+      <link rel="stylesheet" href="../../assets/stylesheets/main.4b4a2bd9.min.css">
       
         
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+        <link rel="stylesheet" href="../../assets/stylesheets/palette.356b1318.min.css">
       
       
 
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@@ -95,6 +99,7 @@
 
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+      
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@@ -119,6 +124,7 @@
     
     
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+        
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       <label class="md-search__icon md-icon" for="__search">
+        
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         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
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@@ -156,7 +165,7 @@
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   <div class="md-source__repository">
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@@ -186,6 +195,7 @@
                     
 
 
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@@ -200,7 +210,7 @@
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-      
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@@ -227,14 +241,18 @@
     
       
       
-      
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       <a href="../../install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
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@@ -242,226 +260,306 @@
     
       
       
-      
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+    
+    
+    
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+        
+        
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+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
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+    Tutorials and Examples
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+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3">
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+            Tutorials and Examples
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+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     
+    
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-          <span class="md-nav__icon md-icon"></span>
-          Introduction
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
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       <a href="../00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
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       <a href="../01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
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       <a href="../16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
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+              
             
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+          </ul>
+        </nav>
+      
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+    
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+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
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+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="true">
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+            Advanced
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+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
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       <a href="../03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
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+
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+              
             
               
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       <a href="../04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
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+  
+
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-            
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+              
             
               
+                
   
   
   
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+  <span class="md-ellipsis">
+    Learning summary statistics
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-            
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+              
             
               
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       <a href="../14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
     
@@ -476,12 +574,24 @@
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          Handling invalid simulations
+          
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <a href="./" class="md-nav__link md-nav__link--active">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
       </a>
       
         
@@ -527,308 +637,412 @@
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
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-          <span class="md-nav__icon md-icon"></span>
-          Diagnostics
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Diagnostics
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../12_diagnostics_posterior_predictive_check/" class="md-nav__link">
-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
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+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
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-            
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+    
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+    
+    
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-      
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-          <span class="md-nav__icon md-icon"></span>
-          Analysis
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-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Analysis
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Analysis
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
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+        
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+  <span class="md-ellipsis">
+    Conditional distributions
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-            
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+              
             
               
+                
   
   
   
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       <a href="../09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
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+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
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+              
             
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+          </ul>
+        </nav>
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+    
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-          Examples
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+        
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+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
+        
           
+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_5">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/00_HH_simulator/" class="md-nav__link">
-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
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+              
             
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+          </ul>
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-          <span class="md-nav__icon md-icon"></span>
-          Contributing
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Contributing
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Contributing
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../contribute/" class="md-nav__link">
-        Guide
+        
+  
+  <span class="md-ellipsis">
+    Guide
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../code_of_conduct/" class="md-nav__link">
-        Code of Conduct
+        
+  
+  <span class="md-ellipsis">
+    Code of Conduct
+  </span>
+  
+
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+              
             
-          
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+          </ul>
+        </nav>
+      
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-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../reference/" class="md-nav__link">
-        API Reference
+        
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
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@@ -836,14 +1050,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../faq/" class="md-nav__link">
-        FAQ
+        
+  
+  <span class="md-ellipsis">
+    FAQ
+  </span>
+  
+
       </a>
     </li>
   
@@ -851,14 +1069,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../credits/" class="md-nav__link">
-        Credits
+        
+  
+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
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     </li>
   
@@ -1097,7 +1319,7 @@ <h2 id="further-options-for-tuning-the-algorithm">Further options for tuning the
       
     
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+    
+    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.f886a092.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.fac441b0.min.js"></script>
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diff --git a/tutorial/09_sensitivity_analysis/index.html b/tutorial/09_sensitivity_analysis/index.html
index 149f72046..4f52d27b1 100644
--- a/tutorial/09_sensitivity_analysis/index.html
+++ b/tutorial/09_sensitivity_analysis/index.html
@@ -16,8 +16,9 @@
       
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+      
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+      <link rel="stylesheet" href="../../assets/stylesheets/main.4b4a2bd9.min.css">
       
         
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+        <link rel="stylesheet" href="../../assets/stylesheets/palette.356b1318.min.css">
       
       
 
+
     
     
+      
+    
     
       
         
@@ -95,6 +99,7 @@
 
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+      
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@@ -119,6 +124,7 @@
     
     
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+        
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-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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   <div class="md-source__repository">
     sbi-dev/sbi
@@ -186,6 +195,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href="../.." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -200,7 +210,7 @@
       <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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   <div class="md-source__repository">
     sbi-dev/sbi
@@ -212,14 +222,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../.." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
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@@ -227,14 +241,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
       </a>
     </li>
   
@@ -242,378 +260,514 @@
     
       
       
-      
-
   
   
     
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
-        <label class="md-nav__title" for="__nav_3">
-          <span class="md-nav__icon md-icon"></span>
-          Tutorials and Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Tutorials and Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            Tutorials and Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
-      
-      
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-        <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
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-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
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-          <span class="md-nav__icon md-icon"></span>
-          Introduction
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
-          Advanced
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_2">
-          <span class="md-nav__icon md-icon"></span>
-          Advanced
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Advanced
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
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-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
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-          <span class="md-nav__icon md-icon"></span>
-          Diagnostics
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Diagnostics
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../12_diagnostics_posterior_predictive_check/" class="md-nav__link">
-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
     
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" checked>
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
-          Analysis
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="true">
-        <label class="md-nav__title" for="__nav_3_4">
-          <span class="md-nav__icon md-icon"></span>
-          Analysis
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" checked>
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Analysis
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Analysis
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../07_conditional_distributions/" class="md-nav__link">
-        Conditional distributions
+        
+  
+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
     
@@ -628,12 +782,24 @@
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          Posterior sensitivity analysis
+          
+  
+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <a href="./" class="md-nav__link md-nav__link--active">
-        Posterior sensitivity analysis
+        
+  
+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
       </a>
       
         
@@ -686,156 +852,204 @@
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
-          Examples
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-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_5">
-          <span class="md-nav__icon md-icon"></span>
-          Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
+        
           
+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_5">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/00_HH_simulator/" class="md-nav__link">
-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
       </a>
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+              
             
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@@ -1123,7 +1345,7 @@ <h2 id="computing-the-sensitivity-of-a-specific-summary-statistic">Computing the
       
     
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+    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.f886a092.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>
     
     
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diff --git a/tutorial/10_crafting_summary_statistics/index.html b/tutorial/10_crafting_summary_statistics/index.html
index 047dd0cca..9c8503066 100644
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+++ b/tutorial/10_crafting_summary_statistics/index.html
@@ -16,8 +16,9 @@
       
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-      <link rel="stylesheet" href="../../assets/stylesheets/main.85bb2934.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.4b4a2bd9.min.css">
       
         
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+        <link rel="stylesheet" href="../../assets/stylesheets/palette.356b1318.min.css">
       
       
 
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+      
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@@ -119,6 +124,7 @@
     
     
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+        
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+        
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@@ -186,6 +195,7 @@
                     
 
 
+
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@@ -200,7 +210,7 @@
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@@ -227,14 +241,18 @@
     
       
       
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       <a href="../../install/" class="md-nav__link">
-        Installation
+        
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+  <span class="md-ellipsis">
+    Installation
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+  
+
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@@ -242,240 +260,326 @@
     
       
       
-      
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+    
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+        
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+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
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+    Tutorials and Examples
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+          </label>
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+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
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+            Tutorials and Examples
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+          <ul class="md-nav__list" data-md-scrollfix>
             
               
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-          <span class="md-nav__icon md-icon"></span>
-          Introduction
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-        <ul class="md-nav__list" data-md-scrollfix>
+        
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+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
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+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
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       <a href="../00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
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-            
-          
+              
             
               
+                
   
   
   
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       <a href="../02_flexible_interface/" class="md-nav__link">
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+        
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+  <span class="md-ellipsis">
+    Flexible interface
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+        
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+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
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       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
     
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" checked>
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
-          Advanced
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="true">
-        <label class="md-nav__title" for="__nav_3_2">
-          <span class="md-nav__icon md-icon"></span>
-          Advanced
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" checked>
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Advanced
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
     
@@ -490,300 +594,404 @@
       
       
       <a href="./" class="md-nav__link md-nav__link--active">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
       </a>
       
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
-          Diagnostics
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_3">
-          <span class="md-nav__icon md-icon"></span>
-          Diagnostics
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Diagnostics
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../12_diagnostics_posterior_predictive_check/" class="md-nav__link">
-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
-          Analysis
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_4">
-          <span class="md-nav__icon md-icon"></span>
-          Analysis
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Analysis
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Analysis
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../07_conditional_distributions/" class="md-nav__link">
-        Conditional distributions
+        
+  
+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
+  
+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
-      
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-        <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
-          Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
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-          <span class="md-nav__icon md-icon"></span>
-          Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
+        
           
+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_5">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/00_HH_simulator/" class="md-nav__link">
-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
-      
-      
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-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
-          Contributing
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_4">
-          <span class="md-nav__icon md-icon"></span>
-          Contributing
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Contributing
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Contributing
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../contribute/" class="md-nav__link">
-        Guide
+        
+  
+  <span class="md-ellipsis">
+    Guide
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../code_of_conduct/" class="md-nav__link">
-        Code of Conduct
+        
+  
+  <span class="md-ellipsis">
+    Code of Conduct
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../reference/" class="md-nav__link">
-        API Reference
+        
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -791,14 +999,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../faq/" class="md-nav__link">
-        FAQ
+        
+  
+  <span class="md-ellipsis">
+    FAQ
+  </span>
+  
+
       </a>
     </li>
   
@@ -806,14 +1018,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../credits/" class="md-nav__link">
-        Credits
+        
+  
+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
       </a>
     </li>
   
@@ -1167,7 +1383,7 @@ <h2 id="17-explicit-recommendations">1.7 Explicit recommendations<a class="heade
       
     
     <a href="https://github.com/sbi-dev/sbi" target="_blank" rel="noopener" title="github.com" class="md-social__link">
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
     </a>
   
 </div>
@@ -1181,10 +1397,11 @@ <h2 id="17-explicit-recommendations">1.7 Explicit recommendations<a class="heade
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.208ed371.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": "../../assets/javascripts/workers/search.f886a092.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.fac441b0.min.js"></script>
+      <script src="../../assets/javascripts/bundle.81fa17fe.min.js"></script>
       
         <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML"></script>
       
diff --git a/tutorial/11_sampler_interface/index.html b/tutorial/11_sampler_interface/index.html
index d862dacab..67d955977 100644
--- a/tutorial/11_sampler_interface/index.html
+++ b/tutorial/11_sampler_interface/index.html
@@ -16,8 +16,9 @@
       
         <link rel="next" href="../04_density_estimators/">
       
+      
       <link rel="icon" href="../../assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.1.9">
+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.4.8">
     
     
       
@@ -25,14 +26,17 @@
       
     
     
-      <link rel="stylesheet" href="../../assets/stylesheets/main.85bb2934.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.4b4a2bd9.min.css">
       
         
-        <link rel="stylesheet" href="../../assets/stylesheets/palette.a6bdf11c.min.css">
+        <link rel="stylesheet" href="../../assets/stylesheets/palette.356b1318.min.css">
       
       
 
+
+    
     
+      
     
     
       
@@ -95,6 +99,7 @@
 
     </a>
     <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 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
     <div class="md-header__title" data-md-component="header-title">
@@ -119,6 +124,7 @@
     
     
       <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-.27A6.516 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>
       <div class="md-search" data-md-component="search" role="dialog">
@@ -127,12 +133,15 @@
     <form class="md-search__form" name="search">
       <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-.27A6.516 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>
       <nav class="md-search__options" aria-label="Search">
         
         <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 12 19 6.41Z"/></svg>
         </button>
       </nav>
@@ -156,7 +165,7 @@
         <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -186,6 +195,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href="../.." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -200,7 +210,7 @@
       <a href="http://github.com/sbi-dev/sbi" 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 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     sbi-dev/sbi
@@ -212,14 +222,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../.." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
       </a>
     </li>
   
@@ -227,14 +241,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
       </a>
     </li>
   
@@ -242,170 +260,226 @@
     
       
       
-      
-
   
   
     
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
-          Tutorials and Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
-        <label class="md-nav__title" for="__nav_3">
-          <span class="md-nav__icon md-icon"></span>
-          Tutorials and Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Tutorials and Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            Tutorials and Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
-          Introduction
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_1">
-          <span class="md-nav__icon md-icon"></span>
-          Introduction
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
     
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" checked>
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
-          Advanced
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="true">
-        <label class="md-nav__title" for="__nav_3_2">
-          <span class="md-nav__icon md-icon"></span>
-          Advanced
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" checked>
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Advanced
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
     
@@ -420,370 +494,504 @@
       
       
       <a href="./" class="md-nav__link md-nav__link--active">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
       </a>
       
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
-          Diagnostics
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_3">
-          <span class="md-nav__icon md-icon"></span>
-          Diagnostics
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Diagnostics
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../12_diagnostics_posterior_predictive_check/" class="md-nav__link">
-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
-          Analysis
-          <span class="md-nav__icon md-icon"></span>
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-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
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-          <span class="md-nav__icon md-icon"></span>
-          Analysis
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Analysis
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Analysis
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
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       <a href="../07_conditional_distributions/" class="md-nav__link">
-        Conditional distributions
+        
+  
+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
+  
+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
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-      
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-          <span class="md-nav__icon md-icon"></span>
-          Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
+        
           
+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_5">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/00_HH_simulator/" class="md-nav__link">
-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
-          Contributing
-          <span class="md-nav__icon md-icon"></span>
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-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_4">
-          <span class="md-nav__icon md-icon"></span>
-          Contributing
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Contributing
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Contributing
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../contribute/" class="md-nav__link">
-        Guide
+        
+  
+  <span class="md-ellipsis">
+    Guide
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../code_of_conduct/" class="md-nav__link">
-        Code of Conduct
+        
+  
+  <span class="md-ellipsis">
+    Code of Conduct
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../reference/" class="md-nav__link">
-        API Reference
+        
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -791,14 +999,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../faq/" class="md-nav__link">
-        FAQ
+        
+  
+  <span class="md-ellipsis">
+    FAQ
+  </span>
+  
+
       </a>
     </li>
   
@@ -806,14 +1018,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../credits/" class="md-nav__link">
-        Credits
+        
+  
+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
       </a>
     </li>
   
@@ -1030,7 +1246,7 @@ <h2 id="main-syntax-for-snpe">Main syntax for SNPE<a class="headerlink" href="#m
       
     
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-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
     </a>
   
 </div>
@@ -1044,10 +1260,11 @@ <h2 id="main-syntax-for-snpe">Main syntax for SNPE<a class="headerlink" href="#m
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.208ed371.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": "../../assets/javascripts/workers/search.f886a092.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.fac441b0.min.js"></script>
+      <script src="../../assets/javascripts/bundle.81fa17fe.min.js"></script>
       
         <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML"></script>
       
diff --git a/tutorial/12_diagnostics_posterior_predictive_check/index.html b/tutorial/12_diagnostics_posterior_predictive_check/index.html
index 25d9d64c9..869f95ce0 100644
--- a/tutorial/12_diagnostics_posterior_predictive_check/index.html
+++ b/tutorial/12_diagnostics_posterior_predictive_check/index.html
@@ -16,8 +16,9 @@
       
         <link rel="next" href="../13_diagnostics_simulation_based_calibration/">
       
+      
       <link rel="icon" href="../../assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.1.9">
+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.4.8">
     
     
       
@@ -25,15 +26,18 @@
       
     
     
-      <link rel="stylesheet" href="../../assets/stylesheets/main.85bb2934.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.4b4a2bd9.min.css">
       
         
-        <link rel="stylesheet" href="../../assets/stylesheets/palette.a6bdf11c.min.css">
+        <link rel="stylesheet" href="../../assets/stylesheets/palette.356b1318.min.css">
       
       
 
+
     
     
+      
+    
     
       
         
@@ -95,6 +99,7 @@
 
     </a>
     <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 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
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@@ -119,6 +124,7 @@
     
     
       <label class="md-header__button md-icon" for="__search">
+        
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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-.27A6.516 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>
       <nav class="md-search__options" aria-label="Search">
         
         <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 12 19 6.41Z"/></svg>
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@@ -156,7 +165,7 @@
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-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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   <div class="md-source__repository">
     sbi-dev/sbi
@@ -186,6 +195,7 @@
                     
 
 
+
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   <label class="md-nav__title" for="__drawer">
     <a href="../.." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -200,7 +210,7 @@
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   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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@@ -212,14 +222,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../.." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
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@@ -227,14 +241,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
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     </li>
   
@@ -242,288 +260,390 @@
     
       
       
-      
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+    
+    
+    
+    
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-      
-      
-      
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-      
-      
-      
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-          Tutorials and Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
-        <label class="md-nav__title" for="__nav_3">
-          <span class="md-nav__icon md-icon"></span>
-          Tutorials and Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Tutorials and Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            Tutorials and Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     
+    
+    
+    
+    
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-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
-      
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-        <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
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-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
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-          <span class="md-nav__icon md-icon"></span>
-          Introduction
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
-      
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-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
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-          <span class="md-nav__icon md-icon"></span>
-          Advanced
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Advanced
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
     
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" checked>
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
-          Diagnostics
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="true">
-        <label class="md-nav__title" for="__nav_3_3">
-          <span class="md-nav__icon md-icon"></span>
-          Diagnostics
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" checked>
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Diagnostics
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
     
@@ -538,12 +658,24 @@
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          Posterior predictive checks
+          
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <a href="./" class="md-nav__link md-nav__link--active">
-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
       </a>
       
         
@@ -582,246 +714,328 @@
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
-          Analysis
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_4">
-          <span class="md-nav__icon md-icon"></span>
-          Analysis
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Analysis
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Analysis
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../07_conditional_distributions/" class="md-nav__link">
-        Conditional distributions
+        
+  
+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
+  
+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
-      
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-          Examples
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-          Examples
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+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
+        
           
+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_5">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/00_HH_simulator/" class="md-nav__link">
-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
+              
             
-          
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-      </nav>
+          </ul>
+        </nav>
+      
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-      
-
   
   
   
     
+    
+    
+    
+    
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-      
-      
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-          <span class="md-nav__icon md-icon"></span>
-          Contributing
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Contributing
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Contributing
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../contribute/" class="md-nav__link">
-        Guide
+        
+  
+  <span class="md-ellipsis">
+    Guide
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../code_of_conduct/" class="md-nav__link">
-        Code of Conduct
+        
+  
+  <span class="md-ellipsis">
+    Code of Conduct
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../reference/" class="md-nav__link">
-        API Reference
+        
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -829,14 +1043,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../faq/" class="md-nav__link">
-        FAQ
+        
+  
+  <span class="md-ellipsis">
+    FAQ
+  </span>
+  
+
       </a>
     </li>
   
@@ -844,14 +1062,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../credits/" class="md-nav__link">
-        Credits
+        
+  
+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
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     </li>
   
@@ -1048,7 +1270,7 @@ <h2 id="performing-a-ppc-over-a-toy-example">Performing a PPC over a toy example
       
     
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@@ -1062,10 +1284,11 @@ <h2 id="performing-a-ppc-over-a-toy-example">Performing a PPC over a toy example
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+    
+    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.f886a092.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>
     
     
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diff --git a/tutorial/13_diagnostics_simulation_based_calibration/index.html b/tutorial/13_diagnostics_simulation_based_calibration/index.html
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@@ -16,8 +16,9 @@
       
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+      
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+      <link rel="stylesheet" href="../../assets/stylesheets/main.4b4a2bd9.min.css">
       
         
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+        <link rel="stylesheet" href="../../assets/stylesheets/palette.356b1318.min.css">
       
       
 
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+      
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@@ -119,6 +124,7 @@
     
     
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+        
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         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
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+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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@@ -186,6 +195,7 @@
                     
 
 
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@@ -200,7 +210,7 @@
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@@ -227,14 +241,18 @@
     
       
       
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       <a href="../../install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
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+  
+
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@@ -242,302 +260,410 @@
     
       
       
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+    
+    
+    
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+        
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+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
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+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3">
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+            Tutorials and Examples
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+          <ul class="md-nav__list" data-md-scrollfix>
             
               
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-          <span class="md-nav__icon md-icon"></span>
-          Introduction
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-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
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       <a href="../00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
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+  
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-            
-          
+              
             
               
+                
   
   
   
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       <a href="../02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
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+  
+
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-            
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+              
             
               
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-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
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+        
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+  </span>
+  
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+        
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+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
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+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
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+            Advanced
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+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
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-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
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-            
-          
+              
             
               
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       <a href="../11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
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@@ -552,12 +678,24 @@
       
       
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@@ -1038,7 +1260,7 @@
 
 
 <h1 id="simulation-based-calibration-in-sbi">Simulation-based Calibration in SBI<a class="headerlink" href="#simulation-based-calibration-in-sbi" title="Permanent link">&para;</a></h1>
-<p>After a density estimator has been trained with simulated data to obtain a posterior, the estimator should be made subject to several diagnostic tests, before being used for inference given the actual observed data. <em>Posterior Predictive Checks</em> (see tutorial 12) provide one way to &ldquo;critique&rdquo; a trained estimator via its predictive performance. Another important approach to such diagnostics is simulation-based calibration as reported by <a href="https://arxiv.org/abs/1804.06788">Talts et al, 2018</a>.</p>
+<p>After a density estimator has been trained with simulated data to obtain a posterior, the estimator should be made subject to several diagnostic tests, before being used for inference given the actual observed data. <em>Posterior Predictive Checks</em> (see <a href="https://sbi-dev.github.io/sbi/tutorial/12_diagnostics_posterior_predictive_check/">previous tutorial</a>) provide one way to &ldquo;critique&rdquo; a trained estimator via its predictive performance. Another important approach to such diagnostics is simulation-based calibration as developed by <a href="https://www.tandfonline.com/doi/abs/10.1198/106186006X136976">Cook et al, 2006</a> and <a href="https://arxiv.org/abs/1804.06788">Talts et al, 2018</a>.</p>
 <p><strong>Simulation-based calibration</strong> (SBC) provides a (qualitative) view and a quantitive measure to check, whether the uncertainties of the posterior are balanced, i.e., neither over-confident nor under-confident. As such, SBC can be viewed as a necessary condition (but not sufficient) for a valid inference algorithm: If SBC checks fail, this tells you that your inference is invalid. If SBC checks pass, this is no guarantee that the posterior estimation is working.</p>
 <h2 id="in-a-nutshell">In a nutshell<a class="headerlink" href="#in-a-nutshell" title="Permanent link">&para;</a></h2>
 <p>To run SBC,  </p>
@@ -1080,7 +1302,7 @@ <h2 id="a-healthy-posterior">A healthy posterior<a class="headerlink" href="#a-h
 <span class="kn">from</span> <span class="nn">sbi.simulators</span> <span class="kn">import</span> <span class="n">linear_gaussian</span><span class="p">,</span> <span class="n">diagonal_linear_gaussian</span>
 </code></pre></div>
 <div class="highlight"><pre><span></span><code><span class="n">num_dim</span> <span class="o">=</span> <span class="mi">2</span>
-<span class="n">num_simulations</span> <span class="o">=</span> <span class="mi">5_000</span>
+<span class="n">num_simulations</span> <span class="o">=</span> <span class="mi">10_000</span>
 
 <span class="n">prior_mean</span> <span class="o">=</span> <span class="n">ones</span><span class="p">(</span><span class="n">num_dim</span><span class="p">)</span>
 <span class="n">prior_cov</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">eye</span><span class="p">(</span><span class="n">num_dim</span><span class="p">)</span>
@@ -1112,7 +1334,7 @@ <h2 id="an-ideal-case">An ideal case<a class="headerlink" href="#an-ideal-case"
 <span class="n">_</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
 <span class="n">theta</span><span class="p">,</span> <span class="n">x</span> <span class="o">=</span> <span class="n">simulate_for_sbi</span><span class="p">(</span><span class="n">simulator</span><span class="p">,</span> <span class="n">prior</span><span class="p">,</span> <span class="n">num_simulations</span><span class="p">)</span>
 </code></pre></div>
-<div class="codehilite"><pre><span></span><code>Running 5000 simulations.:   0%|          | 0/5000 [00:00&lt;?, ?it/s]
+<div class="codehilite"><pre><span></span><code>Running 10000 simulations.:   0%|          | 0/10000 [00:00&lt;?, ?it/s]
 </code></pre></div>
 
 <div class="highlight"><pre><span></span><code><span class="n">_</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
@@ -1134,7 +1356,7 @@ <h2 id="an-ideal-case">An ideal case<a class="headerlink" href="#an-ideal-case"
 <span class="c1"># append simulations and run training.</span>
 <span class="n">inferer</span><span class="o">.</span><span class="n">append_simulations</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">train</span><span class="p">();</span>
 </code></pre></div>
-<div class="codehilite"><pre><span></span><code> Neural network successfully converged after 65 epochs.
+<div class="codehilite"><pre><span></span><code> Neural network successfully converged after 63 epochs.
 </code></pre></div>
 
 <div class="highlight"><pre><span></span><code><span class="n">posterior</span> <span class="o">=</span> <span class="n">inferer</span><span class="o">.</span><span class="n">build_posterior</span><span class="p">()</span>
@@ -1197,7 +1419,7 @@ <h3 id="ranks-versus-uniform-distribution">Ranks versus Uniform distribution<a c
 <span class="p">)</span>
 </code></pre></div>
 <div class="codehilite"><pre><span></span><code>kolmogorov-smirnov p-values 
-check_stats[&#39;ks_pvals&#39;] = [0.1261853  0.07933955]
+check_stats[&#39;ks_pvals&#39;] = [0.28580528 0.12618546]
 </code></pre></div>
 
 <p>The Kolmogorov-Smirnov (KS test, see also <a href="https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test#Two-sample_Kolmogorov%E2%80%93Smirnov_test">here</a>) as used by <code>check_sbc</code> provides p-values <code>pvals</code> on the null hypothesis that the samples from <code>ranks</code> are drawn from a uniform distribution (in other words <code>H_0: PDF(ranks) == PDF(uniform)</code>). We are provided two values as our problem is two-dimensional - one p-value for each dimension. </p>
@@ -1207,14 +1429,14 @@ <h3 id="ranks-versus-uniform-distribution">Ranks versus Uniform distribution<a c
 <span class="p">)</span>
 </code></pre></div>
 <div class="codehilite"><pre><span></span><code>c2st accuracies 
-check_stats[&#39;c2st_ranks&#39;] = [0.604  0.5935]
+check_stats[&#39;c2st_ranks&#39;] = [0.5765 0.5735]
 </code></pre></div>
 
 <p>The second tier of metrics comparing <code>ranks</code> with a uniform distributions is a <code>c2st</code> test (see <a href="http://arxiv.org/abs/1610.06545">here</a> for details). This is a nonparametric two sample test based on training a classifier to differented one of the ensembles (<code>ranks</code> versus samples from a uniform distribution) by being trained on the other. The values reported are the accuracies from cross-validation. If you see values around <code>0.5</code>, the classifier was unable to differentiate both ensembles, i.e. <code>ranks</code> are very uniform. If the values are high towards <code>1</code>, this matches the case where <code>ranks</code> is very unlike a uniform distribution.</p>
 <h3 id="data-averaged-posterior-dap-versus-prior">Data averaged posterior (DAP) versus prior<a class="headerlink" href="#data-averaged-posterior-dap-versus-prior" title="Permanent link">&para;</a></h3>
 <div class="highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;- c2st accuracies check_stats[&#39;c2st_dap&#39;] = </span><span class="si">{</span><span class="n">check_stats</span><span class="p">[</span><span class="s1">&#39;c2st_dap&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </code></pre></div>
-<div class="codehilite"><pre><span></span><code>- c2st accuracies check_stats[&#39;c2st_dap&#39;] = [0.5195 0.4785]
+<div class="codehilite"><pre><span></span><code>- c2st accuracies check_stats[&#39;c2st_dap&#39;] = [0.474 0.488]
 </code></pre></div>
 
 <p>The last metric reported is again based on <code>c2st</code> computed per dimension of <code>theta</code>. If you see values around <code>0.5</code>, the <code>c2st</code> classifier was unable to differentiate both ensembles for each dimension of <code>theta</code>, i.e. <code>dap</code> are much like (if not identical to) the prior. If the values are very high towards <code>1</code>, this represents the case where <code>dap</code> is very unlike the prior distribution.</p>
@@ -1367,7 +1589,7 @@ <h2 id="a-dispersed-posterior">A dispersed posterior<a class="headerlink" href="
       
     
     <a href="https://github.com/sbi-dev/sbi" target="_blank" rel="noopener" title="github.com" class="md-social__link">
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@@ -1381,10 +1603,11 @@ <h2 id="a-dispersed-posterior">A dispersed posterior<a class="headerlink" href="
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+    
+    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.f886a092.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>
     
     
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@@ -16,8 +16,9 @@
       
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+        <link rel="stylesheet" href="../../assets/stylesheets/palette.356b1318.min.css">
       
       
 
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@@ -186,6 +195,7 @@
                     
 
 
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@@ -200,7 +210,7 @@
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+  
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+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
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+            Introduction
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+          <ul class="md-nav__list" data-md-scrollfix>
             
               
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+    Flexible interface
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+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
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       <a href="../03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
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     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
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-            
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+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
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+    Custom density estimators
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+    Learning summary statistics
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
     
@@ -462,12 +554,24 @@
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          SBI with trial-based data
+          
+  
+  <span class="md-ellipsis">
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+  
+
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         </label>
       
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-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
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@@ -574,322 +678,432 @@
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+              
             
               
+                
   
   
   
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-        Handling invalid simulations
+        
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+    Handling invalid simulations
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+  
+
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+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
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-        Guide
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+  <span class="md-ellipsis">
+    Guide
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+                
   
   
   
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@@ -897,14 +1111,18 @@
     
       
       
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       <a href="../../faq/" class="md-nav__link">
-        FAQ
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+  <span class="md-ellipsis">
+    FAQ
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-        Credits
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+    Credits
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@@ -1500,7 +1722,7 @@ <h3 id="amortized-inference">Amortized inference<a class="headerlink" href="#amo
       
     
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+    
+    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.f886a092.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>
     
     
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-      </ul>
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-</li>
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-        <li class="md-nav__item">
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-    Trial-based inference with NLE
-  </a>
-  
-</li>
-      
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-    Trial-based SBI with mixed data types
-  </a>
-  
-</li>
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-        <li class="md-nav__item">
-  <a href="#toy-problem-for-mnle" class="md-nav__link">
-    Toy problem for MNLE
-  </a>
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-    <nav class="md-nav" aria-label="Toy problem for MNLE">
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-        
-          <li class="md-nav__item">
-  <a href="#obtain-reference-posterior-samples-via-analytical-likelihood-and-mcmc" class="md-nav__link">
-    Obtain reference-posterior samples via analytical likelihood and MCMC
-  </a>
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-</li>
-        
-          <li class="md-nav__item">
-  <a href="#train-mnle-and-generate-samples-via-mcmc" class="md-nav__link">
-    Train MNLE and generate samples via MCMC
-  </a>
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-  <a href="#compare-mnle-and-reference-posterior" class="md-nav__link">
-    Compare MNLE and reference posterior
-  </a>
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-        
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-  <a href="#repeat-inference-with-different-x_o-that-has-more-trials" class="md-nav__link">
-    Repeat inference with different x_o that has more trials
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-              <article class="md-content__inner md-typeset">
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-
-<h1 id="sbi-with-trial-based-data-and-models-of-mixed-data-types">SBI with trial-based data and models of mixed data types<a class="headerlink" href="#sbi-with-trial-based-data-and-models-of-mixed-data-types" title="Permanent link">&para;</a></h1>
-<p>Trial-based data often has the property that the individual trials can be assumed to be independent and identically distributed (iid), i.e., they are assumed to have the same underlying model parameters. For example, in a decision-making experiments, the experiment is often repeated in trials with the same experimental settings and conditions. The corresponding set of trials is then assumed to be &ldquo;iid&rdquo;. </p>
-<h3 id="amortization-of-neural-network-training-with-likelihood-based-sbi">Amortization of neural network training with likelihood-based SBI<a class="headerlink" href="#amortization-of-neural-network-training-with-likelihood-based-sbi" title="Permanent link">&para;</a></h3>
-<p>For some SBI variants the iid assumption can be exploited: when using a likelihood-based SBI method (<code>SNLE</code>, <code>SNRE</code>) one can train the density or ratio estimator on single-trial data, and then perform inference with <code>MCMC</code>. Crucially, because the data is iid and the estimator is trained on single-trial data, one can repeat the inference with a different <code>x_o</code> (a different set of trials, or different number of trials) without having to retrain the density estimator. One can interpet this as amortization of the SBI training: we can obtain a neural likelihood, or likelihood-ratio estimate for new <code>x_o</code>s without retraining, but we still have to run <code>MCMC</code> or <code>VI</code> to do inference. </p>
-<p>In addition, one can not only change the number of trials of a new <code>x_o</code>, but also the entire inference setting. For example, one can apply hierarchical inference scenarios with changing hierarchical denpendencies between the model parameters&ndash;all without having to retrain the density estimator because that is based on estimating single-trail likelihoods.</p>
-<p>Let us first have a look how trial-based inference works in <code>SBI</code> before we discuss models with &ldquo;mixed data types&rdquo;.</p>
-<h2 id="sbi-with-trial-based-data">SBI with trial-based data<a class="headerlink" href="#sbi-with-trial-based-data" title="Permanent link">&para;</a></h2>
-<p>For illustration we use a simple linear Gaussian simulator, as in previous tutorials. The simulator takes a single parameter (vector), the mean of the Gaussian and its variance is set to one. We define a Gaussian prior over the mean and perform inference. The observed data is again a from a Gaussian with some fixed &ldquo;ground-truth&rdquo; parameter <span class="arithmatex">\(\theta_o\)</span>. Crucially, the observed data <code>x_o</code> can consist of multiple samples given the same ground-truth parameters and these samples are then iid: </p>
-<div class="arithmatex">\[ 
-\theta \sim \mathcal{N}(\mu_0,\; \Sigma_0) \\
-x | \theta \sim \mathcal{N}(\theta,\; \Sigma=I) \\
-\mathbf{x_o} = \{x_o^i\}_{i=1}^N \sim  \mathcal{N}(\theta_o,\; \Sigma=I)
-\]</div>
-<p>For this toy problem the ground-truth posterior is well defined, it is again a Gaussian, centered on the mean of <span class="arithmatex">\(\mathbf{x_o}\)</span> and with variance scaled by the number of trials <span class="arithmatex">\(N\)</span>, i.e., the more trials we observe, the more information about the underlying <span class="arithmatex">\(\theta_o\)</span> we have and the more concentrated the posteriors becomes.</p>
-<p>We will illustrate this below:</p>
-<div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-
-<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">zeros</span><span class="p">,</span> <span class="n">ones</span><span class="p">,</span> <span class="n">eye</span>
-<span class="kn">from</span> <span class="nn">torch.distributions</span> <span class="kn">import</span> <span class="n">MultivariateNormal</span>
-<span class="kn">from</span> <span class="nn">sbi.inference</span> <span class="kn">import</span> <span class="n">SNLE</span><span class="p">,</span> <span class="n">prepare_for_sbi</span><span class="p">,</span> <span class="n">simulate_for_sbi</span>
-<span class="kn">from</span> <span class="nn">sbi.analysis</span> <span class="kn">import</span> <span class="n">pairplot</span>
-<span class="kn">from</span> <span class="nn">sbi.utils.metrics</span> <span class="kn">import</span> <span class="n">c2st</span>
-
-<span class="kn">from</span> <span class="nn">sbi.simulators.linear_gaussian</span> <span class="kn">import</span> <span class="p">(</span>
-    <span class="n">linear_gaussian</span><span class="p">,</span>
-    <span class="n">true_posterior_linear_gaussian_mvn_prior</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="c1"># Seeding</span>
-<span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">1</span><span class="p">);</span>
-</code></pre></div>
-<div class="highlight"><pre><span></span><code><span class="c1"># Gaussian simulator</span>
-<span class="n">theta_dim</span> <span class="o">=</span> <span class="mi">2</span>
-<span class="n">x_dim</span> <span class="o">=</span> <span class="n">theta_dim</span>
-
-<span class="c1"># likelihood_mean will be likelihood_shift+theta</span>
-<span class="n">likelihood_shift</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.0</span> <span class="o">*</span> <span class="n">zeros</span><span class="p">(</span><span class="n">x_dim</span><span class="p">)</span>
-<span class="n">likelihood_cov</span> <span class="o">=</span> <span class="mf">0.3</span> <span class="o">*</span> <span class="n">eye</span><span class="p">(</span><span class="n">x_dim</span><span class="p">)</span>
-
-<span class="n">prior_mean</span> <span class="o">=</span> <span class="n">zeros</span><span class="p">(</span><span class="n">theta_dim</span><span class="p">)</span>
-<span class="n">prior_cov</span> <span class="o">=</span> <span class="n">eye</span><span class="p">(</span><span class="n">theta_dim</span><span class="p">)</span>
-<span class="n">prior</span> <span class="o">=</span> <span class="n">MultivariateNormal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="n">prior_mean</span><span class="p">,</span> <span class="n">covariance_matrix</span><span class="o">=</span><span class="n">prior_cov</span><span class="p">)</span>
-
-<span class="c1"># Define Gaussian simulator</span>
-<span class="n">simulator</span><span class="p">,</span> <span class="n">prior</span> <span class="o">=</span> <span class="n">prepare_for_sbi</span><span class="p">(</span>
-    <span class="k">lambda</span> <span class="n">theta</span><span class="p">:</span> <span class="n">linear_gaussian</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">likelihood_shift</span><span class="p">,</span> <span class="n">likelihood_cov</span><span class="p">),</span> <span class="n">prior</span>
-<span class="p">)</span>
-
-<span class="c1"># Use built-in function to obtain ground-truth posterior given x_o</span>
-<span class="k">def</span> <span class="nf">get_true_posterior_samples</span><span class="p">(</span><span class="n">x_o</span><span class="p">,</span> <span class="n">num_samples</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
-    <span class="k">return</span> <span class="n">true_posterior_linear_gaussian_mvn_prior</span><span class="p">(</span>
-        <span class="n">x_o</span><span class="p">,</span> <span class="n">likelihood_shift</span><span class="p">,</span> <span class="n">likelihood_cov</span><span class="p">,</span> <span class="n">prior_mean</span><span class="p">,</span> <span class="n">prior_cov</span>
-    <span class="p">)</span><span class="o">.</span><span class="n">sample</span><span class="p">((</span><span class="n">num_samples</span><span class="p">,))</span>
-</code></pre></div>
-<h3 id="the-analytical-posterior-concentrates-around-true-parameters-with-increasing-number-of-iid-trials">The analytical posterior concentrates around true parameters with increasing number of IID trials<a class="headerlink" href="#the-analytical-posterior-concentrates-around-true-parameters-with-increasing-number-of-iid-trials" title="Permanent link">&para;</a></h3>
-<div class="highlight"><pre><span></span><code><span class="n">num_trials</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">20</span><span class="p">]</span>
-<span class="n">theta_o</span> <span class="o">=</span> <span class="n">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">theta_dim</span><span class="p">)</span>
-
-<span class="c1"># Generate multiple x_os with increasing number of trials.</span>
-<span class="n">xos</span> <span class="o">=</span> <span class="p">[</span><span class="n">theta_o</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">nt</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="k">for</span> <span class="n">nt</span> <span class="ow">in</span> <span class="n">num_trials</span><span class="p">]</span>
-
-<span class="c1"># Obtain analytical posterior samples for each of them.</span>
-<span class="n">ss</span> <span class="o">=</span> <span class="p">[</span><span class="n">get_true_posterior_samples</span><span class="p">(</span><span class="n">xo</span><span class="p">,</span> <span class="mi">5000</span><span class="p">)</span> <span class="k">for</span> <span class="n">xo</span> <span class="ow">in</span> <span class="n">xos</span><span class="p">]</span>
-</code></pre></div>
-<div class="highlight"><pre><span></span><code><span class="c1"># Plot them in one pairplot as contours (obtained via KDE on the samples).</span>
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">pairplot</span><span class="p">(</span>
-    <span class="n">ss</span><span class="p">,</span>
-    <span class="n">points</span><span class="o">=</span><span class="n">theta_o</span><span class="p">,</span>
-    <span class="n">diag</span><span class="o">=</span><span class="s2">&quot;kde&quot;</span><span class="p">,</span>
-    <span class="n">upper</span><span class="o">=</span><span class="s2">&quot;contour&quot;</span><span class="p">,</span>
-    <span class="n">kde_offdiag</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">bins</span><span class="o">=</span><span class="mi">50</span><span class="p">),</span>
-    <span class="n">kde_diag</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">bins</span><span class="o">=</span><span class="mi">100</span><span class="p">),</span>
-    <span class="n">contour_offdiag</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">levels</span><span class="o">=</span><span class="p">[</span><span class="mf">0.95</span><span class="p">]),</span>
-    <span class="n">points_colors</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;k&quot;</span><span class="p">],</span>
-    <span class="n">points_offdiag</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">marker</span><span class="o">=</span><span class="s2">&quot;*&quot;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">10</span><span class="p">),</span>
-<span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">sca</span><span class="p">(</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span>
-    <span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">nt</span><span class="si">}</span><span class="s2"> trials&quot;</span> <span class="k">if</span> <span class="n">nt</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">nt</span><span class="si">}</span><span class="s2"> trial&quot;</span> <span class="k">for</span> <span class="n">nt</span> <span class="ow">in</span> <span class="n">num_trials</span><span class="p">]</span>
-    <span class="o">+</span> <span class="p">[</span><span class="sa">r</span><span class="s2">&quot;$\theta_o$&quot;</span><span class="p">],</span>
-    <span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span>
-<span class="p">);</span>
-</code></pre></div>
-<div class="codehilite"><pre><span></span><code>/home/janfb/qode/sbi/sbi/analysis/plot.py:425: UserWarning: No contour levels were found within the data range.
-  levels=opts[&quot;contour_offdiag&quot;][&quot;levels&quot;],
-</code></pre></div>
-
-<p><img alt="png" src="../14_multi-trial-data-and-mixed-data-types_files/14_multi-trial-data-and-mixed-data-types_6_1.png" /></p>
-<p>Indeed, with increasing number of trials the posterior density concentrates around the true underlying parameter.</p>
-<h2 id="trial-based-inference-with-nle">Trial-based inference with NLE<a class="headerlink" href="#trial-based-inference-with-nle" title="Permanent link">&para;</a></h2>
-<p>(S)NLE can easily perform inference given multiple IID x because it is based on learning the likelihood. Once the likelihood is learned on single trials, i.e., a neural network that given a single observation and a parameter predicts the likelihood of that observation given the parameter, one can perform MCMC to obtain posterior samples. </p>
-<p>MCMC relies on evaluating ratios of likelihoods of candidate parameters to either accept or reject them to be posterior samples. When inferring the posterior given multiple IID observation, these likelihoods are just the joint likelihoods of each IID observation given the current parameter candidate. Thus, given a neural likelihood from SNLE, we can calculate these joint likelihoods and perform MCMC given IID data, we just have to multiply together (or add in log-space) the individual trial-likelihoods (<code>sbi</code> takes care of that).</p>
-<div class="highlight"><pre><span></span><code><span class="c1"># Train SNLE.</span>
-<span class="n">inferer</span> <span class="o">=</span> <span class="n">SNLE</span><span class="p">(</span><span class="n">prior</span><span class="p">,</span> <span class="n">show_progress_bars</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">density_estimator</span><span class="o">=</span><span class="s2">&quot;mdn&quot;</span><span class="p">)</span>
-<span class="n">theta</span><span class="p">,</span> <span class="n">x</span> <span class="o">=</span> <span class="n">simulate_for_sbi</span><span class="p">(</span><span class="n">simulator</span><span class="p">,</span> <span class="n">prior</span><span class="p">,</span> <span class="mi">10000</span><span class="p">,</span> <span class="n">simulation_batch_size</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
-<span class="n">inferer</span><span class="o">.</span><span class="n">append_simulations</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">training_batch_size</span><span class="o">=</span><span class="mi">100</span><span class="p">);</span>
-</code></pre></div>
-<div class="codehilite"><pre><span></span><code>Running 10000 simulations.:   0%|          | 0/10000 [00:00&lt;?, ?it/s]
-
-
- Neural network successfully converged after 40 epochs.
-</code></pre></div>
-
-<div class="highlight"><pre><span></span><code><span class="c1"># Obtain posterior samples for different number of iid xos.</span>
-<span class="n">samples</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="n">num_samples</span> <span class="o">=</span> <span class="mi">5000</span>
-
-<span class="n">mcmc_parameters</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
-    <span class="n">num_chains</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
-    <span class="n">thin</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-    <span class="n">warmup_steps</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
-    <span class="n">init_strategy</span><span class="o">=</span><span class="s2">&quot;proposal&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">mcmc_method</span> <span class="o">=</span> <span class="s2">&quot;slice_np_vectorized&quot;</span>
-
-<span class="n">posterior</span> <span class="o">=</span> <span class="n">inferer</span><span class="o">.</span><span class="n">build_posterior</span><span class="p">(</span>
-    <span class="n">mcmc_method</span><span class="o">=</span><span class="n">mcmc_method</span><span class="p">,</span>
-    <span class="n">mcmc_parameters</span><span class="o">=</span><span class="n">mcmc_parameters</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="c1"># Generate samples with MCMC given the same set of x_os as above.</span>
-<span class="k">for</span> <span class="n">xo</span> <span class="ow">in</span> <span class="n">xos</span><span class="p">:</span>
-    <span class="n">samples</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">posterior</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">sample_shape</span><span class="o">=</span><span class="p">(</span><span class="n">num_samples</span><span class="p">,),</span> <span class="n">x</span><span class="o">=</span><span class="n">xo</span><span class="p">))</span>
-</code></pre></div>
-<div class="codehilite"><pre><span></span><code>MCMC init with proposal: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [00:00&lt;00:00, 13448.45it/s]
-100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 75000/75000 [00:34&lt;00:00, 2173.97it/s]
-/home/janfb/qode/sbi/sbi/utils/sbiutils.py:282: UserWarning: An x with a batch size of 5 was passed. It will be interpreted as a batch of independent and identically
-            distributed data X={x_1, ..., x_n}, i.e., data generated based on the
-            same underlying (unknown) parameter. The resulting posterior will be with
-            respect to entire batch, i.e,. p(theta | X).
-  respect to entire batch, i.e,. p(theta | X).&quot;&quot;&quot;
-MCMC init with proposal: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [00:00&lt;00:00, 16636.14it/s]
-100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 75000/75000 [00:40&lt;00:00, 1871.97it/s]
-/home/janfb/qode/sbi/sbi/utils/sbiutils.py:282: UserWarning: An x with a batch size of 15 was passed. It will be interpreted as a batch of independent and identically
-            distributed data X={x_1, ..., x_n}, i.e., data generated based on the
-            same underlying (unknown) parameter. The resulting posterior will be with
-            respect to entire batch, i.e,. p(theta | X).
-  respect to entire batch, i.e,. p(theta | X).&quot;&quot;&quot;
-MCMC init with proposal: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [00:00&lt;00:00, 16856.78it/s]
-100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 75000/75000 [01:00&lt;00:00, 1234.76it/s]
-/home/janfb/qode/sbi/sbi/utils/sbiutils.py:282: UserWarning: An x with a batch size of 20 was passed. It will be interpreted as a batch of independent and identically
-            distributed data X={x_1, ..., x_n}, i.e., data generated based on the
-            same underlying (unknown) parameter. The resulting posterior will be with
-            respect to entire batch, i.e,. p(theta | X).
-  respect to entire batch, i.e,. p(theta | X).&quot;&quot;&quot;
-MCMC init with proposal: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [00:00&lt;00:00, 16580.90it/s]
-100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 75000/75000 [01:13&lt;00:00, 1020.25it/s]
-</code></pre></div>
-
-<p>Note that <code>sbi</code> warns about <code>iid-x</code> with increasing number of trial here. We ignore the warning because that&rsquo;s exactly what we want to do.</p>
-<div class="highlight"><pre><span></span><code><span class="c1"># Plot them in one pairplot as contours (obtained via KDE on the samples).</span>
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">pairplot</span><span class="p">(</span>
-    <span class="n">samples</span><span class="p">,</span>
-    <span class="n">points</span><span class="o">=</span><span class="n">theta_o</span><span class="p">,</span>
-    <span class="n">diag</span><span class="o">=</span><span class="s2">&quot;kde&quot;</span><span class="p">,</span>
-    <span class="n">upper</span><span class="o">=</span><span class="s2">&quot;contour&quot;</span><span class="p">,</span>
-    <span class="n">kde_offdiag</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">bins</span><span class="o">=</span><span class="mi">50</span><span class="p">),</span>
-    <span class="n">kde_diag</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">bins</span><span class="o">=</span><span class="mi">100</span><span class="p">),</span>
-    <span class="n">contour_offdiag</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">levels</span><span class="o">=</span><span class="p">[</span><span class="mf">0.95</span><span class="p">]),</span>
-    <span class="n">points_colors</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;k&quot;</span><span class="p">],</span>
-    <span class="n">points_offdiag</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">marker</span><span class="o">=</span><span class="s2">&quot;*&quot;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">10</span><span class="p">),</span>
-<span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">sca</span><span class="p">(</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span>
-    <span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">nt</span><span class="si">}</span><span class="s2"> trials&quot;</span> <span class="k">if</span> <span class="n">nt</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">nt</span><span class="si">}</span><span class="s2"> trial&quot;</span> <span class="k">for</span> <span class="n">nt</span> <span class="ow">in</span> <span class="n">num_trials</span><span class="p">]</span>
-    <span class="o">+</span> <span class="p">[</span><span class="sa">r</span><span class="s2">&quot;$\theta_o$&quot;</span><span class="p">],</span>
-    <span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span>
-<span class="p">);</span>
-</code></pre></div>
-<div class="codehilite"><pre><span></span><code>/home/janfb/qode/sbi/sbi/analysis/plot.py:425: UserWarning: No contour levels were found within the data range.
-  levels=opts[&quot;contour_offdiag&quot;][&quot;levels&quot;],
-</code></pre></div>
-
-<p><img alt="png" src="../14_multi-trial-data-and-mixed-data-types_files/14_multi-trial-data-and-mixed-data-types_12_1.png" /></p>
-<p>The pairplot above already indicates that (S)NLE is well able to obtain accurate posterior samples also for increasing number of trials (note that we trained the single-round version of SNLE so that we did not have to re-train it for new <span class="arithmatex">\(x_o\)</span>). </p>
-<p>Quantitatively we can measure the accuracy of SNLE by calculating the <code>c2st</code> score between SNLE and the true posterior samples, where the best accuracy is perfect for <code>0.5</code>:</p>
-<div class="highlight"><pre><span></span><code><span class="n">cs</span> <span class="o">=</span> <span class="p">[</span><span class="n">c2st</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">s1</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">s2</span><span class="p">))</span> <span class="k">for</span> <span class="n">s1</span><span class="p">,</span> <span class="n">s2</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">ss</span><span class="p">,</span> <span class="n">samples</span><span class="p">)]</span>
-
-<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">num_trials</span><span class="p">)):</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;c2st score for num_trials=</span><span class="si">{</span><span class="n">num_trials</span><span class="p">[</span><span class="n">_</span><span class="p">]</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="n">cs</span><span class="p">[</span><span class="n">_</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-</code></pre></div>
-<div class="codehilite"><pre><span></span><code>c2st score for num_trials=1: 0.51
-c2st score for num_trials=5: 0.50
-c2st score for num_trials=15: 0.53
-c2st score for num_trials=20: 0.55
-</code></pre></div>
-
-<p>This inference procedure would work similarly when using <code>SNRE</code>. However, note that it does not work for <code>SNPE</code> because in <code>SNPE</code> we are learning the posterior directly so that whenever <code>x_o</code> changes (in terms of the number of trials or the parameter dependencies) the posterior changes and <code>SNPE</code> needs to be trained again. </p>
-<h2 id="trial-based-sbi-with-mixed-data-types">Trial-based SBI with mixed data types<a class="headerlink" href="#trial-based-sbi-with-mixed-data-types" title="Permanent link">&para;</a></h2>
-<p>In some cases, models with trial-based data additionally return data with mixed data types, e.g., continous and discrete data. For example, most computational models of decision-making have continuous reaction times and discrete choices as output. </p>
-<p>This can induce a problem when performing trial-based SBI that relies on learning a neural likelihood. The problem is that it is challenging for most density estimators to handle both, continous and discrete data at the same time. There has been developed a method for solving this problem, it&rsquo;s called <strong>Mixed Neural Likelihood Estimation</strong> (MNLE). It works just like NLE, but with mixed data types. The trick is that it learns two separate density estimators, one for the discrete part of the data, and one for the continuous part, and combines the two to obtain the final neural likelihood. Crucially, the continuous density estimator is trained conditioned on the output of the discrete one, such that statistical dependencies between the discrete and continous data (e.g., between choices and reaction times) are modeled as well. The interested reader is referred to the original paper available <a href="https://www.biorxiv.org/content/10.1101/2021.12.22.473472v2">here</a>.</p>
-<p>MNLE was recently added to <code>sbi</code> (see <a href="https://github.com/mackelab/sbi/pull/638">PR</a>) and follow the same API as <code>SNLE</code>.</p>
-<h2 id="toy-problem-for-mnle">Toy problem for <code>MNLE</code><a class="headerlink" href="#toy-problem-for-mnle" title="Permanent link">&para;</a></h2>
-<p>To illustrate <code>MNLE</code> we set up a toy simulator that outputs mixed data and for which we know the likelihood such we can obtain reference posterior samples via MCMC.</p>
-<p><strong>Simulator</strong>: To simulate mixed data we do the following</p>
-<ul>
-<li>Sample reaction time from <code>inverse Gamma</code></li>
-<li>Sample choices from <code>Binomial</code></li>
-<li>Return reaction time <span class="arithmatex">\(rt \in (0, \infty\)</span> and choice index <span class="arithmatex">\(c \in \{0, 1\}\)</span></li>
-</ul>
-<div class="arithmatex">\[
-c \sim \text{Binomial}(\rho) \\
-rt \sim \text{InverseGamma}(\alpha=2, \beta) \\
-\]</div>
-<p><strong>Prior</strong>: The priors of the two parameters <span class="arithmatex">\(\rho\)</span> and <span class="arithmatex">\(\beta\)</span> are independent. We define a <code>Beta</code> prior over the probabilty parameter of the <code>Binomial</code> used in the simulator and a <code>Gamma</code> prior over the shape-parameter of the <code>inverse Gamma</code> used in the simulator:</p>
-<div class="arithmatex">\[
-p(\beta, \rho) = p(\beta) \; p(\rho) ; \\
-p(\beta) = \text{Gamma}(1, 0.5) \\
-p(\text{probs}) = \text{Beta}(2, 2) 
-\]</div>
-<p>Because the <code>InverseGamma</code> and the <code>Binomial</code> likelihoods are well-defined we can perform MCMC on this problem and obtain reference-posterior samples.</p>
-<div class="highlight"><pre><span></span><code><span class="kn">from</span> <span class="nn">sbi.inference</span> <span class="kn">import</span> <span class="n">MNLE</span>
-<span class="kn">from</span> <span class="nn">pyro.distributions</span> <span class="kn">import</span> <span class="n">InverseGamma</span>
-<span class="kn">from</span> <span class="nn">torch.distributions</span> <span class="kn">import</span> <span class="n">Beta</span><span class="p">,</span> <span class="n">Binomial</span><span class="p">,</span> <span class="n">Gamma</span>
-<span class="kn">from</span> <span class="nn">sbi.utils</span> <span class="kn">import</span> <span class="n">MultipleIndependent</span>
-
-<span class="kn">from</span> <span class="nn">sbi.inference</span> <span class="kn">import</span> <span class="n">MCMCPosterior</span><span class="p">,</span> <span class="n">VIPosterior</span><span class="p">,</span> <span class="n">RejectionPosterior</span>
-<span class="kn">from</span> <span class="nn">sbi.utils.torchutils</span> <span class="kn">import</span> <span class="n">atleast_2d</span>
-
-<span class="kn">from</span> <span class="nn">sbi.utils</span> <span class="kn">import</span> <span class="n">mcmc_transform</span>
-<span class="kn">from</span> <span class="nn">sbi.inference.potentials.base_potential</span> <span class="kn">import</span> <span class="n">BasePotential</span>
-</code></pre></div>
-<div class="highlight"><pre><span></span><code><span class="c1"># Toy simulator for mixed data</span>
-<span class="k">def</span> <span class="nf">mixed_simulator</span><span class="p">(</span><span class="n">theta</span><span class="p">):</span>
-    <span class="n">beta</span><span class="p">,</span> <span class="n">ps</span> <span class="o">=</span> <span class="n">theta</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">1</span><span class="p">],</span> <span class="n">theta</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:]</span>
-
-    <span class="n">choices</span> <span class="o">=</span> <span class="n">Binomial</span><span class="p">(</span><span class="n">probs</span><span class="o">=</span><span class="n">ps</span><span class="p">)</span><span class="o">.</span><span class="n">sample</span><span class="p">()</span>
-    <span class="n">rts</span> <span class="o">=</span> <span class="n">InverseGamma</span><span class="p">(</span><span class="n">concentration</span><span class="o">=</span><span class="mi">2</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">beta</span><span class="p">),</span> <span class="n">rate</span><span class="o">=</span><span class="n">beta</span><span class="p">)</span><span class="o">.</span><span class="n">sample</span><span class="p">()</span>
-
-    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">rts</span><span class="p">,</span> <span class="n">choices</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-
-<span class="c1"># Potential function to perform MCMC to obtain the reference posterior samples.</span>
-<span class="k">class</span> <span class="nc">PotentialFunctionProvider</span><span class="p">(</span><span class="n">BasePotential</span><span class="p">):</span>
-    <span class="n">allow_iid_x</span> <span class="o">=</span> <span class="kc">True</span>  <span class="c1"># type: ignore</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">prior</span><span class="p">,</span> <span class="n">x_o</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">&quot;cpu&quot;</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">prior</span><span class="p">,</span> <span class="n">x_o</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="n">track_gradients</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
-
-        <span class="n">theta</span> <span class="o">=</span> <span class="n">atleast_2d</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
-
-        <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">set_grad_enabled</span><span class="p">(</span><span class="n">track_gradients</span><span class="p">):</span>
-            <span class="n">iid_ll</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">iid_likelihood</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">iid_ll</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">prior</span><span class="o">.</span><span class="n">log_prob</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">iid_likelihood</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">theta</span><span class="p">):</span>
-
-        <span class="n">lp_choices</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span>
-            <span class="p">[</span>
-                <span class="n">Binomial</span><span class="p">(</span><span class="n">probs</span><span class="o">=</span><span class="n">th</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">log_prob</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x_o</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:])</span>
-                <span class="k">for</span> <span class="n">th</span> <span class="ow">in</span> <span class="n">theta</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:]</span>
-            <span class="p">],</span>
-            <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-        <span class="p">)</span>
-
-        <span class="n">lp_rts</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span>
-            <span class="p">[</span>
-                <span class="n">InverseGamma</span><span class="p">(</span>
-                    <span class="n">concentration</span><span class="o">=</span><span class="mi">2</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">beta_i</span><span class="p">),</span> <span class="n">rate</span><span class="o">=</span><span class="n">beta_i</span>
-                <span class="p">)</span><span class="o">.</span><span class="n">log_prob</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x_o</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">1</span><span class="p">])</span>
-                <span class="k">for</span> <span class="n">beta_i</span> <span class="ow">in</span> <span class="n">theta</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">1</span><span class="p">]</span>
-            <span class="p">],</span>
-            <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-        <span class="p">)</span>
-
-        <span class="n">joint_likelihood</span> <span class="o">=</span> <span class="p">(</span><span class="n">lp_choices</span> <span class="o">+</span> <span class="n">lp_rts</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-
-        <span class="k">assert</span> <span class="n">joint_likelihood</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">([</span><span class="n">x_o</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">theta</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]])</span>
-        <span class="k">return</span> <span class="n">joint_likelihood</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-</code></pre></div>
-<div class="highlight"><pre><span></span><code><span class="c1"># Define independent prior.</span>
-<span class="n">prior</span> <span class="o">=</span> <span class="n">MultipleIndependent</span><span class="p">(</span>
-    <span class="p">[</span>
-        <span class="n">Gamma</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.0</span><span class="p">]),</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">0.5</span><span class="p">])),</span>
-        <span class="n">Beta</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">2.0</span><span class="p">]),</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">2.0</span><span class="p">])),</span>
-    <span class="p">],</span>
-    <span class="n">validate_args</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-<span class="p">)</span>
-</code></pre></div>
-<h3 id="obtain-reference-posterior-samples-via-analytical-likelihood-and-mcmc">Obtain reference-posterior samples via analytical likelihood and MCMC<a class="headerlink" href="#obtain-reference-posterior-samples-via-analytical-likelihood-and-mcmc" title="Permanent link">&para;</a></h3>
-<div class="highlight"><pre><span></span><code><span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
-<span class="n">num_trials</span> <span class="o">=</span> <span class="mi">10</span>
-<span class="n">num_samples</span> <span class="o">=</span> <span class="mi">1000</span>
-<span class="n">theta_o</span> <span class="o">=</span> <span class="n">prior</span><span class="o">.</span><span class="n">sample</span><span class="p">((</span><span class="mi">1</span><span class="p">,))</span>
-<span class="n">x_o</span> <span class="o">=</span> <span class="n">mixed_simulator</span><span class="p">(</span><span class="n">theta_o</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">num_trials</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
-</code></pre></div>
-<div class="highlight"><pre><span></span><code><span class="n">true_posterior</span> <span class="o">=</span> <span class="n">MCMCPosterior</span><span class="p">(</span>
-    <span class="n">potential_fn</span><span class="o">=</span><span class="n">PotentialFunctionProvider</span><span class="p">(</span><span class="n">prior</span><span class="p">,</span> <span class="n">x_o</span><span class="p">),</span>
-    <span class="n">proposal</span><span class="o">=</span><span class="n">prior</span><span class="p">,</span>
-    <span class="n">method</span><span class="o">=</span><span class="s2">&quot;slice_np_vectorized&quot;</span><span class="p">,</span>
-    <span class="n">theta_transform</span><span class="o">=</span><span class="n">mcmc_transform</span><span class="p">(</span><span class="n">prior</span><span class="p">,</span> <span class="n">enable_transform</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
-    <span class="o">**</span><span class="n">mcmc_parameters</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">true_samples</span> <span class="o">=</span> <span class="n">true_posterior</span><span class="o">.</span><span class="n">sample</span><span class="p">((</span><span class="n">num_samples</span><span class="p">,))</span>
-</code></pre></div>
-<div class="codehilite"><pre><span></span><code>/home/janfb/qode/sbi/sbi/utils/sbiutils.py:282: UserWarning: An x with a batch size of 10 was passed. It will be interpreted as a batch of independent and identically
-            distributed data X={x_1, ..., x_n}, i.e., data generated based on the
-            same underlying (unknown) parameter. The resulting posterior will be with
-            respect to entire batch, i.e,. p(theta | X).
-  respect to entire batch, i.e,. p(theta | X).&quot;&quot;&quot;
-MCMC init with proposal: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [00:00&lt;00:00, 4365.61it/s]
-100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 35000/35000 [02:39&lt;00:00, 219.06it/s]
-</code></pre></div>
-
-<h3 id="train-mnle-and-generate-samples-via-mcmc">Train MNLE and generate samples via MCMC<a class="headerlink" href="#train-mnle-and-generate-samples-via-mcmc" title="Permanent link">&para;</a></h3>
-<div class="highlight"><pre><span></span><code><span class="c1"># Training data</span>
-<span class="n">num_simulations</span> <span class="o">=</span> <span class="mi">5000</span>
-<span class="n">theta</span> <span class="o">=</span> <span class="n">prior</span><span class="o">.</span><span class="n">sample</span><span class="p">((</span><span class="n">num_simulations</span><span class="p">,))</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">mixed_simulator</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
-
-<span class="c1"># Train MNLE and obtain MCMC-based posterior.</span>
-<span class="n">trainer</span> <span class="o">=</span> <span class="n">MNLE</span><span class="p">()</span>
-<span class="n">estimator</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="n">append_simulations</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
-</code></pre></div>
-<div class="codehilite"><pre><span></span><code> Neural network successfully converged after 84 epochs.
-</code></pre></div>
-
-<div class="highlight"><pre><span></span><code><span class="n">posterior</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="n">build_posterior</span><span class="p">(</span><span class="n">estimator</span><span class="p">,</span> <span class="n">prior</span><span class="p">)</span>
-</code></pre></div>
-<div class="highlight"><pre><span></span><code><span class="c1"># Training data</span>
-<span class="n">num_simulations</span> <span class="o">=</span> <span class="mi">5000</span>
-<span class="n">theta</span> <span class="o">=</span> <span class="n">prior</span><span class="o">.</span><span class="n">sample</span><span class="p">((</span><span class="n">num_simulations</span><span class="p">,))</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">mixed_simulator</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
-
-<span class="c1"># Train MNLE and obtain MCMC-based posterior.</span>
-<span class="n">trainer</span> <span class="o">=</span> <span class="n">MNLE</span><span class="p">(</span><span class="n">prior</span><span class="p">)</span>
-<span class="n">estimator</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="n">append_simulations</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
-<span class="n">mnle_posterior</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="n">build_posterior</span><span class="p">(</span>
-    <span class="n">mcmc_method</span><span class="o">=</span><span class="s2">&quot;slice_np_vectorized&quot;</span><span class="p">,</span> <span class="n">mcmc_parameters</span><span class="o">=</span><span class="n">mcmc_parameters</span>
-<span class="p">)</span>
-<span class="n">mnle_samples</span> <span class="o">=</span> <span class="n">mnle_posterior</span><span class="o">.</span><span class="n">sample</span><span class="p">((</span><span class="n">num_samples</span><span class="p">,),</span> <span class="n">x</span><span class="o">=</span><span class="n">x_o</span><span class="p">)</span>
-</code></pre></div>
-<div class="codehilite"><pre><span></span><code>/home/janfb/qode/sbi/sbi/neural_nets/mnle.py:64: UserWarning: The mixed neural likelihood estimator assumes that x contains
-        continuous data in the first n-1 columns (e.g., reaction times) and
-        categorical data in the last column (e.g., corresponding choices). If
-        this is not the case for the passed `x` do not use this function.
-  this is not the case for the passed `x` do not use this function.&quot;&quot;&quot;
-
-
- Neural network successfully converged after 35 epochs.
-
-MCMC init with proposal: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [00:00&lt;00:00, 6125.93it/s]
-100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 35000/35000 [01:26&lt;00:00, 404.04it/s]
-</code></pre></div>
-
-<h3 id="compare-mnle-and-reference-posterior">Compare MNLE and reference posterior<a class="headerlink" href="#compare-mnle-and-reference-posterior" title="Permanent link">&para;</a></h3>
-<div class="highlight"><pre><span></span><code><span class="c1"># Plot them in one pairplot as contours (obtained via KDE on the samples).</span>
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">pairplot</span><span class="p">(</span>
-    <span class="p">[</span>
-        <span class="n">prior</span><span class="o">.</span><span class="n">sample</span><span class="p">((</span><span class="mi">1000</span><span class="p">,)),</span>
-        <span class="n">true_samples</span><span class="p">,</span>
-        <span class="n">mnle_samples</span><span class="p">,</span>
-    <span class="p">],</span>
-    <span class="n">points</span><span class="o">=</span><span class="n">theta_o</span><span class="p">,</span>
-    <span class="n">diag</span><span class="o">=</span><span class="s2">&quot;kde&quot;</span><span class="p">,</span>
-    <span class="n">upper</span><span class="o">=</span><span class="s2">&quot;contour&quot;</span><span class="p">,</span>
-    <span class="n">kde_offdiag</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">bins</span><span class="o">=</span><span class="mi">50</span><span class="p">),</span>
-    <span class="n">kde_diag</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">bins</span><span class="o">=</span><span class="mi">100</span><span class="p">),</span>
-    <span class="n">contour_offdiag</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">levels</span><span class="o">=</span><span class="p">[</span><span class="mf">0.95</span><span class="p">]),</span>
-    <span class="n">points_colors</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;k&quot;</span><span class="p">],</span>
-    <span class="n">points_offdiag</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">marker</span><span class="o">=</span><span class="s2">&quot;*&quot;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">10</span><span class="p">),</span>
-    <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="sa">r</span><span class="s2">&quot;$\beta$&quot;</span><span class="p">,</span> <span class="sa">r</span><span class="s2">&quot;$\rho$&quot;</span><span class="p">],</span>
-<span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">sca</span><span class="p">(</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span>
-    <span class="p">[</span><span class="s2">&quot;Prior&quot;</span><span class="p">,</span> <span class="s2">&quot;Reference&quot;</span><span class="p">,</span> <span class="s2">&quot;MNLE&quot;</span><span class="p">,</span> <span class="sa">r</span><span class="s2">&quot;$\theta_o$&quot;</span><span class="p">],</span>
-    <span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span>
-<span class="p">);</span>
-</code></pre></div>
-<div class="codehilite"><pre><span></span><code>/home/janfb/qode/sbi/sbi/analysis/plot.py:425: UserWarning: No contour levels were found within the data range.
-  levels=opts[&quot;contour_offdiag&quot;][&quot;levels&quot;],
-</code></pre></div>
-
-<p><img alt="png" src="../14_multi-trial-data-and-mixed-data-types_files/14_multi-trial-data-and-mixed-data-types_29_1.png" /></p>
-<p>We see that the inferred <code>MNLE</code> posterior nicely matches the reference posterior, and how both inferred a posterior that is quite different from the prior.</p>
-<p>Because MNLE training is amortized we can obtain another posterior given a different observation with potentially a different number of trials, just by running MCMC again (without re-training <code>MNLE</code>):</p>
-<h3 id="repeat-inference-with-different-x_o-that-has-more-trials">Repeat inference with different <code>x_o</code> that has more trials<a class="headerlink" href="#repeat-inference-with-different-x_o-that-has-more-trials" title="Permanent link">&para;</a></h3>
-<div class="highlight"><pre><span></span><code><span class="n">num_trials</span> <span class="o">=</span> <span class="mi">100</span>
-<span class="n">x_o</span> <span class="o">=</span> <span class="n">mixed_simulator</span><span class="p">(</span><span class="n">theta_o</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">num_trials</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
-<span class="n">true_samples</span> <span class="o">=</span> <span class="n">true_posterior</span><span class="o">.</span><span class="n">sample</span><span class="p">((</span><span class="n">num_samples</span><span class="p">,),</span> <span class="n">x</span><span class="o">=</span><span class="n">x_o</span><span class="p">)</span>
-<span class="n">mnle_samples</span> <span class="o">=</span> <span class="n">mnle_posterior</span><span class="o">.</span><span class="n">sample</span><span class="p">((</span><span class="n">num_samples</span><span class="p">,),</span> <span class="n">x</span><span class="o">=</span><span class="n">x_o</span><span class="p">)</span>
-</code></pre></div>
-<div class="codehilite"><pre><span></span><code>/home/janfb/qode/sbi/sbi/utils/sbiutils.py:282: UserWarning: An x with a batch size of 100 was passed. It will be interpreted as a batch of independent and identically
-            distributed data X={x_1, ..., x_n}, i.e., data generated based on the
-            same underlying (unknown) parameter. The resulting posterior will be with
-            respect to entire batch, i.e,. p(theta | X).
-  respect to entire batch, i.e,. p(theta | X).&quot;&quot;&quot;
-MCMC init with proposal: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [00:00&lt;00:00, 4685.01it/s]
-100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 35000/35000 [02:47&lt;00:00, 209.25it/s]
-MCMC init with proposal: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [00:00&lt;00:00, 6136.69it/s]
-100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 35000/35000 [08:23&lt;00:00, 69.57it/s]
-</code></pre></div>
-
-<div class="highlight"><pre><span></span><code><span class="c1"># Plot them in one pairplot as contours (obtained via KDE on the samples).</span>
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">pairplot</span><span class="p">(</span>
-    <span class="p">[</span>
-        <span class="n">prior</span><span class="o">.</span><span class="n">sample</span><span class="p">((</span><span class="mi">1000</span><span class="p">,)),</span>
-        <span class="n">true_samples</span><span class="p">,</span>
-        <span class="n">mnle_samples</span><span class="p">,</span>
-    <span class="p">],</span>
-    <span class="n">points</span><span class="o">=</span><span class="n">theta_o</span><span class="p">,</span>
-    <span class="n">diag</span><span class="o">=</span><span class="s2">&quot;kde&quot;</span><span class="p">,</span>
-    <span class="n">upper</span><span class="o">=</span><span class="s2">&quot;contour&quot;</span><span class="p">,</span>
-    <span class="n">kde_offdiag</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">bins</span><span class="o">=</span><span class="mi">50</span><span class="p">),</span>
-    <span class="n">kde_diag</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">bins</span><span class="o">=</span><span class="mi">100</span><span class="p">),</span>
-    <span class="n">contour_offdiag</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">levels</span><span class="o">=</span><span class="p">[</span><span class="mf">0.95</span><span class="p">]),</span>
-    <span class="n">points_colors</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;k&quot;</span><span class="p">],</span>
-    <span class="n">points_offdiag</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">marker</span><span class="o">=</span><span class="s2">&quot;*&quot;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">10</span><span class="p">),</span>
-    <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="sa">r</span><span class="s2">&quot;$\beta$&quot;</span><span class="p">,</span> <span class="sa">r</span><span class="s2">&quot;$\rho$&quot;</span><span class="p">],</span>
-<span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">sca</span><span class="p">(</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span>
-    <span class="p">[</span><span class="s2">&quot;Prior&quot;</span><span class="p">,</span> <span class="s2">&quot;Reference&quot;</span><span class="p">,</span> <span class="s2">&quot;MNLE&quot;</span><span class="p">,</span> <span class="sa">r</span><span class="s2">&quot;$\theta_o$&quot;</span><span class="p">],</span>
-    <span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span>
-<span class="p">);</span>
-</code></pre></div>
-<div class="codehilite"><pre><span></span><code>/home/janfb/qode/sbi/sbi/analysis/plot.py:425: UserWarning: No contour levels were found within the data range.
-  levels=opts[&quot;contour_offdiag&quot;][&quot;levels&quot;],
-</code></pre></div>
-
-<p><img alt="png" src="../14_multi-trial-data-and-mixed-data-types_files/14_multi-trial-data-and-mixed-data-types_33_1.png" /></p>
-<p>Again we can see that the posteriors match nicely. In addition, we observe that the posterior variance reduces as we increase the number of trials, similar to the illustration with the Gaussian example at the beginning of the tutorial. </p>
-<p>A final note: <code>MNLE</code> is trained on single-trial data. Theoretically, density estimation is perfectly accurate only in the limit of infinite training data. Thus, training with a finite amount of training data naturally induces a small bias in the density estimator. As we observed above, this bias is so small that we don&rsquo;t really notice it, e.g., the <code>c2st</code> scores were close to 0.5. However, when we increase the number of trials in <code>x_o</code> dramatically (on the order of 1000s) the small bias can accumulate over the trials and inference with <code>MNLE</code> can become less accurate.</p>
-<div class="highlight"><pre><span></span><code>
-</code></pre></div>
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diff --git a/tutorial/14_multi-trial-data-and-mixed-data-types_files/14_multi-trial-data-and-mixed-data-types_12_1.png b/tutorial/14_multi-trial-data-and-mixed-data-types_files/14_multi-trial-data-and-mixed-data-types_12_1.png
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+++ b/tutorial/15_mcmc_diagnostics_with_arviz/index.html
@@ -16,8 +16,9 @@
       
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+      
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+      <link rel="stylesheet" href="../../assets/stylesheets/main.4b4a2bd9.min.css">
       
         
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+        <link rel="stylesheet" href="../../assets/stylesheets/palette.356b1318.min.css">
       
       
 
+
     
     
+      
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+      
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@@ -119,6 +124,7 @@
     
     
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+        
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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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@@ -156,7 +165,7 @@
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-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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   <div class="md-source__repository">
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@@ -186,6 +195,7 @@
                     
 
 
+
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   <label class="md-nav__title" for="__drawer">
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@@ -200,7 +210,7 @@
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-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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-      
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+    Home
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@@ -227,14 +241,18 @@
     
       
       
-      
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       <a href="../../install/" class="md-nav__link">
-        Installation
+        
+  
+  <span class="md-ellipsis">
+    Installation
+  </span>
+  
+
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@@ -242,316 +260,430 @@
     
       
       
-      
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+        
+        
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+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
+        
           
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Tutorials and Examples
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+  
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+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3">
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+            Tutorials and Examples
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+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     
+    
+    
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-      
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-          Introduction
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-        <ul class="md-nav__list" data-md-scrollfix>
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+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_1" >
+        
           
+          <label class="md-nav__link" for="__nav_3_1" id="__nav_3_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Introduction
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_1">
+            <span class="md-nav__icon md-icon"></span>
+            Introduction
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+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
       </a>
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
+  
+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
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-            
-          
+              
             
               
+                
   
   
   
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       <a href="../16_implemented_methods/" class="md-nav__link">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
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+              
             
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-      </nav>
+          </ul>
+        </nav>
+      
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+    
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+    
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-      
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+        
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+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
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+  
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+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_2">
+            <span class="md-nav__icon md-icon"></span>
+            Advanced
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
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       <a href="../03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
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-            
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+                
   
   
   
     <li class="md-nav__item">
       <a href="../14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
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-            
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+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
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+              
             
               
+                
   
   
   
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       <a href="../10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
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+              
             
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+          </ul>
+        </nav>
+      
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+        
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+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" checked>
+        
           
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
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+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Diagnostics
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../12_diagnostics_posterior_predictive_check/" class="md-nav__link">
-        Posterior predictive checks
+        
+  
+  <span class="md-ellipsis">
+    Posterior predictive checks
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
+  
+  <span class="md-ellipsis">
+    Simulation-based calibration
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
     
@@ -566,12 +698,24 @@
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          Density plots and MCMC diagnostics with ArviZ
+          
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <a href="./" class="md-nav__link md-nav__link--active">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
       </a>
       
         
@@ -637,218 +781,288 @@
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
-          Analysis
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_4">
-          <span class="md-nav__icon md-icon"></span>
-          Analysis
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Analysis
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Analysis
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../07_conditional_distributions/" class="md-nav__link">
-        Conditional distributions
+        
+  
+  <span class="md-ellipsis">
+    Conditional distributions
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
+  
+  <span class="md-ellipsis">
+    Posterior sensitivity analysis
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
-          Examples
-          <span class="md-nav__icon md-icon"></span>
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-      <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_3_5">
-          <span class="md-nav__icon md-icon"></span>
-          Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_5" >
+        
           
+          <label class="md-nav__link" for="__nav_3_5" id="__nav_3_5_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_5">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/00_HH_simulator/" class="md-nav__link">
-        Hodgkin-Huxley example
+        
+  
+  <span class="md-ellipsis">
+    Hodgkin-Huxley example
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../examples/01_decision_making_model/" class="md-nav__link">
-        Decision making model
+        
+  
+  <span class="md-ellipsis">
+    Decision making model
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
-          Contributing
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_4">
-          <span class="md-nav__icon md-icon"></span>
-          Contributing
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Contributing
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Contributing
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../contribute/" class="md-nav__link">
-        Guide
+        
+  
+  <span class="md-ellipsis">
+    Guide
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../../code_of_conduct/" class="md-nav__link">
-        Code of Conduct
+        
+  
+  <span class="md-ellipsis">
+    Code of Conduct
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../reference/" class="md-nav__link">
-        API Reference
+        
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -856,14 +1070,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../faq/" class="md-nav__link">
-        FAQ
+        
+  
+  <span class="md-ellipsis">
+    FAQ
+  </span>
+  
+
       </a>
     </li>
   
@@ -871,14 +1089,18 @@
     
       
       
-      
-
   
   
   
     <li class="md-nav__item">
       <a href="../../credits/" class="md-nav__link">
-        Credits
+        
+  
+  <span class="md-ellipsis">
+    Credits
+  </span>
+  
+
       </a>
     </li>
   
@@ -1181,7 +1403,7 @@ <h3 id="posterior-density-plots">Posterior density  plots<a class="headerlink" h
       
     
     <a href="https://github.com/sbi-dev/sbi" target="_blank" rel="noopener" title="github.com" class="md-social__link">
-      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.0 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 480 512"><!--! Font Awesome Free 6.4.2 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 2023 Fonticons, Inc.--><path d="M186.1 328.7c0 20.9-10.9 55.1-36.7 55.1s-36.7-34.2-36.7-55.1 10.9-55.1 36.7-55.1 36.7 34.2 36.7 55.1zM480 278.2c0 31.9-3.2 65.7-17.5 95-37.9 76.6-142.1 74.8-216.7 74.8-75.8 0-186.2 2.7-225.6-74.8-14.6-29-20.2-63.1-20.2-95 0-41.9 13.9-81.5 41.5-113.6-5.2-15.8-7.7-32.4-7.7-48.8 0-21.5 4.9-32.3 14.6-51.8 45.3 0 74.3 9 108.8 36 29-6.9 58.8-10 88.7-10 27 0 54.2 2.9 80.4 9.2 34-26.7 63-35.2 107.8-35.2 9.8 19.5 14.6 30.3 14.6 51.8 0 16.4-2.6 32.7-7.7 48.2 27.5 32.4 39 72.3 39 114.2zm-64.3 50.5c0-43.9-26.7-82.6-73.5-82.6-18.9 0-37 3.4-56 6-14.9 2.3-29.8 3.2-45.1 3.2-15.2 0-30.1-.9-45.1-3.2-18.7-2.6-37-6-56-6-46.8 0-73.5 38.7-73.5 82.6 0 87.8 80.4 101.3 150.4 101.3h48.2c70.3 0 150.6-13.4 150.6-101.3zm-82.6-55.1c-25.8 0-36.7 34.2-36.7 55.1s10.9 55.1 36.7 55.1 36.7-34.2 36.7-55.1-10.9-55.1-36.7-55.1z"/></svg>
     </a>
   
 </div>
@@ -1195,10 +1417,11 @@ <h3 id="posterior-density-plots">Posterior density  plots<a class="headerlink" h
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.208ed371.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": "../../assets/javascripts/workers/search.f886a092.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.fac441b0.min.js"></script>
+      <script src="../../assets/javascripts/bundle.81fa17fe.min.js"></script>
       
         <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML"></script>
       
diff --git a/tutorial/16_implemented_methods/index.html b/tutorial/16_implemented_methods/index.html
index 25e86e754..08ac5e9a1 100644
--- a/tutorial/16_implemented_methods/index.html
+++ b/tutorial/16_implemented_methods/index.html
@@ -16,8 +16,9 @@
       
         <link rel="next" href="../03_multiround_inference/">
       
+      
       <link rel="icon" href="../../assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.1.9">
+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.4.8">
     
     
       
@@ -25,15 +26,18 @@
       
     
     
-      <link rel="stylesheet" href="../../assets/stylesheets/main.85bb2934.min.css">
+      <link rel="stylesheet" href="../../assets/stylesheets/main.4b4a2bd9.min.css">
       
         
-        <link rel="stylesheet" href="../../assets/stylesheets/palette.a6bdf11c.min.css">
+        <link rel="stylesheet" href="../../assets/stylesheets/palette.356b1318.min.css">
       
       
 
+
     
     
+      
+    
     
       
         
@@ -95,6 +99,7 @@
 
     </a>
     <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 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
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     <div class="md-header__title" data-md-component="header-title">
@@ -119,6 +124,7 @@
     
     
       <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-.27A6.516 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-.27A6.516 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>
       <nav class="md-search__options" aria-label="Search">
         
         <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 12 19 6.41Z"/></svg>
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@@ -156,7 +165,7 @@
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@@ -186,6 +195,7 @@
                     
 
 
+
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   <label class="md-nav__title" for="__drawer">
     <a href="../.." title="sbi" class="md-nav__button md-logo" aria-label="sbi" data-md-component="logo">
@@ -200,7 +210,7 @@
       <a href="http://github.com/sbi-dev/sbi" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
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@@ -212,14 +222,18 @@
     
       
       
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       <a href="../.." class="md-nav__link">
-        Home
+        
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@@ -227,14 +241,18 @@
     
       
       
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       <a href="../../install/" class="md-nav__link">
-        Installation
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+
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@@ -242,108 +260,142 @@
     
       
       
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+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../00_getting_started/" class="md-nav__link">
-        Getting started
+        
+  
+  <span class="md-ellipsis">
+    Getting started
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../02_flexible_interface/" class="md-nav__link">
-        Flexible interface
+        
+  
+  <span class="md-ellipsis">
+    Flexible interface
+  </span>
+  
+
       </a>
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../01_gaussian_amortized/" class="md-nav__link">
-        Amortized inference
+        
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+  <span class="md-ellipsis">
+    Amortized inference
+  </span>
+  
+
       </a>
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-            
-          
+              
             
               
+                
   
   
     
@@ -358,12 +410,24 @@
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          Implemented algorithms
+          
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+  <span class="md-ellipsis">
+    Implemented algorithms
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+  
+
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <a href="./" class="md-nav__link md-nav__link--active">
-        Implemented algorithms
+        
+  
+  <span class="md-ellipsis">
+    Implemented algorithms
+  </span>
+  
+
       </a>
       
         
@@ -416,426 +480,576 @@
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
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-          
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+    
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-      
-      
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-          Advanced
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+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_2" >
+        
           
+          <label class="md-nav__link" for="__nav_3_2" id="__nav_3_2_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Advanced
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_2_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_2">
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+            Advanced
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+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../03_multiround_inference/" class="md-nav__link">
-        Multi-round inference
+        
+  
+  <span class="md-ellipsis">
+    Multi-round inference
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../11_sampler_interface/" class="md-nav__link">
-        Sampling algorithms in sbi
+        
+  
+  <span class="md-ellipsis">
+    Sampling algorithms in sbi
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../04_density_estimators/" class="md-nav__link">
-        Custom density estimators
+        
+  
+  <span class="md-ellipsis">
+    Custom density estimators
+  </span>
+  
+
       </a>
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../05_embedding_net/" class="md-nav__link">
-        Learning summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Learning summary statistics
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../14_iid_data_and_permutation_invariant_embeddings/" class="md-nav__link">
-        SBI with trial-based data
+        
+  
+  <span class="md-ellipsis">
+    SBI with trial-based data
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../08_restriction_estimator/" class="md-nav__link">
-        Handling invalid simulations
+        
+  
+  <span class="md-ellipsis">
+    Handling invalid simulations
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../10_crafting_summary_statistics/" class="md-nav__link">
-        Crafting summary statistics
+        
+  
+  <span class="md-ellipsis">
+    Crafting summary statistics
+  </span>
+  
+
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+              
             
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-      </nav>
+          </ul>
+        </nav>
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+        
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+        
           
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+            Diagnostics
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       <a href="../12_diagnostics_posterior_predictive_check/" class="md-nav__link">
-        Posterior predictive checks
+        
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+    Posterior predictive checks
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
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       <a href="../13_diagnostics_simulation_based_calibration/" class="md-nav__link">
-        Simulation-based calibration
+        
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+    Simulation-based calibration
+  </span>
+  
+
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-            
-          
+              
             
               
+                
   
   
   
     <li class="md-nav__item">
       <a href="../15_mcmc_diagnostics_with_arviz/" class="md-nav__link">
-        Density plots and MCMC diagnostics with ArviZ
+        
+  
+  <span class="md-ellipsis">
+    Density plots and MCMC diagnostics with ArviZ
+  </span>
+  
+
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+              
             
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-      </nav>
+          </ul>
+        </nav>
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+        
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+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
           
+          <label class="md-nav__link" for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
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+    Conditional distributions
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-            
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+              
             
               
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       <a href="../09_sensitivity_analysis/" class="md-nav__link">
-        Posterior sensitivity analysis
+        
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+    Posterior sensitivity analysis
+  </span>
+  
+
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+              
             
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+          </ul>
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-        Hodgkin-Huxley example
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+    Hodgkin-Huxley example
+  </span>
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+
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+    Decision making model
+  </span>
+  
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-<h1 id="using-variational-inference-for-building-posteriors">Using Variational Inference for Building Posteriors<a class="headerlink" href="#using-variational-inference-for-building-posteriors" title="Permanent link">&para;</a></h1>
-<p>In the previous tutorial, we saw how to build the posterior and how to specialize on one specific observation <code>x_o</code>. If one uses SNPE, then the posterior can be sampled from directly, yet this comes at the expense of necessary correction terms during training, since the samples are obtained from the &ldquo;wrong&rdquo; prior for <code>num_rounds &gt; 1</code>. For SNLE or SNRE, MCMC sampling is required, which is computationally expensive. With SNVI (sequential neural variational inference), it is possible to directly sample from the posterior without any corrections during training or without expensive MCMC for sampling. This is possible by learning the posterior with variational inference techniques. For this, an additional network (one for the likelihood or likelihood-to-evidence-ratio) must be trained first.</p>
-<h2 id="main-syntax">Main syntax<a class="headerlink" href="#main-syntax" title="Permanent link">&para;</a></h2>
-<div class="highlight"><pre><span></span><code><span class="n">inference</span> <span class="o">=</span> <span class="n">SNLE</span><span class="p">(</span><span class="n">prior</span><span class="p">)</span>
-
-<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_rounds</span><span class="p">):</span>
-    <span class="n">theta</span><span class="p">,</span> <span class="n">x</span> <span class="o">=</span> <span class="n">simulate_for_sbi</span><span class="p">(</span><span class="n">simulator</span><span class="p">,</span> <span class="n">proposal</span><span class="p">,</span> <span class="n">num_simulations</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>
-
-    <span class="c1"># In `SNLE` and `SNRE`, you should not pass the `proposal` to `</span>
-    <span class="c1"># .append_simulations()`.</span>
-    <span class="n">likelihood_estimator</span> <span class="o">=</span> <span class="n">inference</span><span class="o">.</span><span class="n">append_simulations</span><span class="p">(</span>
-        <span class="n">theta</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span>
-    <span class="p">)</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
-
-    <span class="c1"># Obtain potential (learned likelihood * prior) and theta transformation.</span>
-    <span class="n">potential_fn</span><span class="p">,</span> <span class="n">theta_transform</span> <span class="o">=</span> <span class="n">likelihood_estimator_based_potential</span><span class="p">(</span>
-        <span class="n">likelihood_estimator</span><span class="p">,</span> <span class="n">prior</span><span class="p">,</span> <span class="n">x_o</span>
-    <span class="p">)</span>
-
-    <span class="c1"># Build posterior via variational inference.</span>
-    <span class="n">posterior</span> <span class="o">=</span> <span class="n">VIPosterior</span><span class="p">(</span>
-        <span class="n">potential_fn</span><span class="p">,</span> <span class="n">prior</span><span class="p">,</span> <span class="s2">&quot;maf&quot;</span><span class="p">,</span> <span class="n">theta_transform</span><span class="p">,</span> <span class="n">vi_method</span><span class="o">=</span><span class="s2">&quot;fKL&quot;</span><span class="p">,</span>
-    <span class="p">)</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
-    <span class="n">proposal</span> <span class="o">=</span> <span class="n">posterior</span>
-</code></pre></div>
-<h2 id="linear-gaussian-example">Linear Gaussian example<a class="headerlink" href="#linear-gaussian-example" title="Permanent link">&para;</a></h2>
-<p>Below, we give a full example of inferring the posterior distribution with SNVI over multiple rounds. For this, we take the same example as in the previous tutorial.</p>
-<div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">torch</span>
-
-<span class="kn">from</span> <span class="nn">sbi.inference</span> <span class="kn">import</span> <span class="p">(</span>
-    <span class="n">likelihood_estimator_based_potential</span><span class="p">,</span>
-    <span class="n">SNLE</span><span class="p">,</span>
-    <span class="n">prepare_for_sbi</span><span class="p">,</span>
-    <span class="n">simulate_for_sbi</span><span class="p">,</span>
-    <span class="n">VIPosterior</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="kn">from</span> <span class="nn">sbi</span> <span class="kn">import</span> <span class="n">utils</span> <span class="k">as</span> <span class="n">utils</span>
-<span class="kn">from</span> <span class="nn">sbi</span> <span class="kn">import</span> <span class="n">analysis</span> <span class="k">as</span> <span class="n">analysis</span>
-
-<span class="n">_</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-</code></pre></div>
-<div class="highlight"><pre><span></span><code><span class="n">num_dim</span> <span class="o">=</span> <span class="mi">3</span>
-<span class="n">prior</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">BoxUniform</span><span class="p">(</span><span class="n">low</span><span class="o">=-</span><span class="mi">2</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">num_dim</span><span class="p">),</span> <span class="n">high</span><span class="o">=</span><span class="mi">2</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">num_dim</span><span class="p">))</span>
-</code></pre></div>
-<div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">linear_gaussian</span><span class="p">(</span><span class="n">theta</span><span class="p">):</span>
-    <span class="k">return</span> <span class="n">theta</span> <span class="o">+</span> <span class="mf">1.0</span> <span class="o">+</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn_like</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.1</span>
-</code></pre></div>
-<div class="highlight"><pre><span></span><code><span class="n">simulator</span><span class="p">,</span> <span class="n">prior</span> <span class="o">=</span> <span class="n">prepare_for_sbi</span><span class="p">(</span><span class="n">linear_gaussian</span><span class="p">,</span> <span class="n">prior</span><span class="p">)</span>
-</code></pre></div>
-<p>Here, we decide to learn the likelihood directly, but learning the likelihood-to-evidence ratio would also be a viable option.</p>
-<div class="highlight"><pre><span></span><code><span class="n">inference</span> <span class="o">=</span> <span class="n">SNLE</span><span class="p">(</span><span class="n">prior</span><span class="o">=</span><span class="n">prior</span><span class="p">)</span>
-</code></pre></div>
-<p>Now we can run inference, where we first learn the likelihood, which is then in turn used to learn a posterior through variational inference.</p>
-<div class="highlight"><pre><span></span><code><span class="n">num_rounds</span> <span class="o">=</span> <span class="mi">2</span>
-<span class="n">x_o</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">3</span><span class="p">,)</span>
-
-<span class="n">posteriors</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="n">proposal</span> <span class="o">=</span> <span class="n">prior</span>
-
-<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_rounds</span><span class="p">):</span>
-    <span class="n">theta</span><span class="p">,</span> <span class="n">x</span> <span class="o">=</span> <span class="n">simulate_for_sbi</span><span class="p">(</span><span class="n">simulator</span><span class="p">,</span> <span class="n">proposal</span><span class="p">,</span> <span class="n">num_simulations</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>
-    <span class="n">likelihood_estimator</span> <span class="o">=</span> <span class="n">inference</span><span class="o">.</span><span class="n">append_simulations</span><span class="p">(</span>
-        <span class="n">theta</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span>
-    <span class="p">)</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
-    <span class="n">potential_fn</span><span class="p">,</span> <span class="n">theta_transform</span> <span class="o">=</span> <span class="n">likelihood_estimator_based_potential</span><span class="p">(</span>
-        <span class="n">likelihood_estimator</span><span class="p">,</span> <span class="n">prior</span><span class="p">,</span> <span class="n">x_o</span>
-    <span class="p">)</span>
-
-    <span class="n">posterior</span> <span class="o">=</span> <span class="n">VIPosterior</span><span class="p">(</span>
-        <span class="n">potential_fn</span><span class="p">,</span> <span class="n">prior</span><span class="p">,</span> <span class="s2">&quot;maf&quot;</span><span class="p">,</span> <span class="n">theta_transform</span><span class="p">,</span> <span class="n">vi_method</span><span class="o">=</span><span class="s2">&quot;fKL&quot;</span><span class="p">,</span>
-    <span class="p">)</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
-    <span class="n">posteriors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">posterior</span><span class="p">)</span>
-    <span class="n">proposal</span> <span class="o">=</span> <span class="n">posterior</span>
-</code></pre></div>
-<div class="codehilite"><pre><span></span><code>Running 500 simulations.:   0%|          | 0/500 [00:00&lt;?, ?it/s]
-
-
- Neural network successfully converged after 50 epochs.
-
-
-  0%|          | 0/2000 [00:00&lt;?, ?it/s]
-
-
-
-Converged with loss: -4.23
-Quality Score: -0.218    Good: Smaller than 0.5  Bad: Larger than 1.0            NOTE: Less sensitive to mode collapse.
-
-
-
-Running 500 simulations.:   0%|          | 0/500 [00:00&lt;?, ?it/s]
-
-
- Neural network successfully converged after 47 epochs.
-
-
-  0%|          | 0/2000 [00:00&lt;?, ?it/s]
-
-
-
-Converged with loss: -4.2
-Quality Score: 0.025     Good: Smaller than 0.5  Bad: Larger than 1.0            NOTE: Less sensitive to mode collapse.
-</code></pre></div>
-
-<p>After having learned the posterior, we use <code>.pairplot()</code> to visualize the samples from the learned posterior.</p>
-<div class="highlight"><pre><span></span><code><span class="n">posterior_samples</span> <span class="o">=</span> <span class="n">posterior</span><span class="o">.</span><span class="n">sample</span><span class="p">((</span><span class="mi">10000</span><span class="p">,),</span> <span class="n">x</span><span class="o">=</span><span class="n">x_o</span><span class="p">)</span>
-
-<span class="c1"># Plot posterior samples.</span>
-<span class="n">_</span> <span class="o">=</span> <span class="n">analysis</span><span class="o">.</span><span class="n">pairplot</span><span class="p">(</span>
-    <span class="n">posterior_samples</span><span class="p">,</span> <span class="n">limits</span><span class="o">=</span><span class="p">[[</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]],</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
-<span class="p">)</span>
-</code></pre></div>
-<p><img alt="png" src="../17_vi_posteriors_files/17_vi_posteriors_15_0.png" /></p>
-
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