From e4f9ca8f8238515948fde753edcf3481747c8bc5 Mon Sep 17 00:00:00 2001
From: bennybp <ben@bennyp.org>
Date: Mon, 22 Apr 2024 21:23:28 +0000
Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20MolSSI/Q?=
 =?UTF-8?q?CManyBody@0b3f18315a21d79b6009453e80c24ee5822072ca=20?=
 =?UTF-8?q?=F0=9F=9A=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 404.html                                      |   75 +-
 api/index.html                                |  155 +-
 core-interface/index.html                     |  735 +++++
 .../index.html                                |  136 +-
 how-to-guides/index.html                      |   79 +-
 index.html                                    |  149 +-
 keywords/index.html                           |  641 ++++
 objects.inv                                   |  Bin 284 -> 264 bytes
 reference/index.html                          | 2585 -----------------
 {tutorials => results}/index.html             |  118 +-
 search/search_index.json                      |    2 +-
 sitemap.xml                                   |   23 +-
 sitemap.xml.gz                                |  Bin 268 -> 274 bytes
 13 files changed, 1630 insertions(+), 3068 deletions(-)
 create mode 100644 core-interface/index.html
 rename {explanation => high-level-interface}/index.html (89%)
 create mode 100644 keywords/index.html
 delete mode 100644 reference/index.html
 rename {tutorials => results}/index.html (87%)

diff --git a/404.html b/404.html
index 96cf2fe..8885665 100644
--- a/404.html
+++ b/404.html
@@ -16,7 +16,7 @@
     
     
       
-        <title>QCManyBody Docs</title>
+        <title>QCManyBody Documentation</title>
       
     
     
@@ -81,7 +81,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="/QCManyBody/." title="QCManyBody Docs" class="md-header__button md-logo" aria-label="QCManyBody Docs" data-md-component="logo">
+    <a href="/QCManyBody/." title="QCManyBody Documentation" class="md-header__button md-logo" aria-label="QCManyBody Documentation" data-md-component="logo">
       
   <img src="/QCManyBody/logo.png" alt="logo">
 
@@ -94,7 +94,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            QCManyBody Docs
+            QCManyBody Documentation
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -212,7 +212,7 @@
   
     
   
-  QCManyBody Docs
+  Home
 
       </a>
     </li>
@@ -224,12 +224,12 @@
   
   
     <li class="md-tabs__item">
-      <a href="/QCManyBody/tutorials/" class="md-tabs__link">
+      <a href="/QCManyBody/high-level-interface/" class="md-tabs__link">
         
   
     
   
-  Tutorials
+  High-Level Interface
 
       </a>
     </li>
@@ -241,12 +241,12 @@
   
   
     <li class="md-tabs__item">
-      <a href="/QCManyBody/api/" class="md-tabs__link">
+      <a href="/QCManyBody/core-interface/" class="md-tabs__link">
         
   
     
   
-  API Documentation
+  Core Interface
 
       </a>
     </li>
@@ -275,29 +275,12 @@
   
   
     <li class="md-tabs__item">
-      <a href="/QCManyBody/reference/" class="md-tabs__link">
-        
-  
-    
-  
-  Reference
-
-      </a>
-    </li>
-  
-
-      
-        
-  
-  
-  
-    <li class="md-tabs__item">
-      <a href="/QCManyBody/explanation/" class="md-tabs__link">
+      <a href="/QCManyBody/api/" class="md-tabs__link">
         
   
     
   
-  Explanation
+  API Documentation
 
       </a>
     </li>
@@ -326,12 +309,12 @@
 
 <nav class="md-nav md-nav--primary md-nav--lifted" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="/QCManyBody/." title="QCManyBody Docs" class="md-nav__button md-logo" aria-label="QCManyBody Docs" data-md-component="logo">
+    <a href="/QCManyBody/." title="QCManyBody Documentation" class="md-nav__button md-logo" aria-label="QCManyBody Documentation" data-md-component="logo">
       
   <img src="/QCManyBody/logo.png" alt="logo">
 
     </a>
-    QCManyBody Docs
+    QCManyBody Documentation
   </label>
   
     <div class="md-nav__source">
@@ -359,7 +342,7 @@
         
   
   <span class="md-ellipsis">
-    QCManyBody Docs
+    Home
   </span>
   
 
@@ -375,11 +358,11 @@
   
   
     <li class="md-nav__item">
-      <a href="/QCManyBody/tutorials/" class="md-nav__link">
+      <a href="/QCManyBody/high-level-interface/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    Tutorials
+    High-Level Interface
   </span>
   
 
@@ -395,11 +378,11 @@
   
   
     <li class="md-nav__item">
-      <a href="/QCManyBody/api/" class="md-nav__link">
+      <a href="/QCManyBody/core-interface/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    API Documentation
+    Core Interface
   </span>
   
 
@@ -435,31 +418,11 @@
   
   
     <li class="md-nav__item">
-      <a href="/QCManyBody/reference/" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    Reference
-  </span>
-  
-
-      </a>
-    </li>
-  
-
-    
-      
-      
-  
-  
-  
-  
-    <li class="md-nav__item">
-      <a href="/QCManyBody/explanation/" class="md-nav__link">
+      <a href="/QCManyBody/api/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    Explanation
+    API Documentation
   </span>
   
 
diff --git a/api/index.html b/api/index.html
index d77545c..9f3f748 100644
--- a/api/index.html
+++ b/api/index.html
@@ -11,18 +11,16 @@
         <link rel="canonical" href="https://molssi.github.io/QCManyBody/api/">
       
       
-        <link rel="prev" href="../tutorials/">
+        <link rel="prev" href="../how-to-guides/">
       
       
-        <link rel="next" href="../how-to-guides/">
-      
       
       <link rel="icon" href="../logo.png">
       <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.5.18">
     
     
       
-        <title>API Documentation - QCManyBody Docs</title>
+        <title>API Documentation - QCManyBody Documentation</title>
       
     
     
@@ -92,7 +90,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href=".." title="QCManyBody Docs" class="md-header__button md-logo" aria-label="QCManyBody Docs" data-md-component="logo">
+    <a href=".." title="QCManyBody Documentation" class="md-header__button md-logo" aria-label="QCManyBody Documentation" data-md-component="logo">
       
   <img src="../logo.png" alt="logo">
 
@@ -105,7 +103,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            QCManyBody Docs
+            QCManyBody Documentation
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -223,7 +221,7 @@
   
     
   
-  QCManyBody Docs
+  Home
 
       </a>
     </li>
@@ -235,12 +233,12 @@
   
   
     <li class="md-tabs__item">
-      <a href="../tutorials/" class="md-tabs__link">
+      <a href="../high-level-interface/" class="md-tabs__link">
         
   
     
   
-  Tutorials
+  High-Level Interface
 
       </a>
     </li>
@@ -250,16 +248,14 @@
         
   
   
-    
-  
   
-    <li class="md-tabs__item md-tabs__item--active">
-      <a href="./" class="md-tabs__link">
+    <li class="md-tabs__item">
+      <a href="../core-interface/" class="md-tabs__link">
         
   
     
   
-  API Documentation
+  Core Interface
 
       </a>
     </li>
@@ -286,31 +282,16 @@
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../reference/" class="md-tabs__link">
-        
-  
     
   
-  Reference
-
-      </a>
-    </li>
-  
-
-      
-        
-  
   
-  
-    <li class="md-tabs__item">
-      <a href="../explanation/" class="md-tabs__link">
+    <li class="md-tabs__item md-tabs__item--active">
+      <a href="./" class="md-tabs__link">
         
   
     
   
-  Explanation
+  API Documentation
 
       </a>
     </li>
@@ -339,12 +320,12 @@
 
 <nav class="md-nav md-nav--primary md-nav--lifted" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href=".." title="QCManyBody Docs" class="md-nav__button md-logo" aria-label="QCManyBody Docs" data-md-component="logo">
+    <a href=".." title="QCManyBody Documentation" class="md-nav__button md-logo" aria-label="QCManyBody Documentation" data-md-component="logo">
       
   <img src="../logo.png" alt="logo">
 
     </a>
-    QCManyBody Docs
+    QCManyBody Documentation
   </label>
   
     <div class="md-nav__source">
@@ -372,7 +353,47 @@
         
   
   <span class="md-ellipsis">
-    QCManyBody Docs
+    Home
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../high-level-interface/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    High-Level Interface
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../core-interface/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Core Interface
   </span>
   
 
@@ -388,11 +409,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../tutorials/" class="md-nav__link">
+      <a href="../how-to-guides/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    Tutorials
+    How-To Guides
   </span>
   
 
@@ -484,66 +505,6 @@
   
 
     
-      
-      
-  
-  
-  
-  
-    <li class="md-nav__item">
-      <a href="../how-to-guides/" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    How-To Guides
-  </span>
-  
-
-      </a>
-    </li>
-  
-
-    
-      
-      
-  
-  
-  
-  
-    <li class="md-nav__item">
-      <a href="../reference/" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    Reference
-  </span>
-  
-
-      </a>
-    </li>
-  
-
-    
-      
-      
-  
-  
-  
-  
-    <li class="md-nav__item">
-      <a href="../explanation/" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    Explanation
-  </span>
-  
-
-      </a>
-    </li>
-  
-
-    
   </ul>
 </nav>
                   </div>
diff --git a/core-interface/index.html b/core-interface/index.html
new file mode 100644
index 0000000..288502e
--- /dev/null
+++ b/core-interface/index.html
@@ -0,0 +1,735 @@
+
+<!doctype html>
+<html lang="en" class="no-js">
+  <head>
+    
+      <meta charset="utf-8">
+      <meta name="viewport" content="width=device-width,initial-scale=1">
+      
+      
+      
+        <link rel="canonical" href="https://molssi.github.io/QCManyBody/core-interface/">
+      
+      
+        <link rel="prev" href="../high-level-interface/">
+      
+      
+        <link rel="next" href="../how-to-guides/">
+      
+      
+      <link rel="icon" href="../logo.png">
+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.5.18">
+    
+    
+      
+        <title>Core Interface - QCManyBody Documentation</title>
+      
+    
+    
+      <link rel="stylesheet" href="../assets/stylesheets/main.66ac8b77.min.css">
+      
+        
+        <link rel="stylesheet" href="../assets/stylesheets/palette.06af60db.min.css">
+      
+      
+
+
+    
+    
+      
+    
+    
+      
+        
+        
+        <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+        <link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto:300,300i,400,400i,700,700i%7CRoboto+Mono:400,400i,700,700i&display=fallback">
+        <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
+      
+    
+    
+      <link rel="stylesheet" href="../assets/_mkdocstrings.css">
+    
+      <link rel="stylesheet" href="../stylesheets/extra.css">
+    
+    <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    
+      
+
+    
+    
+    
+  </head>
+  
+  
+    
+    
+      
+    
+    
+    
+    
+    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="custom" data-md-color-accent="indigo">
+  
+    
+    <input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off">
+    <input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off">
+    <label class="md-overlay" for="__drawer"></label>
+    <div data-md-component="skip">
+      
+        
+        <a href="#core-interface" class="md-skip">
+          Skip to content
+        </a>
+      
+    </div>
+    <div data-md-component="announce">
+      
+    </div>
+    
+    
+      
+
+<header class="md-header" data-md-component="header">
+  <nav class="md-header__inner md-grid" aria-label="Header">
+    <a href=".." title="QCManyBody Documentation" class="md-header__button md-logo" aria-label="QCManyBody Documentation" data-md-component="logo">
+      
+  <img src="../logo.png" alt="logo">
+
+    </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">
+      <div class="md-header__ellipsis">
+        <div class="md-header__topic">
+          <span class="md-ellipsis">
+            QCManyBody Documentation
+          </span>
+        </div>
+        <div class="md-header__topic" data-md-component="header-topic">
+          <span class="md-ellipsis">
+            
+              Core Interface
+            
+          </span>
+        </div>
+      </div>
+    </div>
+    
+      
+        <form class="md-header__option" data-md-component="palette">
+  
+    
+    
+    
+    <input class="md-option" data-md-color-media="(prefers-color-scheme: light)" data-md-color-scheme="default" data-md-color-primary="custom" data-md-color-accent="indigo"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_0">
+    
+      <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 2a7 7 0 0 1 7 7c0 2.38-1.19 4.47-3 5.74V17a1 1 0 0 1-1 1H9a1 1 0 0 1-1-1v-2.26C6.19 13.47 5 11.38 5 9a7 7 0 0 1 7-7M9 21v-1h6v1a1 1 0 0 1-1 1h-4a1 1 0 0 1-1-1m3-17a5 5 0 0 0-5 5c0 2.05 1.23 3.81 3 4.58V16h4v-2.42c1.77-.77 3-2.53 3-4.58a5 5 0 0 0-5-5Z"/></svg>
+      </label>
+    
+  
+    
+    
+    
+    <input class="md-option" data-md-color-media="(prefers-color-scheme: dark)" data-md-color-scheme="slate" data-md-color-primary="custom" data-md-color-accent="indigo"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_1">
+    
+      <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 2a7 7 0 0 0-7 7c0 2.38 1.19 4.47 3 5.74V17a1 1 0 0 0 1 1h6a1 1 0 0 0 1-1v-2.26c1.81-1.27 3-3.36 3-5.74a7 7 0 0 0-7-7M9 21a1 1 0 0 0 1 1h4a1 1 0 0 0 1-1v-1H9v1Z"/></svg>
+      </label>
+    
+  
+</form>
+      
+    
+    
+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+    
+    
+    
+      <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">
+  <label class="md-search__overlay" for="__search"></label>
+  <div class="md-search__inner" role="search">
+    <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>
+      
+    </form>
+    <div class="md-search__output">
+      <div class="md-search__scrollwrap" data-md-scrollfix>
+        <div class="md-search-result" data-md-component="search-result">
+          <div class="md-search-result__meta">
+            Initializing search
+          </div>
+          <ol class="md-search-result__list" role="presentation"></ol>
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+    
+    
+      <div class="md-header__source">
+        <a href="https://github.com/MolSSI/QCManyBody" 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 6.5.2 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 2024 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">
+    MolSSI/QCManyBody
+  </div>
+</a>
+      </div>
+    
+  </nav>
+  
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+
+
+<h1 id="core-interface">Core Interface</h1>
+<p>The core interface of QCManyBody is designed to allow for more flexibility in how the calculations are run.
+The primary responsibilities of the core interface are:</p>
+<ol>
+<li>Given a molecule and desired levels of MBE, return the fragments and levels that must be computed for each fragment</li>
+<li>Given a dictionary of the results of those computations, analyze those results and calculate the desired manybody properties</li>
+</ol>
+<p>Note that the user is expected to run the calculations themselves, and the core interface does not provide any tools for
+running the calculations.</p>
+<h2 id="using-the-core-interface">Using the core interface</h2>
+<p>The core interface is accessed through the <a class="autorefs autorefs-internal" href="../api/#qcmanybody.ManyBodyCalculator"><code>ManyBodyCalculator</code></a>
+class.</p>
+<p>The first step is to create a molecule. This molecule is a QCElemental molecule object, and must contain fragments.</p>
+<pre><code class="language-python">import qcmanybody as qcmb
+import qcelemental as qcel
+
+# Create a molecule with 3 hydrogen atoms, each as its own fragment
+mol = qcel.models.Molecule(symbols=['h', 'h', 'h'],
+                           geometry=[[0,0,0],[0,0,2],[0,0,4]],
+                           fragments=[[0], [1], [2]])
+</code></pre>
+<p>Next, create a <code>ManyBodyCalculator</code> object. This object is constructed using the molecule,
+the desired BSSE correction, levels of MBE, and other options of the MBE.</p>
+<pre><code class="language-python">mbc = qcmb.ManyBodyCalculator(
+    molecule=mol,
+    bsse_type=['cp'],
+    levels={1: 'mp2/aug-cc-pvtz', 2: 'b3lyp/def2-svp', 3: 'hf/sto-3g'},
+    return_total_data=True,
+    supersystem_ie_only=False
+)
+</code></pre>
+<p>The <code>levels</code> option is a dictionary that specifies the n-body level as a key, then an arbitrary
+string as the description of the calculation to be performed at the n-body level. This string is
+termed the 'model chemistry' is completely arbitrary; it only has meaning to the user, and the user is expected to map these strings
+to some meaningful definition of a calculation.</p>
+<p><strong>Note</strong>: The core interface is less flexible than the high-level interface when it comes to the <code>levels</code> option.
+    In the core interface, all levels must be accounted for (that is, keys must go from 1 to the maximum
+    nbody you would like to calculate). All levels must be present even if the model chemistry
+    is the same for all levels.</p>
+<p>For a complete discussion of the other options available in the <code>ManyBodyCalculator</code> object, see the
+<a href="../keywords/">keywords discussion</a>
+the <a class="autorefs autorefs-internal" href="../api/#qcmanybody.ManyBodyCalculator"><code>ManyBodyCalculator API documentation</code></a>.</p>
+<p>The next step is to obtain the calculations to be run from the <code>ManyBodyCalculator</code> object.
+This is done with a python generator function <code>iterate_molecules</code> that returns
+a tuple. This tuple contains</p>
+<ol>
+<li>The string describing the calculation to be run (the model chemistry string, as defined in the <code>levels</code> dictionary)</li>
+<li>A label for the calculation. This label is opaque to the user but used to identify the calculation when analyzing the results.</li>
+<li>A <code>Molecule</code> object that contains the cluster of fragments to be computed.</li>
+</ol>
+<pre><code class="language-python">calculation_results = {}
+for model_chemistry, label, mol_cluster in mbc.iterate_molecules():
+    calculation_results[label] = run_calculation(mol_cluster, model_chemistry)
+</code></pre>
+<p>Note that it is entirely up to the user to run the calculation somehow - this level of interface
+does not provide any tools for running the calculations.</p>
+<h3 id="results-dictionary">Results dictionary</h3>
+<p>The data returned from the calculations is expected to be stored in a nested dictionary.
+The level is the opaque label as given from the <code>QCManyBodyCalculator</code>.
+The second level is the name of the property.</p>
+<pre><code class="language-python">calculation_results = {
+    'label1': {
+        'energy': -1.0,
+        'gradient': [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],
+    },
+    'label2': {
+        'energy': -2.0,
+        'gradient': [[4.0, 5.0, 6.0], [7.0, 8.0, 9.0]],
+    },
+    'label3': {
+        'energy': -3.0,
+        'gradient': [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]],
+    },
+}
+</code></pre>
+<h3 id="analysis">Analysis</h3>
+<p>This result dictionary is all that is needed to perform the final analysis and calculation
+of the MBE properties.</p>
+<pre><code class="language-python">final_results = mbc.analyze(calculation_results)
+</code></pre>
+<p>For a discussion about what the results contain, see the <a href="../results/">results documentation</a>.</p>
+
+
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-  <h1>Explanation</h1>
-
-<p>This part of the project documentation focuses on an
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-chance to read about the background of the project,
-as well as reasoning about how it was implemented.</p>
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-<p><strong>Note:</strong> Expand this section by considering the
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-<li>Provide multiple examples and approaches of how
-    to work with it</li>
-<li>Help the reader make connections</li>
-<li>Avoid writing instructions or technical descriptions
-    here</li>
-</ul>
+<h1 id="high-level-interface">High-Level Interface</h1>
 
 
 
diff --git a/how-to-guides/index.html b/how-to-guides/index.html
index 208fb52..6866290 100644
--- a/how-to-guides/index.html
+++ b/how-to-guides/index.html
@@ -11,10 +11,10 @@
         <link rel="canonical" href="https://molssi.github.io/QCManyBody/how-to-guides/">
       
       
-        <link rel="prev" href="../api/">
+        <link rel="prev" href="../core-interface/">
       
       
-        <link rel="next" href="../reference/">
+        <link rel="next" href="../api/">
       
       
       <link rel="icon" href="../logo.png">
@@ -22,7 +22,7 @@
     
     
       
-        <title>How-To Guides - QCManyBody Docs</title>
+        <title>How-To Guides - QCManyBody Documentation</title>
       
     
     
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href=".." title="QCManyBody Docs" class="md-header__button md-logo" aria-label="QCManyBody Docs" data-md-component="logo">
+    <a href=".." title="QCManyBody Documentation" class="md-header__button md-logo" aria-label="QCManyBody Documentation" data-md-component="logo">
       
   <img src="../logo.png" alt="logo">
 
@@ -100,7 +100,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            QCManyBody Docs
+            QCManyBody Documentation
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -218,7 +218,7 @@
   
     
   
-  QCManyBody Docs
+  Home
 
       </a>
     </li>
@@ -230,12 +230,12 @@
   
   
     <li class="md-tabs__item">
-      <a href="../tutorials/" class="md-tabs__link">
+      <a href="../high-level-interface/" class="md-tabs__link">
         
   
     
   
-  Tutorials
+  High-Level Interface
 
       </a>
     </li>
@@ -247,12 +247,12 @@
   
   
     <li class="md-tabs__item">
-      <a href="../api/" class="md-tabs__link">
+      <a href="../core-interface/" class="md-tabs__link">
         
   
     
   
-  API Documentation
+  Core Interface
 
       </a>
     </li>
@@ -283,29 +283,12 @@
   
   
     <li class="md-tabs__item">
-      <a href="../reference/" class="md-tabs__link">
-        
-  
-    
-  
-  Reference
-
-      </a>
-    </li>
-  
-
-      
-        
-  
-  
-  
-    <li class="md-tabs__item">
-      <a href="../explanation/" class="md-tabs__link">
+      <a href="../api/" class="md-tabs__link">
         
   
     
   
-  Explanation
+  API Documentation
 
       </a>
     </li>
@@ -334,12 +317,12 @@
 
 <nav class="md-nav md-nav--primary md-nav--lifted" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href=".." title="QCManyBody Docs" class="md-nav__button md-logo" aria-label="QCManyBody Docs" data-md-component="logo">
+    <a href=".." title="QCManyBody Documentation" class="md-nav__button md-logo" aria-label="QCManyBody Documentation" data-md-component="logo">
       
   <img src="../logo.png" alt="logo">
 
     </a>
-    QCManyBody Docs
+    QCManyBody Documentation
   </label>
   
     <div class="md-nav__source">
@@ -367,7 +350,7 @@
         
   
   <span class="md-ellipsis">
-    QCManyBody Docs
+    Home
   </span>
   
 
@@ -383,11 +366,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../tutorials/" class="md-nav__link">
+      <a href="../high-level-interface/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    Tutorials
+    High-Level Interface
   </span>
   
 
@@ -403,11 +386,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../api/" class="md-nav__link">
+      <a href="../core-interface/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    API Documentation
+    Core Interface
   </span>
   
 
@@ -451,31 +434,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../reference/" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    Reference
-  </span>
-  
-
-      </a>
-    </li>
-  
-
-    
-      
-      
-  
-  
-  
-  
-    <li class="md-nav__item">
-      <a href="../explanation/" class="md-nav__link">
+      <a href="../api/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    Explanation
+    API Documentation
   </span>
   
 
diff --git a/index.html b/index.html
index 52fc8a7..adf9896 100644
--- a/index.html
+++ b/index.html
@@ -12,7 +12,7 @@
       
       
       
-        <link rel="next" href="tutorials/">
+        <link rel="next" href="high-level-interface/">
       
       
       <link rel="icon" href="logo.png">
@@ -20,7 +20,7 @@
     
     
       
-        <title>QCManyBody Docs</title>
+        <title>QCManyBody Documentation</title>
       
     
     
@@ -76,7 +76,7 @@
     <div data-md-component="skip">
       
         
-        <a href="#welcome-to-mkdocs" class="md-skip">
+        <a href="#qcmanybody-documentation" class="md-skip">
           Skip to content
         </a>
       
@@ -90,7 +90,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="." title="QCManyBody Docs" class="md-header__button md-logo" aria-label="QCManyBody Docs" data-md-component="logo">
+    <a href="." title="QCManyBody Documentation" class="md-header__button md-logo" aria-label="QCManyBody Documentation" data-md-component="logo">
       
   <img src="logo.png" alt="logo">
 
@@ -103,13 +103,13 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            QCManyBody Docs
+            QCManyBody Documentation
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
           <span class="md-ellipsis">
             
-              QCManyBody Docs
+              Home
             
           </span>
         </div>
@@ -223,7 +223,7 @@
   
     
   
-  QCManyBody Docs
+  Home
 
       </a>
     </li>
@@ -235,12 +235,12 @@
   
   
     <li class="md-tabs__item">
-      <a href="tutorials/" class="md-tabs__link">
+      <a href="high-level-interface/" class="md-tabs__link">
         
   
     
   
-  Tutorials
+  High-Level Interface
 
       </a>
     </li>
@@ -252,12 +252,12 @@
   
   
     <li class="md-tabs__item">
-      <a href="api/" class="md-tabs__link">
+      <a href="core-interface/" class="md-tabs__link">
         
   
     
   
-  API Documentation
+  Core Interface
 
       </a>
     </li>
@@ -286,29 +286,12 @@
   
   
     <li class="md-tabs__item">
-      <a href="reference/" class="md-tabs__link">
-        
-  
-    
-  
-  Reference
-
-      </a>
-    </li>
-  
-
-      
-        
-  
-  
-  
-    <li class="md-tabs__item">
-      <a href="explanation/" class="md-tabs__link">
+      <a href="api/" class="md-tabs__link">
         
   
     
   
-  Explanation
+  API Documentation
 
       </a>
     </li>
@@ -337,12 +320,12 @@
 
 <nav class="md-nav md-nav--primary md-nav--lifted" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="." title="QCManyBody Docs" class="md-nav__button md-logo" aria-label="QCManyBody Docs" data-md-component="logo">
+    <a href="." title="QCManyBody Documentation" class="md-nav__button md-logo" aria-label="QCManyBody Documentation" data-md-component="logo">
       
   <img src="logo.png" alt="logo">
 
     </a>
-    QCManyBody Docs
+    QCManyBody Documentation
   </label>
   
     <div class="md-nav__source">
@@ -379,7 +362,7 @@
           
   
   <span class="md-ellipsis">
-    QCManyBody Docs
+    Home
   </span>
   
 
@@ -390,7 +373,7 @@
         
   
   <span class="md-ellipsis">
-    QCManyBody Docs
+    Home
   </span>
   
 
@@ -412,9 +395,18 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#commands" class="md-nav__link">
+  <a href="#installation" class="md-nav__link">
+    <span class="md-ellipsis">
+      Installation
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#package-overview" class="md-nav__link">
     <span class="md-ellipsis">
-      Commands
+      Package Overview
     </span>
   </a>
   
@@ -423,7 +415,7 @@
         <li class="md-nav__item">
   <a href="#table-of-contents" class="md-nav__link">
     <span class="md-ellipsis">
-      Table Of Contents
+      Table of Contents
     </span>
   </a>
   
@@ -444,11 +436,11 @@
   
   
     <li class="md-nav__item">
-      <a href="tutorials/" class="md-nav__link">
+      <a href="high-level-interface/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    Tutorials
+    High-Level Interface
   </span>
   
 
@@ -464,11 +456,11 @@
   
   
     <li class="md-nav__item">
-      <a href="api/" class="md-nav__link">
+      <a href="core-interface/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    API Documentation
+    Core Interface
   </span>
   
 
@@ -504,31 +496,11 @@
   
   
     <li class="md-nav__item">
-      <a href="reference/" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    Reference
-  </span>
-  
-
-      </a>
-    </li>
-  
-
-    
-      
-      
-  
-  
-  
-  
-    <li class="md-nav__item">
-      <a href="explanation/" class="md-nav__link">
+      <a href="api/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    Explanation
+    API Documentation
   </span>
   
 
@@ -564,9 +536,18 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#commands" class="md-nav__link">
+  <a href="#installation" class="md-nav__link">
+    <span class="md-ellipsis">
+      Installation
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#package-overview" class="md-nav__link">
     <span class="md-ellipsis">
-      Commands
+      Package Overview
     </span>
   </a>
   
@@ -575,7 +556,7 @@
         <li class="md-nav__item">
   <a href="#table-of-contents" class="md-nav__link">
     <span class="md-ellipsis">
-      Table Of Contents
+      Table of Contents
     </span>
   </a>
   
@@ -599,21 +580,31 @@
   
 
 
-<h1 id="welcome-to-mkdocs">Welcome to MkDocs</h1>
-<p>For full documentation visit <a href="https://www.mkdocs.org">mkdocs.org</a>.</p>
-<h2 id="commands">Commands</h2>
-<ul>
-<li><code>mkdocs new [dir-name]</code> - Create a new project.</li>
-<li><code>mkdocs serve</code> - Start the live-reloading docs server.</li>
-<li><code>mkdocs build</code> - Build the documentation site.</li>
-<li><code>mkdocs -h</code> - Print help message and exit.</li>
-</ul>
-<h2 id="table-of-contents">Table Of Contents</h2>
+<h1 id="qcmanybody-documentation">QCManyBody Documentation</h1>
+<p>QCManyBody is a python package for running quantum chemistry manybody expansions and interaction calculations in a
+package-independent way.</p>
+<h2 id="installation">Installation</h2>
+<p>Currently, the package is only available on <a href="https://github.com/MolSSI/QCManyBody">GitHub</a>. To install directly
+from GitHub, use the following command:</p>
+<pre><code class="language-bash">pip install git+https://github.com/MolSSI/QCManyBody.git
+</code></pre>
+<h2 id="package-overview">Package Overview</h2>
+<p>The package has two main interfaces. The high-level interface allows for a comprehensive workflow, where the user
+provides complete information about the calculation, including the full specification (method, basis set, etc.) of the
+manybody calculation. This is designed to work with <a href="https://github.com/MolSSI/QCEngine">QCEngine</a> or other packages
+that implement the <a href="https://github.com/MolSSI/QCSchema">QCSchema</a>.</p>
+<p>For more information, see (High-level interface)(high-level-interface.md).</p>
+<p>QCManyBody also has a core low-level interface that allows for more flexibility in how the calculations are run. This
+interface generally takes a molecule and an arbitrary definition of quantum chemistry specifications, and expects
+the user to run them themselves.</p>
+<p>For more information, see (Core interface)(core-interface.md).</p>
+<h2 id="table-of-contents">Table of Contents</h2>
 <ol>
-<li><a href="tutorials/">Tutorials</a></li>
+<li><a href="keywords/">Common Keywords and Options</a></li>
+<li><a href="high-level-interface/">High-level Interface</a></li>
+<li><a href="core-interface/">Core Interface</a></li>
+<li><a href="results/">Results</a></li>
 <li><a href="how-to-guides/">How-To Guides</a></li>
-<li><a href="reference/">Reference</a></li>
-<li><a href="explanation/">Explanation</a></li>
 </ol>
 
 
diff --git a/keywords/index.html b/keywords/index.html
new file mode 100644
index 0000000..a311b7b
--- /dev/null
+++ b/keywords/index.html
@@ -0,0 +1,641 @@
+
+<!doctype html>
+<html lang="en" class="no-js">
+  <head>
+    
+      <meta charset="utf-8">
+      <meta name="viewport" content="width=device-width,initial-scale=1">
+      
+      
+      
+        <link rel="canonical" href="https://molssi.github.io/QCManyBody/keywords/">
+      
+      
+      
+      
+      <link rel="icon" href="../logo.png">
+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.5.18">
+    
+    
+      
+        <title>Keywords and options - QCManyBody Documentation</title>
+      
+    
+    
+      <link rel="stylesheet" href="../assets/stylesheets/main.66ac8b77.min.css">
+      
+        
+        <link rel="stylesheet" href="../assets/stylesheets/palette.06af60db.min.css">
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+      
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+      <link rel="stylesheet" href="../assets/_mkdocstrings.css">
+    
+      <link rel="stylesheet" href="../stylesheets/extra.css">
+    
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+    <label class="md-overlay" for="__drawer"></label>
+    <div data-md-component="skip">
+      
+        
+        <a href="#keywords-and-options" class="md-skip">
+          Skip to content
+        </a>
+      
+    </div>
+    <div data-md-component="announce">
+      
+    </div>
+    
+    
+      
+
+<header class="md-header" data-md-component="header">
+  <nav class="md-header__inner md-grid" aria-label="Header">
+    <a href=".." title="QCManyBody Documentation" class="md-header__button md-logo" aria-label="QCManyBody Documentation" data-md-component="logo">
+      
+  <img src="../logo.png" alt="logo">
+
+    </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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+            QCManyBody Documentation
+          </span>
+        </div>
+        <div class="md-header__topic" data-md-component="header-topic">
+          <span class="md-ellipsis">
+            
+              Keywords and options
+            
+          </span>
+        </div>
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+      <div class="md-search" data-md-component="search" role="dialog">
+  <label class="md-search__overlay" for="__search"></label>
+  <div class="md-search__inner" role="search">
+    <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>
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+                  </div>
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+            
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+              
+              <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="#required" class="md-nav__link">
+    <span class="md-ellipsis">
+      Required
+    </span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Required">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#molecule" class="md-nav__link">
+    <span class="md-ellipsis">
+      Molecule
+    </span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bsse_type" class="md-nav__link">
+    <span class="md-ellipsis">
+      bsse_type
+    </span>
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+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#levels-and-max_nbody" class="md-nav__link">
+    <span class="md-ellipsis">
+      levels and max_nbody
+    </span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#keywords-and-options_1" class="md-nav__link">
+    <span class="md-ellipsis">
+      Keywords and Options
+    </span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Keywords and Options">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#return_total_data" class="md-nav__link">
+    <span class="md-ellipsis">
+      return_total_data
+    </span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#supersystem_ie_only" class="md-nav__link">
+    <span class="md-ellipsis">
+      supersystem_ie_only
+    </span>
+  </a>
+  
+</li>
+        
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+    </nav>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+                  </div>
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+            
+          
+          
+            <div class="md-content" data-md-component="content">
+              <article class="md-content__inner md-typeset">
+                
+                  
+
+  
+  
+
+
+<h1 id="keywords-and-options">Keywords and options</h1>
+<h2 id="required">Required</h2>
+<h3 id="molecule">Molecule</h3>
+<h3 id="bsse_type">bsse_type</h3>
+<h3 id="levels-and-max_nbody">levels and max_nbody</h3>
+<h2 id="keywords-and-options_1">Keywords and Options</h2>
+<p>Both the high-level interface and core interface share the same terminology with respect to options</p>
+<h3 id="return_total_data">return_total_data</h3>
+<p>When set to true, the manybody calculation will return the total data (energy/gradient/hessian/property) of the system.
+If not, the return will only contain interaction data.</p>
+<p>Note that the calculation of counterpoise corrected total properties implies the calculation of the energies of monomers
+in the monomer basis, hence specifying <code>return_total_data = True</code> may carry out more computations than.
+For some properties such as gradients and hessians, <code>return_total_data = False</code> is rarely useful.</p>
+<h3 id="supersystem_ie_only">supersystem_ie_only</h3>
+<p>Target the supersystem total/interaction energy (IE) data over the many-body expansion (MBE)
+analysis, thereby omitting intermediate-body calculations. When false, each n-body level
+in the MBE up through <code>max_nbody</code> will be computed. When true (only allowed for <code>max_nbody = nfragments</code>),
+only compute enough for the overall interaction/total energy: max_nbody-body and 1-body.</p>
+<p>When true, properties <code>INTERACTION {driver} THROUGH {max_nbody}-BODY</code> will always be available;
+<code>TOTAL {driver} THROUGH {max_nbody}-BODY</code> will be available depending on <code>return_total_data</code>; and
+<code>{max_nbody}-BODY CONTRIBUTION TO {driver}</code> won't be available (except for dimers).</p>
+<p>This keyword produces no savings for a two-fragment molecule. But for the interaction energy of a three-fragment molecule, for example, 2-body
+subsystems can be skipped with <code>supersystem_ie_only=True</code> Do not use with <code>vmfc</code> in <code>bsse_type</code>
+as it cannot produce savings.</p>
+
+
+
+
+
+
+
+
+
+
+
+
+                
+              </article>
+            </div>
+          
+          
+<script>var target=document.getElementById(location.hash.slice(1));target&&target.name&&(target.checked=target.name.startsWith("__tabbed_"))</script>
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+</html>
\ No newline at end of file
diff --git a/objects.inv b/objects.inv
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deleted file mode 100644
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-an <strong>information-oriented</strong> approach. Use it as a
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-              <summary>Source code in <code>qcmanybody/manybody.py</code></summary>
-              <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 30</span>
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-<span class="normal">523</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">class</span> <span class="nc">ManyBodyCalculator</span><span class="p">:</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">molecule</span><span class="p">:</span> <span class="n">Molecule</span><span class="p">,</span>
-        <span class="n">bsse_type</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">BsseEnum</span><span class="p">],</span>
-        <span class="n">levels</span><span class="p">:</span> <span class="n">Mapping</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="n">Literal</span><span class="p">[</span><span class="s2">&quot;supersystem&quot;</span><span class="p">]],</span> <span class="nb">str</span><span class="p">],</span>
-        <span class="n">return_total_data</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
-        <span class="n">supersystem_ie_only</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
-    <span class="p">):</span>
-        <span class="c1"># TODO</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">embedding_charges</span> <span class="o">=</span> <span class="p">{}</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">molecule</span> <span class="o">=</span> <span class="n">molecule</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bsse_type</span> <span class="o">=</span> <span class="p">[</span><span class="n">BsseEnum</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">bsse_type</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">return_total_data</span> <span class="o">=</span> <span class="n">return_total_data</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">supersystem_ie_only</span> <span class="o">=</span> <span class="n">supersystem_ie_only</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">nfragments</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">molecule</span><span class="o">.</span><span class="n">fragments</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">levels</span> <span class="o">=</span> <span class="n">levels</span>
-
-        <span class="c1"># Levels without supersystem</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">levels_no_ss</span> <span class="o">=</span> <span class="p">{</span><span class="nb">int</span><span class="p">(</span><span class="n">k</span><span class="p">):</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">levels</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">k</span> <span class="o">!=</span> <span class="s2">&quot;supersystem&quot;</span><span class="p">}</span>
-
-        <span class="c1"># Just a set of all the modelchems</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">mc_levels</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">levels</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">max_nbody</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">levels_no_ss</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bsse_type</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;No BSSE correction specified&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">BsseEnum</span><span class="o">.</span><span class="n">vmfc</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bsse_type</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">levels</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="c1"># For single-modelchem VMFC, NOCP &amp; sometimes CP are produced for free</span>
-            <span class="k">if</span> <span class="n">BsseEnum</span><span class="o">.</span><span class="n">nocp</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bsse_type</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">bsse_type</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">BsseEnum</span><span class="o">.</span><span class="n">nocp</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">BsseEnum</span><span class="o">.</span><span class="n">cp</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bsse_type</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_nbody</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">nfragments</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">bsse_type</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">BsseEnum</span><span class="o">.</span><span class="n">cp</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">return_bsse_type</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bsse_type</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="c1">###############################</span>
-        <span class="c1"># Build nbodies_per_mc_level</span>
-        <span class="c1"># TODO - use Lori&#39;s code</span>
-        <span class="c1"># TODO - dict to list of lists to handle non-contiguous levels</span>
-        <span class="c1"># TODO multilevel and supersystem_ie_only=T not allowed together</span>
-        <span class="c1"># TODO supersystem in levels is not to be trusted -- nfrag only and skips levels</span>
-        <span class="n">max_level</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">levels_no_ss</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-
-        <span class="k">if</span> <span class="nb">set</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_level</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span> <span class="o">!=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">levels_no_ss</span><span class="o">.</span><span class="n">keys</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;Levels must be contiguous from 1 to </span><span class="si">{</span><span class="n">max_level</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">nbodies_per_mc_level</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">list</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="n">mc_level</span><span class="p">:</span> <span class="p">[]</span> <span class="k">for</span> <span class="n">mc_level</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mc_levels</span><span class="p">}</span>
-        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">levels_no_ss</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">nbodies_per_mc_level</span><span class="p">[</span><span class="n">v</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">nbodies_per_mc_level</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">nbodies_per_mc_level</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
-
-        <span class="c1"># Supersystem is always at the end</span>
-        <span class="k">if</span> <span class="s2">&quot;supersystem&quot;</span> <span class="ow">in</span> <span class="n">levels</span><span class="p">:</span>
-            <span class="n">ss_mc</span> <span class="o">=</span> <span class="n">levels</span><span class="p">[</span><span class="s2">&quot;supersystem&quot;</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">nbodies_per_mc_level</span><span class="p">[</span><span class="n">ss_mc</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">&quot;supersystem&quot;</span><span class="p">)</span>
-
-        <span class="c1"># To be built on the fly</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">mc_compute_dict</span> <span class="o">=</span> <span class="kc">None</span>
-
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">array_equal</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">molecule</span><span class="o">.</span><span class="n">fragments</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">molecule</span><span class="o">.</span><span class="n">symbols</span><span class="p">))):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;&quot;&quot;QCManyBody: non-contiguous fragments could be implemented but aren&#39;t at present&quot;&quot;&quot;</span><span class="p">)</span>
-
-        <span class="c1"># Build size and slices dictionaries. Assumes fragments are contiguous</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">fragment_size_dict</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">fragment_slice_dict</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="n">iat</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">for</span> <span class="n">ifr</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">nfragments</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-            <span class="n">nat</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">molecule</span><span class="o">.</span><span class="n">fragments</span><span class="p">[</span><span class="n">ifr</span> <span class="o">-</span> <span class="mi">1</span><span class="p">])</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">fragment_size_dict</span><span class="p">[</span><span class="n">ifr</span><span class="p">]</span> <span class="o">=</span> <span class="n">nat</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">fragment_slice_dict</span><span class="p">[</span><span class="n">ifr</span><span class="p">]</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">iat</span><span class="p">,</span> <span class="n">iat</span> <span class="o">+</span> <span class="n">nat</span><span class="p">)</span>
-            <span class="n">iat</span> <span class="o">+=</span> <span class="n">nat</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">has_supersystem</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
-        <span class="k">return</span> <span class="s2">&quot;supersystem&quot;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">levels</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">compute_map</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="n">Set</span><span class="p">[</span><span class="n">FragBasIndex</span><span class="p">]]]]:</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mc_compute_dict</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">mc_compute_dict</span>
-
-        <span class="c1"># Build the compute lists</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">mc_compute_dict</span> <span class="o">=</span> <span class="p">{}</span>
-
-        <span class="k">for</span> <span class="n">mc</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mc_levels</span><span class="p">:</span>
-            <span class="n">nbodies</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nbodies_per_mc_level</span><span class="p">[</span><span class="n">mc</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">mc_compute_dict</span><span class="p">[</span><span class="n">mc</span><span class="p">]</span> <span class="o">=</span> <span class="n">build_nbody_compute_list</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">bsse_type</span><span class="p">,</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">nfragments</span><span class="p">,</span>
-                <span class="n">nbodies</span><span class="p">,</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">return_total_data</span><span class="p">,</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">supersystem_ie_only</span><span class="p">,</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">max_nbody</span><span class="p">,</span>
-            <span class="p">)</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">mc_compute_dict</span>
-
-    <span class="k">def</span> <span class="nf">resize_gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">grad</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">bas</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="o">*</span><span class="p">,</span> <span class="n">reverse</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">resize_gradient</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="n">bas</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fragment_size_dict</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fragment_slice_dict</span><span class="p">,</span> <span class="n">reverse</span><span class="o">=</span><span class="n">reverse</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">resize_hessian</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hess</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">bas</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="o">*</span><span class="p">,</span> <span class="n">reverse</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">resize_hessian</span><span class="p">(</span><span class="n">hess</span><span class="p">,</span> <span class="n">bas</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fragment_size_dict</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fragment_slice_dict</span><span class="p">,</span> <span class="n">reverse</span><span class="o">=</span><span class="n">reverse</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">iterate_molecules</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">,</span> <span class="n">Molecule</span><span class="p">]]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Iterate over all the molecules needed for the computation.</span>
-
-<span class="sd">        Yields model chemistry, label, and molecule.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">done_molecules</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-
-        <span class="k">for</span> <span class="n">mc</span><span class="p">,</span> <span class="n">compute_dict</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">compute_map</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="c1"># TODO - this is a bit of a hack. Lots of duplication when reaching higher nbody</span>
-            <span class="k">for</span> <span class="n">compute_list</span> <span class="ow">in</span> <span class="n">compute_dict</span><span class="p">[</span><span class="s2">&quot;all&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
-                <span class="k">for</span> <span class="n">real_atoms</span><span class="p">,</span> <span class="n">basis_atoms</span> <span class="ow">in</span> <span class="n">compute_list</span><span class="p">:</span>
-                    <span class="n">label</span> <span class="o">=</span> <span class="n">labeler</span><span class="p">(</span><span class="n">mc</span><span class="p">,</span> <span class="n">real_atoms</span><span class="p">,</span> <span class="n">basis_atoms</span><span class="p">)</span>
-                    <span class="k">if</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">done_molecules</span><span class="p">:</span>
-                        <span class="k">continue</span>
-
-                    <span class="n">ghost_atoms</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">basis_atoms</span><span class="p">)</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">real_atoms</span><span class="p">))</span>
-
-                    <span class="c1"># Shift to zero-indexing</span>
-                    <span class="n">real_atoms_0</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">real_atoms</span><span class="p">]</span>
-                    <span class="n">ghost_atoms_0</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">ghost_atoms</span><span class="p">]</span>
-                    <span class="n">mol</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">molecule</span><span class="o">.</span><span class="n">get_fragment</span><span class="p">(</span><span class="n">real_atoms_0</span><span class="p">,</span> <span class="n">ghost_atoms_0</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">group_fragments</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-                    <span class="n">mol</span> <span class="o">=</span> <span class="n">mol</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">update</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fix_com&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span> <span class="s2">&quot;fix_orientation&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">})</span>
-
-                    <span class="c1"># if self.embedding_charges:</span>
-                    <span class="c1">#    embedding_frags = list(set(range(1, self.nfragments + 1)) - set(pair[1]))</span>
-                    <span class="c1">#    charges = []</span>
-                    <span class="c1">#    for frag in embedding_frags:</span>
-                    <span class="c1">#        positions = self.molecule.extract_subsets(frag).geometry().np.tolist()</span>
-                    <span class="c1">#        charges.extend([[chg, i] for i, chg in zip(positions, self.embedding_charges[frag])])</span>
-                    <span class="c1">#    data[&#39;keywords&#39;][&#39;function_kwargs&#39;].update({&#39;external_potentials&#39;: charges})</span>
-
-                    <span class="n">done_molecules</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">label</span><span class="p">)</span>
-                    <span class="k">yield</span> <span class="n">mc</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">mol</span>
-
-    <span class="k">def</span> <span class="nf">_assemble_nbody_components</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">property_label</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
-        <span class="n">component_results</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Union</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]],</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Assembles N-body components for a single derivative level and a single model chemistry level</span>
-<span class="sd">        into interaction quantities according to requested BSSE treatment(s).</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># which level are we assembling?</span>
-        <span class="n">delabeled</span> <span class="o">=</span> <span class="p">[</span><span class="n">delabeler</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">component_results</span><span class="o">.</span><span class="n">keys</span><span class="p">()]</span>
-        <span class="n">mc_level_labels</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">delabeled</span><span class="p">}</span>
-
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">mc_level_labels</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Multiple model chemistries passed into _assemble_nbody_components: </span><span class="si">{</span><span class="n">mc_level_labels</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-
-        <span class="n">mc_level</span> <span class="o">=</span> <span class="n">mc_level_labels</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">mc_level</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mc_levels</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Model chemistry </span><span class="si">{</span><span class="n">mc_level</span><span class="si">}</span><span class="s2"> not found in </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">mc_levels</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-
-        <span class="c1"># get the range of nbodies and the required calculations for this level</span>
-        <span class="n">bsse_type</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bsse_type</span>
-        <span class="n">return_bsse_type</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">return_bsse_type</span>
-        <span class="n">nbodies</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nbodies_per_mc_level</span><span class="p">[</span><span class="n">mc_level</span><span class="p">]</span>
-        <span class="k">if</span> <span class="s2">&quot;supersystem&quot;</span> <span class="ow">in</span> <span class="n">nbodies</span><span class="p">:</span>
-            <span class="n">nbodies</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_nbody</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-            <span class="n">bsse_type</span> <span class="o">=</span> <span class="p">[</span><span class="n">BsseEnum</span><span class="o">.</span><span class="n">nocp</span><span class="p">]</span>
-            <span class="n">return_bsse_type</span> <span class="o">=</span> <span class="n">BsseEnum</span><span class="o">.</span><span class="n">nocp</span>
-
-        <span class="n">max_nbody</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">nbodies</span><span class="p">)</span>
-        <span class="n">compute_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">compute_map</span><span class="p">[</span><span class="n">mc_level</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">all_same_shape</span><span class="p">(</span><span class="n">component_results</span><span class="o">.</span><span class="n">values</span><span class="p">()):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;All values in data dictionary must have the same shape.&quot;</span><span class="p">)</span>
-
-        <span class="c1"># Use first data value to determine shape</span>
-        <span class="n">first_key</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">component_results</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
-        <span class="n">property_shape</span> <span class="o">=</span> <span class="n">find_shape</span><span class="p">(</span><span class="n">component_results</span><span class="p">[</span><span class="n">first_key</span><span class="p">])</span>
-
-        <span class="c1"># Accumulation dictionaries</span>
-        <span class="c1"># * {bsse_type}_by_level is filled by sum_cluster_data to contain for NOCP</span>
-        <span class="c1">#   &amp; CP the summed total energies (or other property) of each nb-body. That is:</span>
-        <span class="c1">#   * NOCP: {1: 1b@1b,    2: 2b@2b,      ..., max_nbody: max_nbody-b@max_nbody-b} and</span>
-        <span class="c1">#   * CP:   {1: 1b@nfr-b, 2: 2b@nfr-b,   ..., max_nbody: max_nbody-b@nfr-b}.</span>
-        <span class="c1">#   VMFC bookkeeping is different. For key 1 it contains the summed 1b total energies.</span>
-        <span class="c1">#   But for higher keys, it contains each nb-body (non-additive) contribution to the energy.</span>
-        <span class="c1">#   * VMFC: {1: 1b@1b,    2: 2b contrib, ..., max_nbody: max_nbody-b contrib}</span>
-        <span class="n">cp_by_level</span> <span class="o">=</span> <span class="p">{</span><span class="n">n</span><span class="p">:</span> <span class="n">shaped_zero</span><span class="p">(</span><span class="n">property_shape</span><span class="p">)</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">nbodies</span><span class="p">[</span><span class="o">-</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="n">nocp_by_level</span> <span class="o">=</span> <span class="p">{</span><span class="n">n</span><span class="p">:</span> <span class="n">shaped_zero</span><span class="p">(</span><span class="n">property_shape</span><span class="p">)</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">nbodies</span><span class="p">[</span><span class="o">-</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="n">vmfc_by_level</span> <span class="o">=</span> <span class="p">{</span><span class="n">n</span><span class="p">:</span> <span class="n">shaped_zero</span><span class="p">(</span><span class="n">property_shape</span><span class="p">)</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">nbodies</span><span class="p">[</span><span class="o">-</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="c1"># * {bsse_type}_body_dict is usually filled with total energies (or other property).</span>
-        <span class="c1">#   Multiple model chemistry levels may be involved.</span>
-        <span class="c1">#   Generally, all consecutive keys between 1 and max_nbody will be present in the body_dict,</span>
-        <span class="c1">#   but if supersystem_ie_only=T, only 1b and nfr-b are present, or if &quot;supersystem&quot; in levels, ???</span>
-        <span class="c1">#   * TOT: {1: 1b@1b, 2: 2b tot prop with bsse_type treatment, ..., max_nbody: max_nbody-b tot prop with bsse_type treatment}</span>
-        <span class="c1">#   If 1b@1b (monomers in monomer basis) aren&#39;t available, which can happen when return_total_data=F</span>
-        <span class="c1">#   and 1b@1b aren&#39;t otherwise needed, body_dict contains interaction energies (or other property).</span>
-        <span class="c1">#   * IE: {1: shaped_zero, 2: 2b interaction prop using bsse_type, ..., max_nbody: max_nbody-b interaction prop using bsse_type}</span>
-        <span class="c1">#   For both TOT and IE cases, body_dict values are cummulative, not additive. For TOT, total,</span>
-        <span class="c1">#   interaction, and contribution data in ManyBodyResultProperties can be computed in</span>
-        <span class="c1">#   collect_vars. For IE, interaction and contribution data can be computed.</span>
-        <span class="n">cp_body_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">n</span><span class="p">:</span> <span class="n">shaped_zero</span><span class="p">(</span><span class="n">property_shape</span><span class="p">)</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">nbodies</span><span class="p">[</span><span class="o">-</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="n">nocp_body_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">n</span><span class="p">:</span> <span class="n">shaped_zero</span><span class="p">(</span><span class="n">property_shape</span><span class="p">)</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">nbodies</span><span class="p">[</span><span class="o">-</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="n">vmfc_body_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">n</span><span class="p">:</span> <span class="n">shaped_zero</span><span class="p">(</span><span class="n">property_shape</span><span class="p">)</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">nbodies</span><span class="p">[</span><span class="o">-</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="c1"># Sum up all of the levels</span>
-        <span class="c1"># * compute_dict[bt][nb] holds all the computations needed to compute nb</span>
-        <span class="c1">#   *not* all the nb-level computations, so build the latter</span>
-        <span class="n">cp_compute_list</span> <span class="o">=</span> <span class="p">{</span><span class="n">nb</span><span class="p">:</span> <span class="nb">set</span><span class="p">()</span> <span class="k">for</span> <span class="n">nb</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">nbodies</span><span class="p">[</span><span class="o">-</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="n">nocp_compute_list</span> <span class="o">=</span> <span class="p">{</span><span class="n">nb</span><span class="p">:</span> <span class="nb">set</span><span class="p">()</span> <span class="k">for</span> <span class="n">nb</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">nbodies</span><span class="p">[</span><span class="o">-</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="k">for</span> <span class="n">nb</span> <span class="ow">in</span> <span class="n">nbodies</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">compute_dict</span><span class="p">[</span><span class="s2">&quot;cp&quot;</span><span class="p">][</span><span class="n">nb</span><span class="p">]:</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">v</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="n">cp_compute_list</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">])]</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">compute_dict</span><span class="p">[</span><span class="s2">&quot;nocp&quot;</span><span class="p">][</span><span class="n">nb</span><span class="p">]:</span>
-                <span class="n">nocp_compute_list</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">w</span><span class="p">[</span><span class="mi">0</span><span class="p">])]</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">nb</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">nbodies</span><span class="p">[</span><span class="o">-</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="n">cp_by_level</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span> <span class="o">=</span> <span class="n">sum_cluster_data</span><span class="p">(</span><span class="n">component_results</span><span class="p">,</span> <span class="n">cp_compute_list</span><span class="p">[</span><span class="n">nb</span><span class="p">],</span> <span class="n">mc_level</span><span class="p">)</span>
-            <span class="n">nocp_by_level</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span> <span class="o">=</span> <span class="n">sum_cluster_data</span><span class="p">(</span><span class="n">component_results</span><span class="p">,</span> <span class="n">nocp_compute_list</span><span class="p">[</span><span class="n">nb</span><span class="p">],</span> <span class="n">mc_level</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">nb</span> <span class="ow">in</span> <span class="n">compute_dict</span><span class="p">[</span><span class="s2">&quot;vmfc_levels&quot;</span><span class="p">]:</span>
-                <span class="n">vmfc_by_level</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span> <span class="o">=</span> <span class="n">sum_cluster_data</span><span class="p">(</span>
-                    <span class="n">component_results</span><span class="p">,</span> <span class="n">compute_dict</span><span class="p">[</span><span class="s2">&quot;vmfc_levels&quot;</span><span class="p">][</span><span class="n">nb</span><span class="p">],</span> <span class="n">mc_level</span><span class="p">,</span> <span class="n">vmfc</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">nb</span><span class="o">=</span><span class="n">nb</span>
-                <span class="p">)</span>
-
-        <span class="c1"># Extract data for monomers in monomer basis for CP total data</span>
-        <span class="k">if</span> <span class="mi">1</span> <span class="ow">in</span> <span class="n">nbodies</span><span class="p">:</span>
-            <span class="n">monomers_in_monomer_basis</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">compute_dict</span><span class="p">[</span><span class="s2">&quot;nocp&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">v</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="n">monomer_sum</span> <span class="o">=</span> <span class="n">sum_cluster_data</span><span class="p">(</span><span class="n">component_results</span><span class="p">,</span> <span class="nb">set</span><span class="p">(</span><span class="n">monomers_in_monomer_basis</span><span class="p">),</span> <span class="n">mc_level</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">monomer_sum</span> <span class="o">=</span> <span class="n">shaped_zero</span><span class="p">(</span><span class="n">property_shape</span><span class="p">)</span>
-
-        <span class="c1"># Compute cp</span>
-        <span class="k">if</span> <span class="n">BsseEnum</span><span class="o">.</span><span class="n">cp</span> <span class="ow">in</span> <span class="n">bsse_type</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">nb</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">nbodies</span><span class="p">[</span><span class="o">-</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="k">if</span> <span class="n">nb</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">nfragments</span><span class="p">:</span>
-                    <span class="n">cp_body_dict</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span> <span class="o">=</span> <span class="n">cp_by_level</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span> <span class="o">-</span> <span class="n">bsse</span>
-                    <span class="k">continue</span>
-
-                <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">nb</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-                    <span class="n">take_nk</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">comb</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nfragments</span> <span class="o">-</span> <span class="n">k</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">nb</span> <span class="o">-</span> <span class="n">k</span><span class="p">)</span>
-                    <span class="n">sign</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">**</span> <span class="p">(</span><span class="n">nb</span> <span class="o">-</span> <span class="n">k</span><span class="p">)</span>
-                    <span class="n">cp_body_dict</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span> <span class="o">+=</span> <span class="n">take_nk</span> <span class="o">*</span> <span class="n">sign</span> <span class="o">*</span> <span class="n">cp_by_level</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
-
-                <span class="k">if</span> <span class="n">nb</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="n">bsse</span> <span class="o">=</span> <span class="n">cp_body_dict</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span> <span class="o">-</span> <span class="n">monomer_sum</span>
-                    <span class="n">cp_body_dict</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span> <span class="o">=</span> <span class="n">copy_value</span><span class="p">(</span><span class="n">monomer_sum</span><span class="p">)</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">cp_body_dict</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span> <span class="o">-=</span> <span class="n">bsse</span>
-
-        <span class="c1"># Compute nocp</span>
-        <span class="k">if</span> <span class="n">BsseEnum</span><span class="o">.</span><span class="n">nocp</span> <span class="ow">in</span> <span class="n">bsse_type</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">nb</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">nbodies</span><span class="p">[</span><span class="o">-</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="k">if</span> <span class="n">nb</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">nfragments</span><span class="p">:</span>
-                    <span class="n">nocp_body_dict</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span> <span class="o">=</span> <span class="n">nocp_by_level</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span>
-                    <span class="k">continue</span>
-
-                <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">nb</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-                    <span class="n">take_nk</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">comb</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nfragments</span> <span class="o">-</span> <span class="n">k</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">nb</span> <span class="o">-</span> <span class="n">k</span><span class="p">)</span>
-                    <span class="n">sign</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">**</span> <span class="p">(</span><span class="n">nb</span> <span class="o">-</span> <span class="n">k</span><span class="p">)</span>
-                    <span class="n">nocp_body_dict</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span> <span class="o">+=</span> <span class="n">take_nk</span> <span class="o">*</span> <span class="n">sign</span> <span class="o">*</span> <span class="n">nocp_by_level</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
-
-        <span class="c1"># Compute vmfc</span>
-        <span class="k">if</span> <span class="n">BsseEnum</span><span class="o">.</span><span class="n">vmfc</span> <span class="ow">in</span> <span class="n">bsse_type</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">nb</span> <span class="ow">in</span> <span class="n">nbodies</span><span class="p">:</span>
-                <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">nb</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-                    <span class="n">vmfc_body_dict</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span> <span class="o">+=</span> <span class="n">vmfc_by_level</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
-
-        <span class="c1"># Collect specific and generalized returns</span>
-        <span class="n">results</span> <span class="o">=</span> <span class="p">{</span>
-            <span class="sa">f</span><span class="s2">&quot;cp_</span><span class="si">{</span><span class="n">property_label</span><span class="si">}</span><span class="s2">_body_dict&quot;</span><span class="p">:</span> <span class="n">cp_body_dict</span><span class="p">,</span>
-            <span class="sa">f</span><span class="s2">&quot;nocp_</span><span class="si">{</span><span class="n">property_label</span><span class="si">}</span><span class="s2">_body_dict&quot;</span><span class="p">:</span> <span class="n">nocp_body_dict</span><span class="p">,</span>
-            <span class="sa">f</span><span class="s2">&quot;vmfc_</span><span class="si">{</span><span class="n">property_label</span><span class="si">}</span><span class="s2">_body_dict&quot;</span><span class="p">:</span> <span class="n">vmfc_body_dict</span><span class="p">,</span>
-        <span class="p">}</span>
-
-        <span class="c1"># Overall return body dict &amp; value for this property</span>
-        <span class="n">results</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">property_label</span><span class="si">}</span><span class="s2">_body_dict&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">return_bsse_type</span><span class="o">.</span><span class="n">value</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">property_label</span><span class="si">}</span><span class="s2">_body_dict&quot;</span><span class="p">]</span>
-        <span class="n">results</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;ret_</span><span class="si">{</span><span class="n">property_label</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">copy_value</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">property_label</span><span class="si">}</span><span class="s2">_body_dict&quot;</span><span class="p">][</span><span class="n">max_nbody</span><span class="p">])</span>
-
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">return_total_data</span><span class="p">:</span>
-            <span class="n">results</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;ret_</span><span class="si">{</span><span class="n">property_label</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">]</span> <span class="o">-=</span> <span class="n">results</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">property_label</span><span class="si">}</span><span class="s2">_body_dict&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
-
-        <span class="k">return</span> <span class="n">results</span>
-
-    <span class="k">def</span> <span class="nf">_analyze</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">property_label</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
-        <span class="n">property_results</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Union</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]],</span>  <span class="c1"># Label to results</span>
-    <span class="p">):</span>
-        <span class="c1"># Initialize with zeros</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">all_same_shape</span><span class="p">(</span><span class="n">property_results</span><span class="o">.</span><span class="n">values</span><span class="p">()):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;All values in data dictionary must have the same shape.&quot;</span><span class="p">)</span>
-
-        <span class="c1"># Use first data value to determine shape</span>
-        <span class="n">first_key</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">property_results</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
-        <span class="n">property_shape</span> <span class="o">=</span> <span class="n">find_shape</span><span class="p">(</span><span class="n">property_results</span><span class="p">[</span><span class="n">first_key</span><span class="p">])</span>
-
-        <span class="n">property_result</span> <span class="o">=</span> <span class="n">shaped_zero</span><span class="p">(</span><span class="n">property_shape</span><span class="p">)</span>
-        <span class="n">property_body_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">bt</span><span class="o">.</span><span class="n">value</span><span class="p">:</span> <span class="p">{}</span> <span class="k">for</span> <span class="n">bt</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bsse_type</span><span class="p">}</span>
-        <span class="n">property_body_contribution</span> <span class="o">=</span> <span class="p">{</span><span class="n">bt</span><span class="o">.</span><span class="n">value</span><span class="p">:</span> <span class="p">{}</span> <span class="k">for</span> <span class="n">bt</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bsse_type</span><span class="p">}</span>
-
-        <span class="c1"># results per model chemistry</span>
-        <span class="n">mc_results</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="n">species_results</span> <span class="o">=</span> <span class="p">{}</span>
-
-        <span class="c1"># sort by nbody level, ignore supersystem</span>
-        <span class="n">sorted_nbodies</span> <span class="o">=</span> <span class="p">[(</span><span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">nbodies_per_mc_level</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">v</span> <span class="o">!=</span> <span class="p">[</span><span class="s2">&quot;supersystem&quot;</span><span class="p">]]</span>
-        <span class="n">sorted_nbodies</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">sorted_nbodies</span><span class="p">,</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-        <span class="k">for</span> <span class="n">mc_label</span><span class="p">,</span> <span class="n">nbody_list</span> <span class="ow">in</span> <span class="n">sorted_nbodies</span><span class="p">:</span>
-            <span class="c1"># filter to only one model chemistry</span>
-            <span class="n">filtered_results</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">property_results</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">delabeler</span><span class="p">(</span><span class="n">k</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">mc_label</span><span class="p">}</span>
-
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">filtered_results</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">nbody_list</span> <span class="o">==</span> <span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
-                    <span class="c1"># Note A.2: Note A.1 holds, but for the special case of CP-only</span>
-                    <span class="c1">#   and rtd=False and multilevel with a separate level for</span>
-                    <span class="c1">#   1-body, the skipped tasks run afoul of sanity checks, so</span>
-                    <span class="c1">#   we&#39;ll add a dummy result.</span>
-                    <span class="n">filtered_results</span> <span class="o">=</span> <span class="p">{</span><span class="n">labeler</span><span class="p">(</span><span class="n">mc_label</span><span class="p">,</span> <span class="p">[</span><span class="mi">1000</span><span class="p">],</span> <span class="p">[</span><span class="mi">1000</span><span class="p">]):</span> <span class="n">shaped_zero</span><span class="p">(</span><span class="n">property_shape</span><span class="p">)}</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;No data found for model chemistry </span><span class="si">{</span><span class="n">mc_label</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-
-            <span class="n">nb_component_results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_assemble_nbody_components</span><span class="p">(</span><span class="n">property_label</span><span class="p">,</span> <span class="n">filtered_results</span><span class="p">)</span>
-            <span class="n">mc_results</span><span class="p">[</span><span class="n">mc_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">nb_component_results</span>
-
-            <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">nbody_list</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span>
-                <span class="n">property_bsse_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">bt</span><span class="o">.</span><span class="n">value</span><span class="p">:</span> <span class="n">shaped_zero</span><span class="p">(</span><span class="n">property_shape</span><span class="p">)</span> <span class="k">for</span> <span class="n">bt</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bsse_type</span><span class="p">}</span>
-
-                <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-                    <span class="k">if</span> <span class="n">m</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                        <span class="k">continue</span>
-
-                    <span class="c1"># Subtract the (n-1)-body contribution from the n-body contribution to get the n-body effect</span>
-                    <span class="n">sign</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">**</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">m</span> <span class="o">//</span> <span class="n">n</span><span class="p">)</span>
-                    <span class="k">for</span> <span class="n">bt</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bsse_type</span><span class="p">:</span>
-                        <span class="n">property_bsse_dict</span><span class="p">[</span><span class="n">bt</span><span class="o">.</span><span class="n">value</span><span class="p">]</span> <span class="o">+=</span> <span class="p">(</span>
-                            <span class="n">sign</span> <span class="o">*</span> <span class="n">mc_results</span><span class="p">[</span><span class="n">mc_label</span><span class="p">][</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">bt</span><span class="o">.</span><span class="n">value</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">property_label</span><span class="si">}</span><span class="s2">_body_dict&quot;</span><span class="p">][</span><span class="n">m</span><span class="p">]</span>
-                        <span class="p">)</span>
-
-                <span class="n">property_result</span> <span class="o">+=</span> <span class="n">property_bsse_dict</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">return_bsse_type</span><span class="p">]</span>
-                <span class="k">for</span> <span class="n">bt</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bsse_type</span><span class="p">:</span>
-                    <span class="n">property_body_contribution</span><span class="p">[</span><span class="n">bt</span><span class="o">.</span><span class="n">value</span><span class="p">][</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">property_bsse_dict</span><span class="p">[</span><span class="n">bt</span><span class="o">.</span><span class="n">value</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">has_supersystem</span><span class="p">:</span>
-            <span class="c1"># Get the MC label for supersystem tasks</span>
-            <span class="n">supersystem_mc_level</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">levels</span><span class="p">[</span><span class="s2">&quot;supersystem&quot;</span><span class="p">]</span>
-
-            <span class="c1"># Super system recovers higher order effects at a lower level</span>
-            <span class="n">frag_range</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">nfragments</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-
-            <span class="n">ss_cresults</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">property_results</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">delabeler</span><span class="p">(</span><span class="n">k</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">supersystem_mc_level</span><span class="p">}</span>
-            <span class="n">ss_component_results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_assemble_nbody_components</span><span class="p">(</span><span class="n">property_label</span><span class="p">,</span> <span class="n">ss_cresults</span><span class="p">)</span>
-            <span class="n">mc_results</span><span class="p">[</span><span class="n">supersystem_mc_level</span><span class="p">]</span> <span class="o">=</span> <span class="n">ss_component_results</span>
-
-            <span class="c1"># Compute components at supersystem level of theory</span>
-            <span class="n">ss_label</span> <span class="o">=</span> <span class="n">labeler</span><span class="p">(</span><span class="n">supersystem_mc_level</span><span class="p">,</span> <span class="n">frag_range</span><span class="p">,</span> <span class="n">frag_range</span><span class="p">)</span>
-            <span class="n">supersystem_result</span> <span class="o">=</span> <span class="n">property_results</span><span class="p">[</span><span class="n">ss_label</span><span class="p">]</span>
-            <span class="n">property_result</span> <span class="o">+=</span> <span class="n">supersystem_result</span> <span class="o">-</span> <span class="n">ss_component_results</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">property_label</span><span class="si">}</span><span class="s2">_body_dict&quot;</span><span class="p">][</span><span class="bp">self</span><span class="o">.</span><span class="n">max_nbody</span><span class="p">]</span>
-
-            <span class="k">for</span> <span class="n">bt</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bsse_type</span><span class="p">:</span>
-                <span class="n">property_body_contribution</span><span class="p">[</span><span class="n">bt</span><span class="p">][</span><span class="bp">self</span><span class="o">.</span><span class="n">nfragments</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
-                    <span class="n">supersystem_result</span> <span class="o">-</span> <span class="n">ss_component_results</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">property_label</span><span class="si">}</span><span class="s2">_body_dict&quot;</span><span class="p">][</span><span class="bp">self</span><span class="o">.</span><span class="n">max_nbody</span><span class="p">]</span>
-                <span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">bt</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bsse_type</span><span class="p">:</span>
-            <span class="n">bstr</span> <span class="o">=</span> <span class="n">bt</span><span class="o">.</span><span class="n">value</span>
-            <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">property_body_contribution</span><span class="p">[</span><span class="n">bstr</span><span class="p">]:</span>
-                <span class="n">property_body_dict</span><span class="p">[</span><span class="n">bstr</span><span class="p">][</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span>
-                    <span class="p">[</span>
-                        <span class="n">property_body_contribution</span><span class="p">[</span><span class="n">bstr</span><span class="p">][</span><span class="n">i</span><span class="p">]</span>
-                        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
-                        <span class="k">if</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">property_body_contribution</span><span class="p">[</span><span class="n">bstr</span><span class="p">]</span>
-                    <span class="p">]</span>
-                <span class="p">)</span>
-
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">return_total_data</span><span class="p">:</span>
-            <span class="c1"># Remove monomer contribution for interaction data</span>
-            <span class="n">property_result</span> <span class="o">-=</span> <span class="n">property_body_dict</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">return_bsse_type</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
-
-        <span class="n">nbody_results</span> <span class="o">=</span> <span class="p">{</span>
-            <span class="sa">f</span><span class="s2">&quot;ret_</span><span class="si">{</span><span class="n">property_label</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">:</span> <span class="n">property_result</span><span class="p">,</span>
-            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">property_label</span><span class="si">}</span><span class="s2">_body_dict&quot;</span><span class="p">:</span> <span class="n">property_body_dict</span><span class="p">,</span>
-            <span class="s2">&quot;mc_results&quot;</span><span class="p">:</span> <span class="n">mc_results</span><span class="p">,</span>
-        <span class="p">}</span>
-        <span class="k">return</span> <span class="n">nbody_results</span>
-
-    <span class="k">def</span> <span class="nf">analyze</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">component_results</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Union</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]]],</span>
-    <span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        component_results</span>
-<span class="sd">            Nested dictionary with results from all individual molecular system</span>
-<span class="sd">            calculations, including all subsystem/basis combinations, all model</span>
-<span class="sd">            chemistries, and all properties (e.g., e/g/h).</span>
-
-<span class="sd">            For example, the below is the format for a nocp gradient run on a</span>
-<span class="sd">            helium dimer with 1-body at CCSD and 2-body at MP2. The outer string</span>
-<span class="sd">            key can be generated with the ``qcmanybody.utils.labeler`` function.</span>
-<span class="sd">            The inner string key is any property; QCManyBody presently knows how</span>
-<span class="sd">            to process energy/gradient/Hessian.</span>
-
-<span class="sd">            {&#39;[&quot;ccsd&quot;, [1], [1]]&#39;: {&#39;energy&#39;: -2.87, &#39;gradient&#39;: array([[0., 0., 0.]])},</span>
-<span class="sd">             &#39;[&quot;ccsd&quot;, [2], [2]]&#39;: {&#39;energy&#39;: -2.87, &#39;gradient&#39;: array([[0., 0., 0.]])},</span>
-<span class="sd">             &#39;[&quot;mp2&quot;, [1], [1]]&#39;: {&#39;energy&#39;: -2.86, &#39;gradient&#39;: array([[0., 0., 0.]])},</span>
-<span class="sd">             &#39;[&quot;mp2&quot;, [2], [2]]&#39;: {&#39;energy&#39;: -2.86, &#39;gradient&#39;: array([[0., 0., 0.]])},</span>
-<span class="sd">             &#39;[&quot;mp2&quot;, [1, 2], [1, 2]]&#39;: {&#39;energy&#39;: -5.73, &#39;gradient&#39;: array([[ 0., 0., 0.0053], [ 0., 0., -0.0053]])},</span>
-<span class="sd">            }</span>
-
-<span class="sd">        Return</span>
-<span class="sd">        ------</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># All properties that were passed to us</span>
-        <span class="c1"># * seed with &quot;energy&quot; so free/no-op jobs can process</span>
-        <span class="n">available_properties</span><span class="p">:</span> <span class="n">Set</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;energy&quot;</span><span class="p">}</span>
-        <span class="k">for</span> <span class="n">property_data</span> <span class="ow">in</span> <span class="n">component_results</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
-            <span class="n">available_properties</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">property_data</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-
-        <span class="c1"># reorganize to component_results_inv[property][label] = 1.23</span>
-        <span class="n">component_results_inv</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="p">{}</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">available_properties</span><span class="p">}</span>
-
-        <span class="k">for</span> <span class="n">cluster_label</span><span class="p">,</span> <span class="n">property_data</span> <span class="ow">in</span> <span class="n">component_results</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="k">for</span> <span class="n">property_label</span><span class="p">,</span> <span class="n">property_value</span> <span class="ow">in</span> <span class="n">property_data</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-                <span class="n">component_results_inv</span><span class="p">[</span><span class="n">property_label</span><span class="p">][</span><span class="n">cluster_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">property_value</span>
-
-        <span class="c1"># Remove any missing data</span>
-        <span class="n">component_results_inv</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">component_results_inv</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">v</span><span class="p">}</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">component_results_inv</span><span class="p">:</span>
-            <span class="c1"># Note B: Rarely, &quot;no results&quot; is expected, like for CP-only,</span>
-            <span class="c1">#   rtd=False, and max_nbody=1. We&#39;ll add a dummy entry so</span>
-            <span class="c1">#   processing can continue.</span>
-            <span class="n">component_results_inv</span><span class="p">[</span><span class="s2">&quot;energy&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;[&quot;dummy&quot;, [1000], [1000]]&#39;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">}</span>
-
-        <span class="c1"># Actually analyze</span>
-        <span class="n">is_embedded</span> <span class="o">=</span> <span class="nb">bool</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">embedding_charges</span><span class="p">)</span>
-        <span class="n">component_properties</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">dict</span><span class="p">)</span>
-        <span class="n">all_results</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="n">nbody_dict</span> <span class="o">=</span> <span class="p">{}</span>
-<span class="c1">#        all_results[&quot;energy_body_dict&quot;] = {&quot;cp&quot;: {1: 0.0}}</span>
-
-        <span class="k">for</span> <span class="n">property_label</span><span class="p">,</span> <span class="n">property_results</span> <span class="ow">in</span> <span class="n">component_results_inv</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="c1"># Expand gradient and hessian</span>
-            <span class="k">if</span> <span class="n">property_label</span> <span class="o">==</span> <span class="s2">&quot;gradient&quot;</span><span class="p">:</span>
-                <span class="n">property_results</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">resize_gradient</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">delabeler</span><span class="p">(</span><span class="n">k</span><span class="p">)[</span><span class="mi">2</span><span class="p">])</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">property_results</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
-            <span class="k">if</span> <span class="n">property_label</span> <span class="o">==</span> <span class="s2">&quot;hessian&quot;</span><span class="p">:</span>
-                <span class="n">property_results</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">resize_hessian</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">delabeler</span><span class="p">(</span><span class="n">k</span><span class="p">)[</span><span class="mi">2</span><span class="p">])</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">property_results</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
-
-            <span class="n">r</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_analyze</span><span class="p">(</span><span class="n">property_label</span><span class="p">,</span> <span class="n">property_results</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">property_results</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-                <span class="n">component_properties</span><span class="p">[</span><span class="n">k</span><span class="p">][</span><span class="s2">&quot;calcinfo_natom&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">molecule</span><span class="o">.</span><span class="n">symbols</span><span class="p">)</span>
-                <span class="n">component_properties</span><span class="p">[</span><span class="n">k</span><span class="p">][</span><span class="sa">f</span><span class="s2">&quot;return_</span><span class="si">{</span><span class="n">property_label</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
-            <span class="n">all_results</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">r</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">bt</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bsse_type</span><span class="p">:</span>
-            <span class="n">print_nbody_energy</span><span class="p">(</span>
-                <span class="n">all_results</span><span class="p">[</span><span class="s2">&quot;energy_body_dict&quot;</span><span class="p">][</span><span class="n">bt</span><span class="p">],</span>
-                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">bt</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span><span class="si">}</span><span class="s2">-corrected multilevel many-body expansion&quot;</span><span class="p">,</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">nfragments</span><span class="p">,</span>
-                <span class="n">is_embedded</span><span class="p">,</span>
-            <span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">property_label</span> <span class="ow">in</span> <span class="n">available_properties</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">bt</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bsse_type</span><span class="p">:</span>
-                <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">has_supersystem</span><span class="p">:</span>  <span class="c1"># skipped levels?</span>
-                    <span class="n">nbody_dict</span><span class="o">.</span><span class="n">update</span><span class="p">(</span>
-                        <span class="n">collect_vars</span><span class="p">(</span>
-                            <span class="n">bt</span><span class="o">.</span><span class="n">upper</span><span class="p">(),</span>
-                            <span class="n">property_label</span><span class="o">.</span><span class="n">upper</span><span class="p">(),</span>
-                            <span class="n">all_results</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">property_label</span><span class="si">}</span><span class="s2">_body_dict&quot;</span><span class="p">][</span><span class="n">bt</span><span class="p">],</span>
-                            <span class="bp">self</span><span class="o">.</span><span class="n">max_nbody</span><span class="p">,</span>
-                            <span class="n">is_embedded</span><span class="p">,</span>
-                            <span class="bp">self</span><span class="o">.</span><span class="n">supersystem_ie_only</span><span class="p">,</span>
-                        <span class="p">)</span>
-                    <span class="p">)</span>
-
-        <span class="n">all_results</span><span class="p">[</span><span class="s2">&quot;results&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">nbody_dict</span>
-        <span class="n">all_results</span><span class="p">[</span><span class="s2">&quot;component_properties&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">component_properties</span>
-
-        <span class="c1"># Make dictionary with &quot;1cp&quot;, &quot;2cp&quot;, etc</span>
-        <span class="n">ebd</span> <span class="o">=</span> <span class="n">all_results</span><span class="p">[</span><span class="s2">&quot;energy_body_dict&quot;</span><span class="p">]</span>
-        <span class="n">all_results</span><span class="p">[</span><span class="s2">&quot;energy_body_dict&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="o">+</span> <span class="n">bt</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">bt</span> <span class="ow">in</span> <span class="n">ebd</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">ebd</span><span class="p">[</span><span class="n">bt</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
-
-        <span class="k">return</span> <span class="n">all_results</span>
-</code></pre></div></td></tr></table></div>
-            </details>
-
-  
-
-  <div class="doc doc-children">
-
-
-
-
-
-
-
-
-
-
-<div class="doc doc-object doc-function">
-
-
-
-<h2 id="qcmanybody.ManyBodyCalculator.analyze" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">analyze</span><span class="p">(</span><span class="n">component_results</span><span class="p">)</span></code>
-
-</h2>
-
-
-  <div class="doc doc-contents ">
-  
-
-
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>component_results</code></td>
-          <td>
-                <code><a class="autorefs autorefs-external" title="typing.Dict" href="https://docs.python.org/3.12/library/typing.html#typing.Dict">Dict</a>[<a class="autorefs autorefs-external" href="https://docs.python.org/3.12/library/stdtypes.html#str">str</a>, <a class="autorefs autorefs-external" title="typing.Dict" href="https://docs.python.org/3.12/library/typing.html#typing.Dict">Dict</a>[<a class="autorefs autorefs-external" href="https://docs.python.org/3.12/library/stdtypes.html#str">str</a>, <a class="autorefs autorefs-external" title="typing.Union" href="https://docs.python.org/3.12/library/typing.html#typing.Union">Union</a>[<a class="autorefs autorefs-external" href="https://docs.python.org/3.12/library/functions.html#float">float</a>, <a class="autorefs autorefs-external" title="numpy.ndarray" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray">ndarray</a>]]]</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>Nested dictionary with results from all individual molecular system
-calculations, including all subsystem/basis combinations, all model
-chemistries, and all properties (e.g., e/g/h).</p>
-<p>For example, the below is the format for a nocp gradient run on a
-helium dimer with 1-body at CCSD and 2-body at MP2. The outer string
-key can be generated with the <code>qcmanybody.utils.labeler</code> function.
-The inner string key is any property; QCManyBody presently knows how
-to process energy/gradient/Hessian.</p>
-<p>{'["ccsd", [1], [1]]': {'energy': -2.87, 'gradient': array([[0., 0., 0.]])},
- '["ccsd", [2], [2]]': {'energy': -2.87, 'gradient': array([[0., 0., 0.]])},
- '["mp2", [1], [1]]': {'energy': -2.86, 'gradient': array([[0., 0., 0.]])},
- '["mp2", [2], [2]]': {'energy': -2.86, 'gradient': array([[0., 0., 0.]])},
- '["mp2", [1, 2], [1, 2]]': {'energy': -5.73, 'gradient': array([[ 0., 0., 0.0053], [ 0., 0., -0.0053]])},
-}</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-<details class="return" open>
-  <summary>Return</summary>
-  
-</details>
-          <details class="quote">
-            <summary>Source code in <code>qcmanybody/manybody.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">422</span>
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-<span class="normal">523</span></pre></div></td><td class="code"><div><pre><span></span><code>    <span class="k">def</span> <span class="nf">analyze</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">component_results</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Union</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]]],</span>
-    <span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        component_results</span>
-<span class="sd">            Nested dictionary with results from all individual molecular system</span>
-<span class="sd">            calculations, including all subsystem/basis combinations, all model</span>
-<span class="sd">            chemistries, and all properties (e.g., e/g/h).</span>
-
-<span class="sd">            For example, the below is the format for a nocp gradient run on a</span>
-<span class="sd">            helium dimer with 1-body at CCSD and 2-body at MP2. The outer string</span>
-<span class="sd">            key can be generated with the ``qcmanybody.utils.labeler`` function.</span>
-<span class="sd">            The inner string key is any property; QCManyBody presently knows how</span>
-<span class="sd">            to process energy/gradient/Hessian.</span>
-
-<span class="sd">            {&#39;[&quot;ccsd&quot;, [1], [1]]&#39;: {&#39;energy&#39;: -2.87, &#39;gradient&#39;: array([[0., 0., 0.]])},</span>
-<span class="sd">             &#39;[&quot;ccsd&quot;, [2], [2]]&#39;: {&#39;energy&#39;: -2.87, &#39;gradient&#39;: array([[0., 0., 0.]])},</span>
-<span class="sd">             &#39;[&quot;mp2&quot;, [1], [1]]&#39;: {&#39;energy&#39;: -2.86, &#39;gradient&#39;: array([[0., 0., 0.]])},</span>
-<span class="sd">             &#39;[&quot;mp2&quot;, [2], [2]]&#39;: {&#39;energy&#39;: -2.86, &#39;gradient&#39;: array([[0., 0., 0.]])},</span>
-<span class="sd">             &#39;[&quot;mp2&quot;, [1, 2], [1, 2]]&#39;: {&#39;energy&#39;: -5.73, &#39;gradient&#39;: array([[ 0., 0., 0.0053], [ 0., 0., -0.0053]])},</span>
-<span class="sd">            }</span>
-
-<span class="sd">        Return</span>
-<span class="sd">        ------</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># All properties that were passed to us</span>
-        <span class="c1"># * seed with &quot;energy&quot; so free/no-op jobs can process</span>
-        <span class="n">available_properties</span><span class="p">:</span> <span class="n">Set</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;energy&quot;</span><span class="p">}</span>
-        <span class="k">for</span> <span class="n">property_data</span> <span class="ow">in</span> <span class="n">component_results</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
-            <span class="n">available_properties</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">property_data</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-
-        <span class="c1"># reorganize to component_results_inv[property][label] = 1.23</span>
-        <span class="n">component_results_inv</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="p">{}</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">available_properties</span><span class="p">}</span>
-
-        <span class="k">for</span> <span class="n">cluster_label</span><span class="p">,</span> <span class="n">property_data</span> <span class="ow">in</span> <span class="n">component_results</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="k">for</span> <span class="n">property_label</span><span class="p">,</span> <span class="n">property_value</span> <span class="ow">in</span> <span class="n">property_data</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-                <span class="n">component_results_inv</span><span class="p">[</span><span class="n">property_label</span><span class="p">][</span><span class="n">cluster_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">property_value</span>
-
-        <span class="c1"># Remove any missing data</span>
-        <span class="n">component_results_inv</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">component_results_inv</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">v</span><span class="p">}</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">component_results_inv</span><span class="p">:</span>
-            <span class="c1"># Note B: Rarely, &quot;no results&quot; is expected, like for CP-only,</span>
-            <span class="c1">#   rtd=False, and max_nbody=1. We&#39;ll add a dummy entry so</span>
-            <span class="c1">#   processing can continue.</span>
-            <span class="n">component_results_inv</span><span class="p">[</span><span class="s2">&quot;energy&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;[&quot;dummy&quot;, [1000], [1000]]&#39;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">}</span>
-
-        <span class="c1"># Actually analyze</span>
-        <span class="n">is_embedded</span> <span class="o">=</span> <span class="nb">bool</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">embedding_charges</span><span class="p">)</span>
-        <span class="n">component_properties</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">dict</span><span class="p">)</span>
-        <span class="n">all_results</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="n">nbody_dict</span> <span class="o">=</span> <span class="p">{}</span>
-<span class="c1">#        all_results[&quot;energy_body_dict&quot;] = {&quot;cp&quot;: {1: 0.0}}</span>
-
-        <span class="k">for</span> <span class="n">property_label</span><span class="p">,</span> <span class="n">property_results</span> <span class="ow">in</span> <span class="n">component_results_inv</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="c1"># Expand gradient and hessian</span>
-            <span class="k">if</span> <span class="n">property_label</span> <span class="o">==</span> <span class="s2">&quot;gradient&quot;</span><span class="p">:</span>
-                <span class="n">property_results</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">resize_gradient</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">delabeler</span><span class="p">(</span><span class="n">k</span><span class="p">)[</span><span class="mi">2</span><span class="p">])</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">property_results</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
-            <span class="k">if</span> <span class="n">property_label</span> <span class="o">==</span> <span class="s2">&quot;hessian&quot;</span><span class="p">:</span>
-                <span class="n">property_results</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">resize_hessian</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">delabeler</span><span class="p">(</span><span class="n">k</span><span class="p">)[</span><span class="mi">2</span><span class="p">])</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">property_results</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
-
-            <span class="n">r</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_analyze</span><span class="p">(</span><span class="n">property_label</span><span class="p">,</span> <span class="n">property_results</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">property_results</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-                <span class="n">component_properties</span><span class="p">[</span><span class="n">k</span><span class="p">][</span><span class="s2">&quot;calcinfo_natom&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">molecule</span><span class="o">.</span><span class="n">symbols</span><span class="p">)</span>
-                <span class="n">component_properties</span><span class="p">[</span><span class="n">k</span><span class="p">][</span><span class="sa">f</span><span class="s2">&quot;return_</span><span class="si">{</span><span class="n">property_label</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
-            <span class="n">all_results</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">r</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">bt</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bsse_type</span><span class="p">:</span>
-            <span class="n">print_nbody_energy</span><span class="p">(</span>
-                <span class="n">all_results</span><span class="p">[</span><span class="s2">&quot;energy_body_dict&quot;</span><span class="p">][</span><span class="n">bt</span><span class="p">],</span>
-                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">bt</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span><span class="si">}</span><span class="s2">-corrected multilevel many-body expansion&quot;</span><span class="p">,</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">nfragments</span><span class="p">,</span>
-                <span class="n">is_embedded</span><span class="p">,</span>
-            <span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">property_label</span> <span class="ow">in</span> <span class="n">available_properties</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">bt</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bsse_type</span><span class="p">:</span>
-                <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">has_supersystem</span><span class="p">:</span>  <span class="c1"># skipped levels?</span>
-                    <span class="n">nbody_dict</span><span class="o">.</span><span class="n">update</span><span class="p">(</span>
-                        <span class="n">collect_vars</span><span class="p">(</span>
-                            <span class="n">bt</span><span class="o">.</span><span class="n">upper</span><span class="p">(),</span>
-                            <span class="n">property_label</span><span class="o">.</span><span class="n">upper</span><span class="p">(),</span>
-                            <span class="n">all_results</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">property_label</span><span class="si">}</span><span class="s2">_body_dict&quot;</span><span class="p">][</span><span class="n">bt</span><span class="p">],</span>
-                            <span class="bp">self</span><span class="o">.</span><span class="n">max_nbody</span><span class="p">,</span>
-                            <span class="n">is_embedded</span><span class="p">,</span>
-                            <span class="bp">self</span><span class="o">.</span><span class="n">supersystem_ie_only</span><span class="p">,</span>
-                        <span class="p">)</span>
-                    <span class="p">)</span>
-
-        <span class="n">all_results</span><span class="p">[</span><span class="s2">&quot;results&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">nbody_dict</span>
-        <span class="n">all_results</span><span class="p">[</span><span class="s2">&quot;component_properties&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">component_properties</span>
-
-        <span class="c1"># Make dictionary with &quot;1cp&quot;, &quot;2cp&quot;, etc</span>
-        <span class="n">ebd</span> <span class="o">=</span> <span class="n">all_results</span><span class="p">[</span><span class="s2">&quot;energy_body_dict&quot;</span><span class="p">]</span>
-        <span class="n">all_results</span><span class="p">[</span><span class="s2">&quot;energy_body_dict&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="o">+</span> <span class="n">bt</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">bt</span> <span class="ow">in</span> <span class="n">ebd</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">ebd</span><span class="p">[</span><span class="n">bt</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
-
-        <span class="k">return</span> <span class="n">all_results</span>
-</code></pre></div></td></tr></table></div>
-          </details>
-  </div>
-
-</div>
-
-
-<div class="doc doc-object doc-function">
-
-
-
-<h2 id="qcmanybody.ManyBodyCalculator.iterate_molecules" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">iterate_molecules</span><span class="p">()</span></code>
-
-</h2>
-
-
-  <div class="doc doc-contents ">
-  
-      <p>Iterate over all the molecules needed for the computation.</p>
-<p>Yields model chemistry, label, and molecule.</p>
-
-          <details class="quote">
-            <summary>Source code in <code>qcmanybody/manybody.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">139</span>
-<span class="normal">140</span>
-<span class="normal">141</span>
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-<span class="normal">143</span>
-<span class="normal">144</span>
-<span class="normal">145</span>
-<span class="normal">146</span>
-<span class="normal">147</span>
-<span class="normal">148</span>
-<span class="normal">149</span>
-<span class="normal">150</span>
-<span class="normal">151</span>
-<span class="normal">152</span>
-<span class="normal">153</span>
-<span class="normal">154</span>
-<span class="normal">155</span>
-<span class="normal">156</span>
-<span class="normal">157</span>
-<span class="normal">158</span>
-<span class="normal">159</span>
-<span class="normal">160</span>
-<span class="normal">161</span>
-<span class="normal">162</span>
-<span class="normal">163</span>
-<span class="normal">164</span>
-<span class="normal">165</span>
-<span class="normal">166</span>
-<span class="normal">167</span>
-<span class="normal">168</span>
-<span class="normal">169</span>
-<span class="normal">170</span>
-<span class="normal">171</span>
-<span class="normal">172</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">iterate_molecules</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">,</span> <span class="n">Molecule</span><span class="p">]]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Iterate over all the molecules needed for the computation.</span>
-
-<span class="sd">    Yields model chemistry, label, and molecule.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">done_molecules</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-
-    <span class="k">for</span> <span class="n">mc</span><span class="p">,</span> <span class="n">compute_dict</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">compute_map</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-        <span class="c1"># TODO - this is a bit of a hack. Lots of duplication when reaching higher nbody</span>
-        <span class="k">for</span> <span class="n">compute_list</span> <span class="ow">in</span> <span class="n">compute_dict</span><span class="p">[</span><span class="s2">&quot;all&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
-            <span class="k">for</span> <span class="n">real_atoms</span><span class="p">,</span> <span class="n">basis_atoms</span> <span class="ow">in</span> <span class="n">compute_list</span><span class="p">:</span>
-                <span class="n">label</span> <span class="o">=</span> <span class="n">labeler</span><span class="p">(</span><span class="n">mc</span><span class="p">,</span> <span class="n">real_atoms</span><span class="p">,</span> <span class="n">basis_atoms</span><span class="p">)</span>
-                <span class="k">if</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">done_molecules</span><span class="p">:</span>
-                    <span class="k">continue</span>
-
-                <span class="n">ghost_atoms</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">basis_atoms</span><span class="p">)</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">real_atoms</span><span class="p">))</span>
-
-                <span class="c1"># Shift to zero-indexing</span>
-                <span class="n">real_atoms_0</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">real_atoms</span><span class="p">]</span>
-                <span class="n">ghost_atoms_0</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">ghost_atoms</span><span class="p">]</span>
-                <span class="n">mol</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">molecule</span><span class="o">.</span><span class="n">get_fragment</span><span class="p">(</span><span class="n">real_atoms_0</span><span class="p">,</span> <span class="n">ghost_atoms_0</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">group_fragments</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-                <span class="n">mol</span> <span class="o">=</span> <span class="n">mol</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">update</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fix_com&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span> <span class="s2">&quot;fix_orientation&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">})</span>
-
-                <span class="c1"># if self.embedding_charges:</span>
-                <span class="c1">#    embedding_frags = list(set(range(1, self.nfragments + 1)) - set(pair[1]))</span>
-                <span class="c1">#    charges = []</span>
-                <span class="c1">#    for frag in embedding_frags:</span>
-                <span class="c1">#        positions = self.molecule.extract_subsets(frag).geometry().np.tolist()</span>
-                <span class="c1">#        charges.extend([[chg, i] for i, chg in zip(positions, self.embedding_charges[frag])])</span>
-                <span class="c1">#    data[&#39;keywords&#39;][&#39;function_kwargs&#39;].update({&#39;external_potentials&#39;: charges})</span>
-
-                <span class="n">done_molecules</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">label</span><span class="p">)</span>
-                <span class="k">yield</span> <span class="n">mc</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">mol</span>
-</code></pre></div></td></tr></table></div>
-          </details>
-  </div>
-
-</div>
-
-
-
-  </div>
-
-  </div>
-
-
-</div>
-
-
-<div class="doc doc-object doc-function">
-
-
-
-
-<a id="qcmanybody.builder.build_nbody_compute_list"></a>
-  <div class="doc doc-contents first">
-  
-      <p>Generates lists of N-Body computations needed for requested BSSE treatments.</p>
-
-
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>bsse_type</code></td>
-          <td>
-                <code><a class="autorefs autorefs-external" title="typing.Iterable" href="https://docs.python.org/3.12/library/typing.html#typing.Iterable">Iterable</a>[<span title="qcmanybody.models.BsseEnum">BsseEnum</span>]</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>Requested BSSE treatments.</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>nfragments</code></td>
-          <td>
-                <code><a class="autorefs autorefs-external" href="https://docs.python.org/3.12/library/functions.html#int">int</a></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>Number of distinct fragments comprising the full molecular supersystem.</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>nbodies</code></td>
-          <td>
-                <code><a class="autorefs autorefs-external" title="typing.Iterable" href="https://docs.python.org/3.12/library/typing.html#typing.Iterable">Iterable</a>[<a class="autorefs autorefs-external" title="typing.Union" href="https://docs.python.org/3.12/library/typing.html#typing.Union">Union</a>[<a class="autorefs autorefs-external" href="https://docs.python.org/3.12/library/functions.html#int">int</a>, <a class="autorefs autorefs-external" title="typing.Literal" href="https://docs.python.org/3.12/library/typing.html#typing.Literal">Literal</a>[&#39;supersystem&#39;]]]</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>List of n-body levels (e.g., <code>[2]</code> or <code>[1, 2]</code> or <code>["supersystem"]</code>) for which to generate tasks.
-Note the natural 1-indexing, so <code>[1]</code> covers one-body contributions.</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>return_total_data</code></td>
-          <td>
-                <code><a class="autorefs autorefs-external" href="https://docs.python.org/3.12/library/functions.html#bool">bool</a></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>Whether the total data (True; energy/gradient/Hessian) of the molecular system has been requested,
-as opposed to interaction data (False).</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>supersystem_ie_only</code></td>
-          <td>
-                <code><a class="autorefs autorefs-external" href="https://docs.python.org/3.12/library/functions.html#bool">bool</a></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>Target the supersystem total/interaction energy (IE) data over the many-body expansion (MBE) "
-analysis, thereby omitting intermediate-body calculations.</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>supersystem_max_nbody</code></td>
-          <td>
-                <code><a class="autorefs autorefs-external" title="typing.Optional" href="https://docs.python.org/3.12/library/typing.html#typing.Optional">Optional</a>[<a class="autorefs autorefs-external" href="https://docs.python.org/3.12/library/functions.html#int">int</a>]</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>Maximum n-body to use for a supersystem calculation. Must be specified if "supersystem" is in <code>nbodies</code></p>
-            </div>
-          </td>
-          <td>
-                <code>None</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-
-
-  <p><span class="doc-section-title">Returns:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Type</th>
-        <th>Description</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td>
-                <code>compute_dict</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>Dictionary containing subdicts enumerating compute lists for each possible BSSE treatment.
-Subdict keys are n-body levels and values are sets of all the <code>mc_(frag, bas)</code> indices
-needed to compute that n-body level. A given index can appear multiple times within a
-subdict and among subdicts.</p>
-<pre><code>compute_dict["cp"] = {
-    1: set(),
-    2: {((1,), (1, 2)),
-        ((2,), (1, 2)),
-        ((1, 2), (1, 2))}
-}
-</code></pre>
-<p>Subdicts below are always returned. Any may be empty if not requested through <em>bsse_type</em>.</p>
-<ul>
-<li><code>'all'</code> |w---w| full list of computations required</li>
-<li><code>'cp'</code> |w---w| list of computations required for CP procedure</li>
-<li><code>'nocp'</code> |w---w| list of computations required for non-CP procedure</li>
-<li><code>'vmfc_compute'</code> |w---w| list of computations required for VMFC procedure</li>
-<li><code>'vmfc_levels'</code> |w---w| list of levels required for VMFC procedure</li>
-</ul>
-            </div>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>qcmanybody/builder.py</code></summary>
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-    <span class="n">bsse_type</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">BsseEnum</span><span class="p">],</span>
-    <span class="n">nfragments</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
-    <span class="n">nbodies</span><span class="p">:</span> <span class="n">Iterable</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="n">Literal</span><span class="p">[</span><span class="s2">&quot;supersystem&quot;</span><span class="p">]]],</span>
-    <span class="n">return_total_data</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
-    <span class="n">supersystem_ie_only</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
-    <span class="n">supersystem_max_nbody</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="n">Set</span><span class="p">[</span><span class="n">FragBasIndex</span><span class="p">]]]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Generates lists of N-Body computations needed for requested BSSE treatments.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    bsse_type</span>
-<span class="sd">        Requested BSSE treatments.</span>
-<span class="sd">    nfragments</span>
-<span class="sd">        Number of distinct fragments comprising the full molecular supersystem.</span>
-<span class="sd">    nbodies</span>
-<span class="sd">        List of n-body levels (e.g., `[2]` or `[1, 2]` or `[&quot;supersystem&quot;]`) for which to generate tasks.</span>
-<span class="sd">        Note the natural 1-indexing, so `[1]` covers one-body contributions.</span>
-<span class="sd">    return_total_data</span>
-<span class="sd">        Whether the total data (True; energy/gradient/Hessian) of the molecular system has been requested,</span>
-<span class="sd">        as opposed to interaction data (False).</span>
-<span class="sd">    supersystem_ie_only</span>
-<span class="sd">        Target the supersystem total/interaction energy (IE) data over the many-body expansion (MBE) &quot;</span>
-<span class="sd">        analysis, thereby omitting intermediate-body calculations.</span>
-<span class="sd">    supersystem_max_nbody</span>
-<span class="sd">        Maximum n-body to use for a supersystem calculation. Must be specified if &quot;supersystem&quot; is in `nbodies`</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    compute_dict</span>
-<span class="sd">        Dictionary containing subdicts enumerating compute lists for each possible BSSE treatment.</span>
-<span class="sd">        Subdict keys are n-body levels and values are sets of all the `mc_(frag, bas)` indices</span>
-<span class="sd">        needed to compute that n-body level. A given index can appear multiple times within a</span>
-<span class="sd">        subdict and among subdicts.</span>
-
-<span class="sd">            compute_dict[&quot;cp&quot;] = {</span>
-<span class="sd">                1: set(),</span>
-<span class="sd">                2: {((1,), (1, 2)),</span>
-<span class="sd">                    ((2,), (1, 2)),</span>
-<span class="sd">                    ((1, 2), (1, 2))}</span>
-<span class="sd">            }</span>
-
-<span class="sd">        Subdicts below are always returned. Any may be empty if not requested through *bsse_type*.</span>
-
-<span class="sd">        * ``&#39;all&#39;`` |w---w| full list of computations required</span>
-<span class="sd">        * ``&#39;cp&#39;`` |w---w| list of computations required for CP procedure</span>
-<span class="sd">        * ``&#39;nocp&#39;`` |w---w| list of computations required for non-CP procedure</span>
-<span class="sd">        * ``&#39;vmfc_compute&#39;`` |w---w| list of computations required for VMFC procedure</span>
-<span class="sd">        * ``&#39;vmfc_levels&#39;`` |w---w| list of levels required for VMFC procedure</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">include_supersystem</span> <span class="o">=</span> <span class="kc">False</span>
-    <span class="k">if</span> <span class="s2">&quot;supersystem&quot;</span> <span class="ow">in</span> <span class="n">nbodies</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">supersystem_max_nbody</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;supersystem_max_nbody must be provided if &#39;supersystem&#39; contains nbodies&quot;</span><span class="p">)</span>
-
-        <span class="n">include_supersystem</span> <span class="o">=</span> <span class="kc">True</span>
-
-    <span class="n">nbodies</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">nbodies</span> <span class="k">if</span> <span class="n">x</span> <span class="o">!=</span> <span class="s2">&quot;supersystem&quot;</span><span class="p">]</span>
-
-    <span class="c1"># What levels do we need?</span>
-    <span class="n">fragment_range</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">nfragments</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
-
-    <span class="c1"># Need nbodies and all lower-body in full basis</span>
-    <span class="n">cp_compute_list</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="nb">set</span><span class="p">()</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">nbodies</span><span class="p">}</span>
-    <span class="n">nocp_compute_list</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="nb">set</span><span class="p">()</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">nbodies</span><span class="p">}</span>
-    <span class="n">vmfc_compute_list</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="nb">set</span><span class="p">()</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">nbodies</span><span class="p">}</span>
-    <span class="n">vmfc_level_list</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="nb">set</span><span class="p">()</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">nbodies</span><span class="p">}</span>  <span class="c1"># Need to sum something slightly different</span>
-
-    <span class="c1"># Verify proper passing of bsse_type. already validated in Computer</span>
-    <span class="n">bsse_type_remainder</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">bsse_type</span><span class="p">)</span> <span class="o">-</span> <span class="p">{</span><span class="n">e</span><span class="o">.</span><span class="n">value</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">BsseEnum</span><span class="p">}</span>
-    <span class="k">if</span> <span class="n">bsse_type_remainder</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Unrecognized BSSE type(s): </span><span class="si">{</span><span class="n">bsse_type_remainder</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-
-    <span class="c1"># Build up compute sets</span>
-    <span class="k">if</span> <span class="s2">&quot;cp&quot;</span> <span class="ow">in</span> <span class="n">bsse_type</span><span class="p">:</span>
-        <span class="c1"># Everything is in counterpoise/nfr-mer basis</span>
-        <span class="n">basis_tuple</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">fragment_range</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">supersystem_ie_only</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">sublevel</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">nfragments</span><span class="p">]:</span>
-                <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">combinations</span><span class="p">(</span><span class="n">fragment_range</span><span class="p">,</span> <span class="n">sublevel</span><span class="p">):</span>
-                    <span class="n">cp_compute_list</span><span class="p">[</span><span class="n">nfragments</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">basis_tuple</span><span class="p">))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">nb</span> <span class="ow">in</span> <span class="n">nbodies</span><span class="p">:</span>
-                <span class="c1"># Note A.1: nb=1 is skipped because the nfr-mer-basis monomer</span>
-                <span class="c1">#   contributions cancel at 1-body. These skipped tasks will be</span>
-                <span class="c1">#   ordered anyways if higher bodies are requested. Monomers for</span>
-                <span class="c1">#   the purpose of total energies use monomer basis, not these</span>
-                <span class="c1">#   skipped tasks. See coordinating Note A.2 .</span>
-                <span class="k">if</span> <span class="n">nb</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="k">for</span> <span class="n">sublevel</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">nb</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-                        <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">combinations</span><span class="p">(</span><span class="n">fragment_range</span><span class="p">,</span> <span class="n">sublevel</span><span class="p">):</span>
-                            <span class="n">cp_compute_list</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">basis_tuple</span><span class="p">))</span>
-
-    <span class="k">if</span> <span class="s2">&quot;nocp&quot;</span> <span class="ow">in</span> <span class="n">bsse_type</span><span class="p">:</span>
-        <span class="c1"># Everything in natural/n-mer basis</span>
-        <span class="k">if</span> <span class="n">supersystem_ie_only</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">sublevel</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">nfragments</span><span class="p">]:</span>
-                <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">combinations</span><span class="p">(</span><span class="n">fragment_range</span><span class="p">,</span> <span class="n">sublevel</span><span class="p">):</span>
-                    <span class="n">nocp_compute_list</span><span class="p">[</span><span class="n">nfragments</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="p">))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">nb</span> <span class="ow">in</span> <span class="n">nbodies</span><span class="p">:</span>
-                <span class="k">for</span> <span class="n">sublevel</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">nb</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-                    <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">combinations</span><span class="p">(</span><span class="n">fragment_range</span><span class="p">,</span> <span class="n">sublevel</span><span class="p">):</span>
-                        <span class="n">nocp_compute_list</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="p">))</span>
-
-    <span class="k">if</span> <span class="s2">&quot;vmfc&quot;</span> <span class="ow">in</span> <span class="n">bsse_type</span><span class="p">:</span>
-        <span class="c1"># Like a CP for all combinations of pairs or greater</span>
-        <span class="k">for</span> <span class="n">nb</span> <span class="ow">in</span> <span class="n">nbodies</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">cp_combos</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">combinations</span><span class="p">(</span><span class="n">fragment_range</span><span class="p">,</span> <span class="n">nb</span><span class="p">):</span>
-                <span class="n">basis_tuple</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">cp_combos</span><span class="p">)</span>
-                <span class="k">for</span> <span class="n">interior_nbody</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">nb</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-                    <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">combinations</span><span class="p">(</span><span class="n">cp_combos</span><span class="p">,</span> <span class="n">interior_nbody</span><span class="p">):</span>
-                        <span class="n">combo_tuple</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">basis_tuple</span><span class="p">)</span>
-                        <span class="n">vmfc_compute_list</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">combo_tuple</span><span class="p">)</span>
-                        <span class="n">vmfc_level_list</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">basis_tuple</span><span class="p">)]</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">combo_tuple</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">return_total_data</span> <span class="ow">and</span> <span class="mi">1</span> <span class="ow">in</span> <span class="n">nbodies</span><span class="p">:</span>
-        <span class="c1"># Monomers in monomer basis</span>
-        <span class="n">nocp_compute_list</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">set</span><span class="p">())</span>
-        <span class="k">for</span> <span class="n">ifr</span> <span class="ow">in</span> <span class="n">fragment_range</span><span class="p">:</span>
-            <span class="n">nocp_compute_list</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">(((</span><span class="n">ifr</span><span class="p">,),</span> <span class="p">(</span><span class="n">ifr</span><span class="p">,)))</span>
-
-    <span class="k">if</span> <span class="n">include_supersystem</span><span class="p">:</span>
-        <span class="c1"># Add supersystem info to the compute list (nocp only)</span>
-        <span class="k">for</span> <span class="n">nb</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">supersystem_max_nbody</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-            <span class="n">cp_compute_list</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="n">nb</span><span class="p">,</span> <span class="nb">set</span><span class="p">())</span>
-            <span class="n">nocp_compute_list</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="n">nb</span><span class="p">,</span> <span class="nb">set</span><span class="p">())</span>
-            <span class="n">vmfc_compute_list</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="n">nb</span><span class="p">,</span> <span class="nb">set</span><span class="p">())</span>
-            <span class="k">for</span> <span class="n">sublevel</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">nb</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-                <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">combinations</span><span class="p">(</span><span class="n">fragment_range</span><span class="p">,</span> <span class="n">sublevel</span><span class="p">):</span>
-                    <span class="n">nocp_compute_list</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="p">))</span>
-
-        <span class="c1"># Add the total supersystem (nfragments@nfragments)</span>
-        <span class="n">nocp_compute_list</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="n">nfragments</span><span class="p">,</span> <span class="nb">set</span><span class="p">())</span>
-        <span class="n">nocp_compute_list</span><span class="p">[</span><span class="n">nfragments</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="nb">tuple</span><span class="p">(</span><span class="n">fragment_range</span><span class="p">),</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">fragment_range</span><span class="p">)))</span>
-
-    <span class="c1"># Build a comprehensive compute range</span>
-    <span class="c1"># * do not use list length to count number of {nb}-body computations</span>
-    <span class="n">compute_list</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="nb">set</span><span class="p">()</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">nbodies</span><span class="p">}</span>
-    <span class="k">for</span> <span class="n">nb</span> <span class="ow">in</span> <span class="n">nbodies</span><span class="p">:</span>
-        <span class="n">compute_list</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span> <span class="o">|=</span> <span class="n">cp_compute_list</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span>
-        <span class="n">compute_list</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span> <span class="o">|=</span> <span class="n">nocp_compute_list</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span>
-        <span class="n">compute_list</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span> <span class="o">|=</span> <span class="n">vmfc_compute_list</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span>
-
-    <span class="k">if</span> <span class="n">include_supersystem</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">nb</span><span class="p">,</span> <span class="n">lst</span> <span class="ow">in</span> <span class="n">nocp_compute_list</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="n">compute_list</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="n">nb</span><span class="p">,</span> <span class="nb">set</span><span class="p">())</span>
-            <span class="n">compute_list</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span> <span class="o">|=</span> <span class="n">lst</span>
-
-    <span class="c1"># Rearrange compute_list from key nb having values to compute all of that nb</span>
-    <span class="c1">#   to key nb including values of that nb. Use for counting.</span>
-    <span class="n">compute_list_count</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="nb">set</span><span class="p">()</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">nbodies</span><span class="p">}</span>
-    <span class="k">for</span> <span class="n">nb</span> <span class="ow">in</span> <span class="n">nbodies</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">nbset</span> <span class="ow">in</span> <span class="n">compute_list</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
-            <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">nbset</span><span class="p">:</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">item</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="n">nb</span><span class="p">:</span>
-                    <span class="n">compute_list_count</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">item</span><span class="p">)</span>
-
-    <span class="n">compute_dict</span> <span class="o">=</span> <span class="p">{</span>
-        <span class="s2">&quot;all&quot;</span><span class="p">:</span> <span class="n">compute_list</span><span class="p">,</span>
-        <span class="s2">&quot;cp&quot;</span><span class="p">:</span> <span class="n">cp_compute_list</span><span class="p">,</span>
-        <span class="s2">&quot;nocp&quot;</span><span class="p">:</span> <span class="n">nocp_compute_list</span><span class="p">,</span>
-        <span class="s2">&quot;vmfc_compute&quot;</span><span class="p">:</span> <span class="n">vmfc_compute_list</span><span class="p">,</span>
-        <span class="s2">&quot;vmfc_levels&quot;</span><span class="p">:</span> <span class="n">vmfc_level_list</span><span class="p">,</span>
-    <span class="p">}</span>
-    <span class="k">return</span> <span class="n">compute_dict</span>
-</code></pre></div></td></tr></table></div>
-          </details>
-  </div>
-
-</div>
-
-
-
-
-
-
-
-
-
-
-
-
-                
-              </article>
-            </div>
-          
-          
-<script>var target=document.getElementById(location.hash.slice(1));target&&target.name&&(target.checked=target.name.startsWith("__tabbed_"))</script>
-        </div>
-        
-      </main>
-      
-        <footer class="md-footer">
-  
-  <div class="md-footer-meta md-typeset">
-    <div class="md-footer-meta__inner md-grid">
-      <div class="md-copyright">
-  
-    <div class="md-copyright__highlight">
-      Copyright &copy; 2024 QCManyBody Developers
-    </div>
-  
-  
-    Made with
-    <a href="https://squidfunk.github.io/mkdocs-material/" target="_blank" rel="noopener">
-      Material for MkDocs
-    </a>
-  
-</div>
-      
-    </div>
-  </div>
-</footer>
-      
-    </div>
-    <div class="md-dialog" data-md-component="dialog">
-      <div class="md-dialog__inner md-typeset"></div>
-    </div>
-    
-    
-    <script id="__config" type="application/json">{"base": "..", "features": ["navigation.tabs"], "search": "../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
-    
-    
-      <script src="../assets/javascripts/bundle.3220b9d7.min.js"></script>
-      
-    
-  </body>
-</html>
\ No newline at end of file
diff --git a/tutorials/index.html b/results/index.html
similarity index 87%
rename from tutorials/index.html
rename to results/index.html
index 8e0b124..cb50c0d 100644
--- a/tutorials/index.html
+++ b/results/index.html
@@ -8,13 +8,9 @@
       
       
       
-        <link rel="canonical" href="https://molssi.github.io/QCManyBody/tutorials/">
+        <link rel="canonical" href="https://molssi.github.io/QCManyBody/results/">
       
       
-        <link rel="prev" href="..">
-      
-      
-        <link rel="next" href="../api/">
       
       
       <link rel="icon" href="../logo.png">
@@ -22,7 +18,7 @@
     
     
       
-        <title>Tutorials - QCManyBody Docs</title>
+        <title>Results - QCManyBody Documentation</title>
       
     
     
@@ -87,7 +83,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href=".." title="QCManyBody Docs" class="md-header__button md-logo" aria-label="QCManyBody Docs" data-md-component="logo">
+    <a href=".." title="QCManyBody Documentation" class="md-header__button md-logo" aria-label="QCManyBody Documentation" data-md-component="logo">
       
   <img src="../logo.png" alt="logo">
 
@@ -100,13 +96,13 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            QCManyBody Docs
+            QCManyBody Documentation
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
           <span class="md-ellipsis">
             
-              Tutorials
+              Results
             
           </span>
         </div>
@@ -218,7 +214,7 @@
   
     
   
-  QCManyBody Docs
+  Home
 
       </a>
     </li>
@@ -228,16 +224,14 @@
         
   
   
-    
-  
   
-    <li class="md-tabs__item md-tabs__item--active">
-      <a href="./" class="md-tabs__link">
+    <li class="md-tabs__item">
+      <a href="../high-level-interface/" class="md-tabs__link">
         
   
     
   
-  Tutorials
+  High-Level Interface
 
       </a>
     </li>
@@ -249,12 +243,12 @@
   
   
     <li class="md-tabs__item">
-      <a href="../api/" class="md-tabs__link">
+      <a href="../core-interface/" class="md-tabs__link">
         
   
     
   
-  API Documentation
+  Core Interface
 
       </a>
     </li>
@@ -283,29 +277,12 @@
   
   
     <li class="md-tabs__item">
-      <a href="../reference/" class="md-tabs__link">
-        
-  
-    
-  
-  Reference
-
-      </a>
-    </li>
-  
-
-      
-        
-  
-  
-  
-    <li class="md-tabs__item">
-      <a href="../explanation/" class="md-tabs__link">
+      <a href="../api/" class="md-tabs__link">
         
   
     
   
-  Explanation
+  API Documentation
 
       </a>
     </li>
@@ -334,12 +311,12 @@
 
 <nav class="md-nav md-nav--primary md-nav--lifted" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href=".." title="QCManyBody Docs" class="md-nav__button md-logo" aria-label="QCManyBody Docs" data-md-component="logo">
+    <a href=".." title="QCManyBody Documentation" class="md-nav__button md-logo" aria-label="QCManyBody Documentation" data-md-component="logo">
       
   <img src="../logo.png" alt="logo">
 
     </a>
-    QCManyBody Docs
+    QCManyBody Documentation
   </label>
   
     <div class="md-nav__source">
@@ -367,7 +344,7 @@
         
   
   <span class="md-ellipsis">
-    QCManyBody Docs
+    Home
   </span>
   
 
@@ -380,26 +357,18 @@
       
   
   
-    
   
   
-  
-    <li class="md-nav__item md-nav__item--active">
-      
-      <input class="md-nav__toggle md-toggle" type="checkbox" id="__toc">
-      
-      
-      
-      <a href="./" class="md-nav__link md-nav__link--active">
+    <li class="md-nav__item">
+      <a href="../high-level-interface/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    Tutorials
+    High-Level Interface
   </span>
   
 
       </a>
-      
     </li>
   
 
@@ -411,11 +380,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../api/" class="md-nav__link">
+      <a href="../core-interface/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    API Documentation
+    Core Interface
   </span>
   
 
@@ -451,31 +420,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../reference/" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    Reference
-  </span>
-  
-
-      </a>
-    </li>
-  
-
-    
-      
-      
-  
-  
-  
-  
-    <li class="md-nav__item">
-      <a href="../explanation/" class="md-nav__link">
+      <a href="../api/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    Explanation
+    API Documentation
   </span>
   
 
@@ -518,26 +467,9 @@
   
 
 
-  <h1>Tutorials</h1>
+  <h1>Results</h1>
+
 
-<p>This part of the project documentation focuses on a
-<strong>learning-oriented</strong> approach. You'll learn how to
-get started with the code in this project.</p>
-<blockquote>
-<p><strong>Note:</strong> Expand this section by considering the
-following points:</p>
-</blockquote>
-<ul>
-<li>Help newcomers with getting started</li>
-<li>Teach readers about your library by making them
-    write code</li>
-<li>Inspire confidence through examples that work for
-    everyone, repeatably</li>
-<li>Give readers an immediate sense of achievement</li>
-<li>Show concrete examples, no abstractions</li>
-<li>Provide the minimum necessary explanation</li>
-<li>Avoid any distractions</li>
-</ul>
 
 
 
diff --git a/search/search_index.json b/search/search_index.json
index 7e1a769..09fa65b 100644
--- a/search/search_index.json
+++ b/search/search_index.json
@@ -1 +1 @@
-{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"Welcome to MkDocs","text":"<p>For full documentation visit mkdocs.org.</p>"},{"location":"#commands","title":"Commands","text":"<ul> <li><code>mkdocs new [dir-name]</code> - Create a new project.</li> <li><code>mkdocs serve</code> - Start the live-reloading docs server.</li> <li><code>mkdocs build</code> - Build the documentation site.</li> <li><code>mkdocs -h</code> - Print help message and exit.</li> </ul>"},{"location":"#table-of-contents","title":"Table Of Contents","text":"<ol> <li>Tutorials</li> <li>How-To Guides</li> <li>Reference</li> <li>Explanation</li> </ol>"},{"location":"api/","title":"API Documentation","text":"Source code in <code>qcmanybody/manybody.py</code> <pre><code>class ManyBodyCalculator:\n    def __init__(\n        self,\n        molecule: Molecule,\n        bsse_type: Sequence[BsseEnum],\n        levels: Mapping[Union[int, Literal[\"supersystem\"]], str],\n        return_total_data: bool,\n        supersystem_ie_only: bool,\n    ):\n        # TODO\n        self.embedding_charges = {}\n\n        self.molecule = molecule\n        self.bsse_type = [BsseEnum(x) for x in bsse_type]\n        self.return_total_data = return_total_data\n        self.supersystem_ie_only = supersystem_ie_only\n        self.nfragments = len(molecule.fragments)\n\n        self.levels = levels\n\n        # Levels without supersystem\n        self.levels_no_ss = {int(k): v for k, v in levels.items() if k != \"supersystem\"}\n\n        # Just a set of all the modelchems\n        self.mc_levels = set(self.levels.values())\n\n        self.max_nbody = max(self.levels_no_ss.keys())\n\n        if len(self.bsse_type) == 0:\n            raise ValueError(\"No BSSE correction specified\")\n\n        if BsseEnum.vmfc in self.bsse_type and len(set(self.levels.values())) == 1:\n            # For single-modelchem VMFC, NOCP &amp; sometimes CP are produced for free\n            if BsseEnum.nocp not in self.bsse_type:\n                self.bsse_type.append(BsseEnum.nocp)\n            if BsseEnum.cp not in self.bsse_type and self.max_nbody == self.nfragments:\n                self.bsse_type.append(BsseEnum.cp)\n\n        self.return_bsse_type = self.bsse_type[0]\n\n        ###############################\n        # Build nbodies_per_mc_level\n        # TODO - use Lori's code\n        # TODO - dict to list of lists to handle non-contiguous levels\n        # TODO multilevel and supersystem_ie_only=T not allowed together\n        # TODO supersystem in levels is not to be trusted -- nfrag only and skips levels\n        max_level = max(self.levels_no_ss.keys())\n\n        if set(range(1, max_level + 1)) != set(self.levels_no_ss.keys()):\n            raise ValueError(f\"Levels must be contiguous from 1 to {max_level}\")\n\n        self.nbodies_per_mc_level: Dict[str, list] = {mc_level: [] for mc_level in self.mc_levels}\n        for k, v in self.levels_no_ss.items():\n            self.nbodies_per_mc_level[v].append(k)\n\n        self.nbodies_per_mc_level = {k: sorted(v) for k, v in self.nbodies_per_mc_level.items()}\n\n        # Supersystem is always at the end\n        if \"supersystem\" in levels:\n            ss_mc = levels[\"supersystem\"]\n            self.nbodies_per_mc_level[ss_mc].append(\"supersystem\")\n\n        # To be built on the fly\n        self.mc_compute_dict = None\n\n        if not np.array_equal(np.concatenate(self.molecule.fragments), np.arange(len(self.molecule.symbols))):\n            raise ValueError(\"\"\"QCManyBody: non-contiguous fragments could be implemented but aren't at present\"\"\")\n\n        # Build size and slices dictionaries. Assumes fragments are contiguous\n        self.fragment_size_dict = {}\n        self.fragment_slice_dict = {}\n        iat = 0\n        for ifr in range(1, self.nfragments + 1):\n            nat = len(self.molecule.fragments[ifr - 1])\n            self.fragment_size_dict[ifr] = nat\n            self.fragment_slice_dict[ifr] = slice(iat, iat + nat)\n            iat += nat\n\n    @property\n    def has_supersystem(self) -&gt; bool:\n        return \"supersystem\" in self.levels\n\n    @property\n    def compute_map(self) -&gt; Dict[str, Dict[str, Dict[int, Set[FragBasIndex]]]]:\n        if self.mc_compute_dict is not None:\n            return self.mc_compute_dict\n\n        # Build the compute lists\n        self.mc_compute_dict = {}\n\n        for mc in self.mc_levels:\n            nbodies = self.nbodies_per_mc_level[mc]\n            self.mc_compute_dict[mc] = build_nbody_compute_list(\n                self.bsse_type,\n                self.nfragments,\n                nbodies,\n                self.return_total_data,\n                self.supersystem_ie_only,\n                self.max_nbody,\n            )\n\n        return self.mc_compute_dict\n\n    def resize_gradient(self, grad: np.ndarray, bas: Tuple[int, ...], *, reverse: bool = False) -&gt; np.ndarray:\n        return resize_gradient(grad, bas, self.fragment_size_dict, self.fragment_slice_dict, reverse=reverse)\n\n    def resize_hessian(self, hess: np.ndarray, bas: Tuple[int, ...], *, reverse: bool = False) -&gt; np.ndarray:\n        return resize_hessian(hess, bas, self.fragment_size_dict, self.fragment_slice_dict, reverse=reverse)\n\n    def iterate_molecules(self) -&gt; Iterable[Tuple[str, str, Molecule]]:\n        \"\"\"Iterate over all the molecules needed for the computation.\n\n        Yields model chemistry, label, and molecule.\n        \"\"\"\n\n        done_molecules = set()\n\n        for mc, compute_dict in self.compute_map.items():\n            # TODO - this is a bit of a hack. Lots of duplication when reaching higher nbody\n            for compute_list in compute_dict[\"all\"].values():\n                for real_atoms, basis_atoms in compute_list:\n                    label = labeler(mc, real_atoms, basis_atoms)\n                    if label in done_molecules:\n                        continue\n\n                    ghost_atoms = list(set(basis_atoms) - set(real_atoms))\n\n                    # Shift to zero-indexing\n                    real_atoms_0 = [x - 1 for x in real_atoms]\n                    ghost_atoms_0 = [x - 1 for x in ghost_atoms]\n                    mol = self.molecule.get_fragment(real_atoms_0, ghost_atoms_0, orient=False, group_fragments=False)\n                    mol = mol.copy(update={\"fix_com\": True, \"fix_orientation\": True})\n\n                    # if self.embedding_charges:\n                    #    embedding_frags = list(set(range(1, self.nfragments + 1)) - set(pair[1]))\n                    #    charges = []\n                    #    for frag in embedding_frags:\n                    #        positions = self.molecule.extract_subsets(frag).geometry().np.tolist()\n                    #        charges.extend([[chg, i] for i, chg in zip(positions, self.embedding_charges[frag])])\n                    #    data['keywords']['function_kwargs'].update({'external_potentials': charges})\n\n                    done_molecules.add(label)\n                    yield mc, label, mol\n\n    def _assemble_nbody_components(\n        self,\n        property_label: str,\n        component_results: Dict[str, Union[float, np.ndarray]],\n    ) -&gt; Dict[str, Any]:\n        \"\"\"Assembles N-body components for a single derivative level and a single model chemistry level\n        into interaction quantities according to requested BSSE treatment(s).\n        \"\"\"\n\n        # which level are we assembling?\n        delabeled = [delabeler(k) for k in component_results.keys()]\n        mc_level_labels = {x[0] for x in delabeled}\n\n        if len(mc_level_labels) != 1:\n            raise RuntimeError(f\"Multiple model chemistries passed into _assemble_nbody_components: {mc_level_labels}\")\n\n        mc_level = mc_level_labels.pop()\n        if mc_level not in self.mc_levels:\n            raise RuntimeError(f\"Model chemistry {mc_level} not found in {self.mc_levels}\")\n\n        # get the range of nbodies and the required calculations for this level\n        bsse_type = self.bsse_type\n        return_bsse_type = self.return_bsse_type\n        nbodies = self.nbodies_per_mc_level[mc_level]\n        if \"supersystem\" in nbodies:\n            nbodies = list(range(1, self.max_nbody + 1))\n            bsse_type = [BsseEnum.nocp]\n            return_bsse_type = BsseEnum.nocp\n\n        max_nbody = max(nbodies)\n        compute_dict = self.compute_map[mc_level]\n\n        if not all_same_shape(component_results.values()):\n            raise ValueError(\"All values in data dictionary must have the same shape.\")\n\n        # Use first data value to determine shape\n        first_key = next(iter(component_results.keys()))\n        property_shape = find_shape(component_results[first_key])\n\n        # Accumulation dictionaries\n        # * {bsse_type}_by_level is filled by sum_cluster_data to contain for NOCP\n        #   &amp; CP the summed total energies (or other property) of each nb-body. That is:\n        #   * NOCP: {1: 1b@1b,    2: 2b@2b,      ..., max_nbody: max_nbody-b@max_nbody-b} and\n        #   * CP:   {1: 1b@nfr-b, 2: 2b@nfr-b,   ..., max_nbody: max_nbody-b@nfr-b}.\n        #   VMFC bookkeeping is different. For key 1 it contains the summed 1b total energies.\n        #   But for higher keys, it contains each nb-body (non-additive) contribution to the energy.\n        #   * VMFC: {1: 1b@1b,    2: 2b contrib, ..., max_nbody: max_nbody-b contrib}\n        cp_by_level = {n: shaped_zero(property_shape) for n in range(1, nbodies[-1] + 1)}\n        nocp_by_level = {n: shaped_zero(property_shape) for n in range(1, nbodies[-1] + 1)}\n        vmfc_by_level = {n: shaped_zero(property_shape) for n in range(1, nbodies[-1] + 1)}\n\n        # * {bsse_type}_body_dict is usually filled with total energies (or other property).\n        #   Multiple model chemistry levels may be involved.\n        #   Generally, all consecutive keys between 1 and max_nbody will be present in the body_dict,\n        #   but if supersystem_ie_only=T, only 1b and nfr-b are present, or if \"supersystem\" in levels, ???\n        #   * TOT: {1: 1b@1b, 2: 2b tot prop with bsse_type treatment, ..., max_nbody: max_nbody-b tot prop with bsse_type treatment}\n        #   If 1b@1b (monomers in monomer basis) aren't available, which can happen when return_total_data=F\n        #   and 1b@1b aren't otherwise needed, body_dict contains interaction energies (or other property).\n        #   * IE: {1: shaped_zero, 2: 2b interaction prop using bsse_type, ..., max_nbody: max_nbody-b interaction prop using bsse_type}\n        #   For both TOT and IE cases, body_dict values are cummulative, not additive. For TOT, total,\n        #   interaction, and contribution data in ManyBodyResultProperties can be computed in\n        #   collect_vars. For IE, interaction and contribution data can be computed.\n        cp_body_dict = {n: shaped_zero(property_shape) for n in range(1, nbodies[-1] + 1)}\n        nocp_body_dict = {n: shaped_zero(property_shape) for n in range(1, nbodies[-1] + 1)}\n        vmfc_body_dict = {n: shaped_zero(property_shape) for n in range(1, nbodies[-1] + 1)}\n\n        # Sum up all of the levels\n        # * compute_dict[bt][nb] holds all the computations needed to compute nb\n        #   *not* all the nb-level computations, so build the latter\n        cp_compute_list = {nb: set() for nb in range(1, nbodies[-1] + 1)}\n        nocp_compute_list = {nb: set() for nb in range(1, nbodies[-1] + 1)}\n\n        for nb in nbodies:\n            for v in compute_dict[\"cp\"][nb]:\n                if len(v[1]) != 1:\n                    cp_compute_list[len(v[0])].add(v)\n            for w in compute_dict[\"nocp\"][nb]:\n                nocp_compute_list[len(w[0])].add(w)\n\n        for nb in range(1, nbodies[-1] + 1):\n            cp_by_level[nb] = sum_cluster_data(component_results, cp_compute_list[nb], mc_level)\n            nocp_by_level[nb] = sum_cluster_data(component_results, nocp_compute_list[nb], mc_level)\n            if nb in compute_dict[\"vmfc_levels\"]:\n                vmfc_by_level[nb] = sum_cluster_data(\n                    component_results, compute_dict[\"vmfc_levels\"][nb], mc_level, vmfc=True, nb=nb\n                )\n\n        # Extract data for monomers in monomer basis for CP total data\n        if 1 in nbodies:\n            monomers_in_monomer_basis = [v for v in compute_dict[\"nocp\"][1] if len(v[1]) == 1]\n            monomer_sum = sum_cluster_data(component_results, set(monomers_in_monomer_basis), mc_level)\n        else:\n            monomer_sum = shaped_zero(property_shape)\n\n        # Compute cp\n        if BsseEnum.cp in bsse_type:\n            for nb in range(1, nbodies[-1] + 1):\n                if nb == self.nfragments:\n                    cp_body_dict[nb] = cp_by_level[nb] - bsse\n                    continue\n\n                for k in range(1, nb + 1):\n                    take_nk = math.comb(self.nfragments - k - 1, nb - k)\n                    sign = (-1) ** (nb - k)\n                    cp_body_dict[nb] += take_nk * sign * cp_by_level[k]\n\n                if nb == 1:\n                    bsse = cp_body_dict[nb] - monomer_sum\n                    cp_body_dict[nb] = copy_value(monomer_sum)\n                else:\n                    cp_body_dict[nb] -= bsse\n\n        # Compute nocp\n        if BsseEnum.nocp in bsse_type:\n            for nb in range(1, nbodies[-1] + 1):\n                if nb == self.nfragments:\n                    nocp_body_dict[nb] = nocp_by_level[nb]\n                    continue\n\n                for k in range(1, nb + 1):\n                    take_nk = math.comb(self.nfragments - k - 1, nb - k)\n                    sign = (-1) ** (nb - k)\n                    nocp_body_dict[nb] += take_nk * sign * nocp_by_level[k]\n\n        # Compute vmfc\n        if BsseEnum.vmfc in bsse_type:\n            for nb in nbodies:\n                for k in range(1, nb + 1):\n                    vmfc_body_dict[nb] += vmfc_by_level[k]\n\n        # Collect specific and generalized returns\n        results = {\n            f\"cp_{property_label}_body_dict\": cp_body_dict,\n            f\"nocp_{property_label}_body_dict\": nocp_body_dict,\n            f\"vmfc_{property_label}_body_dict\": vmfc_body_dict,\n        }\n\n        # Overall return body dict &amp; value for this property\n        results[f\"{property_label}_body_dict\"] = results[f\"{return_bsse_type.value}_{property_label}_body_dict\"]\n        results[f\"ret_{property_label}\"] = copy_value(results[f\"{property_label}_body_dict\"][max_nbody])\n\n        if not self.return_total_data:\n            results[f\"ret_{property_label}\"] -= results[f\"{property_label}_body_dict\"][1]\n\n        return results\n\n    def _analyze(\n        self,\n        property_label: str,\n        property_results: Dict[str, Union[float, np.ndarray]],  # Label to results\n    ):\n        # Initialize with zeros\n        if not all_same_shape(property_results.values()):\n            raise ValueError(\"All values in data dictionary must have the same shape.\")\n\n        # Use first data value to determine shape\n        first_key = next(iter(property_results.keys()))\n        property_shape = find_shape(property_results[first_key])\n\n        property_result = shaped_zero(property_shape)\n        property_body_dict = {bt.value: {} for bt in self.bsse_type}\n        property_body_contribution = {bt.value: {} for bt in self.bsse_type}\n\n        # results per model chemistry\n        mc_results = {}\n        species_results = {}\n\n        # sort by nbody level, ignore supersystem\n        sorted_nbodies = [(k, v) for k, v in self.nbodies_per_mc_level.items() if v != [\"supersystem\"]]\n        sorted_nbodies = sorted(sorted_nbodies, reverse=True, key=lambda x: x[1])\n        for mc_label, nbody_list in sorted_nbodies:\n            # filter to only one model chemistry\n            filtered_results = {k: v for k, v in property_results.items() if delabeler(k)[0] == mc_label}\n\n            if not filtered_results:\n                if nbody_list == [1]:\n                    # Note A.2: Note A.1 holds, but for the special case of CP-only\n                    #   and rtd=False and multilevel with a separate level for\n                    #   1-body, the skipped tasks run afoul of sanity checks, so\n                    #   we'll add a dummy result.\n                    filtered_results = {labeler(mc_label, [1000], [1000]): shaped_zero(property_shape)}\n                else:\n                    raise RuntimeError(f\"No data found for model chemistry {mc_label}\")\n\n            nb_component_results = self._assemble_nbody_components(property_label, filtered_results)\n            mc_results[mc_label] = nb_component_results\n\n            for n in nbody_list[::-1]:\n                property_bsse_dict = {bt.value: shaped_zero(property_shape) for bt in self.bsse_type}\n\n                for m in range(n - 1, n + 1):\n                    if m == 0:\n                        continue\n\n                    # Subtract the (n-1)-body contribution from the n-body contribution to get the n-body effect\n                    sign = (-1) ** (1 - m // n)\n                    for bt in self.bsse_type:\n                        property_bsse_dict[bt.value] += (\n                            sign * mc_results[mc_label][f\"{bt.value}_{property_label}_body_dict\"][m]\n                        )\n\n                property_result += property_bsse_dict[self.return_bsse_type]\n                for bt in self.bsse_type:\n                    property_body_contribution[bt.value][n] = property_bsse_dict[bt.value]\n\n        if self.has_supersystem:\n            # Get the MC label for supersystem tasks\n            supersystem_mc_level = self.levels[\"supersystem\"]\n\n            # Super system recovers higher order effects at a lower level\n            frag_range = tuple(range(1, self.nfragments + 1))\n\n            ss_cresults = {k: v for k, v in property_results.items() if delabeler(k)[0] == supersystem_mc_level}\n            ss_component_results = self._assemble_nbody_components(property_label, ss_cresults)\n            mc_results[supersystem_mc_level] = ss_component_results\n\n            # Compute components at supersystem level of theory\n            ss_label = labeler(supersystem_mc_level, frag_range, frag_range)\n            supersystem_result = property_results[ss_label]\n            property_result += supersystem_result - ss_component_results[f\"{property_label}_body_dict\"][self.max_nbody]\n\n            for bt in self.bsse_type:\n                property_body_contribution[bt][self.nfragments] = (\n                    supersystem_result - ss_component_results[f\"{property_label}_body_dict\"][self.max_nbody]\n                )\n\n        for bt in self.bsse_type:\n            bstr = bt.value\n            for n in property_body_contribution[bstr]:\n                property_body_dict[bstr][n] = sum(\n                    [\n                        property_body_contribution[bstr][i]\n                        for i in range(1, n + 1)\n                        if i in property_body_contribution[bstr]\n                    ]\n                )\n\n        if not self.return_total_data:\n            # Remove monomer contribution for interaction data\n            property_result -= property_body_dict[self.return_bsse_type][1]\n\n        nbody_results = {\n            f\"ret_{property_label}\": property_result,\n            f\"{property_label}_body_dict\": property_body_dict,\n            \"mc_results\": mc_results,\n        }\n        return nbody_results\n\n    def analyze(\n        self,\n        component_results: Dict[str, Dict[str, Union[float, np.ndarray]]],\n    ):\n        \"\"\"\n\n        Parameters\n        ----------\n        component_results\n            Nested dictionary with results from all individual molecular system\n            calculations, including all subsystem/basis combinations, all model\n            chemistries, and all properties (e.g., e/g/h).\n\n            For example, the below is the format for a nocp gradient run on a\n            helium dimer with 1-body at CCSD and 2-body at MP2. The outer string\n            key can be generated with the ``qcmanybody.utils.labeler`` function.\n            The inner string key is any property; QCManyBody presently knows how\n            to process energy/gradient/Hessian.\n\n            {'[\"ccsd\", [1], [1]]': {'energy': -2.87, 'gradient': array([[0., 0., 0.]])},\n             '[\"ccsd\", [2], [2]]': {'energy': -2.87, 'gradient': array([[0., 0., 0.]])},\n             '[\"mp2\", [1], [1]]': {'energy': -2.86, 'gradient': array([[0., 0., 0.]])},\n             '[\"mp2\", [2], [2]]': {'energy': -2.86, 'gradient': array([[0., 0., 0.]])},\n             '[\"mp2\", [1, 2], [1, 2]]': {'energy': -5.73, 'gradient': array([[ 0., 0., 0.0053], [ 0., 0., -0.0053]])},\n            }\n\n        Return\n        ------\n\n        \"\"\"\n\n        # All properties that were passed to us\n        # * seed with \"energy\" so free/no-op jobs can process\n        available_properties: Set[str] = {\"energy\"}\n        for property_data in component_results.values():\n            available_properties.update(property_data.keys())\n\n        # reorganize to component_results_inv[property][label] = 1.23\n        component_results_inv = {k: {} for k in available_properties}\n\n        for cluster_label, property_data in component_results.items():\n            for property_label, property_value in property_data.items():\n                component_results_inv[property_label][cluster_label] = property_value\n\n        # Remove any missing data\n        component_results_inv = {k: v for k, v in component_results_inv.items() if v}\n        if not component_results_inv:\n            # Note B: Rarely, \"no results\" is expected, like for CP-only,\n            #   rtd=False, and max_nbody=1. We'll add a dummy entry so\n            #   processing can continue.\n            component_results_inv[\"energy\"] = {'[\"dummy\", [1000], [1000]]': 0.0}\n\n        # Actually analyze\n        is_embedded = bool(self.embedding_charges)\n        component_properties = defaultdict(dict)\n        all_results = {}\n        nbody_dict = {}\n#        all_results[\"energy_body_dict\"] = {\"cp\": {1: 0.0}}\n\n        for property_label, property_results in component_results_inv.items():\n            # Expand gradient and hessian\n            if property_label == \"gradient\":\n                property_results = {k: self.resize_gradient(v, delabeler(k)[2]) for k, v in property_results.items()}\n            if property_label == \"hessian\":\n                property_results = {k: self.resize_hessian(v, delabeler(k)[2]) for k, v in property_results.items()}\n\n            r = self._analyze(property_label, property_results)\n            for k, v in property_results.items():\n                component_properties[k][\"calcinfo_natom\"] = len(self.molecule.symbols)\n                component_properties[k][f\"return_{property_label}\"] = v\n            all_results.update(r)\n\n        for bt in self.bsse_type:\n            print_nbody_energy(\n                all_results[\"energy_body_dict\"][bt],\n                f\"{bt.upper()}-corrected multilevel many-body expansion\",\n                self.nfragments,\n                is_embedded,\n            )\n\n        for property_label in available_properties:\n            for bt in self.bsse_type:\n                if not self.has_supersystem:  # skipped levels?\n                    nbody_dict.update(\n                        collect_vars(\n                            bt.upper(),\n                            property_label.upper(),\n                            all_results[f\"{property_label}_body_dict\"][bt],\n                            self.max_nbody,\n                            is_embedded,\n                            self.supersystem_ie_only,\n                        )\n                    )\n\n        all_results[\"results\"] = nbody_dict\n        all_results[\"component_properties\"] = component_properties\n\n        # Make dictionary with \"1cp\", \"2cp\", etc\n        ebd = all_results[\"energy_body_dict\"]\n        all_results[\"energy_body_dict\"] = {str(k) + bt: v for bt in ebd for k, v in ebd[bt].items()}\n\n        return all_results\n</code></pre>"},{"location":"api/#qcmanybody.ManyBodyCalculator.analyze","title":"<code>analyze(component_results)</code>","text":"<p>Parameters:</p> Name Type Description Default <code>component_results</code> <code>Dict[str, Dict[str, Union[float, ndarray]]]</code> <p>Nested dictionary with results from all individual molecular system calculations, including all subsystem/basis combinations, all model chemistries, and all properties (e.g., e/g/h).</p> <p>For example, the below is the format for a nocp gradient run on a helium dimer with 1-body at CCSD and 2-body at MP2. The outer string key can be generated with the <code>qcmanybody.utils.labeler</code> function. The inner string key is any property; QCManyBody presently knows how to process energy/gradient/Hessian.</p> <p>{'[\"ccsd\", [1], [1]]': {'energy': -2.87, 'gradient': array([[0., 0., 0.]])},  '[\"ccsd\", [2], [2]]': {'energy': -2.87, 'gradient': array([[0., 0., 0.]])},  '[\"mp2\", [1], [1]]': {'energy': -2.86, 'gradient': array([[0., 0., 0.]])},  '[\"mp2\", [2], [2]]': {'energy': -2.86, 'gradient': array([[0., 0., 0.]])},  '[\"mp2\", [1, 2], [1, 2]]': {'energy': -5.73, 'gradient': array([[ 0., 0., 0.0053], [ 0., 0., -0.0053]])}, }</p> required Return Source code in <code>qcmanybody/manybody.py</code> <pre><code>    def analyze(\n        self,\n        component_results: Dict[str, Dict[str, Union[float, np.ndarray]]],\n    ):\n        \"\"\"\n\n        Parameters\n        ----------\n        component_results\n            Nested dictionary with results from all individual molecular system\n            calculations, including all subsystem/basis combinations, all model\n            chemistries, and all properties (e.g., e/g/h).\n\n            For example, the below is the format for a nocp gradient run on a\n            helium dimer with 1-body at CCSD and 2-body at MP2. The outer string\n            key can be generated with the ``qcmanybody.utils.labeler`` function.\n            The inner string key is any property; QCManyBody presently knows how\n            to process energy/gradient/Hessian.\n\n            {'[\"ccsd\", [1], [1]]': {'energy': -2.87, 'gradient': array([[0., 0., 0.]])},\n             '[\"ccsd\", [2], [2]]': {'energy': -2.87, 'gradient': array([[0., 0., 0.]])},\n             '[\"mp2\", [1], [1]]': {'energy': -2.86, 'gradient': array([[0., 0., 0.]])},\n             '[\"mp2\", [2], [2]]': {'energy': -2.86, 'gradient': array([[0., 0., 0.]])},\n             '[\"mp2\", [1, 2], [1, 2]]': {'energy': -5.73, 'gradient': array([[ 0., 0., 0.0053], [ 0., 0., -0.0053]])},\n            }\n\n        Return\n        ------\n\n        \"\"\"\n\n        # All properties that were passed to us\n        # * seed with \"energy\" so free/no-op jobs can process\n        available_properties: Set[str] = {\"energy\"}\n        for property_data in component_results.values():\n            available_properties.update(property_data.keys())\n\n        # reorganize to component_results_inv[property][label] = 1.23\n        component_results_inv = {k: {} for k in available_properties}\n\n        for cluster_label, property_data in component_results.items():\n            for property_label, property_value in property_data.items():\n                component_results_inv[property_label][cluster_label] = property_value\n\n        # Remove any missing data\n        component_results_inv = {k: v for k, v in component_results_inv.items() if v}\n        if not component_results_inv:\n            # Note B: Rarely, \"no results\" is expected, like for CP-only,\n            #   rtd=False, and max_nbody=1. We'll add a dummy entry so\n            #   processing can continue.\n            component_results_inv[\"energy\"] = {'[\"dummy\", [1000], [1000]]': 0.0}\n\n        # Actually analyze\n        is_embedded = bool(self.embedding_charges)\n        component_properties = defaultdict(dict)\n        all_results = {}\n        nbody_dict = {}\n#        all_results[\"energy_body_dict\"] = {\"cp\": {1: 0.0}}\n\n        for property_label, property_results in component_results_inv.items():\n            # Expand gradient and hessian\n            if property_label == \"gradient\":\n                property_results = {k: self.resize_gradient(v, delabeler(k)[2]) for k, v in property_results.items()}\n            if property_label == \"hessian\":\n                property_results = {k: self.resize_hessian(v, delabeler(k)[2]) for k, v in property_results.items()}\n\n            r = self._analyze(property_label, property_results)\n            for k, v in property_results.items():\n                component_properties[k][\"calcinfo_natom\"] = len(self.molecule.symbols)\n                component_properties[k][f\"return_{property_label}\"] = v\n            all_results.update(r)\n\n        for bt in self.bsse_type:\n            print_nbody_energy(\n                all_results[\"energy_body_dict\"][bt],\n                f\"{bt.upper()}-corrected multilevel many-body expansion\",\n                self.nfragments,\n                is_embedded,\n            )\n\n        for property_label in available_properties:\n            for bt in self.bsse_type:\n                if not self.has_supersystem:  # skipped levels?\n                    nbody_dict.update(\n                        collect_vars(\n                            bt.upper(),\n                            property_label.upper(),\n                            all_results[f\"{property_label}_body_dict\"][bt],\n                            self.max_nbody,\n                            is_embedded,\n                            self.supersystem_ie_only,\n                        )\n                    )\n\n        all_results[\"results\"] = nbody_dict\n        all_results[\"component_properties\"] = component_properties\n\n        # Make dictionary with \"1cp\", \"2cp\", etc\n        ebd = all_results[\"energy_body_dict\"]\n        all_results[\"energy_body_dict\"] = {str(k) + bt: v for bt in ebd for k, v in ebd[bt].items()}\n\n        return all_results\n</code></pre>"},{"location":"api/#qcmanybody.ManyBodyCalculator.iterate_molecules","title":"<code>iterate_molecules()</code>","text":"<p>Iterate over all the molecules needed for the computation.</p> <p>Yields model chemistry, label, and molecule.</p> Source code in <code>qcmanybody/manybody.py</code> <pre><code>def iterate_molecules(self) -&gt; Iterable[Tuple[str, str, Molecule]]:\n    \"\"\"Iterate over all the molecules needed for the computation.\n\n    Yields model chemistry, label, and molecule.\n    \"\"\"\n\n    done_molecules = set()\n\n    for mc, compute_dict in self.compute_map.items():\n        # TODO - this is a bit of a hack. Lots of duplication when reaching higher nbody\n        for compute_list in compute_dict[\"all\"].values():\n            for real_atoms, basis_atoms in compute_list:\n                label = labeler(mc, real_atoms, basis_atoms)\n                if label in done_molecules:\n                    continue\n\n                ghost_atoms = list(set(basis_atoms) - set(real_atoms))\n\n                # Shift to zero-indexing\n                real_atoms_0 = [x - 1 for x in real_atoms]\n                ghost_atoms_0 = [x - 1 for x in ghost_atoms]\n                mol = self.molecule.get_fragment(real_atoms_0, ghost_atoms_0, orient=False, group_fragments=False)\n                mol = mol.copy(update={\"fix_com\": True, \"fix_orientation\": True})\n\n                # if self.embedding_charges:\n                #    embedding_frags = list(set(range(1, self.nfragments + 1)) - set(pair[1]))\n                #    charges = []\n                #    for frag in embedding_frags:\n                #        positions = self.molecule.extract_subsets(frag).geometry().np.tolist()\n                #        charges.extend([[chg, i] for i, chg in zip(positions, self.embedding_charges[frag])])\n                #    data['keywords']['function_kwargs'].update({'external_potentials': charges})\n\n                done_molecules.add(label)\n                yield mc, label, mol\n</code></pre>"},{"location":"explanation/","title":"Explanation","text":"<p>This part of the project documentation focuses on an understanding-oriented approach. You'll get a chance to read about the background of the project, as well as reasoning about how it was implemented.</p> <p>Note: Expand this section by considering the following points:</p> <ul> <li>Give context and background on your library</li> <li>Explain why you created it</li> <li>Provide multiple examples and approaches of how     to work with it</li> <li>Help the reader make connections</li> <li>Avoid writing instructions or technical descriptions     here</li> </ul>"},{"location":"reference/","title":"Reference","text":"<p>This part of the project documentation focuses on an information-oriented approach. Use it as a reference for the technical implementation of the <code>QCManyBody</code> project code.</p> Source code in <code>qcmanybody/manybody.py</code> <pre><code>class ManyBodyCalculator:\n    def __init__(\n        self,\n        molecule: Molecule,\n        bsse_type: Sequence[BsseEnum],\n        levels: Mapping[Union[int, Literal[\"supersystem\"]], str],\n        return_total_data: bool,\n        supersystem_ie_only: bool,\n    ):\n        # TODO\n        self.embedding_charges = {}\n\n        self.molecule = molecule\n        self.bsse_type = [BsseEnum(x) for x in bsse_type]\n        self.return_total_data = return_total_data\n        self.supersystem_ie_only = supersystem_ie_only\n        self.nfragments = len(molecule.fragments)\n\n        self.levels = levels\n\n        # Levels without supersystem\n        self.levels_no_ss = {int(k): v for k, v in levels.items() if k != \"supersystem\"}\n\n        # Just a set of all the modelchems\n        self.mc_levels = set(self.levels.values())\n\n        self.max_nbody = max(self.levels_no_ss.keys())\n\n        if len(self.bsse_type) == 0:\n            raise ValueError(\"No BSSE correction specified\")\n\n        if BsseEnum.vmfc in self.bsse_type and len(set(self.levels.values())) == 1:\n            # For single-modelchem VMFC, NOCP &amp; sometimes CP are produced for free\n            if BsseEnum.nocp not in self.bsse_type:\n                self.bsse_type.append(BsseEnum.nocp)\n            if BsseEnum.cp not in self.bsse_type and self.max_nbody == self.nfragments:\n                self.bsse_type.append(BsseEnum.cp)\n\n        self.return_bsse_type = self.bsse_type[0]\n\n        ###############################\n        # Build nbodies_per_mc_level\n        # TODO - use Lori's code\n        # TODO - dict to list of lists to handle non-contiguous levels\n        # TODO multilevel and supersystem_ie_only=T not allowed together\n        # TODO supersystem in levels is not to be trusted -- nfrag only and skips levels\n        max_level = max(self.levels_no_ss.keys())\n\n        if set(range(1, max_level + 1)) != set(self.levels_no_ss.keys()):\n            raise ValueError(f\"Levels must be contiguous from 1 to {max_level}\")\n\n        self.nbodies_per_mc_level: Dict[str, list] = {mc_level: [] for mc_level in self.mc_levels}\n        for k, v in self.levels_no_ss.items():\n            self.nbodies_per_mc_level[v].append(k)\n\n        self.nbodies_per_mc_level = {k: sorted(v) for k, v in self.nbodies_per_mc_level.items()}\n\n        # Supersystem is always at the end\n        if \"supersystem\" in levels:\n            ss_mc = levels[\"supersystem\"]\n            self.nbodies_per_mc_level[ss_mc].append(\"supersystem\")\n\n        # To be built on the fly\n        self.mc_compute_dict = None\n\n        if not np.array_equal(np.concatenate(self.molecule.fragments), np.arange(len(self.molecule.symbols))):\n            raise ValueError(\"\"\"QCManyBody: non-contiguous fragments could be implemented but aren't at present\"\"\")\n\n        # Build size and slices dictionaries. Assumes fragments are contiguous\n        self.fragment_size_dict = {}\n        self.fragment_slice_dict = {}\n        iat = 0\n        for ifr in range(1, self.nfragments + 1):\n            nat = len(self.molecule.fragments[ifr - 1])\n            self.fragment_size_dict[ifr] = nat\n            self.fragment_slice_dict[ifr] = slice(iat, iat + nat)\n            iat += nat\n\n    @property\n    def has_supersystem(self) -&gt; bool:\n        return \"supersystem\" in self.levels\n\n    @property\n    def compute_map(self) -&gt; Dict[str, Dict[str, Dict[int, Set[FragBasIndex]]]]:\n        if self.mc_compute_dict is not None:\n            return self.mc_compute_dict\n\n        # Build the compute lists\n        self.mc_compute_dict = {}\n\n        for mc in self.mc_levels:\n            nbodies = self.nbodies_per_mc_level[mc]\n            self.mc_compute_dict[mc] = build_nbody_compute_list(\n                self.bsse_type,\n                self.nfragments,\n                nbodies,\n                self.return_total_data,\n                self.supersystem_ie_only,\n                self.max_nbody,\n            )\n\n        return self.mc_compute_dict\n\n    def resize_gradient(self, grad: np.ndarray, bas: Tuple[int, ...], *, reverse: bool = False) -&gt; np.ndarray:\n        return resize_gradient(grad, bas, self.fragment_size_dict, self.fragment_slice_dict, reverse=reverse)\n\n    def resize_hessian(self, hess: np.ndarray, bas: Tuple[int, ...], *, reverse: bool = False) -&gt; np.ndarray:\n        return resize_hessian(hess, bas, self.fragment_size_dict, self.fragment_slice_dict, reverse=reverse)\n\n    def iterate_molecules(self) -&gt; Iterable[Tuple[str, str, Molecule]]:\n        \"\"\"Iterate over all the molecules needed for the computation.\n\n        Yields model chemistry, label, and molecule.\n        \"\"\"\n\n        done_molecules = set()\n\n        for mc, compute_dict in self.compute_map.items():\n            # TODO - this is a bit of a hack. Lots of duplication when reaching higher nbody\n            for compute_list in compute_dict[\"all\"].values():\n                for real_atoms, basis_atoms in compute_list:\n                    label = labeler(mc, real_atoms, basis_atoms)\n                    if label in done_molecules:\n                        continue\n\n                    ghost_atoms = list(set(basis_atoms) - set(real_atoms))\n\n                    # Shift to zero-indexing\n                    real_atoms_0 = [x - 1 for x in real_atoms]\n                    ghost_atoms_0 = [x - 1 for x in ghost_atoms]\n                    mol = self.molecule.get_fragment(real_atoms_0, ghost_atoms_0, orient=False, group_fragments=False)\n                    mol = mol.copy(update={\"fix_com\": True, \"fix_orientation\": True})\n\n                    # if self.embedding_charges:\n                    #    embedding_frags = list(set(range(1, self.nfragments + 1)) - set(pair[1]))\n                    #    charges = []\n                    #    for frag in embedding_frags:\n                    #        positions = self.molecule.extract_subsets(frag).geometry().np.tolist()\n                    #        charges.extend([[chg, i] for i, chg in zip(positions, self.embedding_charges[frag])])\n                    #    data['keywords']['function_kwargs'].update({'external_potentials': charges})\n\n                    done_molecules.add(label)\n                    yield mc, label, mol\n\n    def _assemble_nbody_components(\n        self,\n        property_label: str,\n        component_results: Dict[str, Union[float, np.ndarray]],\n    ) -&gt; Dict[str, Any]:\n        \"\"\"Assembles N-body components for a single derivative level and a single model chemistry level\n        into interaction quantities according to requested BSSE treatment(s).\n        \"\"\"\n\n        # which level are we assembling?\n        delabeled = [delabeler(k) for k in component_results.keys()]\n        mc_level_labels = {x[0] for x in delabeled}\n\n        if len(mc_level_labels) != 1:\n            raise RuntimeError(f\"Multiple model chemistries passed into _assemble_nbody_components: {mc_level_labels}\")\n\n        mc_level = mc_level_labels.pop()\n        if mc_level not in self.mc_levels:\n            raise RuntimeError(f\"Model chemistry {mc_level} not found in {self.mc_levels}\")\n\n        # get the range of nbodies and the required calculations for this level\n        bsse_type = self.bsse_type\n        return_bsse_type = self.return_bsse_type\n        nbodies = self.nbodies_per_mc_level[mc_level]\n        if \"supersystem\" in nbodies:\n            nbodies = list(range(1, self.max_nbody + 1))\n            bsse_type = [BsseEnum.nocp]\n            return_bsse_type = BsseEnum.nocp\n\n        max_nbody = max(nbodies)\n        compute_dict = self.compute_map[mc_level]\n\n        if not all_same_shape(component_results.values()):\n            raise ValueError(\"All values in data dictionary must have the same shape.\")\n\n        # Use first data value to determine shape\n        first_key = next(iter(component_results.keys()))\n        property_shape = find_shape(component_results[first_key])\n\n        # Accumulation dictionaries\n        # * {bsse_type}_by_level is filled by sum_cluster_data to contain for NOCP\n        #   &amp; CP the summed total energies (or other property) of each nb-body. That is:\n        #   * NOCP: {1: 1b@1b,    2: 2b@2b,      ..., max_nbody: max_nbody-b@max_nbody-b} and\n        #   * CP:   {1: 1b@nfr-b, 2: 2b@nfr-b,   ..., max_nbody: max_nbody-b@nfr-b}.\n        #   VMFC bookkeeping is different. For key 1 it contains the summed 1b total energies.\n        #   But for higher keys, it contains each nb-body (non-additive) contribution to the energy.\n        #   * VMFC: {1: 1b@1b,    2: 2b contrib, ..., max_nbody: max_nbody-b contrib}\n        cp_by_level = {n: shaped_zero(property_shape) for n in range(1, nbodies[-1] + 1)}\n        nocp_by_level = {n: shaped_zero(property_shape) for n in range(1, nbodies[-1] + 1)}\n        vmfc_by_level = {n: shaped_zero(property_shape) for n in range(1, nbodies[-1] + 1)}\n\n        # * {bsse_type}_body_dict is usually filled with total energies (or other property).\n        #   Multiple model chemistry levels may be involved.\n        #   Generally, all consecutive keys between 1 and max_nbody will be present in the body_dict,\n        #   but if supersystem_ie_only=T, only 1b and nfr-b are present, or if \"supersystem\" in levels, ???\n        #   * TOT: {1: 1b@1b, 2: 2b tot prop with bsse_type treatment, ..., max_nbody: max_nbody-b tot prop with bsse_type treatment}\n        #   If 1b@1b (monomers in monomer basis) aren't available, which can happen when return_total_data=F\n        #   and 1b@1b aren't otherwise needed, body_dict contains interaction energies (or other property).\n        #   * IE: {1: shaped_zero, 2: 2b interaction prop using bsse_type, ..., max_nbody: max_nbody-b interaction prop using bsse_type}\n        #   For both TOT and IE cases, body_dict values are cummulative, not additive. For TOT, total,\n        #   interaction, and contribution data in ManyBodyResultProperties can be computed in\n        #   collect_vars. For IE, interaction and contribution data can be computed.\n        cp_body_dict = {n: shaped_zero(property_shape) for n in range(1, nbodies[-1] + 1)}\n        nocp_body_dict = {n: shaped_zero(property_shape) for n in range(1, nbodies[-1] + 1)}\n        vmfc_body_dict = {n: shaped_zero(property_shape) for n in range(1, nbodies[-1] + 1)}\n\n        # Sum up all of the levels\n        # * compute_dict[bt][nb] holds all the computations needed to compute nb\n        #   *not* all the nb-level computations, so build the latter\n        cp_compute_list = {nb: set() for nb in range(1, nbodies[-1] + 1)}\n        nocp_compute_list = {nb: set() for nb in range(1, nbodies[-1] + 1)}\n\n        for nb in nbodies:\n            for v in compute_dict[\"cp\"][nb]:\n                if len(v[1]) != 1:\n                    cp_compute_list[len(v[0])].add(v)\n            for w in compute_dict[\"nocp\"][nb]:\n                nocp_compute_list[len(w[0])].add(w)\n\n        for nb in range(1, nbodies[-1] + 1):\n            cp_by_level[nb] = sum_cluster_data(component_results, cp_compute_list[nb], mc_level)\n            nocp_by_level[nb] = sum_cluster_data(component_results, nocp_compute_list[nb], mc_level)\n            if nb in compute_dict[\"vmfc_levels\"]:\n                vmfc_by_level[nb] = sum_cluster_data(\n                    component_results, compute_dict[\"vmfc_levels\"][nb], mc_level, vmfc=True, nb=nb\n                )\n\n        # Extract data for monomers in monomer basis for CP total data\n        if 1 in nbodies:\n            monomers_in_monomer_basis = [v for v in compute_dict[\"nocp\"][1] if len(v[1]) == 1]\n            monomer_sum = sum_cluster_data(component_results, set(monomers_in_monomer_basis), mc_level)\n        else:\n            monomer_sum = shaped_zero(property_shape)\n\n        # Compute cp\n        if BsseEnum.cp in bsse_type:\n            for nb in range(1, nbodies[-1] + 1):\n                if nb == self.nfragments:\n                    cp_body_dict[nb] = cp_by_level[nb] - bsse\n                    continue\n\n                for k in range(1, nb + 1):\n                    take_nk = math.comb(self.nfragments - k - 1, nb - k)\n                    sign = (-1) ** (nb - k)\n                    cp_body_dict[nb] += take_nk * sign * cp_by_level[k]\n\n                if nb == 1:\n                    bsse = cp_body_dict[nb] - monomer_sum\n                    cp_body_dict[nb] = copy_value(monomer_sum)\n                else:\n                    cp_body_dict[nb] -= bsse\n\n        # Compute nocp\n        if BsseEnum.nocp in bsse_type:\n            for nb in range(1, nbodies[-1] + 1):\n                if nb == self.nfragments:\n                    nocp_body_dict[nb] = nocp_by_level[nb]\n                    continue\n\n                for k in range(1, nb + 1):\n                    take_nk = math.comb(self.nfragments - k - 1, nb - k)\n                    sign = (-1) ** (nb - k)\n                    nocp_body_dict[nb] += take_nk * sign * nocp_by_level[k]\n\n        # Compute vmfc\n        if BsseEnum.vmfc in bsse_type:\n            for nb in nbodies:\n                for k in range(1, nb + 1):\n                    vmfc_body_dict[nb] += vmfc_by_level[k]\n\n        # Collect specific and generalized returns\n        results = {\n            f\"cp_{property_label}_body_dict\": cp_body_dict,\n            f\"nocp_{property_label}_body_dict\": nocp_body_dict,\n            f\"vmfc_{property_label}_body_dict\": vmfc_body_dict,\n        }\n\n        # Overall return body dict &amp; value for this property\n        results[f\"{property_label}_body_dict\"] = results[f\"{return_bsse_type.value}_{property_label}_body_dict\"]\n        results[f\"ret_{property_label}\"] = copy_value(results[f\"{property_label}_body_dict\"][max_nbody])\n\n        if not self.return_total_data:\n            results[f\"ret_{property_label}\"] -= results[f\"{property_label}_body_dict\"][1]\n\n        return results\n\n    def _analyze(\n        self,\n        property_label: str,\n        property_results: Dict[str, Union[float, np.ndarray]],  # Label to results\n    ):\n        # Initialize with zeros\n        if not all_same_shape(property_results.values()):\n            raise ValueError(\"All values in data dictionary must have the same shape.\")\n\n        # Use first data value to determine shape\n        first_key = next(iter(property_results.keys()))\n        property_shape = find_shape(property_results[first_key])\n\n        property_result = shaped_zero(property_shape)\n        property_body_dict = {bt.value: {} for bt in self.bsse_type}\n        property_body_contribution = {bt.value: {} for bt in self.bsse_type}\n\n        # results per model chemistry\n        mc_results = {}\n        species_results = {}\n\n        # sort by nbody level, ignore supersystem\n        sorted_nbodies = [(k, v) for k, v in self.nbodies_per_mc_level.items() if v != [\"supersystem\"]]\n        sorted_nbodies = sorted(sorted_nbodies, reverse=True, key=lambda x: x[1])\n        for mc_label, nbody_list in sorted_nbodies:\n            # filter to only one model chemistry\n            filtered_results = {k: v for k, v in property_results.items() if delabeler(k)[0] == mc_label}\n\n            if not filtered_results:\n                if nbody_list == [1]:\n                    # Note A.2: Note A.1 holds, but for the special case of CP-only\n                    #   and rtd=False and multilevel with a separate level for\n                    #   1-body, the skipped tasks run afoul of sanity checks, so\n                    #   we'll add a dummy result.\n                    filtered_results = {labeler(mc_label, [1000], [1000]): shaped_zero(property_shape)}\n                else:\n                    raise RuntimeError(f\"No data found for model chemistry {mc_label}\")\n\n            nb_component_results = self._assemble_nbody_components(property_label, filtered_results)\n            mc_results[mc_label] = nb_component_results\n\n            for n in nbody_list[::-1]:\n                property_bsse_dict = {bt.value: shaped_zero(property_shape) for bt in self.bsse_type}\n\n                for m in range(n - 1, n + 1):\n                    if m == 0:\n                        continue\n\n                    # Subtract the (n-1)-body contribution from the n-body contribution to get the n-body effect\n                    sign = (-1) ** (1 - m // n)\n                    for bt in self.bsse_type:\n                        property_bsse_dict[bt.value] += (\n                            sign * mc_results[mc_label][f\"{bt.value}_{property_label}_body_dict\"][m]\n                        )\n\n                property_result += property_bsse_dict[self.return_bsse_type]\n                for bt in self.bsse_type:\n                    property_body_contribution[bt.value][n] = property_bsse_dict[bt.value]\n\n        if self.has_supersystem:\n            # Get the MC label for supersystem tasks\n            supersystem_mc_level = self.levels[\"supersystem\"]\n\n            # Super system recovers higher order effects at a lower level\n            frag_range = tuple(range(1, self.nfragments + 1))\n\n            ss_cresults = {k: v for k, v in property_results.items() if delabeler(k)[0] == supersystem_mc_level}\n            ss_component_results = self._assemble_nbody_components(property_label, ss_cresults)\n            mc_results[supersystem_mc_level] = ss_component_results\n\n            # Compute components at supersystem level of theory\n            ss_label = labeler(supersystem_mc_level, frag_range, frag_range)\n            supersystem_result = property_results[ss_label]\n            property_result += supersystem_result - ss_component_results[f\"{property_label}_body_dict\"][self.max_nbody]\n\n            for bt in self.bsse_type:\n                property_body_contribution[bt][self.nfragments] = (\n                    supersystem_result - ss_component_results[f\"{property_label}_body_dict\"][self.max_nbody]\n                )\n\n        for bt in self.bsse_type:\n            bstr = bt.value\n            for n in property_body_contribution[bstr]:\n                property_body_dict[bstr][n] = sum(\n                    [\n                        property_body_contribution[bstr][i]\n                        for i in range(1, n + 1)\n                        if i in property_body_contribution[bstr]\n                    ]\n                )\n\n        if not self.return_total_data:\n            # Remove monomer contribution for interaction data\n            property_result -= property_body_dict[self.return_bsse_type][1]\n\n        nbody_results = {\n            f\"ret_{property_label}\": property_result,\n            f\"{property_label}_body_dict\": property_body_dict,\n            \"mc_results\": mc_results,\n        }\n        return nbody_results\n\n    def analyze(\n        self,\n        component_results: Dict[str, Dict[str, Union[float, np.ndarray]]],\n    ):\n        \"\"\"\n\n        Parameters\n        ----------\n        component_results\n            Nested dictionary with results from all individual molecular system\n            calculations, including all subsystem/basis combinations, all model\n            chemistries, and all properties (e.g., e/g/h).\n\n            For example, the below is the format for a nocp gradient run on a\n            helium dimer with 1-body at CCSD and 2-body at MP2. The outer string\n            key can be generated with the ``qcmanybody.utils.labeler`` function.\n            The inner string key is any property; QCManyBody presently knows how\n            to process energy/gradient/Hessian.\n\n            {'[\"ccsd\", [1], [1]]': {'energy': -2.87, 'gradient': array([[0., 0., 0.]])},\n             '[\"ccsd\", [2], [2]]': {'energy': -2.87, 'gradient': array([[0., 0., 0.]])},\n             '[\"mp2\", [1], [1]]': {'energy': -2.86, 'gradient': array([[0., 0., 0.]])},\n             '[\"mp2\", [2], [2]]': {'energy': -2.86, 'gradient': array([[0., 0., 0.]])},\n             '[\"mp2\", [1, 2], [1, 2]]': {'energy': -5.73, 'gradient': array([[ 0., 0., 0.0053], [ 0., 0., -0.0053]])},\n            }\n\n        Return\n        ------\n\n        \"\"\"\n\n        # All properties that were passed to us\n        # * seed with \"energy\" so free/no-op jobs can process\n        available_properties: Set[str] = {\"energy\"}\n        for property_data in component_results.values():\n            available_properties.update(property_data.keys())\n\n        # reorganize to component_results_inv[property][label] = 1.23\n        component_results_inv = {k: {} for k in available_properties}\n\n        for cluster_label, property_data in component_results.items():\n            for property_label, property_value in property_data.items():\n                component_results_inv[property_label][cluster_label] = property_value\n\n        # Remove any missing data\n        component_results_inv = {k: v for k, v in component_results_inv.items() if v}\n        if not component_results_inv:\n            # Note B: Rarely, \"no results\" is expected, like for CP-only,\n            #   rtd=False, and max_nbody=1. We'll add a dummy entry so\n            #   processing can continue.\n            component_results_inv[\"energy\"] = {'[\"dummy\", [1000], [1000]]': 0.0}\n\n        # Actually analyze\n        is_embedded = bool(self.embedding_charges)\n        component_properties = defaultdict(dict)\n        all_results = {}\n        nbody_dict = {}\n#        all_results[\"energy_body_dict\"] = {\"cp\": {1: 0.0}}\n\n        for property_label, property_results in component_results_inv.items():\n            # Expand gradient and hessian\n            if property_label == \"gradient\":\n                property_results = {k: self.resize_gradient(v, delabeler(k)[2]) for k, v in property_results.items()}\n            if property_label == \"hessian\":\n                property_results = {k: self.resize_hessian(v, delabeler(k)[2]) for k, v in property_results.items()}\n\n            r = self._analyze(property_label, property_results)\n            for k, v in property_results.items():\n                component_properties[k][\"calcinfo_natom\"] = len(self.molecule.symbols)\n                component_properties[k][f\"return_{property_label}\"] = v\n            all_results.update(r)\n\n        for bt in self.bsse_type:\n            print_nbody_energy(\n                all_results[\"energy_body_dict\"][bt],\n                f\"{bt.upper()}-corrected multilevel many-body expansion\",\n                self.nfragments,\n                is_embedded,\n            )\n\n        for property_label in available_properties:\n            for bt in self.bsse_type:\n                if not self.has_supersystem:  # skipped levels?\n                    nbody_dict.update(\n                        collect_vars(\n                            bt.upper(),\n                            property_label.upper(),\n                            all_results[f\"{property_label}_body_dict\"][bt],\n                            self.max_nbody,\n                            is_embedded,\n                            self.supersystem_ie_only,\n                        )\n                    )\n\n        all_results[\"results\"] = nbody_dict\n        all_results[\"component_properties\"] = component_properties\n\n        # Make dictionary with \"1cp\", \"2cp\", etc\n        ebd = all_results[\"energy_body_dict\"]\n        all_results[\"energy_body_dict\"] = {str(k) + bt: v for bt in ebd for k, v in ebd[bt].items()}\n\n        return all_results\n</code></pre> <p>Generates lists of N-Body computations needed for requested BSSE treatments.</p> <p>Parameters:</p> Name Type Description Default <code>bsse_type</code> <code>Iterable[BsseEnum]</code> <p>Requested BSSE treatments.</p> required <code>nfragments</code> <code>int</code> <p>Number of distinct fragments comprising the full molecular supersystem.</p> required <code>nbodies</code> <code>Iterable[Union[int, Literal['supersystem']]]</code> <p>List of n-body levels (e.g., <code>[2]</code> or <code>[1, 2]</code> or <code>[\"supersystem\"]</code>) for which to generate tasks. Note the natural 1-indexing, so <code>[1]</code> covers one-body contributions.</p> required <code>return_total_data</code> <code>bool</code> <p>Whether the total data (True; energy/gradient/Hessian) of the molecular system has been requested, as opposed to interaction data (False).</p> required <code>supersystem_ie_only</code> <code>bool</code> <p>Target the supersystem total/interaction energy (IE) data over the many-body expansion (MBE) \" analysis, thereby omitting intermediate-body calculations.</p> required <code>supersystem_max_nbody</code> <code>Optional[int]</code> <p>Maximum n-body to use for a supersystem calculation. Must be specified if \"supersystem\" is in <code>nbodies</code></p> <code>None</code> <p>Returns:</p> Type Description <code>compute_dict</code> <p>Dictionary containing subdicts enumerating compute lists for each possible BSSE treatment. Subdict keys are n-body levels and values are sets of all the <code>mc_(frag, bas)</code> indices needed to compute that n-body level. A given index can appear multiple times within a subdict and among subdicts.</p> <pre><code>compute_dict[\"cp\"] = {\n    1: set(),\n    2: {((1,), (1, 2)),\n        ((2,), (1, 2)),\n        ((1, 2), (1, 2))}\n}\n</code></pre> <p>Subdicts below are always returned. Any may be empty if not requested through bsse_type.</p> <ul> <li><code>'all'</code> |w---w| full list of computations required</li> <li><code>'cp'</code> |w---w| list of computations required for CP procedure</li> <li><code>'nocp'</code> |w---w| list of computations required for non-CP procedure</li> <li><code>'vmfc_compute'</code> |w---w| list of computations required for VMFC procedure</li> <li><code>'vmfc_levels'</code> |w---w| list of levels required for VMFC procedure</li> </ul> Source code in <code>qcmanybody/builder.py</code> <pre><code>def build_nbody_compute_list(\n    bsse_type: Iterable[BsseEnum],\n    nfragments: int,\n    nbodies: Iterable[Union[int, Literal[\"supersystem\"]]],\n    return_total_data: bool,\n    supersystem_ie_only: bool,\n    supersystem_max_nbody: Optional[int] = None,\n) -&gt; Dict[str, Dict[int, Set[FragBasIndex]]]:\n    \"\"\"Generates lists of N-Body computations needed for requested BSSE treatments.\n\n    Parameters\n    ----------\n    bsse_type\n        Requested BSSE treatments.\n    nfragments\n        Number of distinct fragments comprising the full molecular supersystem.\n    nbodies\n        List of n-body levels (e.g., `[2]` or `[1, 2]` or `[\"supersystem\"]`) for which to generate tasks.\n        Note the natural 1-indexing, so `[1]` covers one-body contributions.\n    return_total_data\n        Whether the total data (True; energy/gradient/Hessian) of the molecular system has been requested,\n        as opposed to interaction data (False).\n    supersystem_ie_only\n        Target the supersystem total/interaction energy (IE) data over the many-body expansion (MBE) \"\n        analysis, thereby omitting intermediate-body calculations.\n    supersystem_max_nbody\n        Maximum n-body to use for a supersystem calculation. Must be specified if \"supersystem\" is in `nbodies`\n\n    Returns\n    -------\n    compute_dict\n        Dictionary containing subdicts enumerating compute lists for each possible BSSE treatment.\n        Subdict keys are n-body levels and values are sets of all the `mc_(frag, bas)` indices\n        needed to compute that n-body level. A given index can appear multiple times within a\n        subdict and among subdicts.\n\n            compute_dict[\"cp\"] = {\n                1: set(),\n                2: {((1,), (1, 2)),\n                    ((2,), (1, 2)),\n                    ((1, 2), (1, 2))}\n            }\n\n        Subdicts below are always returned. Any may be empty if not requested through *bsse_type*.\n\n        * ``'all'`` |w---w| full list of computations required\n        * ``'cp'`` |w---w| list of computations required for CP procedure\n        * ``'nocp'`` |w---w| list of computations required for non-CP procedure\n        * ``'vmfc_compute'`` |w---w| list of computations required for VMFC procedure\n        * ``'vmfc_levels'`` |w---w| list of levels required for VMFC procedure\n\n    \"\"\"\n\n    include_supersystem = False\n    if \"supersystem\" in nbodies:\n        if supersystem_max_nbody is None:\n            raise ValueError(\"supersystem_max_nbody must be provided if 'supersystem' contains nbodies\")\n\n        include_supersystem = True\n\n    nbodies: List[int] = [x for x in nbodies if x != \"supersystem\"]\n\n    # What levels do we need?\n    fragment_range = range(1, nfragments + 1)\n\n    # Need nbodies and all lower-body in full basis\n    cp_compute_list = {x: set() for x in nbodies}\n    nocp_compute_list = {x: set() for x in nbodies}\n    vmfc_compute_list = {x: set() for x in nbodies}\n    vmfc_level_list = {x: set() for x in nbodies}  # Need to sum something slightly different\n\n    # Verify proper passing of bsse_type. already validated in Computer\n    bsse_type_remainder = set(bsse_type) - {e.value for e in BsseEnum}\n    if bsse_type_remainder:\n        raise RuntimeError(f\"Unrecognized BSSE type(s): {bsse_type_remainder}\")\n\n    # Build up compute sets\n    if \"cp\" in bsse_type:\n        # Everything is in counterpoise/nfr-mer basis\n        basis_tuple = tuple(fragment_range)\n\n        if supersystem_ie_only:\n            for sublevel in [1, nfragments]:\n                for x in itertools.combinations(fragment_range, sublevel):\n                    cp_compute_list[nfragments].add((x, basis_tuple))\n        else:\n            for nb in nbodies:\n                # Note A.1: nb=1 is skipped because the nfr-mer-basis monomer\n                #   contributions cancel at 1-body. These skipped tasks will be\n                #   ordered anyways if higher bodies are requested. Monomers for\n                #   the purpose of total energies use monomer basis, not these\n                #   skipped tasks. See coordinating Note A.2 .\n                if nb &gt; 1:\n                    for sublevel in range(1, nb + 1):\n                        for x in itertools.combinations(fragment_range, sublevel):\n                            cp_compute_list[nb].add((x, basis_tuple))\n\n    if \"nocp\" in bsse_type:\n        # Everything in natural/n-mer basis\n        if supersystem_ie_only:\n            for sublevel in [1, nfragments]:\n                for x in itertools.combinations(fragment_range, sublevel):\n                    nocp_compute_list[nfragments].add((x, x))\n        else:\n            for nb in nbodies:\n                for sublevel in range(1, nb + 1):\n                    for x in itertools.combinations(fragment_range, sublevel):\n                        nocp_compute_list[nb].add((x, x))\n\n    if \"vmfc\" in bsse_type:\n        # Like a CP for all combinations of pairs or greater\n        for nb in nbodies:\n            for cp_combos in itertools.combinations(fragment_range, nb):\n                basis_tuple = tuple(cp_combos)\n                for interior_nbody in range(1, nb + 1):\n                    for x in itertools.combinations(cp_combos, interior_nbody):\n                        combo_tuple = (x, basis_tuple)\n                        vmfc_compute_list[nb].add(combo_tuple)\n                        vmfc_level_list[len(basis_tuple)].add(combo_tuple)\n\n    if return_total_data and 1 in nbodies:\n        # Monomers in monomer basis\n        nocp_compute_list.setdefault(1, set())\n        for ifr in fragment_range:\n            nocp_compute_list[1].add(((ifr,), (ifr,)))\n\n    if include_supersystem:\n        # Add supersystem info to the compute list (nocp only)\n        for nb in range(1, supersystem_max_nbody + 1):\n            cp_compute_list.setdefault(nb, set())\n            nocp_compute_list.setdefault(nb, set())\n            vmfc_compute_list.setdefault(nb, set())\n            for sublevel in range(1, nb + 1):\n                for x in itertools.combinations(fragment_range, sublevel):\n                    nocp_compute_list[nb].add((x, x))\n\n        # Add the total supersystem (nfragments@nfragments)\n        nocp_compute_list.setdefault(nfragments, set())\n        nocp_compute_list[nfragments].add((tuple(fragment_range), tuple(fragment_range)))\n\n    # Build a comprehensive compute range\n    # * do not use list length to count number of {nb}-body computations\n    compute_list = {x: set() for x in nbodies}\n    for nb in nbodies:\n        compute_list[nb] |= cp_compute_list[nb]\n        compute_list[nb] |= nocp_compute_list[nb]\n        compute_list[nb] |= vmfc_compute_list[nb]\n\n    if include_supersystem:\n        for nb, lst in nocp_compute_list.items():\n            compute_list.setdefault(nb, set())\n            compute_list[nb] |= lst\n\n    # Rearrange compute_list from key nb having values to compute all of that nb\n    #   to key nb including values of that nb. Use for counting.\n    compute_list_count = {x: set() for x in nbodies}\n    for nb in nbodies:\n        for nbset in compute_list.values():\n            for item in nbset:\n                if len(item[0]) == nb:\n                    compute_list_count[nb].add(item)\n\n    compute_dict = {\n        \"all\": compute_list,\n        \"cp\": cp_compute_list,\n        \"nocp\": nocp_compute_list,\n        \"vmfc_compute\": vmfc_compute_list,\n        \"vmfc_levels\": vmfc_level_list,\n    }\n    return compute_dict\n</code></pre>"},{"location":"reference/#qcmanybody.ManyBodyCalculator.analyze","title":"<code>analyze(component_results)</code>","text":"<p>Parameters:</p> Name Type Description Default <code>component_results</code> <code>Dict[str, Dict[str, Union[float, ndarray]]]</code> <p>Nested dictionary with results from all individual molecular system calculations, including all subsystem/basis combinations, all model chemistries, and all properties (e.g., e/g/h).</p> <p>For example, the below is the format for a nocp gradient run on a helium dimer with 1-body at CCSD and 2-body at MP2. The outer string key can be generated with the <code>qcmanybody.utils.labeler</code> function. The inner string key is any property; QCManyBody presently knows how to process energy/gradient/Hessian.</p> <p>{'[\"ccsd\", [1], [1]]': {'energy': -2.87, 'gradient': array([[0., 0., 0.]])},  '[\"ccsd\", [2], [2]]': {'energy': -2.87, 'gradient': array([[0., 0., 0.]])},  '[\"mp2\", [1], [1]]': {'energy': -2.86, 'gradient': array([[0., 0., 0.]])},  '[\"mp2\", [2], [2]]': {'energy': -2.86, 'gradient': array([[0., 0., 0.]])},  '[\"mp2\", [1, 2], [1, 2]]': {'energy': -5.73, 'gradient': array([[ 0., 0., 0.0053], [ 0., 0., -0.0053]])}, }</p> required Return Source code in <code>qcmanybody/manybody.py</code> <pre><code>    def analyze(\n        self,\n        component_results: Dict[str, Dict[str, Union[float, np.ndarray]]],\n    ):\n        \"\"\"\n\n        Parameters\n        ----------\n        component_results\n            Nested dictionary with results from all individual molecular system\n            calculations, including all subsystem/basis combinations, all model\n            chemistries, and all properties (e.g., e/g/h).\n\n            For example, the below is the format for a nocp gradient run on a\n            helium dimer with 1-body at CCSD and 2-body at MP2. The outer string\n            key can be generated with the ``qcmanybody.utils.labeler`` function.\n            The inner string key is any property; QCManyBody presently knows how\n            to process energy/gradient/Hessian.\n\n            {'[\"ccsd\", [1], [1]]': {'energy': -2.87, 'gradient': array([[0., 0., 0.]])},\n             '[\"ccsd\", [2], [2]]': {'energy': -2.87, 'gradient': array([[0., 0., 0.]])},\n             '[\"mp2\", [1], [1]]': {'energy': -2.86, 'gradient': array([[0., 0., 0.]])},\n             '[\"mp2\", [2], [2]]': {'energy': -2.86, 'gradient': array([[0., 0., 0.]])},\n             '[\"mp2\", [1, 2], [1, 2]]': {'energy': -5.73, 'gradient': array([[ 0., 0., 0.0053], [ 0., 0., -0.0053]])},\n            }\n\n        Return\n        ------\n\n        \"\"\"\n\n        # All properties that were passed to us\n        # * seed with \"energy\" so free/no-op jobs can process\n        available_properties: Set[str] = {\"energy\"}\n        for property_data in component_results.values():\n            available_properties.update(property_data.keys())\n\n        # reorganize to component_results_inv[property][label] = 1.23\n        component_results_inv = {k: {} for k in available_properties}\n\n        for cluster_label, property_data in component_results.items():\n            for property_label, property_value in property_data.items():\n                component_results_inv[property_label][cluster_label] = property_value\n\n        # Remove any missing data\n        component_results_inv = {k: v for k, v in component_results_inv.items() if v}\n        if not component_results_inv:\n            # Note B: Rarely, \"no results\" is expected, like for CP-only,\n            #   rtd=False, and max_nbody=1. We'll add a dummy entry so\n            #   processing can continue.\n            component_results_inv[\"energy\"] = {'[\"dummy\", [1000], [1000]]': 0.0}\n\n        # Actually analyze\n        is_embedded = bool(self.embedding_charges)\n        component_properties = defaultdict(dict)\n        all_results = {}\n        nbody_dict = {}\n#        all_results[\"energy_body_dict\"] = {\"cp\": {1: 0.0}}\n\n        for property_label, property_results in component_results_inv.items():\n            # Expand gradient and hessian\n            if property_label == \"gradient\":\n                property_results = {k: self.resize_gradient(v, delabeler(k)[2]) for k, v in property_results.items()}\n            if property_label == \"hessian\":\n                property_results = {k: self.resize_hessian(v, delabeler(k)[2]) for k, v in property_results.items()}\n\n            r = self._analyze(property_label, property_results)\n            for k, v in property_results.items():\n                component_properties[k][\"calcinfo_natom\"] = len(self.molecule.symbols)\n                component_properties[k][f\"return_{property_label}\"] = v\n            all_results.update(r)\n\n        for bt in self.bsse_type:\n            print_nbody_energy(\n                all_results[\"energy_body_dict\"][bt],\n                f\"{bt.upper()}-corrected multilevel many-body expansion\",\n                self.nfragments,\n                is_embedded,\n            )\n\n        for property_label in available_properties:\n            for bt in self.bsse_type:\n                if not self.has_supersystem:  # skipped levels?\n                    nbody_dict.update(\n                        collect_vars(\n                            bt.upper(),\n                            property_label.upper(),\n                            all_results[f\"{property_label}_body_dict\"][bt],\n                            self.max_nbody,\n                            is_embedded,\n                            self.supersystem_ie_only,\n                        )\n                    )\n\n        all_results[\"results\"] = nbody_dict\n        all_results[\"component_properties\"] = component_properties\n\n        # Make dictionary with \"1cp\", \"2cp\", etc\n        ebd = all_results[\"energy_body_dict\"]\n        all_results[\"energy_body_dict\"] = {str(k) + bt: v for bt in ebd for k, v in ebd[bt].items()}\n\n        return all_results\n</code></pre>"},{"location":"reference/#qcmanybody.ManyBodyCalculator.iterate_molecules","title":"<code>iterate_molecules()</code>","text":"<p>Iterate over all the molecules needed for the computation.</p> <p>Yields model chemistry, label, and molecule.</p> Source code in <code>qcmanybody/manybody.py</code> <pre><code>def iterate_molecules(self) -&gt; Iterable[Tuple[str, str, Molecule]]:\n    \"\"\"Iterate over all the molecules needed for the computation.\n\n    Yields model chemistry, label, and molecule.\n    \"\"\"\n\n    done_molecules = set()\n\n    for mc, compute_dict in self.compute_map.items():\n        # TODO - this is a bit of a hack. Lots of duplication when reaching higher nbody\n        for compute_list in compute_dict[\"all\"].values():\n            for real_atoms, basis_atoms in compute_list:\n                label = labeler(mc, real_atoms, basis_atoms)\n                if label in done_molecules:\n                    continue\n\n                ghost_atoms = list(set(basis_atoms) - set(real_atoms))\n\n                # Shift to zero-indexing\n                real_atoms_0 = [x - 1 for x in real_atoms]\n                ghost_atoms_0 = [x - 1 for x in ghost_atoms]\n                mol = self.molecule.get_fragment(real_atoms_0, ghost_atoms_0, orient=False, group_fragments=False)\n                mol = mol.copy(update={\"fix_com\": True, \"fix_orientation\": True})\n\n                # if self.embedding_charges:\n                #    embedding_frags = list(set(range(1, self.nfragments + 1)) - set(pair[1]))\n                #    charges = []\n                #    for frag in embedding_frags:\n                #        positions = self.molecule.extract_subsets(frag).geometry().np.tolist()\n                #        charges.extend([[chg, i] for i, chg in zip(positions, self.embedding_charges[frag])])\n                #    data['keywords']['function_kwargs'].update({'external_potentials': charges})\n\n                done_molecules.add(label)\n                yield mc, label, mol\n</code></pre>"},{"location":"tutorials/","title":"Tutorials","text":"<p>This part of the project documentation focuses on a learning-oriented approach. You'll learn how to get started with the code in this project.</p> <p>Note: Expand this section by considering the following points:</p> <ul> <li>Help newcomers with getting started</li> <li>Teach readers about your library by making them     write code</li> <li>Inspire confidence through examples that work for     everyone, repeatably</li> <li>Give readers an immediate sense of achievement</li> <li>Show concrete examples, no abstractions</li> <li>Provide the minimum necessary explanation</li> <li>Avoid any distractions</li> </ul>"}]}
\ No newline at end of file
+{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"QCManyBody Documentation","text":"<p>QCManyBody is a python package for running quantum chemistry manybody expansions and interaction calculations in a package-independent way.</p>"},{"location":"#installation","title":"Installation","text":"<p>Currently, the package is only available on GitHub. To install directly from GitHub, use the following command:</p> <pre><code>pip install git+https://github.com/MolSSI/QCManyBody.git\n</code></pre>"},{"location":"#package-overview","title":"Package Overview","text":"<p>The package has two main interfaces. The high-level interface allows for a comprehensive workflow, where the user provides complete information about the calculation, including the full specification (method, basis set, etc.) of the manybody calculation. This is designed to work with QCEngine or other packages that implement the QCSchema.</p> <p>For more information, see (High-level interface)(high-level-interface.md).</p> <p>QCManyBody also has a core low-level interface that allows for more flexibility in how the calculations are run. This interface generally takes a molecule and an arbitrary definition of quantum chemistry specifications, and expects the user to run them themselves.</p> <p>For more information, see (Core interface)(core-interface.md).</p>"},{"location":"#table-of-contents","title":"Table of Contents","text":"<ol> <li>Common Keywords and Options</li> <li>High-level Interface</li> <li>Core Interface</li> <li>Results</li> <li>How-To Guides</li> </ol>"},{"location":"api/","title":"API Documentation","text":"Source code in <code>qcmanybody/manybody.py</code> <pre><code>class ManyBodyCalculator:\n    def __init__(\n        self,\n        molecule: Molecule,\n        bsse_type: Sequence[BsseEnum],\n        levels: Mapping[Union[int, Literal[\"supersystem\"]], str],\n        return_total_data: bool,\n        supersystem_ie_only: bool,\n    ):\n        # TODO\n        self.embedding_charges = {}\n\n        self.molecule = molecule\n        self.bsse_type = [BsseEnum(x) for x in bsse_type]\n        self.return_total_data = return_total_data\n        self.supersystem_ie_only = supersystem_ie_only\n        self.nfragments = len(molecule.fragments)\n\n        self.levels = levels\n\n        # Levels without supersystem\n        self.levels_no_ss = {int(k): v for k, v in levels.items() if k != \"supersystem\"}\n\n        # Just a set of all the modelchems\n        self.mc_levels = set(self.levels.values())\n\n        self.max_nbody = max(self.levels_no_ss.keys())\n\n        if len(self.bsse_type) == 0:\n            raise ValueError(\"No BSSE correction specified\")\n\n        if BsseEnum.vmfc in self.bsse_type and len(set(self.levels.values())) == 1:\n            # For single-modelchem VMFC, NOCP &amp; sometimes CP are produced for free\n            if BsseEnum.nocp not in self.bsse_type:\n                self.bsse_type.append(BsseEnum.nocp)\n            if BsseEnum.cp not in self.bsse_type and self.max_nbody == self.nfragments:\n                self.bsse_type.append(BsseEnum.cp)\n\n        self.return_bsse_type = self.bsse_type[0]\n\n        ###############################\n        # Build nbodies_per_mc_level\n        # TODO - use Lori's code\n        # TODO - dict to list of lists to handle non-contiguous levels\n        # TODO multilevel and supersystem_ie_only=T not allowed together\n        # TODO supersystem in levels is not to be trusted -- nfrag only and skips levels\n        max_level = max(self.levels_no_ss.keys())\n\n        if set(range(1, max_level + 1)) != set(self.levels_no_ss.keys()):\n            raise ValueError(f\"Levels must be contiguous from 1 to {max_level}\")\n\n        self.nbodies_per_mc_level: Dict[str, list] = {mc_level: [] for mc_level in self.mc_levels}\n        for k, v in self.levels_no_ss.items():\n            self.nbodies_per_mc_level[v].append(k)\n\n        self.nbodies_per_mc_level = {k: sorted(v) for k, v in self.nbodies_per_mc_level.items()}\n\n        # Supersystem is always at the end\n        if \"supersystem\" in levels:\n            ss_mc = levels[\"supersystem\"]\n            self.nbodies_per_mc_level[ss_mc].append(\"supersystem\")\n\n        # To be built on the fly\n        self.mc_compute_dict = None\n\n        if not np.array_equal(np.concatenate(self.molecule.fragments), np.arange(len(self.molecule.symbols))):\n            raise ValueError(\"\"\"QCManyBody: non-contiguous fragments could be implemented but aren't at present\"\"\")\n\n        # Build size and slices dictionaries. Assumes fragments are contiguous\n        self.fragment_size_dict = {}\n        self.fragment_slice_dict = {}\n        iat = 0\n        for ifr in range(1, self.nfragments + 1):\n            nat = len(self.molecule.fragments[ifr - 1])\n            self.fragment_size_dict[ifr] = nat\n            self.fragment_slice_dict[ifr] = slice(iat, iat + nat)\n            iat += nat\n\n    @property\n    def has_supersystem(self) -&gt; bool:\n        return \"supersystem\" in self.levels\n\n    @property\n    def compute_map(self) -&gt; Dict[str, Dict[str, Dict[int, Set[FragBasIndex]]]]:\n        if self.mc_compute_dict is not None:\n            return self.mc_compute_dict\n\n        # Build the compute lists\n        self.mc_compute_dict = {}\n\n        for mc in self.mc_levels:\n            nbodies = self.nbodies_per_mc_level[mc]\n            self.mc_compute_dict[mc] = build_nbody_compute_list(\n                self.bsse_type,\n                self.nfragments,\n                nbodies,\n                self.return_total_data,\n                self.supersystem_ie_only,\n                self.max_nbody,\n            )\n\n        return self.mc_compute_dict\n\n    def resize_gradient(self, grad: np.ndarray, bas: Tuple[int, ...], *, reverse: bool = False) -&gt; np.ndarray:\n        return resize_gradient(grad, bas, self.fragment_size_dict, self.fragment_slice_dict, reverse=reverse)\n\n    def resize_hessian(self, hess: np.ndarray, bas: Tuple[int, ...], *, reverse: bool = False) -&gt; np.ndarray:\n        return resize_hessian(hess, bas, self.fragment_size_dict, self.fragment_slice_dict, reverse=reverse)\n\n    def iterate_molecules(self) -&gt; Iterable[Tuple[str, str, Molecule]]:\n        \"\"\"Iterate over all the molecules needed for the computation.\n\n        Yields model chemistry, label, and molecule.\n        \"\"\"\n\n        done_molecules = set()\n\n        for mc, compute_dict in self.compute_map.items():\n            # TODO - this is a bit of a hack. Lots of duplication when reaching higher nbody\n            for compute_list in compute_dict[\"all\"].values():\n                for real_atoms, basis_atoms in compute_list:\n                    label = labeler(mc, real_atoms, basis_atoms)\n                    if label in done_molecules:\n                        continue\n\n                    ghost_atoms = list(set(basis_atoms) - set(real_atoms))\n\n                    # Shift to zero-indexing\n                    real_atoms_0 = [x - 1 for x in real_atoms]\n                    ghost_atoms_0 = [x - 1 for x in ghost_atoms]\n                    mol = self.molecule.get_fragment(real_atoms_0, ghost_atoms_0, orient=False, group_fragments=False)\n                    mol = mol.copy(update={\"fix_com\": True, \"fix_orientation\": True})\n\n                    # if self.embedding_charges:\n                    #    embedding_frags = list(set(range(1, self.nfragments + 1)) - set(pair[1]))\n                    #    charges = []\n                    #    for frag in embedding_frags:\n                    #        positions = self.molecule.extract_subsets(frag).geometry().np.tolist()\n                    #        charges.extend([[chg, i] for i, chg in zip(positions, self.embedding_charges[frag])])\n                    #    data['keywords']['function_kwargs'].update({'external_potentials': charges})\n\n                    done_molecules.add(label)\n                    yield mc, label, mol\n\n    def _assemble_nbody_components(\n        self,\n        property_label: str,\n        component_results: Dict[str, Union[float, np.ndarray]],\n    ) -&gt; Dict[str, Any]:\n        \"\"\"Assembles N-body components for a single derivative level and a single model chemistry level\n        into interaction quantities according to requested BSSE treatment(s).\n        \"\"\"\n\n        # which level are we assembling?\n        delabeled = [delabeler(k) for k in component_results.keys()]\n        mc_level_labels = {x[0] for x in delabeled}\n\n        if len(mc_level_labels) != 1:\n            raise RuntimeError(f\"Multiple model chemistries passed into _assemble_nbody_components: {mc_level_labels}\")\n\n        mc_level = mc_level_labels.pop()\n        if mc_level not in self.mc_levels:\n            raise RuntimeError(f\"Model chemistry {mc_level} not found in {self.mc_levels}\")\n\n        # get the range of nbodies and the required calculations for this level\n        bsse_type = self.bsse_type\n        return_bsse_type = self.return_bsse_type\n        nbodies = self.nbodies_per_mc_level[mc_level]\n        if \"supersystem\" in nbodies:\n            nbodies = list(range(1, self.max_nbody + 1))\n            bsse_type = [BsseEnum.nocp]\n            return_bsse_type = BsseEnum.nocp\n\n        max_nbody = max(nbodies)\n        compute_dict = self.compute_map[mc_level]\n\n        if not all_same_shape(component_results.values()):\n            raise ValueError(\"All values in data dictionary must have the same shape.\")\n\n        # Use first data value to determine shape\n        first_key = next(iter(component_results.keys()))\n        property_shape = find_shape(component_results[first_key])\n\n        # Accumulation dictionaries\n        # * {bsse_type}_by_level is filled by sum_cluster_data to contain for NOCP\n        #   &amp; CP the summed total energies (or other property) of each nb-body. That is:\n        #   * NOCP: {1: 1b@1b,    2: 2b@2b,      ..., max_nbody: max_nbody-b@max_nbody-b} and\n        #   * CP:   {1: 1b@nfr-b, 2: 2b@nfr-b,   ..., max_nbody: max_nbody-b@nfr-b}.\n        #   VMFC bookkeeping is different. For key 1 it contains the summed 1b total energies.\n        #   But for higher keys, it contains each nb-body (non-additive) contribution to the energy.\n        #   * VMFC: {1: 1b@1b,    2: 2b contrib, ..., max_nbody: max_nbody-b contrib}\n        cp_by_level = {n: shaped_zero(property_shape) for n in range(1, nbodies[-1] + 1)}\n        nocp_by_level = {n: shaped_zero(property_shape) for n in range(1, nbodies[-1] + 1)}\n        vmfc_by_level = {n: shaped_zero(property_shape) for n in range(1, nbodies[-1] + 1)}\n\n        # * {bsse_type}_body_dict is usually filled with total energies (or other property).\n        #   Multiple model chemistry levels may be involved.\n        #   Generally, all consecutive keys between 1 and max_nbody will be present in the body_dict,\n        #   but if supersystem_ie_only=T, only 1b and nfr-b are present, or if \"supersystem\" in levels, ???\n        #   * TOT: {1: 1b@1b, 2: 2b tot prop with bsse_type treatment, ..., max_nbody: max_nbody-b tot prop with bsse_type treatment}\n        #   If 1b@1b (monomers in monomer basis) aren't available, which can happen when return_total_data=F\n        #   and 1b@1b aren't otherwise needed, body_dict contains interaction energies (or other property).\n        #   * IE: {1: shaped_zero, 2: 2b interaction prop using bsse_type, ..., max_nbody: max_nbody-b interaction prop using bsse_type}\n        #   For both TOT and IE cases, body_dict values are cummulative, not additive. For TOT, total,\n        #   interaction, and contribution data in ManyBodyResultProperties can be computed in\n        #   collect_vars. For IE, interaction and contribution data can be computed.\n        cp_body_dict = {n: shaped_zero(property_shape) for n in range(1, nbodies[-1] + 1)}\n        nocp_body_dict = {n: shaped_zero(property_shape) for n in range(1, nbodies[-1] + 1)}\n        vmfc_body_dict = {n: shaped_zero(property_shape) for n in range(1, nbodies[-1] + 1)}\n\n        # Sum up all of the levels\n        # * compute_dict[bt][nb] holds all the computations needed to compute nb\n        #   *not* all the nb-level computations, so build the latter\n        cp_compute_list = {nb: set() for nb in range(1, nbodies[-1] + 1)}\n        nocp_compute_list = {nb: set() for nb in range(1, nbodies[-1] + 1)}\n\n        for nb in nbodies:\n            for v in compute_dict[\"cp\"][nb]:\n                if len(v[1]) != 1:\n                    cp_compute_list[len(v[0])].add(v)\n            for w in compute_dict[\"nocp\"][nb]:\n                nocp_compute_list[len(w[0])].add(w)\n\n        for nb in range(1, nbodies[-1] + 1):\n            cp_by_level[nb] = sum_cluster_data(component_results, cp_compute_list[nb], mc_level)\n            nocp_by_level[nb] = sum_cluster_data(component_results, nocp_compute_list[nb], mc_level)\n            if nb in compute_dict[\"vmfc_levels\"]:\n                vmfc_by_level[nb] = sum_cluster_data(\n                    component_results, compute_dict[\"vmfc_levels\"][nb], mc_level, vmfc=True, nb=nb\n                )\n\n        # Extract data for monomers in monomer basis for CP total data\n        if 1 in nbodies:\n            monomers_in_monomer_basis = [v for v in compute_dict[\"nocp\"][1] if len(v[1]) == 1]\n            monomer_sum = sum_cluster_data(component_results, set(monomers_in_monomer_basis), mc_level)\n        else:\n            monomer_sum = shaped_zero(property_shape)\n\n        # Compute cp\n        if BsseEnum.cp in bsse_type:\n            for nb in range(1, nbodies[-1] + 1):\n                if nb == self.nfragments:\n                    cp_body_dict[nb] = cp_by_level[nb] - bsse\n                    continue\n\n                for k in range(1, nb + 1):\n                    take_nk = math.comb(self.nfragments - k - 1, nb - k)\n                    sign = (-1) ** (nb - k)\n                    cp_body_dict[nb] += take_nk * sign * cp_by_level[k]\n\n                if nb == 1:\n                    bsse = cp_body_dict[nb] - monomer_sum\n                    cp_body_dict[nb] = copy_value(monomer_sum)\n                else:\n                    cp_body_dict[nb] -= bsse\n\n        # Compute nocp\n        if BsseEnum.nocp in bsse_type:\n            for nb in range(1, nbodies[-1] + 1):\n                if nb == self.nfragments:\n                    nocp_body_dict[nb] = nocp_by_level[nb]\n                    continue\n\n                for k in range(1, nb + 1):\n                    take_nk = math.comb(self.nfragments - k - 1, nb - k)\n                    sign = (-1) ** (nb - k)\n                    nocp_body_dict[nb] += take_nk * sign * nocp_by_level[k]\n\n        # Compute vmfc\n        if BsseEnum.vmfc in bsse_type:\n            for nb in nbodies:\n                for k in range(1, nb + 1):\n                    vmfc_body_dict[nb] += vmfc_by_level[k]\n\n        # Collect specific and generalized returns\n        results = {\n            f\"cp_{property_label}_body_dict\": cp_body_dict,\n            f\"nocp_{property_label}_body_dict\": nocp_body_dict,\n            f\"vmfc_{property_label}_body_dict\": vmfc_body_dict,\n        }\n\n        # Overall return body dict &amp; value for this property\n        results[f\"{property_label}_body_dict\"] = results[f\"{return_bsse_type.value}_{property_label}_body_dict\"]\n        results[f\"ret_{property_label}\"] = copy_value(results[f\"{property_label}_body_dict\"][max_nbody])\n\n        if not self.return_total_data:\n            results[f\"ret_{property_label}\"] -= results[f\"{property_label}_body_dict\"][1]\n\n        return results\n\n    def _analyze(\n        self,\n        property_label: str,\n        property_results: Dict[str, Union[float, np.ndarray]],  # Label to results\n    ):\n        # Initialize with zeros\n        if not all_same_shape(property_results.values()):\n            raise ValueError(\"All values in data dictionary must have the same shape.\")\n\n        # Use first data value to determine shape\n        first_key = next(iter(property_results.keys()))\n        property_shape = find_shape(property_results[first_key])\n\n        property_result = shaped_zero(property_shape)\n        property_body_dict = {bt.value: {} for bt in self.bsse_type}\n        property_body_contribution = {bt.value: {} for bt in self.bsse_type}\n\n        # results per model chemistry\n        mc_results = {}\n        species_results = {}\n\n        # sort by nbody level, ignore supersystem\n        sorted_nbodies = [(k, v) for k, v in self.nbodies_per_mc_level.items() if v != [\"supersystem\"]]\n        sorted_nbodies = sorted(sorted_nbodies, reverse=True, key=lambda x: x[1])\n        for mc_label, nbody_list in sorted_nbodies:\n            # filter to only one model chemistry\n            filtered_results = {k: v for k, v in property_results.items() if delabeler(k)[0] == mc_label}\n\n            if not filtered_results:\n                if nbody_list == [1]:\n                    # Note A.2: Note A.1 holds, but for the special case of CP-only\n                    #   and rtd=False and multilevel with a separate level for\n                    #   1-body, the skipped tasks run afoul of sanity checks, so\n                    #   we'll add a dummy result.\n                    filtered_results = {labeler(mc_label, [1000], [1000]): shaped_zero(property_shape)}\n                else:\n                    raise RuntimeError(f\"No data found for model chemistry {mc_label}\")\n\n            nb_component_results = self._assemble_nbody_components(property_label, filtered_results)\n            mc_results[mc_label] = nb_component_results\n\n            for n in nbody_list[::-1]:\n                property_bsse_dict = {bt.value: shaped_zero(property_shape) for bt in self.bsse_type}\n\n                for m in range(n - 1, n + 1):\n                    if m == 0:\n                        continue\n\n                    # Subtract the (n-1)-body contribution from the n-body contribution to get the n-body effect\n                    sign = (-1) ** (1 - m // n)\n                    for bt in self.bsse_type:\n                        property_bsse_dict[bt.value] += (\n                            sign * mc_results[mc_label][f\"{bt.value}_{property_label}_body_dict\"][m]\n                        )\n\n                property_result += property_bsse_dict[self.return_bsse_type]\n                for bt in self.bsse_type:\n                    property_body_contribution[bt.value][n] = property_bsse_dict[bt.value]\n\n        if self.has_supersystem:\n            # Get the MC label for supersystem tasks\n            supersystem_mc_level = self.levels[\"supersystem\"]\n\n            # Super system recovers higher order effects at a lower level\n            frag_range = tuple(range(1, self.nfragments + 1))\n\n            ss_cresults = {k: v for k, v in property_results.items() if delabeler(k)[0] == supersystem_mc_level}\n            ss_component_results = self._assemble_nbody_components(property_label, ss_cresults)\n            mc_results[supersystem_mc_level] = ss_component_results\n\n            # Compute components at supersystem level of theory\n            ss_label = labeler(supersystem_mc_level, frag_range, frag_range)\n            supersystem_result = property_results[ss_label]\n            property_result += supersystem_result - ss_component_results[f\"{property_label}_body_dict\"][self.max_nbody]\n\n            for bt in self.bsse_type:\n                property_body_contribution[bt][self.nfragments] = (\n                    supersystem_result - ss_component_results[f\"{property_label}_body_dict\"][self.max_nbody]\n                )\n\n        for bt in self.bsse_type:\n            bstr = bt.value\n            for n in property_body_contribution[bstr]:\n                property_body_dict[bstr][n] = sum(\n                    [\n                        property_body_contribution[bstr][i]\n                        for i in range(1, n + 1)\n                        if i in property_body_contribution[bstr]\n                    ]\n                )\n\n        if not self.return_total_data:\n            # Remove monomer contribution for interaction data\n            property_result -= property_body_dict[self.return_bsse_type][1]\n\n        nbody_results = {\n            f\"ret_{property_label}\": property_result,\n            f\"{property_label}_body_dict\": property_body_dict,\n            \"mc_results\": mc_results,\n        }\n        return nbody_results\n\n    def analyze(\n        self,\n        component_results: Dict[str, Dict[str, Union[float, np.ndarray]]],\n    ):\n        \"\"\"\n\n        Parameters\n        ----------\n        component_results\n            Nested dictionary with results from all individual molecular system\n            calculations, including all subsystem/basis combinations, all model\n            chemistries, and all properties (e.g., e/g/h).\n\n            For example, the below is the format for a nocp gradient run on a\n            helium dimer with 1-body at CCSD and 2-body at MP2. The outer string\n            key can be generated with the ``qcmanybody.utils.labeler`` function.\n            The inner string key is any property; QCManyBody presently knows how\n            to process energy/gradient/Hessian.\n\n            {'[\"ccsd\", [1], [1]]': {'energy': -2.87, 'gradient': array([[0., 0., 0.]])},\n             '[\"ccsd\", [2], [2]]': {'energy': -2.87, 'gradient': array([[0., 0., 0.]])},\n             '[\"mp2\", [1], [1]]': {'energy': -2.86, 'gradient': array([[0., 0., 0.]])},\n             '[\"mp2\", [2], [2]]': {'energy': -2.86, 'gradient': array([[0., 0., 0.]])},\n             '[\"mp2\", [1, 2], [1, 2]]': {'energy': -5.73, 'gradient': array([[ 0., 0., 0.0053], [ 0., 0., -0.0053]])},\n            }\n\n        Return\n        ------\n\n        \"\"\"\n\n        # All properties that were passed to us\n        # * seed with \"energy\" so free/no-op jobs can process\n        available_properties: Set[str] = {\"energy\"}\n        for property_data in component_results.values():\n            available_properties.update(property_data.keys())\n\n        # reorganize to component_results_inv[property][label] = 1.23\n        component_results_inv = {k: {} for k in available_properties}\n\n        for cluster_label, property_data in component_results.items():\n            for property_label, property_value in property_data.items():\n                component_results_inv[property_label][cluster_label] = property_value\n\n        # Remove any missing data\n        component_results_inv = {k: v for k, v in component_results_inv.items() if v}\n        if not component_results_inv:\n            # Note B: Rarely, \"no results\" is expected, like for CP-only,\n            #   rtd=False, and max_nbody=1. We'll add a dummy entry so\n            #   processing can continue.\n            component_results_inv[\"energy\"] = {'[\"dummy\", [1000], [1000]]': 0.0}\n\n        # Actually analyze\n        is_embedded = bool(self.embedding_charges)\n        component_properties = defaultdict(dict)\n        all_results = {}\n        nbody_dict = {}\n#        all_results[\"energy_body_dict\"] = {\"cp\": {1: 0.0}}\n\n        for property_label, property_results in component_results_inv.items():\n            # Expand gradient and hessian\n            if property_label == \"gradient\":\n                property_results = {k: self.resize_gradient(v, delabeler(k)[2]) for k, v in property_results.items()}\n            if property_label == \"hessian\":\n                property_results = {k: self.resize_hessian(v, delabeler(k)[2]) for k, v in property_results.items()}\n\n            r = self._analyze(property_label, property_results)\n            for k, v in property_results.items():\n                component_properties[k][\"calcinfo_natom\"] = len(self.molecule.symbols)\n                component_properties[k][f\"return_{property_label}\"] = v\n            all_results.update(r)\n\n        for bt in self.bsse_type:\n            print_nbody_energy(\n                all_results[\"energy_body_dict\"][bt],\n                f\"{bt.upper()}-corrected multilevel many-body expansion\",\n                self.nfragments,\n                is_embedded,\n            )\n\n        for property_label in available_properties:\n            for bt in self.bsse_type:\n                if not self.has_supersystem:  # skipped levels?\n                    nbody_dict.update(\n                        collect_vars(\n                            bt.upper(),\n                            property_label.upper(),\n                            all_results[f\"{property_label}_body_dict\"][bt],\n                            self.max_nbody,\n                            is_embedded,\n                            self.supersystem_ie_only,\n                        )\n                    )\n\n        all_results[\"results\"] = nbody_dict\n        all_results[\"component_properties\"] = component_properties\n\n        # Make dictionary with \"1cp\", \"2cp\", etc\n        ebd = all_results[\"energy_body_dict\"]\n        all_results[\"energy_body_dict\"] = {str(k) + bt: v for bt in ebd for k, v in ebd[bt].items()}\n\n        return all_results\n</code></pre>"},{"location":"api/#qcmanybody.ManyBodyCalculator.analyze","title":"<code>analyze(component_results)</code>","text":"<p>Parameters:</p> Name Type Description Default <code>component_results</code> <code>Dict[str, Dict[str, Union[float, ndarray]]]</code> <p>Nested dictionary with results from all individual molecular system calculations, including all subsystem/basis combinations, all model chemistries, and all properties (e.g., e/g/h).</p> <p>For example, the below is the format for a nocp gradient run on a helium dimer with 1-body at CCSD and 2-body at MP2. The outer string key can be generated with the <code>qcmanybody.utils.labeler</code> function. The inner string key is any property; QCManyBody presently knows how to process energy/gradient/Hessian.</p> <p>{'[\"ccsd\", [1], [1]]': {'energy': -2.87, 'gradient': array([[0., 0., 0.]])},  '[\"ccsd\", [2], [2]]': {'energy': -2.87, 'gradient': array([[0., 0., 0.]])},  '[\"mp2\", [1], [1]]': {'energy': -2.86, 'gradient': array([[0., 0., 0.]])},  '[\"mp2\", [2], [2]]': {'energy': -2.86, 'gradient': array([[0., 0., 0.]])},  '[\"mp2\", [1, 2], [1, 2]]': {'energy': -5.73, 'gradient': array([[ 0., 0., 0.0053], [ 0., 0., -0.0053]])}, }</p> required Return Source code in <code>qcmanybody/manybody.py</code> <pre><code>    def analyze(\n        self,\n        component_results: Dict[str, Dict[str, Union[float, np.ndarray]]],\n    ):\n        \"\"\"\n\n        Parameters\n        ----------\n        component_results\n            Nested dictionary with results from all individual molecular system\n            calculations, including all subsystem/basis combinations, all model\n            chemistries, and all properties (e.g., e/g/h).\n\n            For example, the below is the format for a nocp gradient run on a\n            helium dimer with 1-body at CCSD and 2-body at MP2. The outer string\n            key can be generated with the ``qcmanybody.utils.labeler`` function.\n            The inner string key is any property; QCManyBody presently knows how\n            to process energy/gradient/Hessian.\n\n            {'[\"ccsd\", [1], [1]]': {'energy': -2.87, 'gradient': array([[0., 0., 0.]])},\n             '[\"ccsd\", [2], [2]]': {'energy': -2.87, 'gradient': array([[0., 0., 0.]])},\n             '[\"mp2\", [1], [1]]': {'energy': -2.86, 'gradient': array([[0., 0., 0.]])},\n             '[\"mp2\", [2], [2]]': {'energy': -2.86, 'gradient': array([[0., 0., 0.]])},\n             '[\"mp2\", [1, 2], [1, 2]]': {'energy': -5.73, 'gradient': array([[ 0., 0., 0.0053], [ 0., 0., -0.0053]])},\n            }\n\n        Return\n        ------\n\n        \"\"\"\n\n        # All properties that were passed to us\n        # * seed with \"energy\" so free/no-op jobs can process\n        available_properties: Set[str] = {\"energy\"}\n        for property_data in component_results.values():\n            available_properties.update(property_data.keys())\n\n        # reorganize to component_results_inv[property][label] = 1.23\n        component_results_inv = {k: {} for k in available_properties}\n\n        for cluster_label, property_data in component_results.items():\n            for property_label, property_value in property_data.items():\n                component_results_inv[property_label][cluster_label] = property_value\n\n        # Remove any missing data\n        component_results_inv = {k: v for k, v in component_results_inv.items() if v}\n        if not component_results_inv:\n            # Note B: Rarely, \"no results\" is expected, like for CP-only,\n            #   rtd=False, and max_nbody=1. We'll add a dummy entry so\n            #   processing can continue.\n            component_results_inv[\"energy\"] = {'[\"dummy\", [1000], [1000]]': 0.0}\n\n        # Actually analyze\n        is_embedded = bool(self.embedding_charges)\n        component_properties = defaultdict(dict)\n        all_results = {}\n        nbody_dict = {}\n#        all_results[\"energy_body_dict\"] = {\"cp\": {1: 0.0}}\n\n        for property_label, property_results in component_results_inv.items():\n            # Expand gradient and hessian\n            if property_label == \"gradient\":\n                property_results = {k: self.resize_gradient(v, delabeler(k)[2]) for k, v in property_results.items()}\n            if property_label == \"hessian\":\n                property_results = {k: self.resize_hessian(v, delabeler(k)[2]) for k, v in property_results.items()}\n\n            r = self._analyze(property_label, property_results)\n            for k, v in property_results.items():\n                component_properties[k][\"calcinfo_natom\"] = len(self.molecule.symbols)\n                component_properties[k][f\"return_{property_label}\"] = v\n            all_results.update(r)\n\n        for bt in self.bsse_type:\n            print_nbody_energy(\n                all_results[\"energy_body_dict\"][bt],\n                f\"{bt.upper()}-corrected multilevel many-body expansion\",\n                self.nfragments,\n                is_embedded,\n            )\n\n        for property_label in available_properties:\n            for bt in self.bsse_type:\n                if not self.has_supersystem:  # skipped levels?\n                    nbody_dict.update(\n                        collect_vars(\n                            bt.upper(),\n                            property_label.upper(),\n                            all_results[f\"{property_label}_body_dict\"][bt],\n                            self.max_nbody,\n                            is_embedded,\n                            self.supersystem_ie_only,\n                        )\n                    )\n\n        all_results[\"results\"] = nbody_dict\n        all_results[\"component_properties\"] = component_properties\n\n        # Make dictionary with \"1cp\", \"2cp\", etc\n        ebd = all_results[\"energy_body_dict\"]\n        all_results[\"energy_body_dict\"] = {str(k) + bt: v for bt in ebd for k, v in ebd[bt].items()}\n\n        return all_results\n</code></pre>"},{"location":"api/#qcmanybody.ManyBodyCalculator.iterate_molecules","title":"<code>iterate_molecules()</code>","text":"<p>Iterate over all the molecules needed for the computation.</p> <p>Yields model chemistry, label, and molecule.</p> Source code in <code>qcmanybody/manybody.py</code> <pre><code>def iterate_molecules(self) -&gt; Iterable[Tuple[str, str, Molecule]]:\n    \"\"\"Iterate over all the molecules needed for the computation.\n\n    Yields model chemistry, label, and molecule.\n    \"\"\"\n\n    done_molecules = set()\n\n    for mc, compute_dict in self.compute_map.items():\n        # TODO - this is a bit of a hack. Lots of duplication when reaching higher nbody\n        for compute_list in compute_dict[\"all\"].values():\n            for real_atoms, basis_atoms in compute_list:\n                label = labeler(mc, real_atoms, basis_atoms)\n                if label in done_molecules:\n                    continue\n\n                ghost_atoms = list(set(basis_atoms) - set(real_atoms))\n\n                # Shift to zero-indexing\n                real_atoms_0 = [x - 1 for x in real_atoms]\n                ghost_atoms_0 = [x - 1 for x in ghost_atoms]\n                mol = self.molecule.get_fragment(real_atoms_0, ghost_atoms_0, orient=False, group_fragments=False)\n                mol = mol.copy(update={\"fix_com\": True, \"fix_orientation\": True})\n\n                # if self.embedding_charges:\n                #    embedding_frags = list(set(range(1, self.nfragments + 1)) - set(pair[1]))\n                #    charges = []\n                #    for frag in embedding_frags:\n                #        positions = self.molecule.extract_subsets(frag).geometry().np.tolist()\n                #        charges.extend([[chg, i] for i, chg in zip(positions, self.embedding_charges[frag])])\n                #    data['keywords']['function_kwargs'].update({'external_potentials': charges})\n\n                done_molecules.add(label)\n                yield mc, label, mol\n</code></pre>"},{"location":"core-interface/","title":"Core Interface","text":"<p>The core interface of QCManyBody is designed to allow for more flexibility in how the calculations are run. The primary responsibilities of the core interface are:</p> <ol> <li>Given a molecule and desired levels of MBE, return the fragments and levels that must be computed for each fragment</li> <li>Given a dictionary of the results of those computations, analyze those results and calculate the desired manybody properties</li> </ol> <p>Note that the user is expected to run the calculations themselves, and the core interface does not provide any tools for running the calculations.</p>"},{"location":"core-interface/#using-the-core-interface","title":"Using the core interface","text":"<p>The core interface is accessed through the <code>ManyBodyCalculator</code> class.</p> <p>The first step is to create a molecule. This molecule is a QCElemental molecule object, and must contain fragments.</p> <pre><code>import qcmanybody as qcmb\nimport qcelemental as qcel\n\n# Create a molecule with 3 hydrogen atoms, each as its own fragment\nmol = qcel.models.Molecule(symbols=['h', 'h', 'h'],\n                           geometry=[[0,0,0],[0,0,2],[0,0,4]],\n                           fragments=[[0], [1], [2]])\n</code></pre> <p>Next, create a <code>ManyBodyCalculator</code> object. This object is constructed using the molecule, the desired BSSE correction, levels of MBE, and other options of the MBE.</p> <pre><code>mbc = qcmb.ManyBodyCalculator(\n    molecule=mol,\n    bsse_type=['cp'],\n    levels={1: 'mp2/aug-cc-pvtz', 2: 'b3lyp/def2-svp', 3: 'hf/sto-3g'},\n    return_total_data=True,\n    supersystem_ie_only=False\n)\n</code></pre> <p>The <code>levels</code> option is a dictionary that specifies the n-body level as a key, then an arbitrary string as the description of the calculation to be performed at the n-body level. This string is termed the 'model chemistry' is completely arbitrary; it only has meaning to the user, and the user is expected to map these strings to some meaningful definition of a calculation.</p> <p>Note: The core interface is less flexible than the high-level interface when it comes to the <code>levels</code> option.     In the core interface, all levels must be accounted for (that is, keys must go from 1 to the maximum     nbody you would like to calculate). All levels must be present even if the model chemistry     is the same for all levels.</p> <p>For a complete discussion of the other options available in the <code>ManyBodyCalculator</code> object, see the keywords discussion the <code>ManyBodyCalculator API documentation</code>.</p> <p>The next step is to obtain the calculations to be run from the <code>ManyBodyCalculator</code> object. This is done with a python generator function <code>iterate_molecules</code> that returns a tuple. This tuple contains</p> <ol> <li>The string describing the calculation to be run (the model chemistry string, as defined in the <code>levels</code> dictionary)</li> <li>A label for the calculation. This label is opaque to the user but used to identify the calculation when analyzing the results.</li> <li>A <code>Molecule</code> object that contains the cluster of fragments to be computed.</li> </ol> <pre><code>calculation_results = {}\nfor model_chemistry, label, mol_cluster in mbc.iterate_molecules():\n    calculation_results[label] = run_calculation(mol_cluster, model_chemistry)\n</code></pre> <p>Note that it is entirely up to the user to run the calculation somehow - this level of interface does not provide any tools for running the calculations.</p>"},{"location":"core-interface/#results-dictionary","title":"Results dictionary","text":"<p>The data returned from the calculations is expected to be stored in a nested dictionary. The level is the opaque label as given from the <code>QCManyBodyCalculator</code>. The second level is the name of the property.</p> <pre><code>calculation_results = {\n    'label1': {\n        'energy': -1.0,\n        'gradient': [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],\n    },\n    'label2': {\n        'energy': -2.0,\n        'gradient': [[4.0, 5.0, 6.0], [7.0, 8.0, 9.0]],\n    },\n    'label3': {\n        'energy': -3.0,\n        'gradient': [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]],\n    },\n}\n</code></pre>"},{"location":"core-interface/#analysis","title":"Analysis","text":"<p>This result dictionary is all that is needed to perform the final analysis and calculation of the MBE properties.</p> <pre><code>final_results = mbc.analyze(calculation_results)\n</code></pre> <p>For a discussion about what the results contain, see the results documentation.</p>"},{"location":"high-level-interface/","title":"High-Level Interface","text":""},{"location":"keywords/","title":"Keywords and options","text":""},{"location":"keywords/#required","title":"Required","text":""},{"location":"keywords/#molecule","title":"Molecule","text":""},{"location":"keywords/#bsse_type","title":"bsse_type","text":""},{"location":"keywords/#levels-and-max_nbody","title":"levels and max_nbody","text":""},{"location":"keywords/#keywords-and-options_1","title":"Keywords and Options","text":"<p>Both the high-level interface and core interface share the same terminology with respect to options</p>"},{"location":"keywords/#return_total_data","title":"return_total_data","text":"<p>When set to true, the manybody calculation will return the total data (energy/gradient/hessian/property) of the system. If not, the return will only contain interaction data.</p> <p>Note that the calculation of counterpoise corrected total properties implies the calculation of the energies of monomers in the monomer basis, hence specifying <code>return_total_data = True</code> may carry out more computations than. For some properties such as gradients and hessians, <code>return_total_data = False</code> is rarely useful.</p>"},{"location":"keywords/#supersystem_ie_only","title":"supersystem_ie_only","text":"<p>Target the supersystem total/interaction energy (IE) data over the many-body expansion (MBE) analysis, thereby omitting intermediate-body calculations. When false, each n-body level in the MBE up through <code>max_nbody</code> will be computed. When true (only allowed for <code>max_nbody = nfragments</code>), only compute enough for the overall interaction/total energy: max_nbody-body and 1-body.</p> <p>When true, properties <code>INTERACTION {driver} THROUGH {max_nbody}-BODY</code> will always be available; <code>TOTAL {driver} THROUGH {max_nbody}-BODY</code> will be available depending on <code>return_total_data</code>; and <code>{max_nbody}-BODY CONTRIBUTION TO {driver}</code> won't be available (except for dimers).</p> <p>This keyword produces no savings for a two-fragment molecule. But for the interaction energy of a three-fragment molecule, for example, 2-body subsystems can be skipped with <code>supersystem_ie_only=True</code> Do not use with <code>vmfc</code> in <code>bsse_type</code> as it cannot produce savings.</p>"}]}
\ No newline at end of file
diff --git a/sitemap.xml b/sitemap.xml
index 224d229..dc0f7f4 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -2,32 +2,37 @@
 <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
     <url>
          <loc>https://molssi.github.io/QCManyBody/</loc>
-         <lastmod>2024-04-21</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://molssi.github.io/QCManyBody/api/</loc>
-         <lastmod>2024-04-21</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
-         <loc>https://molssi.github.io/QCManyBody/explanation/</loc>
-         <lastmod>2024-04-21</lastmod>
+         <loc>https://molssi.github.io/QCManyBody/core-interface/</loc>
+         <lastmod>2024-04-22</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>https://molssi.github.io/QCManyBody/high-level-interface/</loc>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://molssi.github.io/QCManyBody/how-to-guides/</loc>
-         <lastmod>2024-04-21</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
-         <loc>https://molssi.github.io/QCManyBody/reference/</loc>
-         <lastmod>2024-04-21</lastmod>
+         <loc>https://molssi.github.io/QCManyBody/keywords/</loc>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
-         <loc>https://molssi.github.io/QCManyBody/tutorials/</loc>
-         <lastmod>2024-04-21</lastmod>
+         <loc>https://molssi.github.io/QCManyBody/results/</loc>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
 </urlset>
\ No newline at end of file
diff --git a/sitemap.xml.gz b/sitemap.xml.gz
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